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2025 |
Agricultureal Security Areas of Adams County, Pennsylvania
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| Adams County |
2025 |
Land Conservancy Easements of Adams County, Pennsylvania
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| Adams County |
2025 |
Mt Joy Twp Preserved Farms of Adams County, Pennsylvania
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| Adams County |
2025 |
Adams County Municipal Boundaies
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| Adams County |
2025 |
Adams County parcels
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| Adams County |
2025 |
Preserved Farms of Adams County, Pennsylvania
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| Adams County |
2025 |
Adams County streets
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| Adams County |
2018 |
Digitized regional surficial geology map for northwestern Pennsylvania. The complete citation for the paper map is: Map of the Glacial Deposits of Northwestern Pennsylvania by V.C. Shepps, J.B. Droste, and R.F. Sitler under the supervision of G.W.White, 1959, Bulletin G-32, Pennsylvania Geologic Survey. The original map was published at a scale of 1:125,000. Three counties are fully covered by these deposits and mapped (Crawford, Erie, and Mercer); five more are partially covered and mapped (Beaver, Butler, Lawrence, Warren, and Venango).
This GIS dataset shows the areal distribution of 14 distinct glacial deposits from the Pleistocene Epoch and Recent stream alluvium and/or bedrock exposures. All mapping activity and lithologic interpretations were made by the original authors of the map (Shepps et al., 1959); the authors of this electronic geospatial resource (Shaffer et al., 2005) have only provided digital transfer of the map features and attributes and documentation of the process. Purpose:
This dataset identifies the distribution of surficial unconsolidated sediments (glacial deposits and stream alluvium) in northwestern PA. This map is the only published comprehensive regional surficial geologic map available for the region. The original map was digitized to provide an electronic version for GIS work. Potential users should read the Supplemental Information Section (below) to determine the appropriate scale for using these data. More detailed surficial geologic maps may exist within the mapped area in either paper or electronic format. Researchers are encouraged to check with the U.S. Geological Survey, Pennsylvania Geologic Survey, PASDA, and/or Geology Departments at local universities and colleges for other maps.
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| Allegheny College |
2010 |
Digitized sediment thickness map for western Crawford County, Pennsylvania. (The term western applies to all portions of the county west of 80 degrees 07' 30") The complete citation for the source map is: Map Showing the Thickness of Glacial Deposits and Locations of Wells in Western Crawford County, Pennsylvania by George R. Schiner and John T. Gallaher, 1979, W46, Plate 2. The original map was published at a scale of 1:50,000. The map is a part of the Pennsylvania Geologic Survey Water Resource Report 46, Geology and Groundwater Resources of Western Crawford County, Pennsylvania. This GIS dataset shows the areal distribution of glacial deposits and estimated well yields across the western portion of Crawford County. Water well locations published on the source map have not been included here. All mapping activity and thickness interpretations were made by the original authors of the map (Shiner and Gallaher, 1979); the authors of this electronic geospatial resource (Shaffer et al., 2010) have only transferred the map features, assigned attributes, and documented our process.
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| Allegheny College |
2010 |
Digitized sediment thickness map for Erie County, Pennsylvania. The complete citation for the source map is: Map Showing the Thickness of Unconsolidated Deposits, Locations of Selected Wells, and Seismic-Refraction Cross Sections by David B. Richards, J. Jack McCoy, and John T. Gallaher, 1987, W62, Plate 2. The original map was published at a scale of 1:62,500. The map is a part of the Pennsylvania Geologic Survey Water Resource Report 62, Groundwater Resources of Erie County, Pennsylvania. This GIS dataset shows the areal distribution of sediment thickness across Erie County. Water well locations and geophysical data on the source map have not been included here. All mapping activity and thickness interpretations were made by the original authors of the map (Richards et al., 1987); the authors of this electronic geospatial resource (Shaffer et al., 2010) have only transferred the map features, assigned attributes, and documented our process.
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| Allegheny College |
2025 |
This dataset contains Address Points in Allegheny County. The Address Points were created by GDR for the Allegheny County CAD project, October 2008. Data is updated by County staff as changes and corrections are found, on a continuous basis. Updates are sent to PASDA monthly.
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| Allegheny County |
2002 |
3 Rivers Wet Weather Demonstration Program Outreach Planning Basins
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| Allegheny County |
2002 |
Derived from original MAPINDX: Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5" grid tics and italicized grid coordinate markers and outlines of map sheet boundaries. Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.
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| Allegheny County |
2016 |
This dataset contains the Allegheny County boundary.
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| Allegheny County |
2025 |
Footprints for all buildings and out buildings in Allegheny County. Buildings are captured by following the edge of the roof line. All near orthogonal corners are square. Interim roof lines, such as dormers, are not shown. Minor structures such as carports, decks, patios, stairs, etc. which are part of the structure are not shown. Building Footprints were updated as a result of a flyover in 2004. Buildings less than 400 square feet were not captured. Special consideration was given to garages, less than 400 square feet, and were digitized if greater than 200 square feet.
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| Allegheny County |
2002 |
Outlines of public and private cemeteries greater than one acre in size. Areas were delineated following a generalized line along the outside edge of the area. Individual features within the cemetery are not shown.
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| Allegheny County |
2006 |
Contour data was created as a result of a county flyover in the spring of 2004. Contours were created from a DTM Feature Dataset, upgraded by Baker and T-3 to support National Mapping Accuracy Standards (NMAS) for contours . Intermediate Contours are contours spaced at 5 foot intervals. Depression Contours are contours showing the edges and slope in a depression at 5 foot intervals. Contours are coded separately for delineation between depression and intermediate contours.
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| Allegheny County |
2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2008 |
Data was created to portray the boundaries of the Councils of Government (COG's) in Allegheny County.
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| Allegheny County |
2012 |
Data was created to portray the boundaries of the County Council Districts in Allegheny County.
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| Allegheny County |
2017 |
This dataset contains Farmers Market locations in Allegheny County.
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| Allegheny County |
2000 |
In an effort to expedite the permit review process for Water Obstruction and Encroachment Applications, the Pennsylvania Department of Environmental Protection initiated a plan to replace hard-copy maps with digital GIS sets. The project is referred to as the 105 Spatial Data System (105SDS) Pennsylvania river floodplains and coastal floodplains are two of many spatial data sets that were used in the 105SDS project. As a result of work completed by Law Environmental, Inc. on the statewide low-level radioactive waste siting project, DEP received two coverages depicting river and coastal floodplains. However, due to the process used in constructing these data sets, there were many areas throughout the state in which floodplains were not digitized. The primary purpose of this task was to complete the digital floodplain mapping in these areas.
Purpose: INTENDED USE OF DATA; Created to do permit reviews for Water Obstruction and Encroachment Applications. LIMITATIONS OF DATA; Due to the nature of transferring the floodplains from the Federal Emergency Management Agency maps to plotted 1:24000 scale maps this coverage should be considered to be the "best representation" of the data but not as accurate as, for example, a map of Global Positioning System's floodplain coordinates
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| Allegheny County |
2010 |
The Greenways feature class consists of a compilation of the following data: agricultural easements, Allegheny Land Trust GREENPRINT, Conservation Streams buffered by 50 ft, Forested Floodplains, City of Pittsburgh designated Greenways, Land Trust Properties, Rivers buffered by 100 ft, sensitive slopes, wetlands 1 acre or more buffered by 50 ft, golf courses, parks and trails. Building footprints have been deleted from the feature class, but can be added back (see ModelWorkspace\Buildings_in_Greenways).
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| Allegheny County |
2016 |
Rivers, Lakes, Ponds, Reservoirs, Hidden Lakes, Reservoirs or Ponds: If greater than 25 feet and less than 30 feet wide, is captured as a double line stream. If greater than 30 feet wide it is captured as a river. Lakes are large standing bodies of water greater than 5 acres in size. Ponds are large standing bodies of water greater than 1 acre and less than 5 acres in size. Reservoirs are man made embankments of water. Included in this definition are both covered and uncovered water tanks.
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| Allegheny County |
2016 |
Rivers, Lakes, Ponds, Reservoirs, Hidden Lakes, Reservoirs or Ponds: If greater than 25 feet and less than 30 feet wide, is captured as a double line stream. If greater than 30 feet wide it is captured as a river. Lakes are large standing bodies of water greater than 5 acres in size. Ponds are large standing bodies of water greater than 1 acre and less than 5 acres in size. Reservoirs are man made embankments of water. Included in this definition are both covered and uncovered water tanks.
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| Allegheny County |
2000 |
Derived from original MAPINDX: Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5" grid tics and italicized grid coordinate markers and outlines of map sheet boundaries. Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.
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| Allegheny County |
2002 |
Data was created to portray the Libraries in Allegheny County.
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| Allegheny County |
2024 |
Data was created to portray the boundaries of the 130 Municipalities in Allegheny County the attribute table includes additional descriptive informations including Council of Government (COG) affiliation, School District, Congressional District, FIPS Code, County Municipal Code and County Council District.
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| Allegheny County |
2000 |
The Allegheny County Natural Heritage Inventory identifies and maps Allegheny County's most significant natural areas. The NHI study (Natural Heritage Inventory) investigated plant and animal species and communities that are unique or uncommon in Allegheny County; it also explored areas important for general wildlife habitat, education and scientific study.
The inventory does not confer protection on any of the areas listed here.
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| Allegheny County |
2000 |
NWI (National Wetlands Inventory) digital data files are records of wetlands location and classification as defined by the U.S. Fish & Wildlife Service. This dataset is one of a series available in 7.5 minute by 7.5 minute blocks containing ground planimetric coordinates of wetlands point, line, and area features and wetlands attributes. When completed, the series will provide coverage for all of the contiguous United States, Hawaii, Alaska, and U.S. protectorates in the Pacific and Caribbean. The digital data as well as the hardcopy maps that were used as the source for the digital data are produced and distributed by the U.S. Fish & Wildlife Service's National Wetlands Inventory project. This data set is derived from the national coverage and only includes the area within the boundary of Allegheny County.
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| Allegheny County |
2025 |
Boundary outlines of individual properties in Allegheny County.
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| Allegheny County |
2000 |
Paved and unpaved parking lots that accommodate more than ten (10) cars.
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| Allegheny County |
2024 |
Was derived from the data included in original file CULTUREF. Delineates the area following a generalized line along the outside edge of all parks in Allegheny County
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| Allegheny County |
2000 |
Data was created to portray in-ground community pools, does not include residential pools.
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| Allegheny County |
2002 |
Point locations of county and municipal buildings, halls, public works sites, government buildings.
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| Allegheny County |
2006 |
Railroad Centerlines are collected by digitizing the apparent center of each set of rails. The Rail Line Edge is the apparent Right of Way. Rights of Way and Centerlines that are currently being used for rail traffic are coded as In Use. Rights of Way and Centerlines that no longer maintain rail traffic but have tracks remaining, as exemplified by plants growing through or around the tracks or right of way, are coded as Abandoned. Rights of Way and Centerlines that have the tracks removed are coded as Old. The Rights of Way and Centerlines that have the tracks removed and have been converted to trails are coded as Rails to Trail. For rail lines that cannot be discerned between Old and Rails to Trails, the Rights of Way and Centerlines shall be coded as Old.
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| Allegheny County |
2006 |
The Retaining Wall Feature Dataset contains photogrammetrically compiled Retaining Walls - structures of concrete, brick, stone, wood, etc. retaining earth and adjacent to a road, railroad, edge or stream that are over five (5) feet high and 200 feet long.
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| Allegheny County |
2010 |
Slope data
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| Allegheny County |
2000 |
Data was created to portray the soils in Allegheny County.
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| Allegheny County |
2006 |
Spot Elevation data was created as a result of a county flyover in the spring of 2004. They were created from a DTM Feature Dataset, upgraded by Baker and T-3 to support National Mapping Accuracy Standards (NMAS). Spot Elevations are used to show additional elevation information. They are located in flat areas where contours may be sparse or spaced far apart, at road and railroad intersections, on the road centerline at the ends of bridges, on the road centerline over the center of culverts that have a span of five (5)-foot or greater, at the crest of all tops of hills, at saddles, within depressions and where the ground is visible in obscured areas.
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| Allegheny County |
2025 |
This dataset contains street centerlines for vehicular and foot traffic in Allegheny County. Street Centerlines are classified as Primary Road, Secondary Road, Unpaved Road, Limited Access Road, Connecting Road, Jeep Trail, Walkway, Stairway, Alleyway and Unknown.
A Primary Road is a street paved with either concrete or asphalt that has two (2) or more lanes in each direction. A Secondary Road is a residential type hard surface road, or any hard surface road with only one (1) lane in each direction. An Unpaved Road is any road covered with packed dirt or gravel. A Limited Access Road is one that can only be accessed from a Connecting Road such as an Interstate Highway. A Connecting Road is a ramp connecting a Limited Access Road to a surface street. A Walkway is a paved or unpaved foot track that connects two (2) roads together. Walkways within College Campuses will also be shown. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. Walkways will not have an Edge of Pavement feature. A Stairway is a paved or wooden structure that connects two (2) roads together. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. An Alleyway is a road, usually narrower than a Secondary Road that runs between, but parallel to, two (2) Secondary Roads. Generally, Outbuildings will be adjacent to Alleyways. A Jeep Trail is a vehicular trail used for recreation. A Jeep Trail will not have an associated edge of pavement feature. A road coded as Unknown is a road, which in the judgment of the photogrammetrist, does not fall into any of the categories listed.
Centerlines will be visually placed between the edges of pavement. One (1) centerline will be placed between each edge of pavement. Roads with medial strips, such as Limited Access Roads, will have two (2) centerlines for those portions of the road where the medial strip is present.
For roads that terminate with a cul-de-sac, the centerline shall continue through the center of the cul-de-sac and stop at the edge of pavement.
All attribute data will remain for all Street Centerlines that are not updated. For Street Centerlines that are new, the only attribute field that will be populated is the FeatureCode and UPDATE_YEAR. If a Street Centerline is graphically modified, the existing attribute data will remain and the UPDATE_YEAR will be set to 2004. The attribute values for 2004 Street Centerlines should be considered suspicious until verified.
The ArcInfo Street Centerline coverage that is being updated has 800 segments of Paper Streets, 66 segments of Vacated Streets and 78 segments of Steps. Street Centerlines that are coded as Paper Streets in the OWNER field will remain unchanged in the updated dataset unless the area has been developed. In the event the area has been developed, the Street Centerlines will be modified to reflect the true condition of the visible roads.
Street Centerlines that are coded as Vacated in the OWNER field will also remain unchanged in the updated dataset. In the event the area coinciding with the Vacated Streets has been developed, the Vacated Street Centerlines will be removed in order to reflect the true condition of the area.
Street Centerlines that are coded as Steps in the OWNER field will be updated to reflect the current condition of the area.
The Street Centerlines dataset consists of an external table that links to the supplied coverages and the Geodatabase created for this project using the "-ID" (UserID) field. In order to maintain the link to the external table and not loose valuable data the decision was made to keep all database information currently in the Street Centerline dataset. When a Street Centerline is modified during the update process, the field "UPDATE_YEAR" is set to 2004. All other database attributes will remain unchanged from the original values. All Street Centerline database data with an "UPDATE_YEAR" of 2004 should be verified before used. In some occasions the Street Centerline was divided into two (2) sections to allow for a new road intersection. Both sections of the resulting Street Centerline will have the same database attributes including Address Range. All new Street Centerlines will have zero (0) for "SystemID" and "UserID".
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| Allegheny County |
2016 |
Edge of Pavements are the edges of all public thoroughfares including paved roads, unpaved roads, bridges, overpasses, tunnels, alleys, stairways and airfield runways.
Paved Roads are roads that are surfaced with concrete or asphalt. Roads that have been tarred and chipped are also considered paved. Roads surfaced with gravel or packed dirt are considered Unpaved. An Alleyway is a road, usually narrower than surrounding roads that runs between, but parallel to, two paved roads. An Alley can be either paved or unpaved. Generally, Outbuildings will be adjacent to Alleyways. Road pavement edges that are underneath bridges or overpasses are coded as Hidden. Bridges and Overpasses are delineated along the outside edge of the structure. Portions of bridges that are under other bridges or other objects are coded as Hidden Bridge. Tunnels are shown as lines connecting the apparent width of the tunnel through the earth. Stairways are major pedestrian thoroughfares, either paved or wooden, connecting two (2) roads and typically found on steep hills. Airfield Runways are the edges of Runways, Taxiways and other airfield pavement areas. Driveways that are greater than 300' in length will be digitized and coded as either Paved Road or Unpaved Road.
Paved Road edges show the width of the paved area. If the paved road has a paved shoulder, the paved shoulder is shown as part of the paved road. The placement of an unpaved road is subject to interpretation by the photogrammetrist since the road edge may be indefinite due to the imprecise nature of an unpaved surface.
Driveways that are over 300' in length are digitized. For a driveway that terminates at a building, it shall be drawn to the building. Driveways will also have a centerline for its entire length.
Parking lots are not digitized. The entrance to a parking lot for shopping areas shall be shown. Driving areas around and through parking lots serving shopping areas and other businesses are considered part of the parking lot. If the driving area through a parking lot connects to two (2) or more exterior roads, the main thoroughfare through the parking lot shall be shown as a road.
The driving area through an apartment complex or condominium complex shall be digitized as a road. The road is needed in these areas to show access to the individual dwelling places.
Edges of Pavement that are updated will have the database field StreetCL_FC populated with the Feature Code of the Street Centerline. (The Street Centerline Feature Code is more descriptive than the general code used for Edge of Pavement.)
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| Allegheny County |
2010 |
Allegheny County Urban Tree Canopy. High resolution land cover dataset for Allegheny County, Pennsylvania.
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| Allegheny County |
2024 |
Data was created to portray the boundaries of the Voting Districts in Allegheny County.
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| Allegheny County |
2000 |
Polygons marking borders of watersheds in Allegheny county.
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| Allegheny County |
2011 |
Stands of trees (coniferous and deciduous) too numerous to plot as individual trees. The area is delineated following a generalized line along the outside edge of tree trunks. Areas are captured if at least one acre in size or of major significance especially in urban areas.
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| Allegheny County |
2024 |
This dataset demarcates the zip code boundaries that lie within Allegheny County. These are not clipped to the Allgeheny County boundary.
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| Allegheny County |
2010 |
Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2010 |
Tile Index - Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2013 |
Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2013 |
Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2015 |
Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2015 |
Tile Index - Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2017 |
Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2017 |
Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2017 |
Tile Index - Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2017 |
Tile Index - Orthophotos of Allegheny County, Pennsylvania - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Aerial survey of Allegheny County,Pennsylvania. Orthophotos serve a variety of purposes, from interim maps to field references for earth science investigations and analysis. The digital orthophoto is useful as a layer of a geographic information system and as a tool for revision of digital line graphs and topographic maps.
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| Allegheny County |
2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2017 |
Tile Index - Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2024 |
The purpose of this project was to conduct an assessment of tree canopy change that occurred between 2015 and 2020 utiliing LiDAR data and a previously prepared canopy dataset. Comprehensive canopy change statistics are provided for various geographies down to the parcel-level scale. Tree canopy was extracted from LiDAR data collected in the fall of 2019 and spring of 2020 in ArcMap. A digital surface model (DSM) was created by interpolating the maximum values of the first returns of each laser pulse across a 3-foot grid surface (raster). A speckled output was created because some pulses can entirely or partially pass-through tree canopy before detecting a return, so maximum focal statistics in a 3 by 3 rectangular grid window were applied to the DSM to create a smooth surface. Another raster representing the elevations of solid surfaces which LiDAR does not penetrate - usually ground and buildings, but occasionally dense evergreens as well, was created by interpolating the minimum values of the last returns (which are also the first return in instances of single return). Mean focal statistics in a 3 by 3 cell window were applied to this raster. The last return raster was subtracted from the first return raster, creating a canopy height model (CHM) – a representation of the heights of objects with complex return structures above the ground. In addition to trees, this includes built structures such as power lines, poles, transmission towers, gantries, etc. The edges of buildings also appeared in the CHM as a result of different cell assignment and focal statistics types applied to the first and last return rasters. The heights of dense evergreens were underestimated due to the inability of LiDAR to penetrate to the ground for a proper base for height. A constant raster of CHM cells with a height greater than 15 feet was created. Holes less than 500 square feet were filled to eliminate dubious small gaps while preserving discernable canopy gaps. This raster was then shrunk by 2 cells and expanded back by 2 cells. This process eliminated narrow or small features such as building edges, power lines, and poles. This raster was then converted into a vector polygon format for editing.
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| Allegheny County |
2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
2004 |
Database containing more than 33,466 records on water quality from 1986 to the present from 622 testing sites throughout Pennsylvania. Information in records includes at least alkalinity and Ph and includes nitrates and phosphates for some sites since 1996.
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| Alliance for Aquatic Resource Monitoring |
2002 |
AMERICAN FORESTS conducted an urban ecosystem analysis of the Delaware Valley region to provide community leaders with detailed information about the region's tree cover and its environmental and economic impacts. The analysis documents what landscape changes have occurred over time and how these changes have impacted the environmental services the urban forest provides to the region. The study used Geographic Information Systems (GIS) technology to connect image analysis of the area to ecological assessment of tree cover change trends over the last 15 years. In addition, AMERICAN FORESTS created a "green data layer" -a digital tool that local communities can use to integrate urban forest ecology into their future planning.
Frankford Tacony, Mill Creek and Cobbs Creek in Pennsylvania and the Big Timber in New Jersey were selected for analysis and to create a detailed (4-meter resolution) digital landcover. The detailed analysis used a high-resolution satellite image as the basis for creating a "green data layer" or classified land cover image. This digital mapping tool enables planners to integrate green infrastructure into their future planning.
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| American Forests |
1985 |
AMERICAN FORESTS conducted an urban ecosystem analysis of the Delaware Valley region to provide community leaders with detailed information about the region's tree cover and its environmental and economic impacts. The analysis documents what landscape changes have occurred over time and how these changes have impacted the environmental services the urban forest provides to the region. The study used Geographic Information Systems (GIS) technology to connect image analysis of the area to ecological assessment of tree cover change trends over the last 15 years. This file represents a reclassification of 30-meter resolution Landsat Thematic Mapper imagery.
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| American Forests |
1993 |
AMERICAN FORESTS conducted an urban ecosystem analysis of the Delaware Valley region to provide community leaders with detailed information about the region's tree cover and its environmental and economic impacts. The analysis documents what landscape changes have occurred over time and how these changes have impacted the environmental services the urban forest provides to the region. The study used Geographic Information Systems (GIS) technology to connect image analysis of the area to ecological assessment of tree cover change trends over the last 15 years. This file represents a reclassification of 30-meter resolution Landsat Thematic Mapper imagery.
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| American Forests |
2000 |
AMERICAN FORESTS conducted an urban ecosystem analysis of the Delaware Valley region to provide community leaders with detailed information about the region's tree cover and its environmental and economic impacts. The analysis documents what landscape changes have occurred over time and how these changes have impacted the environmental services the urban forest provides to the region. The study used Geographic Information Systems (GIS) technology to connect image analysis of the area to ecological assessment of tree cover change trends over the last 15 years. This file represents a reclassification of 30-meter resolution Landsat Thematic Mapper imagery.
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| American Forests |
2005 |
The state of PA that falls within the Higlands Regional Study Area designated by the USFS. Plus, the extended area designated by the Appalachian Mountain Club to the border of Maryland.
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| Appalachian Mountain Club |
2005 |
A Critical Treasure is a recognized priority area for additional land conservation efforts in the Highlands that has significant value of open space preservation, watershed protection, habitats for plants or wildlife, or outdoor recreation.
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| Appalachian Mountain Club |
2005 |
Corridors are made up of the least disturbed and therefore the best potential lands available to unite core conservation areas. These are key landscapes that provide opportunities for biodiversity while contributing to the greenway’s overall ecological richness.
The hubs and corridors are the major concentration of the map and serve as the core conservation areas in the PA Highlands Greenway. The hub and spoke system was created by AMC by combining the results of two analysis techniques, stronghold analysis and the Natural Lands Trust’s Smart ConservationTM.. Stronghold analysis identifies areas that are least affected by man-made disturbances such as roads and fragmentation of the natural landscape while Smart Conservation determines the ecological health of an area. The combination of these two analyses in conjunction with the knowledge of local stakeholders was used to prioritize core conservation areas.
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| Appalachian Mountain Club |
2007 |
Hubs are areas that have a high natural resource value such as large intact forests or abundant wildlife. They have little fragmentation and include unfragemented forests and farmland. Hubs are both undisturbed natural lands and adjacent protected lands such as state parks, forests or state game lands.
The hubs and corridors are the major concentration of the map and serve as the core conservation areas in the PA Highlands Greenway. The hub and spoke system was created by AMC by combining the results of two analysis techniques, stronghold analysis and the Natural Lands Trust’s Smart ConservationTM.. Stronghold analysis identifies areas that are least affected by man-made disturbances such as roads and fragmentation of the natural landscape while Smart Conservation determines the ecological health of an area. The combination of these two analyses in conjunction with the knowledge of local stakeholders was used to prioritize core conservation areas.
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| Appalachian Mountain Club |
2005 |
States that fall within the Higlands Regional Study Area designated by the USFS, portions of CT, NJ, NY, and PA. PA Highlands boundary was extended by the Appalachian Mountain Club to the border of Maryland.
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| Appalachian Mountain Club |
2012 |
Portion of Pennsylvania that fall within the Higlands Regional Study Area.
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| Appalachian Mountain Club |
2003 |
This data set represents the most current depiction of the Appalachian National Scenic Trail centerline for the portion of the trail passing through Pennsylvania. Locational information used to create this data set were obtained from both Global Positioning Systems (GPS) survey data collected between 1998-2001 and information digitized from USGS topographical maps and Appalachian Trail maps.
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| Appalachian Trail Conference |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
Metadata
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KMZ
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
Metadata
|
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KMZ
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
Metadata
|
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KMZ
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2003 |
This map layer portrays our current understanding of the distributions of
United States and Canadian bat species during the past 100-150 years.
Specimen and capture data were obtained from a variety of data sources,
including U.S. State natural heritage programs, Canadian conservation data
centers, published literature, unpublished reports, museum collections,
and personal communications from university, federal, State and local
biologists. Records are all specimen, roost, capture, or positive visual
identification-based; no acoustic-only identifications were used for this
map layer. This map layer reflects minor changes to the July, 2002, data
set.
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| Bat Conservation International |
2023 |
Boundary outlines of individual properties in Bedford County. The Parcel Dataset was developed primarily for the purposes of identifying land parcels for tax billing and tax assessment purposes.
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| Bedford County |
2025 |
All farms that are enrolled in the Agricultural Security Area program (Act 43).
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| Berks County, Pennsylvania |
2025 |
This dataset contains Street Centerlines in Berks County.
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| Berks County, Pennsylvania |
2025 |
This dataset depicts the future land use for Berks County Pennsylvania. To determine the future land use, each tax parcel was first designated an existing land use. Further analysis of municipal zoning, comprehensive plans, the County Future Land Use Plan (2020), municipal sewer service areas (existing and proposed) from Act 537 plans, preserved/eased land, soils, floodplains, steep slopes, 2008 aerial photography and highway accessibility was used to designate a future land use for each tax parcel. In some instances, there are multiple future land use designations on a parcel. The Berks County Comprehensive Plan 2030 was adopted on September 26, 2013.The following are descriptions of the 8 future land use designations used:Existing Development: Land that has already experienced concentrated commercial, industrial, institutional, and residential development at various densities. Larger areas typically serve as multi-purpose centers; and provide opportunities for in-fill development, rehabilitation and revitalization efforts.Designated Growth: Areas of vacant land that typically surround existing development areas; located within an existing sewer service area; and are zoned for multi-purpose development at concentrated densities. Areas with this designation is suitable for mixed-use development in the immediate future due to the presence of public infrastructure services needed to accommodate concentrated growth.Future Growth: Areas of vacant land located within development-based zoning districts and are typically adjacent to or within a proposed sewer service area. Public infrastructure is planned to accommodate multi-purpose concentrated growth.Rural Conservation: Land characterized by low density development; is predominately wooded and may contain steep slopes or isolated farmland. Due to the absence of public infrastructure services, this area is appropriate for flexible development paterns that reflect any environmental constraints. The area includes land eased for open space, natural resource or woodland conservation.Agricutural Preservation: Lands that show a strong presence of agricultural activity with prime agricultural soils; is typically zoned as Effective Agricultural Preservation; or farms preserved by agricultural conservation easements. Objectives are to protect the agricultural land base, minimize land use conflicts and promote the long-term viability of the agricultural community.Environmental Hazard: Land located within a floodplain, wetland watercourse, water body and their associated riparian buffers. The purpose is to recognize their environmental importance, as well as protect the components and their natural function. Theses areas should be left in their natural state and are inappropriate for infrastructure investment.Permanent Open Space and Recreation: Land owned by Federal, State, County, local government, and homeowner associations for the purpose of providing public park and recreation opportunities, open space conservation and the protection of watershed lands.Transportation Network: Federal, state, and local roads, as well as railroad networks. Maintenance of existing facilities and improvement services to increase safety and capacity will be supported.
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| Berks County, Pennsylvania |
2025 |
This polygon data set represents the boundaries of all election precincts within the County of Berks. Additionally, the associated attributes reflect information regarding which state and congressional districts by which its consituents are represented.
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| Berks County, Pennsylvania |
2025 |
This dataset contains Recreation Areas in Berks County.
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| Berks County, Pennsylvania |
2025 |
Data created by and for the primary use of the Berks County Department of Emergency Services. Used to support communication center functions for the purposes of E911 response and for the proper dispatching of emergency services calls. Additionally, can be used in aiding in transportation and public safety planning, design, and development, as well as other uses by various Berks County departments, agencies and offices.
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| Berks County, Pennsylvania |
2025 |
These address points represent current, and believed active physical 911 addresses. Sub-address data is incomplete and generally not spatially accurate to location within the parent structure/address. This is a constantly evolving dataset that sees both additions and deletions, as well as spatial adjustments on a regular basis. This dataset is a smaller subset of what is used by 911 dispatch, and thus we encourage any observed errors to be reported immediately to the Blair County GIS Department at GIS@blairco.org, or by calling the office at 814-693-2535.
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| Blair County |
2025 |
This dataset is widely used. It supports functions such as: 911 addressing, location reference, cartography, 911 dispatch, etc... Data is maintained by BCGIS Department. Because this data is a critical component to 911 addressing, if any errors are observed we kindly ask that the Blair County GIS Department be notified directly by sending an email to: GIS@blairco.org, or by calling the office at: 814-693-2535. Every effort is made to provide an accurate and current representation of the roads within the County, however we do not guarantee the validity nor spatial accuracy of this dataset.
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| Blair County |
2025 |
These parcels represent the current mapped parcels, to represent tax areas, not to serve as surveyed boundaries. Parcels polygons should never be considered survey accurate, nor should they be used in place of a survey by a registered land surveyor for any means other than general reference to the taxable area described on a deed. For any noticeable mapping errors please notify the Blair County Assessment Office at: blairassessment@blairco.org or by calling the office at: (814) 693-3110.
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| Blair County |
2023 |
Bradford County Access Lines
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| Bradford County |
2023 |
Bradford County Addresses
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| Bradford County |
2023 |
Bradford County Municipal Boundaries
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| Bradford County |
2023 |
Bradford County Parcel Hooks
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| Bradford County |
2023 |
Bradford County Parcels
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| Bradford County |
2023 |
Bradford County Road Centerlines
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| Bradford County |
2019 |
BCRA’s Present Water Service Area
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| Brodhead Creek Regional Authority |
2019 |
BCRA’s Additional Ten Year Projected Water Service Area (areas reasonably anticipated to be served within at least the next 10 years).
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| Brodhead Creek Regional Authority |
2023 |
Agricultural Preservation Program
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| Bucks County |
2022 |
Drop Box Locations
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| Bucks County |
2023 |
Fire Stations
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| Bucks County |
2023 |
Land Trust Owned
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| Bucks County |
2020 |
Magisterial Districts
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| Bucks County |
2020 |
Major Roads
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| Bucks County |
2023 |
Municipal Boundary
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| Bucks County |
2023 |
Municipal Parks Open Space
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| Bucks County |
2023 |
Municipal Zoning
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| Bucks County |
2023 |
Natural Areas Program
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| Bucks County |
2020 |
PA Congressional Districts
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| Bucks County |
2020 |
PA State Assembly Districts
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| Bucks County |
2020 |
PA State Senate Districts
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| Bucks County |
2024 |
Parcels
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| Bucks County |
2023 |
Police Departments
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| Bucks County |
2022 |
Bucks County Polling Places
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| Bucks County |
2023 |
Proposed Developments
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| Bucks County |
2023 |
Road Centerlines
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| Bucks County |
2020 |
School Districts
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| Bucks County |
2023 |
Schools
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| Bucks County |
2024 |
Site Address Points
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| Bucks County |
2023 |
Trails
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| Bucks County |
2022 |
Bucks County Voting Precincts
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| Bucks County |
2022 |
Watersheds
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| Bucks County |
2023 |
Butler County, Pennsylvania address points.
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| Butler County |
2022 |
Municipalities boundary outlines of Butler County.
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| Butler County |
2023 |
Boundary outlines of individual properties in Butler County.
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| Butler County |
2023 |
Street Centerlines
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| Butler County |
2025 |
This dataset contains wetlands in Cambria County.
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| Cambria County |
2024 |
This dataset contains wetlands in Cambria County.
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| Cambria County |
2023 |
This dataset contains airport runways for Cambria County.
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| Cambria County |
2023 |
This dataset contains bridges of for Cambria County.
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| Cambria County |
2023 |
This dataset contains ditches for Cambria County.
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| Cambria County |
2023 |
This dataset contains driveways for Cambria County.
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| Cambria County |
2003 |
A collection of imagery over Cambria County PA from the year 2003. This image catalog spans years of aerial photography and digital ortho photo production generated for Cambria County Pennsylvania. Project year and criteria, available technology, and service providers are factors in the difference of products provided in this archive. While project deliverables seek to provide consistent and spatially accurate results Cambria County nor any of the service providers or agencies providing access to this catalog can be held liable for misuse or misinterpretation of the images. These images are provided for reference only.
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| Cambria County |
2006 |
A collection of imagery over Cambria County PA from the year 2006. This image catalog spans years of aerial photography and digital ortho photo production generated for Cambria County Pennsylvania. Project year and criteria, available technology, and service providers are factors in the difference of products provided in this archive. While project deliverables seek to provide consistent and spatially accurate results Cambria County nor any of the service providers or agencies providing access to this catalog can be held liable for misuse or misinterpretation of the images. These images are provided for reference only.
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| Cambria County |
2009 |
A collection of imagery over Cambria County PA from the year 2009. This image catalog spans years of aerial photography and digital ortho photo production generated for Cambria County Pennsylvania. Project year and criteria, available technology, and service providers are factors in the difference of products provided in this archive. While project deliverables seek to provide consistent and spatially accurate results Cambria County nor any of the service providers or agencies providing access to this catalog can be held liable for misuse or misinterpretation of the images. These images are provided for reference only.
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| Cambria County |
2017 |
A collection of imagery over Cambria County PA from the year 2017. This image catalog spans years of aerial photography and digital ortho photo production generated for Cambria County Pennsylvania. Project year and criteria, available technology, and service providers are factors in the difference of products provided in this archive. While project deliverables seek to provide consistent and spatially accurate results Cambria County nor any of the service providers or agencies providing access to this catalog can be held liable for misuse or misinterpretation of the images. These images are provided for reference only.
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| Cambria County |
2018 |
A collection of imagery over Cambria County PA from the year 2018. This image catalog spans years of aerial photography and digital ortho photo production generated for Cambria County Pennsylvania. Project year and criteria, available technology, and service providers are factors in the difference of products provided in this archive. While project deliverables seek to provide consistent and spatially accurate results Cambria County nor any of the service providers or agencies providing access to this catalog can be held liable for misuse or misinterpretation of the images. These images are provided for reference only.
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| Cambria County |
2023 |
This dataset contains islands for Cambria County.
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| Cambria County |
2023 |
This dataset contains railroads for Cambria County.
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| Cambria County |
2023 |
This dataset contains rivers of Cambria County.
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| Cambria County |
2023 |
This dataset contains streams of Cambria County.
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| Cambria County |
2025 |
This dataset contains street centerlines for Cambria County.
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| Cambria County |
2023 |
This dataset contains structures of Cambria County.
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| Cambria County |
2023 |
This dataset contains waterbodies in Cambria County.
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| Cambria County |
2023 |
This dataset contains wetlands in Cambria County.
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| Cambria County |
2024 |
This dataset contains street centerlines for vehicular and foot traffic in Carbon County, Pennsylvania
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| Carbon County |
2024 |
Data was created to portray the boundaries of the 23 Municipalities in Carbon County, Pennsylvania
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| Carbon County |
2020 |
Railroad Centerlines in Carbon County, Pennsylvania
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| Carbon County |
2020 |
School Districts in Carbon County, Pennsylvania
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| Carbon County |
2020 |
Soils types in Carbon County, Pennsylvania
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| Carbon County |
2020 |
Polylgon geometric features representing the waterbodiies of Carbon County, Pennsylvania
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| Carbon County |
2020 |
Geometric features representing the waterways Carbon County, Pennsylvania
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| Carbon County |
2020 |
Zip code boundaries that lie within Carbon County
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| Carbon County |
2020 |
Boundaries of Carbon County Zoning Base Districts
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| Carbon County |
2006 |
The 3 Rivers 2nd Nature project was directed by artists/researchers Tim Collins and Reiko Goto. The project addressed the meaning, form, and function of public space and nature in Allegheny County, PA, U.S.A. This is the region that encompasses the former steel industry capital of the United States, Pittsburgh PA, U.S.A. 3 Rivers 2nd Nature focused upon the three major rivers; the Allegheny, the Monongahela, and the Ohio Rivers, as well as the streams and subwatersheds. This five-year project revisited questions of nature and post-industrial public space, first addressed on the Nine Mile Run Greenway Project. The focus of the work is research to benefit the public realm, applied as strategic knowledge with accompanying outreach programs intended to enable creative public advocacy and change.
The 3 Rivers 2nd Nature conducted integrative analysis and instrumental planning based upon the rigorous field studies that began in the year 2000. The work effort focused upon partnerships to accomplish interdisciplinary analysis, spatial mapping, and concept design within and among specific communities. The work culminated with an ecological design plan and a water quality policy report that analyzed alternatives for ongoing water quality sampling. Finally, the project team has organized the "Monongahela Conferences" and the subsequent 2005 "Groundworks" exhibition (October 2005) to examine the artist's role in social and environmental change.
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| Carnegie Mellon University |
2021 |
This data is from the Healthy People 2020 project done by the Centers for Disease Control and Prevention. Data includes: Incidence Rate (95% Confidence Interval) - The incidence rate is based upon 100,000 people and is an annual rate (or average annual rate) based on the time period indicated. Rates are age-adjusted by 5-year age groups to the 2000 U.S. standard million population. Recent Trends - This is an interpretation of the AAPC/APC: Rising when 95% confidence interval of AAPC/APC is above 0. Stable when 95% confidence interval of AAPC/APC includes 0. Falling when 95% confidence interval of AAPC/APC is below 0. AAPC/APC (95% Confidence Interval) - the change in rate over time Average Annual Percent Change - AAPCs are based upon APCs that were calculated by Joinpoint Regression Program Annual Percent Change - APCs calculated in SEER*Stat.
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| Center for Disease Control |
2023 |
Affordable Housing
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| Centre County |
2023 |
Electric Company Boundaries
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| Centre County |
2024 |
This Parcels layer contains basic geometry and attribute information. It is maintained by the Tax Assessment staff with the assistance of the GIS Department. This contains NO ownership information. Please contact the GIS Department for this dataset.
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| Centre County |
2023 |
Source Water Protection
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| Centre County |
2023 |
Streets
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| Centre County |
2023 |
Voting Locations
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| Centre County |
2023 |
Water Lines
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| Centre County |
2023 |
Water Treatment Plants
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| Centre County |
2018 |
Centre County Aerials 1938 from the Penn Pilot Program
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| Centre County |
2018 |
Centre County Aerials 1957 from the Penn Pilot Program
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| Centre County |
2018 |
Centre County Aerials 1971 from the Penn Pilot Program
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| Centre County |
2023 |
Tunnels
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| Centre County |
2020 |
EPA will provide the most effective basins funding for nitrogen reduction from the most cost-effective BMPs in the agricultural sector to the Chesapeake Bay watershed jurisdictions that have committed to reducing the agricultural contribution of nitrogen in their Phase III Watershed implementation Plans (WIPs), i.e. Delaware, Maryland, New York, Pennsylvania, Virginia, and West Virginia. The District of Columbia does not have an agricultural commitment through 2025. Using the state Phase III WIPs, each identified nitrogen reduction commitment between now and 2025. The total load of these obligations to reduce nitrogen from Agriculture was added and then a percentage for each of those jurisdictions was determined. The $6 million MEB money will be allocated using the individual percentages for those jurisdictions to complete implementation work in the most effective basins identified within their boundaries.
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2000 |
A land cover map of the Chesapeake Bay Watershed plus the non-watershed components of counties that intersect the Watershed. The map has 17 cover classes modified from the Anderson Level II system. Landsat remotely sensed data and additional thematic data bases were used to create the map. A decision-tree classification was used, together with ad hoc algorithms to improve local accuracies. Landsat 7 ETM+ data were acquired for three seasons. The spatial accuracy is nominally 30m, but accuracy increases rapidly at larger minimum mapping units and, as a general rule, the map should not be used below 1ha (100m x 100m) resolution. The map extends beyond the watershed but has not been validated outside the watershed boundary and the intersecting counties.
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| Chesapeake Bay Program |
2000 |
Multiple TIFF and layer files pertaining to Cultural Assessment of the Chesapeake Bay watershed Growth and development not only threaten lands of high value for water quality and habitat, but also cultural lands that directly connect many citizens in the Bay watershed to the land. Important in preserving heritage and traditional values, cultural lands often define sense of place. The objective of the cultural assessment is to identify lands that provide cultural assets and further inform preservation efforts with spatial information about cultural lands. This spatial information about cultural resources can provide an added value to land preservation because cultural lands provide a historic perspective for interpreting land and people's relationship with it. Additionally, the value of an historic or cultural site is often intrinsically tied to the landscape context in which it is located. MORE INFORMATION -
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| Chesapeake Bay Program |
2000 |
Multiple TIFF and layer files pertaining to forest economics of the Chesapeake Bay watershed. The objective of this analysis was to identify forested lands with the highest economic value. The identification of economically important forest lands focuses on the potential for future economic benefits associated with timber management activities. This considers not only the potential economic return from forest harvest operations, but also the long-term economic sustainability of forest land management and the local importance of the timber management and wood products industry. Other economic benefits related to forest lands, such as tourism and hunting, are not considered in this model. MORE INFORMATION -
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| Chesapeake Bay Program |
2000 |
Multiple TIFF and layer files pertaining to Landscape ecological assessment of the Chesapeake Bay watershed. The Chesapeake Bay Watershed, located in the Mid-Atlantic Region of the United States, is experiencing rapid habitat loss and fragmentation from sprawling low-density development. The bay itself is heavily stressed by excess sediment and nutrient runoff. Three states, the District of Columbia, and the federal government signed an agreement in 2000 to address these problems. The commitments included an assessment of the watershed's resource lands, and targeting the most valued lands for protection. As part of this task, the Resource Lands Assessment identified an ecological network comprised of large contiguous blocks (hubs) of forests, wetlands, and streams, interconnected by corridors to allow animal and plant propagule dispersal and migration. Hubs were prioritized by ecoregion, by analyzing a variety of ecological parameters, including: rare species presence, rarity and population viability; vegetation and vertebrate richness; habitat area, condition, and diversity; intactness and remoteness; connectivity potential; and the nature of the surrounding landscape. I found that much of the watershed was still fairly intact, although this varied dramatically by ecoregion. Current protection also varied, and an assessment of vulnerability will help focus protection efforts among the most valuable hubs and corridors. MORE INFORMATION -
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| Chesapeake Bay Program |
2000 |
Shapefile and raster layers paired with STATSGO data from NRCS to determine prime farmland within the Chesapeake Bay watershed. MORE INFORMATION -
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| Chesapeake Bay Program |
2000 |
Grid of the Chesapeake Bay Watershed with cell values representing relative value for the maintenance of water quality.
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| Chesapeake Bay Program |
2000 |
Multiple layers make up this complete project, all layers and pertaining documentation is included in this file.
The vulnerability layer evaluates the relative potential risk of future land conversion to urban uses. Vulnerability is defined as function of suitability for development and proximity to growth "hot spots." The vulnerability layers useful as a stand-alone layer to evaluate development trends, but can also be combined with the other RLA layers to prioritize land conservation efforts. MORE INFORMATION - ftp://www.pasda.psu.edu/pub/pasda/chesbp/Vulnerability_methods3.doc
ftp://www.pasda.psu.edu/pub/pasda/chesbp/Vulnerability_poster.pdf
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| Chesapeake Bay Program |
2023 |
This dataset contains Address Points in Chester County
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| Chester County |
2011 |
One of the planimetric layers developed and owned by Chester County as part of the Chester County Land Records System (ChesCO-LRS) project. The airports were photogrammetrically compiled (stereo digitized) from the aerial photography taken April 07, 1993.
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| Chester County |
2013 |
One of the planimetric layers developed and owned by Chester
County as part of the Chester County Land Records System
(ChesCO-LRS) project. The bridge data was photogrammetrically compiled (stereo digitized) from the aerial photography taken April 07, 1993 and other sources.
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| Chester County |
2015 |
Footprints for all buildings and out buildings in Chester County.
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| Chester County |
2013 |
One of the planimetric layers developed and owned by Chester
County as part of the Chester County Land Records System
(ChesCO-LRS) project. The cemetery data was photogrammetrically compiled (stereo digitized) from the aerial photography taken April 07, 1993.
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| Chester County |
2011 |
The data describes the boundary of the Chester County. The boundary was captured from the County tax assessment maps. Original maps at 1"=400' and 1"=100' scales were compiled at 1"=200' through the digital tax parcel conversion project.
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| Chester County |
2020 |
Polling place locations and information within Crawford County PA
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| Chester County |
2013 |
Higher education facilities in Chester County. The attributing
contains education facility name.
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| Chester County |
2023 |
The data describes the municipal boundaries within Chester County. There are 73 municipalities within Chester County. The boundaries were captured from the County tax assessment maps. Original maps at 1"=400' and 1"=100' scales were compiled at 1"=200' through the digital tax parcel conversion project.
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| Chester County |
2023 |
Boundary outlines of individual properties in Chester County
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| Chester County |
2013 |
One of the planimetric layers developed and owned by Chester County as part of the Chester County Land Records System (ChesCO-LRS) project. The railroads data was photogrammetrically compiled (stereo digitized) from the aerial photography taken April 07, 1993.
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| Chester County |
2020 |
This dataset contains road centerlines for vehicular and foot traffic in Chester County.
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| Chester County |
2013 |
School Districts in Chester County.
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| Chester County |
2013 |
Schools in Chester County with their names in the attribute files.
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| Chester County |
2017 |
Zipcodes of Chester County
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| Chester County |
2013 |
2003 Municipal Edits with the 2003 County Edit (removal of natural), along with additional county edits (extending underlying landscapes to align to center of certain natural features)
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| Chester County |
2013 |
Natural Features Overlay
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| Chester County |
2011 |
Public landings and other river access points to the Choptank River and its tributaries, on Maryland's Eastern Shore.
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| Choptank River Heritage |
2011 |
Shows environmental education and preservation sites in or near the watershed of the Choptank River, on Maryland's Eastern Shore, that are of interest to tourism planners, historians, and environmentalists.
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| Choptank River Heritage |
2011 |
Shows historic sites in or near the watershed of the Choptank River, on Maryland's Eastern Shore, that are of interest to tourism planners, historians, and environmentalists.
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| Choptank River Heritage |
2011 |
This map shows NRHP sites in the Mid-Shore counties of Kent, Queen Anne, Caroline, Talbot, and Dorchester, on Maryland's Eastern Shore. Map data was provided to the Choptank River Heritage Center (CRHC) by the office of the Chief Archeologist/GIS Coordinator, Maryland Historical Trust, Maryland Department of Planning, 100 Community Place, Crownsville, MD 21032.
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| Choptank River Heritage |
2010 |
Shows watershed of the Choptank River and its tributaries, on Maryland's Eastern Shore.
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| Choptank River Heritage |
2025 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983
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| City of Philadelphia |
2016 |
PhillyRising’s 19 active neighborhoods as of FY15. These areas were identified in partnership with the Police Department for their high rates of violent crime and quality of life issues, including poverty. PhillyRising strives to advance City services and community partnerships within these neighborhoods.
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| City of Philadelphia |
2016 |
The Choice Neighborhoods program is administered by the U.S. Department of Housing and Urban Development (HUD). It supports locally driven strategies to address struggling neighborhoods with distressed public or HUD-assisted housing through a comprehensive approach to neighborhood transformation.
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| City of Philadelphia |
2025 |
boat launches along the two major bodies of water, the Schuylkill River and the Delaware River. These boat launched have direct access into either one of these bodies of water.
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| City of Philadelphia |
2025 |
The points of pools owned and managed by Philadelphia Parks and Recreation. This layer was created to be used for the Philadelphia Parks and Recreation website.
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| City of Philadelphia |
2025 |
The points of spraygounds owned and managed by Philadelphia Parks and Recreation. This layer was created to be used for the Philadelphia Parks and Recreation website.
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| City of Philadelphia |
2025 |
Displays the locations of adult exercise equipment located within or are maintained by Philadelphia Parks and Recreation (PPR).
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| City of Philadelphia |
2025 |
Footprints of buildings and structures located on Philadelphia Parks and Recreation (PPR) properties or utilized directly by PPR.
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| City of Philadelphia |
2025 |
Point representations of Philadelphia Parks and Recreation (PPR) help locators. These are virtual points representing signage locations and their vehicular access points.
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| City of Philadelphia |
2025 |
Displays the locations of playgrounds within PPR Boundaries. Playgrounds designated as similar age range equipment within a definable distance (not each piece of equipment).
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| City of Philadelphia |
2025 |
Point location feature for all PPR Program Sites. This includes all recreation centers, playgrounds, older adult centers, swimming pools, and environmental education centers.
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| City of Philadelphia |
2025 |
Location and boundaries of lands that Philadelphia Parks and Recreation is responsible or has a distinct role in maintaining or managing.
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| City of Philadelphia |
2025 |
Displays the locations of outdoor tennis courts located within Philadelphia Parks and Recreation boundaries.
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| City of Philadelphia |
2023 |
Locations of trees that Philadelphia Parks and Recreation (PPR) inventories within the limits of the City of Philadelphia. This dataset is a snapshot in time.
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| City of Philadelphia |
2004 |
Planimetric Coverage containing the delineation of topographic contours at ten foot intervals. Annotation of Contour Line elevations exists. Annotation viewable at 1" = 200'. The city-wide contour download file is approximately 250 megabytes.
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| City of Philadelphia |
2004 |
Planimetric Coverage containing the delineation of topographic contours at two foot intervals. Annotation of Contour Line elevations exists. Annotation viewable at 1" = 200'. The city-wide contour download file is approximately 250 megabytes.
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| City of Philadelphia |
2008 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2008. DEMs were generated from the raw data.
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| City of Philadelphia |
2010 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2010. DEMs were generated from the raw data.
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| City of Philadelphia |
2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. The GIS Services Group at OIT generated these 10ft Contours for the 2015 1ft Contours.
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| City of Philadelphia |
2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. Lidar Data Products for the Philadelphia, PA collection area including a 5ft Digital Elevation Model (DEM), and tiled1ft Contours.
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| City of Philadelphia |
2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. The GIS Services Group at OIT generated these 2ft Contours from the 2015 1ft Contours.
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| City of Philadelphia |
2018 |
DEM/Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2018 |
DEM/Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEM/Hillshade was generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2018 |
Planimetric Coverage containing the delineation of topographic contours at ten foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels
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| City of Philadelphia |
2018 |
Planimetric Coverage containing the delineation of topographic contours at two foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2022 |
Contours 10ft - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2022 |
DEM - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2022 |
Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2022 |
Topographic Contours 1ft - Planimetric Coverage containing the delineation of topographic contours at one foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
1996 |
Philadelphia aerial photography
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| City of Philadelphia |
1996 |
Philadelphia aerial photography
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| City of Philadelphia |
2000 |
Philadelphia aerial photography
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| City of Philadelphia |
2000 |
Philadelphia aerial photography
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| City of Philadelphia |
2004 |
Philadelphia aerial photography
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| City of Philadelphia |
2004 |
Philadelphia aerial photography
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| City of Philadelphia |
2005 |
Philadelphia aerial photography
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| City of Philadelphia |
2005 |
Philadelphia aerial photography
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| City of Philadelphia |
2008 |
Philadelphia aerial photography
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| City of Philadelphia |
2008 |
Philadelphia aerial photography
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| City of Philadelphia |
2009 |
Philadelphia aerial photography
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| City of Philadelphia |
2009 |
Philadelphia aerial photography
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| City of Philadelphia |
2010 |
Philadelphia aerial photography
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| City of Philadelphia |
2010 |
Philadelphia aerial photography
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| City of Philadelphia |
2011 |
Philadelphia aerial photography
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| City of Philadelphia |
2011 |
Philadelphia aerial photography - Leaf On
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| City of Philadelphia |
2011 |
Philadelphia aerial photography
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| City of Philadelphia |
2011 |
Philadelphia aerial photography
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| City of Philadelphia |
2012 |
Philadelphia aerial photography
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| City of Philadelphia |
2012 |
Philadelphia aerial photography
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| City of Philadelphia |
2014 |
Philadelphia aerial photography
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| City of Philadelphia |
2014 |
Philadelphia aerial photography
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| City of Philadelphia |
2015 |
Philadelphia aerial photography
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| City of Philadelphia |
2015 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography 3 inch pixels tile index. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography city wide mosaic. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 1 meter pixels. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 1 meter pixels tile index. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 3 inch pixels. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 3 inch pixels tile index. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2018 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2018 |
Tile Index - Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2019 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2019 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2020 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2020 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2022 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2022 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2023 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2023 |
Tile Index - Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2024 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2024 |
Tile Index - Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2012 |
Philadelphia bike network and supporting datasets is an experimental dataset that combines various City of Philadelphia departments' datasets to support bike network routing. These datasets are in an experimental stage and are not yet approved for wide use. Use with caution is recommended. Datasets include Bike Network, Connector Streets, Regional Routes, and Trails and Side paths. For source, date, limitations and additional data for each supporting dataset, please see metadata.
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| City of Philadelphia |
2012 |
This dataset is a combination of both bike racks installed in 2008 as a result of Adopt-a-rack program and racks installed by converting parking meters by the Philadelphia Parking Authority.
Those racks installed in 2008 as part of the City's first Adopt-a-rack program were requested by civic and business associations who "adopted" them and promised to clear abandoned bikes, remove graffiti and maintain them if they were damaged. These bike racks are in the style of 'staple' or 'hoop' racks.
The PPA racks are those mounted to the former parking meter posts. They are the green discs that have been installed through Center City and parts of University City.
The PROJECT field defines the associated project of installation, either Adopt-a-rack or PPA.
The STAND_ADD field is most appropriate for address locating or geocoding purposes.
Please note the field NUM_RACKS represents the number of racks at a given address. Each record may represent more than one bike rack.
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| City of Philadelphia |
2008 |
Files used to comprise a 3D building model of the City of Philadelphia
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| City of Philadelphia |
2010 |
Files used to comprise a 3D building model of the City of Philadelphia
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| City of Philadelphia |
2015 |
Files used to comprise a 3D building model of the City of Philadelphia
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| City of Philadelphia |
2017 |
Planimetric Coverage containing the delineation of buildings or related structure outlines that represent the footprints of buildings within the City of Philadelphia.
Outlines are based on imagery captured in early 2015.
FEATURES DELINEATED:
-Residential, commercial and industrial buildings
-Isolated garages, mobile homes, sheds
-Greenhouses and silos
-Buildings under construction that at least have walls
-Trailer boxes with windows or doors
Data was captured from AccuPLUS Orthomosaic tiles created off PAPHIL15-LEAF-OFF imagery, flown by Pictometry.
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| City of Philadelphia |
2010 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas. Some boundary details clarified by Philadelphia City Planning Commission May 2011
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| City of Philadelphia |
2025 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas. Some boundary details clarified by Philadelphia City Planning Commission May 2011
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| City of Philadelphia |
2025 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas. Some boundary details clarified by Philadelphia City Planning Commission May 2011
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| City of Philadelphia |
2000 |
Census bureau statistical units. Most commonly used statistical unit. This version, released January 2004 contains a fix, replacing tract 172 which was omitted from previous versions.
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| City of Philadelphia |
2010 |
Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data. Census tracts generally have between 1,500 and 8,000 people, with an optimum size of 4,000 people. Counties with fewer people have a single census tract.) When first delineated, census tracts are designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over many decades so that statistical comparisons can be made from decennial census to decennial census. However, physical changes in street patterns caused by highway construction, new developments, and so forth, may require occasional boundary revisions. In addition, census tracts occasionally are split due to population growth or combined as a result of substantial population decline.Census tracts are identified by a four-digit basic number and may have a two-digit numeric suffix; or example, 6059.02. The decimal point separating the four-digit basic tract number from the two-digit suffix is shown in the printed reports and on census maps. In computer-readable files, the decimal point is implied. Many census tracts do not have a suffix; in such cases, the suffix field is either left blank or is zero-filled. Leading zeros in a census tract number (for example, 002502) are shown only in computer-readable files. Census tract suffixes may range from .01 to .98. For the 1990 census, the .99 suffix was reserved for census tracts/block numbering areas (BNAs) that contained only crews-of-vessels population; for Census 2000, the crews-of-vessels population is part of the related census tract.Census tract numbers range from 1 to 9999 and are unique within a county or statistically equivalent entity. The U.S. Census Bureau reserves the basic census tract numbers 9400 to 9499 for census tracts delineated within or to encompass American Indian reservations and off-reservation trust lands that exist in multiple states or counties. The number 0000 in computer-readable files identifies a census tract delineated to provide complete coverage of water area in territorial seas and the Great Lakes. Projection: Lambert Conformal ConicXY Coordinate System: NAD 1983 StatePlane Pennsylvania South FIPS 3702 (US Feet)Datum: NAD 1983Units of Measurement: Foot_US
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| City of Philadelphia |
2025 |
Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data. Census tracts generally have between 1,500 and 8,000 people, with an optimum size of 4,000 people. Counties with fewer people have a single census tract.) When first delineated, census tracts are designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over many decades so that statistical comparisons can be made from decennial census to decennial census. However, physical changes in street patterns caused by highway construction, new developments, and so forth, may require occasional boundary revisions. In addition, census tracts occasionally are split due to population growth or combined as a result of substantial population decline.Census tracts are identified by a four-digit basic number and may have a two-digit numeric suffix; or example, 6059.02. The decimal point separating the four-digit basic tract number from the two-digit suffix is shown in the printed reports and on census maps. In computer-readable files, the decimal point is implied. Many census tracts do not have a suffix; in such cases, the suffix field is either left blank or is zero-filled. Leading zeros in a census tract number (for example, 002502) are shown only in computer-readable files. Census tract suffixes may range from .01 to .98. For the 1990 census, the .99 suffix was reserved for census tracts/block numbering areas (BNAs) that contained only crews-of-vessels population; for Census 2000, the crews-of-vessels population is part of the related census tract.Census tract numbers range from 1 to 9999 and are unique within a county or statistically equivalent entity. The U.S. Census Bureau reserves the basic census tract numbers 9400 to 9499 for census tracts delineated within or to encompass American Indian reservations and off-reservation trust lands that exist in multiple states or counties. The number 0000 in computer-readable files identifies a census tract delineated to provide complete coverage of water area in territorial seas and the Great Lakes. Projection: Lambert Conformal ConicXY Coordinate System: NAD 1983 StatePlane Pennsylvania South FIPS 3702 (US Feet)Datum: NAD 1983Units of Measurement: Foot_US
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| City of Philadelphia |
2016 |
This polygon layer represents the Market Areas for each representative from the Department of Commerce Office of Business Services (OBS). All polygons (nearly all of which correspond to zip codes) associated with an OBS representative’s name comprise that representative’s Market Area.
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| City of Philadelphia |
2016 |
This data set reflects the recipients, award amounts, and project sites for grant money disbursed by the Philadelphia Commerce Department for the Storefront Improvement Program whereby businesses are provided the funds to improve the exterior of their storefront and beautify the commercial corridor on which they operate.
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| City of Philadelphia |
2016 |
correctional facility points in the City, administered by the Philadelphia Prisons System
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| City of Philadelphia |
1990 |
District boundaries of municipal legislators.
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| City of Philadelphia |
2000 |
District Boundaries of Municipal Legislators
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| City of Philadelphia |
2024 |
This map data layer represents the city council districts for the City of Philadelphia, PA. The city council districts data layer was determined by the boundaries of the Philadelphia County voting precincts and the City Ordinance that established the districts based on the precincts.
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| City of Philadelphia |
2024 |
This dataset is composed of the entire City of Philadelphia's parcels based on their legal descriptions.
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| City of Philadelphia |
2012 |
Commercial/industrial zones with special amenities.
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| City of Philadelphia |
2012 |
Commercial/industrial zones with special amenities
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| City of Philadelphia |
2016 |
Philadelphia Fire Department Fire Station Locations.
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| City of Philadelphia |
2016 |
Polygon boundaries of geographic market areas in the city of Philadelphia.
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| City of Philadelphia |
2025 |
Point geometric features representing planned and complete Green Stormwater Infrastructure (GSI).
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| City of Philadelphia |
2025 |
Philadelphia Green stormwater infrastructure public projects on parcels for the Big Green Map
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| City of Philadelphia |
2013 |
Green City, Clean Waters is Philadelphia's 25-year plan to protect and enhance our watersheds by managing stormwater with green infrastructure. This feature class represents Green Stormwater Infrastructure (GSI) locations that are currently in the design phase or have been constructed.
Features updated: 01/25/2013
Attributes updated: 01/25/2013
Metadata updated 01/25/2013
Update Frequency - monthly
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| City of Philadelphia |
2015 |
Planimetric Coverage containing the delineation of impervious surfaces for studying and calculating drainage runoff. This coverage shows surface features that are visible on the aerial photography, and is sometimes referred to as the landbase.
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| City of Philadelphia |
2008 |
High resolution land cover dataset for Philadelphia. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at ten square feet. The primary sources used to derive this land cover layer were 2008 Orthophotography and 2008 LiDAR LAS data. Ancillary data sources included GIS data (building footprints, road polygons, and hydrography) provided by City of Philadelphia. This land cover dataset is considered current as of 2008. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30700 corrections were made to the classification.
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| City of Philadelphia |
2018 |
High resolution land cover dataset for Philadelphia,Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2018 LiDAR data and 2017 NAIP imagery. Ancillary data sources included GIS data provided by Philadelphia,Pennsylvania or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3500 and all observable errors were corrected.
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| City of Philadelphia |
2012 |
Free Library of Philadelphia locations (branches and Central) are represented as point features. Feature Update Date: 05-2012
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| City of Philadelphia |
2008 |
LiDAR data collection performed over the City of Philadelphia, PA in April of 2008. Products generated include Breaklines, 10ft DEM and 5ft DEM.
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| City of Philadelphia |
2010 |
LiDAR data collection performed over the City of Philadelphia, PA in April of 2010. Products generated include Breaklines, 10ft DEM and 5ft DEM.
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| City of Philadelphia |
2015 |
This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2018 |
2018 LiDAR – 8ppm – Classified. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. Key attribute field names and descriptions: LiDAR Classification Categories: 0 Created Not Classified 1 Unclassified 2 Ground 3 Low vegetation 4 Vegetation 5 High vegetation 6 Building 9 Water 17 Bridge Deck
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| City of Philadelphia |
2022 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2025 |
The Litter Index is used to compare the relative cleanliness of different areas of the city of Philadelphia.
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| City of Philadelphia |
2025 |
This file features polylines representing the major trails in the Philadelphia Parks & Recreation (PPR) system. It is not comprehensive of all trails thoughout the system. Other minor and rogue trails do exist and are not represented here. Data Development:Data were created by PP&R staff using historic information from the Fairmount Park Commission and Department of Recreation data archives. The data was primarily sourced using GPS technology. In certain cases data were created through orthophotography, georeferenced civil plans, and hard copy mapsTrail data is updated on a regular basis to indicate new trails, closed trails, and trails undergoing restoration or expansion.
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| City of Philadelphia |
2000 |
District boundaries of municipal legislators.
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| City of Philadelphia |
2025 |
This feature class is the administrative boundaries for the Philadelphia Police Department's districts.
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| City of Philadelphia |
2025 |
This feature class is the administrative boundaries for the Philadelphia Police Department's divisions. Divisions are subdivided into districts.
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| City of Philadelphia |
2025 |
There are currently 65 Police Service Areas (PSA) boundaries in Philadelphia with two to four per District. These boundaries replaced a much smaller boundary, Sectors in 2009.
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| City of Philadelphia |
2016 |
The Center City District (CCD) is a business improvement district. The mission is to keep Center City clean, safe, and fun. CCD also makes phyiscal improvements to center city by installing and maintain lighting, signs, banners trees and landscape.
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| City of Philadelphia |
2016 |
This layer displays their policing boundary for the Center City District (CCD) business improvement district.
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| City of Philadelphia |
2016 |
This feature class contains polygons that illustrate 10 correctional facilities in the City, adminstered by Philadelphia Prisons System.
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| City of Philadelphia |
2016 |
This feature class contains points that represent 10 correctional facilities in the City, administered by Philadelphia Prisons System.
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| City of Philadelphia |
2016 |
Point data of all First Judicial District of PA courts.
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| City of Philadelphia |
2016 |
This data was developed for cartographic use -- specifically, as reference information for the Police Athletic League.
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| City of Philadelphia |
2025 |
Point locations of the Police District Headquarters in Philadelphia
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| City of Philadelphia |
2025 |
The shapefile is the administrative boundaries for Police Sectors. Sectors are the logical
means by which police districts are subdivided. Each district is made up of 12-30 sectors.
In theory, a police patrol car is assigned to each sector and a supervisor for each 3-4 sectors.
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| City of Philadelphia |
2016 |
Political subdivisions. Feature Update Date: 09-2012
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| City of Philadelphia |
2016 |
Polygon boundaries of PPR's operational districts as established by PPR's GIS staff and reviewed, revised, and approved by PPR's executive staff.
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| City of Philadelphia |
2009 |
This is a boundary file identifying Philadelphia development certified areas
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| City of Philadelphia |
2012 |
Historic Sites on the National Register. Updated by the Planning Commission in Fall, 2010.
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| City of Philadelphia |
2012 |
Historic Sites on the Philadelphia Register. Updated by the Planning Commission in Fall, 2010.
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| City of Philadelphia |
2016 |
The location of schools in the City of Philadelphia with attribute information for address, grade level, type, and status indicating whether the school is open, proposed for closure or closed. The status field is intended to provide up to date information on school facility management conducted by all institution types.KEY ATTRIBUTE FIELDSGradeLevel – generalized description of grade levels as elementary, middle or high schoolGradeOrg – Grade Organizational Levels - estimate of grade levels range at the school, e.g., K-5Instit_Type – Institutional Type - public, private, parochial or charterActive – The current designation as active, closed or closing with expected closure date. Feature Update Date: 03-2012
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| City of Philadelphia |
2016 |
Inventory of fixed assets owned or leased by the City of Philadelphia including buildings, structures, piers, and properties (not including surplus properties). Also known as the Master Facilities database. Critical infrastructure omitted from public version.
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| City of Philadelphia |
2016 |
This layer contains the railroad line capture from 2004 orthophotography. Sanborn’s methods for capturing railroad lines included aerial imagery and the 2004 DEM.
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| City of Philadelphia |
2004 |
This layer contains the railroad line capture from 2004 orthophotography. Sanborn’s methods for capturing railroad lines included aerial imagery and the 2004 DEM. This is one of the planimetric coverages developed as part of the aerial survey project of 1996 and updated using new aerial photography collected between 25 March 2004 and 23 April 2004.
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| City of Philadelphia |
2016 |
information on solar technology installations across Philadelphia.
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| City of Philadelphia |
2025 |
Network of streets within City of Philadelphia with bike lanes and/or bike-friendly markings.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Bridge Division in maintaining and referencing the bridges of the City of Philadelphia.
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| City of Philadelphia |
2016 |
This layer identifies the point locations of the city owned bridges that are maintained by the Bridge Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer identifies the 286 city plan boundaries for the Surveys Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
Edge of drivable pavement, or travelway for all drivable pavement citywide, including the edge of traffic flow islands and drivable pavement within parks
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| City of Philadelphia |
2016 |
Polygons representing the area of drivable pavement citywide, as well as interior non-street polygons.
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| City of Philadelphia |
2016 |
Just internal polygons with "FCODE = 9999" representing the area of drivable pavement citywide, as well as interior non-street polygons.
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| City of Philadelphia |
2016 |
This layer delineates the six districts of the Highway Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer delineates the six districts of the Highway Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer delineates the fifty-six sections of the Highway Division of the City of Philadelphia Streets Department. Sections can be aggregated into districts and subdivided into subsections.
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| City of Philadelphia |
2016 |
This layer delineates the 703 subsections of the Highway Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer delineates the Arcs of the Polygons of the 703 subsections of the Highway Division of the City of Philadelphia Streets Department. Subsections can be aggregated into sections and districts.
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| City of Philadelphia |
2016 |
Street segments listed on the Philadelphia Register of Historic Places as part of the Historic Street Paving Thematic District, as amended by the Historical Commission December 12th, 2014.
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| City of Philadelphia |
2016 |
Polygons for street segments listed on the Philadelphia Register of Historic Places as part of the Historic Street Paving Thematic District, as amended by the Historical Commission December 12th, 2014.
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| City of Philadelphia |
2016 |
This layer identifies the active intersection controls for the Street Lighting and Traffic Engineering Divisions of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer identifies boundaries for City Leaf Collection Services.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
To define the boundaries for Leaf Collection within the City of Philadelphia
The arc layer contains street name attributes for labeling the outside of the polygons. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
This layer identifies boundaries for City Leaf Collection Services.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
The Litter Index is used to compare the relative cleanliness of different areas of the city of Philadelphia.
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| City of Philadelphia |
2016 |
To map streets with no through trucks in the City of Philadelphia.
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| City of Philadelphia |
2016 |
City of Philadelphia paving plan for 2015
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| City of Philadelphia |
2016 |
The Recycling Diversion Rate is the rubbish collection tonage divided by the recycling collection tonnage by sanitation on collection day.
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| City of Philadelphia |
2025 |
The data is used to determine the day of collection for a given location and set of households in the City of Philadelphia. The file is also used to aggregate data such as households, tonnage, and mileage.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
Coverage of all the sanitation areas containing line, point, and polygon data used by the City of Philadelphia.
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| City of Philadelphia |
2016 |
Coverage of all the sanitation districts containing line, point, and polygon data used by the City.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
Coverage of all the sanitation districts containing line, point, and polygon data used by the City.
The arc layer contains data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2025 |
Used citywide as base layer for many purposes/applications. The street centerline is available for reference purposes only and does not represent exact engineering specifiactions. The Philadelphia Streets Department makes no guarantees as to the accuracy of the layer.
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| City of Philadelphia |
2016 |
Created to show the City of Philadelphia's street lighting route districts.
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| City of Philadelphia |
2025 |
The street nodes layer was developed for use by agencies citywide including PWD, PCPC, Police, BRT, Health, etc.
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| City of Philadelphia |
2025 |
This layer was developed to aid the Street Lighting Division in planning, referencing, and maintaining the active street poles within the City of Philadelphia. Examples include: providing information regarding group replacement projects and any individual edits, using tables from layer for billing, and aiding cityworks.
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| City of Philadelphia |
2016 |
The Arterial layer was developed to aid various city agencies with planning, organizing, and maintaining the streets of the City of Philadelphia. These agencies include PWD, PCPC, Police, BRT, Health, etc.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
This is the arc file of the Traffic Districts layer. It was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
Big Belly brand waste baskets maintained/collected by the City of Philadelphia.
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| City of Philadelphia |
2016 |
Non Big Belly waste baskets maintained/collected by the City of Philadelphia
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| City of Philadelphia |
2016 |
The purpose of this dataset is to represent the Zip Code areas for the City of Philadelphia. The edges of Zip Codes are slightly modified for logical and cartographic purposes.
The arc layer contains data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
Philadelphia. The edges of Zip Codes are slightly modified for logical and cartographic purposes.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
1996 |
Planimetric Coverage containing the delineation of topographic contours. Annotation of Contour Line elevations exists. Annotation viewable at 1" = 200?
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| City of Philadelphia |
2004 |
This dataset is composed of the entire City of Philadelphia's transparcels based on their legal descriptions. Feature update 2004
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| City of Philadelphia |
2025 |
This point layer represents dams. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
This layer represents Green Stormwater Infrastructure Stormwater Management Practice types. Integrating Green Stormwater Infrastructure (GSI) into a highly developed area such as Philadelphia requires a decentralized and creative approach to planning and design. Various tools can be implemented to accomplish this, including stormwater planters, rain gardens and green roofs. All of these tools help to reduce runoff volume and filter pollutants by intercepting stormwater runoff before it enters the City's combined sewer system. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
This point layer contains all the wastewater and stormwater inlet locations. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
This point layer contains all the stormwater outfalls. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
The primary purpose of PWD_PARCEL layer is to calculate parcel-based stormwater charges for PWD customers under the new parcel-based stormwater billing program. The layer was created from the DOR_PARCELS layer in 2005 after it was decided that none of the other City parcel layers could meet the needs of PWD's stormwater billing program. Those needs are generally that the parcel delineations match up to what people actually own, that there is an accurate assessment of the impervious area on the parcel, and that there is owner information associated with the parcel. Over the past 5 years, PWD has made corrections based off deeds on file with DOR, BRT information, and other City records. PWD also matched up each DOR parcel to a corresponding BRT record that contained the owner information for that parcel.
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| City of Philadelphia |
2025 |
The purpose of this data is to describe the Rain Gauges both locationally and via their attributes.
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| City of Philadelphia |
2016 |
Combined Sewer Service area within Philadelphia
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| City of Philadelphia |
2016 |
Green City, Clean Waters is Philadelphia's 25-year plan to protect and enhance our watersheds by managing stormwater with green infrastructure. This feature class represents Green Stormwater Infrastructure (GSI) locations that are currently in the design phase or have been constructed.
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| City of Philadelphia |
2016 |
District planning areas for Green City, Clean Waters stormwater management strategic planning.
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| City of Philadelphia |
2016 |
Point geometric features representing land development projects in compliance with PWD’s Stormwater Regulations.
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| City of Philadelphia |
2016 |
Philadelphia streams as mapped by Charles Ellet in 1842 and Previous study of historic streams conducted by PWD.
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| City of Philadelphia |
2016 |
Philadelphia shoreline of Delaware River and Schuylkill River, digitized as mapped by Charles Ellet in 1842. Data is digitized from georeferenced map scans.
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| City of Philadelphia |
2025 |
Polyline geometric features representing the center flow line of all waterways in Philadelphia's five major watersheds.
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| City of Philadelphia |
2025 |
Polygon geometric features representing the waterways and impoundments in Philadelphia's five major watersheds.
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| City of Philadelphia |
2025 |
Polygon feature class representing major watersheds in the Philadelphia and Region.
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| City of Philadelphia |
2025 |
Polygon feature class represtenting major watersheds in the Philadelphia Region.
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| City of Philadelphia |
2016 |
Rain Barrel installation locations prior to RainCheck.
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| City of Philadelphia |
2025 |
RainCheck program installation sites
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| City of Philadelphia |
2016 |
Hydrographic polygon features included in Philadelphia Hydrology Map. This map was officially adopted by City Council as the official map of Philadelphia Watercoures Designated for Protection on September 13th, 2012. The geographic data depicts watercourses within Philadelphia County as they appear on the map and will not be edited or updated.
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| City of Philadelphia |
2016 |
Hydrographic polygon features included in Philadelphia Hydrology Map. This map was officially adopted by City Council as the official map of Philadelphia Watercoures Designated for Protection on September 13th, 2012. The geographic data depicts watercourses within Philadelphia County as they appear on the map and will not be edited or updated.
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| City of Philadelphia |
2016 |
The Zoning Code establishes a registry of community organizations to be managed by the Philadelphia City Planning Commission. The RCO system is designed to improve community notification of proposed developments and make community involvement more predictable across the city. RCOs became effective on August 22, 2012. Polygon boundaries of Registered Community Organizations (RCO) as established under Section 14-303 (12) of the City of Philadelphia Zoning Code enacted December 15, 2011 and made effective August 22, 2012. RCO’s register annually with the City Planning Commission and are notified of projects requiring Zoning Board approval or Civic Design Review. RCO boundaries may overlap as permitted by the Zoning Code.KEY ATTRIBUTE FIELDS:RCO Name: Name of Registered Community OrganizationType: Type of RCO.Expire Date: Date RCO registration with PCPC expiresExpire Year: Year RCO registration with PCPC expires Philadelphia Zoning-Related Websites:Registered Community Organizations -- http://www.phila.gov/CityPlanning/projectreviews/Pages/RegisteredCommunityOrganizationsZoning Code -- http://www.amlegal.com/library/pa/philadelphia.shtml and navigate to Title 14Zonng Map -- http://www.phila.gov/map On pop-up menu scroll down to 'Zoning'Zoning Administration Manual -- http://www.phila.gov/CityPlanning/projectreviews/Pages/Zoning.aspxDATA DEVELOPMENT:Features produced in ArcGIS Desktop using PA South Stateplane coordinates, NAD83, US Foot. Boundaries were constructed by PCPC using self-reported geographic descriptions by the RCO. Feature Update Date: 01-2013
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| City of Philadelphia |
2024 |
Geopolitical Areas
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| Columbia County |
2024 |
Roads of Columbia County, Pennsylvania
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| Columbia County |
2024 |
This dataset contains Address Points in Columbia County, Pennsylvania
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| Columbia County |
2024 |
Tax Parcels of Columbia County, Pennsylvania
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| Columbia County |
2010 |
Protected areas are cornerstones of national and international conservation strategies. By way of these designations, lands and waters are set-aside in-perpetuity to preserve functioning natural ecosystems, act as refuges for species, and maintain ecological processes. Complementary conservation strategies preserve land for the sustainable use of natural resources, or for the protection of significant geologic and cultural features or open space. PAD-US 1.1 (CBI Edition) attempts to include all available spatial data on these places. It is our goal to publish the most comprehensive geospatial data set of U. S. protected areas to date.
PAD-US 1.1 (CBI Edition) is limited to the continental U.S., Alaska, and Hawaii. It does not include protected areas data for U.S. territories at this time.
The PAD-US 1.1 (CBI Edition) data set portrays the nation's protected areas with a standardized spatial geometry and numerous valuable attributes on land ownership, management designations, and conservation status (using national GAP and international IUCN coding systems). The PAD-US 1.1 (CBI Edition) defines protected area to include all lands dedicated to the preservation of biology diversity and to other natural, recreation and cultural uses, and managed for these purposes through legal or other effective means (adapted from IUCN definition). The database represents the full range of conservation designations that preserve these natural resources in the United States. Our database does not distinguish a protection threshold above which biodiversity is considered secure. Instead, a complete suite of protected area attributes are provided for each polygon with the purpose of giving users the information they need to define the most relevant conservation thresholds for their own objectives and requirements. Collaborating with the nation's leading data providers, the goal is to provide an annual update.
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| Conservation Biology Institute |
2018 |
BLS (Basic Life Support - EMT) ambulance coverage within Crawford County, PA
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| Crawford County |
2018 |
Crawford County, Pennsylvania address points. Location and information of address points in Crawford County,Pennsylvania. This feature service has restricted fields available for this open data version. Certain fields have been redacted. The full dataset (as seen via the GIS mapping applications) is available via cost by contacting Crawford County. The full dataset available at cost provides all records that are not redacted by law. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
Location of aiports and information about them within Crawford County PA
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| Crawford County |
2018 |
Advanced Life Support (ALS) / paramedic service coverage within Crawford County, PA
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| Crawford County |
2018 |
Crawford County, Pennsylvania ID Points. ID points compliment the tax parcel layer and provide the location of subdivisions (general area) and individual lots. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
Campgrounds location and individual site information within Crawford County, PA
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| Crawford County |
2018 |
Locations of cemeteries, and access road information
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| Crawford County |
2018 |
Fire department area coverage in Crawford County, PA
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| Crawford County |
2018 |
Lake boundaries were digitized from various years of ortho-imagery
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| Crawford County |
2020 |
Location of landmarks and places of interest within Crawford County, PA
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| Crawford County |
2018 |
Crawford County PA municipal election precincts areas
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| Crawford County |
2024 |
Tax parcels for Crawford County PA. This information is constantly updated and maintained by the Crawford county assessment office (814-333-7302) or gisassessment@co.crawford.pa.us
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| Crawford County |
2018 |
Voting districts for the PA House of Representatives within Crawford County PA
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| Crawford County |
2018 |
PA Senate voting districts within Crawford County PA
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| Crawford County |
2018 |
Municipal and State Police reponse areas
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| Crawford County |
2020 |
Polling place locations and information within Crawford County PA
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| Crawford County |
2018 |
Locations of Fire, EMS,and Police stations and other miscelanous Public Safety agencies in Crawford County, PA
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| Crawford County |
2018 |
QRS (Quick Response Service) coverage areas within Crawford County, PA. QRS service is provided by fire departments to provide basic care until a BLS or ALS ambulance service can arrive. These are commonly found in areas in which a department doesn't run BLS/ALS services.
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| Crawford County |
2018 |
Location and information of railroad tracks in Crawford County PA. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
Location and information of recreational public trails used for hiking, biking, walking, running within Crawford County, PA
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| Crawford County |
2018 |
Rescue department coverage areas within Crawford County, PA
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| Crawford County |
2018 |
Rivers/streams digitized from various years of ortho-imagery
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| Crawford County |
2018 |
School district coverage areas in Crawford County, PA
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| Crawford County |
2018 |
Crawford County, Pennsylvania softlines. Softlines compliment the tax parcel layer and show information such as (not limited to) land hooks, streams, railroads, lot lines, free hooks. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2024 |
Crawford County, Pennsylvania street centerline. Street centerline location and information within Crawford County, Pennsylvania. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
US Congressional voting districts within Crawford County, PA
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| Crawford County |
2018 |
US Senate Voting Districts within Crawford County, PA
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| Crawford County |
2023 |
Address Points for Cumberland County, PA. These represent addressable structures.
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| Cumberland County |
2023 |
Hiking and multi-use land trails in Cumberland County, Pennsylvania.
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| Cumberland County |
2021 |
Real estate boundaries and parcel identification number for Cumberland County, Pennsylvania. These boundaries do not represent survey accuracy
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| Cumberland County |
2023 |
Public parks located in Cumberland County, Pennsylvania.
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| Cumberland County |
2023 |
Real estate boundaries and parcel identification number for Cumberland County, Pennsylvania. These boundaries do not represent survey accuracy
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| Cumberland County |
2022 |
The parcels are intended to provide a graphical representation of deeded properties in Dauphin County and be used for basemapping, thematic mapping and analysis.
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| Dauphin County |
2022 |
Road centerlines for Dauphin County. Used for the emergency dispatch system, mapping, and reference.
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| Dauphin County |
2011 |
Half meter GSD represented in Geographic Decimal Degrees, natural color (RGB), 8-bit per band digital orthophotography for approximately 14,035 square miles in Pennsylvania. The imagery was collected using the Leica Geosystems ADS40-SH51 during Fall, 2010 and Spring, 2011 at an average altitude of 4,800 meters above ground level. An auto correlated elevation model was used as vertical control. Airborne GPS/IMU was used as a basis for Analytical Aerial Triangulation (AT). Following rectification of imagery, manually placed seamlines were used to mosaic into a seamless coverage. The orthophotography is georeferenced to Geographic NAD83 decimal degrees.
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| DCNR PAMAP Program |
2011 |
Tile Index - Half meter GSD represented in Geographic Decimal Degrees, natural color (RGB), 8-bit per band digital orthophotography for approximately 14,035 square miles in Pennsylvania. The imagery was collected using the Leica Geosystems ADS40-SH51 during Fall, 2010 and Spring, 2011 at an average altitude of 4,800 meters above ground level. An auto correlated elevation model was used as vertical control. Airborne GPS/IMU was used as a basis for Analytical Aerial Triangulation (AT). Following rectification of imagery, manually placed seamlines were used to mosaic into a seamless coverage. The orthophotography is georeferenced to Geographic NAD83 decimal degrees.
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| DCNR PAMAP Program |
2010 |
(2010) Pennsylvania area natural color seamless orthoimagery acquired by Aerials Express at a 45cm pixel-resolution. Flight operations began on 12/11/09 and ended on 04/10/10 using an (DMC) camera with an approximate forward overlap of 60% and side overlap of 30% with an approximate Ground Sample Distance of (44 cm). The dataset is projected as Universal Transverse Mercator (UTM) 17 on the North American Datum of 1983. The PAMAP Program LiDAR Data of Pennsylvania; West Virginia Statewide Digital Elevation Models; USGS National Elevation Dataset (NED) - (Used in the respective sequential order) were utilized as the Digital Elevation Model in ortho-processing.
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| DCNR PAMAP Program |
2010 |
TILE INDEX North - (2010) Pennsylvania area natural color seamless orthoimagery acquired by Aerials Express at a 45cm pixel-resolution. Flight operations began on 12/11/09 and ended on 04/10/10 using an (DMC) camera with an approximate forward overlap of 60% and side overlap of 30% with an approximate Ground Sample Distance of (44 cm). The dataset is projected as Universal Transverse Mercator (UTM) 17 on the North American Datum of 1983. The PAMAP Program LiDAR Data of Pennsylvania; West Virginia Statewide Digital Elevation Models; USGS National Elevation Dataset (NED) - (Used in the respective sequential order) were utilized as the Digital Elevation Model in ortho-processing.
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| DCNR PAMAP Program |
2010 |
TILE INDEX South - (2010) Pennsylvania area natural color seamless orthoimagery acquired by Aerials Express at a 45cm pixel-resolution. Flight operations began on 12/11/09 and ended on 04/10/10 using an (DMC) camera with an approximate forward overlap of 60% and side overlap of 30% with an approximate Ground Sample Distance of (44 cm). The dataset is projected as Universal Transverse Mercator (UTM) 17 on the North American Datum of 1983. The PAMAP Program LiDAR Data of Pennsylvania; West Virginia Statewide Digital Elevation Models; USGS National Elevation Dataset (NED) - (Used in the respective sequential order) were utilized as the Digital Elevation Model in ortho-processing.
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| DCNR PAMAP Program |
2006 - 2008 |
Mosaics of PAMAP DEMs by PAMAP Lidar Delivery Zones -
This dataset, produced by the PAMAP Program, consists of a raster digital elevation model with a horizontal ground resolution of 3.2 feet. The model was constructed from PAMAP LiDAR (Light Detection and Ranging) elevation points. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
2006 - 2008 |
This dataset, produced by the PAMAP Program, consists of topographic contours mapped at an interval of 2 feet. Contours were derived from a bare-earth digital elevation model constructed from PAMAP LiDAR (Light Detection and Ranging) elevation points. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
2006 - 2008 |
This dataset, produced by the PAMAP Program, consists of a raster digital elevation model with a horizontal ground resolution of 3.2 feet. The model was constructed from PAMAP LiDAR (Light Detection and Ranging) elevation points. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
2007 |
This dataset consists of building/structure/address points aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
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| DCNR PAMAP Program |
2007 |
This dataset consists of building outlines aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
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| DCNR PAMAP Program |
2003 - 2006 |
Orthoimagery for south-central Pennsylvania captured in April of 2003. An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. For this dataset, the natural color orthoimages were produced at 2-feet pixel resolution. The design accuracy is estimated not to exceed 4.8 feet at the 95% confidence level. Each orthoimage provides imagery for a 10,000 by 10,000 feet block on the ground. The projected coordinate system is Pennsylvania State Plane with a NAD83 datum. There is no image overlap been adjacent files. The ortho image filenames were derived from the northwest corner of each ortho tile using the first four digits of the northing and easting coordinates referenced to the Pennsylvania State Plane coordinate system, followed by the State designator "PA", and the State Plane zone designator "S".
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| DCNR PAMAP Program |
2007 - 2008 |
This dataset, produced by the PAMAP Program, consists of an orthorectified digital raster image (i.e. orthoimage) with a horizontal ground resolution of 1 foot. An orthoimage is a remotely sensed image that has been positionally corrected for camera lens distortion, vertical displacement, and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Source images were captured in natural color at a negative scale of 1:19200. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
2003 - 2006 |
County Mosaics MR.SID format - An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The PAMAP 2005 natural color orthoimages were produced at 1-foot pixel resolution. Each orthoimage provides imagery for a 10,000 x 10,000 ft. block on the ground. The projected coordinate system is Pennsylvania State Plane South with a NAD83 datum. There is no image overlap been adjacent files. The orthoimage filenames were derived from the northwest corner of each ortho tile using the first four digits of the northing and easting coordinates referenced to the Pennsylvania State Plane coordinate system, followed by the State designator "PA," and the State Plane zone designator "S." This dataset consists of 10000 x 10000 ft. uncompressed natural color (24-bit) GeoTIFF files at a pixel resolution of 1 foot. The imagery was captured at a negative scale of 1:19200 for the purpose of producing orthophotos.
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| DCNR PAMAP Program |
2007 - 2008 |
County Mosaics JPEG 2000 format - This dataset, produced by the PAMAP Program, consists of an orthorectified digital raster image (i.e. orthoimage) with a horizontal ground resolution of 1 foot. An orthoimage is a remotely sensed image that has been positionally corrected for camera lens distortion, vertical displacement, and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Source images were captured in natural color at a negative scale of 1:19200. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
2007 |
This dataset consists of hydrography (streams, rivers) aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
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| DCNR PAMAP Program |
2007 |
This dataset consists of hydrography (waterbodies) aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
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| DCNR PAMAP Program |
2006 - 2008 |
This dataset consists of classified LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
2007 |
This dataset consists of county and municipal boundaries aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
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| DCNR PAMAP Program |
2007 |
This dataset consists of railroad lines aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
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| DCNR PAMAP Program |
2007 |
This dataset consists of road centerlines aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
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| DCNR PAMAP Program |
2005 |
PAMAP 10,000 feet x 10,000 feet tile index covering counties in the southern State Plane zone of Pennsylvania. This version has been updated to include additional tiles within a 5000 feet buffer of the Pennsylvania border. Also, the one tile overlap along the border between the north-south State Plane zones has been removed.
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| DCNR PAMAP Program |
2005 |
PAMAP 10,000 feet x 10,000 feet tile index covering counties in the southern State Plane zone of Pennsylvania. This version has been updated to include additional tiles within a 5000 feet buffer of the Pennsylvania border. Also, the one tile overlap along the border between the north-south State Plane zones has been removed.
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| DCNR PAMAP Program |
2006 - 2008 |
This dataset, provided by the PAMAP Program, consists of vectors used to classify LiDAR points and aesthetically enhance contour lines. The vectors are commonly delineated along features such as road edges, railroads, bridge decks, double line hydro (20' wide and greater), lakes and ponds, swamps and marshes, and extreme terrain breaks (cliffs, retaining walls, etc.). PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
2022 |
Trails of Delaware County, Pennsylvania
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| Delaware County |
2021 |
Protected Lands of Delaware County, Pennsylvania
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| Delaware County |
2023 |
This dataset contains street centerlines for vehicular and foot traffic in Delaware County, Pennsylvania
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| Delaware County |
2023 |
This polygon layer represents School District boundaries of Delaware County, Pennsylvania
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| Delaware County |
2023 |
This layer includes all of the current sidewalks of Delaware County, Pennsylvania
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| Delaware County |
2023 |
Municipal Zoning of Delaware County, Pennsylvania
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| Delaware County |
2004 |
108th Congressional Districts for the Delaware River Basin Area. These were compiled from the 4 basin states and combined into one layer.
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| Delaware River Basin Commission DRBC |
2004 |
County Seats (county capitals) for the Delaware River Basin Area
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| Delaware River Basin Commission DRBC |
2004 |
line boundary of the Delaware River basin
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| Delaware River Basin Commission DRBC |
2004 |
polygon boundary of the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
watershed boundaries for the Delaware River Basin at the HUC 8 level
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| Delaware River Basin Commission DRBC |
2004 |
County boundaries clipped to the Delaware River Basin.
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| Delaware River Basin Commission DRBC |
2004 |
County boundaries that touch the Delaware River Basin, not clipped to the basin boundary
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| Delaware River Basin Commission DRBC |
2004 |
creeks and rivers clipped to the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
major cities and towns within Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
Reservoirs located within the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
roads clipped to the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
sub-basins within the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
watershed boundaries for the Delaware River Basin at the HUC 11 level
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| Delaware River Basin Commission DRBC |
2004 |
Boundary of the Delaware river within the Delaware River Basin.
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| Delaware River Basin Commission DRBC |
2004 |
Bridges over the Delaware river within the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
islands located on the Delaware River within the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
Line boundary for the state of Delaware
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| Delaware River Basin Commission DRBC |
2004 |
Delaware watersheds within the Delaware River Basin.
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| Delaware River Basin Commission DRBC |
2004 |
Major municipal boundaries for the Delaware River Basin Area 2004
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| Delaware River Basin Commission DRBC |
2004 |
Municipal boundaries for New York clipped to the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
Municipal boundaries for Pennsylvania clipped to the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
New Jersey municipal boundaries
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| Delaware River Basin Commission DRBC |
2004 |
Route of the Cape May-Lewis ferry
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| Delaware River Basin Commission DRBC |
2004 |
Watershed boundaries for New York clipped to the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
Watershed boundaries for Pennsylvania clipped to the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
Watersheds for New Jersey coded to 14 digit Hydrologic Unit Codes (HUC) clipped to the Delaware River Basin
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| Delaware River Basin Commission DRBC |
2004 |
boundary of the West Branch of the Delaware River
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| Delaware River Basin Commission DRBC |
2000 |
An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map.
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| Delaware Valley Regional Planning Commission |
2000 |
Tile Index - An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map.
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| Delaware Valley Regional Planning Commission |
2005 |
Mosaic - An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. This project consists of the creation of 3-band, 24 bit color digital orthophoto tiles for the 5-county, Pennsylvania portion DVRPC?s region utilizing a Leica ADS40 digital imaging system. The tiles were delivered in both GeoTIFF and MrSID MG3 formats. A GeoTIFF is a TIFF file which has geographic (or cartographic) data embedded as tags within the TIFF file. The geographic data can then be used to position the image in the correct location and geometry within a geographic information system (GIS) display. MrSID (Multi-resolution Seamless Image Database) is a proprietary, wavelet-based, image compression file format (*.sid) developed and patented by LizardTech, Inc. A 20:1 compression ratio was used for the MrSIDs. The complete data set contains 1,540 full ortho tiles in Pennsylvania State Plane South coordinate system, NAD83. The individual tiles measure 5,055' x 8,745' at a 1.0' pixel size. There is no image overlap between adjacent tiles. Counties include: Bucks, Chester, Delaware, Montgomery, and Philadelphia.
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| Delaware Valley Regional Planning Commission |
2005 |
An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. This project consists of the creation of 3-band, 24 bit color digital orthophoto tiles for the 5-county, Pennsylvania portion DVRPC?s region utilizing a Leica ADS40 digital imaging system. The tiles were delivered in both GeoTIFF and MrSID MG3 formats. A GeoTIFF is a TIFF file which has geographic (or cartographic) data embedded as tags within the TIFF file. The geographic data can then be used to position the image in the correct location and geometry within a geographic information system (GIS) display. MrSID (Multi-resolution Seamless Image Database) is a proprietary, wavelet-based, image compression file format (*.sid) developed and patented by LizardTech, Inc. A 20:1 compression ratio was used for the MrSIDs. The complete data set contains 1,540 full ortho tiles in Pennsylvania State Plane South coordinate system, NAD83. The individual tiles measure 5,055' x 8,745' at a 1.0' pixel size. There is no image overlap between adjacent tiles.
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| Delaware Valley Regional Planning Commission |
2005 |
TILE INDEX - An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. This project consists of the creation of 3-band, 24 bit color digital orthophoto tiles for the 5-county, Pennsylvania portion DVRPC?s region utilizing a Leica ADS40 digital imaging system. The tiles were delivered in both GeoTIFF and MrSID MG3 formats. A GeoTIFF is a TIFF file which has geographic (or cartographic) data embedded as tags within the TIFF file. The geographic data can then be used to position the image in the correct location and geometry within a geographic information system (GIS) display. MrSID (Multi-resolution Seamless Image Database) is a proprietary, wavelet-based, image compression file format (*.sid) developed and patented by LizardTech, Inc. A 20:1 compression ratio was used for the MrSIDs. The complete data set contains 1,540 full ortho tiles in Pennsylvania State Plane South coordinate system, NAD83. The individual tiles measure 5,055' x 8,745' at a 1.0' pixel size. There is no image overlap between adjacent tiles.
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| Delaware Valley Regional Planning Commission |
2005 |
An elevation or topgraphic contour is a line that connects a series of points of equal elevation and is used to illustrate topography, or relief, on a map. It shows the height of ground above Mean Sea Level (M.S.L.). Numerous contour lines that are close together indicate hilly or mountainous terrain; when far apart, they represent a gentler slope. This layer consists of contours at a five foot interval for DVRPC's 9-county region and was generated from an aerial topographic survey in 2005.The Delaware Valley Regional Planning Commission's (DVRPC) 9-county region is made up of the following: Bucks, Chester, Delaware, Montgomery, and Philadelphia counties in Pennsylvania; and Burlington, Camden, Gloucester, and Mercer counties in New Jersey.
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| Delaware Valley Regional Planning Commission |
2005 |
A spot elevation is an elevation measurement taken at a single location. It shows the height of ground above Mean Sea Level (M.S.L.). This layer consists of spot elevations for DVRPC's 9-county region and was generated from an aerial topographic survey in 2005.The Delaware Valley Regional Planning Commission's (DVRPC) 9-county region is made up of the following: Bucks, Chester, Delaware, Montgomery, and Philadelphia counties in Pennsylvania; and Burlington, Camden, Gloucester, and Mercer counties in New Jersey.
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| Delaware Valley Regional Planning Commission |
2010 |
Mosaic - An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. This project consists of the creation of 3-band, 24 bit color digital orthophoto tiles for the 5-county, Pennsylvania portion DVRPC?s region utilizing a Leica ADS40 digital imaging system. The tiles were delivered in both GeoTIFF and MrSID MG3 formats. A GeoTIFF is a TIFF file which has geographic (or cartographic) data embedded as tags within the TIFF file. The geographic data can then be used to position the image in the correct location and geometry within a geographic information system (GIS) display. MrSID (Multi-resolution Seamless Image Database) is a proprietary, wavelet-based, image compression file format (*.sid) developed and patented by LizardTech, Inc. A 20:1 compression ratio was used for the MrSIDs. The complete data set contains 1,540 full ortho tiles in Pennsylvania State Plane South coordinate system, NAD83. The individual tiles measure 5,055' x 8,745' at a 1.0' pixel size. There is no image overlap between adjacent tiles. Counties include: Bucks, Chester, Delaware, Montgomery, and Philadelphia.
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| Delaware Valley Regional Planning Commission |
2010 |
An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. This project consists of the creation of 3-band, 24 bit color digital orthophoto tiles for the 5-county, Pennsylvania portion DVRPC?s region utilizing a Leica ADS40 digital imaging system. The tiles were delivered in both GeoTIFF and MrSID MG3 formats. A GeoTIFF is a TIFF file which has geographic (or cartographic) data embedded as tags within the TIFF file. The geographic data can then be used to position the image in the correct location and geometry within a geographic information system (GIS) display. MrSID (Multi-resolution Seamless Image Database) is a proprietary, wavelet-based, image compression file format (*.sid) developed and patented by LizardTech, Inc. A 20:1 compression ratio was used for the MrSIDs. The complete data set contains 1,540 full ortho tiles in Pennsylvania State Plane South coordinate system, NAD83. The individual tiles measure 5,055' x 8,745' at a 1.0' pixel size. There is no image overlap between adjacent tiles.
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| Delaware Valley Regional Planning Commission |
2010 |
TILE INDEX - An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. This project consists of the creation of 3-band, 24 bit color digital orthophoto tiles for the 5-county, Pennsylvania portion DVRPC?s region utilizing a Leica ADS40 digital imaging system. The tiles were delivered in both GeoTIFF and MrSID MG3 formats. A GeoTIFF is a TIFF file which has geographic (or cartographic) data embedded as tags within the TIFF file. The geographic data can then be used to position the image in the correct location and geometry within a geographic information system (GIS) display. MrSID (Multi-resolution Seamless Image Database) is a proprietary, wavelet-based, image compression file format (*.sid) developed and patented by LizardTech, Inc. A 20:1 compression ratio was used for the MrSIDs. The complete data set contains 1,540 full ortho tiles in Pennsylvania State Plane South coordinate system, NAD83. The individual tiles measure 5,055' x 8,745' at a 1.0' pixel size. There is no image overlap between adjacent tiles.
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| Delaware Valley Regional Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the DVRPC - PA project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Delaware Valley Regional Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the DVRPC - PA project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Delaware Valley Regional Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the DVRPC - PA project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Delaware Valley Regional Planning Commission |
2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
2020 |
This orthoimagery consists of 1-foot pixel resolution, 3-band, natural color county mosaics in JPEG 2000 format covering the Delaware Valley Regional Planning Commission’s (DVRPC) 9-county region (Bucks, Chester, Delaware, Montgomery, and Philadelphia counties in Pennsylvania; and Burlington, Camden, Gloucester, and Mercer counties in New Jersey). This orthoimagery was acquired in the late winter/early spring of 2020. An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map.
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| Delaware Valley Regional Planning Commission |
1959 - 1995 |
Prior to the year 2000, DVRPC’s aerial imagery consisted of mylar aerial photo
enlargements or “atlas sheets”. These atlas sheets were produced from 9x9" aerial
photos. The imagery dates from the years 1959, 1965, 1970, 1975, 1980, 1985, 1990,
& 1995. The 1959s and 1965s primarily cover the urbanized portion of
the DVRPC region (the DVRPC region is made up of nine counties: Bucks, Chester,
Delaware, Montgomery, and Philadelphia in Pennsylvania; Burlington, Camden,
Gloucester, and Mercer in New Jersey). Subsequent years provide full coverage of the
region, minus the occasional missing scan.
In order to increase the efficiency of using the historical aerial imagery, the sheets were
scanned into TIFF (Tagged Image File Format) files. Each TIFF file ranges between 35-
40MB in size. Unlike DVRPC’s more recent aerial imagery (2000 and later), the
historical aerials are not “orthorectified” or “orthocorrected”. In other words, they are
simply aerial images with no spatial reference or uniform scale. Through the process of
georeferencing, the scanned images can be assigned a spatial reference which will
enable them to be used more readily in a GIS environment. That said, georeferencing is
not orthorectifying or orthocorrecting. What it does allow is for the scan to be displayed
relative to other spatially referenced GIS layers. A georeferenced scan does not have
the properties of an actual orthoimage. Whereas an orthoimage can be used for making
accurate measurements, a georeferenced image cannot, as it does not have the spatial
accuracy and uniform scale of an orthoimage.
ftp://ftp.pasda.psu.edu/pub/pasda/dvrpc/DVRPC_Historical_Aerials/Indexes/DVRPC_Historical_Aerial_Index_Maps.pdf
https://www.dvrpc.org/webmaps/TileIndex/
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| Delaware Valley Regional Planning Commission |
2006 |
The Eastern Brook Trout Joint Venture (EBTJV) is the nation's first pilot project under the National Fish Habitat Initiative, which directs locally-driven efforts that build private and public partnerships to improve fish habitat. The long-term goals of the EBTJV are to develop a comprehensive restoration and education strategy to improve aquatic habitat, to raise education awareness, and to raise federal, state and local funds for brook trout conservation.
In 2005, in recognition of the need to address regional and range-wide threats to brook trout, a group of public and private entities formed the EBTJV to halt the decline of brook trout and restore fishable populations. The group spearheaded a range-wide assessment of brook trout populations and threats to brook trout and brook trout habitat in the Eastern United States (report forthcoming). Seventeen states are currently drafting strategies to prioritize policy changes and on-the-ground actions to improve water quality and restore brook trout habitat and populations in their individual state using locally-driven, incentive-based, and non-regulatory programs.
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| Eastern Brook Trout Joint Venture |
2022 |
Water Quality of the Large Discharges from Mines in the Anthracite Region of Eastern Pennsylvania. Developed by USGS to showcase the locations and water quality of the anthracite discharges
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| Eastern Pennsylvania Coalition for Abandoned Mine Reclamation |
2014 |
Boundaries of 9,895 watersheds in Pennsylvania indicated in the Pennsylvania gazetteer of streams. These boundaries enclose catchment areas for named streams officially recognized by the Board on Geographic Names and other unofficially named streams that flow through named hollows. ERRI extracted, reprojected and edgematched datasets for major watersheds produced by the Water Resources Division of the U.S. Geological Survey into this smallsheds coverage of the state of Pennsylvania. EPCAMR spatially joined PA DEP 104 Major Sheds, Act 167 Stormwater and PA River Basins layers and incorporated them into the attributes to show features as pieces of a bigger watershed (2009).
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| Eastern Pennsylvania Coalition for Abandoned Mine Reclamation |
2025 |
Boundary outlines of individual properties in Erie County. The Parcel Dataset was developed primarily for the purposes of identifying land parcels for tax billing and tax assessment purposes.
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| Erie County |
2008 |
2008 update to the boundary of the Erie National Wildlife Refuge - Coverage showing stewardship of managed conservation lands throughout the Commonwealth. Includes federal, state, county and privately owned lands including National and State Parks, Wildlife Refuges and Forests, county parks, and private conservancy lands
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| Erie National Wildlife Refuge |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2013 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2020 |
A 1% Annual Chance Flood Depth Grid represents the height of a 100 year flood surface elevation above ground or bottom of a stream channel measured in feet. The values are captured per cell of a defined area. The cell size for 1% Annual Chance Flood Depth Grids is variable. It is calculated by subtracting the height of the ground surface elevation from the surface height of the 1% annual chance flood. A Flood Depth Grid can assist a community in understanding, communicating, and relaying the variability and severity of flooding in a mapped floodplain.
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2025 |
The FIRM is the basis for floodplain management, mitigation, and insurance activities for the National Flood Insurance Program (NFIP). Insurance applications include enforcement of the mandatory purchase requirement of the Flood Disaster Protection Act, which "... requires the purchase of flood insurance by property owners who are being assisted by Federal programs or by Federally supervised, regulated or insured agencies or institutions in the acquisition or improvement of land facilities located or to be located in identified areas having special flood hazards," Section 2 (b) (4) of the Flood Disaster Protection Act of 1973. In addition to the identification of Special Flood Hazard Areas (SFHAs), the risk zones shown on the FIRMs are the basis for the establishment of premium rates for flood coverage offered through the NFIP. The DFIRM Database presents the flood risk information depicted on the FIRM in a digital format suitable for use in electronic mapping applications. The DFIRM database is a subset of the Digital FIS database that serves to archive the information collected during the FIS. The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting
data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance
flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM
Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps
(FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data,
where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).
The file is georeferenced to earth's surface using the UTM projection and coordinate system.The
specifications for the horizontal control of DFIRM data files are consistent with those required for
mapping at a scale of 1:12,000.
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| Federal Emergency Management Agency |
2005 |
various layers clipped to counties - The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting
data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance
flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM
Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps
(FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data,
where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).
The file is georeferenced to earth's surface using the UTM projection and coordinate system. The
specifications for the horizontal control of DFIRM data files are consistent with those required for
mapping at a scale of 1:12,000.
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2015 |
FEMA Historic Products - Once a FIRM or FIS has been superseded by a new version, it will be categorized as a Historic Product. After they move to this status, these products are no longer official and binding under the NFIP (National Flood Insurance Program). However, Historic Products may serve as valuable reference information and provide a record of an area's changing flood risks over time. They are made available on the Map Service Center for these purposes. Letters of Map Change (LOMC) that were issued to amend Historic Products when they were still effective are also listed here. A LOMC is a formal document that communicates an official modification to an effective Flood Insurance Rate Map (FIRM). LOMCs are issued in place of a physical alteration and re-publication of the map. Regulatory Mapping Products Flood Insurance Rate Map (FIRM) - The official map of a community on which FEMA has delineated both the special hazard areas and the risk premium zones applicable to the community. Full FIRM panels are quite large (36' x 25.875'), so most users will prefer to print out a smaller selected portion called a FIRMette. This can be accomplished by selecting "View" once you have located your FIRM or by using the FIRMette - Desktop application. Flood Insurance Study (FIS) - A compilation and presentation of flood risk data for specific watercourses, lakes and coastal flood hazard areas within a community. The FIS report provides a detailed written account of a flood hazard mapping study and its findings. Letters of Map Change (LOMC) - Documents, including different types of Letters of Map Revision (LOMR) and Letters of Map Amendment (LOMA), that are issued by FEMA to revise or amend the flood hazard information shown on the FIRM without requiring the FIRM to be physically revised and republished. In addition, FEMA issues a formal determination letter, called a LOMC Revalidation or LOMC-VALID letter - when one or more previously issued LOMCs are found to still be valid during a new flood mapping study of an area. Revalidation letters are included in the LOMC product results on this site.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2013 |
A HAZUS flood analysis is an estimation of economic and social losses associated with a specific return period of flooding. Results are provided as direct and indirect losses, and direct and induced damages. Components which are modeled in the flood analysis include direct damage to buildings, essential facilities, transportation facilities and lifelines. Other components include direct losses associated with the cost of repair or replacement of buildings, essential facilities, transportation facilities, and income loss, shelter and recovery needs.
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2024 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2018 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2024 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2024 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2023 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2018 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2012 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2011 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2002 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2024 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2014 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2017 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2023 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2016 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2017 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2017 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2010 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2023 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2011 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2015 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2017 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2022 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2012 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2020 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2023 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2024 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2018 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2016 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2016 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2025 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2023 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2008 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2024 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2023 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2000 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2014 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2015 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2021 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2014 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2017 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2020 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2019 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2016 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2012 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2020 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Dataum of 1983 (NSRS-2007).
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| Federal Emergency Management Agency |
2025 |
The geographic extent of individual parcels within Franklin County with NO associated CAMA (Computer Assisted Mass Appraisal - tax base) or parcel information. Polygons derived from original documents (deeds & surveys) by the Sidwell Co in 2013 and maintained since by County GIS staff.
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| Franklin County |
2021 |
GIS dataset for GeoCoding and Routing. Multiple Centerline datasets can be used for GeoCoding as sub-locators can be created and configured within a Composite Locator.
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| Fulton County |
2021 |
Boundary outlines of individual properties in Fulton County.
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| Fulton County |
2021 |
Structures resents all officially addressed and non-addressed structures in Fulton County, PA. Addressed structures may include but not limited to buildings of residence, work, or recreation
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| Fulton County |
2025 |
The project contains mapping data for a Berks Nature Return on Environment (ROE) project. The work was prepared for Berks Nature to support its conservation activities in Berks County, Pa. as part of its Business of Nature project. Project descriptions can be found on the Berks Nature website at https://berksnature.org/land-protection/business-of-nature/ . The project contains the GIS datasets used in the ROE evaluations and online applications. The contents, which include metadata descriptions, include several feature datasets, as follows: 1) BC Study Area– The study area which encompasses all portions of Berks County Pa. 2) BC ROE Parcels – Parcels in the project study area with attribute values 3) BC Riparian Buffers – Riparian buffer areas adjacent to surface water features 4) BC ROE Values by Acre – Berks Nature Return on Environment Findings 5) BC Green Ribbon Landscapes – Conservation buffer areas 6) BC ROE Recommended Actions – Areas designated for priority conservation consideration 7) BC Forest Size – Forested areas classified by size 8) ROE Findings raster - The project also contains a raster dataset version of the BC ROE Values by Acre used to assist in online rendering.
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| Greener Planning |
2023 |
The GIS file includes results from the 2022 Lebanon County Return on Environment (ROE) study. Findings include values for natural systems services, protection status, Green Ribbon Landscapes (GRL) location and priorities (recommended actions ). The values are derived using a methodology that calculates ROE values using information on site conditions, peer review studies, and defined ecoprice values. ROE findings from 2022 Lebanon County Return on Environment study. The feature dataset includes values for natural systems services, priorities (actions), protection status, and Green Ribbon Landscapes location.
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| Greener Planning |
2023 |
The GIS file includes results from the 2022 Monroe County Return on Environment (ROE) study. Findings include values for natural systems services, protection status, Green Ribbon Landscapes (GRL) location and priorities (recommended actions ). The values are derived using a methodology that calculates ROE values using information on site conditions, peer review studies, and defined ecoprice values. ROE findings from 2022 Monroe County Return on Environment study. The feature dataset includes values for natural systems services, priorities (actions), protection status, and Green Ribbon Landscapes location
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| Greener Planning |
2024 |
The ArcGIS Package contains mapping data for a Swatara Creek Greenway Return on Environment (ROE) project. The work was prepared for the Manada Conservancy for its work in the Swatara Creek watershed in Dauphin County. The contents, which include detailed metadata descriptions, include several feature datasets, as follows: 1) SG Watershed – The study area or Swatara Creek watershed in Dauphin County 2) SG Parcels – Parcels in the project study area with attribute values 3) SG Riparian Areas – Riparian buffer areas adjacent to surface water features 4) SG ROE Values - Swatara Creek Greenway Return on Environment Findings 5) SG Green Ribbon Landscapes – Conservation buffer areas 6) SG Forest Size – Forested areas 7) SG Tree Canopy – detailed tree canopy in buffer areas. The project also contains a raster version of the SG ROE Values to assist in online rendering. Metadata information is included for each of the participating layers.
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| Greener Planning |
2000 |
Watershed conservation plan for the Neshaminy Creek. Contains multiple datasets including park boundaries, cultural places, floodplains, hydrology, municipal boundaries, land use, roads, railroads, soils, and zoning
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| Heritage Conservancy |
2012 |
During 2011 - 2012, the Pennsylvania Department of Environmental Protection (PA DEP), as part of the Coastal Zone Management (CZM) Program and Pennsylvania Stream ReLeaf, funded Heritage Conservancy to develop a rapid assessment method to identify and map sections of stream lacking riparian forest buffers. Montgomery County Planning Commission provided in-kind funding for the project as well as completing the assessment portion in Montgomery Count. Heritage Conservancy completed the assessment for the areas in Bucks County and Philadelphia County. Both organizations then mapped waterways lacking riparian forest buffers. The assessment included the main stem, tributaries and small headwater streams. The 1' pixel resolution 2010 aerials from DVRPC served as the basis for the riparian conditions along the waterways. The forest buffer conditions were classified and digitized into a Geographic Information System (GIS). This dataset is an assessment of the same area completed back in 200-2004. The same methodology of creating 50 foot buffers from the edge of water to assess the tree cover in this area was used. The stream centerline is used to represent the classification of whether one side, both sides, or neither side has tree cover. In addition to the three categories from the previous assessment, the category of culvert was added to incorporate areas where there was not an opportunity for tree cover.
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| Heritage Conservancy |
2000 |
During 2000 - 2004, the Pennsylvania Department of Environmental Protection (PA DEP), as part of the Coastal Zone Management (CZM) Program and Pennsylvania Stream ReLeaf, funded Heritage Conservancy to develop a rapid assessment method to identify and map sections of stream lacking riparian forest buffers. The conservancy then assessed various watersheds in southeastern Pennsylvania and mapped waterways lacking riparian forest buffers. The assessment included the main stem, tributaries and small headwater streams.
Interpretation of 1" = 400' black-and-white high altitude aerial photographs, Orthophotos, and videotape from helicopter over flights were used to determine the presence or absence of a forested buffer for 1,200 miles of stream. The forest buffer conditions were classified and digitized into a Geographic Information System (GIS).
A series of large-scale (1" = 400') maps were produced showing sections of stream bank lacking forest buffers. Local conservation groups were given the maps to assist them in targeting areas for riparian buffer plantings to improve water quality.
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| Heritage Conservancy |
2007 |
This is a county wide analysis of Berks County waterways to determine riparian buffer values given a 50 ft., 50% canopy coverage requirement.
The analysis was done by first overlaying the stream shapefile on the orthophotos. Next a 50 ft. buffer shapefile was derived from the streamlines and added. Using the newly created buffer shapefile as a guide, visual analysis was done for entire shapefile. The canopy coverage percentage was interpreted by Heritage Conservancy and buffer shapefile was used to define the 50 ft. distance requirement. At a scale of 1:2,000, the streamlines were attributed with one of three riparian values (full, half, none).
- Full Value: both banks of the stream contain 50% or greater canopy coverage for 50 ft from the stream.
- Half Value: only one bank of the stream contains 50% or greater canopy coverage.
- None Value: neither stream bank contains 50% or greater canopy coverage.
The threshold for determining whether or not a shoreline segment or a stream centerline segment should be used for the analysis was if the waterway was wider than 100 ft for a linear distance of approximately 400 ft. If a waterway was wider than 100 ft. for a linear distance of approximately 400 ft., shorelines would be used rather than stream centerlines. Having the threshold include a 400 ft linear distance requirement made many ponds be represented by a centerline rather than a shoreline. In cases were the original data contained two shorelines to represent a stream that was not greater than 100 ft wide, a new centerline was created by Heritage Conservancy by using the orthophotos and the shorelines as guides.
Ponds that were delineated to represent headwaters were not included in this analysis. Analysis and editing would begin at the tributary originating from the pond, but the pond itself would not be given a riparian value.
Edits to the shape of the streamline were done when they were visually obvious. The point of this analysis was not to delineate new stream lines, but rather to give existing streamlines riparian buffer attributes. The large majority of the segments are identical to the original Berks County Planning Commission streamlines.
Stream and shorelines were cut into segments based on the riparian attribute value.
Ten sites were chosen for on-site verification and they were visited in July 2007.
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| Heritage Conservancy |
2022 |
Access Lines represents access/drive ways to 911 addressed structures in Huntingdon County, PA. These features typically represent un-named driveways, right of ways, or paths that may be used by 911 services in the case of an emergency. Changes to this data are updated on a weekly basis, or at the county’s discretion. Beginning with December 2022 this data follows the NG911 schema.
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| Huntingdon County |
2022 |
"AddressPoints" was created to support Huntingdon County, PA in its mission to serve government, public, and private entities. This layer is maintained by Huntingdon County Mapping Dept. for the county PSAP (Public Saftey Answering Point, 911) to assist in routing emergency service calls. "AccessLines" is also used by Huntingdon County offices to assist in day to day office/field work.
Beginning with December 2022 this data follows the NG911 schema.
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| Huntingdon County |
2022 |
Roads represents all offical road center lines in Huntingdon County, PA. These features represent all Huntingdon County roads: federal & state, municipal & borough, public & private.The original data was derived from PennDOT road centerlines and expanded by the Huntingdon County Planning and Mapping departments. Currently the data is maintained and updated by the Mapping Department on a weekly basis, or at the county’s discretion.
Beginning with December 2022 this data follows the NG911 schema
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| Huntingdon County |
2023 |
Centerlines
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| Indiana County |
2023 |
Major Roads
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| Indiana County |
2023 |
Lakes
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| Indiana County |
2023 |
Municipal Boundaries
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| Indiana County |
2023 |
Parcels
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| Indiana County |
2023 |
Railroads
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| Indiana County |
2008 |
Boundaries of municipalities within Juniata County, Pennsylvania
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| Juniata County |
2024 |
Polygon coverage representing Juniata County, Pennsylvania properties
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| Juniata County |
2024 |
Points of addressable structures within Juniata County, Pennsylvania
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| Juniata County |
2024 |
Line coverage representing Juniata County, Pennsylvania street centerlines
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| Juniata County |
2009 |
PA CleanWays' illegal dump surveys educate state, county and local officials, as well as citizens, about the problem of illegal dumping and provide valuable data about the dumpsites and the community in which they reside.
Illegal dumping mostly occurs in remote and secluded places, rural areas where few persons live, and the roads are less traveled. However, for many people who are residents of an urban area, an illegal dump is often within a one-mile radius of their home. Overall, very few people are aware of the widespread problem of illegal dumping in Pennsylvania.
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| Keep Pennsylvania Beautiful |
2010 |
Keep Pennsylvania Beautiful illegal dump surveys educate state, county and local officials, as well as citizens, about the problem of illegal dumping and provide valuable data about the dumpsites and the community in which they reside.
Illegal dumping mostly occurs in remote and secluded places, rural areas where few persons live, and the roads are less traveled. However, for many people who are residents of an urban area, an illegal dump is often within a one-mile radius of their home. Overall, very few people are aware of the widespread problem of illegal dumping in Pennsylvania.
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| Keep Pennsylvania Beautiful |
2011 |
Keep Pennsylvania Beautiful illegal dump surveys educate state, county and local officials, as well as citizens, about the problem of illegal dumping and provide valuable data about the dumpsites and the community in which they reside.
Illegal dumping mostly occurs in remote and secluded places, rural areas where few persons live, and the roads are less traveled. However, for many people who are residents of an urban area, an illegal dump is often within a one-mile radius of their home. Overall, very few people are aware of the widespread problem of illegal dumping in Pennsylvania.
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| Keep Pennsylvania Beautiful |
1900 |
Historical maps in TIFF format of Lancaster County
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| Lancaster County |
2008 |
The data describes the boundary of the Lancaster County.
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| Lancaster County |
2008 |
This layer consists of polygons representing bridges in Lancaster County. It contains both covered and non-covered bridges.
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| Lancaster County |
2018 |
This layer polygon coverage that depict the outlines of buildings larger than 10 feet by 10 feet in size as captured from aerial photography. Includes primary structures such as residential, agricultural, business, and industrial and large ancillary structures such as barns, detached garages, out buildings, storage sheds, silos, etc.
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| Lancaster County |
2008 |
This polygon coverage includes both recent and historical cemeteries. County supplied source data to identify historical locations. Dataset consists of all cemeteries which were identifiable in the 1993 photography, plus historical cemeteries which were mapped using other source data and or verification methods.
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| Lancaster County |
2008 |
This dataset consists of lines representing elevation contours at a 5 foot interval, with index contours at a 25 foot interval.
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| Lancaster County |
2008 |
Polygons within this coverage represent the outlines of each covered bridge in Lancaster County, PA, as determined via aerial photographs.
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| Lancaster County |
2019 |
Polygons representing photogrammetrically interpreted edges of paved and unpaved drives greater than 200 ft. in length. Features were updated using Pictometry 6 inch resolution aerial photos flown in the spring of 2016 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2008 |
Surveyed positions of monuments for geodetic control.
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| Lancaster County |
2014 |
Hydrography Centerlines. This dataset contains lines representing streams and the centerlines of rivers wider than 10 feet. The features are pointed in the direction of streamflow.
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| Lancaster County |
2014 |
Hydrography Polygons. This dataset contains areas representing water bodies and rivers wider than 10 feet.
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| Lancaster County |
2022 |
Polygon coverage representing the predominant land use or land cover, as interpreted from aerial photography. Note: GDI delineated the land use boundaries using aerial photographs dated March 1993 and using a minimum mapping unit of one-half acre. Based on acquired aerial photography, subsequent updates have been made using aerial photos from: 1998, 2002, 2005, 2008, 2012, 2016, 2018. ADR compiled land use polygons for all surface water features. NWI wetlands areas were identified using boundaries from a Quad-based wetland coverage; the wetland boundaries were then delineated using the aerial photography. Land uses are categorized using the Modified Anderson Level II classification system. Quality control check plots were produced at 1"=250' scale the whole county. Planning Commission staff reviewed all of the check plots and forwarded comments and corrections to GDI. GDI made some of the corrections but did not make others, claiming that they were below the half-acre minimum mapping unit limit. Planning Commission staff has also made corrections where they have been found.
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| Lancaster County |
2025 |
Polygon layer representing the Lancaster County municipal boundaries, as mapped by the assessment office.
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| Lancaster County |
2025 |
Polygon layer representing the real Lancaster County property, as mapped by the assessment office.
Data created for Lancaster County Assessment Office. Intended for illustration and demonstration purposes only.
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| Lancaster County |
2019 |
This polygon coverage represents photogrammetrically interpreted surfaces and edges of parking lots. All lots in excess of 1000 square feet will be compiled. Features were updated using Pictometry 6 inch resolution aerial photos flown in the spring of 2016 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2025 |
Polygon layer representing public parks, public and semi-public recreation land and other conservation land and easements. Based on the tax parcel layer and aerial photographs.
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| Lancaster County |
2008 |
This line coverage contains arc features representing photogrammetrically interpreted Rail
Road centerlines. County Planning Commission supplied reference map to identify active
features from abandoned features.
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| Lancaster County |
2025 |
This line coverage contains arc features representing all photogrammetrically interpreted street centerlines in the county, including roads built during or after 1993. RPT will resolve and generate road names.
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| Lancaster County |
2019 |
Arcs representing photogrammetrically interpreted surfaces and edges of public and private roads. Features were updated using NGA and USGS 30 centimeter aerial photos flown from March 26th through April 3rd, 2012 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2019 |
Polygons representing photogrammetrically interpreted surfaces and edges of public and private roads. Features were updated using Pictometry 6 inch resolution aerial photos flown in the spring of 2016 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2008 |
Boundaries of Lancaster County School Districts
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| Lancaster County |
2025 |
Comprehensive layer of the trail network in Lancaster County. Trails data has been updated to support uploading to the DCNR's http://www.explorepatrails.com/ site Last update was based on 2018 orthophotos.
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| Lancaster County |
2025 |
This a county-wide coverage that contains polygons showing the urban and village growth boundaries in the county. Growth boundaries are split along municipal boundaries.
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| Lancaster County |
2024 |
0.5-foot pixel resolution digital orthoimagery produced for Lancaster County, PA by the Sanborn Map Company. The orthoimagery provided is 4-band (R, G, B, NIR), 8-bit per pixel and delivered in section tiles in uncompressed TIFF/TFW format. A compressed mosaic is also provided in SID/SDW format (20:1). The data was flown between March 12, 2024 and March 25, 2024 at an altitude of 8200 feet AGL using an UltraCam Osprey camera and complies with the American Society for Photogrammetry and Remote Sensing Accuracy Standards (ASPRS) for Class 1, large scale maps at 1" = 100'. This orthoimagery is published in Pennsylvania South State Plane, NAD83(2011), FIPS Zone 3702 (US Survey Feet).
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| Lancaster County |
2012 |
Color InfraRed (CIR) Orthoimages State Plane for Lancaster County, Pennsylvania 2012
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| Lancaster County |
2012 |
Color InfraRed (CIR) Orthoimages State Plane for Lancaster County, Pennsylvania 2012
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| Lancaster County |
2012 |
Color InfraRed (CIR) Orthoimages State Plane for Lancaster County, Pennsylvania 2012
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| Lancaster County |
2012 |
True Color (RGB) Orthoimages State Plane for Lancaster County, Pennsylvania 2012
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| Lancaster County |
2012 |
True Color (RGB) Orthoimages State Plane for Lancaster County, Pennsylvania 2012
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| Lancaster County |
2012 |
True Color (RGB) Orthoimages State Plane for Lancaster County, Pennsylvania 2012
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| Lancaster County |
2021 |
Abandoned Railroads of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Footprints for buildings in Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Municipal Boundaries of Lehigh County, Pennslyvania
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| Lehigh County |
2021 |
Boundary outlines of individual properties in Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Railroads of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Road Centerlines of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Political Wards of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Political Wards of Lehigh County, Pennsylvania
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| Lehigh County |
2010 |
Digital orthophoto covering Lehigh and Northampton County, Pennsylvania that was flow in spring 2010. An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map.
Flown during leaf off conditions between March 27, 2010 and April 22, 2010.
Digital imagery at 1''=200 design scale natural color aerial photography at 1' pixel resolution.
Image tile dimensions are 5000' by 5000' and 1000tiles cover the entirety of Lehigh and Northampton Counties.
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| Lehigh Valley Planning Commission |
2010 |
TILE INDEX - Digital orthophoto covering Lehigh and Northampton County, Pennsylvania that was flow in spring 2010. An orthoimage is remotely sensed image data that has been positionally corrected for camera lens distortion, vertical displacement and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map.
Flown during leaf off conditions between March 27, 2010 and April 22, 2010.
Digital imagery at 1''=200 design scale natural color aerial photography at 1' pixel resolution.
Image tile dimensions are 5000' by 5000' and 1000tiles cover the entirety of Lehigh and Northampton Counties.
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| Lehigh Valley Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the LVPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Lehigh Valley Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the LVPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Lehigh Valley Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the LVPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Lehigh Valley Planning Commission |
2022 |
Luzerne County Municipal boundaries which defines the current municpal configurations based on best available information. Data may also contain various administrative, planning and property data. Best Fit to 1"=200' orthophotography, County Tax Parcels, GPS field work, Provided Surveys, Road Docket Plots and/or other sources.
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| Luzerne County |
2022 |
School District Boundaries of Luzerne County, Pennsylvania
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| Luzerne County |
2025 |
Property or Parcel Boundaries of all known properties in Lycoming County, PA
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2020 |
Lycoming County UnNamed Roads. These are primarily private roads or driveways, but may contain Commonwealth maintenance roads which do not require a name.
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| Lycoming County |
2020 |
Lycoming County Named Roads. This contains both private and public roads which have an address range and an approved road name as per the Lycoming County Addressing Ordinance 96-3
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| Lycoming County |
2020 |
Addressed Structures or Sites in Lycoming County as per the Lycoming County Addessing Ordinance 96-3
Note: Zip Code values are estimated and not verified.
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| Lycoming County |
2024 |
A Polygon dataset that represents the lands of local airport
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| Mercer County |
2024 |
Polygons that represent the total lands of a college or university campus
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| Mercer County |
2024 |
A point data set of the local libraries
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| Mercer County |
2024 |
A point data set that represents the various municipal buildings
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| Mercer County |
2024 |
The Parcel Dataset was developed primarily for the purpose of identifying land parcels for tax billing and tax assessment purposes.
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| Mercer County |
2024 |
A line dataset of the trails in the greater Mercer County Area
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| Mercer County |
2023 |
Mercer County, Pennsylvania building footprints digitized from 2015 aerial imagery.
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| Mercer County |
2024 |
Boundary outlines of individual properties in Mifflin County. The Parcel Dataset was developed primarily for the purposes of identifying land parcels for tax billing and tax assessment purposes.
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| Mifflin County |
1995 |
Mifflin County aerial photography
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| Mifflin County |
2022 |
Boundary outlines of individual properties in Monroe County
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| Monroe County |
2022 |
This dataset contains street centerlines for vehicular and foot traffic in Monroe County, Pennsylvania
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| Monroe County |
2025 |
This layer shows site structure address points in Montgomery County, Pennsylvania. Site Structure Address Points (SSAP) represent a physical address of a structure. This address may differ from a mailing address and even a property parcel address. The layer is was developed in accordance with NENA NG911 guidelines. The layer also contains trail and highway mile markers which are needed for 911 location services.
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| Montgomery County |
2022 |
Bridges of Montgomery County, Pennsylvania
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| Montgomery County |
2025 |
This layer shows the building outlines for Montgomery County, Pennsylvania. The original layer was purchased from NearMap in the spring of 2020. Updates and additions to the original layer were made using parcel and current spring 2022 aerial imagery.
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| Montgomery County |
2022 |
This is Montgomery County's geographic boundary. The original source data has been edited to best match orthophotography, natural features and other known boundary markers. This data is for reference purposes only.
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| Montgomery County |
2022 |
Electric Service Areas
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| Montgomery County |
2022 |
Electric Transmission Lines
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| Montgomery County |
2022 |
EMS Ambulance Districts
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| Montgomery County |
2022 |
EMS Ambulance Stations
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| Montgomery County |
2022 |
Fire Districts
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| Montgomery County |
2022 |
Fire Stations
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| Montgomery County |
2025 |
Historical Attraction
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| Montgomery County |
2022 |
Hospitals
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| Montgomery County |
2022 |
Libraries
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| Montgomery County |
2022 |
Municipal Boundaries
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| Montgomery County |
2025 |
Parcel boundaries and property information for Montgomery County, Pennsylvania. This data is a download from the county's assessment database and is updated monthly.
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| Montgomery County |
2022 |
Parks
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| Montgomery County |
2022 |
Police Districts
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| Montgomery County |
2022 |
Police Stations
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| Montgomery County |
2022 |
School Districts
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| Montgomery County |
2022 |
Schools
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| Montgomery County |
2022 |
Sewer Service Areas
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| Montgomery County |
2025 |
Street Centerline
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| Montgomery County |
2022 |
Trails
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| Montgomery County |
2022 |
Water Service Areas
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| Montgomery County |
2024 |
Geopolitical Areas
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| Montour County |
2024 |
Roads of Monroe County, Pennsylvania
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| Montour County |
2024 |
This dataset contains Address Points in Monroe County, Pennsylvania
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| Montour County |
2024 |
Tax Parcels of Monroe County, Pennsylvania
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| Montour County |
1999 |
This layer includes the parcels within the municipality along with condominiums and mobile homes. Also included, are the county's tax data along with municipal data such as street address, lot number and subdivision, build out status, land use, zoning, septic or sewer, and housing starts on two year intervals since 1990.
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| Municipality of Murrysville |
1999 |
The Zoning coverage is a polygon layer reflecting Murrysville's zoning districts and their boundaries. The coverage is designed to overlay the parcel coverage, aiding in the initial determination of a parcel or plan's zoning or zonings. Additional attribute data such as setbacks and minimum lot size are included.
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| Municipality of Murrysville |
2003 |
The streets coverage represents all local roads within the Municipality of Murrysville. Where applicable, roads are defined by their local names rather than state and township route numbers. Address ranges are also included.
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| Municipality of Murrysville |
2002 |
The Shuttle Radar Topography Mission (SRTM) is a partnership between
NASA and the National Geospatial-Intelligence Agency (NGA). Flown aboard the NASA
Space Shuttle Endeavour (11-22 February 2000), SRTM fulfilled its mission to map the
world in three dimensions. The USGS is under agreement with NGA and NASA's Jet
Propulsion Laboratory to distribute the C-band data. SRTM utilized dual Spaceborne
Imaging Radar (SIR-C) and dual X-band Synthetic Aperture Radar (X-SAR) configured as
a baseline interferometer to successfully collect data over 80 per cent of the
Earth's land surface, everything between 60 degrees North and 56 degrees South
latitude.
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| National Aeronautics and Space Administration NASA |
2018 |
This data contains a set of geodetic control stations maintained by the National Geodetic Survey. Each geodetic control station in this dataset has either a precise Latitude/Longitude used for horizontal control or a precise Orthometric Height used for vertical control, or both. The National Geodetic Survey (NGS) serves as the Nation's depository for geodetic data. The NGS distributes geodetic data worldwide to a variety of users. These geodetic data include the final results of geodetic surveys, software programs to format, compute, verify, and adjust original survey observations or to convert values from one geodetic datum to another, and publications that describe how to obtain and use Geodetic Data products and services.
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| National Geodetic Survey |
2001 |
This data set represents the most current depiction of the Appalachian National Scenic Trail centerline. Locational information used to create this data set were obtained from both Global Positioning Systems (GPS) survey data collected between 1998-2001 and information digitized from USGS topographical maps and Appalachian Trail maps.
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| National Park Service |
2001 |
This data set represents the most current depiction of the Major Parks and Forests polygon along the Appalachian Trail. Locational information used to create this data set were obtained from both Global Positioning Systems (GPS) survey data collected between 1998-2001 and information digitized from USGS topographical maps and Appalachian Trail maps.
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| National Park Service |
2001 |
This data set represents the most current depiction of the Appalachian National Scenic Trail Shelter locations. Locational information used to create this data set were obtained from both Global Positioning Systems (GPS) survey data collected between 1998-2001 and information digitized from USGS topographical maps and Appalachian Trail maps
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| National Park Service |
2015 |
These ESRI shape files are of National Park Service tract and boundary data that was created by the Land Resources Division. Tracts are numbered and created by the regional cartographic staff at the Land Resources Program Centers and are associated to the Land Status Maps. This data should be used to display properties that NPS owns and properties that NPS may have some type of interest such as scenic easements or right of ways. Spatial data contained herein represent the administrative boundary of the Upper Delaware Scenic & Recreational River (UPDE) as defined in the 1986 Upper Delaware Scenic & Recreational River Management Plan. The data are intended for general GIS analysis, display, planning, and reference purposes only. The spatial data contained herein are a digital representation of the Upper Delaware Scenic & Recreational River's administrative boundary. These digital data were derived from the official version of the boundary as represented in hard-copy form in the 1986 Upper Delaware Scenic & Recreational River Management Plan, as approved in the 1987 Record of Decision. The plan boundary, which was established to meet the resource protection requirements set forth in the Wild and Scenic Rvers Act and in the Special Statutory Provisions for the Upper Delaware, was delineated so as to encompass "lands from which runoff drains directly into the river and tributary drainages upstream to the first prominent topographic feature" (River Management Plan, 1986). The authoritative area of the boundary as given in the 1986 River Management Plan is 55,574.5 acres. At present, the National Park Service owns approximately 31 acres within the boundary, and the remaining land is mostly privately owned, with some state and local government holdings. The boundary was digitized based on the eight pages of maps from the River Management Plan georeferenced to 7.5 minute quadrangle topo maps from the USGS Historical Topographic Map Collection. National Park Service, Upper Delaware Scenic & Recreational River . Credits: Shannon L. Thol - Graduate Certificate in GIS, Master of Geographic Information Systems - Under contract by National Park Service, Upper Delaware Scenic & Recreational River to complete task of digitizing Upper Delaware Scenic & Recreational River's administrative boundary.
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| National Park Service |
2015 |
These ESRI shape files are of National Park Service tract and boundary data that was created by the Land Resources Division. Tracts are numbered and created by the regional cartographic staff at the Land Resources Program Centers and are associated to the Land Status Maps. This data should be used to display properties that NPS owns and properties that NPS may have some type of interest such as scenic easements or right of ways. Spatial data contained herein represent the administrative boundary of the Upper Delaware Scenic & Recreational River (UPDE) as defined in the 1986 Upper Delaware Scenic & Recreational River Management Plan. The data are intended for general GIS analysis, display, planning, and reference purposes only. The spatial data contained herein are a digital representation of the Upper Delaware Scenic & Recreational River's administrative boundary. These digital data were derived from the official version of the boundary as represented in hard-copy form in the 1986 Upper Delaware Scenic & Recreational River Management Plan, as approved in the 1987 Record of Decision. The plan boundary, which was established to meet the resource protection requirements set forth in the Wild and Scenic Rvers Act and in the Special Statutory Provisions for the Upper Delaware, was delineated so as to encompass "lands from which runoff drains directly into the river and tributary drainages upstream to the first prominent topographic feature" (River Management Plan, 1986). The authoritative area of the boundary as given in the 1986 River Management Plan is 55,574.5 acres. At present, the National Park Service owns approximately 31 acres within the boundary, and the remaining land is mostly privately owned, with some state and local government holdings. The boundary was digitized based on the eight pages of maps from the River Management Plan georeferenced to 7.5 minute quadrangle topo maps from the USGS Historical Topographic Map Collection. National Park Service, Upper Delaware Scenic & Recreational River . Credits: Shannon L. Thol - Graduate Certificate in GIS, Master of Geographic Information Systems - Under contract by National Park Service, Upper Delaware Scenic & Recreational River to complete task of digitizing Upper Delaware Scenic & Recreational River's administrative boundary.
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| National Park Service |
2015 |
These ESRI shape files are of National Park Service tract and boundary data that was created by the Land Resources Division. Tracts are numbered and created by the regional cartographic staff at the Land Resources Program Centers and are associated to the Land Status Maps. This data should be used to display properties that NPS owns and properties that NPS may have some type of interest such as scenic easements or right of ways. Spatial data contained herein represent the administrative boundary of the Upper Delaware Scenic & Recreational River (UPDE) as defined in the 1986 Upper Delaware Scenic & Recreational River Management Plan. The data are intended for general GIS analysis, display, planning, and reference purposes only. The spatial data contained herein are a digital representation of the Upper Delaware Scenic & Recreational River's administrative boundary. These digital data were derived from the official version of the boundary as represented in hard-copy form in the 1986 Upper Delaware Scenic & Recreational River Management Plan, as approved in the 1987 Record of Decision. The plan boundary, which was established to meet the resource protection requirements set forth in the Wild and Scenic Rvers Act and in the Special Statutory Provisions for the Upper Delaware, was delineated so as to encompass "lands from which runoff drains directly into the river and tributary drainages upstream to the first prominent topographic feature" (River Management Plan, 1986). The authoritative area of the boundary as given in the 1986 River Management Plan is 55,574.5 acres. At present, the National Park Service owns approximately 31 acres within the boundary, and the remaining land is mostly privately owned, with some state and local government holdings. The boundary was digitized based on the eight pages of maps from the River Management Plan georeferenced to 7.5 minute quadrangle topo maps from the USGS Historical Topographic Map Collection. National Park Service, Upper Delaware Scenic & Recreational River . Credits: Shannon L. Thol - Graduate Certificate in GIS, Master of Geographic Information Systems - Under contract by National Park Service, Upper Delaware Scenic & Recreational River to complete task of digitizing Upper Delaware Scenic & Recreational River's administrative boundary.
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| National Park Service |
2009 |
United States Biomass Resource Assessment
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| National Renewable Energy Laboratory NREL |
2007 |
Monthly and annual average solar resource potential for 48 Contiguous United States.
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| National Renewable Energy Laboratory NREL |
2003 |
Wind Resource maps (50 m) for Delaware, Maryland, North Carolina, New Jersey, Pennsylvania, Virginia, and West Virginia - jpeg format
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| National Renewable Energy Laboratory NREL |
2003 |
Annual average wind resource potential of the mid-Atlantic United States at a 50 meter height.
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| National Renewable Energy Laboratory NREL |
2006 |
This data set contains vector lines and polygons representing data used in
the creation of the Environmental Sensitivity Index (ESI) for the Hudson River. The data layer contains all annotation used in producing the atlas. This data set comprises a portion of the ESI data for the Hudson River. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats,
sensitive biological resources, and human-use resources. Data includes: HYDRO, ESI, WETLANDS, BIRDS, FISH, INVERT, MAMMAL(Marine), MAMMAL(Terrestrial), HABITATS, REPTILES, MGT, INDEX, SOCECON, RVRMILES, SENSITIV, STAGING.
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| National Weather Service NOAA NWS |
2006 |
This data set contains vector lines and polygons representing data used in
the creation of the Environmental Sensitivity Index (ESI) for the Hudson River. The data layer contains all annotation used in producing the atlas. This data set comprises a portion of the ESI data for the Hudson River. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats,
sensitive biological resources, and human-use resources. Data includes: HYDRO, ESI, WETLANDS, BIRDS, FISH, INVERT, MAMMAL(Marine), MAMMAL(Terrestrial), HABITATS, REPTILES, MGT, INDEX, SOCECON, RVRMILES, SENSITIV, STAGING.
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| National Weather Service NOAA NWS |
2005 |
This Historical North Atlantic Hurricane Tracks file of major storms
with landfall in the United States contains the six-hourly (0000, 0600,
1200, 1800 UTC) center locations and intensities for all northern
Atlantic major storms from 1851 through 2004. Major storms are those
that made landfall in the United States and that were classified on the
Saffir-Simpson Hurricane Scale as Category 3, 4, or 5 at the time of
landfall. Landfalling storms are defined as those storms whose center
is reported to have either crossed or passed directly adjacent to the
United States coastline, and which came ashore with tropical storm
intensity or greater (sustained surface winds of 34 knots or 39 miles
per hour or greater). In 2000, 2001, 2002, and 2003 there were no
major landfalling hurricanes. This a replacement for the January 2005
map layer distributed as Historical North Atlantic Hurricane Tracks ?
Major Storms with Landfall in the United States, 1851-2003.
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| National Weather Service NOAA NWS |
2005 |
This map layer shows tornado touchdown points in the United States, Puerto
Rico, and the U.S. Virgin Islands, from 1950 to 2004. Statistical data
were obtained from the National Weather Service, Storm Prediction Center
(SPC). The SPC data originate from the Severe Thunderstorm Database and
the National Oceanic and Atmospheric Administration (NOAA) Storm Data
publication. This layer is clipped to Pennsylvania
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| National Weather Service NOAA NWS |
1991 |
The Centre County Natural Heritage Inventory is designed to identify and map important biotic (living) and ecological resources present in Centre County. This information helps county, state, and municipal government, the public, and business interests plan development with the preservation of these environmentally important sites in mind.
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| Natural Heritage Inventory |
2016 |
Natural Lands Trust works proactively to protect the natural heritage of the Philadelphia region for today and generations to come. This file contains various
layers and maps for Schuylkill Action Network - Land Prioritization Strategy Model Included maps are:
Composite Resource Protection Prioritization, Composite Resource Protection Prioritization 3 color, DVRPC Future Development Year 2020-2030, Future Development on High Priority CRP Areas, NLT SC Conservation Resource Prioritization, PWD Source Water Model.
Also included are the GIS data layers that support these maps.
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| Natural Lands Trust |
2023 |
These are the NLT preserves that are currently open to the public. Trails, parking areas, and other information can be found on the NLT website: https://natlands.org/category/preserves-to-visit/list-of-preserves/
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| Natural Lands Trust |
2007 |
Natural Lands Trust works proactively to protect the natural heritage of the Philadelphia region for today and generations to come. This file contains various
layers and maps for Schuylkill Action Network - Land Prioritization Strategy Model Included maps are:
Composite Resource Protection Prioritization, Composite Resource Protection Prioritization 3 color, DVRPC Future Development Year 2020-2030, Future Development on High Priority CRP Areas, NLT SC Conservation Resource Prioritization, PWD Source Water Model.
Also included are the GIS data layers that support these maps.
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| Natural Lands Trust |
2016 |
In the original APCAW (v2) the Open Space Institute (OSI) sought to develop a metric to measure the relative capacity of small scale (HUC12) watersheds to produce clean surface and ground water. In 2014, two different HUC12-based metrics (i.e., “Ability to Produce Good Quality Surface Water” and “Ability to Produce Good Quality Ground Water”) were calculated and used by OSI to evaluate land protection projects. In 2015, OSI combined these two metrics into one metric that considers watershed conditions (e.g. land cover, terrain, and hydrology) and their effects on the abundance and quality of surface and ground water within a reasonably-sized watershed (i.e., HUC12 boundary). The revised index the APCAWv2 makes a number of changes to the original work, notably watershed boundaries are now based on the National Hydrography Dataset Plus version 2 Horizon Systems and EPA. The NHDplusv2 lateral drainage areas were originally defined by the USGS 1:100 k watershed boundaries. For more information on the spatial boundaries used in this analysis .
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| Open Space Institute |
2015 |
Two seamless soils datasets based on USDA’s SSURGO and STATSGO databases were created for the entire DRB region, and information pertaining to various soil-related factors such as erodibility (k factor), available water - holding capacity, texture, etc. were compiled and summarized for discrete mapping units at these two scales. The SSURGO (Soil Survey Geographic) database is compiled at the detailed county-level survey scale that most soil information users are familiar with, and has two basic components: 1) digital boundaries of the detailed soil mapping units, and 2) tabular information on a wide range of soil parameters associated with each mapping unit. The STATSGO (State Soil Geographic) database summarizes similar soils information at a much more generalized “soil association” scale. Both of these datasets for the DRB area were downloaded from USDA’s “geospatial data” site at http://datagateway.nrcs.usda.gov. Once downloaded, considerable effort was then expended to first
seam together the data from the separate states overlapping the DRB, and then to “populate” both soil databases by linking a number of “attribute tables” to the soils polygons contained within the DRB boundary. In this case, over 325,000 soil polygons were populated with information extracted from about a dozen different attribute tables.
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| Open Space Institute |
2006 |
Boundary of the Delaware Estuary
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| Partnership for the Delaware Estuary |
2020 |
To outline the general boundary of the preserved farmland easements derived from property descriptions provided to the State Bureau of Farmland Preservation as part of the preservation process submission. The Preserved Farmland Boundary data layer is to be used to depict the approximate boundary of Preserved Farmland. This file is not to be used as a survey to determine property lines.
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| Pennsylvania Department of Agriculture |
2001 |
The 1980 Pennsylvania state geologic map (Berg and others, 1980) shows the areal distribution of 194 bedrock geologic units. The units range in geologic age from Quaternary to Precambrian and encompass a wide variety of lithologies. The map was compiled and published in a transverse mercator projection at 1:250,000 scale. The two digital data sets include 195 geologic units, which, for the most part, closely correspond to those shown on the 1980 map. The data sets were prepared using ArcInfo software and are provided in a geographic coordinate system (units in decimal degrees). Although it is possible to portray digital data at any scale, the geologic formational contacts, faults, and dikes represented in these data sets are not intended to be used at any scale finer than 1:250,000.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
PaGS assembled 214,851 relevant well records from Pennsylvania, GroundWater Information System (PaGWIS) and other unpublished PaGS reports. Each well used in the analysis contains a measurement of the depth to bedrock (in feet) and/or a notation indicating if bedrock was encountered during well drilling, these attributes allow well records to be separated into two datasets – bedrock wells (wells that penetrate bedrock) and drift wells (wells that did not encounter bedrock).Topographic Position Index (TPI) is a quantitative landform analysis that uses land surface elevation data to determine landforms such as ridge, upper slope, middle/flat slope, lower slope, and valley. A composite TPI raster for each of Pennsylvania’s 23 physiographic sections was generated. Each well data point was attributed to a physiographic section and assigned a TPI value based on its location. The square root of depth-to-bedrock was calculated for each well. A linear regression relationship between the TPI and the square root of sediment thickness was established for five TPI classes (ridge, upper slope, middle/flat slope, lower slope, and valley) in each of the 23 physiographic sections. This statistical relationship was used to create a surrogate model for depth to bedrock to predict sediment thickness across the state. Synthetic data points were generated from the surrogate model to fill in areas of low well data density. A combination of bedrock well data points and synthetic data points were used to generate the first-iteration sediment thickness model through a natural neighbor interpolation technique. Iterative refinements to the sediment thickness model were made by comparing model predictions to drift well data points. If the total depth of the drift well was less than the predicted thickness of sediment at that location, then the drift well data point was ignored. If the total depth of a drift well was greater than the predicted thickness of sediment at that location, then the drift well data point was added to dataset and a new sediment thickness model was generated. In total, 413, 474 data points were used in the modeling process – 207,130 empirically derived well points and 206, 344 synthetic points derived from the surrogate model.The final sediment thickness model was resampled to a 100-meter grid digital raster conforming to a similar resolution surface topography digital elevation raster. The surface topography grid was smoothed to remove detail before subtracting the sediment thickness to create a bedrock elevation map. The degree of smoothing was applied proportionally to the magnitude of sediment thickness. Portions of the surface topography grid that correspond to sediment thickness greater than 365 feet received the maximum amount of smoothing; likewise, portions of the surface topography grid that correspond to zero sediment thickness received no smoothing. The remaining portions of the surface topography grid that correspond to sediment thickness between 0 and 365 feet received gradational smoothing proportional to the sediment thickness.This 100-meter grid bedrock elevation raster was calculated by subtracting the sediment thickness model from the conditionally-smoothed surface topography digital elevation raster.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Bedrock Geology of Pennsylvania Geologic Units
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Civilian Conservation Corps Camps
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Coal Fields in Pennsylvania Anthracite
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| Pennsylvania Department of Conservation and Natural Resources |
2022 |
Coal Fields in Pennsylvania High Volatile Bituminous
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Coal Fields in Pennsylvania Low Volatile Bituminous
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Coal Fields in Pennsylvania Medium Volatile Bituminous
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| Pennsylvania Department of Conservation and Natural Resources |
2022 |
Coal Fields in Pennsylvania Semi Anthracite
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
State-designated Scenic River centerlines. Pennsylvania has 13 state-designated scenic rivers. State legislation allows waterway segments to be designated as part of the Scenic Rivers System. This designation is intended to protect the natural, aesthetic, and recreational values of a waterway. The protection efforts are largely carried out through a partnership between DCNR and other state agencies, whereby construction projects in the vicinity of a designated Scenic River are required to undergo a more rigorous permitting process, and may be required to adjust the project design and/or construction practices to ensure that the natural and aesthetic values of the waterway are maintained. Visit https://www.dcnr.pa.gov/Conservation/Water/RiversConservation/ScenicRivers/Pages/default.aspx for more information.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Earthquakes in Pennsylvania 1724 - 2003
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| Pennsylvania Department of Conservation and Natural Resources |
2017 |
Boundaries of the PA DCNR, Bureau of Forestry Districts. These districts represent the regional management organization of the Bureau.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Kids in Nature Regions of Pennsylvania
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Late Wisconsinan Glacial Border in Pennsylvania
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| Pennsylvania Department of Conservation and Natural Resources |
2022 |
Maximum Elevations in Pennsylvania Counties
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
The Local Park data layer is to be used to depict the approximate boundary of Local Parks.
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| Pennsylvania Department of Conservation and Natural Resources |
2015 |
The PA Local Parks Access Points data layer is to be used to depict the approximate location of PA Local Parks Access Points.
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Pennsylvania Publicly Owned Streambeds
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Pennsylvania State Forest ADA Accessible Sites
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Pennsylvania State Forest DMAP Units
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Pennsylvania State Forest Gated Roads Open for Deer Season
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Pennsylvania State Forest Mature Oak Stands
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Pennsylvania State Forest Meadows Food Plots and Other Herbaceous Openings
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
Representation of mineral rights, leased tracts, etc. on lands managed by the Bureau of Forestry. Updates to this dataset are ongoing and this data is not intended to be a legal representation of ownership. This data may contain inaccuracies or incomplete information. This dataset includes ownership information where a "tract" number has been assigned by the Bureau of Forestry (leased tracts and severed rights lands where the ownership of said rights has been verified). This dataset was created from several different datasets of unknown lineage. The boundaries do not exactly match the Bureau of Forestry’s state forest boundary data. It is an estimate of the boundaries only and is not intended to be a legal description or depiction of boundaries. This data may contain inaccuracies or incomplete information. Ownership Type Code Values: 1=Leased Tract, 6=Other or Unknown, 2=Severed Right, 3=Leased Storage The data is intended for demonstration, education, planning, and monitoring purposes only. The user is advised not to use this dataset beyond its stated accuracy. To do so may yield unusual, undesirable, or incorrect results. Users of this dataset shall save the Commonwealth harmless from any suits, claims, or actions arising out of the use of or any defect in the data files or accompanying documentation. The Commonwealth excludes any and all implied warranties and makes no warranty or representation with respect to the data files or accompanying documentation, including quality, performance, merchantability, or fitness for a particular purpose. These data files and documentation are provided as is and the user assumes the entire risk as to their quality and performance. This data was based on the best available information at the time and may contain errors or omissions.
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Forest stands on State Forest Land classified as either a 1) FF:Hemlock-(White-Pine)-Forest, 2) FA:Dry-White-Pine-(Hemlock)-Oak-Forest, 3) FB:Hemlock-(White-Pine)-Northern-Hardwood-Forest, 4) FR:Hemlock-(White-Pine)-Red-Oak-Mixed-Hardwood-Forest, 5) FTHemlock-Tuliptree-Birch-Forest, 6) FM:Hemlock-Rich-Mesic-Hardwood-Forest, 7) UF:Hemlock-Palustrine-Forest, or 8) UB:Hemlock-Mixed-Hardwood-Palustrine-Forest.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Pennsylvania State Forest Young Aspen Stands
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
State Park Amenities
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
State Park Campground Entrances
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
Pennsylvania State Park Campground Tables locations
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
State Park Hiking Trails
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
State Park Ponds and Lakes
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
State Park Roads
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Boundaries of the PA DCNR, Bureau of Forestry Districts. These districts represent the regional management organization of the Bureau.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Physiographic Provinces of Pennsylvania
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Physiographic Sections of Pennsylvania
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
This feature class contains lines for rail-trails in the state of Pennsylvania, as prepared by the Rails-to-Trails Conservancy. The majority of data was collected using GPS units and checked for quality and accuracy against high-resolution aerial imagery. See Method. Data was collected between November 2007 and August 2008.
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| Pennsylvania Department of Conservation and Natural Resources |
2008 |
This feature class contains points associated with the line feature class for rail-trails in the state of Pennsylvania, as prepared by the Rails-to-Trails Conservancy. The majority of data was collected using GPS units and checked for quality and accuracy against high-resolution aerial imagery. See Method. Data was collected between November 2007 and August 2008. See Subtype Code for point type.
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
State-designated Scenic River centerlines. Pennsylvania has 13 state-designated scenic rivers. State legislation allows waterway segments to be designated as part of the Scenic Rivers System. This designation is intended to protect the natural, aesthetic, and recreational values of a waterway. The protection efforts are largely carried out through a partnership between DCNR and other state agencies, whereby construction projects in the vicinity of a designated Scenic River are required to undergo a more rigorous permitting process, and may be required to adjust the project design and/or construction practices to ensure that the natural and aesthetic values of the waterway are maintained. Visit https://www.dcnr.pa.gov/Conservation/Water/RiversConservation/ScenicRivers/Pages/default.aspx for more information.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Central office locations for the PA DCNR, Bureau of Forestry Districts. The districts represent the regional management organization of the Bureau.
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
The state forest boundary coverage is being updated frequently. It is derived from survey descriptions and will be, and has been in certain areas, adjusted to GPS boundary corners.
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
State Park Boundaries as of February 2009
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Surficial Geology of Pennsylvania Eskers
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Surficial Geology of Pennsylvania Varves
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Pennsylvania DCNR Bureau of Recreation and Conservation Grants Trails
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
Pennsylvania DCNR Bureau of Recreation and Conservation Regions
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Trail Gaps
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
TreeVitalize is a public-private partnership established by DCNR to help build capacity within communities to plan for, plant, and care for trees, and to offer educational trainings to help citizens understand the diverse benefits of trees and the importance of properly planting and maintaining them.
TreeVitalize offers a broad range of services to support sustainable urban and community forestry programs across the state.
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| Pennsylvania Department of Conservation and Natural Resources |
2007 |
This point dataset represents an (incomplete) inventory of karst features (herein categorized as sinkholes, surface depressions, surface mines, or cave entrances) that have been cataloged in Pennsylvania by the staff of the Pennsylvania Geological Survey since 1985. County-wide karst-feature inventories for fourteen counties (Adams, Berks, Bucks, Centre, Chester, Cumberland, Dauphin, Franklin, Lancaster, Lebanon, Lehigh, Montgomery, Northampton, and York) were originally released as hard-copy open file reports by the agency. The user of this dataset would be well served to consult one of those reports for specifics on how the inventories were conducted. Data are also provided from incidental reports of karst features as well as partial or cursory inventories in eight additional counties (Bedford, Blair, Clinton, Fulton, Huntingdon, Juniata, Lycoming, and Mifflin).
The majority of these features were identified on contact prints of USDA, USGS, and other aerial photography. Photo locations were then transferred to paper copies of USGS 7.5 minute quadrangles where the coordinates for the locations were determined using a constantly evolving array of technologies from mylar overlay grids, through early proprietary mapping software and more recently with GIS and GPS technology. . Some features in urbanized areas were identified via questionnaires that were sent to the municipalities in the counties being inventoried. The locations of surface mines and cave entrances were garnered from historical geological publications.
Locations of historical iron mines and other activities often associated with karst features were garnered from historical geological publications.
Field surveys were undertaken to establish protocol for future mapping efforts as well as to verify the field presence of interpreted features. Field verified sites are not identified in the data set.
Karst features change considerably over time. Sinkholes become filled or subsidence is repaired. One of the motivations for the karst inventory program was the rapid development taking place in many of the counties where karst could be a problem. Development obliterates much of the evidence for underlying karst features.
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
This feature class contains centerline data for trails in the state of Pennsylvania, as prepared by the Pennsylvania Department of Conservation and Natural Resources, Rails-to-Trails Conservancy, PA Fish and Boat Commission, and the Keystone Trails Association. The majority of the data was collected using GPS units and checked for quality and accuracy against high-resolution aerial imagery. See Method. GPS data was collected between November 2007 and October 2009. Trail updates are submitted by users through explorepatrails.com and are evaluated and updated on a regular basis.
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| Pennsylvania Department of Conservation and Natural Resources |
2017 |
This feature class contains points associated with the line feature class for trails in the state of Pennsylvania, as prepared by the PA DCNR, Rails-to-Trails Conservancy, PA Fish and Boat Commission, and Keystone Trails Association. The majority of the data was collected using GPS units and checked for quality and accuracy against high-resolution aerial imagery. See Method. Data was collected between November 2007 and October 2009. See Subtype Code for point type. Trail updates are submitted by users through explorepatrails.com and are evaluated and updated on a regular basis.
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| Pennsylvania Department of Conservation and Natural Resources |
2017 |
Water trails designated by the Pennsylvania Fish and Boat Commission in Pennsylvania. Visit http://sites.state.pa.us/PA_Exec/Fish_Boat/watertrails/trailindex.htm for more information.Allows for mapping on a statewide or regional scale of water trail extents in Pennsylvania.The data set was created by selecting the appropriate segments from the 1:24000 National Hydrography Dataset (NHD) flowing waters layer corresponding to the water trail extents. The segments were merged together to form the water trail lines.
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
This dataset represents a snapshot of forest fragmentation on state forest land as of December 31, 2012. The data was derived from the Bureau of Forestry’s fragmentation analysis in the April 2014 Shale Gas Monitoring Report using the University of Connecticut’s Landscape Fragmentation Tool (LTF) v 2.0 and the Bureau of Forestry’s data. The results categorize state forest land into: 1) small core patches less than 100 hectares, 2) Medium core patches with an area between 100 and 200 hectares, 3) Large core patches greater than 200 hectares, 4) Non-forested area, 5) Forest Edge, 6) Perforated forest, and 7) Patch forest. The default 100 meters was used to define forest edge. The fragmentation model considers all changes and is not limited to just shale gas activities (non-shale gas related changes do affect the results of this analysis too). April 2014 Shale Gas Monitoring Report Fragmentation
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
This dataset represents a snapshot of forest fragmentation on state forest land prior to shale-gas development (pre-2008).
The data was derived from the Bureau of Forestry’s fragmentation analysis in the April 2014 Shale Gas Monitoring Report using the University of Connecticut’s Landscape Fragmentation Tool (LTF) v 2.0 and the Bureau of Forestry’s data. The results categorize state forest land into: 1) small core patches less than 100 hectares, 2) Medium core patches with an area between 100 and 200 hectares, 3) Large core patches greater than 200 hectares, 4) Non-forested area, 5) Forest Edge, 6) Perforated forest, and 7) Patch forest. The default 100 meters was used to define forest edge. The fragmentation model considers all changes and is not limited to just shale gas activities (non-shale gas related changes do affect the results of this analysis too). April 2014 Shale Gas Monitoring Report Fragmentation
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
State forest roads impacted by shale gas development as of December 31, 2012. This dataset was used in part to develop the Bureau of Forestry’s Shale Gas Monitoring Report (released April 2014). This data represents primarily State Forest managed roads impacted by shale gas development as of 12/31/2012. This data was based on the best available information at the time and may contain errors or omissions. April 2014 Shale Gas Monitoring Report Infrastructure
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
Infrastructure pad sites on state forest land as of December 31, 2012. This dataset was used in part to develop the Bureau of Forestry’s Shale Gas Monitoring Report (released April 2014). This feature class represents any pads related to gas development and is not limited to natural gas wellhead pads only. The Bureau of Forestry considers any hardened non-linear surface related to shale gas development to be a pad for monitoring purposes. This feature class contains the best available data as of 12/31/2012. Pad Type Code Values: 1=Gas Well, 2=Road ROW, 3=Pipeline, 4=Compressor, 5=Freshwater Impoundment, 6=Water Withdraw, 7=Storage, 8=Stone Pit/Quarry, 9=Meter/Valve/Tap, 10=Monitoring, 11=Oil Well, 12=Oil and Gas Well. April 2014 Shale Gas Monitoring Report Infrastructure
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
Limits of Clearance (LOC) for infrastructure development of shale gas activities on state forest land as of December 31, 2012. This dataset was used in part to develop the Bureau of Forestry’s Shale Gas Monitoring Report (released April 2014). This dataset is intended to represent areas where vegetation would be removed or altered due to shale gas development. This area tends to be equal to or less than DEP permitted Limits of Disturbance (LOD). The permitted LOD can include erosion & sedimentation measures that do not impact surface vegetation (such as silt sock). LOC typically include the area where gas infrastructure is developed such as roads, pads and pipelines and the additional workspace cleared for the project. Shale gas related development projects tend to be adjacent to each other and could possibly be attributed to several types of development (pads, roads, pipelines). The individual features have been arbitrarily assigned to a single type of development to avoid counting the same areas several times. Portions of the LOC may be reclaimed or allowed to revert back to natural vegetation within the near future, and is therefore not necessarily an accurate measure of vegetated areas permanently converted to non-vegetation. This data is an estimate of the area that was cleared for a shale gas related project. There may be inaccuracies in the vector or attribute data. This data is intended for demonstration, monitoring, planning, educational, and research purposes only.Project Type Code Values: 1=Gas Well, 2=Road ROW, 3=Pipeline, 4=Compressor, 5=Freshwater Impoundment, 6=Water Withdraw, 7=Storage, 8=Stone Pit/Quarry, 9=Meter/Valve/Tap, 10=Monitoring, 11=Oil Well, 12=Oil and Gas Well. April 2014 Shale Gas Monitoring Report Infrastructure
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
Ownership data used in the generation of the Bureau of Forestry’s Shale-Gas Monitoring Report (April 2014). This dataset was used in part to develop the Bureau of Forestry’s Shale Gas Monitoring Report (released April 2014). Representation of mineral rights, leased tracts, etc. on lands managed by the Bureau of Forestry as of December 31, 2012. Updates to this dataset are ongoing and this data is not intended to be a legal representation of ownership. This data may contain inaccuracies or incomplete information. This dataset includes ownership information where a "tract" number has been assigned by the Bureau of Forestry (leased tracts and severed rights lands where the ownership of said rights has been verified). This dataset was created from several different datasets of unknown lineage. The boundaries do not exactly match the Bureau of Forestry’s state forest boundary data. It is an estimate of the boundaries only and is not intended to be a legal description or depiction of boundaries. This data may contain inaccuracies or incomplete information. April 2014 Shale Gas Monitoring Report Infrastructure
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
Pipelines permitted through a DCNR natural gas lease or developed on state forest lands where DCNR does not own the mineral rights. This dataset was used in part to develop the Bureau of Forestry’s Shale Gas Monitoring Report (released April 2014). This dataset represents pipelines where construction has been started or completed before December 31, 2012 and permitted through a DCNR natural gas lease or developed on state forest lands where DCNR does not own the mineral rights.Pipeline Type Code Values: 1=Gathering, 2=Marketing, 3=Water, 4=Other or Unknown, 5=Meter/Valve/Tap, 6=Trunk. April 2014 Shale Gas Monitoring Report Infrastructure
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| Pennsylvania Department of Conservation and Natural Resources |
1995 |
Late in 1994, the Pennsylvania Bureau of Topographic and Geologic Survey was
asked to develop a digital physiographic provinces map at 1:100,000 scale. The
then-available physiographic provinces map was compiled by the Survey at
1:500,000 scale and published at 1:2,000,000 scale in 1989. A new physiographic
provinces map was recompiled on county 1:50,000-scale topographic maps having
20-foot contour intervals. Boundaries based primarily on geology were positioned
using published geological maps. Most boundaries were positioned by topographic
interpretation. The use of a 20-foot contour interval (a 200-foot interval was
used in 1989) resulted in the repositioning of some boundaries. New
scale-enhanced understanding of topographic/geologic patterns in the Appalachian
Plateaus province resulted in the creation of three new sections and the revision
of other section boundaries. The new compilation was reduced 50 percent and
transferred to 1:100,000-scale mylar base maps. The province and section
boundaries and the late Wisconsinan glacial border were digitized from the
mylars, edgematched, assembled into a single dataset, and attributed with
physiographic province and section names using UNIX-based Arc/Info. The late
Wisconsinan glacial border, which coincides with province and section boundaries
in some places, was copied to a separate dataset and removed from the dataset
containing the province and section boundaries. There are two datasets for the
late Wisconsinan glacial border and the physiographic province and section
boundaries. The original datasets are accurate at 1:100,000 scale. The other
datasets have been generalized to 1:500,000-scale accuracy for more regional
work.
A companion dataset consisting of the state and county boundaries of Pennsylvania
was compiled from the U.S. Geological Survey (USGS) 1:100,000-scale
digital-line-graph (DLG) files for boundaries. The dataset has been attributed
with the county names.
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| Pennsylvania Department of Conservation and Natural Resources |
1995 |
Late in 1994, the Pennsylvania Bureau of Topographic and Geologic Survey was
asked to develop a digital physiographic provinces map at 1:100,000 scale. The
then-available physiographic provinces map was compiled by the Survey at
1:500,000 scale and published at 1:2,000,000 scale in 1989. A new physiographic
provinces map was recompiled on county 1:50,000-scale topographic maps having
20-foot contour intervals. Boundaries based primarily on geology were positioned
using published geological maps. Most boundaries were positioned by topographic
interpretation. The use of a 20-foot contour interval (a 200-foot interval was
used in 1989) resulted in the repositioning of some boundaries. New
scale-enhanced understanding of topographic/geologic patterns in the Appalachian
Plateaus province resulted in the creation of three new sections and the revision
of other section boundaries. The new compilation was reduced 50 percent and
transferred to 1:100,000-scale mylar base maps. The province and section
boundaries and the late Wisconsinan glacial border were digitized from the
mylars, edgematched, assembled into a single dataset, and attributed with
physiographic province and section names using UNIX-based Arc/Info. The late
Wisconsinan glacial border, which coincides with province and section boundaries
in some places, was copied to a separate dataset and removed from the dataset
containing the province and section boundaries. There are two datasets for the
late Wisconsinan glacial border and the physiographic province and section
boundaries. The original datasets are accurate at 1:100,000 scale. The other
datasets have been generalized to 1:500,000-scale accuracy for more regional
work.
A companion dataset consisting of the state and county boundaries of Pennsylvania
was compiled from the U.S. Geological Survey (USGS) 1:100,000-scale
digital-line-graph (DLG) files for boundaries. The dataset has been attributed
with the county names.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
An intermediate product of the Pennsylvania Hydrography Dataset (PAHD) generation. This product is the result of a conflation study with existing hydrography originated by the Allegheny County Division of Computer Services Geographic Information Systems Group. This product is not intended to be a finalized component of the Pennsylvania Hydrography Dataset (PAHD): these are provisional data that have undergone no manual refinement. The Modeled_PAHD_Flowpath geometries represent an intermediate product that was created from a workflow that was examining, among other things, the application of conflation steps, monotonicity, and Topographic Positioning Index (TPI) products toward an automated elevation-derived hydrography (EDH) workflow.
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| Pennsylvania Department of Conservation and Natural Resources |
2011 |
Open-file reports are informal releases that allow quicker public access to geologic data. They also may be released when the information is expected to be updated frequently, or when a limited audience is expected. Open-file reports are compiled by Survey staff and/or outside cooperators. They are reviewed for conformity with the open-file publication guidelines of the Bureau of Topographic and Geologic Survey but have not gone through a formal editorial review process. In terms of technical and geological content, the standards for preparation of texts for informal release should not vary in quality, accuracy, or precision from the standards applied to similar geologic texts destined for formal publication.
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
This dataset can help inform trail and trailhead planning by identifying areas with the greatest need for trail access opportunities. It calculates 10-minute drive “service areas” around trailheads in Pennsylvania. Regions outside these service areas are defined as low, medium, or high need based on population density, youth density, and low-income households. Trailhead data came from Explore PA Trails. The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
This dataset provides county-level summary statistics on who has 10-minute drive access to trailheads in Pennsylvania and who doesn’t. The statistics derive from overlaying 2018 US Census block group data with the Outdoor Recreation Access: 10-Minute Drive to Trailheads layer. Statistics include overall number of citizens served as well as demographic breakdowns by age, income, race/ethnicity, and social vulnerability. The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation.
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
This dataset can help inform fishing and boating access planning by identifying areas with the greatest need for water access opportunities. It calculates 10-minute drive “service areas” around fishing and boating access points in Pennsylvania. Regions outside these service areas are defined as low, medium, or high need based on population density, youth density, and low-income households. Water access data came from Explore PA Trails and the Pennsylvania Fish and Boat Commission’s Access Points (Fishing and Boating). The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation.
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
This dataset provides county-level summary statistics on who lives within a 10-minute drive to fishing and boating access in Pennsylvania and who doesn’t. The statistics derive from overlaying 2018 US Census block group data with the Outdoor Recreation Access: 10-Minute Drive to Water Access layer. Statistics include overall number of citizens served as well as demographic breakdowns by age, income, race/ethnicity, and social vulnerability. The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation.
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
This dataset can help inform park and trailhead planning by identifying areas with the greatest need for outdoor recreation opportunities. It calculates 10-minute walk “service areas” around open-access lands and trailheads in Pennsylvania. Regions outside these service areas are defined as low, medium, or high need based on population density, youth density, and low-income households. Open-access land data came from PA Conserved Land and Explore PA Local Parks, while trailhead data came from Explore PA Trails. The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation.
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
This dataset provides county-level summary statistics on who has 10-minute walk access to outdoor recreation in Pennsylvania and who doesn’t. The statistics derive from overlaying 2018 US Census block group data with the Outdoor Recreation Access: 10-Minute Walk layer. Statistics include overall number of citizens served as well as demographic breakdowns by age, income, race/ethnicity, and social vulnerability. The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation.
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
This dataset provides municipal-level summary statistics on who has 10-minute walk access to outdoor recreation in Pennsylvania and who doesn’t. The statistics derive from overlaying 2018 US Census block group data with the Outdoor Recreation Access: 10-Minute Walk layer. Statistics include overall number of citizens served as well as demographic breakdowns by age, income, race/ethnicity, and social vulnerability. The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation.
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| Pennsylvania Department of Conservation and Natural Resources |
2020 |
Developed by the US Forest Service, the Recreation Opportunity Spectrum (ROS) classifies land into categories based on the probable recreation experiences it affords. This 30-m raster dataset adapts the Forest Service method to fit the landscape and local data in Pennsylvania. ROS classifies land based on land use, a location’s distance from roads, and the amount of human disturbance nearby. These classifications can help determine potential recreation opportunities that lands across the commonwealth can provide. The dataset assigns one of the following values to each cell statewide in Pennsylvania: Urban, Crop, Water, Disturbed, Rural, Front Country, Mid Country, and Back Country. CAUTION: This dataset covers the full state of Pennsylvania. It examines recreation potential, not whether lands are open to the public. The Trust for Public Land produced this dataset to support Pennsylvania’s 2020-2024 Statewide Comprehensive Outdoor Recreation Plan (SCORP). This dataset was financed in part by a grant from the Community Conservation Partnerships Program, Environmental Stewardship Fund, under the administration of the Pennsylvania Department of Conservation and Natural Resources, Bureau of Recreation and Conservation.
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Hydrography layers for the study area covered by the Surficial Geologic Map of Bessemer, New Castle South, Portersville, and the Pennsylvania portion of the New Middletown quads. The vector data herein were derived from the most recently available Quality Level 2 (QL2) lidar data using geomorphon analysis and least-cost analysis. These data are a subset of the larger Pennsylvania Hydrography Dataset (PAHD), which is in the process of being generated. They were produced using QL2 lidar deliverables and most will have a minimum horizontal accuracy of 1 meter and a minimum vertical accuracy of 0.5 meter at a 1:2,400 scale. This geodatabase contains a dataset of hydrography features of the Bessemer, New Castle South, Portersville, and the Pennsylvania portion of the New Middletown quadrangles
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Hydrography layers for the study area covered by Map 24–09.0: Hydrography map showing automated stream permanence identification for the Catawissa 7.5-minute quadrangle, Columbia County, Pennsylvania. The vector data herein were derived from the most recently available Quality Level 2 (QL2) lidar data using geomorphon analysis and least-cost analysis. These data are a subset of the larger Pennsylvania Hydrography Dataset (PAHD), which is in the process of being generated. They were produced using QL2 lidar deliverables and most will have a minimum horizontal accuracy of 1 meter and a minimum vertical accuracy of 0.5 meter at a 1:2,400 scale. This geodatabase contains a dataset of derived hydrography features of the Catawissa quadrangle as well as field validation points collected in the study area.
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| Pennsylvania Department of Conservation and Natural Resources |
2006 |
This data set represents the state forest management areas including part of the state forest boundary.
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| Pennsylvania Department of Conservation and Natural Resources |
1995 |
Late in 1994, the Pennsylvania Bureau of Topographic and Geologic Survey was
asked to develop a digital physiographic provinces map at 1:100,000 scale. The
then-available physiographic provinces map was compiled by the Survey at
1:500,000 scale and published at 1:2,000,000 scale in 1989. A new physiographic
provinces map was recompiled on county 1:50,000-scale topographic maps having
20-foot contour intervals. Boundaries based primarily on geology were positioned
using published geological maps. Most boundaries were positioned by topographic
interpretation. The use of a 20-foot contour interval (a 200-foot interval was
used in 1989) resulted in the repositioning of some boundaries. New
scale-enhanced understanding of topographic/geologic patterns in the Appalachian
Plateaus province resulted in the creation of three new sections and the revision
of other section boundaries. The new compilation was reduced 50 percent and
transferred to 1:100,000-scale mylar base maps. The province and section
boundaries and the late Wisconsinan glacial border were digitized from the
mylars, edgematched, assembled into a single dataset, and attributed with
physiographic province and section names using UNIX-based Arc/Info. The late
Wisconsinan glacial border, which coincides with province and section boundaries
in some places, was copied to a separate dataset and removed from the dataset
containing the province and section boundaries. There are two datasets for the
late Wisconsinan glacial border and the physiographic province and section
boundaries. The original datasets are accurate at 1:100,000 scale. The other
datasets have been generalized to 1:500,000-scale accuracy for more regional
work.
A companion dataset consisting of the state and county boundaries of Pennsylvania
was compiled from the U.S. Geological Survey (USGS) 1:100,000-scale
digital-line-graph (DLG) files for boundaries. The dataset has been attributed
with the county names.
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| Pennsylvania Department of Conservation and Natural Resources |
1995 |
Late in 1994, the Pennsylvania Bureau of Topographic and Geologic Survey was
asked to develop a digital physiographic provinces map at 1:100,000 scale. The
then-available physiographic provinces map was compiled by the Survey at
1:500,000 scale and published at 1:2,000,000 scale in 1989. A new physiographic
provinces map was recompiled on county 1:50,000-scale topographic maps having
20-foot contour intervals. Boundaries based primarily on geology were positioned
using published geological maps. Most boundaries were positioned by topographic
interpretation. The use of a 20-foot contour interval (a 200-foot interval was
used in 1989) resulted in the repositioning of some boundaries. New
scale-enhanced understanding of topographic/geologic patterns in the Appalachian
Plateaus province resulted in the creation of three new sections and the revision
of other section boundaries. The new compilation was reduced 50 percent and
transferred to 1:100,000-scale mylar base maps. The province and section
boundaries and the late Wisconsinan glacial border were digitized from the
mylars, edgematched, assembled into a single dataset, and attributed with
physiographic province and section names using UNIX-based Arc/Info. The late
Wisconsinan glacial border, which coincides with province and section boundaries
in some places, was copied to a separate dataset and removed from the dataset
containing the province and section boundaries. There are two datasets for the
late Wisconsinan glacial border and the physiographic province and section
boundaries. The original datasets are accurate at 1:100,000 scale. The other
datasets have been generalized to 1:500,000-scale accuracy for more regional
work.
A companion dataset consisting of the state and county boundaries of Pennsylvania
was compiled from the U.S. Geological Survey (USGS) 1:100,000-scale
digital-line-graph (DLG) files for boundaries. The dataset has been attributed
with the county names.
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| Pennsylvania Department of Conservation and Natural Resources |
2008 |
This polygon feature class is a digital representation of physiographic provinces of Pennsylvania. It is a result of visual interpretation of the topography of Pennsylvania as illustrated by 20-feet countour intervals on 1:50,000 scale county-size topographic maps. There has been no field investigation or aerial photograph interpretation involved in the production of this dataset. The feature class has the attribute field called "PHYSNO", which has been padded to 14 character length for all records.
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| Pennsylvania Department of Conservation and Natural Resources |
2008 |
This polygon feature class is a digital representation of physiographic sections of Pennsylvania. It is a result of visual interpretation of the topography of Pennsylvania as illustrated by 20-feet countour intervals on 1:50,000 scale county-size topographic maps. There has been no field investigation or aerial photograph interpretation involved in the production of this dataset. The feature class has the attribute field called "PHYSNO", assigned as a unique number identifier. The number represents two steps in the units hierarchy (province-section) and has been padded to 14 character length for all records.
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| Pennsylvania Department of Conservation and Natural Resources |
1998 |
The land surface of Pennsylvania is anything but flat and horizontal. The surface comprises complex arrangements of differences in elevation above mean sea level. The spatial arrangement of these elevation differences comprises topography. Topography that has describable uniformity throughout some areal dimension is termed a landform. Landforms can be classified, boundaries can be drawn, and a map can be created. A preliminary landform map was compiled at approximately 1:605,500 scale. The map has four levels of landform subdivision: province, section, region, and district. These units range in size from many hundreds of square miles (province) to less than 200 square miles (district). The greatest topographic similarity for a given landform unit occurs in the smallest subdivision (district). The greatest topographic dissimilarity for a given landform unit occurs in the largest subdivision (province).
The landform map was digitized using ARC/INFO software. Polygon attributes include name of physiographic province, section, district, and area, and the numerical designation of the landform unit. Arcs were attributed according to whether they form the boundary of one or more of the following: province, section, region, district, or state.
These data sets are preliminary and will be superseded in 1999 by more detailed data sets prepared from 1:50,000-scale compilation maps having five levels of landform subdivision.
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
The expected recreation experience and characterization of wild character that users can expect to find on the state forest system as of December 31, 2012. This dataset was used in part to develop the Bureau of Forestry’s Shale Gas Monitoring Report (released April 2014). The Bureau of Forestry uses a modified version of the Recreational Opportunity Spectrum (ROS) to estimate the wild character of state forest land and the experience that recreationists can be expected to find in different portions of state forest system. This dataset represents the excepted ROS experience as of December 31, 2012. The Bureau of Forestry’s ROS zones are defined as: Primitive: greater than 1,000 acres and more than 1 mile from motorized roads, trails, and railroads. Semi-Primitive: greater than 500 acres and more than 1/2 mile from motorized roads, trails, and railroads.Semi-Primitive Non-motorized: greater than 250 acres and more than 1/4 mile from motorized roads, trails, and railroads.Semi-Developed and Developed (Other Zones): No minimum size or remoteness criteria. The ROS model considers all changes. Non-shale gas related changes do affect the results of the analysis. April 2014 Shale Gas Monitoring Report ROS
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| Pennsylvania Department of Conservation and Natural Resources |
2014 |
The expected recreation experience and characterization of wild character that users can expect to find on the state forest system prior to shale-gas development (pre-2008). This dataset was used in part to develop the Bureau of Forestry’s Shale Gas Monitoring Report (released April 2014). The Bureau of Forestry uses a modified version of the Recreational Opportunity Spectrum (ROS) to estimate the wild character of state forest land and the experience that recreationists can be expected to find in different portions of state forest system. This dataset represents the excepted ROS experience prior to Shale-Gas development (pre-2008). The Bureau of Forestry’s ROS zones are defined as: Primitive: greater than 1,000 acres and more than 1 mile from motorized roads, trails, and railroads. Semi-Primitive: greater than 500 acres and more than 1/2 mile from motorized roads, trails, and railroads.Semi-Primitive Non-motorized: greater than 250 acres and more than 1/4 mile from motorized roads, trails, and railroads.Semi-Developed and Developed (Other Zones): No minimum size or remoteness criteria. The ROS model considers all changes. Non-shale gas related changes do affect the results of the analysis. April 2014 Shale Gas Monitoring Report ROS
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| Pennsylvania Department of Conservation and Natural Resources |
2000 |
Contains multiple Rivers Conservation Plans for Pennsylvania. Each folder includes various files with data about that particular river study area.
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| Pennsylvania Department of Conservation and Natural Resources |
2025 |
This data set portrays the approximate location of Abandoned Mine Land Problem Areas containing public health, safety, and public welfare problems created by past coal mining. It is a subset of data contained in the Office of Surface Mining (OSM) Abandoned Mine Land Inventory. This layer identifies AML Points representing specific locations within an AML Inventory Site, examples include AML discharge.
This data set provides information needed to implement Title IV Abandoned Mine Reclamation, of the Surface Mining Control and Reclamation Act (SMCRA) of 1977. One of the major uses of this data set is for the reporting of annual Abandoned Mine Land Program accomplishments to Congress. In addition, the data is used in the National Atlas of the United States for geographic display and analysis at the national level, and for large regional areas.
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| Pennsylvania Department of Environmental Protection |
2025 |
This data set portrays the approximate location of Abandoned Mine Land Problem Areas containing public health, safety, and public welfare problems created by past coal mining. It is a subset of data contained in the Office of Surface Mining (OSM) Abandoned Mine Land Inventory. This layer identifies AML Polygons representing specific areas to large to be represented by points within the entire, AML Inventory Site, examples include AML dangerous highwalls.
This data set provides information needed to implement Title IV Abandoned Mine Reclamation, of the Surface Mining Control and Reclamation Act (SMCRA) of 1977. One of the major uses of this data set is for the reporting of annual Abandoned Mine Land Program accomplishments to Congress. In addition, the data is used in the National Atlas of the United States for geographic display and analysis at the national level, and for large regional areas.
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| Pennsylvania Department of Environmental Protection |
2025 |
The AML (Abandoned Mine Land) Inventory is a collection of areas where surface features of abandoned mines are present. Presently the data is shown using three layers. AML Inventory Sites is used to show the entire boundary of a problem area. AML Points and AML Polygons are used to show specific problems within a designated inventory site. The inventory Does Not Include complete and comprehensive coverage of abandoned underground mines, surface or underground mines that were permitted and closed after 1982, or active surface or underground mines. For further information concerning mining in your area, please contact the local DEP office.
This data set portrays the approximate location of Abandoned Mine Land Problem Areas containing public health, safety, and public welfare problems created by past coal mining. It is a subset of data contained in the Office of Surface Mining (OSM) Abandoned Mine Land Inventory. This layer represents the AML Inventory Sites, which are the boundary of an entire problem area. All related AML point/polygon features must fit within the boundary of the designated problem area.
This data set provides information needed to implement Title IV Abandoned Mine Reclamation, of the Surface Mining Control and Reclamation Act (SMCRA) of 1977. One of the major uses of this data set is for the reporting of annual Abandoned Mine Land Program accomplishments to Congress. In addition, the data is used in the National Atlas of the United States for geographic display and analysis at the national level, and for large regional areas.
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| Pennsylvania Department of Environmental Protection |
2025 |
Pennsylvania state law requires those who wish to conduct mining activities within the Commonwealth submit and get approval by the Pennsylvania Department of Environmental Protection (DEP) for permits related to those activities. These permits are written to cover various aspects of the mining operations, such as: reclamation, water quality protection, air quality protection, waste disposal and mine subsidence control. The DEP California District Office reviews permits related to Bituminous coal underground mining. Module 6.1 of the Application for Bituminous Underground Mine requires a Location Map be submitted with the permit. The Location Map should be a 7.5 Minute USGS map covering the area within one (1) mile of the underground permit area boundaries. This dataset contains the digitized underground permit area boundaries of the active underground bituminous mines in Pennsylvania based from the Location Maps submitted with the permit applications and permit renewal applications.
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| Pennsylvania Department of Environmental Protection |
2025 |
Represents the Primary Facility type Air Emission Plant (AEP) point features. Air Emissions Plant is a DEP primary facility type related to the Air Quality Program. The sub-facility types related to the Air Emissions Plant that are included are: Air Pollution Control Device, Combustion Unit, Fuel Material Location, General Administrative Location, Incinerator, Point of Air Emission, and Process.
Air Pollution Control Device:
Facility that removes one or more pollutants from an exhaust stream. Examples include a baghouse or wet scrubber.
Combustion Unit:
Facility that burns coal, oil, or natural gas. Combustion units are used to produce either electricity, steam, hot gases, or some combination of these. Examples include a utility boiler or gas turbine.
Fuel Material Location:
Facility for storage of fuels shared by multiple combustion units, incinerators, or processes. Examples include oil storage tanks and larger natural gas supply lines.
General Administrative Location:
An administrative location is created automatically for every new air emission plant primary facility. It is used for locational data to represent the entire primary facility, instead of assigning lat/longs to each sub-facility. The General Administrative Location sub-facility may be obsolete in the future, once eFACTS allows locations at the primary level.
Incinerator:
Facility that destroys solid waste products using a variety of fuels. Examples include municipal waste incinerators and hospital infectious waste incinerators.
Point of Air Emission:
Exact location or structure from which all other air emission plant sub-facilities exhaust their emissions. Examples may include a steel or masonry smokestack; however, a point of air emission may also represent fugitive emissions that escaped from other points of a facility.
Process:
Facility that produces or modifies a product, and creates an air emission from either the materials used or a fuel consumed. Examples include coating lines or cement kilns.
Continuous Emission Monitoring Point:
Devices attached to smokestacks that monitor specific pollutants, i.e. sulfur dioxide, nitrogen oxides, etc.
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| Pennsylvania Department of Environmental Protection |
1999 |
This coverage represents the point locations and data for 1,106 groundwater
quality monitoring points sampled under the PA DEP Fixed Station
Network (FSN) and Ambient Survey groundwater monitoring program.
Sample data were collected from August 1985 to December 1998. Monitoring
points were typically homeowner wells, springs, public water supplies, or industrial
wells. Locations were chosen to represent regional background conditions
within a groundwater basin. Samples were collected by prioritized
groundwater basins. Monitoring points represent some of the top 100
priority basins of the 478 groundwater basins. Monitoring points were
analyzed for 27 different analytes including pH, alkalinity, total dissolved
solids, ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, phosphorus,
total organic carbon, total hardness, calcium, magnesium, sodium,
potassium, chloride, sulfate, silica, arsenic, barium, cadmium, chromium,
copper, iron, lead, manganese, zinc, mercury, and turbidity. Data for
alkalinity through silica are in mg/L, whereas values listed for arsenic
through mercury are in ug/L. Turbidity is in NTUs. PH is a laboratory pH value.
Analyte values listed with the coverage are means. Monitoring points have been
sampled up to 34 times. Trend analysis results are included for 474 monitoring
points by parameter. The Kendall Tau rank correlation test was used to test for trends
in ground water quality over time. This test is a nonparametric procedure that is
similar to the standard correlation coefficient to assess the presence of a relationship
between variables. The Kendall Tau value represents a probability that there is a
relationship between two variables. A significance level of .05 was used with the
calculated Kendall Tau correlation coefficients. Trend analysis was performed on
monitoring points using SAS statistical software.
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| Pennsylvania Department of Environmental Protection |
2025 |
A layer containing the permit boundaries of Anthracite Coal Surface Mines. Data was converted over from the physical "Mylar Review" system for surface mine permit location look-ups. The physical topographic maps and mylar overlays have been scanned, georeferenced, and vectorized to create the permit boundary polygon layers with an index number attribute. The corresponding index cards have been data entered in a spreadsheet. The polygon layers' attribute tables were joined with the spreadsheet. More recent surface mine permit boundaries and attributes were digitized directly as shapefiles. This layer represents a combination of various surface mine permit tracking systems. All future surface mine permit boundaries will be updated here, as the mylar system is phased out.
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| Pennsylvania Department of Environmental Protection |
2025 |
Beneficial Land Use is a DEP primary facility type related to the Water Pollution Control Program. The sub-facility type related to Beneficial Land Use is the Parcel. A parcel refers to the land application site that is proposed to received biosolids or residential septage. Land application for biosolids and septage means beneficial use, meaning it is applied to land as a soil amendment/fertilizer.
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| Pennsylvania Department of Environmental Protection |
2025 |
A layer containing the permit boundaries of Bituminous Coal Surface Mines. Data was converted over from the physical "Mylar Review" system for surface mine permit location look-ups. The physical topographic maps and mylar overlays have been scanned, georeferenced, and vectorized to create the permit boundary polygon layers with an index number attribute. The corresponding index cards have been data entered in a spreadsheet. The polygon layers' attribute tables were joined with the spreadsheet. More recent surface mine permit boundaries and attributes were digitized directly as shapefiles. This layer represents a combination of various surface mine permit tracking systems. All future surface mine permit boundaries will be updated here, as the mylar system is phased out.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Captive Hazardous Waste Operation is a DEP primary facility type related to the Waste Management Hazardous Waste Program. The sub-facility types related to Captive Hazardous Waste Operations that are included in eMapPA are: Boiler/Industrial Furnace, Disposal Facility, Hazardous Generator, Incinerator, Recycling Facility, Storage Facility, and Treatment Facility. Captive Hazardous Waste Operation Sub-Facility Types
Boiler or Industrial Furnace:
Facility permitted by DEP to burn or process hazardous waste generated onsite, to recover thermal energy, or to accomplish recovery of materials in association with a manufacturing process.
Disposal Facility:
Facility permitted by DEP to dispose of hazardous waste generated onsite by incineration, or by intentionally placing the waste in or on land or water in specially designed and constructed containment units where the waste will remain after closure of the facility.
Hazardous Generator:
A site where hazardous waste is first produced. The hazardous waste may be accumulated onsite at this facility for a prescribed limited amount of time (usually between 90 and 270 days) without first obtaining a storage permit from the Department, as long as it is done in accordance with prescribed standards.
Incinerator:
Facility permitted by DEP to burn or thermally combust hazardous waste generated onsite in an enclosed device using controlled flame. Devices meeting the criteria for classification as a boiler, industrial furnace, carbon regeneration unit, or sludge dryer are not incinerators.
Recycling Facility:
Facility permitted by DEP to treat hazardous waste generated onsite, making it suitable for upcoming recovery of a usable product or material.
Storage Facility:
Facility permitted by DEP to hold hazardous waste generated onsite for a temporary period (not to exceed one year). At the end of that period the hazardous waste is treated, disposed of, or stored elsewhere. Facilities accumulating hazardous wastes generated onsite in accordance with prescribed generator accumulation standards for a prescribed limited amount of time (usually between 90 and 270 days) are NOT storage facilities.
Treatment Facility:
Facility permitted by DEP to change the physical, chemical or biological character or composition of hazardous waste that is generated onsite. The purpose is to neutralize the waste or to render the waste non-hazardous, safer for transport, suitable for recovery, suitable for storage, or reduced in volume.
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| Pennsylvania Department of Environmental Protection |
2025 |
Represents the Primary Facility type Coal Mining Operation (CMO) point features. A Coal Mining Operation is a DEP primary facility type related to the Mining Program. The sub-facility types related to Coal Mining Operations that are included are: Deep Mine Underground mining of coal. Includes, but is not limited to, portal, tunnel, slope, and drift mines.
Discharge Point Discharge of water from an area as a result of coal mining activities.
Mineral Preparation Plant Facility at which coal is cleaned and processed.
Post Mining Treatment Post-mining discharges are groundwater seeps and flows that occur after a mine has been completed and reclaimed. Many of these discharges have become contaminated by contacting acid producing rock in the mine environment. Untreated discharges that enter clean streams cause acidification, which immediately kills much of the aquatic life. Coal mines that are predicted to have discharges are not permitted; however, coal mining operators are required to treat post-mining discharges in cases where the predictions do not come true. Through advances in predictive science, less than 2 percent of the permits issued today result in a post-mining discharge. New technologies, including alkaline addition and special handling of acid producing material, are being studied in order to help address the remaining 2 percent.
Refuse Disposal Facility An area used for disposal or storage of waste coal, rock, shale, slate, clay, and other coal mining related materials.
Refuse Reprocessing Facility at which coal is extracted from waste coal, rock, shale, slate, clay, and other coal mining related material, i.e., coal refuse.
Surface Mine Surface mining of coal by removing material which lies above the coal seam. Includes, but is not limited to, strip, auger, quarry, dredging, and leaching mines. A Coal Mining Operation is a DEP primary facility type related to the Mining Program. The sub-facility types related to Coal Mining Operations that are included in eMapPA are: Coal-Aboveground Storage Tank - aboveground tanks greater than 250 gallons used to store a regulated substance, motor oil or fuel on a coalmine permit. These tanks are regulated under the coal mining regulations since they are specifically exempted from the storage tank regulations. Discharge Point - Discharge of water from an area as a result of coal mining activities. Mineral Preparation Plant - Facility at which coal is cleaned and processed. Mining Stormwater GP - General permit for Stormwater discharges associated with coal mining activities in which the main pollutant is sediment. Discharge is not into a High Quality or Exceptional Value designated stream. NPDES Discharge Point - An effluent discharge at a coal mine operation permitted under the National Pollutant Discharge Elimination System. Post Mining Treatment - Post-mining discharges are groundwater seeps and flows that occur after a mine has been completed and reclaimed. Many of these discharges have become contaminated by contacting acid producing rock in the mine environment. Untreated discharges that enter clean streams cause acidification, which immediately kills much of the aquatic life. Coal mines that are predicted to have discharges are not permitted; however, coal mining operators are required to treat post-mining discharges in cases where the predictions do not come true. Through advances in predictive science, less than 2 percent of the permits issued today result in a post-mining discharge. New technologies, including alkaline addition and special handling of acid producing material, are being studied in order to help address the remaining 2 percent. Refuse Disposal Facility - An area used for disposal or storage of waste coal, rock, shale, slate, clay, and other coal mining related materials. Refuse Reprocessing - Facility at which coal is extracted from waste coal, rock, shale, slate, clay, and other coal mining related material, i.e., coal refuse. Surface Mine - Surface mining of coal by removing material which lies above the coal seam. Includes, but is not limited to, strip, auger, quarry, dredging and leaching mines. Underground Mine - Deep mining of coal. Includes, but is not limited to, portal, tunnel, slope and drift mines.
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| Pennsylvania Department of Environmental Protection |
2025 |
Coal Pillar Locations are pillars of coal that must remain in place to provide support for a coal mine.
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| Pennsylvania Department of Environmental Protection |
2025 |
Coal Pillar Locations are pillars of coal that must remain in place to provide support for an oil and gas well on the surface.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Commercial Hazardous Waste Operation is a DEP primary facility type related to the Waste Management Hazardous Waste Program. The sub-facility types related to Commercial Hazardous Waste Operations that are included are: Disposal Facility, Hazardous Generator, Recycling Facility, Storage Facility, and Treatment Facility._____Disposal Facility:
Facility permitted by DEP to accept hazardous waste generated off-site, and dispose of the waste by incineration, or by intentionally placing the waste in or on land or water in specially designed and constructed containment units where it will remain after closure of the facility.
Hazardous Generator:
A site where hazardous waste is first produced. The hazardous waste may be accumulated on-site at this facility for a prescribed limited amount of time (usually between 90 and 270 days) without first obtaining a storage permit from the Department, as long as it is done in accordance with prescribed standards.
Recycling Facility:
Facility permitted by DEP to accept hazardous waste generated off-site, and treat the waste to make it suitable for upcoming recovery of a usable product or material.
Storage Facility:
Facility permitted by DEP to accept hazardous waste generated off-site, and hold the waste for a temporary period (not to exceed one year). At the end of that period the hazardous waste is treated or disposed of at the same facility, or treated, disposed, or stored elsewhere. Additional permitting is necessary if the waste is treated or disposed at the storage facility itself.
Treatment Facility:
Facility permitted by DEP to change the physical, chemical or biological character or composition of hazardous waste that is generated off-site. The purpose is to neutralize the waste, or to render the waste non-hazardous, safer for transport, suitable for recovery, suitable for storage, or reduced in volume.
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| Pennsylvania Department of Environmental Protection |
2025 |
The conservation well layer identifies the permitted surface location of oil and gas conservation wells that have not been plugged. These include active, regulatory inactive, orphaned, and abandoned wells. A conservation well is any well which penetrates the Onondaga horizon, or in those areas in which the Onondaga horizon is nearer to the surface than thirty-eight hundred feet, any well which exceeds a depth of thirty-eight hundred feet beneath the surface.
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| Pennsylvania Department of Environmental Protection |
2025 |
The plugged conservation well layer identifies the permitted surface location of oil and gas conservation wells that have been plugged. A conservation well is any well which penetrates the Onondaga horizon, or in those areas in which the Onondaga horizon is nearer to the surface than thirty-eight hundred feet, any well which exceeds a depth of thirty-eight hundred feet beneath the surface.
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| Pennsylvania Department of Environmental Protection |
2025 |
The purpose of this dataset is to provide DEP staff and other environmental professionals with a spatially referenced dataset of the permit area boundaries of active permitted coal refuse disposal areas (CRDAs) in Pennsylvania.
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| Pennsylvania Department of Environmental Protection |
2024 |
DEP office locations
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| Pennsylvania Department of Environmental Protection |
2024 |
Spatially displays the six regions of Pennsylvania Department of Environmental Protection
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| Pennsylvania Department of Environmental Protection |
2025 |
Coal mining has occurred in Pennsylvania for over a century. This dataset tries to identify the areas of the various coal seams in Pennsylvania that have been extracted by various underground mining techniques. This information can be used for many environmental related issues, including mine land reclamation and determination of needs for Mine Subsidence Insurance. The information in this dataset was gathered from digitizing the area of extracted coal identified on historic and modern underground mine maps. The maps to these coal mines are stored at many various public and private locations (if they still exist at all) throughout the commonwealth, they have been scanned to create a digital archive, and georeferenced to their approximate location for use in a geographic information system (GIS). The dataset is continuously updated as new maps are processed and is not considered “completed”, i.e. just because an area in Pennsylvania is not identified in this dataset as mined, does not mean the area was not mined.
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| Pennsylvania Department of Environmental Protection |
2025 |
An Encroachment Location is a DEP primary facility type related to the Water Resources Management Water Obstructions Program. There are many sub-facility types relating to Encroachment Locations, ranging from Boat Launch Ramps to Dredging to Wetland Impact, that are included in eMapPA. Furthermore, these sub-facilities may pertain to more than one primary facility kind as listed: Abandoned Mine Reclamation, Mineral Resources, Oil and Gas, Soils and Waterways, Waterways Engineering, and Water Quality.
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| Pennsylvania Department of Environmental Protection |
2025 |
An Encroachment Location for Oil & Gas is a DEP primary facility type related to the Oil and Gas Program. The sub-facilities that fall under Oil and Gas Encroachment also exist under Encroachment Locations. The difference is in the DEP program that regulates the facilities. Sub Facility types include:___Bridge--- A bridge across a stream required to provide access primarily to an oil and gas location Culvert--- A culvert installed to provide access primarily to an oil and gas location. Stream Bank Protection definition - Oil and Gas Stream Bank Protection facilities are physical barriers or practices put in place to minimize stream bank disturbance from Oil and Gas related activities. Intake structure - means the total physical structure and any associated constructed waterways used to withdraw water from waters of the Commonwealth. The intake structure extends from the point at which water is withdrawn from the surface water source up to, and including, the intake pumps.
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| Pennsylvania Department of Environmental Protection |
2010 |
This layer identifies 2010 Pennsylvania Census Tracts which meet the PADEP definition for Environmental Justice Areas by having a poverty rate of 20% or greater or a non-white population of 30% or greated. Percentages were rounded up based on a decimal value of .5 or greater for purposes of creating this layer. Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data.
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| Pennsylvania Department of Environmental Protection |
2015 |
This layer identifies 2015 Pennsylvania Census Tracts which meet the PADEP definition for Environmental Justice Areas by having a poverty rate of 20% or greater or a non-white population of 30% or greated. Percentages were rounded up based on a decimal value of .5 or greater for purposes of creating this layer. Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data.
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| Pennsylvania Department of Environmental Protection |
2023 |
Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data. This is the first decennial census for which the entire United States is covered by census tracts. For the 1990 census, some counties had census tracts and others had block numbering areas (BNAs). In preparation for Census 2000, all BNAs were replaced by census tracts, which may or may not cover the same areas. Census tracts generally have between 1,500 and 8,000 people, with an optimum size of 4,000 people. (Counties with fewer people have a single census tract.) When first delineated, census tracts are designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over many decades so that statistical comparisons can be made from decennial census to decennial census. However, physical changes in street patterns caused by highway construction, new developments, and so forth, may require occasional boundary revisions. In addition, census tracts occasionally are split due to population growth or combined as a result of substantial population decline. Census tracts are identified by a four-digit basic number and may have a two-digit numeric suffix; for example, 6059.02. The decimal point separating the four-digit basic tract number from the two-digit suffix is shown in the printed reports and on census maps. In computer-readable files, the decimal point is implied. Many census tracts do not have a suffix; in such cases, the suffix field is either left blank or is zero-filled. Leading zeros in a census tract number (for example, 002502) are shown only in computer-readable files. Census tract suffixes may range from .01 to .98. For the 1990 census, the .99 suffix was reserved for census tracts/block numbering areas (BNAs) that contained only crews-of-vessels population; for Census 2000, the crews-of-vessels population is part of the related census tract. Census tract numbers range from 1 to 9999 and are unique within a county or statistically equivalent entity. The U.S. Census Bureau reserves the basic census tract numbers 9400 to 9499 for census tracts delineated within or to encompass American Indian reservations and off-reservation trust lands that exist in multiple states or counties. The number 0000 in computer-readable files identifies a census tract delineated to provide complete coverage of water area in territorial seas and the Great Lakes.
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| Pennsylvania Department of Environmental Protection |
2025 |
An Erosion and Sediment Control Facility is a DEP primary facility type related to the Water Pollution Control program. The following is a list of sub-facility types related to Erosion and Sediment Control Facilities that are included in eMapPA: Agricultural Activities, Commercial or Industrial Development, Government Facilities, Oil and Gas Development, Private Road or Residence, Public Road Construction, Recreational Activities, Remediation/Restoration, Residential Subdivision, Sewerage or Water Systems, Silviculture, or Utility Facility and/or Transmission Line. Any of the above development activities that may discharge stormwater during construction fall under the erosion and sediment control permit category.
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| Pennsylvania Department of Environmental Protection |
2013 |
These well locations were derived from historical mine maps known as the WPA, Ksheet, and Hsheet collections. These locations are provided for informational purposes only and should not be sole means of decision making and are in no way a substitute for actual on the ground observation. In 1859, the United States’ first commercial oil well was drilled in Venango County, Pennsylvania. In the 150 years subsequent to this, an unknown number of oil and gas wells have been drilled in the state. A current estimate by the Independent Petroleum Association of America places that number at approximately 325,000. Of those 325,000 wells, over 200,000 are still unaccounted for. As these wells are found and verified, they are cataloged in the Department of Environmental Protection’s (DEP) Abandoned and Orphan Well database to facilitate plugging. There are currently over 8,200 wells listed in this database (2013). With so many unknown oil and gas wells scattered across Pennsylvania and the environmental threats that they pose, identification remains a vital component of DEP’s Oil and Gas Program. Currently, the DEP, Office of Active and Abandoned Mine Operations is involved in many projects dealing with historic and active mine map restoration and geo-referencing. These maps, which vary in age, not only contain information on historic mine locations, but also oil and gas locations. Through collaboration between the Bureau of Mining Programs and the Bureau of Oil and Gas Planning and Program Management, potential oil and gas well locations were assembled using three mine map collections. These collections include the WPA mine map collection, Ksheets collection, and the Hsheets collection. From these sources, over 30,000 potential historic oil and gas well locations were derived. The Bureau of Oil and Gas Planning and Program Management is constantly looking for historic sources to help locate oil and gas wells in the state that remain unaccounted for. This particular dataset was created using georeferenced mine maps of various/unknown accuracy and various/unknown coordinate systems to various base maps, including but not limited to USGS topographic maps and PAMAP aerial photography. The locations were then digitized using the georeferenced mine maps. These locations are provided for informational purposes only and should not be sole means of decision making and are in no way a substitute for actual field observations.
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| Pennsylvania Department of Environmental Protection |
2004 |
This is the Department's legacy master stream coverage. The last updates to this layer occurred in late 2004. This layer has been replaced by the High Resolution National Hydrography Dataset (NHD) and as such should only be used to aid in identifying legacy stream codes.
This layer was digitized at a scale of 1/24,000. All streams are connected and have flow direction. Each stream is identified by a unique 5-digit stream code found in the WRDS (Water Resource Data System) field. The streams are further divided into segments. The first stream segment flows from the headwaters to the first tributary. Subsequent segments are tributary to tributary. The final segment flows from the last tributary to the mouth which for purposes of this data layer is defined as the point at which the stream crosses the Pennsylvania boundary. The unique segment identifier is constructed from three concatenated fields. The stream code, downstream river mile, and finally the upstream river mile of the segment boundaries (12345_8.000_ 12.000). The river miles are measured from the mouth, at the Pennsylvania boundary, upstream to the headwaters.
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| Pennsylvania Department of Environmental Protection |
2025 |
Locations at which surface water sampling has been done in order to determine if surface waters are attaining or non-attaining designated uses. Station records are created by the DEP Biologists when they do surface water sampling.
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| Pennsylvania Department of Environmental Protection |
2025 |
An Industrial Mineral Mining Operation is a DEP primary facility type related to the Industrial Mineral Mining Program. The sub-facility types included in eMapPA are: Deep Mine - Underground mining of industrial minerals, i.e., noncoal mining. Includes, but is not limited to, industrial minerals extracted from beneath the surface by means of shafts, tunnels, adits or other mining openings. Discharge Point - Discharge of water from an area as a result of industrial mining activities, i.e. noncoal mining. Mineral Preparation Plant - Facility at which industrial minerals (i.e. noncoal minerals) are cleaned and processed. Mining Stormwater GP - General permit for Stormwater discharges associated with industrial mineral mining activities in which the main pollutant is sediment. Discharge is not into a High Quality or Exceptional Value designated stream. NPDES Discharge Point - National Pollutant Discharge Elimination System effluent discharge point for Industrial Mineral (Noncoal) Mine Sites. Post Mining Treatment - Inactive Industrial Mine with a permitted treatment facility. Surface Mine - Surface mining of industrial minerals (i.e. noncoal minerals) by removing material which lies about the industrial minerals. Includes, but is not limited to, strip, augur, quarry, dredging and leaching mines.
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| Pennsylvania Department of Environmental Protection |
2025 |
A layer containing the permit boundaries of Industrial Mineral Surface Mines. Data was converted over from the physical "Mylar Review" system for surface mine permit location look-ups. The physical topographic maps and mylar overlays have been scanned, georeferenced, and vectorized to create the permit boundary polygon layers with an index number attribute. The corresponding index cards have been data entered in a spreadsheet. The polygon layers' attribute tables were joined with the spreadsheet. More recent surface mine permit boundaries and attributes were digitized directly as shapefiles. This layer represents a combination of various surface mine permit tracking systems. All future surface mine permit boundaries will be updated here, as the mylar system is phased out.
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| Pennsylvania Department of Environmental Protection |
2025 |
This layer shows only attaining segments of the Integrated List. The Streams Integrated List represents stream assessments in an integrated format for the Clean Water Act Section 305(b) reporting and Section 303(d) listing. Streams are bodies of flowing surface water that collectively form a network that drains a catchment or basin. PA DEP protects 4 stream water uses: aquatic life, fish consumption, potable water supply, and recreation. The 305(b) layers represents stream segments that have been evaluated for attainment of those uses. If a stream segment is not attaining any one of its 4 uses, it is considered impaired.
· Aquatic Life use attainment - The integrity reflected in any component of the biological community. (i.e. fish or fish food organisms)
· Fish Consumption use attainment - The risk posed to people by the consumption of aquatic organisms (ex. fish, shellfish, frogs, turtles, crayfish, etc.)
· Recreational use attainment - The risk associated with human recreation activities in or on a water body. (i.e. exposure to bacteria and other disease causing organisms through water contact recreation like swimming or water skiing)
· Potable Water Supply use attainment - The risk posed to people by the ingestion of drinking water
Segments that have appeared on an approved Category 5 Integrated Listing are the entries labeled as approved. Integrated Lists are submitted for approval every other year. Segments entered subsequent to the latest approved Category 5 listing are labeled tentative. After appearing on an approved listing, the tentative entries move to approved. The Stream Integrated List is provided as two separate layers determined if the stream is attaining or not attaining its designated uses.
DEP Streams Integrated List layer is maintained by the PADEP Office of Water Management, Bureau of Water Supply & Wastewater Management, Water Quality Assessment and Standards Division. The layer is based on the High Resolution National Hydrography Dataset (NHD). Additional update information is provided by Bureau of Watershed Management, Water Use Planning Division.
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| Pennsylvania Department of Environmental Protection |
2025 |
This layer shows only attaining lakes of the Integrated List. The Lakes Integrated List represents lake assessments in an integrated format for the Clean Water Act Section 305(b) reporting and Section 303(d) listing. PA DEP protects 4 lake water uses: aquatic life, fish consumption, potable water supply, and recreation. The 305(b) layers represent lakes that have been evaluated for attainment of those uses. If a lake is not attaining any one of its 4 uses, it is considered impaired. Aquatic Life use attainment - The integrity reflected in any component of the biological community (i.e. fish or fish food organisms). Fish Consumption use attainment - The risk posed to people by the consumption of aquatic organisms (ex. fish, shellfish, frogs, turtles, crayfish, etc.). Recreational use attainment - The risk associated with human recreation activities in or on a water body (i.e. exposure to bacteria and other disease causing organisms through water contact recreation like swimming or water skiing). Potable Water Supply use attainment - The risk posed to people by the ingestion of drinking water. Lakes that have appeared on an approved Category 5 Integrated Listing are the entries labeled as approved. Integrated Lists are submitted for approval every other year. Lakes entered subsequent to the latest approved Category 5 listing are labeled tentative. After appearing on an approved listing, the tentative entries move to approved. The Lake Integrated List is provided as two separate layers determined if the lake is attaining or not attaining its designated uses. DEP Lakes Integrated List layers are maintained by the PADEP Office of Water Management, Bureau of Water Supply & Wastewater Management, Water Quality Assessment and Standards Division. The layer is based on the High Resolution National Hydrography Dataset (NHD). Additional update information is provided by Bureau of Watershed Management, Water Use Planning Division.
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| Pennsylvania Department of Environmental Protection |
2025 |
This layer shows only non-attaining segments of the Integrated List. The Streams Integrated List represents stream assessments in an integrated format for the Clean Water Act Section 305(b) reporting and Section 303(d) listing. Streams are bodies of flowing surface water that collectively form a network that drains a catchment or basin. PA DEP protects 4 stream water uses: aquatic life, fish consumption, potable water supply, and recreation. The 305(b) layers represents stream segments that have been evaluated for attainment of those uses. If a stream segment is not attaining any one of its 4 uses, it is considered impaired.
· Aquatic Life use attainment - The integrity reflected in any component of the biological community. (i.e. fish or fish food organisms)
· Fish Consumption use attainment - The risk posed to people by the consumption of aquatic organisms (ex. fish, shellfish, frogs, turtles, crayfish, etc.)
· Recreational use attainment - The risk associated with human recreation activities in or on a water body. (i.e. exposure to bacteria and other disease causing organisms through water contact recreation like swimming or water skiing)
· Potable Water Supply use attainment - The risk posed to people by the ingestion of drinking water
Segments that have appeared on an approved Category 5 Integrated Listing are the entries labeled as approved. Integrated Lists are submitted for approval every other year. Segments entered subsequent to the latest approved Category 5 listing are labeled tentative. After appearing on an approved listing, the tentative entries move to approved. The Stream Integrated List is provided as two separate layers determined if the stream is attaining or not attaining its designated uses.
DEP Streams Integrated List layer is maintained by the PADEP Office of Water Management, Bureau of Water Supply & Wastewater Management, Water Quality Assessment and Standards Division. The layer is based on the High Resolution National Hydrography Dataset (NHD). Additional update information is provided by Bureau of Watershed Management, Water Use Planning Division.
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| Pennsylvania Department of Environmental Protection |
2025 |
This layer shows only non attaining lakes of the Integrated List. The Lakes Integrated List represents lake assessments in an integrated format for the Clean Water Act Section 305(b) reporting and Section 303(d) listing. PA DEP protects 4 lake water uses: aquatic life, fish consumption, potable water supply, and recreation. The 305(b) layers represent lakes that have been evaluated for attainment of those uses. If a lake is not attaining any one of its 4 uses, it is considered impaired. Aquatic Life use attainment - The integrity reflected in any component of the biological community (i.e. fish or fish food organisms). Fish Consumption use attainment - The risk posed to people by the consumption of aquatic organisms (ex. fish, shellfish, frogs, turtles, crayfish, etc.). Recreational use attainment - The risk associated with human recreation activities in or on a water body (i.e. exposure to bacteria and other disease causing organisms through water contact recreation like swimming or water skiing). Potable Water Supply use attainment - The risk posed to people by the ingestion of drinking water. Lakes that have appeared on an approved Category 5 Integrated Listing are the entries labeled as approved. Integrated Lists are submitted for approval every other year. Lakes entered subsequent to the latest approved Category 5 listing are labeled tentative. After appearing on an approved listing, the tentative entries move to approved. The Lake Integrated List is provided as two separate layers determined if the lake is attaining or not attaining its designated uses. DEP Lakes Integrated List layers are maintained by the PADEP Office of Water Management, Bureau of Water Supply & Wastewater Management, Water Quality Assessment and Standards Division. The layer is based on the High Resolution National Hydrography Dataset (NHD). Additional update information is provided by Bureau of Watershed Management, Water Use Planning Division.
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| Pennsylvania Department of Environmental Protection |
2025 |
Land Recycling Cleanup Locations (LRCL) are divided into one or more sub-facilities categorized as media: Air, Contained Release or Abandoned Container, Groundwater, Sediment, Soil, Surface Water, and Waste. Media is the environmental resource that is associated with the cleanup effort. The following primary facility kinds describe the Acts from which cleanup locations are derived:
Act2 Land Recycling and Environmental Remediation Standards
The first declaration of Section 102 of the policy provides a brief description of the purpose of Act2:
The elimination of public health and environmental hazards on existing commercial and industrial land across this Commonwealth is vital to their use and reuse as sources of employment, housing, recreation, and open-space areas. The reuse of industrial land is an important component of a sound land use policy that will help prevent the needless development of prime farmland, open-space areas and natural areas and reduce public costs for installing new water, sewer, and highway infrastructure.
CERCLA Comprehensive Environmental Response, Compensation, and Liability Act, also known as the Superfund
This act was passed by Congress as a federal law in December of 1980, creating a tax on chemical and petroleum industries to:
Identify and respond to sites from which releases of hazardous substances into the environment have occurred or could potentially occur
Ensure they are cleaned up by responsible parties or through government funding
Evaluate damages to natural resources
HSCA Hazardous Sites Cleanup Act
[This Act] provides the Department of Environmental Protection (DEP) with the funding and the authority to conduct cleanup actions at sites where hazardous substances have been released. HSCA also provides DEP with enforcement authorities to force the persons who are responsible for releases of hazardous substances to conduct cleanup actions or to repay public funds spent on a DEP funded cleanup action. HSCA funds are also used to pay the state share of costs of cleanup actions at Pennsylvania sites in the Federal Superfund program. Under the provisions of HSCA, most HSCA sites involve bankrupt facility owners, abandoned facilities, and inappropriate disposal of hazardous substances. As a general rule, HSCA sites do not include active facilities with financially viable owners.
Other
The Other primary facility kind includes a mixture of various different cleanup sites, no further action sites, and potential sites. This is optional data that the regional offices are not required to maintain.
STSP Storage Tank Spill and Prevention Act
Releases and/or ruptures from improperly installed or faulty storage tanks contaminate the Commonwealth's land and water resources. This act was passed to prevent such contamination through "improved safeguards on the installation and construction of storage tanks."
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| Pennsylvania Department of Environmental Protection |
2025 |
Coal mining has occurred in Pennsylvania for over a century. A method of coal mining known as Longwall Mining has become more prevalent in recent decades.
Longwall mining involves full extraction of the coal seam in the longwall panel. The longwall mining machinery cuts or shears the coal off the face of a long wall panel of coal in a single pass. The machinery will then advance forward and perform another pass along the coal face. The broken off coal is hauled to the surface using conveyor belts. As the machinery advances forward into the coal panel, the mine roof is allowed to collapse behind it. This dataset identifies the footprint of these longwall panels relative to the surface.
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| Pennsylvania Department of Environmental Protection |
2025 |
Mine Drainage Treatment/Land Reclamation Locations are clean-up projects that are working to eliminate some form of abandoned mine. The following sub-facility types are included: Abandoned Coal Refuse Pile Reclamation, Abandoned Deep Mine Reclamation, Abandoned Mine Drainage Treatment, Abandoned Oil & Gas Well Reclamation, Abandoned Surface Mine Reclamation, Internal Monitoring Point.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Municipal Waste Operation is a DEP primary facility type related to the Waste Management Municipal Waste Program. The sub-facility types related to Municipal Waste Operations that are included are: Composting:
Includes facilities that use land for processing municipal waste by composting. Composting is a process that biologically decomposes organic waste under controlled anaerobic or aerobic conditions to yield a humus-like product.
Land Application:
Includes facilities that uses agricultural utilization or land reclamation of waste. Sewage sludge is land-applied for its nutrient value or as a soil conditioner.
Landfill - Abandoned:
The Abandoned Landfill Inventory Project collects geospatial and descriptive data for closed and abandoned landfills throughout the state of Pennsylvania. Locations for sites were determined from historic records such as microfiche, index card, topographic map, and staff personal files, and then compiled into site lists. Each of the six DEP regions is staffed with summer interns who physically locate the sites and collect the data for the project.
The purpose of the Abandoned Landfill Inventory Project is to determine the location of abandoned and closed landfills in order to catalog potential environmental hazards. The data is intended for internal government and public consumption, in order to keep property sales, clean-up efforts, and land development well informed.
For metadata on the ALI Project, see Abandoned Landfill Metadata.
Landfill:
A landfill is a facility that uses land for the disposal of municipal waste.
Processing Facility:
A processing facility is a transfer station, composting facility, resource recovery facility, or a facility that reduces the volume or bulk of municipal waste for offsite reuse.
Resource Recovery:
A resource recovery is a facility that provides for the extraction and utilization of materials or energy from municipal waste. The facility can be a mechanical extraction facility or a combustion facility.
Transfer Station:
A transfer station is a facility that receives and processes or temporarily stores municipal waste at a location other than the generation site. This sub-facility facilitates the transportation or transfer of municipal waste to a processing or disposal facility.
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| Pennsylvania Department of Environmental Protection |
2025 |
Points digitized from the historic Mylar tracking system, attributed with fields from BMP spreadsheet of Mylar index cards.A layer containing points of portal entries and coal seam elevations of coal mines. Data was converted over from the physical "Mylar Review" system for surface mine permit location look-ups. The physical topographic maps and mylar overlays have been scanned, georeferenced, and vectorized to create the point layers with an index number attribute. The corresponding index cards have been data entered in a spreadsheet. The point layers' attribute tables were joined with the spreadsheet. More recent coal feature points and attributes were digitized directly as shapefiles. This layer represents a combination of various coal mine tracking systems.
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| Pennsylvania Department of Environmental Protection |
2025 |
An Oil and Gas Location is a DEP primary facility type related to the Oil & Gas Program. The sub-facility types related to Oil and Gas that are included in this layer are:_____ Land Application -- An area where drilling cuttings or waste are disposed by land application
Well-- A well associated with oil and/or gas production
Pit -- An approved pit that is used for storage of oil and gas well fluids . Some sub facility types are not included in this layer due to security policies.
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| Pennsylvania Department of Environmental Protection |
2025 |
An Oil and Gas Water Pollution Control Facility is a DEP primary facility type related to the Oil & Gas Program. The following are the sub-facility types related to Water Pollution Control that are included in eMapPA: Discharge point - The outfall from a wastewater treatment facility for oil and gas fluids. Internal Monitoring Point - A monitoring point within the wastewater treatment system where samples are collected. Treatment Plant - A facility for treating oil and gas wastewater to achieve permit effluent limits.
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| Pennsylvania Department of Environmental Protection |
2025 |
The following data set contains all the Oil & Gas Wells in Pennsylvania that the Dept of Enviromental Protection has locational information on. The wells are broken into two formation types of conventional and unconventional wells.
A conventional well is a bore hole drilled or being drilled for the purpose of or to be used for the production of oil or natureal gas from only conventional formation(s). A conventional formation is any formation that does not meet the statutory definition of an unconventional formation.
An unconventional gas well is a bore hole drilled or being drilled for the purpose of or to be used for the production of natural gas from an unconventional formation. Unconventional formation is a geological shale formation existing below the base of the Elk Sandstone or its geologic equivalent stratigraphic interval where natural gas generally cannot be produced at economic flow rates or in economic volumes except by vertical or horizontal well bores stimulated by hydraulic fracture treatments or by using multilateral well bores or other techniques to expose more of the formation to the well bore.
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| Pennsylvania Department of Environmental Protection |
2025 |
A layer containing the permit boundaries of historic underground coal mine permits in Pennsylvania. Data was converted over from the physical "Mylar Review" system for mine permit location look-ups. The physical topographic maps and mylar overlays have been scanned, georeferenced, and vectorized to create the permit boundary polygon layers with an index number attribute. The corresponding index cards have been data entered in a spreadsheet. The polygon layers' attribute tables were joined with the spreadsheet. There are no planned updates for this layer, see the Department's Active Underground Permit Boundaries layer for newer mine permits. This layer is for informational purposes only and is known to not be a complete listing of all historic permits.
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| Pennsylvania Department of Environmental Protection |
2024 |
The Active Underground Coal Mine Map Mosaic map service is an imagery composite of georeferenced underground coal mine maps of mining operations currently or recently in active status in Pennsylvania.
When viewing detailed mine workings depicted on the maps in areas where multiple mines exist adjacent to each other, parts of one map may overlap, obstructing the view of adjacent maps. Through the mosaic process, the irrelevant parts of a map are cropped, so that the maps edge-match without overlap, creating a near seamless image of underground mine workings.
Pennsylvania state law requires those who wish to conduct mining activities within the Commonwealth submit and get approval by the Pennsylvania Department of Environmental Protection (DEP) for permits related to those activities. Every six months, operators must submit an Exhibit 22.6 Six-Month Subsidence Map that shows which areas inside their approved permit boundary have been mined in the previous six-month period and where the operator plans to mine within the permit boundary in the following six-month period. “Six-month” map submittal schedules from the various mine operators are spread out through the year. Review and approval of these “six-month” maps by DEP can take varying amounts of time depending on the complexity of each individual permit. “Six-month” maps are updated and added in the mosaic only after they are approved by DEP. Therefore, the timeframe of the depicted mine workings on the mosaiced maps may vary across the individual mine permits.
There is an inherit loss of accuracy in the georeferencing process and a georeferenced map may not align correctly with base maps and/or established coordinate systems. Therefore, these georeferenced maps should ONLY be used for reference information only and NOT be used for engineering and/or human safety related issues.
Uncropped, individual georeferenced maps can be viewed on the Pennsylvania Mine Map Atlas at www.paminemaps.psu.edu . Detailed metadata on the individual maps can be found in the Pennsylvania Historic Underground Mine Map Inventory System (PHUMMIS) at www.PHUMMIS.pa.gov .
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| Pennsylvania Department of Environmental Protection |
2024 |
The Pennsylvania Bituminous Coal Region Mine Map Mosaic map service is an imagery composite of georeferenced historic underground coal mine maps. THIS MAP SERVICE IS CURRENTLY UNFINISHED. THE SPATIAL ACCURACY AND ALLIGNMENT OF MAP IMAGES IS CURRENTLY BEING REVIEWED. USERS SHOULD CONSIDER THIS A DRAFT VERSION AND NOT RELY ON ANY INFORMATION CURRENTLY BEING PRESENTED IN THE SERVICE.
When viewing detailed mine workings depicted on the maps in areas where multiple mines exist adjacent to each other, parts of one map may overlap, obstructing the view of adjacent maps. Through the mosaic process, the irrelevant parts of a map are cropped, so that the maps edge-match without overlap, creating a near seamless image of underground mine workings.
Coal mining has occurred in Pennsylvania for over two centuries. The maps, if they still exist at all, of these coal mines are stored at various public and private locations throughout the Commonwealth. The Pennsylvania Department of Environmental Protection (DEP) has tried to preserve the historic information contained on these maps by scanning the maps into digital images. These maps are then georeferenced to their approximate spatial location to meet a variety of end-user needs, including mine land reclamation, gas well development, mine safety planning, and determination of needs for Mine Subsidence Insurance.
There is an inherit loss of accuracy in the georeferencing process and a georeferenced map may not align correctly with base maps and/or established coordinate systems. Therefore, these georeferenced maps should ONLY be used for reference information only and NOT be used for engineering and/or human safety related issues.
Uncropped, individual georeferenced maps can be viewed on the Pennsylvania Mine Map Atlas at www.paminemaps.psu.edu . Detailed metadata on the individual maps can be found in the Pennsylvania Historic Underground Mine Map Inventory System (PHUMMIS) at www.PHUMMIS.pa.gov .
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| Pennsylvania Department of Environmental Protection |
2004 |
The Coastal Zone Boundary delineates Pennsylvania's two coastal zones: the Delaware Estuary Coastal Zone located in southeastern Pennsylvania, and the Lake Erie Coastal Zone located in the northwestern part of the state. The federal Coastal Zone Management Act (Act) defines the coastal zone as coastal waters and the adjacent shorelands, strongly influenced by each other. The zone extends inland from the shoreline only to extend necessary to control shorelands, the uses of which have a direct and significant impact on the coastal waters. The boundary is used as a starting point by persons or agencies to determine if their proposed activities will affect the coastal zones, and are subject to review by the Pennsylvania Coastal Zone Management Program. Further, local municipalities, authorities, state agencies and certain non-profit organizations within these boundaries are eligible for Coastal Zone grants to support the goals and objectives of the Act at the local and state level.
Federal actions (eg. federal development activities, federal permits and licenses, federal assistance, and Outer Continental Shelf activities) occurring within the coastal boundary, or outside the boundary but impacting upon it, are subject to the federal consistency review requirements of the Act. In addition, applications for state Department of Environmental Protection permits for activities located in the coastal zones are subject to review and approval by Pennsylvania's CZM Program.
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| Pennsylvania Department of Environmental Protection |
2012 |
Coal mining has occurred in Pennsylvania for over a century. The maps of these coal mines are stored at various public and private locations (if they still exist at all) throughout the commonwealth. The Pennsylvania Department of Environmental Protection (DEP), Office of Active and Abandoned Mine Operations is involved in many projects dealing with historic and active mine map restoration and digitization. The DEP has tried to preserve the historic information contained on these maps by scanning the maps into a digital image. The preferred format for archiving these maps is an uncompressed Tagged Image File Format (.TIF) at 400 DPI resolution and 24-bit RGB color depth. Some archival images are stored at a lower DPI due to scanner limitations and/or extremely large size of the original hardcopy. Various scanners were used at different locations at different times. Detailed metadata on the individual map sheets can be found in the Pennsylvania Historic Underground Mine Map Inventory System (PHUMMIS). Many of the mine map images have been georeferenced after an archive image is made. These maps have been georeferenced to provide the approximate spatial location so that they can be used for many environmental related issues, including mine land reclamation and determination of needs for Mine Subsidence Insurance. It is understood that there is an inherit loss of accuracy in the georeferencing process and the georeferenced map may not align correctly with base maps and/or established coordinate systems. Therefore these georeferenced maps should ONLY be used for reference information only and NOT be used for an engineering and/or human safety related issues.
Before a map is georeferenced, it is compressed into a MrSID file format using LizardTech’s GeoExpress software. The compressed image is georeferenced in ESRI’s ArcGIS software. An attempt is made to match a minimum of four control points between the source map image and target basemaps/coordinates. The points used to link the mine map to a base map include, but are not limited to: road crossings, monuments, property boundaries, water crossings, structures, coordinates, railroads, etc. When four control points cannot be obtained with this process, additional points may have been used from other previously georeferenced mine maps. Some maps were previously georeferenced in other file formats, if the georeferencing was deemed sufficient, a MrSID image of the same map may have been georeferenced to the previous image using the four corners of the map image. The extents of the georeferenced images are then recorded in a feature class to create a spatial Underground Mine Map Index.
It should be noted that the “Mine Map Index” overlay displayed on the PA Mine Map Atlas web map application may not contain features for all available georeferenced maps due to duplication of maps and/or visual presentation of the Atlas web map, but the index shapefile available for FTP download from PASDA will contain features for all available quality-controlled georeferenced underground mine maps as of its publish date.
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| Pennsylvania Department of Environmental Protection |
2024 |
Coal mining has occurred in Pennsylvania for over a century. The maps of these coal mines are stored at various public and private locations (if they still exist at all) throughout the commonwealth. The Pennsylvania Department of Environmental Protection (DEP), Office of Active and Abandoned Mine Operations is involved in many projects dealing with historic and active mine map restoration and digitization. The DEP has tried to preserve the historic information contained on these maps by scanning the maps into a digital image. The preferred format for archiving these maps is an uncompressed Tagged Image File Format (.TIF) at 400 DPI resolution and 24-bit RGB color depth. Some archival images are stored at a lower DPI due to scanner limitations and/or extremely large size of the original hardcopy. Various scanners were used at different locations at different times. Detailed metadata on the individual map sheets can be found in the Pennsylvania Historic Underground Mine Map Inventory System (PHUMMIS). Many of the mine map images have been georeferenced after an archive image is made. These maps have been georeferenced to provide the approximate spatial location so that they can be used for many environmental related issues, including mine land reclamation and determination of needs for Mine Subsidence Insurance. It is understood that there is an inherit loss of accuracy in the georeferencing process and the georeferenced map may not align correctly with base maps and/or established coordinate systems. Therefore these georeferenced maps should ONLY be used for reference information only and NOT be used for an engineering and/or human safety related issues.
Before a map is georeferenced, it is compressed into a MrSID file format using LizardTech’s GeoExpress software. The compressed image is georeferenced in ESRI’s ArcGIS software. An attempt is made to match a minimum of four control points between the source map image and target basemaps/coordinates. The points used to link the mine map to a base map include, but are not limited to: road crossings, monuments, property boundaries, water crossings, structures, coordinates, railroads, etc. When four control points cannot be obtained with this process, additional points may have been used from other previously georeferenced mine maps. Some maps were previously georeferenced in other file formats, if the georeferencing was deemed sufficient, a MrSID image of the same map may have been georeferenced to the previous image using the four corners of the map image. The extents of the georeferenced images are then recorded in a feature class to create a spatial Underground Mine Map Index.
It should be noted that the “Mine Map Index” overlay displayed on the PA Mine Map Atlas web map application may not contain features for all available georeferenced maps due to duplication of maps and/or visual presentation of the Atlas web map, but the index shapefile available for FTP download from PASDA will contain features for all available quality-controlled georeferenced underground mine maps as of its publish date.
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| Pennsylvania Department of Environmental Protection |
2023 |
Pennsylvania municipalities with NPDES MS4 permits or waivers, have been identified by the Pennsylvania Department of Environmental Protection. Attribute data indicates whether the municipality holds a general or individual NPDES MS4 permit (PAG-13), or whether the municipality qualified for a waiver from the permitting requirement.
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| Pennsylvania Department of Environmental Protection |
2025 |
As part of Pennsylvania's State Water Plan this data set is used to determine non-public water supply areas (self-supplied). It is also used to help determine the population served and water supply demand.
Boundaries of current public water supplier's (PWS) service areas. This data set contains the present service area boundary of the water system and does not contain locations of surface and groundwater sources, storage facilities, transmission and distribution system lines, and interconnections with other water systems. Revisions, updates and additions are done on an as needed basis. All boundaries should be considered approximate.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Radiation Facility is a DEP primary facility type related to the Radiation Protection Program. The sub-facility types related to Radiation that are included are:___
Accelerator -- Electronic machine producing high energy radiation,
Mammography Quality Stds Act Tube -- Specialized X-ray equipment for mammography,
X-ray Machine -- A facility where X-ray machines other than accelerators are used
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| Pennsylvania Department of Environmental Protection |
2025 |
A Residual Waste Operation is a DEP primary facility type related to the Waste Management Residual Waste Program. Residual waste is waste generated at an industrial, mining, or wastewater treatment facility. The sub-facility types related to Residual Waste that are included are:____ Generator:
A generator is a person, company, institution, or municipality that produces or creates residual waste. Residual waste is waste generated at an industrial, mining, or wastewater treatment facility.
Impoundment:
An impoundment is a facility designed to hold an accumulation of liquid wastes.
Incinerator:
An incinerator is an enclosed device using controlled combustion to thermally break down residual waste.
Land Application:
Land application is a facility that uses agricultural utilization or land reclamation of waste. Residual waste is land applied for its nutrient value or as a soil conditioner.
Landfill:
A landfill is a facility that uses land for the disposal of residual waste.
Processing Facility:
A processing facility is a transfer station, compost facility, resource recovery facility, or a facility that reduces the volume or bulk of residual waste for off-site reuse.
Transfer Station:
A transfer station receives and processes or temporarily stores residual waste at a location other than the generation site. This sub-facility facilitates the transportation or transfer of residual waste to a processing or disposal facility.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Storage Tank Location is a DEP primary facility type, and the storage tanks at the facility are the sub-facilities. Active storage tanks are aboveground or underground tanks regulated under the Storage Tank and Spill Prevention Act (35 P.S. §6021) and 25 Pa. Code Chapter 245. Active storage tanks are in a status of “currently in use” or “temporarily out of use”, which means the tanks still exist in a regulated status. These tanks are currently registered to hold a regulated substance, which could be a petroleum product or a hazardous substance. Above ground storage tanks with a capacity greater than 21,000 gallons, and aboveground storage tanks that contain highly hazardous substances, are removed from this layer.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Storage Tank Location is a DEP primary facility type, and the storage tanks at the facility are the sub-facilities. Inactive storage tanks are aboveground or underground tanks that were once regulated under the Storage Tank and Spill Prevention Act 35 P.S. 6021 and 25 Pa. Code Chapter 245. Inactive storage tanks include those tanks that have been removed, permanently closed, exempted from regulation, transferred to a different facility record, or otherwise removed from registration with DEP. These tanks previously held a regulated substance, which could have been a petroleum product or a hazardous substance.
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| Pennsylvania Department of Environmental Protection |
2025 |
Stormwater management involves the control of water that runs off the surface of the land from rain or melting ice or snow. The volume, or amount of runoff and its rate of runoff, substantially increase as land development occurs. Construction of impervious surfaces, such as roofs and parking lots, and the installation of storm sewer pipes which efficiently collect and discharge runoff, prevent the infiltration of rainfall into the soil. Management of stormwater is necessary to compensate for the possible impacts of development such as frequent flooding, erosion and sedimentation problems, concentration of flow on adjacent properties, damages to roads, bridges and other infrastructure as well as non-point source pollution washed off from impervious surfaces. The Pennsylvania legislature enacted the Storm Water Management Act (No.167) in 1978 to authorize a program of comprehensive watershed stormwater management which retains local implementation and enforcement of stormwater ordinances similar to local responsibility of administration of subdivision and land development regulations. Under the Act, the Department of Environmental Protection (DEP) provides grant money to counties to develop stormwater management plans for designated watersheds. This planning effort results in the incorporation of sound engineering standards and criteria into local codes and ordinances to manage runoff from new development in a coordinated, watershed-wide approach. Counties develop stormwater plans for each of their watersheds within their boundaries. DEP develops grant agreements with counties to pay for 75 percent of the cost to prepare the plans. Upon completion of a plan by a county and approval by DEP, municipalities located in the watershed adopt ordinances consistent with the plan. Developers are then required to follow the local drainage regulations that incorporate the standards of the watershed plan when preparing their land development plan. Although not all watersheds have been studied, developers in non-studied areas are still required to follow any local drainage regulations adopted under the Municipalities Planning Code. County boundaries within Pennsylvania as delineated for the PennDOT Type 10 general highway map.
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| Pennsylvania Department of Environmental Protection |
2006 |
The Stream ReLeaf layer represents the locations of projects that restore and maintain riparian buffers along streams, lakes and ponds of the Commonwealth. A riparian buffer is an area of vegetation that is maintained along the shore of a water body to protect water quality along with stabilizing stream channels and banks. Buffers can reduce the pollutants entering a stream, lake or pond by filtering and altering the form of sediments, nutrients and other chemicals in runoff from surrounding lands. Pennsylvania has the goal of eventually restoring buffers along all of our streams, lakes and ponds.
The project locations that fall within 150 meters of the National Hydrography Network (NHD) have been snapped to the nearest stream location on the NHDFlowline layer. All other project locations are plotted as they are entered.
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| Pennsylvania Department of Environmental Protection |
2025 |
National Hydrography Dataset NHDFlowline layer with a spatial representation of designated water uses defined in Title 25 Environmental Protection, Department of Environmental Protection, Chapter 93, Water Quality Standards. The Pennsylvania Code just cited provides a list of all streams or watersheds (basins) in the state along with their associated designated water uses. This GIS layer displays these uses spatially on an interactive stream map. Public users can drill down to locations on the map to view and map the designated uses of the water bodies of interest. The layer can also be used in conjunction with other spatially referenced data for spatial analyses.
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| Pennsylvania Department of Environmental Protection |
2025 |
National Hydrography Dataset NHDFlowline layer with spatial representation of existing water uses defined in Title 25 Environmental Protection, Department of Environmental Protection, Chapter 93, Water Quality Standards. The Pennsylvania Code just cited provides a definition for distinguishing between designated and existing use classification. This GIS layer displays these uses spatially on an interactive stream map. Public users can drill down to locations on the map to view and map the existing uses of the water bodies of interest. The layer can also be used in conjunction with other spatially referenced data for spatial analyses.
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| Pennsylvania Department of Environmental Protection |
2025 |
The Clean Water Act Section 303(d) establishes the Total Maximum Daily Load (TMDL) program. The purpose of the TMDL program is to identify sources of pollution and allocate pollutant loads in places where water quality goals are not being achieved. This layer shows the list of waters for which technology-based or other required pollution controls are not stringent enough to meet water quality standards. The TMDLs themselves specify a pollutant budget that must be achieved to meet state water quality standards and allocates pollutant loads among pollution sources in a watershed, e.g., point and nonpoint sources.
TMDLs can be developed for several categories such as: point sources (permitted sewage and industrial discharges); nonpoint sources (agriculture and urban runoff); lakes; abandoned mine drainage (also called acid mine drainage or AMD); specific bioaccumulative chemicals (PCBs and chlordane that contaminate fish, resulting in fish advisories limiting or banning the number of fish that a person can safely consume); and complex situations (combinations of different types).
This layer is based on the High Resolution National Hydrography Dataset (NHD). The Lake TMDLs are not included in this layer.
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| Pennsylvania Department of Environmental Protection |
2025 |
The Clean Water Act Section 303(d) establishes the Total Maximum Daily Load (TMDL) program. The purpose of the TMDL program is to identify sources of pollution and allocate pollutant loads in places where water quality goals are not being achieved. This layer shows the list of waters for which technology-based or other required pollution controls are not stringent enough to meet water quality standards. The TMDLs themselves specify a pollutant budget that must be achieved to meet state water quality standards and allocates pollutant loads among pollution sources in a watershed, e.g., point and nonpoint sources.
This layer represents lakes with TMDL associated with them. Lakes have characteristics that differentiate TMDLs from other waters. Lakes are not free-flowing like streams, but are contained waters that trap pollutants for long periods. Most lake impairments are from high nutrient or sediment loads. Wherever possible, lake TMDLs are developed with the information in the lake study reports that were sponsored by local watershed groups or other local interests. Target acceptable pollutant loads are set at the level of a watershed largely unaffected by human induced impacts. Load allocations are given to the pollutant sources using the same methods as nonpoint source TMDLs. Other indicators of water quality are also considered in the evaluation of a lake. One indicator is the Trophic Status Index (TSI), which refers to the degree of nutrient enrichment in the lake. Nutrient enrichment causes growths of algae that consume oxygen and interfere with the health of the aquatic organisms in the lake. The TSI is affected by factors such as lake volume, water residence time and nutrient loads to the lake. After target loads are set, the TSI is evaluated under reduced nutrient load conditions to assure that the pollutant reductions will bring the TSI into an acceptable range. Implementation of lake TMDLs is best accomplished though local participation. This layer is based on the High Resolution National Hydrography Dataset (NHD).
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| Pennsylvania Department of Environmental Protection |
2009 |
Unpaved roads layer for Pennsylvania created by the Bureau of Watershed Management, Conservation Districts And Nutrient Management Division.
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| Pennsylvania Department of Environmental Protection |
2013 |
Areas of PA that are unsuitable for mining purposes
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| Pennsylvania Department of Environmental Protection |
2025 |
This data set contains all approved water sources within water management plans (WMP). A WMP contains water sources utilized in the fracture stimulation of Marcellus Shale natural gas wells in Pennsylvania.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Water Pollution Control Facility is a DEP primary facility type related to the Water Pollution Control Program. The sub-facility types related to Water Pollution Control that are included in eMapPA are: Agricultural Activities - The management and use of farming resources for the production of crops, livestock or poultry. Biosolids Treatment - Indicates that the facility treats sewage sludge to produce a material that can be beneficially used, biosolids. Compost/Processing - Indicates that the facility treats sewage sludge by composting to produce a material that can be beneficially used, biosolids. Conveyance System - Sewage system without treatment. Discharge Point - Discharge point to stream. Groundwater Monitoring Point. Internal Monitoring Point - Used to monitor internal processes - not a discharge. Land Discharge - Land application of wastewater. Manure Management - Activities related to or supporting storage, collection, handling, transport, application, planning, record keeping, generation or other manure management activities. Outfall structure - Outfall structure to stream. Pesticide Treatment Area - These SFs are created to address treatment areas that in reality are often an entire water body, such as a pond. The lat/long coordinates are supposed to be entered at the mid-point or center of the treatment area. Pipeline or Conduit - Pipes or other smaller diameter conveyances that are used to transport or supply liquids or slurries from collection, storage or supply facilities or areas to other facilities or areas for storage, modification or use. These can be for longer-term, medium-term or short-term and would include design, capacity, maintenance, safety, inspection, accident and varying use and weather considerations. Production Service Unit - Catch all sub-facility that covers a variety of industries participating in a multitude of activities such as concentrated animal feeding, pharmaceuticals, paper, steel, utilities, etc. The majority of PSUs are classified as Industrial Waste or Stormwater-Industrial (Primary Facility kind). Pump Station - Sewage pump station. Septage Land Application - Indicates that the septage hauler treats residential septage for land application, meaning that it can be applied to land as a soil amendment/fertilizer. Storage Unit - Storage of wastewater. Treatment Plant - Sewage or industrial wastewater treatment plant.
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| Pennsylvania Department of Environmental Protection |
2025 |
A Water Resource is a DEP primary facility type related to the Water Use Planning Program. The sub-facility types related to Water Resources that are included are:____
Discharge:
A Discharge subfacility type represents the return of water used at a Water Resources primary facility. The subfacility type may be a sewage treatment plant, instream discharge, spray irrigation field, groundwater recharge, on-lot septic or an unidentified facility type.
Ground Water Withdrawal:
A Ground Water subfacility type represents the withdrawal of water used at a Water Resources primary facility. The subfacility type may be a well, spring, quarry, infiltration gallery, deep mine, surface mine or an unidentified facility type.
Interconnection:
An Interconnection subfacility type represents the point of interconnection between Water Resources primary facilities. The subfacility type may be for an interconnection between two public water supply agencies or between a public water supply agency and a commercial or industrial water user.
Storage:
A Storage subfacility type represents the storage of water used at a Water Resources primary facility. The subfacility type represents raw or treated water storage and may be a quarry, standpipe, open off-stream reservoir, closed off-stream reservoir, instream reservoir, hydroelectric dam, natural lake, pond, silt dam, hydroelectric pumped storage or an unidentified facility type.
Surface Water Withdrawal:
A Surface Water subfacility type represents the withdrawal of water used at a Water Resources primary facility. The subfacility type may be an instream diversion, intake from a dam, natural lake, pond, river well, or an unidentified facility type.
Water Allocation:
A Water Allocation subfacility type represents a permit issued for a surface water withdrawal by a Water Resources Public Water Supply (PWS) Agency primary facility. The subfacility type (permit) may be issued for all PWS Water Resources' surface water withdrawals, ground water withdrawals except ground water wells, or interconnections used by a receiving PWS from a surface water PWS.
Primary facility kinds, 'Water Purveyor' and 'Electric Use', are removed.
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| Pennsylvania Department of Environmental Protection |
2012 |
WPA Mapping was drawn in the 1930’s as part of the “Works Progress Administration”. All mining shown on the WPA Maps is assumed to have taken place ‘Prior to 1935’. The WPA Maps were laid out based on the 15’ USGS Topographic Quadrangles, consisting of nine (9) 5' sections. The lower right corner of the WPA Maps includes the sheet name and coal seam covered by the sheet. The latitude and longitude of each corner of the WPA Map is shown. The adjacent WPA Map is listed at each corner and mid way along each edge. The contour lines on the WPA Maps indicate the elevation of the coal seam in feet “above sea level”. The coal seam outcrop is shown using a heavy black line. The outcrop is the point where the coal seam elevation and the surface elevation are equal. The speckled areas on the maps indicate completely mined out areas. The symbol that looks like a ladder indicates a mined out area where passage ways ("Mains") were first developed. Clear areas, which are inside the outcrop, were not mined as of 1935. Occasionally mine names and operator names appear around the mined out areas, however clear mine boundaries are not shown. Oil and gas wells are shown on the maps as star-like symbols.
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| Pennsylvania Department of Environmental Protection |
2012 |
TILE INDEX - WPA Mapping was drawn in the 1930’s as part of the “Works Progress Administration”. All mining shown on the WPA Maps is assumed to have taken place ‘Prior to 1935’. The WPA Maps were laid out based on the 15’ USGS Topographic Quadrangles, consisting of nine (9) 5' sections. The lower right corner of the WPA Maps includes the sheet name and coal seam covered by the sheet. The latitude and longitude of each corner of the WPA Map is shown. The adjacent WPA Map is listed at each corner and mid way along each edge. The contour lines on the WPA Maps indicate the elevation of the coal seam in feet “above sea level”. The coal seam outcrop is shown using a heavy black line. The outcrop is the point where the coal seam elevation and the surface elevation are equal. The speckled areas on the maps indicate completely mined out areas. The symbol that looks like a ladder indicates a mined out area where passage ways ("Mains") were first developed. Clear areas, which are inside the outcrop, were not mined as of 1935. Occasionally mine names and operator names appear around the mined out areas, however clear mine boundaries are not shown. Oil and gas wells are shown on the maps as star-like symbols.
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| Pennsylvania Department of Environmental Protection |
2024 |
The PA Ambulatory Surgery Centers layer was developed to show the geospatial locations of Ambulatory Surgery Centers in Pennsylvania. It can be used in various emergency and health related maps. An ambulatory Surgery Center is a facility for surgeries to be performed on a person who is admitted to and discharged from the location on the same day. When possible, efforts were made to confirm the rooftop location of each Ambulatory Surgery Center. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Birth Centers layer was developed to show the geospatial locations of birth facilities in Pennsylvania. It can be used in various emergency and health related maps. A birth center is a medical facility, specializing in childbirth, that is less restrictive and more homelike than a hospital. When possible, efforts were made to confirm the rooftop location of each birth facility. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Community Mental Health Centers was developed to show the geospatial locations of community mental health centers in Pennsylvania. It can be used in various emergency and health related maps. A community mental health center is a facility that provides prevention, treatment, and rehabilitation mental health services, sometimes organized as a practical alternative to the largely custodial care given in mental hospitals.. When possible, efforts were made to confirm the rooftop location of each community mental health center. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2022 |
The PA Drug and Alcohol Treatment Facilities layer was developed to show the geospatial locations of drug and alcohol treatment facilities in Pennsylvania. It can be used in various emergency and health related maps. A drug and alcohol treatment facility is facility that specializes in the evaluation and treatment of drug addiction, alcoholism and associated disorders. When possible, efforts were made to confirm the rooftop location of each drug and alcohol treatment facility. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2022 |
The PA EMS Regional Councils layer was developed to show the boundaries for Regional EMS Councils in Pennsylvania. It can be used in various emergency and health related maps. An EMS Regional Council is an independent advisory board to the Pennsylvania Department of Health and other agencies in matters related to emergency medical services. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Home Health Agencies layer was developed to show the geospatial locations of home health agencies in Pennsylvania. It can be used in various emergency and health related maps. A home health agency is an organization that delivers skilled nursing and other therapeutic services to a patient’s personal residence, rather than in a more traditional healthcare facility setting. When possible, efforts were made to confirm the rooftop location of each home health agency. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Hospice layer was developed to show the geospatial locations of hospices in Pennsylvania. It can be used in various emergency and health related maps. A hospice is a home providing care for the sick or terminally ill. When possible, efforts were made to confirm the rooftop location of each hospice. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Hospitals layer was developed to show the geospatial locations of Hospitals in Pennsylvania. It can be used in various emergency and health related maps. A hospital is an institution in which sick or injured persons are given medical or surgical treatment. When possible, efforts were made to confirm the rooftop location of each Hospital. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Intermediate Care Facilities layer was developed to show the geospatial locations of intermediate care facilities in Pennsylvania. It can be used in various emergency and health related maps. An intermediate care facility is a location where care is provided to acute care patients who are medically stable but too unstable to be treated in alternative healthcare settings such as home, ambulatory, or traditional skilled long term care. When possible, efforts were made to confirm the rooftop location of each intermediate care facility. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2025 |
The PA Medical Marijuana Dispensaries layer was developed to show the geospatial locations of medical marijuana dispensaries in Pennsylvania. It can be used in various emergency and health related maps. A medical marijuana dispensary is a facility where medical marijuana is dispensed. When possible, efforts were made to confirm the rooftop location of each medical marijuana dispensary. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Nursing Homes layer was developed to show the geospatial locations of nursing homes in Pennsylvania. It can be used in various emergency and health related maps. A nursing home is a place for people who don't need to be in a hospital but can't be cared for at home. When possible, efforts were made to confirm the rooftop location of each nursing home. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2025 |
The PA State Health Centers layer was developed to show the geospatial locations of state health centers in Pennsylvania. It can be used in various emergency and health related maps. A state health center is A facility that provides (ambulatory) medical and sanitary services to a specific group in a population. When possible, efforts were made to confirm the rooftop location of each state health center. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Physical Speech Therapists layer was developed to show the geospatial locations of physical speech therapists in Pennsylvania. It can be used in various emergency and health related maps. A physical speech therapist is where the assessment and treatment for communication problems and speech disorders are performed. When possible, efforts were made to confirm the rooftop location of each physical speech therapists. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACT@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Portable X-Ray layer was developed to show the geospatial locations of portable X-ray machine in Pennsylvania. It can be used in various emergency and health related maps. A portable X-ray machine allows radiographers, vets and dental professionals to take X-ray images of patients without having to call them into a special lead-lined room. When possible, efforts were made to confirm the rooftop location of each portable X-ray machine. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Psychiatric Residential Treatment Facility layer was developed to show the geospatial locations of psychiatric residential treatment facilities in Pennsylvania. It can be used in various emergency and health related maps. A psychiatric residential treatment facility is a facility that provides out-of-home psychiatric care to children, adolescents, and young adults in a non-hospital, highly structured setting. When possible, efforts were made to confirm the rooftop location of each psychiatric residential treatment facility. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2024 |
The PA Rural Health Clinics layer was developed to show the geospatial locations of rural health clinics in Pennsylvania. It can be used in various emergency and health related maps. A rural health clinic is a clinic that is located inside a rural area designated as a shortage area, is not a rehabilitation agency or a facility primarily for the care and treatment of mental diseases. When possible, efforts were made to confirm the rooftop location of each rural health clinic. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude are recorded in the WGS 1984 coordinate system in decimal degrees. For questions about this dataset, please contact the Pennsylvania Department of Health Division of Health Informatics at RA-DHICONTACTUS@pa.gov or 717-782-2448.
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| Pennsylvania Department of Health |
2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
2025 |
Point Location of PennDOT's Highway Beautification Management System (HBMS) Billboards.
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| Pennsylvania Department of Transportation |
2005 |
Aerial Photography of four flight lines, captures April 4, 2005 during a spring flood event in southeastern Pennsylvania.
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| Pennsylvania Department of Transportation |
2025 |
electric vehicle registrations in Pennsylvania by County. Data is provided by Pennsylvania Department of Transportation Driver and Vehicle Services and updated on a quarterly basis. This data includes the following types of vehicles: battery electric, plug-in hybrid electric, hybrid electric and fuel cell.
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| Pennsylvania Department of Transportation |
2025 |
electric vehicle registrations in Pennsylvania by United States Postal Service ZIP code. Data is provided by Pennsylvania Department of Transportation Driver and Vehicle Services and updated on a quarterly basis. ZIP code areas are based on U.S. Census Bureau data and adjusted to match PennDOT boundaries where possible. This data includes the following types of vehicles: battery electric, plug-in hybrid electric, hybrid electric and fuel cell.
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| Pennsylvania Department of Transportation |
2025 |
Locations of Pennsylvania At-Grade Intersections
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| Pennsylvania Department of Transportation |
2025 |
Bonded roads bonds with bond holder information.
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| Pennsylvania Department of Transportation |
2025 |
Bridge locations within Pennsylvania
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| Pennsylvania Department of Transportation |
2025 |
Locations and attributes of drainage pipe structures that intersect with a state route
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| Pennsylvania Department of Transportation |
2017 |
Driver Vehicle Services locations - There are approximately 3,100 locations, both PennDOT owned or leased and third party Titles and Tags and Online DL locations. The Excel has addresses geocoded with LL’s; PennDOT and third party can be filtered on the PENNDOT column ‘Y’ = PennDOT, ‘N’ = third party.
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| Pennsylvania Department of Transportation |
2024 |
location of Pennsylvania Department of Transportation owned facilities. Included: maintenance districts, stockpiles, photo exam centers, welcome centers
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| Pennsylvania Department of Transportation |
2020 |
locations of multiple modes of transport
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| Pennsylvania Department of Transportation |
2025 |
Locations of road segments that have posted weight restrictions
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| Pennsylvania Department of Transportation |
2025 |
Pennsylvania Railroad Crossing Points of Intersection
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| Pennsylvania Department of Transportation |
2025 |
Rails Lines within Pennsylvania
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| Pennsylvania Department of Transportation |
2025 |
PennDOT's Transportation Improvement Projects. This dataset is Lines only and is part of the Multi-Modal Project Management System (MPMS). Combine with Transportation Improvement Projects (Lines) to view all data.
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| Pennsylvania Department of Transportation |
2025 |
PennDOT's Transportation Improvement Projects. This dataset is Points only and is part of the Multi-Modal Project Management System (MPMS). Combine with Transportation Improvement Projects (Points) to view all data.
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| Pennsylvania Department of Transportation |
2025 |
Locations of Park and Ride facilities in Pennsylvania.
These PennDOT-maintained Park and Ride facilities offer a safe, convenient location for commuters to leave their automobiles and travel to their destinations in carpools, vanpools or buses.
Ride sharing reduces the total number of vehicle miles of travel and improves air quality. It reduces road deterioration, saves fuel, reduces congestion and limits our carbon footprint. It’s also a great way to meet new friends and enjoy your commute.
A park and ride facility can offer a transit provider convenient access to many patrons. These locations may also expand the area public transit service covers as the result of a larger customer base and they reduce transit agency operating expense by eliminating the need for the buses to circulate through residential neighborhoods.
For more information on this layer, you can use the Data Dictionary available in both web and spreadsheet format.
Updates to this layer are made as needed. Any errors or omissions should be reported to the PennDOT Geographic Information Division (ra-penndotmaps@pa.gov).
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| Pennsylvania Department of Transportation |
2025 |
Information on roadway shoulders in Pennsylvania including size, condition and material from PennDOT's Roadway Management System (RMS).
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| Pennsylvania Department of Transportation |
2013 |
FAA Part 77 Surface Areas for each public use airport in Pennsylvania. This file includes the five FAA-designated surface areas for each airport and was developed for the statewide Airport Hazard Zoning workshops held through the summer of 2010.
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| Pennsylvania Department of Transportation |
2013 |
FAA Part 77 Surface Areas for each public use airport in Pennsylvania. This file includes the five FAA-designated surface areas for each airport and was developed for the statewide Airport Hazard Zoning workshops held through the summer of 2010.
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| Pennsylvania Department of Transportation |
2024 |
BicyclePA Routes for use with PennDOT's PennShare and OpenData applications
BicyclePA routes were designed by experienced bicyclists to provide those who wish to traverse the state by bicycle with a guide to some of the Commonwealth’s highways and rail-trails. Some of these routes contain bike lanes or other facilities designed specifically for bicyclists traveling within the four corners of the Commonwealth. Every bicyclist is responsible for his or her personal safety and welfare and for remaining alert and mindful of conditions on the roads or trails.
There are 10 designated BicyclePA routes- A, E, G, J, JS, L, S, V, Y, and Z.
A: BicyclePA Route A runs north/south from Presque Isle State Park on Lake Erie to the West Virginia border just north of Morgantown in Greene County.
E: BicyclePA Route E runs north/south from the New Jersey border along the Delaware River in Morrisville, PA to the Delaware border in Marcus Hook, PA.
G: BicyclePA Route G runs north/south from the New York border in Lawrenceville, Tioga County to the Maryland border in Bedford County.
J: BicyclePA Route J runs north/south from the New York border in Sayre, Bradford County to the Maryland border in two places: New Freedom, York County and south of Gettysburg in Adams County.
JS: BicyclePA Route JS runs east/west between Seven Valleys in York County and Gettysburg in Adams County.
L: BicyclePA Route L runs north/south from the New York border south of Binghamton in Susquehanna County to the Delaware border north of Wilmington in Chester County.
S: BicyclePA Route S runs east/west from the New Jersey border at the Washington Crossing Military Park on the Delaware River in Bucks County to the West Virginia border east of Wheeling in Washington County.
V: BicyclePA Route V runs east/west from the New Jersey border in Portland (Northampton County) to the Ohio border in Lawrence County, roughly parallel with Interstate 80.
Y: BicyclePA Route Y runs east/west from the New York border in Pike County to the Ohio border in Crawford County.
Z: BicyclePA Route Z runs east/west along the shore of Lake Erie from the New York border to the Ohio border in Erie County.
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| Pennsylvania Department of Transportation |
2025 |
United States Congressional legislative boundaries within Pennsylvania as set forth in Act 34 of 2002. This layer was digitized by PADOT from maps generated by the Reapportionment Commission for the Bureau of Commissions, Elections and Legislation Pennsylvania Dept. of State. These boundaries were used as the legislative district boundaries for the November 2002 general election and are currently being contested in the judicial system. The current layers are these same district boundary linework attributed with the current legislator's name, party affiliation and home county.
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| Pennsylvania Department of Transportation |
2025 |
County boundaries within Pennsylvania as delineated for the PennDOT Type 10 general highway map.
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| Pennsylvania Department of Transportation |
2025 |
Public information and support for transportation planning, design and development.
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| Pennsylvania Department of Transportation |
2025 |
point locations of Pennsylvania interstate highway exits
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| Pennsylvania Department of Transportation |
2025 |
point locations of Pennsylvania Interstate mile markers
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| Pennsylvania Department of Transportation |
2025 |
Public roads within Pennsylvania. This file includes all identified public roads not maintained by the PA Dept. of Transportation.
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| Pennsylvania Department of Transportation |
2025 |
Boundaries of municipalities within Pennsylvania as delineated for the PennDOT Type 10 general highway maps. Additional information comes from the Pennsylvania Bureau of Municipal Services. This layer contains all classifications of municipality including first and second class townships, boroughs, cities and the town.
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| Pennsylvania Department of Transportation |
2025 |
Pennsylvania Department of Transportation, Bureau of Planning and Research, Cartographic Information Division
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| Pennsylvania Department of Transportation |
2025 |
State senate boundaries within Pennsylvania attributed with names of legislators and party affiliations.
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| Pennsylvania Department of Transportation |
2024 |
The boundary of the Commonwealth as delineated for the PennDOT Type 10 general highway map.
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| Pennsylvania Department of Transportation |
2025 |
State House boundaries within Pennsylvania attributed with names of legislators and party affiliations.
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| Pennsylvania Department of Transportation |
2025 |
State-owned and maintained public roads within Pennsylvania as extracted from the PENNDOT Roadway Management System (RMS). Includes fields describing pavement type, traffic volumes and other information as detailed below.
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| Pennsylvania Department of Transportation |
2025 |
classification of road segments - administrative and reporting purposes like the federal aid system and federal functional classification
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| Pennsylvania Department of Transportation |
2025 |
Traffic volumes; measured and calculated amounts of vehicle traffic that travel the sections of road.
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| Pennsylvania Department of Transportation |
2008 |
point locations of Pennsylvania turnpike toll plazas
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| Pennsylvania Department of Transportation |
2025 |
Urban area boundaries within Pennsylvania based on U.S Census Bureau maps.
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| Pennsylvania Department of Transportation |
2025 |
Planning Agencies Boundaries - All Pennsylvania Metropolitan Planning Organizations (MPOs)
and Rural Planning Organizations (RPOs) Boundaries
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| Pennsylvania Department of Transportation |
2024 |
US Bike Routes in Pennsylvania for use with PennDOT's PennShare and Open Data applications.
The U.S. Bicycle Route System (USBRS) is a developing national network of bicycle routes connecting urban and rural communities via signed roads and trails. Created with public input, U.S. Bicycle Routes direct bicyclists to a preferred route through a city, county, or state-creating opportunities for people everywhere to bicycle for travel, transportation, and recreation. Over 19,000 miles are currently established in 34 states and Washington DC.
There are 4 US Bike Routes that run through Pennsylvania- 11, 30, 36, and 50.
11: US Bike Route 11 runs north/south from the New York border in Lawrenceville, PA to the Maryland border north of Hagerstown, PA.
30: US Bike Route 30 runs east/west along the shore of Lake Erie from the New York border to the Ohio border in Erie County.
36: US Bike Route 36 runs east/west from the New York border in Pike County to the Ohio border in Crawford County.
50: US Bike Route 50 runs east/west from the Maryland border in Somerset County to the West Virginia border in Washington County.
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| Pennsylvania Department of Transportation |
2006 |
This metadata record describes the production of natural color images for the December 01, 2006 Pennsylvania storm assessment project. The area flown consists of major wind damage in Luzerne and Dauphin Counties Pennsylvania
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| Pennsylvania Emergency Management Agency |
2006 |
This metadata record describes the production of natural color images for the June 2006 Pennsylvania flood assessment project. The area flown consists of the upper Susquehanna, Schuylkill, and Delaware rivers in the state of Pennsylvania. All photos were acquired during June 2006 for flood assessment.
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| Pennsylvania Emergency Management Agency |
2018 - 2020 |
This orthoimagery data set includes 0.5-foot (15-centimeter) 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000(JP2) format. The PEMA 2018 0.5-foot Orthoimagery called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5-foot (15 centimeters). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania, US Survey Feet. Orthoimagery data was delivered as 298 individual 0.5-foot (15-centimeter) 1" = 100' GeoTIFF (uncompressed) 10,000-foot x 10,000-foot (3,048-meter x 3,048-meter) tiles. Aerial photography was captured during the spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 496 QC points throughout the Commonwealth of Pennsylvania to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| Pennsylvania Emergency Management Agency |
2018 - 2020 |
This orthoimagery data set includes 0.5-foot (15-centimeter) 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000(JP2) format. The PEMA 2018 0.5-foot Orthoimagery called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5-foot (15 centimeters). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania, US Survey Feet. Orthoimagery data was delivered as 298 individual 0.5-foot (15-centimeter) 1" = 100' GeoTIFF (uncompressed) 10,000-foot x 10,000-foot (3,048-meter x 3,048-meter) tiles. Aerial photography was captured during the spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 496 QC points throughout the Commonwealth of Pennsylvania to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| Pennsylvania Emergency Management Agency |
2018 - 2020 |
This orthoimagery data set includes 0.5-foot (15-centimeter) 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000(JP2) format. The PEMA 2018 0.5-foot Orthoimagery called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5-foot (15 centimeters). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania, US Survey Feet. Orthoimagery data was delivered as 298 individual 0.5-foot (15-centimeter) 1" = 100' GeoTIFF (uncompressed) 10,000-foot x 10,000-foot (3,048-meter x 3,048-meter) tiles. Aerial photography was captured during the spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 496 QC points throughout the Commonwealth of Pennsylvania to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| Pennsylvania Emergency Management Agency |
2018 - 2020 |
This orthoimagery data set includes 0.5-foot (15-centimeter) 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000(JP2) format. The PEMA 2018 0.5-foot Orthoimagery called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5-foot (15 centimeters). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania, US Survey Feet. Orthoimagery data was delivered as 298 individual 0.5-foot (15-centimeter) 1" = 100' GeoTIFF (uncompressed) 10,000-foot x 10,000-foot (3,048-meter x 3,048-meter) tiles. Aerial photography was captured during the spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 496 QC points throughout the Commonwealth of Pennsylvania to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| Pennsylvania Emergency Management Agency |
2018 - 2020 |
This orthoimagery data set includes 0.5-foot (15-centimeter) 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000(JP2) format. The PEMA 2018 0.5-foot Orthoimagery called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5-foot (15 centimeters). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania, US Survey Feet. Orthoimagery data was delivered as 298 individual 0.5-foot (15-centimeter) 1" = 100' GeoTIFF (uncompressed) 10,000-foot x 10,000-foot (3,048-meter x 3,048-meter) tiles. Aerial photography was captured during the spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 496 QC points throughout the Commonwealth of Pennsylvania to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| Pennsylvania Emergency Management Agency |
2018 - 2020 |
This orthoimagery data set includes 0.5-foot (15-centimeter) 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000(JP2) format. The PEMA 2018 0.5-foot Orthoimagery called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5-foot (15 centimeters). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania, US Survey Feet. Orthoimagery data was delivered as 298 individual 0.5-foot (15-centimeter) 1" = 100' GeoTIFF (uncompressed) 10,000-foot x 10,000-foot (3,048-meter x 3,048-meter) tiles. Aerial photography was captured during the spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 496 QC points throughout the Commonwealth of Pennsylvania to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| Pennsylvania Emergency Management Agency |
2018 - 2020 |
This orthoimagery data set includes 0.5-foot (15-centimeter) 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000(JP2) format. The PEMA 2018 0.5-foot Orthoimagery called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5-foot (15 centimeters). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania, US Survey Feet. Orthoimagery data was delivered as 298 individual 0.5-foot (15-centimeter) 1" = 100' GeoTIFF (uncompressed) 10,000-foot x 10,000-foot (3,048-meter x 3,048-meter) tiles. Aerial photography was captured during the spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 496 QC points throughout the Commonwealth of Pennsylvania to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| Pennsylvania Emergency Management Agency |
2021 - 2023 |
This orthoimagery data set includes 0.5 foot 8-bit 4-band (RGBN) digital orthoimage tiles in GeoTIFF, Mr. SID, and JPEG 2000 (JP2) format.
Dataset Description: The PEMA 0.5-Foot Orthoimagery Delivery (US Survey Feet) project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5 foot. Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 HARN StatePlane Pennsylvania North FIPS 3701 Feet, Foot US.
Ground Conditions: Imagery was collected in spring 2022, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet ASPRS horizontal accuracy guidelines, NV5 Geospatial utilized a total of 119 ground control points to assess the horizontal accuracy of the data.
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| Pennsylvania Emergency Management Agency |
2021 - 2023 |
This orthoimagery data set includes 0.5 foot 8-bit 4-band (RGBN) digital orthoimage mosaics in GeoTIFF, Mr. SID, and JPEG 2000 (JP2) format. Dataset Description: The PEMA 2020-2021 0.5-Foot Orthoimagery project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5 foot. Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 HARN StatePlane Pennsylvania Feet, Foot US.. Ground Conditions: Imagery was collected in winter 2020 and 2021, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet ASPRS horizontal accuracy guidelines, NV5 Geospatial utilized a total of 496 ground control points to assess the horizontal accuracy of the data.
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| Pennsylvania Emergency Management Agency |
2021 |
Counties include: Adams, Berks, Bucks, Carbon, Chester, Columbia, Cumberland, Dauphin, Delaware, Franklin, Lackawanna, Lancaster, Lebanon, Lehigh, Monroe, Montgomery, Northampton, Northumberland, Perry, Philadelphia, Pike, Schuylkill, Susquehanna, Wayne, Wyoming, York. Product: This orthoimagery data set includes 0.5 foot 8-bit 4-band (RGBN) digital orthoimage mosaics in GeoTIFF, Mr. SID, and JPEG 2000 (JP2) format. Dataset Description: The PEMA 2020-2021 0.5-Foot Orthoimagery project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5 foot. Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 HARN StatePlane Pennsylvania Feet, Foot US.. Ground Conditions: Imagery was collected in winter 2020 and 2021, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet ASPRS horizontal accuracy guidelines, NV5 Geospatial utilized a total of 496 ground control points to assess the horizontal accuracy of the data.
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| Pennsylvania Emergency Management Agency |
2021 |
Counties include: Adams, Berks, Bucks, Carbon, Chester, Columbia, Cumberland, Dauphin, Delaware, Franklin, Lackawanna, Lancaster, Lebanon, Lehigh, Monroe, Montgomery, Northampton, Northumberland, Perry, Philadelphia, Pike, Schuylkill, Susquehanna, Wayne, Wyoming, York. Product: This orthoimagery data set includes 0.5 foot 8-bit 4-band (RGBN) digital orthoimage mosaics in GeoTIFF, Mr. SID, and JPEG 2000 (JP2) format. Dataset Description: The PEMA 2020-2021 0.5-Foot Orthoimagery project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5 foot. Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 HARN StatePlane Pennsylvania Feet, Foot US.. Ground Conditions: Imagery was collected in winter 2020 and 2021, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet ASPRS horizontal accuracy guidelines, NV5 Geospatial utilized a total of 496 ground control points to assess the horizontal accuracy of the data.
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| Pennsylvania Emergency Management Agency |
2021 |
Counties include: Adams, Berks, Bucks, Carbon, Chester, Columbia, Cumberland, Dauphin, Delaware, Franklin, Lackawanna, Lancaster, Lebanon, Lehigh, Monroe, Montgomery, Northampton, Northumberland, Perry, Philadelphia, Pike, Schuylkill, Susquehanna, Wayne, Wyoming, York. Product: This orthoimagery data set includes 0.5 foot 8-bit 4-band (RGBN) digital orthoimage mosaics in GeoTIFF, Mr. SID, and JPEG 2000 (JP2) format. Dataset Description: The PEMA 2020-2021 0.5-Foot Orthoimagery project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5 foot. Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 HARN StatePlane Pennsylvania Feet, Foot US.. Ground Conditions: Imagery was collected in winter 2020 and 2021, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet ASPRS horizontal accuracy guidelines, NV5 Geospatial utilized a total of 496 ground control points to assess the horizontal accuracy of the data.
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| Pennsylvania Emergency Management Agency |
2021 |
Counties include: Adams, Berks, Bucks, Carbon, Chester, Columbia, Cumberland, Dauphin, Delaware, Franklin, Lackawanna, Lancaster, Lebanon, Lehigh, Monroe, Montgomery, Northampton, Northumberland, Perry, Philadelphia, Pike, Schuylkill, Susquehanna, Wayne, Wyoming, York. Product: This orthoimagery data set includes 0.5 foot 8-bit 4-band (RGBN) digital orthoimage mosaics in GeoTIFF, Mr. SID, and JPEG 2000 (JP2) format. Dataset Description: The PEMA 2020-2021 0.5-Foot Orthoimagery project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5 foot. Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 HARN StatePlane Pennsylvania Feet, Foot US.. Ground Conditions: Imagery was collected in winter 2020 and 2021, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet ASPRS horizontal accuracy guidelines, NV5 Geospatial utilized a total of 496 ground control points to assess the horizontal accuracy of the data.
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| Pennsylvania Emergency Management Agency |
2021 |
Counties include: Adams, Berks, Bucks, Carbon, Chester, Columbia, Cumberland, Dauphin, Delaware, Franklin, Lackawanna, Lancaster, Lebanon, Lehigh, Monroe, Montgomery, Northampton, Northumberland, Perry, Philadelphia, Pike, Schuylkill, Susquehanna, Wayne, Wyoming, York. Product: This orthoimagery data set includes 0.5 foot 8-bit 4-band (RGBN) digital orthoimage mosaics in GeoTIFF, Mr. SID, and JPEG 2000 (JP2) format. Dataset Description: The PEMA 2020-2021 0.5-Foot Orthoimagery project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.5 foot. Project specifications are based on the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. The data were developed based on a horizontal projection/datum of NAD 1983 HARN StatePlane Pennsylvania Feet, Foot US.. Ground Conditions: Imagery was collected in winter 2020 and 2021, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet ASPRS horizontal accuracy guidelines, NV5 Geospatial utilized a total of 496 ground control points to assess the horizontal accuracy of the data.
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
Metadata
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KMZ
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
Metadata
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KMZ
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
Metadata
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KMZ
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
Metadata
|
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KMZ
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
Metadata
|
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|
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KMZ
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
Metadata
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KMZ
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| Pennsylvania Emergency Management Agency |
2007 |
In 2006, PEMA completed a statewide study of a 100 year flood event using FEMA's HAZUS-MH risk analysis software. In 2007, PEMA concluded a broader statewide study using the latest version of HAZUS-MH MR 2.0 that includes damage estimates for 10, 50, 100, 200 and 500 year flood events. The study computed damages in dollars for total economic loss, building and content damage, and other economic impacts. The study also estimated the number of damaged homes and the degree of damage to those homes.
With these results, Statewide GIS shapefiles for economic losses and damaged homes were created for each flood scenario using ArcMap 9.1. These files can be displayed using ArcMap 9.0 or higher version - you do not need the HAZUS software to use them. They can be overlaid on other GIS maps (county, local roads, street maps, municipal maps, etc) to show the location and extent of potential flood damages for each of the flood severity levels.
The Flood Study maps and information contained in these files can by used local emergency management agencies, GIS and planning departments, watershed organizations, and other interested parties for hazard identification and risk assessment, mitigation planning, and flood response training activities. IT IS IMPORTANT TO NOTE: These files are estimates of damages and locations generated by the FEMA HAZUS flood analysis model. These files do not include all possible flood risk areas and are not based on actual past flood events. The data should not be used to determine regulated floodplain areas or the location of individual homes and buildings.
The GIS files for the various flood events have been compressed (ZIP files) and can be downloaded from this webpage. The size of the files range from 4 MBs to 25 MBs - download time may be considerable depending on the speed of your Internet connection. Contained in each ZIP file are Pennsylvania Statewide Flood Study ARCGIS shapefiles generated with the HAZUS Multihazard Flood Risk Analysis program.
The shapefiles represent all census blocks affected by each flood event. The economic loss layer shows total losses in thousands of dollars. The damaged homes layer shows the number of flood-affected residential structures. Additional information for each layer is contained in its attribute table which can be viewed and edited in ArcMap. The display can also be modified or enhanced by changing the layer's properties in ArcMap (change the display to "classified" and the number of classes to "10," for example).
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| Pennsylvania Emergency Management Agency |
2004 |
This metadata record describes the production of
natural color digital orthophotography for all priority
area of the September 2004 Pennsylvania flood
assessment project. All
imagery was acquired during September of 2004.
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| Pennsylvania Emergency Management Agency |
2024 |
This layer contains the locations of fishing and boating access areas that have been verified by the Pennsylvania Fish and Boat Commission and are public or semi-public areas. In addition, accesses identified by water trail partners on water trail guides have been added to the layer.
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| Pennsylvania Fish and Boat Commission |
2019 |
Coverage areas of Pennsylvania Fish and Boat Commission Area Fisheries Managers (AFM).
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| Pennsylvania Fish and Boat Commission |
2019 |
Office locations of Pennsylvania Fish and Boat Commission Area Fisheries Managers (AFM)
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| Pennsylvania Fish and Boat Commission |
2024 |
“Pennsylvania’s Best Fishing Waters” program identifies proven waters for specific species. This listing includes suggestions for distinct species within PA Lakes.
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| Pennsylvania Fish and Boat Commission |
2024 |
“Pennsylvania’s Best Fishing Waters” program identifies proven waters for specific species. This listing includes suggestions for distinct species within PA Streams.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains those lakes within Pennsylvania that have specific special regulations related to boating as defined by the Pennsylvania Fish and Boat Commission
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| Pennsylvania Fish and Boat Commission |
2016 |
This layer contains those streams/rivers within Pennsylvania that have specific special regulations related to boating as defined by the Pennsylvania Fish and Boat Commission
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains those lakes and streams as point features within Pennsylvania that have specific special regulations related to boating as defined by the Pennsylvania Fish and Boat Commission
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| Pennsylvania Fish and Boat Commission |
2025 |
Class A streams are streams that support a population of wild (natural reproduction) trout of sufficient size and abundance to support a long-term and rewarding sport fishery. The Commission does not stock these streams. This GIS layer represents the sections of streams that are designated as such.
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| Pennsylvania Fish and Boat Commission |
2025 |
Class A streams are streams that support a population of wild (natural reproduction) trout of sufficient size and abundance to support a long-term and rewarding sport fishery. The Commission does not stock these streams. This GIS layer respresents the points of streams that are designated as such.
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| Pennsylvania Fish and Boat Commission |
2020 |
This GIS layer contains locations of nurseries that participate in the Commission's Cooperative Nursery Program, "Coops" supplement the Commission's stocking program. For more information about the Cooperative Nursery Program, contact the Commission's Coordinator, Cecil Houser, chouser@state.pa.us or 814-359-5124.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains flowing waters from the Pennsylvania Fish and Boat Commission Fisheries Resource Database located around the Lake Erie Steehhead fishing area. Mapping of the locations of these public access areas throughout Pennsylvania will help promote fishing and boating opportunities in Pennsylvania.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains the locations of fishing access points that have been identified by the Pennsylvania Fish and Boat Commission and are public or semi-public areas.
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| Pennsylvania Fish and Boat Commission |
2021 |
This layer contains the locations of fishing access areas that have been identified by the Pennsylvania Fish and Boat Commission and are public or semi-public areas
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| Pennsylvania Fish and Boat Commission |
2025 |
This layer contains flowing water Sections from the Pennsylvania Fish and Boat Commission Fisheries Resource Database. Water Sections are defined when a water has had some type of sampling conducted and data is being entered into the PFBCMainResource database and ResourceFirstPortal. This layer is directly linked to tables of stocking events for trout, as well as planned and past stockings for warmwater/coolwater species.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains the lakes that are part of the Pennsylvania Fish and Boat Commission Fisheries Resource Database. These include lakes that are currently or have previously been stocked, regulated or sampled.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains the lakes that are part of the Pennsylvania Fish and Boat Commission Fisheries Resource Database. These include lakes that are currently or have previously been stocked, regulated or sampled.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains flowing waters from the Pennsylvania Fish and Boat Commission Fisheries Resource Database that are under fishing special regulations.
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| Pennsylvania Fish and Boat Commission |
2017 |
The data set was created using the PennDOT Counties GIS layer & TeleAtlas North America Roads GIS layers as Region boundaries follow these features. Polygons from the Counties layer were merged to create the Regions and then modified using the roads layers where appropriate. Roads were incorporated by using the Trace Sketch Tool in ArcMap.
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| Pennsylvania Fish and Boat Commission |
2020 |
Water trails designated by the Pennsylvania Fish and Boat Commission in Pennsylvania. Visit http://sites.state.pa.us/PA_Exec/Fish_Boat/watertrails/trailindex.htm for more information.
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| Pennsylvania Fish and Boat Commission |
1998 |
Historical listing (1975-1995) of Pennsylvania fish species occurrences, as documented at 10,780 sampling points on streams and rivers throughout the state. Accessory information, such as type of sampling gear used and scientific names, are included. Threatened and endangered species, however, have been removed from the database for protection purposes. (If you require information concerning the distribution of Threatened and Endangered species in Pennsylvania, contact Andy Shiels at the PA Fish and Boat Commission.)
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| Pennsylvania Fish and Boat Commission |
2020 |
The Pennsylvania Fish and Boat Commission is an independent administrative commission. It consists of ten competent citizens of the Commonwealth who are appointed by the Governor by and with the advice and consent of a majority of the Pennsylvania Senate. Commissioners serve for terms of eight years and continue in office until their successors are appointed.
Two members of the Commission serve at large and are experienced in boating and water safety education and owners of Pennsylvania registered boats. The remaining eight members each represent a specific geographic district. They are persons well informed on the subjects of conservation, restoration, fish and fishing and boats and boating. Commissioners are appointed on a bipartisan basis. This GIS layer is a compilation of those 8 geographic districts.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains the lakes that are part of the Pennsylvania Fish and Boat Commission Fisheries Resource Database. These include lakes that are currently or have previously been stocked, regulated or sampled.
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains the lakes that are part of the Pennsylvania Fish and Boat Commission Fisheries Resource Database. These include lakes that are currently or have previously been stocked, regulated or sampled.
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| Pennsylvania Fish and Boat Commission |
2020 |
The Pennsylvania Fish and Boat Commission owns numerous properties across Pennsylvania. This GIS layer contains point locations on each property
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| Pennsylvania Fish and Boat Commission |
2017 |
Regional Pennsylvania Fish and Boat Commission Bureau of Law Enforcement office locations.
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| Pennsylvania Fish and Boat Commission |
2017 |
The Fish & Boat Commission operates fifteen (15) state fish hatcheries. Northern hatcheries (north of I-80) primarily culture warm/coolwater fish, while southern hatcheries (south of I-80) primarily culture trout. This GIS layer contains the locations of these hatcheries.
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| Pennsylvania Fish and Boat Commission |
2024 |
Many streams, lakes, ponds, and reservoirs are officially classified as "approved trout waters." This means that these waters contain significant portions that are open to public fishing and are stocked with trout. The waters in this layer are lakes (not flowing waters) classified as "approved trout waters" and these are open to trout harvest during the "extended season" (see Commonwealth Inland Waters). Unlisted tributary streams (those not included in this list of "approved trout waters") are not open to harvest of trout during the "extended season." Only approved trout waters and all waters downstream of approved trout waters are open during this period. Spearing fish is not permitted in any of these waters at any time of the year.
These waters are closed to all fishing (including taking of minnows) from March 1 to 8 a.m. on the opening day of the trout season. Some of these waters have been included in the Early Season Trout-Stocked Waters Program and are open from March 1 through March 31. A person shall be deemed to be fishing if he or she has in possession any fishing line, rod, or other device that can be used for fishing while on or in any water or on the banks within 25 feet of any water where fishing is prohibited.
Check with the nearest Fish & Boat Commission office if there is any question about whether or not a water area is "approved."
This layer is current through the new fishing regulations released December of 2009 for the 2010 fishing season.
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| Pennsylvania Fish and Boat Commission |
2024 |
Many streams, lakes, ponds, and reservoirs are officially classified as "approved trout waters." This means that these waters contain significant portions that are open to public fishing and are stocked with trout. The waters in this layer are flowing waters (not lakes) classified as "approved trout waters" and these are open to trout harvest during the "extended season" (see Commonwealth Inland Waters). Unlisted tributary streams (those not included in this list of "approved trout waters") are not open to harvest of trout during the "extended season." Only approved trout waters and all waters downstream of approved trout waters are open during this period. Spearing fish is not permitted in any of these waters at any time of the year.
These waters are closed to all fishing (including taking of minnows) from March 1 to 8 a.m. on the opening day of the trout season. Some of these waters have been included in the Early Season Trout-Stocked Waters Program and are open from March 1 through March 31. A person shall be deemed to be fishing if he or she has in possession any fishing line, rod, or other device that can be used for fishing while on or in any water or on the banks within 25 feet of any water where fishing is prohibited.
Check with the nearest Fish & Boat Commission office if there is any question about whether or not a water area is "approved."
This layer is current through the new fishing regulations released December of 2009 for the 2010 fishing season.
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| Pennsylvania Fish and Boat Commission |
2024 |
Many streams, lakes, ponds, and reservoirs are officially classified as "approved trout waters." This means that these waters contain significant portions that are open to public fishing and are stocked with trout. The waters in this layer are flowing waters (not lakes) classified as "approved trout waters" and these are open to trout harvest during the "extended season" (see Commonwealth Inland Waters). Unlisted tributary streams (those not included in this list of "approved trout waters") are not open to harvest of trout during the "extended season." Only approved trout waters and all waters downstream of approved trout waters are open during this period. Spearing fish is not permitted in any of these waters at any time of the year.
These waters are closed to all fishing (including taking of minnows) from March 1 to 8 a.m. on the opening day of the trout season. Some of these waters have been included in the Early Season Trout-Stocked Waters Program and are open from March 1 through March 31. A person shall be deemed to be fishing if he or she has in possession any fishing line, rod, or other device that can be used for fishing while on or in any water or on the banks within 25 feet of any water where fishing is prohibited.
Check with the nearest Fish & Boat Commission office if there is any question about whether or not a water area is "approved."
This layer is current through the new fishing regulations released December of 2009 for the 2010 fishing season.
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| Pennsylvania Fish and Boat Commission |
2025 |
This layer contains flowing waters from the Pennsylvania Fish and Boat Commission Fisheries Resource Database that support naturally reproducing populations of trout. A wild trout stream section is a biological designation that does not determine how it is managed, therefore, these streams may also be stocked with hatchery trout by the Commission.
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| Pennsylvania Fish and Boat Commission |
2025 |
This layer contains flowing waters in Pennsylvania that support naturally reproducing populations of trout WITH Tributaries that are NOT Commission Approved Wild Trout Waters. Attibute column named Wildtrouts will show if a polyline represents a Commission Approved Wild Trout with an entry of Listed or will have Tributary indicating water is a tributary to a wild trout water. A wild trout stream section is a biological designation that does not determine how it is managed, therefore, these streams may also be stocked with hatchery trout by the Commission. This GIS layer matches the list available on the PFBC web site (excluding Tributaries) at https://www.fishandboat.com/Fish/PennsylvaniaFishes/Trout/Documents/trout_repro.pdf. Also Interactive ArcGIS MAp at https://pfbc.maps.arcgis.com/apps/webappviewer/index.html?id=65a89f6592234019bdc5f095eaf5c6ac
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| Pennsylvania Fish and Boat Commission |
2024 |
This layer contains flowing waters from the Pennsylvania Fish and Boat Commission Fisheries Resource Database that will be stocked with trout in 2017.
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| Pennsylvania Fish and Boat Commission |
2024 |
Suggested WW/CW Lake fishing locations by species, identified by the PA Fish and Boat Commission.
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| Pennsylvania Fish and Boat Commission |
2024 |
Suggested WW/CW Stream fishing locations by species, identified by the PA Fish and Boat Commission.
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| Pennsylvania Fish and Boat Commission |
2022 |
Pennsylvania Fish and Boat Commission Waterways Conservation Officer Districts
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| Pennsylvania Fish and Boat Commission |
2022 |
This layer contains flowing waters from the Pennsylvania Fish and Boat Commission Fisheries Resource Database that are under the Wilderness Trout Designation. Wilderness trout stream management is based upon the provision of a wild trout fishing experience in a remote, natural and unspoiled environment where man's disruptive activities are minimized. Established in 1969, this option was designed to protect and promote native (brook trout) fisheries, the ecological requirements necessary for natural reproduction of trout and wilderness aesthetics. The superior quality of these watersheds is considered an important part of the overall angling experience on wilderness trout streams. Therefore, all stream sections included in this program qualify for the Exceptional Value (EV) special protected water use classification, which represents the highest protection status provided by the Department of Environmental Protection (DEP).
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| Pennsylvania Fish and Boat Commission |
2021 |
Boundaries for managing deer antler restrictions within Pennsylvania which protects yearling bucks from harvest, allowing them to grow.
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| Pennsylvania Game Commission |
2020 |
Defines the boundaries of the Chronic Wasting Disease Managment Areas (DMAs) in Pennsylvania that are governed by special rules and guidelines. Within DMAs, rehabilitation of cervids (deer, elk, and moose); the use or possession of cervid urine-based attractants in an outdoor setting; the removal of high-risk cervid parts; and the feeding of wild, free-ranging cervids are prohibited. Increased testing continues in these area to determine the distribution of the disease. Newly confirmed cases will alter the boudaries of DMAs as the Game Commission continues to manage the disease and minimize its affect on free ranging cervids.
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| Pennsylvania Game Commission |
2021 |
Select areas in Pennsylvania where special restrictions apply to Goose Hunting.
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| Pennsylvania Game Commission |
2021 |
Duck zones are geographical units within states for which duck season dates can be set within federal frameworks but independently of other zones, providing greater flexibility in establishing duck seasons that fit different patterns of species occurrence, habitat conditions, weather, and hunter preferences in different parts of Pennsylvania.
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| Pennsylvania Game Commission |
2020 |
Boundary defines the elk managment area boundary and surrounds the elk managment or hunt zones in Pennsylvania.
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| Pennsylvania Game Commission |
2020 |
Boundarires used to manage and regulate harvests of elk populations according to specific areas in Pennsylvania. The elk zones are managed according to the various subpopulations amoung the herds in Pennsylvania
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| Pennsylvania Game Commission |
2021 |
Designated marked locations at specific locations (gates and parking lots) on state game lands that are used to provide users with identifible locations for requesting emergency assistance.
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| Pennsylvania Game Commission |
2021 |
Game Commission Regions boundaries
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| Pennsylvania Game Commission |
2021 |
Used to define the boundaries of the individual game warden districts and to manage manpower and workload distribution of officers for the Pennsylvania Game Commission.
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| Pennsylvania Game Commission |
2020 |
Line feature to show roads on Game Lands. These include Public, Private and Administrative roads.
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| Pennsylvania Game Commission |
2021 |
Formally recognized trail system managed by the Pennsylvania Game Commission or another entity through an agreement with the Pennsylvania Game Commission.Polyline showing trails located on SGL.
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| Pennsylvania Game Commission |
2021 |
Manmade structure of plastic, metal or wood to demark locations where vehicular traffic is controlled.
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| Pennsylvania Game Commission |
2021 |
Goose zones are for Canada goose managment in Pennsylvania. There are three different goose zones: Southern James Bay Poluation Zone, Resident Population Zone and the Atlantic Population Zone.
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| Pennsylvania Game Commission |
2021 |
Boundaries for 29 PGC Land Management Groups
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| Pennsylvania Game Commission |
2021 |
Office locations
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| Pennsylvania Game Commission |
2020 |
Point locations of parking areas on Pennsylvania State Game Lands.
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| Pennsylvania Game Commission |
1994 |
Part of the Pennsylvania Gap Analysis Project the Breeding Bird Atlas database indicates bird species and nesting behavior of known birds species in Pennsylvania. The database is organized into two primary segments; the block and the species lists (data). Blocks are defined by a code for the USGS topographic map, and the block number. All records are organized by the Atlas region. Various attributes of the Atlas block are incorporated, including geographic features (physiographic locations), effort (hours of fieldwork). The species segment, being replicated the full 320,000 times, was kept to a minimal size, and lists only the species and breeding code, the year and comment. Evidence of birds nesting within an Atlas block was recorded with a set of codes that describe behavior and physical evidence of nesting. These codes are divided into four hierarchical categories, three of which indicate breeding. "Observed," "Possible," "Probable," and "Confirmed" breeding codes represent increasing evidence that nesting occurred within the block a species was recorded. "Probable" and "Confirmed" categories were further segregated into hierarchical codes that represent individual breeding behaviors or physical evidence of nesting. A description of breeding codes may be provided upon request. All of Pennsylvania's 4,928 atlas "blocks" were surveyed, with an average of 65 species per block recorded. A total of 188 breeding species were documented statewide. Data catalogued geographically by atlas "block," USGS 7.5 minute quadrangle, county, and physiographic province. Species files include political status designation and a number of life-history characteristics.
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| Pennsylvania Game Commission |
2016 |
Twenty years after the first Atlas of Breeding Birds in Pennsylvania was published, the Second Atlas of Breeding Birds in Pennsylvania brings our knowledge of the state’s bird populations up to date, documenting current distribution and changes in status for nearly two hundred breeding bird species. More than two thousand dedicated birdwatchers completed surveys of birds across the state from 2004 to 2009. The data amassed reveal the distribution of each species and show changes in distribution since the publication of the first Atlas. The core results of the project are the distribution maps. In fact, up to three maps per species show in fine detail the current distribution of all breeding birds based on the second Atlas, changes in distribution since the first Atlas, and, for more than one hundred species, their density across Pennsylvania.The field project was based at Powdermill Nature Reserve, environmental research center of the Carnegie Museum of Natural History, in Rector, Pennsylvania. Bob Mulvihill and Mike Lanzone were project co-coordinators. Following the recommendations of the North American Ornithological Atlas Committee, the basic survey unit was the “block,” defined as one-sixth of a standard U.S. Geological Survey 7.5-minute topographic map, of which 4,937 were identified for survey coverage. Local coordination was provided by volunteer Regional Coordinators, who provided the foundation for organizing fieldwork. Eventually, 83 individuals served as Regional Coordinators (see the list under the Credits tab).Breeding bird atlases employ a set of codes to record the behaviors associated with nesting activity, ranging from the simplest detection by sight or sound, through confirmation of active nests and fledged young. Birds observed in breeding habitat, and within their particular breeding season, were placed in one of four categories based on breeding evidence — observations outside breeding habitat, or Possible, Probable, or Confirmed breeding — with a two-letter code used for confirmed breeding evidence and single letter codes for all other categories (See Project Methods – Breeding Codes). The second Atlas did not recommend undue effort to confirm breeding of common species, because it was believed that the field time could be better spent compiling species lists from multiple blocks. Obtaining confirmed breeding evidence was stressed for species identified in a particular conservation or priority category or for species simply rare or unexpected within the block.
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| Pennsylvania Game Commission |
2023 |
Defines the individual boundaries of the Pennsylvania State Game Lands for the Management of public resources.
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| Pennsylvania Game Commission |
2021 |
Point locations of Pennsylvania Game Commission public shooting ranges. Rangetype 'R' = Rifle, 'P' = Pistol. Shooting permits or valid Pennsylvania hunting license required in order to use these facilities. Information available on the PGC website and Hunting Digest. Be sure to check the status of the range to ensure that it is open.
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| Pennsylvania Game Commission |
2021 |
Special regulation areas - are counties that may limit the type of arms and ammunition permitted to be used for hunting in these areas. Special Regulation Areas include:In western Pennsylvania, all of Allegheny County.In Southeastern Pennsylvania, all of Bucks, Chester, Delaware, Montgomery and Philadelphia counties, and also during special controlled hunts at Ridley Creek and Tyler state parks.
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| Pennsylvania Game Commission |
2021 |
Wild Pheasant Recovery Areas (WPRA) are areas designated by the Pennsylvania Game Commission for the release of wild pheasants that are trapped in western states and transferred to Pennsylvania. The goal of a WPRA is to establish a sustainable wild pheasant population that can be hunted.
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| Pennsylvania Game Commission |
2021 |
Wildlife Management Units boundaries
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| Pennsylvania Game Commission |
2015 |
This data set represents the state forest management areas including part of the state forest boundary.
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| Pennsylvania Historical and Museum Commission |
2019 |
Pennsylvania North Central Lidar 2019 - Tile Indexes. Includes: LiDAR 2019 PA North 10K QL2, PA North 5K QL1, PA North 5K QL1, North 5K QL2, PA South 10K QL2, PA South 5K QL2
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| Pennsylvania North Central Lidar 2019 - Tile Indexes |
2020 |
Pennsylvania Western Lidar 2020 - Tile Indexes. Includes: LiDAR 2020 QL1 5K SP North, QL2 10K SP North, QL2 10K SP South, QL2 5K SP North, QL2 5K SP South
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| Pennsylvania North Central Lidar 2019 - Tile Indexes |
2023 |
geographical reference points for Pennsylvania State Police Stations.
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| Pennsylvania State Police |
2023 |
PSP Troop Zones derived and dissolved from Tom Tom minor civil division boundaries.
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| Pennsylvania State Police |
2021 |
Municipal boundaries of Perry County, Pennsylvania
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| Perry County |
2021 |
Parcel boundaries of Perry County, Pennsylvania
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| Perry County |
2025 |
Current Land Use
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| Snyder County |
2025 |
Facility Sites
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| Snyder County |
2025 |
FEMA Flood Zone
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| Snyder County |
2025 |
Municipal Boundary
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| Snyder County |
2025 |
Open Space Conservancy
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| Snyder County |
2025 |
Parcels
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| Snyder County |
2025 |
Polling Places
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| Snyder County |
2025 |
Railroads
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| Snyder County |
2025 |
Road Centerline
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| Snyder County |
2025 |
Site Address Point
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| Snyder County |
2025 |
Voting Precincts
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| Snyder County |
2025 |
Waterbodies
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| Snyder County |
2025 |
Waterlines
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| Snyder County |
2025 |
Zoning District
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| Snyder County |
2024 |
House Numbers of Somerset County, Pennsylvania
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| Somerset County |
2024 |
Street Centerlines of Somerset County, Pennsylvania
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| Somerset County |
2012 |
This data set shows the locations of train stops along the Market Frankford Line, Broad Street Line, and Broad Street Spur. This data set also has ridership information as well as detailed information about each train stop location.
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| Southeastern Pennsylvania Transportation Authority |
2012 |
This data set shows the locations of train stops along the Market Frankford Line, Broad Street Line, and Broad Street Spur. This data set also has ridership information as well as detailed information about each train stop location.
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| Southeastern Pennsylvania Transportation Authority |
2012 |
This data set shows the locations of train stops along the Market Frankford Line, Broad Street Line, and Broad Street Spur. This data set also has ridership information as well as detailed information about each train stop location.
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| Southeastern Pennsylvania Transportation Authority |
2016 |
This data set shows the locations of train stops along the Market Frankford Line, Broad Street Line, and Broad Street Spur. This data set also has ridership information as well as detailed information about each train stop location.
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| Southeastern Pennsylvania Transportation Authority |
2016 |
Routes - This data set was developed to assist in service planning purposes.
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| Southeastern Pennsylvania Transportation Authority |
2016 |
This data set shows the locations of train stops along the Market Frankford Line, Broad Street Line, and Broad Street Spur. This data set also has ridership information as well as detailed information about each train stop location.
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| Southeastern Pennsylvania Transportation Authority |
2014 |
Routes - This data set was developed to assist in service planning purposes.
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| Southeastern Pennsylvania Transportation Authority |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2017 |
Landslides and landslide potential for Western Pennsylvania digitized from mid 1970’s - 1980’s USGS Topo sheets. Polygons are locations that were active or have recent evidence of a slide. Landslides and landslide potential for Western Pennsylvania digitized from mid 1970’s - 1980’s USGS Topo sheets. Polygons are locations that were active or have recent evidence of a slide. USGS topo sheets used for digitizing: http://www.dcnr.state.pa.us/topogeo/hazards/landslides/slidepubs/clarksburg_of/index.htm http://www.dcnr.state.pa.us/topogeo/hazards/landslides/slidepubs/canton_of/index.htm http://www.dcnr.state.pa.us/topogeo/hazards/landslides/slidepubs/pittsburgh_of/index.htm http://www.dcnr.state.pa.us/topogeo/hazards/landslides/slidepubs/warren/index.htm
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| Southwestern Pennsylvania Commission |
2020 |
This layer includes all of the current and proposed sidewalks, trails, and bikeways for the township. This is digitized and updated periodically for planning purposes. Some of the existing sidewalks and trails were gpsed but a low percentage.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2015 |
SPC’s Land Use/ Land Cover (LULC) was developed with heads-up digitization methods based on the visual interpretation of SPC’s one-foot resolution black and white orthophotography from 2000-01. Data for Lawrence County was initially digitized using 1997 one-foot by two-foot resolution orthophotography, and was updated with 2000 LandSAT panchromatic satellite imagery. Westmoreland County was digitized using 2003 one-foot resolution orthophotography. In 2006, the SPC LULC was updated based on color aerial photography from the PA DCNR’s PAMAP Program. As a follow up to the land cover developed by SPC in 2000, the 2006 land cover data uses the same classification system and land cover definitions allowing change to be measured across the region. The classification closely resembles USGS Anderson Level I & II standards. SPC’s LULC was updated once again in 2010 based on the aerial imagery collected by the National Agricultural Imagery Program with one-meter resolution. Heads up digitization methods were again applied based on previously adopted definitions set in 2000. SPC’s LULC is designated based on a three-tier hierarchical classification system. Level I contains six LULC types: Urban-Built-Up, Agricultural, Rangeland, Forest, Water, and Barren Land. Levels II and III provide a more thorough classification of the land. Level III classes will be highlighted briefly following Level II definitions. Some Level III classes have been developed consistent with Level II detail for mapping efficiency in accordance with the level of detail and purpose of this project. Other Level III classes have been developed providing higher detail but are not fully utilized so that future mapping can be achieved (at that level of detail) if the need should arise.
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| Southwestern Pennsylvania Commission |
2018 |
Monthly 30-year "normal" dataset covering the Susquehanna River Basin, averaged over the climatological period 1981-2010. Contains spatially gridded average annual precipitation ain inches at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset was heavily peer reviewed, and is available free-of-charge on the PRISM website.
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| Susquehanna River Basin Commission SRBC |
2006 |
30 Year Precipitation dataset (1971-2000) for the Susquehanna River Basin (2006 boundary). This dataset was created from a precipitation raster from the Spatial Climate Analysis Service, Oregon State University. Please use the GRIDCODE field for precipitation in inches. The correct conversion appears in the Entity and Attribute; Overview Description section of this document.
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| Susquehanna River Basin Commission SRBC |
2011 |
Polygon shapefile representing the area underlain by shales with the potential for natural gas extraction in the Susquehanna River Basin. This dataset includes the estimated subsurface extent within the Susquehanna River Basin of the Marcellus, Utica-Antes, Burket, Geneseo, Mandata, Middlesex, Needmore, and Rhinestreet Shales combined. This dataset was created using New York geology from the NYS Museum, NYS Geologic Survey, 1999 and Pennsylvania geology from PA DCNR, 2001. To depict potential natural gas shale area within the Susquehanna River Basin, only display formations with a 'Yes' attribute for the field "GAS_SHALE".
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile of the Chemung Subbasin. This file was created from the 2006 subbasin.shp SRBC dataset.
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| Susquehanna River Basin Commission SRBC |
2006 |
This geology shapefile covers the entire Susquehanna River Basin (2006 boundary). It represents the merging of three state (NY, PA, MD) bedrock geology spatial datasets in order to provide basic rock types of the Susquehanna River Basin. The dominant formation lithology that appears in the attribute table were grouped into 19 general rock types, such as sandstone, shale, schist, limestone, dolomite, etc. These rock types are also categorized by hydrostratigraphic terrain. The hydrostratigraphic terrain helps to identify the way in which water flows over or through these rocks.
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| Susquehanna River Basin Commission SRBC |
2023 |
Different geologic materials, structures, and land uses all influence the rate in which water can recharge underlying aquifers. This dataset combines factors influencing recharge through standardization and weighting assignments using the Multi-Criteria Decision Analysis (MCDA)-GIS framework to identify land-surface areas that have the potential to provide a large fraction of recharge. Percent impervious cover, land surface slope, percent sand and clay, depth to bedrock, drainage density, karst density, and fault density are used to describe recharge potential within the Susquehanna River Basin (basin). Many of these data layers are used to predict baseflow via regional regression equations in ungaged locations throughout the Mid-Atlantic region; baseflow is often used as an approximation of recharge. Input criteria were prioritized using the Analytic Hierarchy Process (AHP), which is an additive weighting model that can be combined with MCDA. Three general “first-level factors” were identified based on three primary zones of infiltration or recharge; those include the land surface, shallow-subsurface (soil) geology, and structural/bedrock geology. Weighting assignments of input datasets are presented in the table below. WeightFirst-Level FactorsWeightSecond-Level Factors40Land Cover / Terrain25Percent Impervious15Land Surface Slope20Shallow-Subsurface Geology15Percent Sand2.5Percent Clay2.5Depth to Bedrock40Structural / Bedrock Geology25Drainage Density10Karst Density5Fault DensityThe resulting raster dataset describes recharge potential as an index from 100-500, with 100 illustrating areas of least recharge potential and 500 illustrating areas of highest recharge potential. This polygon feature class represents all land surface areas that have corresponding pixel values within the 5th (or highest) quantile recharge class. Verification of these areas via desktop methods is described in detail in the study report available on the Commission's website at https://www.srbc.net/our-work/reports-library/Areas of high recharge potential may potentially overlie open water in features such as in rivers, streams, lakes, or reservoirs. Open water locations may be suitable for recharge, however some input datasets may not be applicable and/or complete for these areas. We combined “waterbody” features from the National Hydrography Dataset (USGS, 1999) and areas classified as “open water” in the National Land Cover Database (USGS, 2019) to comprehensively describe surface waters in the Basin. High recharge potential areas overlying surface waters are denoted by “Yes-Water” in the “High_Pot” attribute field. Reclassification of input datasets, and dataset sources are described in the "Lineage" section of the metadata.
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| Susquehanna River Basin Commission SRBC |
2023 |
Different geologic materials, structures, and land uses all influence the rate in which water can recharge underlying aquifers. This dataset combines factors influencing recharge through standardization and weighting assignments using the Multi-Criteria Decision Analysis (MCDA)-GIS framework to identify land-surface areas that have the potential to provide a large fraction of recharge. Percent impervious cover, land surface slope, percent sand and clay, depth to bedrock, drainage density, karst density, and fault density are used to describe recharge potential within the Susquehanna River Basin (basin). Many of these data layers are used to predict baseflow via regional regression equations in ungaged locations throughout the Mid-Atlantic region; baseflow is often used as an approximation of recharge. Input criteria were prioritized using the Analytic Hierarchy Process (AHP), which is an additive weighting model that can be combined with MCDA. Three general “first-level factors” were identified based on three primary zones of infiltration or recharge; those include the land surface, shallow-subsurface (soil) geology, and structural/bedrock geology. Weighting assignments of input datasets are presented in the table below.WeightFirst-Level FactorsWeightSecond-Level Factors40Land Cover / Terrain25Percent Impervious15Land Surface Slope20Shallow-Subsurface Geology15Percent Sand2.5Percent Clay2.5Depth to Bedrock40Structural / Bedrock Geology25Drainage Density10Karst Density5Fault DensityRecharge potential is described as an index from 100-500, with 100 illustrating areas of least recharge potential and 500 illustrating areas of highest recharge potential. This dataset can be extracted and reclassified in user defined areas for local assessments using the quantile classification scheme.Reclassification of input datasets, and dataset sources are described in the "Lineage" section of the metadata.
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| Susquehanna River Basin Commission SRBC |
2006 |
Isoline shapefile created from the 1971-2000 precipitation raster dataset from the Spatial Climate Analysis Service, Oregon State University. This file should be used in conjunction with the 30 Year Precipitation dataset (1971-2000) for the Susquehanna River Basin (2006 boundary).
The attribute field for inches has been interpolated from the raster's GRIDCODE field. The inch values should be viewed as approximate. The conversion is listed in the Entity Attribute Overview section of the document. This section also lists the correct conversion for the 30 Year Precipitation dataset (1971-2000) for the Susquehanna River Basin.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile of the Juniata Subbasin. This file was created from the 2006 subbasin.shp SRBC dataset.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile of the Lower Susquehanna Subbasin. This file was created from the 2006 subbasin.shp SRBC dataset.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile that includes the major watersheds of the Susquehanna River Basin (2006 boundary). This file was created by dissolving the wshed24K dataset into the major watersheds. The groupings were defined by the needs of the Susquehanna River Basin Commission (SRBC) and represent the SRBC's interpretation of major watersheds in the Susquehanna River Basin. This file was created to work in tandem with wshed24k.shp, subbasin.shp, and srb.shp. This dataset does not match the Hydrologic Unit Code (HUC) 11 digit watersheds.
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| Susquehanna River Basin Commission SRBC |
2005 |
The Susquehanna River Basin Commission (SRBC) developed a map package for watershed groups and county conservation districts within the Pennsylvania portion of the basin. Map packages were distributed to 44 non-profit groups in 22 counties, covering 52 watersheds equaling 8,300 square miles in Pennsylvania. The project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Growing Greener Grant Program. The map packages were tailored to each group’s watershed and featured four geographic themes – geology, land cover, soils, and watersheds. In addition, abandoned mine lands, political boundaries and hydrologic features, such as wetlands, were incorporated, where applicable. Large (28’ x 36’) and small (8.5’ x 11’) format map images are included. Organizations can use the data and maps to plan and assess watershed conditions and determine methods to improve the quality of the waters.
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| Susquehanna River Basin Commission SRBC |
2018 |
Point shapefile representing the spatial distribution of median fecal coliform counts (colonies per 100 milliliters) in the Susquehanna River Basin. The results were obtained from the United States Environmental Protection Agency's (USEPA) STORET database for 95 stations in the basin. Samples were collected from 1986 to 2018.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile of the Middle Susquehanna Subbasin. This file was created from the 2006 subbasin.shp SRBC dataset.
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| Susquehanna River Basin Commission SRBC |
2005 |
Contains various shapefiles, pdf's, and jpeg maps pertaining to the Northern Lancaster County Groundwater study. Layer include groundwater basin shapefiles include - river basins, MCD's, census blocks, sewage treatment outfalls, water use, study area, dry stream valley systems, geology, karst modified uplands, impervious surfaces, wetlands.
JPEG maps include study area, basins, withdraws, conductivity, nitrates, geology, water tables, flowlines, mapplate.
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| Susquehanna River Basin Commission SRBC |
2006 |
This is a polygon coverage of Physiographic Divisions of the Susquehanna River Basin (2006 boundary). It was automated from Fenneman's
1:7,000,000-scale map, "Physical Divisions of the United
States," which is based on eight major divisions, 25 provinces,
and 86 sections representing distinctive areas having common
topography, rock types and structure, and geologic and
geomorphic history.
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| Susquehanna River Basin Commission SRBC |
2021 |
Line shapefile depicting isolines created from the 1991-2020 precipitation raster dataset from the Spatial Climate Analysis Service, Oregon State University. This file should be used in conjunction with the 30 Year Precipitation dataset (1991-2020) for the Susquehanna River Basin. The attribute field for inches has been interpolated from the raster's VALUE field. The inch values should be viewed as approximate. The conversion is listed in the Entity Attribute Overview section of the document.
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| Susquehanna River Basin Commission SRBC |
2021 |
Monthly 30-year "normal" dataset covering the Susquehanna River Basin, averaged over the climatological period 1991-2020. Contains spatially gridded average total precipitation at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.
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| Susquehanna River Basin Commission SRBC |
2006 |
This dataset is a digital general soil association
map for the Susquehanna River Basin (2006 boundary) developed by the
National Cooperative Soil Survey. It
consists of a broad based inventory of soils and nonsoil areas
that occur in a repeatable pattern on the landscape and that
can be cartographically shown at the scale mapped. The soil
maps for STATSGO are compiled by generalizing more detailed
soil survey maps. Where more detailed soil survey maps are
not available, data on geology, topography, vegetation, and
climate are assembled, together with Land Remote Sensing
Satellite (LANDSAT) images. Soils of like areas are studied,
and the probable classification and extent of the soils are
determined.
Map unit composition for a STATSGO map is determined by
transecting or sampling areas on the more detailed maps and
expanding the data statistically to characterize the whole map
unit.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are collected in 1-
by 2-degree topographic quadrangle units and merged and
distributed as statewide coverages. The soil map units are
linked to attributes in the Map Unit Interpretations Record
relational data base which gives the proportionate extent of
the component soils and their properties.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile that includes the subbasins of the Susquehanna River Basin (2006 boundary). The 6 subbasins include the Upper Susquehanna, Chemung, Middle Susquehanna, West Branch Susquehanna, Juniata, and Lower Susquehanna. This file was created by dissolving the wshed24K dataset into the 6 major subbasins. The boundary between the Upper Susq. and Chemung Subbasins was updated to match the SRBC delineated subbasin line that exists on 1:24,000 scale USGS topographic maps.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile that represents the Susquehanna River drainage basin. This file was delineated in 2006 from two sources: (1) Susquehanna River Basin Commission hand drawn basin boundary appearing on hardcopy 1:24,000 USGS topographic maps and (2) the Smallsheds dataset from the Pennsylvania Department of Environmental Protection (PADEP).
The Pennsylvania portion of the drainage basin was taken largely from the smallsheds dataset. The New York and Maryland portions were modified from the previous version of the SRB shapefile to match the SRBC hand drawn basin boundary from the USGS topo maps.
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| Susquehanna River Basin Commission SRBC |
2018 |
GIS raster datasets displaying Topographic Wetness Index (TWI) for Adams, Cumberland, Dauphin, Franklin, Lancaster, and York Counties, PA. The TWI rasters were derived from 2016 LiDAR for Dauphin County, 2015 LiDAR for Lancaster and York Counties, and 2006-08 LiDAR for Adams, Cumberland, and Franklin Counties. The TWI rasters were derived from 2015 LiDAR for Lancaster and York Counties and 2006-08 LiDAR for Adams and Franklin Counties. The TauDEM extension (D-Infinity tools) for ArcMap was used to create flow direction, slope, and contributing area rasters. TWI was then calculated using the following equation: Ln (Contributing Area/Slope). The methodology was described by Cody Fink in his 2013 thesis entitled Dynamic Soil Property Change in Response to Natural Gas Development in Pennsylvania. TWI results in a dimensionless raster and should be displayed using a red (low values representing no flow) to blue (high, representing high probability flowpaths) color gradient. TWI results vary depending on raster size and analysis options so value thresholds for probability-based overland flowpaths for water should be field verified.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile of the Upper Susquehanna Subbasin. This file was created from the 2006 subbasin.shp SRBC dataset.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polyline shapefile depicting water trails in the Susquehanna River Basin. The Pennsylvania Fish and Boat Commission defines water trails as recreational corridors between specific locations that can be used for both single day and multiple day trips. They provide safe access to and information about Pennsylvania's waterways while also providing connections to our history, ecology, geology, heritage and wildlife. There are 10 water trails which cover the following 8 rivers: Susquehanna River (lower, middle, & north), West Branch Susquehanna River, Juniata River, Raystown Branch Juniata River, Swatara Creek, Chemung River, Conodoguinet Creek, and Conestoga River.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile that includes approximately 1:24,000 scale watersheds in the Susquehanna River Basin (2006 boundary). This file was created by merging three small watershed datasets; (1) Smallsheds from the Pennsylvania Department of Environmental Protection (PADEP); (2) Maryland Hydrologic Unit Code (HUC) 14 digit watersheds; (3) 1:24,000 scale watersheds from the United States Geologic Survey (USGS). Pennsylvania watersheds are delineated at 1:24,00 while New York and Maryland watersheds are delineated mostly at 1:24,000 but some were delineated at larger or smaller scale. Additionally, the boundary between the Upper Susq. and Chemung Subbasins was updated to match the SRBC delineated subbasin line that exists on 1:24,000 scale USGS topographic maps.
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| Susquehanna River Basin Commission SRBC |
2006 |
Polygon shapefile of the West Branch Susquehanna Subbasin. This file was created from the 2006 subbasin.shp SRBC dataset.
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| Susquehanna River Basin Commission SRBC |
2004 |
This data layer depicts a subset of protected lands information for the Commonwealth of Pennsylvania. The data for all of the Protected Lands Inventory layers was collected from the 1998 PA GAP Analysis Program's Managed Lands data layer as well as from hard copy and digital data provided by land trusts and local governments.
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| The Conservation Fund |
2004 |
This data layer depicts a subset of protected lands information for the Commonwealth of Pennsylvania. The data for all of the Protected Lands Inventory layers was collected from the 1998 PA GAP Analysis Program's Managed Lands data layer as well as from hard copy and digital data provided by land trusts and local governments.
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| The Conservation Fund |
2004 |
This data layer depicts a subset of protected lands information for the Commonwealth of Pennsylvania. The data for all of the Protected Lands Inventory layers was collected from the 1998 PA GAP Analysis Program's Managed Lands data layer as well as from hard copy and digital data provided by land trusts and local governments.
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| The Conservation Fund |
2010 |
PAMAP Program Cycle 1/DVRPC 2005 Digital Orthoimagery High Resolution Orthoimage (2003 - 2006) - cached mapservice
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| The Pennsylvania State University |
1996 |
Agricultural security areas from LLRWS data.
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| The Pennsylvania State University |
2012 |
Pennsylvania bear harvest by county boundary 2003 - 2010
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| The Pennsylvania State University |
2010 |
This dataset is intended to illustrate the potential for biofuel production in the Chesapeake Bay, in a manner that does not compete with current food or fiber production. This data was used in support of a Chesapeake Bay Commission report titled: "Chesapeake Biofuel Policies: Balancing Energy, Economy and Environment" published in 2010.
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| The Pennsylvania State University |
2010 |
This layer shows potential land area for the production of biofuel feedstocks, such as winter barley, winter rye and switchgrass. Areas and corresponding crop production totals are aggregated by HUC6 boundaries for the Chesapeake Bay.
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| The Pennsylvania State University |
2021 |
The goal of this project is to apply the latest in remote sensing technology to map the Lake Erie shoreline in Pennsylvania. Pennsylvania has 77 miles of Lake Erie shoreline contanined entirely within Erie County. Most of the shoreline consists of bluff geomorphologies ranging in height from 5 to 180 feet above the lake level. A bluff is a high bank or bold headland with a broad precipitous cliff face overlooking a lake or sea. Notable exceptions include the mouths of major tributaries and Presque Isle, adjacent to the City of Erie. Nearly all of the shoreline is designated as Bluff Recession Hazard Areas (BRHA) under the framework established in the Bluff Recession and Setback Act (the Act) and companion regulations in Pa. Code Title 25, Chapter 85. Municipalities having BRHAs designated within their jurisdictions are required to enact specific setback ordinances relating to construction and development activities occurring within the BRHAs.The bluff crest is the edge of the bluff.
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| The Pennsylvania State University |
2021 |
The goal of this project is to apply the latest in remote sensing technology to map the Lake Erie shoreline in Pennsylvania. Pennsylvania has 77 miles of Lake Erie shoreline contanined entirely within Erie County. Most of the shoreline consists of bluff geomorphologies ranging in height from 5 to 180 feet above the lake level. A bluff is a high bank or bold headland with a broad precipitous cliff face overlooking a lake or sea. Notable exceptions include the mouths of major tributaries and Presque Isle, adjacent to the City of Erie. Nearly all of the shoreline is designated as Bluff Recession Hazard Areas (BRHA) under the framework established in the Bluff Recession and Setback Act (the Act) and companion regulations in Pa. Code Title 25, Chapter 85. Municipalities having BRHAs designated within their jurisdictions are required to enact specific setback ordinances relating to construction and development activities occurring within the BRHAs.The bluff crest is the edge of the bluff.
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| The Pennsylvania State University |
2021 |
The goal of this project is to apply the latest in remote sensing technology to map the Lake Erie shoreline in Pennsylvania. Pennsylvania has 77 miles of Lake Erie shoreline contanined entirely within Erie County. Most of the shoreline consists of bluff geomorphologies ranging in height from 5 to 180 feet above the lake level. A bluff is a high bank or bold headland with a broad precipitous cliff face overlooking a lake or sea. Notable exceptions include the mouths of major tributaries and Presque Isle, adjacent to the City of Erie. Nearly all of the shoreline is designated as Bluff Recession Hazard Areas (BRHA) under the framework established in the Bluff Recession and Setback Act (the Act) and companion regulations in Pa. Code Title 25, Chapter 85. Municipalities having BRHAs designated within their jurisdictions are required to enact specific setback ordinances relating to construction and development activities occurring within the BRHAs.The bluff crest is the edge of the bluff.
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| The Pennsylvania State University |
2001 |
The aerial photos were captured in the Spring 2001 by LR Kimball Aerial Photography for Cambria County. These photos were given to the Donald W. Hamer Center for Maps and Geospatial Information in 2017. The 1414 photos were flown at four different scales (1:500, 1:600, 1:1000, 1:2000) with varied coverage areas of Cambria County. These were scanned at 800dpi on an Epson Expression 11000 scanner by the Donald W. Hamer Center for Maps and Geospatial Information staff. This collection lacked an index. To generate approximate center points, photos were aligned and georeferenced in three dimensions using Agisoft Metashape and using a minimum number of ground control points, typically between 5 and 12 depending on the size of the project. Estimates of each camera pose, including geographic coordinates, were then exported to create a digital index for the collection. This digital index is intended to facilitate discovery and access, but not for research purposes.
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| The Pennsylvania State University |
1996 |
Major watershed boundaries for the Chesapeake Bay basin.
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| The Pennsylvania State University |
1996 |
Boundaries for the states in the Chesapeake Bay region.
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| The Pennsylvania State University |
2010 |
Pennsylvania deer harvest by county boundary 2003
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| The Pennsylvania State University |
2012 |
Pennsylvania deer harvest by Wildlife Management Units 2004 - 2012
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| The Pennsylvania State University |
2021 |
The Erie PA Hydrology mapping will consist of the following:
1. Island – land areas within rivers and water bodies.
2. Marsh_Swamp – marshes, swamps and areas of intermittent water.
3. River_Poly – double line streams that are wider than 8 feet wide.
4. Streams – single line stream less than 8 feet wide.
5. Waterbody – a quarter acre ponds larger than 200 sq. ft. within 200 feet. of the bluff line and half an acre beyond 200 feet.
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| The Pennsylvania State University |
2021 |
The Erie PA Watershed mapping will consist of the following hydrological units i.e. watersheds and subwatersheds covering river systems draining large hydrologic unit regions, all attributed by a standard nomenclature. A hydrologic unit is a drainage area delineated to nest in a multi-level, hierarchical drainage system. Its boundaries are defined by hydrographic and topographic criteria that delineate an area of land upstream from a specific point on a river, stream or similar surface waters. A hydrologic unit can accept surface water directly from upstream drainage areas, and indirectly from associated surface areas such as remnant, non-contributing, and diversions to form a drainage area with single or multiple outlet points. Hydrologic units are only synonymous with classic watersheds when their boundaries include all the source area contributing surface water to a single defined outlet point. This dataset has gone through extensive quality review process to ensure accuracy and compliance to the Federal Standard for Delineation of Hydrologic Unit Boundaries (http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/water/watersheds/?cid=nrcs143_021630)
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| The Pennsylvania State University |
2021 |
The Erie PA Watershed mapping will consist of the following hydrological units i.e. watersheds and subwatersheds covering river systems draining large hydrologic unit regions, all attributed by a standard nomenclature. A hydrologic unit is a drainage area delineated to nest in a multi-level, hierarchical drainage system. Its boundaries are defined by hydrographic and topographic criteria that delineate an area of land upstream from a specific point on a river, stream or similar surface waters. A hydrologic unit can accept surface water directly from upstream drainage areas, and indirectly from associated surface areas such as remnant, non-contributing, and diversions to form a drainage area with single or multiple outlet points. Hydrologic units are only synonymous with classic watersheds when their boundaries include all the source area contributing surface water to a single defined outlet point. This dataset has gone through extensive quality review process to ensure accuracy and compliance to the Federal Standard for Delineation of Hydrologic Unit Boundaries (http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/water/watersheds/?cid=nrcs143_021630)
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| The Pennsylvania State University |
1996 |
In an effort to expedite the permit review process for Water
Obstruction and Encroachment Applications, the Pennsylvania Department of
Environmental Protection has initiated a plan to replace hard-copy maps
with digital GIS sets. The project is referred to as the 105 Spatial Data
System /8105SDS/9
Pennsylvania river floodplains and coastal floodplains are two of many
spatial data sets that were used in the 105SDS project. As a result of work
completed by Law Environmental, Inc. on the statewide low-level radioactive
waste siting project, DEP received two coverages depicting river and
coastal floodplains. However, due to the process used in constructing these
data sets, there were many areas throughout the state in which floodplains
were not digitized. The primary purpose of this task was to complete the
digital floodplain mapping in these areas.
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| The Pennsylvania State University |
1985 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. Most of the imagery were acquired in 2001. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
1985 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. Most of the imagery were acquired in 2001. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
1985 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. Most of the imagery were acquired in 2001. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
1985 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Fractional vegetation cover for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. Values range from 0 to 100, use of integer rather than decimal values reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. Most of the imagery were acquired in 2001. The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2019 |
Historical markers at the University Park campus and other Penn State locations across the Commonwealth call attention to the University's rich tradition of achievement in higher education and in service to society. A formal series of blue-and-white markers was launched in 1989 by the Penn State Alumni Association and the Office of Strategic Communications. The Alumni Association funds the project while the Strategic Communications office provides management services and coordinates the installation and maintenance of the markers with the Office of Physical Plant. The markers in general commemorate events and locations of broad importance to the intellectual and scientific development of Penn State as one of America's leading public universities.
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| The Pennsylvania State University |
1985 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
1985 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
1985 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2000 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1999 to 2002, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
1985 |
Impervious surface area for Pennsylvania was estimated from Thematic Mapper data using algorithms developed by Dr. Toby Carlson. The Value attribute indicates percentage of the 25 meter grid cell that is impervious and range from 0 to 100 and use integer rather than decimal values for reduced storage volume. Date of the imagery ranged from 1985 to 1987, availability depended on extent of cloud cover at time of acquisition. All images were collected for the late Spring or Summer months (May-August). The Pennsylvania Department of Transportation supported the construction of the impervious surface data, with technical assistance from Eric Warner and Deborah Slawson.
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| The Pennsylvania State University |
2012 |
Impervious surfaces data is used for modelling the sources and amount run-off into Lake Erie. Woolpert's automated land cover feature extraction processes was used to perform impervious surface delineation. Woolpert utilized commercial off-the-shelf (COTS) remote sensing software, proprietary software and applications to perform automated feature analysis incorporating imagery, LiDAR and other ancillary vector data. The automated processes replace the need to manually digitize features.
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| The Pennsylvania State University |
2012 |
2012 Lake Erie Drainage Area (Erie, PA) Digital Orthoimagery/LiDAR Project - In the fall of 2012, Woolpert obtained new aerial LiDAR covering the entire project area (512 square miles). The aerial LiDAR was acquired at a point density average of 1-meter with final products comprised of LAS (ground and above ground points). The aerial LiDAR was collected during leaf-off conditions during the fall 2012 flying season (November). The LiDAR is being delivered as a project area wide dataset, consisting of 2,500' x 2,500' tiles (which matches the ortho tiling system). Adjacent flight lines overlap by an average of 30 percent. LiDAR was collected with Leica ALS LiDAR Systems. The file naming convention is as follows: xxxyyy (Pennsylvania North Zone); Please note that xxx and yyy represent the easting and northing coordinates (respectively) in state plane feet. Each LiDAR file is approximately 40 megabytes in size. Ownership of the data products resides with Penn State and the Pennsylvania Department of Environmental Protection. The LiDAR data will be utilized for the rectification of aerial imagery to produce 1”=100’ scale ortho-imagery with a 6-inch pixel resolution. The LiDAR data will also be used as a component during the future delineation of project area wide impervious surfaces (using remote sensing techniques).
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| The Pennsylvania State University |
2012 |
2012 Lake Erie Drainage Area (Erie, PA) Digital Orthoimagery/LiDAR Project - In the fall of 2012, Woolpert obtained new aerial LiDAR covering the entire project area (512 square miles). The aerial LiDAR was acquired at a point density average of 1-meter with final products comprised of LAS (ground and above ground points). The aerial LiDAR was collected during leaf-off conditions during the fall 2012 flying season (November). The LiDAR is being delivered as a project area wide dataset, consisting of 2,500' x 2,500' tiles (which matches the ortho tiling system). Adjacent flight lines overlap by an average of 30 percent. LiDAR was collected with Leica ALS LiDAR Systems. The file naming convention is as follows: xxxyyy (Pennsylvania North Zone); Please note that xxx and yyy represent the easting and northing coordinates (respectively) in state plane feet. Each LiDAR file is approximately 40 megabytes in size. Ownership of the data products resides with Penn State and the Pennsylvania Department of Environmental Protection. The LiDAR data will be utilized for the rectification of aerial imagery to produce 1”=100’ scale ortho-imagery with a 6-inch pixel resolution. The LiDAR data will also be used as a component during the future delineation of project area wide impervious surfaces (using remote sensing techniques).
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| The Pennsylvania State University |
2012 |
2012 Lake Erie Drainage Area (Erie, PA) Digital Orthoimagery Project - In the fall of 2012, Woolpert obtained new aerial Orthoimagery covering the entire project area (512 square miles). The aerial Orthoimagery was collected during leaf-off conditions during the fall 2012 flying season (November) at 1"=100' scale with a 6-inch pixel resolution. The Orthoimagery is being delivered as a project area wide dataset, consisting of 2,500' x 2,500' tiles. The file naming convention is as follows: xxxyyy (Pennsylvania North Zone); Please note that xxx and yyy represent the easting and northing coordinates (respectively) in state plane feet. Ownership of the data products resides with Penn State and the Pennsylvania Department of Environmental Protection.
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| The Pennsylvania State University |
2012 |
2012 Lake Erie Drainage Area (Erie, PA) Digital Orthoimagery Project - In the fall of 2012, Woolpert obtained new aerial Orthoimagery covering the entire project area (512 square miles). The aerial Orthoimagery was collected during leaf-off conditions during the fall 2012 flying season (November) at 1"=100' scale with a 6-inch pixel resolution. The Orthoimagery is being delivered as a project area wide dataset, consisting of 2,500' x 2,500' tiles. The file naming convention is as follows: xxxyyy (Pennsylvania North Zone); Please note that xxx and yyy represent the easting and northing coordinates (respectively) in state plane feet. Ownership of the data products resides with Penn State and the Pennsylvania Department of Environmental Protection.
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| The Pennsylvania State University |
2017 |
Lake Erie Watershed 2015 Ortho/LiDAR/Hydro Project will consist the following: • New project – wide 1”=100’ scale color digital orthoimagery (with a 6-inch pixel resolution) • New project wide 0.7-meter LiDAR (average point density) • New project wide hydrology • Crest Delineation This task is for a high resolution data set of lidar covering approximately 512 square miles of the Lake Erie Shoreline, PA. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, 2.5' pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, 8-bit intensity images, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. To better understand one of the state’s most vital natural resources and accurately plan for the future, the Pennsylvania Department of Environmental Protection (PADEP), through grant funding provided to the Pennsylvania Sea Grant (PASG) College Program, partnered with Woolpert to acquire imagery and lidar data for the entire Pennsylvania Lake Erie Watershed and all 77 miles of shoreline. - See more at: http://www.xyht.com/aerialuas/heights-april-2017-mapping-the-pennsylvania-lake-erie-watershed/#sthash.n3pVphR6.dpuf
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| The Pennsylvania State University |
2017 |
Lake Erie Watershed 2015 Ortho/LiDAR/Hydro Project will consist the following: • New project – wide 1”=100’ scale color digital orthoimagery (with a 6-inch pixel resolution) • New project wide 0.7-meter LiDAR (average point density) • New project wide hydrology • Crest Delineation This task is for a high resolution data set of lidar covering approximately 512 square miles of the Lake Erie Shoreline, PA. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, 2.5' pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, 8-bit intensity images, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. To better understand one of the state’s most vital natural resources and accurately plan for the future, the Pennsylvania Department of Environmental Protection (PADEP), through grant funding provided to the Pennsylvania Sea Grant (PASG) College Program, partnered with Woolpert to acquire imagery and lidar data for the entire Pennsylvania Lake Erie Watershed and all 77 miles of shoreline. - See more at: http://www.xyht.com/aerialuas/heights-april-2017-mapping-the-pennsylvania-lake-erie-watershed/#sthash.n3pVphR6.dpuf
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| The Pennsylvania State University |
2012 |
TILE INDEX - 2012 Lake Erie Drainage Area (Erie, PA) Digital Orthoimagery Project - In the fall of 2012, Woolpert obtained new aerial Orthoimagery covering the entire project area (512 square miles). The aerial Orthoimagery was collected during leaf-off conditions during the fall 2012 flying season (November) at 1"=100' scale with a 6-inch pixel resolution. The Orthoimagery is being delivered as a project area wide dataset, consisting of 2,500' x 2,500' tiles. The file naming convention is as follows: xxxyyy (Pennsylvania North Zone); Please note that xxx and yyy represent the easting and northing coordinates (respectively) in state plane feet. Ownership of the data products resides with Penn State and the Pennsylvania Department of Environmental Protection.
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| The Pennsylvania State University |
2017 |
Lake Erie Watershed 2015 Ortho/LiDAR/Hydro Project will consist the following: • New project – wide 1”=100’ scale color digital orthoimagery (with a 6-inch pixel resolution) • New project wide 0.7-meter LiDAR (average point density) • New project wide hydrology • Crest Delineation This task is for a high resolution data set of lidar covering approximately 512 square miles of the Lake Erie Shoreline, PA. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, 2.5' pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, 8-bit intensity images, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. To better understand one of the state’s most vital natural resources and accurately plan for the future, the Pennsylvania Department of Environmental Protection (PADEP), through grant funding provided to the Pennsylvania Sea Grant (PASG) College Program, partnered with Woolpert to acquire imagery and lidar data for the entire Pennsylvania Lake Erie Watershed and all 77 miles of shoreline. - See more at: http://www.xyht.com/aerialuas/heights-april-2017-mapping-the-pennsylvania-lake-erie-watershed/#sthash.n3pVphR6.dpuf
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| The Pennsylvania State University |
2017 |
Lake Erie Watershed 2015 Ortho/LiDAR/Hydro Project will consist the following: • New project – wide 1”=100’ scale color digital orthoimagery (with a 6-inch pixel resolution) • New project wide 0.7-meter LiDAR (average point density) • New project wide hydrology • Crest Delineation This task is for a high resolution data set of lidar covering approximately 512 square miles of the Lake Erie Shoreline, PA. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, 2.5' pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, 8-bit intensity images, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. To better understand one of the state’s most vital natural resources and accurately plan for the future, the Pennsylvania Department of Environmental Protection (PADEP), through grant funding provided to the Pennsylvania Sea Grant (PASG) College Program, partnered with Woolpert to acquire imagery and lidar data for the entire Pennsylvania Lake Erie Watershed and all 77 miles of shoreline. - See more at: http://www.xyht.com/aerialuas/heights-april-2017-mapping-the-pennsylvania-lake-erie-watershed/#sthash.n3pVphR6.dpuf
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| The Pennsylvania State University |
2017 |
Lake Erie Watershed 2015 Ortho/LiDAR/Hydro Project will consist the following: • New project – wide 1”=100’ scale color digital orthoimagery (with a 6-inch pixel resolution) • New project wide 0.7-meter LiDAR (average point density) • New project wide hydrology • Crest Delineation This task is for a high resolution data set of lidar covering approximately 512 square miles of the Lake Erie Shoreline, PA. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, 2.5' pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, 8-bit intensity images, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. To better understand one of the state’s most vital natural resources and accurately plan for the future, the Pennsylvania Department of Environmental Protection (PADEP), through grant funding provided to the Pennsylvania Sea Grant (PASG) College Program, partnered with Woolpert to acquire imagery and lidar data for the entire Pennsylvania Lake Erie Watershed and all 77 miles of shoreline. - See more at: http://www.xyht.com/aerialuas/heights-april-2017-mapping-the-pennsylvania-lake-erie-watershed/#sthash.n3pVphR6.dpuf
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| The Pennsylvania State University |
2017 |
Lake Erie Watershed 2015 Ortho/LiDAR/Hydro Project will consist the following: • New project – wide 1”=100’ scale color digital orthoimagery (with a 6-inch pixel resolution) • New project wide 0.7-meter LiDAR (average point density) • New project wide hydrology • Crest Delineation This task is for a high resolution data set of lidar covering approximately 512 square miles of the Lake Erie Shoreline, PA. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, 2.5' pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, 8-bit intensity images, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. To better understand one of the state’s most vital natural resources and accurately plan for the future, the Pennsylvania Department of Environmental Protection (PADEP), through grant funding provided to the Pennsylvania Sea Grant (PASG) College Program, partnered with Woolpert to acquire imagery and lidar data for the entire Pennsylvania Lake Erie Watershed and all 77 miles of shoreline. - See more at: http://www.xyht.com/aerialuas/heights-april-2017-mapping-the-pennsylvania-lake-erie-watershed/#sthash.n3pVphR6.dpuf
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| The Pennsylvania State University |
2017 |
Lake Erie Watershed 2015 Ortho/LiDAR/Hydro Project will consist the following: • New project – wide 1”=100’ scale color digital orthoimagery (with a 6-inch pixel resolution) • New project wide 0.7-meter LiDAR (average point density) • New project wide hydrology • Crest Delineation This task is for a high resolution data set of lidar covering approximately 512 square miles of the Lake Erie Shoreline, PA. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, 2.5' pixel raster DEMs of the bare-earth surface in ERDAS IMG Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, 8-bit intensity images, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. To better understand one of the state’s most vital natural resources and accurately plan for the future, the Pennsylvania Department of Environmental Protection (PADEP), through grant funding provided to the Pennsylvania Sea Grant (PASG) College Program, partnered with Woolpert to acquire imagery and lidar data for the entire Pennsylvania Lake Erie Watershed and all 77 miles of shoreline. - See more at: http://www.xyht.com/aerialuas/heights-april-2017-mapping-the-pennsylvania-lake-erie-watershed/#sthash.n3pVphR6.dpuf
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| The Pennsylvania State University |
2012 |
TILE INDEX - 2012 Lake Erie Drainage Area (Erie, PA) Digital Orthoimagery Project - In the fall of 2012, Woolpert obtained new aerial Orthoimagery covering the entire project area (512 square miles). The aerial Orthoimagery was collected during leaf-off conditions during the fall 2012 flying season (November) at 1"=100' scale with a 6-inch pixel resolution. The Orthoimagery is being delivered as a project area wide dataset, consisting of 2,500' x 2,500' tiles. The file naming convention is as follows: xxxyyy (Pennsylvania North Zone); Please note that xxx and yyy represent the easting and northing coordinates (respectively) in state plane feet. Ownership of the data products resides with Penn State and the Pennsylvania Department of Environmental Protection.
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| The Pennsylvania State University |
2018 |
Land Cover Change by Pennsylvania County, Includes change from 1992 - 2011, 2001 - 2011, 2005 - 2011
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| The Pennsylvania State University |
2018 |
Land Cover Change by Pennsylvania Major River Basin change includes 1992 - 2011, 2001 - 2011, 2005 - 2011, Change is focused on Percent Forest, Percent Agriculture, Percent Developed
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| The Pennsylvania State University |
2018 |
Land Cover Change by Pennsylvania Physiographic Provinces change includes 1992 - 2011, 2001 - 2011, 2005 - 2011, Change is focused on Percent Forest, Percent Agriculture, Percent Developed
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| The Pennsylvania State University |
2018 |
Land Cover Change by Pennsylvania Physiographic Sections change includes 1992 - 2011
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| The Pennsylvania State University |
1996 |
National Park Service information used to establish landmark
boundaries on 1:24,000 USGS topographic maps.
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| The Pennsylvania State University |
2017 |
A Level 1 Landscape Assessment using spatial analysis was conducted to characterize all mapped wetlands from the National Wetland Inventory (NWI) within the Commonwealth of Pennsylvania, USA. Wetland sites, comprising more than 168,000 polygons, were defined as 1-km radius landscape circles around the center point of each wetland polygon. Landscape composition was summarized by extracting information of a set of patches of the same type (i.e., a NLCD land use class) within each circle. The following NLCD land use classes were studied: (1) water, (2) developed, open space, (3) developed, low intensity, (4) developed, medium intensity, (5) developed, high intensity, (6) barren, rock/clay/sand, (7) forest, (8) shrub, scrub, (9) pasture, hay, (10) cultivated crops, and (11) wetlands. In addition, four indicators of human activity -Total Core Area Index, Road Density, Landscape Development Intensity Index, and Impervious Surface- were quantified by integrating land cover and road network information.
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| The Pennsylvania State University |
1998 |
This polygon layer delineates the boundaries of 8 major watersheds in Pennsylvania that have shown through past vertebrate inventory collection records to effectively separate domains for some species of animals. The basins included are Lake Erie, the Allegheny, Ohio, Monongahela, Susquehanna, Delaware, Potomac and Genessee Rivers.
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| The Pennsylvania State University |
1996 |
Boundaries of fish hatcheries on 1:24,000 USGS topographic maps,
locations verified from the National Park Service list.
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| The Pennsylvania State University |
1996 |
Boundary information from the office of the Allegheny National Forest,
taken from 1:24,000 USGS topographic maps
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| The Pennsylvania State University |
1996 |
National Park Service list and boundaries from 1:24,000 USGS
topographic maps.
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| The Pennsylvania State University |
1996 |
Boundaries of National Wildlife Preserves in Pennsylvania.
Boundaries were provided by the United States Department of the Interior,
National Fish and Wildlife Service. Boundaries were digitized from delineations
on 1:24,000 USGS topographic maps.
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| The Pennsylvania State University |
1998 |
The connected network of streams and waterways of Pennsylvania are indicated as single lines in this coverage. Waterways are given connected topology to show the direction of flow from the headwaters of the stream through the watershed to the extent of the coverage. With ARC Network data can be used for watershed modelling
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| The Pennsylvania State University |
1998 |
The Terrabyte images are extractives from Landsat Thematic Mapper images through the SpectraPlex process. This process does not permit the full restoration of the original scene. The SpectraPlex process takes multiple bands of original image data and compresses them to 255 cluster units using isodata as a cluster mechanism. The result of the process is an image layer of cluster numbers with an accompanying table of band means by cluster number.
Color rendition: the shade set is LANDSCOP Portrayal (landscape directed spectral composite portrayal). Total of the visible bands is blue, total of the infrared bands is green, total of all bands modified to help distinguish conifers is red.
The images have been converted to ArcInfo Grid coverages, exported and transferred to compact disk. The original SpectraPlex images are approximately 54 megabytes and extend over the Pennsylvania borders. The images on the Terrabyte Images cd-rom have been clipped to the borders of Pennsylvania and cut by county in Pennsylvania.
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| The Pennsylvania State University |
2007 |
The 2005 land cover for Pennsylvania was created through a mix of
interpretation of remotely sensed data and use of ancillary data sources.
The date actually is a mid-point as the remotely sensed and ancillary data
are representative of the time period 2003-2007.
The coding is based on the Anderson Land Use/Land Cover system, where the
more descriptive detail in the category is reflected by a higher code value.
Further the coding is hierarchical so that each group can be related to other
codes within a general category. For example, in the Anderson system the general
classification of forest is a 4, a deciduous forest is 41, and so on. For a
description of the Anderson system see;
http://landcover.usgs.gov/pdf/anderson.pdf
This project was funded by The PA Department of Conservation and Natural Resources (DCNR)
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| The Pennsylvania State University |
2016 |
Buildings polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Parking lot polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
This line feature class depicts painted lines in parking lots. The lines can be representative of parking spaces and they can also be used to define no parking zones. They were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Major named road polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Minor access road polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Gravel, dirt and other minor unpaved road polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
The polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction. The sidewalks are classified by material (Pavers, Concrete, Bituminous).
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| The Pennsylvania State University |
2016 |
Buildings polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Parking lot polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
This line feature class depicts painted lines in parking lots. The lines can be representative of parking spaces and they can also be used to define no parking zones. They were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Major named road polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Minor access road polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
Gravel, dirt and other minor unpaved road polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
2016 |
This polygon feature class represents sidewalks. The sidewalks are classified by material (Pavers, Concrete, Bituminous). The polygons were originally created in 2014 using planimetric mapping based on 0.25 ft pixel aerial imagery. They are updated as necessary from as-built drawings resulting from new construction.
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| The Pennsylvania State University |
1985-1986 |
These aerial photos were captured in Spring 1985 and 1986. These photos were given to the Donald W. Hamer Center for Maps and Geospatial Information by the Penn State Office of the Physical Plant in 2017. The 450 photos cover: Allentown 1986, Altoona 1985, Beaver 1985, Berks 1985, Capitol 1985, Delaware 1985, Fayette 1985, Hazelton 1985, Hershey 1985, Mckeesport 1985, Mont Alto 1985, New Kensington 1985, Ogontz 1986, Schuylkill 1985, Scranton 1985, Shenango 1985, Wilkes-Barre 1985, York 1985, University Park 1985 campus airport, University Park 1985 Farm, University Park 1985, Rock Springs 1985, and Stone Valley 1985. The scales range from 1"=250' to 1"=350'. The original contractor for the collection of these images was Eastern Mapping Co. 250 Freeport Rd., Blawnox, PA 15238. An index of the location of the flight lines is included for each site.
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| The Pennsylvania State University |
2021 |
Buildings polygons were originally created in 1998 using planimetric mapping. Roof outlines are depicted as if they were on the ground. In 2004 Building CAD files were converted to GIS polygons.Buildings contain the following essential attributes:BLDG_NUM - Building NumberBLDG_NAME - Building Name
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| The Pennsylvania State University |
2021 |
Boundaries of Penn State owned properties
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| The Pennsylvania State University |
2021 |
Croswalks (line) on the Penn State campus
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| The Pennsylvania State University |
2021 |
Croswalks (polygon) on the Penn State campus
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| The Pennsylvania State University |
2020 |
Improvement Other than Building
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| The Pennsylvania State University |
2021 |
Major Roads on the Penn State campus
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| The Pennsylvania State University |
2020 |
Minor Roads on the Penn State campus
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| The Pennsylvania State University |
2021 |
This line feature class represents painted lines in University Park parking lots. The lines can be representative of parking spaces and they can also be used to define no parking zones.
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| The Pennsylvania State University |
2021 |
Parking spot locations on Penn State University Park campus
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| The Pennsylvania State University |
2021 |
This polygon feature class represents paved areas (miscellaneous) on Penn State Campus - University Park.
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| The Pennsylvania State University |
2020 |
Planting beds on the Penn State campus
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| The Pennsylvania State University |
2019 |
Recreation areas on the Penn State campus. includes Baseball, Basketball, Field Hockey, Football, Golf, Intramural, Soccer, Tennis, Track, Volleyball
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| The Pennsylvania State University |
2021 |
Recreation areas on the Penn State campus. includes Baseball, Basketball, Field Hockey, Football, Golf, Intramural, Soccer, Tennis, Track, Volleyball
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| The Pennsylvania State University |
2021 |
Sidewalks at University Park
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| The Pennsylvania State University |
2020 |
Unpaved Roads on the Penn State campus
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| The Pennsylvania State University |
2021 |
This polygon feature class represents walls on Penn State Campus - University Park
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| The Pennsylvania State University |
2017 |
Penn State Campus Imagery (caputured in 2017) includes Agricultural Research Center at Rock Springs
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| The Pennsylvania State University |
2016 |
Penn State Campus Imagery High Level Tile Index
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| The Pennsylvania State University |
2016 |
Penn State Campus Imagery Low Level Tile Index
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| The Pennsylvania State University |
2014 |
Penn State Campus Imagery (caputured in 2012)
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| The Pennsylvania State University |
2015 |
Penn State Campus Imagery (caputured in 2015) includes Stone Valley Recreation Area
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| The Pennsylvania State University |
2017 |
Penn State Campus Imagery (caputured in 2017) includes Agricultural Research Center at Rock Springs
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| The Pennsylvania State University |
2019 |
Penn State Campus Imagery (caputured in 2019)
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| The Pennsylvania State University |
2022 |
Penn State Campus Imagery (caputured in 2022)
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| The Pennsylvania State University |
2016 |
LiDAR and related products for the Penn State University Park Campus 2015. In April of 2015 Woolpert obtained new aerial LiDAR covering the entire project area (+/- 45 sq. miles). The aerial LiDAR was collected during leaf-off conditions at a point density average of 0.5-meter with products comprised of LAS (ground and above ground points) and DEM in IMG format (contains ground only points). The LiDAR is delivered using 1,250’ x 1,250' tiles (matches the ortho tiling system). Adjacent flight lines overlap by an average of 25 percent. LiDAR was collected with a Leica ALS70 LiDAR System. The LiDAR data was produced in the Pennsylvania State Plane NAD83 (2011), NAVD 88 in units of Survey Foot.
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| The Pennsylvania State University |
2017 |
LiDAR and related products for the Penn State University Park Campus 2017
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| The Pennsylvania State University |
2017 |
LiDAR and related products for the Penn State University Park Campus 2017
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| The Pennsylvania State University |
2019 |
LiDAR and related products for the Penn State University Park Campus 2017
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| The Pennsylvania State University |
2022 |
LAS files for the Penn State University Park Campus 2022
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| The Pennsylvania State University |
2016 |
LiDAR and related products for the Penn State University Park Campus 2015. In April of 2015 Woolpert obtained new aerial LiDAR covering the entire project area (+/- 45 sq. miles). The aerial LiDAR was collected during leaf-off conditions at a point density average of 0.5-meter with products comprised of LAS (ground and above ground points) and DEM in IMG format (contains ground only points). The LiDAR is delivered using 1,250’ x 1,250' tiles (matches the ortho tiling system). Adjacent flight lines overlap by an average of 25 percent. LiDAR was collected with a Leica ALS70 LiDAR System. The LiDAR data was produced in the Pennsylvania State Plane NAD83 (2011), NAVD 88 in units of Survey Foot.
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| The Pennsylvania State University |
1949 |
The aerial photos were captured in the Spring 1949. These photos were given to the Donald W. Hamer Center for Maps and Geospatial Information in 2021. The 177 photos were flown at a 1â€:9600’ scale covering portions of Centre County, PA, including the Penn State University Park campus and surrounding towns. These aerial images were obtained from 9"x9" film roll. This collection lacked an index. To generate approximate center points, photos were aligned and georeferenced in three dimensions using Agisoft Metashape and using a minimum number of ground control points, typically between 5 and 12 depending on the size of the project. Estimates of each camera pose, including geographic coordinates, were then exported to create a digital index for the collection. This digital index is intended to facilitate discovery and access, but not for research purposes.
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| The Pennsylvania State University |
1938 - 1980 |
Penn Pilot, a project sponsored by the Pennsylvania Geological Survey, is an online library of digital historical aerial photography for the Commonwealth of Pennsylvania. Using the interactive map provided on this website, you can browse, view, and download thousands of photos covering the Commonwealth from 1937 to 1942 and 1967 to 1972.
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| The Pennsylvania State University |
1998 |
Digitized boundaries of 104 major watersheds defined in the Pennsylvania state water plan. The State Water Plan, originally developed in the 1970s, divided Pennsylvania's major river basins into 20 smaller units (subbasins) for planning purposes. Most of these subbasins are further divided into watershed areas (designated "A", "B", "C" etc.) that range in size from about 100 to 1000 square miles.
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| The Pennsylvania State University |
1997 |
Boundaries of 9,895 watersheds in Pennsylvania indicated in the Pennsylvania gazetteer of streams. These boundaries enclose catchment areas for named streams officially recognized by the Board on Geographic Names and other unofficially named streams that flow through named hollows. ERRI extracted, reprojected and edgematched datasets for major watersheds produced by the Water Resources Division of the U.S. Geological Survey into this smallsheds coverage of the state of Pennsylvania.
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| The Pennsylvania State University |
2003 |
Index of USGS quarter-quadrangles (3.75 minute) covering Pennsylvania. The polygons are attributed with information pertaining to the USGS/PaGS Digital Orthophoto Quarter-Quad (DOQQ) imagery collection available for download through PASDA. Including dates of image acquisition for the complete NAPP II imagery collection (1993-1995) and imagery available as of April 2003 from the NAPP III collection (1999- ).
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| The Pennsylvania State University |
2025 |
This dataset is a compilation of airborne lidar derived digital elevation models for Pennsylvania, organized by HUC8 watershed boundaries and sampled at 3 m resolution. Source data was downloaded from the US Geological Survey National Map, primarily from the 2019 Pennsylvania 3D Elevation Program lidar survey, but supplemented with other available datasets where needed to ensure continuous coverage. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
2025 |
This dataset represents the upstream flow accumulation, or contributing drainage area, in units of square meters, calculated from the dataset “Pennsylvania 3m lidar digital elevation models 2019”, organized by HUC8 watershed. Flow accumulation was calculated using the “carve” approach in TopoToolbox (Schwanghart and Scherler, 2014). Flow accumulation does not account for incoming flow from outside HUC8 regions, and so should be used with caution when interpreting large trunk streams that cross HUC 8 regions. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025. Schwanghart, W., Scherler, D., 2014. TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences. Earth Surface Dynamics, 2, 1-7. https://doi.org/10.5194/esurf-2-1-2014
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| The Pennsylvania State University |
2025 |
Geomorphons (geomorphologic phonotypes) are landforms computed from a digital elevation model based on the principle of pattern recognition and the concept of computer line of sight (Jasiewicz and Stepinski 2013). This geomorphon map was calculated using ArcGIS Pro following the same approach as the dataset “Pennsylvania Geomorphon Landform Maps 2021”, but using the topographic dataset “Pennsylvania 3m lidar digital elevation models 2019”.
The final product includes 10 most common landforms: flat (FL - 1), peak (PK- 2), ridge (RI - 3), shoulder (SH - 4), spur (SP - 5), slope (SL -6), hollow (HL - 7), footslope (FS - 8), valley (VL - 9), and pit (PT - 10). This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
2025 |
This dataset represents local slope angle in units of degrees, calculated from the dataset “Pennsylvania 3m lidar digital elevation models 2019”, organized by HUC8 watershed. Slope was calculated using the Spatial Analyst Slope tool in ArcGIS Pro. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
1996 |
Location of active rail lines in Pennsylvania, digitized from
1:24,000 USGS topographic maps on a stable Mylar base.
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| The Pennsylvania State University |
1996 |
Watersheds of exceptional quality streams
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| The Pennsylvania State University |
2018 |
Land Cover by Breeding Bird Atlas Block based on National Land Cover Dataset 1992
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| The Pennsylvania State University |
2018 |
Land Cover by Breeding Bird Atlas Block based on PAMAP Program Land Cover for Pennsylvania 2005
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| The Pennsylvania State University |
2018 |
Land Cover by Breeding Bird Atlas Block based on National Land Cover Dataset 2011 Keyword RipariaLandCover
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| The Pennsylvania State University |
2004 |
This Website provides an interface for PA Breeding Bird Atlas staff and volunteers to find survey blocks, enter their findings and view results. I developed the component with which users can view a map of a block and print a high-quality PDF map. To print a map, enter the site and click the "Register" link on the left side of the window. Then click the "View Regions & Blocks" link, select a block, click its "Block Map" tab and choose whether to print an air photo or a topo map.
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| The Pennsylvania State University |
2004 |
This Website provides an interface for PA Breeding Bird Atlas staff and volunteers to find survey blocks, enter their findings and view results. I developed the component with which users can view a map of a block and print a high-quality PDF map. To print a map, enter the site and click the "Register" link on the left side of the window. Then click the "View Regions & Blocks" link, select a block, click its "Block Map" tab and choose whether to print an air photo or a topo map.
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| The Pennsylvania State University |
2004 |
This Website provides an interface for PA Breeding Bird Atlas staff and volunteers to find survey blocks, enter their findings and view results. I developed the component with which users can view a map of a block and print a high-quality PDF map. To print a map, enter the site and click the "Register" link on the left side of the window. Then click the "View Regions & Blocks" link, select a block, click its "Block Map" tab and choose whether to print an air photo or a topo map.
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| The Pennsylvania State University |
2018 |
Building Footprints from Individual Counties for the State of Pennsylvania. This dataset is incomplete and Building Footprints will be added as available. Building Footprints Include: Allegheny, Lancaster, Philadelphia
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| The Pennsylvania State University |
1996 |
Location of mined areas, including surface and deep coal and
non-coal mining.
Data incomplete, areas not mapped when screened at small scales during low
level radioactive waste siting analysis.
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| The Pennsylvania State University |
1996 |
Coastal floodplains from LLRWS data.
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| The Pennsylvania State University |
1996 |
Draft ecoregions from the EPA.
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| The Pennsylvania State University |
2000 |
This layer represents a potential habitat model for birds in Pennsylvania at 30 meter resolution. The model associates occurrence of suitable habitat with key environmental factors that can be mapped over the entire region. These key factors include vegetative land cover, presence of human activity, elevation, topographic position, wetland characteristics and stream size and proximity. Areas of potential species presence were tabulated based on current and historical information and a series of conditional statements proceeded using layers derived to depict the key factors on a landscape scale.
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| The Pennsylvania State University |
2000 |
This layer represents a potential habitat model for birds in Pennsylvania at 90 meter resolution. The model associates occurrence of suitable habitat with key environmental factors that can be mapped over the entire region. These key factors include vegetative land cover, presence of human activity, elevation, topographic position, wetland characteristics and stream size and proximity. Areas of potential species presence were tabulated based on current and historical information and a series of conditional statements proceeded using layers derived to depict the key factors on a landscape scale.
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| The Pennsylvania State University |
2000 |
This layer represents a potential habitat model for Fish in Pennsylvania at 90 meter resolution. The model associates occurrence of suitable habitat with key environmental factors that can be mapped over the entire region. These key factors include vegetative land cover, presence of human activity, elevation, topographic position, wetland characteristics and stream size and proximity. Areas of potential species presence were tabulated based on current and historical information and a series of conditional statements proceeded using layers derived to depict the key factors on a landscape scale.
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| The Pennsylvania State University |
2000 |
This layer represents a potential habitat model for Herpetilies in Pennsylvania at 30 meter resolution. The model associates occurrence of suitable habitat with key environmental factors that can be mapped over the entire region. These key factors include vegetative land cover, presence of human activity, elevation, topographic position, wetland characteristics and stream size and proximity. Areas of potential species presence were tabulated based on current and historical information and a series of conditional statements proceeded using layers derived to depict the key factors on a landscape scale.
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| The Pennsylvania State University |
2000 |
This layer represents a potential habitat model for Herpetilies in Pennsylvania at 90 meter resolution. The model associates occurrence of suitable habitat with key environmental factors that can be mapped over the entire region. These key factors include vegetative land cover, presence of human activity, elevation, topographic position, wetland characteristics and stream size and proximity. Areas of potential species presence were tabulated based on current and historical information and a series of conditional statements proceeded using layers derived to depict the key factors on a landscape scale.
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| The Pennsylvania State University |
2000 |
This layer represents a potential habitat model for Mammals in Pennsylvania at 30 meter resolution. The model associates occurrence of suitable habitat with key environmental factors that can be mapped over the entire region. These key factors include vegetative land cover, presence of human activity, elevation, topographic position, wetland characteristics and stream size and proximity. Areas of potential species presence were tabulated based on current and historical information and a series of conditional statements proceeded using layers derived to depict the key factors on a landscape scale.
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| The Pennsylvania State University |
2000 |
This layer represents a potential habitat model for Mammals in Pennsylvania at 90 meter resolution. The model associates occurrence of suitable habitat with key environmental factors that can be mapped over the entire region. These key factors include vegetative land cover, presence of human activity, elevation, topographic position, wetland characteristics and stream size and proximity. Areas of potential species presence were tabulated based on current and historical information and a series of conditional statements proceeded using layers derived to depict the key factors on a landscape scale.
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| The Pennsylvania State University |
1999 |
Coverage showing stewardship of managed conservation lands throughout the Commonwealth. Includes federal, state, county and privately owned lands including National and State Parks, Wildlife Refuges and Forests, county parks, and private conservancy lands
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| The Pennsylvania State University |
2021 |
Geomorphons (geomorphologic phonotypes) are landforms computed from a DEM based on the principle of pattern recognition and the concept of computer line of sight (Jasiewicz and Stepinski 2013). The geomorphon algorithm was executed using an open source package, r.geomorphon, under the GRASS GIS environment. The final product includes 10 most common landforms: flat (FL - 1), peak (PK- 2), ridge (RI - 3), shoulder (SH - 4), spur (SP - 5), slope (SL -6), hollow (HL - 7), footslope (FS - 8), valley (VL - 9), and pit (PT - 10). The county level 3-m LiDAR (Light Detection and Ranging) DEM produced by the PAMAP Program was used for geomorphon calculations. This dataset consists of two sets of geomorphon maps for all 67 counties of Pennsylvania. The first set of maps are geomorphons calculated using the default parameters (i.e. OR = 200 m, IR = 20 m, FT = 1, and FD = 0) adapted from the Chesapeake Conservancy (Baker et al. 2018). The second set of maps are geomorphons calculated using a dynamic zone-parameterization system (i.e. OR = ORzone, IR = 20 m, FT = 1, and FD = 0) based on zones and the average-valley-width (AVW) at drainage area of 26 km2 of each county. The ORs are 200m, 2*AVW, and 4*AVW for zones of headwater, large-valley, and extra-large-valley, respectively. The project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. August 2021.
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| The Pennsylvania State University |
1996 |
Location of inactive rail lines in Pennsylvania, digitized from
1:24,000 USGS topographic maps on a stable Mylar base.
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| The Pennsylvania State University |
2018 |
Pennsylvania Land Cover by Hydrologic Unit Code (HUC) 10, Based on NLCD 2011
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| The Pennsylvania State University |
2018 |
Pennsylvania Land Cover by Hydrologic Unit Code (HUC) 12, Based on NLCD 2011
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| The Pennsylvania State University |
2018 |
Pennsylvania Land Cover by Hydrologic Unit Code (HUC) 12, Based on NLCD 2011
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| The Pennsylvania State University |
2000 |
PALULC2000 is a statewide land cover map generated from Enhanced Thematic Mapper satellite data and three other ancillary data sources. It is an update to the MRLC data layer produced for the state in 1992.
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| The Pennsylvania State University |
1996 |
Major rivers as derived from Pennsylvania Dept. of
Transportation's streams database, which is organized by county.
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| The Pennsylvania State University |
1996 |
Point locations of oil and gas fields from Pennsylvania Department of
Environmental Resources, Bureau of Topographic and Geologic Survey /8PAGS/9 Well
Completion Maps /81:24,000 scale/9 and PAGS and Pennsylvania Department of
Environmental Protection Bureau of Mines well field data.
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| The Pennsylvania State University |
1996 |
Point locations of oil and gas wells from Pennsylvania Department of
Environmental Resources, Bureau of Topographic and Geologic Survey /8PAGS/9 Well
Completion Maps /81:24,000 scale/9 and PAGS and Pennsylvania Department of
Environmental Protection Bureau of Mines well field data.
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| The Pennsylvania State University |
2025 |
Boundary outlines of individual properties for the State of Pennsylvania. This dataset is incomplete and county parcels will be added as available. Attributes contain PIN ID, Source, and Date of Parcels
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| The Pennsylvania State University |
2009 |
This data-set contains public schools only for those counties that have abandoned coal mines. This data-set portrays the approximate location of Public Schools (Regular Elementary, Regular Elementary/Secondary, Occupational CTC, Special Education, Regular Secondary, Comprehensive CTC, Adult CTC, State Operated Educational Facility). The data set was created by first obtaining the street address of each school from the Pennsylvania Departmet of Education (www.edna.ed.state.pa.us). Addresses were then geocoded using ArcMap. Aerial photography was used to assist in the groundtruthing of the points.
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| The Pennsylvania State University |
1996 |
State designated scenic rivers.
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| The Pennsylvania State University |
2025 |
This dataset contains polygon shapefiles of the stream network with contributing area larger than 1 square kilometer in Pennsylvania, organized by HUC8 watershed. Flow paths are derived from the dataset “Pennsylvania 3m lidar flow accumulation 2019”, and stream width is modeled using the hydraulic geometry scaling of width and drainage area from Hack (1957). This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025..
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| The Pennsylvania State University |
2025 |
This dataset contains polyline shapefiles of the intersection between stream channels and valley walls, as calculated from the datasets “Pennsylvania Stream Polygons 2019” and “Pennsylvania Valley Bottom Polygons 2019”. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
2020 |
Street Centerlines from Individual Counties for the State of Pennsylvania. This dataset is incomplete and Street Centerlines will be added as available.
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| The Pennsylvania State University |
2025 |
This dataset contains polygon shapefiles of valley bottoms derived from the dataset “Pennsylvania Geomorphon LandformMaps 2019”. Valley bottoms are defined by geomorphon types Flat (FL-1), Footslope (FS-8), Valley (VL-9), and Pit (PT-10) within a buffer of 300 m on either side of the stream network in the dataset “Pennsylvania Stream Polygons 2019”. Final polygon boundaries were cleaned using iterative boundary expanding and shrinking, and regions smaller than 10,000 square meters were eliminated. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
2025 |
This dataset contains polygon shapefiles of segmented valley bottoms derived from the dataset “Pennsylvania Valley Bottom Polygons 2019”. Valley bottoms were segmented at 200 m intervals, and for each segment the following parameters were calculated and included: DrArea: Upstream drainage area (square meters) ValLength: Valley length (meters) StrLength: Total length of streams (meters) ValWidth: Average valley width, calculated as area divided by valley length (meters) StrWidth: Average total stream width, calculated as stream area divided by stream length (meters) AreaRatio: Stream area divided by area of valley (dimensionless) LngthRatio: Stream length divided by length of valley (dimensionless) Cnfinement: Stream-valley confinement ratio, a confinement index calculated as the length of the stream contact with valley walls divided by the total valley wall length (dimensionless) The calculations in this dataset derive from the datasets “Pennsylvania Valley Bottom Polygons 2019”, “Pennsylvania Stream Polygons 2019”, “Pennsylvania 3m lidar flow accumulation 2019”, and “Pennsylvania Stream Valley Intersections 2019”. To clean the dataset, valley segments were removed where: the valley length or stream length was less than 100 m; where the stream length to valley length ratio was less than 0.75, or where valley wall length divided by valley length was greater than 3. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
2020 |
GIS raster datasets displaying Topographic Wetness Index (TWI) for Pennsylvania by County. TWI raster datasets were derived from 2006-2008 LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum: NAD83, units: feet); blocks in the southern half are in Pennsylvania State Plane South. Raster spatial resolution is 9.6 ft (approximately 3 m).
The TWI, also called Compound Topographic Index (CTI) or Topographic Convergence Index (TCI), is a hydrological-based topographic index that describes the tendency of a cell or area to accumulate and retain water under steady-state conditions. TWI is defined as Ln(Contributing Area/Slope angle). It balances contributing areas capturing the tendency to receive water versus slope angles capturing the tendency to evacuate water. An automated procedure was developed in ArcMap® for the TWI computation. Contributing areas (i.e. cumulative contributing area per unit contour length) were determined based on the D-Infinity model proposed by Tarboton, D. (1997). Lengths were measured considering cell size and whether the direction is adjacent or diagonal. Land surface slope was computed using the Horn’s method.
The project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. August 2020.
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| The Pennsylvania State University |
2009 |
Point shapefiles of road/trail gates maintained by the PSU Forestland Mgmt. Office.
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| The Pennsylvania State University |
2009 |
Line shapefile of the roads constructed and maintained by the PSU Forestland Mgmt. Office located within PSU owned forestland.
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| The Pennsylvania State University |
2009 |
Line shapefile that is an enhanced version of the hydrology for Huntingdon County, PA. Enhancements are small springs and intermittent runs within the PSU Stone Valley Forest.
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| The Pennsylvania State University |
2009 |
Line shapefile of the Trails constructed and maintained by the PSU Forestland Mgmt. Office located within PSU owned forestland.
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| The Pennsylvania State University |
2020 |
Lidar, Hyperspectral Imagery, Orthoimagery for The Pennsylvania State University Stone Valley Experimental Forest.
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| The Pennsylvania State University |
2018 |
Based on Robbins et al., (1989) and Debinski & Holt, (2000) as well as long conversations with PA wildlife experts 100 meters was established as the distance into a forest that edge effects would influence the health of a forest. Many forest species are influenced by disturbance and either avoid or are less successful in edge forest areas.
To create this layer the land cover layer was reclassed to isolate forest classes (deciduous forest, mixed forest and coniferous forest) as well as wetlands into one Forest/Natural class and the remaining disturbed classes (urban, suburban, transitional and annual & perennial herbaceous). At this stage, wildlife experts were not convinced that road footprints were captured appropriately since the land cover missed most state and local roads. PennDOT roads (1996) were used to “burn in” or force the roads into the land cover layer. Once complete a distance function was used to isolate the first 100 meters into any forest as edge forest. The last step isolated the water class from the original land cover and ensured that it remained in the final core forest layer. The resulting raster layer has four classes: Core Forest, Edge Forest, Not Forest and Water.
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| The Pennsylvania State University |
2018 |
Based on Robbins et al., (1989) and Debinski & Holt, (2000) as well as long conversations with PA wildlife experts 100 meters was established as the distance into a forest that edge effects would influence the health of a forest. Many forest species are influenced by disturbance and either avoid or are less successful in edge forest areas.
To create this layer the land cover layer was reclassed to isolate forest classes (deciduous forest, mixed forest and coniferous forest) as well as wetlands into one Forest/Natural class and the remaining disturbed classes (urban, suburban, transitional and annual & perennial herbaceous). At this stage, wildlife experts were not convinced that road footprints were captured appropriately since the land cover missed most state and local roads. PA Tiger roads (2000) were used to “burn in” or force the roads into the land cover layer. Once complete a distance function was used to isolate the first 100 meters into any forest as edge forest. The last step isolated the water class from the original land cover and ensured that it remained in the final core forest layer. The resulting raster layer has four classes: Core Forest, Edge Forest, Not Forest and Water.
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| The Pennsylvania State University |
2018 |
Based on Robbins et al., (1989) and Debinski & Holt, (2000) as well as long conversations with PA wildlife experts 100 meters was established as the distance into a forest that edge effects would influence the health of a forest. Many forest species are influenced by disturbance and either avoid or are less successful in edge forest areas.
To create this layer the land cover layer (PAMAP2005) was reclassed to focus forest classes (deciduous forest, mixed forest and coniferous forest) as well as wetlands into one Forest/Natural class and the remaining disturbed classes (urban, suburban, transitional and annual & perennial herbaceous). At this stage, wildlife experts were not convinced that road footprints were captured appropriately since the land cover missed most state and local roads. PA Tiger roads (2006) were used to “burn in” or force the roads into the land cover layer. Once complete a distance function was used to isolate the first 100 meters into any forest as edge forest. The last step isolated the water class from the original land cover and ensured that it remained in the final core forest layer. The resulting raster layer has four classes: Core Forest, Edge Forest, Not Forest and Water.
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| The Pennsylvania State University |
2018 |
Based on Robbins et al., (1989) and Debinski & Holt, (2000) as well as long conversations with PA wildlife experts 100 meters was established as the distance into a forest that edge effects would influence the health of a forest. Many forest species are influenced by disturbance and either avoid or are less successful in edge forest areas.
To create this layer the land cover layer was reclassed to focus forest classes (deciduous forest, mixed forest and coniferous forest) as well as wetlands into one Forest/Natural class and the remaining disturbed classes (urban, suburban, transitional and annual & perennial herbaceous). At this stage, wildlife experts were not convinced that road footprints were captured appropriately since the land cover missed most state and local roads. PA Tiger roads (2009) were used to “burn in” or force the roads into the land cover layer. Once complete a distance function was used to isolate the first 100 meters into any forest as edge forest. The last step isolated the water class from the original land cover and ensured that it remained in the final core forest layer. The resulting raster layer has four classes: Core Forest, Edge Forest, Not Forest and Water.
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base land cover layer LCSTRAT1992 which was based on satellite imagery collected in the early 1990s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class while grouping agriculture, urban and suburban classes into a non-forest class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Not Forest and Water.
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base land cover layer NLCD2001 which was based on satellite imagery collected in the late 1990s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class while grouping agriculture, urban and suburban classes into a non-forest class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Non-Forest and Water.
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base PAMAP2005 land cover layer which was based on satellite imagery collected in the early 2000s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class while grouping agriculture, urban and suburban classes into a non-forest class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Not Forest and Water.
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base 2011 land cover layer (PANLCD_2011) which was based on satellite imagery collected in the late 2000s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class while grouping agriculture, urban and suburban classes into a non-forest class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Not Forest and Water.
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| The Pennsylvania State University |
2018 |
Originally created for the Pennsylvania Gap Project (Myers et al., 2000) land cover classification and interpretation was completed specifically for Pennsylvania to facilitate potential habitat modeling for all vertebrate species reported to breed within Pennsylvania following a modified Anderson Level 2 system (Anderson et al., 1976). The “Stratification” was our attempt to further classify suburban and urban areas by delineating based on urban reflectance from satellite imagery and road pattern from PennDOT roads (1994). The result allowed us to identify modified land covers such as forested or herbaceous suburban and forested or herbaceous urban hoping to use these to better capture areas as wildlife habitat for more generalist species.
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| The Pennsylvania State University |
2018 |
The National Land Cover Dataset is the result of a national effort for the United States to facilitate resource planning. The Pennsylvania portion was extracted for this project. For more information on the NLCD Program see: https://www.mrlc.gov/nlcd2001.
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| The Pennsylvania State University |
2018 |
Originally created for the PAMAP Project (Warner et al., 2007) land cover classification and interpretation was completed specifically for Pennsylvania to update potential habitat modeling for Pennsylvania and include interpretation to capture changes in urban and suburban development. Following a modified Anderson Level 2 system (Anderson et al., 1976) photo interpretation was expanded to further classify suburban/urban areas based on housing density and to better identify land uses such as golf courses and surface mines.
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| The Pennsylvania State University |
2018 |
The National Land Cover Dataset is the result of a national effort for the United States to facilitate resource planning. The Pennsylvania portion was extracted for this project. For more information on the NLCD Program see: https://www.mrlc.gov/nlcd2011.php
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base land cover layer LCSTRAT1992 which was based on satellite imagery collected in the early 1990s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class, urban and suburban classes into one developed class and agricultural classes into one agricultural class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Agriculture, Developed and Water.
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base land cover layer NLCD2001 which was based on satellite imagery collected in the late 1990s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class, urban and suburban classes into one developed class and agricultural classes into one agricultural class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Agriculture, Developed and Water.
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base PAMAP2005 land cover layer which was based on satellite imagery collected in the early 2000s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class, urban and suburban classes into one developed class and agricultural classes into one agricultural class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Agriculture, Developed and Water.
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| The Pennsylvania State University |
2018 |
This layer was created to facilitate ecological understanding by reclassifying the base 2011 land cover layer (PANLCD_2011) which was based on satellite imagery collected in the late 2000s. To better understand ecology condition using satellite collected land cover data it is often beneficial to focus on more specific land cover types. This layer groups all the forest classes into one forest class, urban and suburban classes into one developed class and agricultural classes into one agricultural class. This allows condition to be assessed using metrics, such as those tabulated using GIS tools found in Fragstats (McGarigal & Marks, 1995), to be consistently calculated within ecological units like watersheds and political units like counties. The resulting raster format layer has three classes: Forest, Agriculture, Developed and Water.
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| The Pennsylvania State University |
2018 |
Many land cover interpretations strive for an Anderson Level 2 (Anderson et al., 1976) classification. The important distinction splits Anderson Level 1 cover types such as Forest and Agriculture into more specific cover types such as Deciduous or Coniferous Forest and Row Crops or Pasture. These divisions provide more information when a modeling effort studies wildlife that favor specific forest types (Mahan et al., 2010) and agricultural models to predict watershed condition (Brooks et al., 2002). While this level of detail is beneficial in these instances many studies seek to model ecological condition where too much detail become challenging to understand. When ecological condition is the goal detailed classifications can produce confusing results, thus, it is common practice to reclassify the original land cover to better target a specified project goal. This version of LCSTRAT1992 targeted forest, suburban and urban classes as import groupings and kept the distinction between pasture and row crop. Several ecological condition projects used this layer as coarse scale step and then compared these results with site level data collected in the field.
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| The Pennsylvania State University |
2018 |
Many land cover interpretations strive for an Anderson Level 2 (Anderson et al., 1976) classification. The important distinction splits Anderson Level 1 cover types such as Forest and Agriculture into more specific cover types such as Deciduous or Coniferous Forest and Row Crops or Pasture. These divisions provide more information when a modeling effort studies wildlife that favor specific forest types (Mahan et al., 2010) and agricultural models to predict watershed condition (Brooks et al., 2002). While this level of detail is beneficial in these instances many studies seek to model ecological condition where too much detail can become challenging to understand. When ecological condition is the goal detailed classifications can produce confusing results, thus, it is common practice to reclassify the original land cover to better target a specified project goal. This version of NLCD2001 targeted forest, suburban and urban classes as import groupings and kept the distinction between pasture and row crop. Several ecological condition projects used this layer as coarse scale step and then compared these results with site level data collected in the field. In particular this layer was used to help plan the Pennsylvania’s second breeding bird atlas project (O’Connell et al., 2004).
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| The Pennsylvania State University |
2018 |
Many land cover interpretations strive for an Anderson Level 2 (Anderson et al., 1976) classification. The important distinction splits Anderson Level 1 cover types such as Forest and Agriculture into more specific cover types such as Deciduous or Coniferous Forest and Row Crops or Pasture. These divisions provide more information when a modeling effort studies wildlife that favor specific forest types (Mahan et al., 2010) and agricultural models to predict watershed condition (Brooks et al., 2002). While this level of detail is beneficial in these instances many studies seek to model ecological condition where too much detail can become challenging to understand. When ecological condition is the goal detailed classifications can produce confusing results, thus, it is common practice to reclassify the original land cover to better target a specified project goal. This version of the 2005 land cover targeted forest, suburban and urban classes as import groupings and kept the distinction between pasture and row crop. Several ecological condition projects used this layer as coarse scale step and then compared these results with site level data collected in the field.
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| The Pennsylvania State University |
2018 |
Many land cover interpretations strive for an Anderson Level 2 (Anderson et al., 1976) classification. The important distinction splits Anderson Level 1 cover types such as Forest and Agriculture into more specific cover types such as Deciduous or Coniferous Forest and Row Crops or Pasture. These divisions provide more information when a modeling effort studies wildlife that favor specific forest types (Mahan et al., 2010) and agricultural models to predict watershed condition (Brooks et al., 2002). While this level of detail is beneficial in these instances many studies seek to model ecological condition where too much detail can become challenging to understand. When ecological condition is the goal detailed classifications can produce confusing results, thus, it is common practice to reclassify the original land cover to better target a specified project goal. This version of the 2011 land cover (PANLCD_2011) targeted forest, suburban and urban classes as import groupings and kept the distinction between pasture and row crop. Several ecological condition projects used this layer as coarse scale step and then compared these results with site level data collected in the field.
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| The Pennsylvania State University |
1996 |
30-meter contours, digital elevation model for the geographic
area coverage of the Spring Creek Watershed
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| The Pennsylvania State University |
1996 |
File shows those United States Geological Survey 7.5-minute
quadrangles which overlay the geographic extent of the watershed.
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| The Pennsylvania State University |
1996 |
Basin area of the Spring Creek Watershed. Includes subbasins for
all tributaries within the Spring Creek Watershed.
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| The Pennsylvania State University |
1996 |
Geographic boundaries of the Spring Creek Watershed.
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| The Pennsylvania State University |
1996 |
Shows 1 km. buffer zone bounding the perimeter of the Spring Creek
Watershed.
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| The Pennsylvania State University |
1996 |
Shows minor civil divisions located in the Spring Creek Watershed
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| The Pennsylvania State University |
1996 |
Shows sources and locations of Polychlorinated biphenyls (PCBs)
and trichloroethylene pollutants in the Spring Creek Watershed.
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| The Pennsylvania State University |
1996 |
Roads and road network located in the Spring Creek Watershed.
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| The Pennsylvania State University |
1996 |
Soil types located in the Spring Creek Watershed. Taken from the
Centre County soils classification.
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| The Pennsylvania State University |
1996 |
STATSGO, state-wide soil coverage for areas within the Spring
Creek Watershed.
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| The Pennsylvania State University |
1996 |
Streams within the Spring Creek Watershed
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| The Pennsylvania State University |
1996 |
Shows wetlands within the Spring Creek Watershed. Wetlands
Classification reference numbers: RZUBH U PUBHn
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| The Pennsylvania State University |
1995 |
The aerial photos were captured in the Spring 1995. These photos were given to the Donald W. Hamer Center for Maps and Geospatial Information in 2015 by the Penn State Office of the Physical Plant. The 213 photos were flown at a 1"=300' scale covering State College, PA. These were scanned at 800dpi on an Epson Expression 11000 scanner by the Donald W. Hamer Center for Maps and Geospatial Information staff. The original contractor for these photos was Photogrammetric Data Services Inc. 2 Fairview Plaza, 5950 Fairview Rd, Suite 345, Charlotte, NC 28210 (704)553-7216.
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| The Pennsylvania State University |
1997 |
The aerial photos were captured in the Spring 1997. These photos were given to the Donald W. Hamer Center for Maps and Geospatial Information in 2015 by the Penn State Office of the Physical Plant. The 33 photos were flown at a 1":2400' scale covering Toftrees development in Patton Township, as part of the State College PA region. These were scanned at 800dpi on an Epson Expression 11000 scanner by the Donald W. Hamer Center for Maps and Geospatial Information staff. The original contractor of these images was Land & Mapping Services 506 Krebs Ave. Clearfield, PA 16830, 814-765-9370.
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| The Pennsylvania State University |
2000 |
various datasets on the Stone Valley Experimental Forest, managed by The Pennsylvania State University. Data includes: roads, contours, lakes, ponds, hydrology, landuse
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| The Pennsylvania State University |
2009 |
The most accurate polygon boundary of the PSU SFR Stone Valley Forest.
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| The Pennsylvania State University |
2010 |
The PSU Forestland Mgmt. Office contracted Purple Lizard Publishing to create this basemap.
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| The Pennsylvania State University |
2023 |
The PSU Forestland Mgmt. Office contracted Purple Lizard Publishing to create this basemap.
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| The Pennsylvania State University |
2009 |
Line shapefile of the rtrails constructed and maintained by the PSU Forestland Mgmt. Office located within Stone Valley Recreation Area
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| The Pennsylvania State University |
2010 |
Pennsylvania turkey harvest by Wildlife Management Units 2003 - 2009
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| The Pennsylvania State University |
2012 |
This data set includes unpaved road locations for Pennsylvania. - fields include length of road. Forestry roads not included.
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| The Pennsylvania State University |
2015 |
he 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.
Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.
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| U S Census Bureau |
2020 |
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
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| U S Census Bureau |
2020 |
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
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| U S Census Bureau |
2020 |
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
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| U S Census Bureau |
2020 |
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
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| U S Census Bureau |
2020 |
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
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| U S Census Bureau |
2020 |
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
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|
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| U S Census Bureau |
2020 |
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
Metadata
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|
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| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
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|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
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Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
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Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
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| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
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|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2009 |
The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent data set or the shapefiles can be combined to cover the whole nation.
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2010 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2010 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).
Metadata
|
Download
|
Preview
|
KMZ
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2010 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).
Metadata
|
Download
|
Preview
|
KMZ
|
Spreadsheet
|
GeoJSON
|
Add to ArcMap:
Image
or
Feature
|
Add to ArcGIS Pro
| More Options...
| U S Census Bureau |
2010 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).
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| U S Census Bureau |
2010 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).
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| U S Census Bureau |
2010 |
he TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2010 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county. For the 2010 Census, Rhode Island is the only State that did not participate in Phase 2 (the Voting District Project) of the Redistricting Data Program and no VTDs exist for this State in the 2010 Census data products. Note that only Montana and Oregon do not have complete coverage of VTDs for the 2010 Census.
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| U S Census Bureau |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2006 |
This table contains the values of all the metrics calculated for the subwatersheds in the Eastern Brook Trout Joint Venture brook trout assessment. The Eastern Brook Trout Joint Venture (EBTJV) is a partnership between state and federal agencies, nongovernmental conservation organizations, and academia. The EBTJV is focused on an effort to protect, restore, and enhance brook trout populations throughout the historic range of the Eastern brook trout. One of the goals of the EBTJV was to produce a subwatershed dataset indicating the current distribution of brook trout populations and their perturbations. This data will aid in quantifying problems, identifying information gaps, and setting restoration priorities. The subwatershed classifications are based on a combination of quantitative information collected by state agencies and qualitative judgment calls by local experts.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2021 |
Compartments are small subdivisions of forest land for purposes of orientation, administration and/or silvicultural operations on the Allegheny National Forest (ANF). Boundaries are defined by natural features, roads or artifically marked.
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| U S Department of Agriculture |
2011 |
The aim of landscape ecosystem classification and mapping is to distinguish appropritely sized ecosystems - useful and functional land units that differ significantly from one another in abiotic characteristics as well as their related biotic components. This subdivision of a large area into distinctive landscape ecosystems provides a much needed framework for integrated resource management planning; for biological conservation; and for comparison of differences in composition, occurrence, interactions, and productivity of plants and animals among ecosystems on the Allegheny National Forest (ANF).
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| U S Department of Agriculture |
2011 |
The aim of landscape ecosystem classification and mapping is to distinguish appropritely sized ecosystems - useful and functional land units that differ significantly from one another in abiotic characteristics as well as their related biotic components. This subdivision of a large area into distinctive landscape ecosystems provides a much needed framework for integrated resource management planning; for biological conservation; and for comparison of differences in composition, occurrence, interactions, and productivity of plants and animals among ecosystems on the Allegheny National Forest (ANF).
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| U S Department of Agriculture |
2011 |
The aim of landscape ecosystem classification and mapping is to distinguish appropritely sized ecosystems - useful and functional land units that differ significantly from one another in abiotic characteristics as well as their related biotic components. This subdivision of a large area into distinctive landscape ecosystems provides a much needed framework for integrated resource management planning; for biological conservation; and for comparison of differences in composition, occurrence, interactions, and productivity of plants and animals among ecosystems on the Allegheny National Forest (ANF).
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| U S Department of Agriculture |
2021 |
This dataset includes all cities, boroughs and most towns and villages within and surrounding the Allegheny National Forest (ANF).
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| U S Department of Agriculture |
2021 |
This Management Area dataset depicts areas of the Allegheny National Forest (ANF) having a unique emphasis, desired condition, list of suitable uses, and standards and guidelines.
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| U S Department of Agriculture |
2021 |
This data depicts roads (Forest, state, township, borough, and non-system) and trails within or in close proximity to the Allegheny National Forest.
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| U S Department of Agriculture |
2023 |
This dataset includes field sampled vegetation data about trees, fuels, down woody material, surface cover and understory vegetation by compartment/stand (vegetation polygon) on the Allegheny National Forest (ANF).
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| U S Department of Agriculture |
2021 |
This feature class depicts warrants and lots within and immediately outside the proclamation boundary of the Allegheny National Forest (ANF).
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2000 |
This map layer shows polygons of average annual precipitation in the
contiguous United States, for the climatological period 1961-1990.
Parameter-elevation Regressions on Independent Slopes Model (PRISM)
derived raster data is the underlying data set from which the polygons
and vectors were created. PRISM is an analytical model that uses point
data and a digital elevation model (DEM) to generate gridded estimates
of annual, monthly and event-based climatic parameters
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
1997 |
This data set includes all soil map units that are defined
as Hydric in the SSURGO data base. The SSURGO data set is a digital
soil survey and is the most detailed level of soil geographic data
developed by the National Cooperative Soil Survey. The information
was collected by digitizing maps, by compiling information onto a
planimetric correct base and digitizing, or by revising digitized
maps using remotely sensed and other information.
The SSURGO data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a full county
format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. Sometimes a special soil features layer (point
and line features) is included. This layer displays the location of
features too small to delineate at the mapping scale, but they are
large enough and contrasting enough to significantly influence use
and management. The soil map units are linked to attributes in the
Map Unit Interpretations Record relational data base, which gives
the proportionate extent of the component soils and their
properties.
|
The data set has been provided to Chester County Departments and
PASDA as an ArcView shapefile by the County of Chester, Department
of Computer and Information Services. The theme has been
reprojected to PA Stateplane (South) NAD83 from its original datum
in accordance with the base map standards of the County of Chester.
The County of Chester serves as the secondary organization in
providing this shapefile, as compared to its originator and primary
organization, the Natural Resources Conservation Service .
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| U S Department of Agriculture |
1997 |
This data set represents Prime Agricultural Soils as defined by
the County of Chester. The designation equates to any soil map unit
designation as 1 or 2 in the Nonirrigated Capability Class from the SSURGO
data base. The SSURGO data set is a digital soil survey and is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was collected by
digitizing maps, by compiling information onto a planimetric
correct base and digitizing, or by revising digitized maps using
remotely sensed and other information.
The SSURGO data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a full county
format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. Sometimes a special soil features layer (point
and line features) is included. This layer displays the location of
features too small to delineate at the mapping scale, but they are
large enough and contrasting enough to significantly influence use
and management. The soil map units are linked to attributes in the
Map Unit Interpretations Record relational data base, which gives
the proportionate extent of the component soils and their
properties.
The data set has been provided to Chester County Departments and PASDA as an
ArcView shapefile by the County of Chester, Department of Computer and
Information Services. The theme has been reprojected to PA Stateplane
NAD83 from its original datum in accordance with the base map standards of
the County of Chester. The County of Chester serves as the secondary
organization in providing this shapefile, as compared to its originator
and primary organization, the Natural Resources Conservation Service.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties
Metadata
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
|
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
|
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|
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|
KMZ
|
GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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|
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KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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|
Preview
|
KMZ
|
GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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|
KMZ
|
GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
|
Download
|
Preview
|
KMZ
|
GeoJSON
|
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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KMZ
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| U S Department of Agriculture |
2002 |
The USDA-NASS 2002 Delaware Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of Delaware
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2004 |
This map layer is commonly called Bailey's ecoregions and shows
ecosystems of regional extent in the United States, Puerto Rico, and the
U.S. Virgin Islands. Four levels of detail are included to show a
hierarchy of ecosystems. The largest ecosystems are domains, which are
groups of related climates and which are differentiated based on
precipitation and temperature. Divisions represent the climates within
domains and are differentiated based on precipitation levels and patterns
as well as temperature. Divisions are subdivided into provinces, which
are differentiated based on vegetation or other natural land covers. The
finest level of detail is described by subregions, called sections, which
are subdivisions of provinces based on terrain features. Also identified
are mountainous areas that exhibit different ecological zones based on
elevation.
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Spreadsheet
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2006 |
This table contains the results of the models used to classify the subwatersheds where the population status was unknown or there was only qualitative data available. The Eastern Brook Trout Joint Venture (EBTJV) is a partnership between state and federal agencies, nongovernmental conservation organizations, and academia. The EBTJV is focused on an effort to protect, restore, and enhance brook trout populations throughout the historic range of the Eastern brook trout. One of the goals of the EBTJV was to produce a subwatershed dataset indicating the current distribution of brook trout populations and their perturbations. This data will aid in quantifying problems, identifying information gaps, and setting restoration priorities. The subwatershed classifications are based on a combination of quantitative information collected by state agencies, qualitative judgment calls by local experts, and classification models based on landscape characteristics.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2017 |
The National High Altitude Photography (NHAP) program was coordinated by the USGS as an interagency project to acquire cloud-free aerial photographs at an altitude of 40,000 feet above mean terrain elevation. Two different camera systems were used to obtain simultaneous coverage of black-and-white (BW) and color infrared (CIR) aerial photographs over the conterminous United States. The color-infrared photographs were taken with an 8.25-inch focal length lens and are at a scale of 1:58,000. The black-and-white photographs were taken with a 6-inch focal length lens and are at a scale of 1:80,000. The NHAP program, which was operational from 1980 to 1989, consists of approximately 500,000 images. Photographs were acquired on 9-inch film and centered over USGS 7.5-minute quadrangles. Not Georeferenced. Statewide historic aerial images for Pennsylvania. Color-infrared photographs from the National High Altitude Photography (NHAP) program, conducted by the USGS. Pennsylvaina imagery was captured between 03/27/1980 & 05/13/1987. The USGS "The National High Altitude Photography (NHAP) program, which was operated from 1980-1989, was coordinated by the U.S. Geological Survey as an interagency project to eliminate duplicate photography in various Government programs. The aim of the program was to cover the 48 conterminous states over a 5-year span. In the NHAP program, black-and-white and color-infrared aerial photographs were obtained on 9-inch film from an altitude of 40,000 feet above mean terrain elevation and are centered over USGS 7.5-minute quadrangles. The color-infrared photographs are at a scale of 1:58,000 (1 inch equals about .9 miles). All NHAP flights were flown in a North to South direction. These photographs are offered as digital images" (https://catalog.data.gov/dataset/nhap-national-high-altitude-aerial-photography-1980-1989 , April 2017). In 2016, Donald W. Hamer Center for Maps & Geospatial Information at The Pennsylvania State University obtained high quality digital images of 9x9 film from USDA - FSA Aerial Photography Field Office (APFO). Digital images were captured at 2,032 dpi with Wehrli and Associates, Inc. RM-6 and RM-3 photogrammetric scanners. The complete collection of 2,607 images contain 60% forward and 30% side to side overlapping coverage for all of Pennsylvania.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
|
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|
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|
KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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|
KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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|
KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
|
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
|
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|
KMZ
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GeoJSON
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2002 |
The USDA-NASS 2002 Maryland Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of Maryland
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2006 |
This table contains the results of the models used to classify the subwatersheds where the population status was unknown or there was only qualitative data available. The Eastern Brook Trout Joint Venture (EBTJV) is a partnership between state and federal agencies, nongovernmental conservation organizations, and academia. The EBTJV is focused on an effort to protect, restore, and enhance brook trout populations throughout the historic range of the Eastern brook trout. One of the goals of the EBTJV was to produce a subwatershed dataset indicating the current distribution of brook trout populations and their perturbations. This data will aid in quantifying problems, identifying information gaps, and setting restoration priorities. The subwatershed classifications are based on a combination of quantitative information collected by state agencies and qualitative judgment calls by local experts.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2006 |
Index of USGS quarter-quadrangles (3.75 minute) covering Pennsylvania used for 2004 National Agricultural Imagery program (NAIP) images. The polygons are attributed with information pertaining to USGS quadrangle names and quarter-quad designations. The polygons are also attributed with the initial identifying portion of the 2004 NAIP image file names.
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| U S Department of Agriculture |
2004 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 3
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP); and, 2 meter GSD
ortho imagery rectified to within +/- 10 meters of reference
DOQQs. The tiling format of NAIP imagery is based on a 3.75'
x 3.75' quarter quadrangle with a 300 meter buffer on all four
sides. NAIP quarter quads are formatted to the UTM coordinate
system using NAD83. NAIP imagery may contain as much as 10%
cloud cover per tile.
This file was generated by compressing NAIP quarter quadrangle
tiles that cover a county. MrSID compression, with mosaic
option, was used. Target values for the compression ratio
are (50:1) and compression levels(9) are used.
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| U S Department of Agriculture |
2004 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2004 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2005 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 3
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP); and, 2 meter GSD
ortho imagery rectified to within +/- 10 meters of reference
DOQQs. The tiling format of NAIP imagery is based on a 3.75'
x 3.75' quarter quadrangle with a 300 meter buffer on all four
sides. NAIP quarter quads are formatted to the UTM coordinate
system using NAD83. NAIP imagery may contain as much as 10%
cloud cover per tile.
This file was generated by compressing NAIP quarter quadrangle
tiles that cover a county. MrSID compression, with mosaic
option, was used. Target values for the compression ratio
are (50:1) and compression levels(9) are used.
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| U S Department of Agriculture |
2008 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 5
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP) or from the
National Agriculture Imagery Program (NAIP); 1 meter GSD ortho
imagery rectified to within +/- 6 meters to true ground.
The tiling format of NAIP imagery is based on a 3.75' x 3.75'
quarter quadrangle with a 300 meter buffer on all four sides.
NAIP quarter quads are formatted to the UTM coordinate system
using NAD83. NAIP imagery may contain as much as 10% cloud
cover per tile. This file was generated by compressing NAIP
quarter quadrangle tiles that cover a county. Two types of
compression may be used for 2008 NAIP imagery: MrSID and JPEG
2000. Target values for the compression ratio are (15:1).
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| U S Department of Agriculture |
2008 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2010 |
4 Band JPEG200 - This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 3
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP); and, 2 meter GSD
ortho imagery rectified to within +/- 10 meters of reference
DOQQs. The tiling format of NAIP imagery is based on a 3.75'
x 3.75' quarter quadrangle with a 300 meter buffer on all four
sides. NAIP quarter quads are formatted to the UTM coordinate
system using NAD83. NAIP imagery may contain as much as 10%
cloud cover per tile.
This file was generated by compressing NAIP quarter quadrangle
tiles that cover a county. MrSID compression, with mosaic
option, was used. Target values for the compression ratio
are (50:1) and compression levels(9) are used.
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| U S Department of Agriculture |
2010 |
4 Band TIFFs - This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 3
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP); and, 2 meter GSD
ortho imagery rectified to within +/- 10 meters of reference
DOQQs. The tiling format of NAIP imagery is based on a 3.75'
x 3.75' quarter quadrangle with a 300 meter buffer on all four
sides. NAIP quarter quads are formatted to the UTM coordinate
system using NAD83. NAIP imagery may contain as much as 10%
cloud cover per tile.
This file was generated by compressing NAIP quarter quadrangle
tiles that cover a county. MrSID compression, with mosaic
option, was used. Target values for the compression ratio
are (50:1) and compression levels(9) are used.
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| U S Department of Agriculture |
2010 |
Tile Index for National Agriculture
Imagery Program (NAIP) 2010. NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 3
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP); and, 2 meter GSD
ortho imagery rectified to within +/- 10 meters of reference
DOQQs. The tiling format of NAIP imagery is based on a 3.75'
x 3.75' quarter quadrangle with a 300 meter buffer on all four
sides. NAIP quarter quads are formatted to the UTM coordinate
system using NAD83. NAIP imagery may contain as much as 10%
cloud cover per tile.
This file was generated by compressing NAIP quarter quadrangle
tiles that cover a county. MrSID compression, with mosaic
option, was used. Target values for the compression ratio
are (50:1) and compression levels(9) are used.
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| U S Department of Agriculture |
2013 |
Tile Index for National Agriculture
Imagery Program (NAIP) 2010. NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 3
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP); and, 2 meter GSD
ortho imagery rectified to within +/- 10 meters of reference
DOQQs. The tiling format of NAIP imagery is based on a 3.75'
x 3.75' quarter quadrangle with a 300 meter buffer on all four
sides. NAIP quarter quads are formatted to the UTM coordinate
system using NAD83. NAIP imagery may contain as much as 10%
cloud cover per tile.
This file was generated by compressing NAIP quarter quadrangle
tiles that cover a county. MrSID compression, with mosaic
option, was used. Target values for the compression ratio
are (50:1) and compression levels(9) are used.
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| U S Department of Agriculture |
2013 |
Tile Index - This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2015 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2015 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2015 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2017 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2017 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. This file was generated by compressing NAIP imagery that cover the county extent. Two types of compression may be used for NAIP imagery: MrSID and JPEG 2000. Target value for the compression ratio is (15:1).
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| U S Department of Agriculture |
2019 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.
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| U S Department of Agriculture |
2019 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.
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| U S Department of Agriculture |
2019 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.
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| U S Department of Agriculture |
2022 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.
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| U S Department of Agriculture |
2022 |
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.
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| U S Department of Agriculture |
2022 |
Tile Index - This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs. Ortho imagery provides an effective, intuitive means of communication about farm program administration between FSA and stakeholders. New technology and innovation is identified by fostering and maintaining a relationship with vendors and government partners, and by keeping pace with the broader geospatial community. As a result of these efforts the NAIP program provides three main products: DOQQ tiles, Compressed County Mosaics (CCM), and Seamline shape files. The Contract specifications for NAIP imagery have changed over time reflecting agency requirements and improving technologies. These changes include image resolution, horizontal accuracy, coverage area, and number of bands. In general, flying seasons are established by FSA and are targeted for peak crop growing conditions. The NAIP acquisition cycle is based on a minimum 3 year refresh of base ortho imagery. The tiling format of the NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 pixel buffer on all four sides. NAIP quarter quads are formatted to the UTM coordinate system using the North American Datum of 1983. NAIP imagery may contain as much as 10% cloud cover per tile.
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| U S Department of Agriculture |
2009 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 5
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP) or from the
National Agriculture Imagery Program (NAIP); 1 meter GSD ortho
imagery rectified to within +/- 6 meters to true ground.
The tiling format of NAIP imagery is based on a 3.75' x 3.75'
quarter quadrangle with a 300 meter buffer on all four sides.
NAIP quarter quads are formatted to the UTM coordinate system
using NAD83. NAIP imagery may contain as much as 10% cloud
cover per tile. This file was generated by compressing NAIP
quarter quadrangle tiles that cover a county. Two types of
compression may be used for 2008 NAIP imagery: MrSID and JPEG
2000. Target values for the compression ratio are (15:1).
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| U S Department of Agriculture |
2009 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 5
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP) or from the
National Agriculture Imagery Program (NAIP); 1 meter GSD ortho
imagery rectified to within +/- 6 meters to true ground.
The tiling format of NAIP imagery is based on a 3.75' x 3.75'
quarter quadrangle with a 300 meter buffer on all four sides.
NAIP quarter quads are formatted to the UTM coordinate system
using NAD83. NAIP imagery may contain as much as 10% cloud
cover per tile. This file was generated by compressing NAIP
quarter quadrangle tiles that cover a county. Two types of
compression may be used for 2008 NAIP imagery: MrSID and JPEG
2000. Target values for the compression ratio are (15:1).
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| U S Department of Agriculture |
2009 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 5
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP) or from the
National Agriculture Imagery Program (NAIP); 1 meter GSD ortho
imagery rectified to within +/- 6 meters to true ground.
The tiling format of NAIP imagery is based on a 3.75' x 3.75'
quarter quadrangle with a 300 meter buffer on all four sides.
NAIP quarter quads are formatted to the UTM coordinate system
using NAD83. NAIP imagery may contain as much as 10% cloud
cover per tile. This file was generated by compressing NAIP
quarter quadrangle tiles that cover a county. Two types of
compression may be used for 2008 NAIP imagery: MrSID and JPEG
2000. Target values for the compression ratio are (15:1).
Metadata
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| U S Department of Agriculture |
2009 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 5
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP) or from the
National Agriculture Imagery Program (NAIP); 1 meter GSD ortho
imagery rectified to within +/- 6 meters to true ground.
The tiling format of NAIP imagery is based on a 3.75' x 3.75'
quarter quadrangle with a 300 meter buffer on all four sides.
NAIP quarter quads are formatted to the UTM coordinate system
using NAD83. NAIP imagery may contain as much as 10% cloud
cover per tile. This file was generated by compressing NAIP
quarter quadrangle tiles that cover a county. Two types of
compression may be used for 2008 NAIP imagery: MrSID and JPEG
2000. Target values for the compression ratio are (15:1).
Metadata
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| U S Department of Agriculture |
2009 |
This data set contains imagery from the National Agriculture
Imagery Program (NAIP). NAIP acquires digital ortho imagery
during the agricultural growing seasons in the continental U.S..
A primary goal of the NAIP program is to enable availability of
ortho imagery within one year of acquisition. NAIP provides two
main products: 1 meter ground sample distance (GSD) ortho
imagery rectified to a horizontal accuracy of within +/- 5
meters of reference digital ortho quarter quads (DOQQ's) from
the National Digital Ortho Program (NDOP) or from the
National Agriculture Imagery Program (NAIP); 1 meter GSD ortho
imagery rectified to within +/- 6 meters to true ground.
The tiling format of NAIP imagery is based on a 3.75' x 3.75'
quarter quadrangle with a 300 meter buffer on all four sides.
NAIP quarter quads are formatted to the UTM coordinate system
using NAD83. NAIP imagery may contain as much as 10% cloud
cover per tile. This file was generated by compressing NAIP
quarter quadrangle tiles that cover a county. Two types of
compression may be used for 2008 NAIP imagery: MrSID and JPEG
2000. Target values for the compression ratio are (15:1).
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| U S Department of Agriculture |
2002 |
The USDA-NASS 2002 New Jersey Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of New Jersey
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2002 |
The USDA-NASS 2002 New York Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of New York
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2006 |
This table contains the pruned classification tree terminal node numbers for each subwatershed in the Eastern Brook Trout Joint Venture brook trout assessment. The tables also lists the classification probabilities for each node. The Eastern Brook Trout Joint Venture (EBTJV) is a partnership between state and federal agencies, nongovernmental conservation organizations, and academia. The EBTJV is focused on an effort to protect, restore, and enhance brook trout populations throughout the historic range of the Eastern brook trout. One of the goals of the EBTJV was to produce a subwatershed dataset indicating the current distribution of brook trout populations and their perturbations. This data will aid in quantifying problems, identifying information gaps, and setting restoration priorities. The subwatershed classifications are based on a combination of quantitative information collected by state agencies and qualitative judgment calls by local experts.
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| U S Department of Agriculture |
2006 |
This table contains the classification tree terminal node numbers for each subwatershed in the Eastern Brook Trout Joint Venture brook trout assessment. The tables also lists the classification probabilities for each node. The Eastern Brook Trout Joint Venture (EBTJV) is a partnership between state and federal agencies, nongovernmental conservation organizations, and academia. The EBTJV is focused on an effort to protect, restore, and enhance brook trout populations throughout the historic range of the Eastern brook trout. One of the goals of the EBTJV was to produce a subwatershed dataset indicating the current distribution of brook trout populations and their perturbations. This data will aid in quantifying problems, identifying information gaps, and setting restoration priorities. The subwatershed classifications are based on a combination of quantitative information collected by state agencies and qualitative judgment calls by local experts.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2002 |
The USDA-NASS 2002 Pennsylvania Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of Pennsylvania.
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2002 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2008 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2009 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2010 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2011 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2012 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2013 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2014 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2015 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2016 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2017 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2018 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2019 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2020 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2021 |
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2006 |
This table contains the ratings of perturbations to brook trout populations by subwatershed. These ratings were designated by biologists taking part in the Eastern Brook Trout Joint Venture population assessment. The Eastern Brook Trout Joint Venture (EBTJV) is a partnership between state and federal agencies, nongovernmental conservation organizations, and academia. The EBTJV is focused on an effort to protect, restore, and enhance brook trout populations throughout the historic range of the Eastern brook trout. One of the goals of the EBTJV was to produce a subwatershed dataset indicating the current distribution of brook trout populations and their perturbations. This data will aid in quantifying problems, identifying information gaps, and setting restoration priorities. The dataset includes additional information concerning the type and quality of perturbations to brook trout populations. The subwatershed classifications are based on a combination of quantitative information collected by state agencies and qualitative judgment calls by local experts.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and miscellaneous areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
This data set consists of georeferenced digital map data and
computerized attribute data. The map data are in a soil survey area
extent format and include a detailed, field verified inventory
of soils and nonsoil areas that normally occur in a repeatable
pattern on the landscape and that can be cartographically shown at
the scale mapped. A special soil features layer (point and line
features) is optional. This layer displays the location of features
too small to delineate at the mapping scale, but they are large
enough and contrasting enough to significantly influence use and
management. The soil map units are linked to attributes in the
National Soil Information System relational database, which gives
the proportionate extent of the component soils and their properties.
Metadata
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2002 |
The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries.
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| U S Department of Agriculture |
2002 |
The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries.
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| U S Department of Agriculture |
2002 |
The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries.
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| U S Department of Agriculture |
2002 |
The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries.
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| U S Department of Agriculture |
2002 |
The United States Department of Agriculture (USDA), National
Agricultural Statistics Service (NASS) area sampling frame is a
delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite
imagery to divide a state into homogenous land use areas (strata) based
on percent cultivated. The strata are typically defined as low, medium or
high percent cultivated, non-agricultural land, urban use, agri-urban, or
water. The boundaries of the strata usually follow identifiable features
such as roads, railroads and waterways. The strata boundaries do not
coincide with any political boundaries, with the exception of state
boundaries.
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| U S Department of Agriculture |
2002 |
The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries.
Metadata
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| U S Department of Agriculture |
2002 |
The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries.
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| U S Department of Agriculture |
2006 |
The Eastern Brook Trout Joint Venture (EBTJV) is a partnership between state and federal agencies, nongovernmental conservation organizations, and academia. The EBTJV is focused on an effort to protect, restore, and enhance brook trout populations throughout the historic range of the Eastern brook trout. One of the goals of the EBTJV was to produce a subwatershed dataset indicating the current distribution of brook trout populations and their perturbations. This data will aid in quantifying problems, identifying information gaps, and setting restoration priorities. The subwatershed classifications are based on a combination of quantitative information collected by state agencies and qualitative judgment calls by local experts.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
|
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KMZ
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
Metadata
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| U S Department of Agriculture |
2012 |
The USDA-NASS 2002, 2008, 2009, 2010, 2011 Pennsylvania Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of Pennsylvania.
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2002 |
The USDA-NASS 2002 Virginia Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of Virginia.
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2002 |
The USDA-NASS 2002 West Virginia Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of West Virginia.
This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.
The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.
There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers: http://www.nass.usda.gov/research/Cropland/SARS1a.htm
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information. This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2024 |
This data set is a digital soil survey and generally is the most
detailed level of soil geographic data developed by the National
Cooperative Soil Survey. The information was prepared by digitizing
maps, by compiling information onto a planimetric correct base
and digitizing, or by revising digitized maps using remotely
sensed and other information.
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| U S Department of Agriculture |
2006 |
This map layer portrays 1979, 1980, and 1981 estimates for total personal
income, per capita personal income, annual number of full-time and part-
time jobs, average wage per job in dollars, population, and per capita
number of jobs, for counties in the United States.
Total personal income is all the income that is received by, or on behalf
of, the residents of a particular area. It is calculated as the sum of
wage and salary disbursements, other labor income, proprietors' income
with inventory valuation and capital consumption adjustments, rental
income of persons with capital consumption adjustment, personal dividend
income, personal interest income, and transfer payments to persons, minus
personal contributions for social insurance.
Per capita personal income is calculated as the total personal income of
the residents of a county divided by the resident population of the
county. The Census Bureau's annual midyear population estimates were used
in the computation.
The average annual number of full-time and part-time jobs includes all
jobs for which wages and salaries are paid, except jury and witness
service and paid employment of prisoners. The jobs are counted at equal
weight, and employees, sole proprietors, and active partners are all
included. Unpaid family workers and volunteers are not included.
Average wage per job is the total wage and salary disbursements divided by
the number of wage and salary jobs in the county. Wage and salary
disbursements consist of the monetary remuneration of employees, including
the compensation of corporate officers; commissions, tips, and bonuses;
and receipts in kind, or pay-in-kind, such as the meals furnished to the
employees of restaurants. It reflects the amount of payments disbursed,
but not necessarily earned during the year.
Per capita number of jobs is calculated as the average annual number of
full-time and part-time jobs in a county divided by the resident
population of the county. The Census Bureau's annual midyear population
estimates were used in the computation.
All dollar estimates are in current dollars, not adjusted for inflation.
The information in this map layer comes from the Regional Economic
Information System (REIS) that is distributed by the Bureau of Economic
Analysis, http://www.bea.gov/.
This is an updated version of the November 2004 map layer.
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| U S Department of Commerce |
2006 |
This map layer portrays 1982 estimates for total personal income, per
capita personal income, annual number of full-time and part-time jobs,
average wage per job in dollars, population, and per capita number of
jobs, for counties in the United States.
Total personal income is all the income that is received by, or on behalf
of, the residents of a particular area. It is calculated as the sum of
wage and salary disbursements, other labor income, proprietors' income
with inventory valuation and capital consumption adjustments, rental
income of persons with capital consumption adjustment, personal dividend
income, personal interest income, and transfer payments to persons, minus
personal contributions for social insurance.
Per capita personal income is calculated as the total personal income of
the residents of a county divided by the resident population of the
county. The Census Bureau's annual midyear population estimates were used
in the computation.
The average annual number of full-time and part-time jobs includes all
jobs for which wages and salaries are paid, except jury and witness
service and paid employment of prisoners. The jobs are counted at equal
weight, and employees, sole proprietors, and active partners are all
included. Unpaid family workers and volunteers are not included.
Average wage per job is the wage and salary disbursements divided by the
number of wage and salary jobs in the county. Wage and salary
disbursements consist of the monetary remuneration of employees, including
the compensation of corporate officers; commissions, tips, and bonuses;
and receipts in kind, or pay-in-kind, such as the meals furnished to the
employees of restaurants. It reflects the amount of payments disbursed,
but not necessarily earned during the year.
Per capita number of jobs is calculated as the average annual number of
full-time and part-time jobs in a county divided by the resident
population of the county. The Census Bureau's annual midyear population
estimates were used in the computation.
All dollar estimates are in current dollars, not adjusted for inflation.
The information in this map layer comes from the Regional Economic
Information System (REIS) that is distributed by the Bureau of Economic
Analysis, http://www.bea.gov/.
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| U S Department of Commerce |
2006 |
This map layer portrays 1988 estimates for total personal income, per
capita personal income, annual number of full-time and part-time jobs,
average wage per job in dollars, population, and per capita number of
jobs, for counties in the United States.
Total personal income is all the income that is received by, or on behalf
of, the residents of a particular area. It is calculated as the sum of
wage and salary disbursements, other labor income, proprietors' income
with inventory valuation and capital consumption adjustments, rental
income of persons with capital consumption adjustment, personal dividend
income, personal interest income, and transfer payments to persons, less
personal contributions for social insurance.
Per capita personal income is calculated as the total personal income of
the residents of a county divided by the resident population of the
county. The Census Bureau's annual midyear population estimates were used
in the computation.
The average annual number of full-time and part-time jobs includes all
jobs for which wages and salaries are paid, except jury and witness
service and paid employment of prisoners. The jobs are counted at equal
weight, and employees, sole proprietors, and active partners are all
included. Unpaid family workers and volunteers are not included.
Average wage per job is the wage and salary disbursements divided by the
number of wage and salary jobs in the county. Wage and salary
disbursements consist of the monetary remuneration of employees, including
the compensation of corporate officers; commissions, tips, and bonuses;
and receipts in kind, or pay-in-kind, such as the meals furnished to the
employees of restaurants. It reflects the amount of payments disbursed,
but not necessarily earned during the year.
Per capita number of jobs is calculated as the average annual number of
full-time and part-time jobs in a county divided by the resident
population of the county. The Census Bureau's annual midyear population
estimates were used in the computation.
All dollar estimates are in current dollars, not adjusted for inflation.
The information in this map layer comes from the Regional Economic
Information System (REIS) that is distributed by the Bureau of Economic
Analysis, http://www.bea.gov/.
This is an updated version of the November 2004 map layer
Metadata
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| U S Department of Commerce |
2006 |
This map layer portrays 1989 and 1990 estimates for total personal
income, per capita personal income, annual number of full-time and part-
time jobs, average wage per job in dollars, population, and per capita
number of jobs, for counties in the United States.
Total personal income is all the income that is received by, or on behalf
of, the residents of a particular area. It is calculated as the sum of
wage and salary disbursements, other labor income, proprietors' income
with inventory valuation and capital consumption adjustments, rental
income of persons with capital consumption adjustment, personal dividend
income, personal interest income, and transfer payments to persons, minus
personal contributions for social insurance.
Per capita personal income is calculated as the total personal income of
the residents of a county divided by the resident population of the
county. The Census Bureau's annual midyear population estimates were used
in the computation.
The average annual number of full-time and part-time jobs includes all
jobs for which wages and salaries are paid, except jury and witness
service and paid employment of prisoners. The jobs are counted at equal
weight, and employees, sole proprietors, and active partners are all
included. Unpaid family workers and volunteers are not included.
Average wage per job is the wage and salary disbursements divided by the
number of wage and salary jobs in the county. Wage and salary
disbursements consist of the monetary remuneration of employees, including
the compensation of corporate officers; commissions, tips, and bonuses;
and receipts in kind, or pay-in-kind, such as the meals furnished to the
employees of restaurants. It reflects the amount of payments disbursed,
but not necessarily earned during the year.
Per capita number of jobs is calculated as the average annual number of
full-time and part-time jobs in a county divided by the resident
population of the county. The Census Bureau's annual midyear population
estimates were used in the computation.
All dollar estimates are in current dollars, not adjusted for inflation.
The information in this map layer comes from the Regional Economic
Information System (REIS) that is distributed by the Bureau of Economic
Analysis, http://www.bea.gov/.
This is an updated version of the November 2004 map layer.
Metadata
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| U S Department of Commerce |
2006 |
This map layer portrays 1991 and 1992 estimates for total personal
income, per capita personal income, annual number of full-time and part-
time jobs, average wage per job in dollars, population, and per capita
number of jobs, for counties in the United States.
Total personal income is all the income that is received by, or on behalf
of, the residents of a particular area. It is calculated as the sum of
wage and salary disbursements, other labor income, proprietors' income
with inventory valuation and capital consumption adjustments, rental
income of persons with capital consumption adjustment, personal dividend
income, personal interest income, and transfer payments to persons, minus
personal contributions for social insurance.
Per capita personal income is calculated as the total personal income of
the residents of a county divided by the resident population of the
county. The Census Bureau's annual midyear population estimates were used
in the computation.
The average annual number of full-time and part-time jobs includes all
jobs for which wages and salaries are paid, except jury and witness
service and paid employment of prisoners. The jobs are counted at equal
weight, and employees, sole proprietors, and active partners are all
included. Unpaid family workers and volunteers are not included.
Average wage per job is the wage and salary disbursements divided by the
number of wage and salary jobs in the county. Wage and salary
disbursements consist of the monetary remuneration of employees, including
the compensation of corporate officers; commissions, tips, and bonuses;
and receipts in kind, or pay-in-kind, such as the meals furnished to the
employees of restaurants. It reflects the amount of payments disbursed,
but not necessarily earned during the year.
Per capita number of jobs is calculated as the average annual number of
full-time and part-time jobs in a county divided by the resident
population of the county. The Census Bureau's annual midyear population
estimates were used in the computation.
All dollar estimates are in current dollars, not adjusted for inflation.
The information in this map layer comes from the Regional Economic
Information System (REIS) that is distributed by the Bureau of Economic
Analysis, http://www.bea.gov/.
This is an updated version of the November 2004 map layer.
Metadata
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| U S Department of Commerce |
2006 |
This map layer portrays 1993 to 2001 estimates for total personal
income, per capita personal income, annual number of full-time and part-
time jobs, average wage per job in dollars, population, and per capita
number of jobs, for counties in the United States.
Total personal income is all the income that is received by, or on behalf
of, the residents of a particular area. It is calculated as the sum of
wage and salary disbursements, other labor income, proprietors' income
with inventory valuation and capital consumption adjustments, rental
income of persons with capital consumption adjustment, personal dividend
income, personal interest income, and transfer payments to persons, minus
personal contributions for social insurance.
Per capita personal income is calculated as the total personal income of
the residents of a county divided by the resident population of the
county. The Census Bureau's annual midyear population estimates were used
in the computation.
The average annual number of full-time and part-time jobs includes all
jobs for which wages and salaries are paid, except jury and witness
service and paid employment of prisoners. The jobs are counted at equal
weight, and employees, sole proprietors, and active partners are all
included. Unpaid family workers and volunteers are not included.
Average wage per job is the wage and salary disbursements divided by the
number of wage and salary jobs in the county. Wage and salary
disbursements consist of the monetary remuneration of employees, including
the compensation of corporate officers; commissions, tips, and bonuses;
and receipts in kind, or pay-in-kind, such as the meals furnished to the
employees of restaurants. It reflects the amount of payments disbursed,
but not necessarily earned during the year.
Per capita number of jobs is calculated as the average annual number of
full-time and part-time jobs in a county divided by the resident
population of the county. The Census Bureau's annual midyear population
estimates were used in the computation.
All dollar estimates are in current dollars, not adjusted for inflation.
The information in this map layer comes from the Regional Economic
Information System (REIS) that is distributed by the Bureau of Economic
Analysis, http://www.bea.gov/.
Metadata
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| U S Department of Commerce |
2005 |
This map layer shows crime statistics for the United States for the
years 2001-2002, drawn from the Uniform Crime Reporting Program data
compiled by the Federal Bureau of Investigation and archived at the
Inter-university Consortium for Political and Social Research (ICPSR).
Crime data are reported by county and are provided for eight crimes:
murder, forcible rape, robbery, aggravated assault, burglary, larceny,
motor vehicle theft, and arson. Statewide allocation data are not
included in this map layer. Crime data are adjusted to compensate for
incomplete reporting by individual law enforcement agencies. See the
online codebook at
for more information. This layer is clipped to Pennsylvania
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| U S Department of Justice |
2010 |
Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 52 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 104 regions whereas the conterminous United States has 84 (U.S. Environmental Protection Agency, 2005). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989).
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| U S Environmental Protection Agency |
2010 |
Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 52 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 104 regions whereas the conterminous United States has 84 (U.S. Environmental Protection Agency, 2005). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989).
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| U S Environmental Protection Agency |
2003 |
This map layer shows Omernik's Level III ecoregions, derived from a
1:7,500,000 map created by J.M. Omernik in 1987 and from refinements of
Omernik's framework that were made for other projects. Ecoregions
describe areas of general similarity in ecosystems and in the type,
quality, and quantity of environmental resources. Omernik's ecoregions
are based on the premise that a hierarchy of ecological regions can be
identified through the analysis of the patterns and the composition of
both living and nonliving phenomena, such as geology, physiography,
vegetation, climate, soils, land use, wildlife, and hydrology, that affect
or reflect differences in ecosystem quality and integrity. All the
characteristics are considered when determining ecoregions, but the
relative importance of each characteristic may vary from one ecoregion to
another. Level III is the most detailed level available nationally for
this system of ecoregions.
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| U S Environmental Protection Agency |
2021 |
The purpose of this map is to assist national, state and local organizations to target their resources and to implement radon-resistant building codes.
The Map of Radon Zones was developed in 1993 to identify areas of the U.S. with the potential for elevated indoor radon levels. The map is intended to help governments and other organizations target risk reduction activities and resources. The Map of Radon Zones should not be used to determine if individual homes need to be tested. No matter where you live, test your home for radon—it’s easy and inexpensive. Fix your home if your radon level is 4 picocuries per liter (pCi/L) or higher. Consider fixing if your level is between 2 and 4 pCi/L.
The Map of Radon Zones was developed using data on indoor radon measurements, geology, aerial radioactivity, soil parameters, and foundation types. EPA recommends that this map be supplemented with any available local data in order to further understand and predict the radon potential for a specific area.
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| U S Environmental Protection Agency |
2008 |
The U.S. Environmental Protection Agency (EPA) Office of Solid Waste and Emergency Response (OSWER) Center for Program Analysis (OCPA) is encouraging the reuse of contaminated lands for siting clean and renewable energy facilities. According to the U.S. Energy Information Administration's Annual Energy Outlook 2008, by 2030 U.S. electricity production will need to increase by nearly 30 percent to meet growing demand. Currently, wind, solar and biomass supply 2.3% of our nation's electricity. While these renewable sources currently make up only a small fraction of energy production, renewable energy production is expected to increase by more than 70% between 2006 and 2030. Identifying and using land located in areas with high quality renewable energy resource will be an essential component of developing more electricity from renewable energy sources.
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| U S Environmental Protection Agency |
1994 |
This data layer includes point locations for Toxic Chemical Release Inventory (TRI) data from the Environmental Protection Agency's Toxic Release Inventory System (TRIS) for all States in EPA Region 3 (PA, MD, DE, DC, VA, WV). This data layer includes yearly information by State about the facilities that report releases and the types of chemicals being released. TRI includes information from facilities on the amounts of over 300 listed toxic chemicals that the facilities release directly to air, water, or land or that are transported (transferred) off-site.
TRI was mandated by the Emergency Planning and Community Right-to-Know Act (EPCRA) which is based on the premise that citizens have a right to know about toxic chemicals in their communities. TRI's purpose is to encourage planning for response to chemical accidents and to provide the public and government information about possible chemical hazards in communities.
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| U S Environmental Protection Agency |
2017 |
This dataset contains the 100 most-requested data fields from the TRI Reporting Form R and Form A. The data in these files are presented in .csv (comma-separated value) format.
Quantities of dioxin and dioxin-like compounds are reported in grams, while all other chemicals are reported in pounds. For descriptions of each data element and other details about the contents and use of these files, see the TRI Basic Data File Documentation. The TRI Reporting Forms and Instructions document is also a helpful reference.
Data includes:
Facility name, address, latitude and longitude coordinates, and industry sector codes
Chemical identification and classification information
Quantities of chemicals released on site at the facility
Quantities of chemicals transferred to Publicly Owned Treatment Works (POTWs)
Quantities of chemicals transferred off site to other locations for release/disposal and further waste management
Quantities of chemicals managed through disposal, energy recovery, recycling and treatment; non-production-related waste quantities; production/activity ratio
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| U S Environmental Protection Agency |
2018 |
This dataset contains the 100 most-requested data fields from the TRI Reporting Form R and Form A. The data in these files are presented in .csv (comma-separated value) format.
Quantities of dioxin and dioxin-like compounds are reported in grams, while all other chemicals are reported in pounds. For descriptions of each data element and other details about the contents and use of these files, see the TRI Basic Data File Documentation. The TRI Reporting Forms and Instructions document is also a helpful reference.
Data includes:
Facility name, address, latitude and longitude coordinates, and industry sector codes
Chemical identification and classification information
Quantities of chemicals released on site at the facility
Quantities of chemicals transferred to Publicly Owned Treatment Works (POTWs)
Quantities of chemicals transferred off site to other locations for release/disposal and further waste management
Quantities of chemicals managed through disposal, energy recovery, recycling and treatment; non-production-related waste quantities; production/activity ratio
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| U S Environmental Protection Agency |
2019 |
This dataset contains the 100 most-requested data fields from the TRI Reporting Form R and Form A. The data in these files are presented in .csv (comma-separated value) format.
Quantities of dioxin and dioxin-like compounds are reported in grams, while all other chemicals are reported in pounds. For descriptions of each data element and other details about the contents and use of these files, see the TRI Basic Data File Documentation. The TRI Reporting Forms and Instructions document is also a helpful reference.
Data includes:
Facility name, address, latitude and longitude coordinates, and industry sector codes
Chemical identification and classification information
Quantities of chemicals released on site at the facility
Quantities of chemicals transferred to Publicly Owned Treatment Works (POTWs)
Quantities of chemicals transferred off site to other locations for release/disposal and further waste management
Quantities of chemicals managed through disposal, energy recovery, recycling and treatment; non-production-related waste quantities; production/activity ratio
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| U S Environmental Protection Agency |
1997 |
NWI digital data files are records of wetlands location and classification as defined by the U.S. Fish & Wildlife Service. This dataset is a compilation of the 7.5 minute blocks containing ground planimetric coordinates of wetlands point, line, and area features and wetlands attributes for Chester County. The digital data as well as the hardcopy maps that were used as the source for the digital data are produced and distributed by the U.S. Fish and Wildlife Service's National Wetlands Inventory project.
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| U S Fish and Wildlife Service |
1997 |
NWI digital data files are records of wetlands location and classification as defined by the U.S. Fish & Wildlife Service. This dataset is a compilation of the 7.5 minute blocks containing ground planimetric coordinates of wetlands point, line, and area features and wetlands attributes for Chester County. The digital data as well as the hardcopy maps that were used as the source for the digital data are produced and distributed by the U.S. Fish and Wildlife Service's National Wetlands Inventory project.
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| U S Fish and Wildlife Service |
2009 |
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the conterminous United States. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979).
Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery.
By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps.
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| U S Fish and Wildlife Service |
2009 |
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the conterminous United States. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979).
Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery.
By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps.
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| U S Fish and Wildlife Service |
2009 |
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the conterminous United States. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979).
Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery.
By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps.
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| U S Fish and Wildlife Service |
2009 |
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the conterminous United States. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979).
Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery.
By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps.
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| U S Fish and Wildlife Service |
2017 |
This data set represents the extent, status, and location of National Wetland Inventory wetland mapping projects for NWI Version 2, Surface Waters and Wetlands. Each project polygon contains information on the type and date of imagery used to map the wetlands and a link to a document about specific mapping techniques and habitat information for that project. Some polygons have been updated in 2017 others not for quite some time. The user need to check the date of each wetland polygon for update years.
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the conterminous United States. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979).
Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery.
By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps.
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| U S Fish and Wildlife Service |
2005 |
NWI digital data files are records of wetlands location and
classification as developed by the U.S. Fish & Wildlife Service.
The classification system was adopted as a national
classification standard in 1996 by the Federal Geographic Data
Committee. This dataset is one of a series available in 7.5
minute by 7.5 minute blocks containing ground planimetric
coordinates of wetlands point, line, and polygon features and
wetlands attributes. When completed, the series will provide
coverage for all of the contiguous United States, Hawaii,
Alaska, and U.S. protectorates in the Pacific and Caribbean.
Coverage includes both digital data and hardcopy maps. The NWI
maps do not show all wetlands since the maps are derived from
aerial photointerpretation with varying limitations due to
scale, photo quality, inventory techniques, and other factors.
Consequently, the maps tend to show wetlands that are readily
photointerpreted given consideration of photo and map scale. In
general, the older NWI maps prepared from 1970s-era black and
white photography (1:80,000 scale) tend to be very conservative,
with many forested and drier-end emergent wetlands (e.g., wet
meadows) not mapped. Maps derived from color infrared
photography tend to yield more accurate results except when this
photography was captured during a dry year, making wetland
identification equally difficult. Proper use of NWI maps
therefore requires knowledge of the inherent limitations of this
mapping. It is suggested that users also consult other
information to aid in wetland detection, such as U.S. Department
of Agriculture soil survey reports and other wetland maps that
may have been produced by state and local governments, and not
rely solely on NWI maps. See section on "Completeness Report"
for more information. Also see an article in the National
Wetlands Newsletter (March-April 1997; Vol. 19/2, pp. 5-12)
entitled "NWI Maps: What They Tell Us" (a free copy of this
article can be ordered from U.S. Fish and Wildlife Service,
ES-NWI, 300 Westgate Center Drive, Hadley, MA 01035, telephone,
413-253-8620).
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| U S Fish and Wildlife Service |
2005 |
NWI digital data files are records of wetlands location and
classification as developed by the U.S. Fish & Wildlife Service.
The classification system was adopted as a national
classification standard in 1996 by the Federal Geographic Data
Committee. This dataset is one of a series available in 7.5
minute by 7.5 minute blocks containing ground planimetric
coordinates of wetlands point, line, and polygon features and
wetlands attributes. When completed, the series will provide
coverage for all of the contiguous United States, Hawaii,
Alaska, and U.S. protectorates in the Pacific and Caribbean.
Coverage includes both digital data and hardcopy maps. The NWI
maps do not show all wetlands since the maps are derived from
aerial photointerpretation with varying limitations due to
scale, photo quality, inventory techniques, and other factors.
Consequently, the maps tend to show wetlands that are readily
photointerpreted given consideration of photo and map scale. In
general, the older NWI maps prepared from 1970s-era black and
white photography (1:80,000 scale) tend to be very conservative,
with many forested and drier-end emergent wetlands (e.g., wet
meadows) not mapped. Maps derived from color infrared
photography tend to yield more accurate results except when this
photography was captured during a dry year, making wetland
identification equally difficult. Proper use of NWI maps
therefore requires knowledge of the inherent limitations of this
mapping. It is suggested that users also consult other
information to aid in wetland detection, such as U.S. Department
of Agriculture soil survey reports and other wetland maps that
may have been produced by state and local governments, and not
rely solely on NWI maps. See section on ""Completeness Report""
for more information. Also see an article in the National
Wetlands Newsletter (March-April 1997; Vol. 19/2, pp. 5-12)
entitled "NWI Maps: What They Tell Us" (a free copy of this
article can be ordered from U.S. Fish and Wildlife Service,
ES-NWI, 300 Westgate Center Drive, Hadley, MA 01035, telephone,
413-253-8620).
Metadata
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|
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KMZ
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Spreadsheet
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| U S Fish and Wildlife Service |
2005 |
NWI digital data files are records of wetlands location and
classification as developed by the U.S. Fish & Wildlife Service.
The classification system was adopted as a national
classification standard in 1996 by the Federal Geographic Data
Committee. This dataset is one of a series available in 7.5
minute by 7.5 minute blocks containing ground planimetric
coordinates of wetlands point, line, and polygon features and
wetlands attributes. When completed, the series will provide
coverage for all of the contiguous United States, Hawaii,
Alaska, and U.S. protectorates in the Pacific and Caribbean.
Coverage includes both digital data and hardcopy maps. The NWI
maps do not show all wetlands since the maps are derived from
aerial photointerpretation with varying limitations due to
scale, photo quality, inventory techniques, and other factors.
Consequently, the maps tend to show wetlands that are readily
photointerpreted given consideration of photo and map scale. In
general, the older NWI maps prepared from 1970s-era black and
white photography (1:80,000 scale) tend to be very conservative,
with many forested and drier-end emergent wetlands (e.g., wet
meadows) not mapped. Maps derived from color infrared
photography tend to yield more accurate results except when this
photography was captured during a dry year, making wetland
identification equally difficult. Proper use of NWI maps
therefore requires knowledge of the inherent limitations of this
mapping. It is suggested that users also consult other
information to aid in wetland detection, such as U.S. Department
of Agriculture soil survey reports and other wetland maps that
may have been produced by state and local governments, and not
rely solely on NWI maps. See section on "Completeness Report"
for more information. Also see an article in the National
Wetlands Newsletter (March-April 1997; Vol. 19/2, pp. 5-12)
entitled "NWI Maps: What They Tell Us" (a free copy of this
article can be ordered from U.S. Fish and Wildlife Service,
ES-NWI, 300 Westgate Center Drive, Hadley, MA 01035, telephone,
413-253-8620).
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| U S Fish and Wildlife Service |
2009 |
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the conterminous United States. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979).
Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery.
By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps.
Metadata
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KMZ
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| U S Fish and Wildlife Service |
2009 |
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the conterminous United States. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979).
Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery.
By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps.
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| U S Fish and Wildlife Service |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
2000 |
Digital Elevation Model (DEM) is the terminology adopted by the
USGS to describe terrain elevation data sets in a digital raster form. The
standard DEM consists of a regular array of elevations cast on a designated
coordinate projection system. The DEM data are stored as a series of
profiles in which the spacing of the elevations along and between each
profile is in regular whole number intervals. The normal orientation of
data is by columns and rows. Each column contains a series of elevations
ordered from south to north with the order of the columns from west to
east. The DEM is formatted as one ASCII header record (A-record),
followed by a series of profile records (B-records) each of which include
a short B-record header followed by a series of ASCII integer elevations
per each profile. The last physical record of the DEM is an accuracy record
(C-record).
7.5-minute DEM (30- by 30-meter data spacing, cast on Universal Transverse
Mercator (UTM) projection). Provides coverage in 7.5- by 7.5-minute
blocks. Each product provides the same coverage as a standard USGS
7.5-minute quadrangle without over edge. Coverage is for the Contiguous
United States, Hawaii, and Puerto Rico.
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| U S Geological Survey |
2000 |
Digital Elevation Model (DEM) is the terminology adopted by the
USGS to describe terrain elevation data sets in a digital raster form. The
standard DEM consists of a regular array of elevations cast on a designated
coordinate projection system. The DEM data are stored as a series of
profiles in which the spacing of the elevations along and between each
profile is in regular whole number intervals. The normal orientation of
data is by columns and rows. Each column contains a series of elevations
ordered from south to north with the order of the columns from west to
east. The DEM is formatted as one ASCII header record (A-record),
followed by a series of profile records (B-records) each of which include
a short B-record header followed by a series of ASCII integer elevations
per each profile. The last physical record of the DEM is an accuracy record
(C-record).
7.5-minute DEM (30- by 30-meter data spacing, cast on Universal Transverse
Mercator (UTM) projection). Provides coverage in 7.5- by 7.5-minute
blocks. Each product provides the same coverage as a standard USGS
7.5-minute quadrangle without over edge. Coverage is for the Contiguous
United States, Hawaii, and Puerto Rico.
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| U S Geological Survey |
2000 |
Digital Elevation Model (DEM) is the terminology adopted by the
USGS to describe terrain elevation data sets in a digital raster form. The
standard DEM consists of a regular array of elevations cast on a designated
coordinate projection system. The DEM data are stored as a series of
profiles in which the spacing of the elevations along and between each
profile is in regular whole number intervals. The normal orientation of
data is by columns and rows. Each column contains a series of elevations
ordered from south to north with the order of the columns from west to
east. The DEM is formatted as one ASCII header record (A-record),
followed by a series of profile records (B-records) each of which include
a short B-record header followed by a series of ASCII integer elevations
per each profile. The last physical record of the DEM is an accuracy record
(C-record).
7.5-minute DEM (30- by 30-meter data spacing, cast on Universal Transverse
Mercator (UTM) projection). Provides coverage in 7.5- by 7.5-minute
blocks. Each product provides the same coverage as a standard USGS
7.5-minute quadrangle without over edge. Coverage is for the Contiguous
United States, Hawaii, and Puerto Rico.
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|
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| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
Metadata
|
Download
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| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2000 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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|
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 200
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| U S Geological Survey |
2020 |
This represents tidal waters of the Chesapeake Bay that are impaired for some part or all of the indicated Bay segment by toxic chemicals based on each state's implementation of the Clean Water Act.
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| U S Geological Survey |
1984 |
To better understand how the land is changing and to relate those changes to water quality trends, the USGS funded the production of a Chesapeake Bay Watershed Land Cover Data Series (CBLCD) representing four dates: 1984, 1992, 2001, and 2006. These data were produced by MDA Federal Inc., under contract to the USGS and were derived from Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper satellite imagery. Each of the four datasets consists of 16 land use and land cover classes (Anderson, et al., 1976). The datasets are temporally comparable and encompass the entire Chesapeake Bay watershed and most intersecting counties.
The 2001 dataset represents the base layer for the Data Series and is composed of NOAA's 2001 Coastal Change Analysis Program (CCAP) dataset in the coastal and northern portion of the watershed coupled with USGS' 2001 National Land Cover Dataset in the western and southwestern portions of the watershed. MDA Federal's Cross Correlation Analysis (CCA) technique was used to produce updates (yr. 2006) and retrospective updates (yrs. 1984 and 1992) to the base layer. CCA identifies significant spectral changes between image pairs within the range of spectral values for each land cover class identified in the 2001 base layer. MDA Federal used Classification and Regression Trees to assign land cover classes to 1984, 1992, and 2006 pixels exhibiting significant deviations from their 2001 expected spectral values. MDA Federal used these methods to develop the 1996 and 2005 land cover change datasets for the Mid-Atlantic coastal area funded by NOAA CCAP.
Land use and land cover interpretations derived from Landsat satellite imagery are based on the sun's reflectance off the land surface, e.g., urban areas have different spectral reflectance characteristics than forests and herbaceous vegetation. For this reason, the data most accurately represent land cover (e.g., tree canopy) compared with land use or management (e.g., forests and pasture). Due to similarities in spectral reflectance characteristics, some land use and land cover classes are easily confused with each other. The spectral characteristics of low density residential areas, for example, may closely resemble the characteristics of forests in a neighborhood with mature trees or of cropland or pasture if large residential lots are composed mostly of lawns. Cropland and pasture may also have similar spectral qualities. Therefore, users should be cautioned against evaluating transitions between cropland and pasture based on the CBLCD. Users should be generally confident, however, that the overall spatial patterns of cropland and pasture in the Bay watershed are accurate because the USGS and NOAA used multi-season imagery to create the 2001 base layer and the data compare favorably with county-level cropland and pasture acreage estimates reported in the 2002 U.S. Census of Agriculture.
The USGS is in the process of interpreting and publishing statistics on the extent, type, and patterns of land cover change from 1984-2006 in the Bay watershed, major tributaries, and counties. The USGS will also be publishing land change forecasts based on observed trends in the CBLCD. These additional interpretations, statistics, and datasets will be publicly released over the coming year following publication.
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| U S Geological Survey |
1992 |
To better understand how the land is changing and to relate those changes to water quality trends, the USGS funded the production of a Chesapeake Bay Watershed Land Cover Data Series (CBLCD) representing four dates: 1984, 1992, 2001, and 2006. These data were produced by MDA Federal Inc., under contract to the USGS and were derived from Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper satellite imagery. Each of the four datasets consists of 16 land use and land cover classes (Anderson, et al., 1976). The datasets are temporally comparable and encompass the entire Chesapeake Bay watershed and most intersecting counties.
The 2001 dataset represents the base layer for the Data Series and is composed of NOAA's 2001 Coastal Change Analysis Program (CCAP) dataset in the coastal and northern portion of the watershed coupled with USGS' 2001 National Land Cover Dataset in the western and southwestern portions of the watershed. MDA Federal's Cross Correlation Analysis (CCA) technique was used to produce updates (yr. 2006) and retrospective updates (yrs. 1984 and 1992) to the base layer. CCA identifies significant spectral changes between image pairs within the range of spectral values for each land cover class identified in the 2001 base layer. MDA Federal used Classification and Regression Trees to assign land cover classes to 1984, 1992, and 2006 pixels exhibiting significant deviations from their 2001 expected spectral values. MDA Federal used these methods to develop the 1996 and 2005 land cover change datasets for the Mid-Atlantic coastal area funded by NOAA CCAP.
Land use and land cover interpretations derived from Landsat satellite imagery are based on the sun's reflectance off the land surface, e.g., urban areas have different spectral reflectance characteristics than forests and herbaceous vegetation. For this reason, the data most accurately represent land cover (e.g., tree canopy) compared with land use or management (e.g., forests and pasture). Due to similarities in spectral reflectance characteristics, some land use and land cover classes are easily confused with each other. The spectral characteristics of low density residential areas, for example, may closely resemble the characteristics of forests in a neighborhood with mature trees or of cropland or pasture if large residential lots are composed mostly of lawns. Cropland and pasture may also have similar spectral qualities. Therefore, users should be cautioned against evaluating transitions between cropland and pasture based on the CBLCD. Users should be generally confident, however, that the overall spatial patterns of cropland and pasture in the Bay watershed are accurate because the USGS and NOAA used multi-season imagery to create the 2001 base layer and the data compare favorably with county-level cropland and pasture acreage estimates reported in the 2002 U.S. Census of Agriculture.
The USGS is in the process of interpreting and publishing statistics on the extent, type, and patterns of land cover change from 1984-2006 in the Bay watershed, major tributaries, and counties. The USGS will also be publishing land change forecasts based on observed trends in the CBLCD. These additional interpretations, statistics, and datasets will be publicly released over the coming year following publication.
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| U S Geological Survey |
2001 |
To better understand how the land is changing and to relate those changes to water quality trends, the USGS funded the production of a Chesapeake Bay Watershed Land Cover Data Series (CBLCD) representing four dates: 1984, 1992, 2001, and 2006. These data were produced by MDA Federal Inc., under contract to the USGS and were derived from Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper satellite imagery. Each of the four datasets consists of 16 land use and land cover classes (Anderson, et al., 1976). The datasets are temporally comparable and encompass the entire Chesapeake Bay watershed and most intersecting counties.
The 2001 dataset represents the base layer for the Data Series and is composed of NOAA's 2001 Coastal Change Analysis Program (CCAP) dataset in the coastal and northern portion of the watershed coupled with USGS' 2001 National Land Cover Dataset in the western and southwestern portions of the watershed. MDA Federal's Cross Correlation Analysis (CCA) technique was used to produce updates (yr. 2006) and retrospective updates (yrs. 1984 and 1992) to the base layer. CCA identifies significant spectral changes between image pairs within the range of spectral values for each land cover class identified in the 2001 base layer. MDA Federal used Classification and Regression Trees to assign land cover classes to 1984, 1992, and 2006 pixels exhibiting significant deviations from their 2001 expected spectral values. MDA Federal used these methods to develop the 1996 and 2005 land cover change datasets for the Mid-Atlantic coastal area funded by NOAA CCAP.
Land use and land cover interpretations derived from Landsat satellite imagery are based on the sun's reflectance off the land surface, e.g., urban areas have different spectral reflectance characteristics than forests and herbaceous vegetation. For this reason, the data most accurately represent land cover (e.g., tree canopy) compared with land use or management (e.g., forests and pasture). Due to similarities in spectral reflectance characteristics, some land use and land cover classes are easily confused with each other. The spectral characteristics of low density residential areas, for example, may closely resemble the characteristics of forests in a neighborhood with mature trees or of cropland or pasture if large residential lots are composed mostly of lawns. Cropland and pasture may also have similar spectral qualities. Therefore, users should be cautioned against evaluating transitions between cropland and pasture based on the CBLCD. Users should be generally confident, however, that the overall spatial patterns of cropland and pasture in the Bay watershed are accurate because the USGS and NOAA used multi-season imagery to create the 2001 base layer and the data compare favorably with county-level cropland and pasture acreage estimates reported in the 2002 U.S. Census of Agriculture.
The USGS is in the process of interpreting and publishing statistics on the extent, type, and patterns of land cover change from 1984-2006 in the Bay watershed, major tributaries, and counties. The USGS will also be publishing land change forecasts based on observed trends in the CBLCD. These additional interpretations, statistics, and datasets will be publicly released over the coming year following publication.
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| U S Geological Survey |
2006 |
To better understand how the land is changing and to relate those changes to water quality trends, the USGS funded the production of a Chesapeake Bay Watershed Land Cover Data Series (CBLCD) representing four dates: 1984, 1992, 2001, and 2006. These data were produced by MDA Federal Inc., under contract to the USGS and were derived from Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper satellite imagery. Each of the four datasets consists of 16 land use and land cover classes (Anderson, et al., 1976). The datasets are temporally comparable and encompass the entire Chesapeake Bay watershed and most intersecting counties.
The 2001 dataset represents the base layer for the Data Series and is composed of NOAA's 2001 Coastal Change Analysis Program (CCAP) dataset in the coastal and northern portion of the watershed coupled with USGS' 2001 National Land Cover Dataset in the western and southwestern portions of the watershed. MDA Federal's Cross Correlation Analysis (CCA) technique was used to produce updates (yr. 2006) and retrospective updates (yrs. 1984 and 1992) to the base layer. CCA identifies significant spectral changes between image pairs within the range of spectral values for each land cover class identified in the 2001 base layer. MDA Federal used Classification and Regression Trees to assign land cover classes to 1984, 1992, and 2006 pixels exhibiting significant deviations from their 2001 expected spectral values. MDA Federal used these methods to develop the 1996 and 2005 land cover change datasets for the Mid-Atlantic coastal area funded by NOAA CCAP.
Land use and land cover interpretations derived from Landsat satellite imagery are based on the sun's reflectance off the land surface, e.g., urban areas have different spectral reflectance characteristics than forests and herbaceous vegetation. For this reason, the data most accurately represent land cover (e.g., tree canopy) compared with land use or management (e.g., forests and pasture). Due to similarities in spectral reflectance characteristics, some land use and land cover classes are easily confused with each other. The spectral characteristics of low density residential areas, for example, may closely resemble the characteristics of forests in a neighborhood with mature trees or of cropland or pasture if large residential lots are composed mostly of lawns. Cropland and pasture may also have similar spectral qualities. Therefore, users should be cautioned against evaluating transitions between cropland and pasture based on the CBLCD. Users should be generally confident, however, that the overall spatial patterns of cropland and pasture in the Bay watershed are accurate because the USGS and NOAA used multi-season imagery to create the 2001 base layer and the data compare favorably with county-level cropland and pasture acreage estimates reported in the 2002 U.S. Census of Agriculture.
The USGS is in the process of interpreting and publishing statistics on the extent, type, and patterns of land cover change from 1984-2006 in the Bay watershed, major tributaries, and counties. The USGS will also be publishing land change forecasts based on observed trends in the CBLCD. These additional interpretations, statistics, and datasets will be publicly released over the coming year following publication.
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| U S Geological Survey |
2023 |
This dataset consists of Pennsylvania bridge over water locations that are being considered for flood data collection. The purpose of this effort is to identify potential locations in Pennsylvania where additional or updated water level data may be needed during or after major storm events. Criteria involved in the identification of potential locations for flood data collection includes locations of bridges over water, scour-critical bridges, densely populated areas, historical hurricane tracks, historical flood event data collection sites, counties with historical Federal Emergency Management Agency (FEMA) disaster declarations, Social Vulnerability Index data, and flood-related disaster data provided by FEMA (insurance claims, individual assistance applications, and repetitive loss records). Pre-identification of these locations in anticipation of a flood event ensures that areas of interest are being targeted and allows for expedited decision-making related to site selection, resulting in safer and more timely installation of equipment.
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| U S Geological Survey |
2016 |
In 2012, catchments were generated in the Delaware River Basin for 8-digit HUCs in the areas underlain by the Marcellus Shale (all of 02040101, 02040102, 02040103, 02040104; and headwater areas of 02040106 and 02040203) based on the National Hydrography Dataset (NHD) Strahler first- and second-order streams. There were areas that did not have a catchment generated so another methodology needed to be used in an attempt to fill in the 'gap areas'. A 900-cell, flow accumulation raster generated for the Pennsylvania StreamStats application was used as a surrogate stream layer with the same Strahler ordering system applied to help fill in the 'gap areas'. Points were manually placed at the downstream end of the Strahler Order 1 and 2 reaches using the surrogate streams as a guide. This manual point placement is different from the automated method used to develop the catchments generated from the NHD flowlines. The manual placement of the catchment pour points does not include a point downstream of a confluence, therefore, not as many catchments are generated in the 'gap-area' processing.
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| U S Geological Survey |
2016 |
In 2012, catchments were generated in the Delaware River Basin for 8-digit HUCs in areas underlain by the Marcellus Shale (all of 02040101, 02040102, 02040103, 02040104; and headwater areas of 02040106 and 02040203) based on the National Hydrography Dataset (NHD) Strahler first- and second-order streams. At that time, the remaining 8-digit HUCs were not included. The completion of HUCs 02040106 and 02040203, along with HUCs 02040105, 02040201, 02040202, 02040205, 02040206, and 02040207 were part of this current project.
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| U S Geological Survey |
2000 |
aerial photography of the Delaware Water Gap including the surrounding watersheds and tributaries
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| U S Geological Survey |
2000 |
Landuse for the Delaware Water Gap and Surrounding watersheds and tributaries
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| U S Geological Survey |
2023 |
As part of a study to quantify floodplain flood attenuation ecosystem services, datasets were developed representing a baseline (current floodplain condition) and counterfactual (floodplain flood storage removed) scenario for 18 sites in the Schuylkill River Watershed, Pennsylvania. This data release contains rasters (3-m resolution) of baseline and counterfactual flood depth grids for the 0.5, 0.2, 0.1, 0.04, 0.02, and 0.01 annual exceedance probability (AEP) scenarios in the Schuylkill River Watershed, Pennsylvania. Depth grid raster datasets were used as input for riverine flood modeling in the Federal Emergency Management Agency HAZUS Program to estimate damages to buildings under various flood intensities. The HAZUS Program is a tool to estimate damages and associated losses due to natural disasters like floods. The data release also contains polyline shapefiles of (1) six floodplain storage volume cross-sections for the 0.01 AEP baseline scenario flood inundation boundary at each USGS streamgage of interest and (2) water surface cross-sections extending across all areas of interest inundation boundaries based on the 0.01 counterfactual scenario boundary. Floodplain storage volume cross-sectional lines (Schuylkill_Volume_xns) were used in the approximation of average floodplain flood water storage capacity of each area of interest. Water surface cross-sections (Schuylkill_DepthGrid_xns) were used for water surface interpolation in depth grid processing
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2017 |
A breakthrough in water resources management occurred in 1961 when President Kennedy and the governors of Delaware, New Jersey, Pennsylvania, and New York for the first time signed concurrent compact legislation into law creating a regional body with the force of law to oversee a unified approach to managing a river system without regard to political boundaries. The members of this regional body - the Delaware River Basin Commission (DRBC) - include the four basin state governors and the Division Engineer, North Atlantic Division, U.S. Army Corps of Engineers, who serves as the federal representative. Commission programs include water quality protection, water supply allocation, regulatory review (permitting), water conservation initiatives, watershed planning, drought management, flood loss reduction, and recreation. Much of the new drilling interest taking place in northeastern Pennsylvania and southern New York is targeted at reaching the natural gas found in the Marcellus Shale formation, which underlies about 36 percent of the Delaware River Basin. Because the Marcellus Shale is considered a tight geologic formation, natural gas deposits were not previously thought to be practically and economically mineable using traditional techniques. New horizontal drilling and extraction methods, coupled with higher energy costs, have given energy companies reason to take a new interest in mining the natural gas deposits within the Marcellus Shale. In connection with natural gas drilling, the commission has identified three major areas of concern: 1.Gas drilling projects in the Marcellus Shale or other formations may have a substantial effect on the water resources of the basin by reducing the flow in streams and/or aquifers used to supply the significant amounts of fresh water needed in the natural gas mining process. 2.On-site drilling operations may potentially add, discharge or cause the release of pollutants into the ground water or surface water. 3.The recovered "frac water" must be treated and disposed of properly. DRBC is identifying methods, geospatial data, and other information to support decision making on how best to oversee the Marcellus Shale drilling in the Delaware River Basin (DRB).
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| U S Geological Survey |
2017 |
A breakthrough in water resources management occurred in 1961 when President Kennedy and the governors of Delaware, New Jersey, Pennsylvania, and New York for the first time signed concurrent compact legislation into law creating a regional body with the force of law to oversee a unified approach to managing a river system without regard to political boundaries. The members of this regional body - the Delaware River Basin Commission (DRBC) - include the four basin state governors and the Division Engineer, North Atlantic Division, U.S. Army Corps of Engineers, who serves as the federal representative. Commission programs include water quality protection, water supply allocation, regulatory review (permitting), water conservation initiatives, watershed planning, drought management, flood loss reduction, and recreation. Much of the new drilling interest taking place in northeastern Pennsylvania and southern New York is targeted at reaching the natural gas found in the Marcellus Shale formation, which underlies about 36 percent of the Delaware River Basin. Because the Marcellus Shale is considered a tight geologic formation, natural gas deposits were not previously thought to be practically and economically mineable using traditional techniques. New horizontal drilling and extraction methods, coupled with higher energy costs, have given energy companies reason to take a new interest in mining the natural gas deposits within the Marcellus Shale. In connection with natural gas drilling, the commission has identified three major areas of concern: 1.Gas drilling projects in the Marcellus Shale or other formations may have a substantial effect on the water resources of the basin by reducing the flow in streams and/or aquifers used to supply the significant amounts of fresh water needed in the natural gas mining process. 2.On-site drilling operations may potentially add, discharge or cause the release of pollutants into the ground water or surface water. 3.The recovered "frac water" must be treated and disposed of properly. DRBC is identifying methods, geospatial data, and other information to support decision making on how best to oversee the Marcellus Shale drilling in the Delaware River Basin (DRB).
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| U S Geological Survey |
2017 |
A breakthrough in water resources management occurred in 1961 when President Kennedy and the governors of Delaware, New Jersey, Pennsylvania, and New York for the first time signed concurrent compact legislation into law creating a regional body with the force of law to oversee a unified approach to managing a river system without regard to political boundaries. The members of this regional body - the Delaware River Basin Commission (DRBC) - include the four basin state governors and the Division Engineer, North Atlantic Division, U.S. Army Corps of Engineers, who serves as the federal representative. Commission programs include water quality protection, water supply allocation, regulatory review (permitting), water conservation initiatives, watershed planning, drought management, flood loss reduction, and recreation. Much of the new drilling interest taking place in northeastern Pennsylvania and southern New York is targeted at reaching the natural gas found in the Marcellus Shale formation, which underlies about 36 percent of the Delaware River Basin. Because the Marcellus Shale is considered a tight geologic formation, natural gas deposits were not previously thought to be practically and economically mineable using traditional techniques. New horizontal drilling and extraction methods, coupled with higher energy costs, have given energy companies reason to take a new interest in mining the natural gas deposits within the Marcellus Shale. In connection with natural gas drilling, the commission has identified three major areas of concern: 1.Gas drilling projects in the Marcellus Shale or other formations may have a substantial effect on the water resources of the basin by reducing the flow in streams and/or aquifers used to supply the significant amounts of fresh water needed in the natural gas mining process. 2.On-site drilling operations may potentially add, discharge or cause the release of pollutants into the ground water or surface water. 3.The recovered "frac water" must be treated and disposed of properly. DRBC is identifying methods, geospatial data, and other information to support decision making on how best to oversee the Marcellus Shale drilling in the Delaware River Basin (DRB).
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| U S Geological Survey |
2017 |
A breakthrough in water resources management occurred in 1961 when President Kennedy and the governors of Delaware, New Jersey, Pennsylvania, and New York for the first time signed concurrent compact legislation into law creating a regional body with the force of law to oversee a unified approach to managing a river system without regard to political boundaries. The members of this regional body - the Delaware River Basin Commission (DRBC) - include the four basin state governors and the Division Engineer, North Atlantic Division, U.S. Army Corps of Engineers, who serves as the federal representative. Commission programs include water quality protection, water supply allocation, regulatory review (permitting), water conservation initiatives, watershed planning, drought management, flood loss reduction, and recreation. Much of the new drilling interest taking place in northeastern Pennsylvania and southern New York is targeted at reaching the natural gas found in the Marcellus Shale formation, which underlies about 36 percent of the Delaware River Basin. Because the Marcellus Shale is considered a tight geologic formation, natural gas deposits were not previously thought to be practically and economically mineable using traditional techniques. New horizontal drilling and extraction methods, coupled with higher energy costs, have given energy companies reason to take a new interest in mining the natural gas deposits within the Marcellus Shale. In connection with natural gas drilling, the commission has identified three major areas of concern: 1.Gas drilling projects in the Marcellus Shale or other formations may have a substantial effect on the water resources of the basin by reducing the flow in streams and/or aquifers used to supply the significant amounts of fresh water needed in the natural gas mining process. 2.On-site drilling operations may potentially add, discharge or cause the release of pollutants into the ground water or surface water. 3.The recovered "frac water" must be treated and disposed of properly. DRBC is identifying methods, geospatial data, and other information to support decision making on how best to oversee the Marcellus Shale drilling in the Delaware River Basin (DRB).
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| U S Geological Survey |
2017 |
A breakthrough in water resources management occurred in 1961 when President Kennedy and the governors of Delaware, New Jersey, Pennsylvania, and New York for the first time signed concurrent compact legislation into law creating a regional body with the force of law to oversee a unified approach to managing a river system without regard to political boundaries. The members of this regional body - the Delaware River Basin Commission (DRBC) - include the four basin state governors and the Division Engineer, North Atlantic Division, U.S. Army Corps of Engineers, who serves as the federal representative. Commission programs include water quality protection, water supply allocation, regulatory review (permitting), water conservation initiatives, watershed planning, drought management, flood loss reduction, and recreation. Much of the new drilling interest taking place in northeastern Pennsylvania and southern New York is targeted at reaching the natural gas found in the Marcellus Shale formation, which underlies about 36 percent of the Delaware River Basin. Because the Marcellus Shale is considered a tight geologic formation, natural gas deposits were not previously thought to be practically and economically mineable using traditional techniques. New horizontal drilling and extraction methods, coupled with higher energy costs, have given energy companies reason to take a new interest in mining the natural gas deposits within the Marcellus Shale. In connection with natural gas drilling, the commission has identified three major areas of concern: 1.Gas drilling projects in the Marcellus Shale or other formations may have a substantial effect on the water resources of the basin by reducing the flow in streams and/or aquifers used to supply the significant amounts of fresh water needed in the natural gas mining process. 2.On-site drilling operations may potentially add, discharge or cause the release of pollutants into the ground water or surface water. 3.The recovered "frac water" must be treated and disposed of properly. DRBC is identifying methods, geospatial data, and other information to support decision making on how best to oversee the Marcellus Shale drilling in the Delaware River Basin (DRB).
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| U S Geological Survey |
2017 |
A breakthrough in water resources management occurred in 1961 when President Kennedy and the governors of Delaware, New Jersey, Pennsylvania, and New York for the first time signed concurrent compact legislation into law creating a regional body with the force of law to oversee a unified approach to managing a river system without regard to political boundaries. The members of this regional body - the Delaware River Basin Commission (DRBC) - include the four basin state governors and the Division Engineer, North Atlantic Division, U.S. Army Corps of Engineers, who serves as the federal representative. Commission programs include water quality protection, water supply allocation, regulatory review (permitting), water conservation initiatives, watershed planning, drought management, flood loss reduction, and recreation. Much of the new drilling interest taking place in northeastern Pennsylvania and southern New York is targeted at reaching the natural gas found in the Marcellus Shale formation, which underlies about 36 percent of the Delaware River Basin. Because the Marcellus Shale is considered a tight geologic formation, natural gas deposits were not previously thought to be practically and economically mineable using traditional techniques. New horizontal drilling and extraction methods, coupled with higher energy costs, have given energy companies reason to take a new interest in mining the natural gas deposits within the Marcellus Shale. In connection with natural gas drilling, the commission has identified three major areas of concern: 1.Gas drilling projects in the Marcellus Shale or other formations may have a substantial effect on the water resources of the basin by reducing the flow in streams and/or aquifers used to supply the significant amounts of fresh water needed in the natural gas mining process. 2.On-site drilling operations may potentially add, discharge or cause the release of pollutants into the ground water or surface water. 3.The recovered "frac water" must be treated and disposed of properly. DRBC is identifying methods, geospatial data, and other information to support decision making on how best to oversee the Marcellus Shale drilling in the Delaware River Basin (DRB).
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| U S Geological Survey |
1992 |
Orthophotos combine the image characteristics of a
photograph with the geometric qualities of a map. The
primary digital orthophotoquad (DOQ) is a 1-meter ground
resolution, quarter-quadrangle (3.75-minutes of latitude
by 3.75-minutes of longitude) image cast on the Universal
Transverse Mercator Projection (UTM) on the North
American Datum of 1983 (NAD83).The geographic extent of
the DOQ is equivalent to a quarter-quad plus The overedge
ranges a minimum of 50 meters to a maximum of 300 meters
beyond the extremes of the primary and secondary corner
points. The overedge is included to facilitate tonal
matching for mosaicking and for the placement of the NAD83
and secondary datum corner ticks. The normal orientation
of data is by lines (rows) and samples (columns). Each
line contains a series of pixels ordered from west to
east with the order of the lines from north to south.
The standard, archived digital orthophoto is formatted as
four ASCII header records, followed by a series of 8-bit
binary image data records. The radiometric image
brightness values are stored as 256 gray levels ranging
from 0 to 255. The metadata embedded in the digital
orthophoto contain a wide range of descriptive
information including format source information,
production instrumentation and dates, and data to assist
with displaying and georeferencing the image.
DOQ images are acquired as a part of the USGS' National Aerial Photography Program (NAPP).
Through NAPP imagery for each state is produced on a 7 year cycle.
These images are the NAPP III cycle which will run from 1997-2001
These DOQQ's are distributed through PASDA as GeoTIFF images as received from USGS.
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| U S Geological Survey |
1997 |
Orthophotos combine the image characteristics of a
photograph with the geometric qualities of a map. The
primary digital orthophotoquad (DOQ) is a 1-meter ground
resolution, quarter-quadrangle (3.75-minutes of latitude
by 3.75-minutes of longitude) image cast on the Universal
Transverse Mercator Projection (UTM) on the North
American Datum of 1983 (NAD83).The geographic extent of
the DOQ is equivalent to a quarter-quad plus The overedge
ranges a minimum of 50 meters to a maximum of 300 meters
beyond the extremes of the primary and secondary corner
points. The overedge is included to facilitate tonal
matching for mosaicking and for the placement of the NAD83
and secondary datum corner ticks. The normal orientation
of data is by lines (rows) and samples (columns). Each
line contains a series of pixels ordered from west to
east with the order of the lines from north to south.
The standard, archived digital orthophoto is formatted as
four ASCII header records, followed by a series of 8-bit
binary image data records. The radiometric image
brightness values are stored as 256 gray levels ranging
from 0 to 255. The metadata embedded in the digital
orthophoto contain a wide range of descriptive
information including format source information,
production instrumentation and dates, and data to assist
with displaying and georeferencing the image.
DOQ images are acquired as a part of the USGS' National Aerial Photography Program (NAPP).
Through NAPP imagery for each state is produced on a 7 year cycle.
These images are the NAPP III cycle which will run from 1997-2001
These DOQQ's are distributed through PASDA as GeoTIFF images as received from USGS.
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| U S Geological Survey |
2000 |
Orthophotos combine the image characteristics of a
photograph with the geometric qualities of a map. The
primary digital orthophotoquad (DOQ) is a 1-meter ground
resolution, quarter-quadrangle (3.75-minutes of latitude
by 3.75-minutes of longitude) image cast on the Universal
Transverse Mercator Projection (UTM) on the North
American Datum of 1983 (NAD83).The geographic extent of
the DOQ is equivalent to a quarter-quad plus The overedge
ranges a minimum of 50 meters to a maximum of 300 meters
beyond the extremes of the primary and secondary corner
points. The overedge is included to facilitate tonal
matching for mosaicking and for the placement of the NAD83
and secondary datum corner ticks. The normal orientation
of data is by lines (rows) and samples (columns). Each
line contains a series of pixels ordered from west to
east with the order of the lines from north to south.
The standard, archived digital orthophoto is formatted as
four ASCII header records, followed by a series of 8-bit
binary image data records. The radiometric image
brightness values are stored as 256 gray levels ranging
from 0 to 255. The metadata embedded in the digital
orthophoto contain a wide range of descriptive
information including format source information,
production instrumentation and dates, and data to assist
with displaying and georeferencing the image.
DOQ images are acquired as a part of the USGS' National Aerial Photography Program (NAPP).
Through NAPP imagery for each state is produced on a 7 year cycle.
These images are the NAPP III cycle which will run from 1997-2001
These DOQQ's are distributed through PASDA as GeoTIFF images as received from USGS.
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| U S Geological Survey |
1997 |
Orthophotos combine the image characteristics of a
photograph with the geometric qualities of a map. The
primary digital orthophotoquad (DOQ) is a 1-meter ground
resolution, quarter-quadrangle (3.75-minutes of latitude
by 3.75-minutes of longitude) image cast on the Universal
Transverse Mercator Projection (UTM) on the North
American Datum of 1983 (NAD83).The geographic extent of
the DOQ is equivalent to a quarter-quad plus The overedge
ranges a minimum of 50 meters to a maximum of 300 meters
beyond the extremes of the primary and secondary corner
points. The overedge is included to facilitate tonal
matching for mosaicking and for the placement of the NAD83
and secondary datum corner ticks. The normal orientation
of data is by lines (rows) and samples (columns). Each
line contains a series of pixels ordered from west to
east with the order of the lines from north to south.
The standard, archived digital orthophoto is formatted as
four ASCII header records, followed by a series of 8-bit
binary image data records. The radiometric image
brightness values are stored as 256 gray levels ranging
from 0 to 255. The metadata embedded in the digital
orthophoto contain a wide range of descriptive
information including format source information,
production instrumentation and dates, and data to assist
with displaying and georeferencing the image.
DOQ images are acquired as a part of the USGS' National Aerial Photography Program (NAPP).
Through NAPP imagery for each state is produced on a 7 year cycle.
These images are the NAPP III cycle which will run from 1997-2001
These DOQQ's are distributed through PASDA as GeoTIFF images as received from USGS.
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| U S Geological Survey |
1993 |
Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquad (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75-minutes of latitude by 3.75-minutes of longitude) image cast on the Universal Transverse Mercator Projection (UTM) on the North American Datum of 1983 (NAD83).The geographic extent of the DOQ is equivalent to a quarter-quad plus The overedge ranges a minimum of 50 meters to a maximum of 300 meters beyond the extremes of the primary and secondary corner points. The overedge is included to facilitate tonal matching for mosaicking and for the placement of the NAD83 and secondary datum corner ticks. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. The standard, archived digital orthophoto is formatted as four ASCII header records, followed by a series of 8-bit binary image data records. The radiometric image brightness values are stored as 256 gray levels ranging from 0 to 255. The metadata embedded in the digital orthophoto contain a wide range of descriptive information including format source information, production instrumentation and dates, and data to assist with displaying and georeferencing the image. DOQ images are acquired as a part of the USGS' National Aerial Photography Program (NAPP). Through NAPP imagery for each state is produced on a 7 year cycle. These images are the NAPP III cycle which will run from 1997-2001 These DOQQ's are distributed through PASDA as GeoTIFF images as received from USGS.
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| U S Geological Survey |
1999 |
Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquad (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75-minutes of latitude by 3.75-minutes of longitude) image cast on the Universal Transverse Mercator Projection (UTM) on the North American Datum of 1983 (NAD83).The geographic extent of the DOQ is equivalent to a quarter-quad plus The overedge ranges a minimum of 50 meters to a maximum of 300 meters beyond the extremes of the primary and secondary corner points. The overedge is included to facilitate tonal matching for mosaicking and for the placement of the NAD83 and secondary datum corner ticks. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. The standard, archived digital orthophoto is formatted as four ASCII header records, followed by a series of 8-bit binary image data records. The radiometric image brightness values are stored as 256 gray levels ranging from 0 to 255. The metadata embedded in the digital orthophoto contain a wide range of descriptive information including format source information, production instrumentation and dates, and data to assist with displaying and georeferencing the image. DOQ images are acquired as a part of the USGS' National Aerial Photography Program (NAPP). Through NAPP imagery for each state is produced on a 7 year cycle. These images are the NAPP III cycle which will run from 1997-2001 These DOQQ's are distributed through PASDA as GeoTIFF images as received from USGS.
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| U S Geological Survey |
2000 |
A scope of work was developed in response to a request by the U. S. Army Corps
of Engineers, Philadelphia District. The request was to perform a topographic
change grid analysis for the Frankford 7.5-minute quadrangle, 1:24,000-scale
topographic map, which includes the Wissinoming neighborhood, and the Germantown 7.5-minute quadrangle, which includes the Logan and Feltonville neighborhoods of the City of Philadelphia. The following tasks were performed under this scope of work: A GPS-corrected GIS grid analysis for each quadrangle was completed and is accompanied by documentation that describes procedures and provides metadata of the informational content of the GIS. A high-resolution global positioning system (GPS) survey was conducted for each topographic quadrangle in order to evaluate and correct systematic discrepancies in elevation between the modern and historic surveys. Prior to release, the fully documented GPS-corrected GIS grid analysis for each quadrangle was reviewed for (1) com-pleteness of documentation and for (2) appropriate analysis and discussion of uncertainties.
The following report is in fulfillment of the tasks outlined in this scope of work and was performed by the U. S. Geological Survey for the U. S. Army Corps of Engineers, Philadelphia District under MIPR agreement number: W25PHS93358288.
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| U S Geological Survey |
2023 |
This dataset consists of Pennsylvania bridge over water locations that are being considered for flood data collection. The purpose of this effort is to identify potential locations in Pennsylvania where additional or updated water level data may be needed during or after major storm events. Criteria involved in the identification of potential locations for flood data collection includes locations of bridges over water, scour-critical bridges, densely populated areas, historical hurricane tracks, historical flood event data collection sites, counties with historical Federal Emergency Management Agency (FEMA) disaster declarations, Social Vulnerability Index data, and flood-related disaster data provided by FEMA (insurance claims, individual assistance applications, and repetitive loss records). Pre-identification of these locations in anticipation of a flood event ensures that areas of interest are being targeted and allows for expedited decision-making related to site selection, resulting in safer and more timely installation of equipment.
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| U S Geological Survey |
2018 |
The Geographic Names Information System contains information about physical and cultural geographic features of all types in the United States, associated areas, and Antarctica, current and historical, but not including roads and highways. The database holds the Federally recognized name of each feature and defines the feature location by state, county, USGS topographic map, and geographic coordinates. Other attributes include names or spellings other than the official name, feature designations, feature classification, historical and descriptive information, and for some categories the geometric boundaries. The database assigns a unique, permanent feature identifier, the Feature ID, as a standard Federal key for accessing, integrating, or reconciling feature data from multiple data sets. The GNIS collects data from a broad program of partnerships with Federal, State, and local government agencies and other authorized contributors. The GNIS provides data to all levels of government and to the public, as well as to numerous applications through a web query site, web map and feature services, file download services, and customized files
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| U S Geological Survey |
2008 |
The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.
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| U S Geological Survey |
1981 |
The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.
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| U S Geological Survey |
2007 |
The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.
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| U S Geological Survey |
2017 |
The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.
Metadata
|
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|
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|
KMZ
|
Spreadsheet
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| U S Geological Survey |
2000 |
A scope of work was developed in response to a request by the U. S. Army Corps
of Engineers, Philadelphia District. The request was to perform a topographic
change grid analysis for the Frankford 7.5-minute quadrangle, 1:24,000-scale
topographic map, which includes the Wissinoming neighborhood, and the Germantown 7.5-minute quadrangle, which includes the Logan and Feltonville neighborhoods of the City of Philadelphia. The following tasks were performed under this scope of work: A GPS-corrected GIS grid analysis for each quadrangle was completed and is accompanied by documentation that describes procedures and provides metadata of the informational content of the GIS. A high-resolution global positioning system (GPS) survey was conducted for each topographic quadrangle in order to evaluate and correct systematic discrepancies in elevation between the modern and historic surveys. Prior to release, the fully documented GPS-corrected GIS grid analysis for each quadrangle was reviewed for (1) com-pleteness of documentation and for (2) appropriate analysis and discussion of uncertainties.
The following report is in fulfillment of the tasks outlined in this scope of work and was performed by the U. S. Geological Survey for the U. S. Army Corps of Engineers, Philadelphia District under MIPR agreement number: W25PHS93358288.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2005 |
Historic USGS 15 minute topographic maps for Pennsylvania as collected from the MapTech Historical Map Collection at '. Scanned map images from MapTech were downloaded, assembled, and registered and rectified via Arc/Info to the UTM Zone 17/18 NAD83 projection.
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| U S Geological Survey |
2006 |
A newly revised version of the historic USGS 15 minute topographic maps for Pennsylvania as collected from the MapTech Historical Map Collection at 'http://historical.maptech.com'. As an improvement to the initial version, the original scanned images from MapTech were downloaded, assembled with mosaicing software, and georeferenced to the statewide Albers NAD83 projection.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2002 |
The USGS Central Region Energy Team assesses oil and gas resources of the United States. The onshore and State water areas of the United States comprise 71 provinces. Within these provinces, Total Petroleum Systems are defined and Assessment Units are defined and assessed. Each of these provinces is defined geologically, and most province boundaries are defined by major geologic changes.
The Appalachian Basin Province is located in the eastern United States, encompassing all or parts of the counties in Alabama, Georgia, Kentucky, Maryland, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia. The main population centers within the study area are Birmingham, Alabama; Buffalo, New York; Cleveland, Ohio; Pittsburgh, Pennsylvania; Chattanooga, Tennessee; and Roanoke, Virginia. The main Interstates are I-20, I-24, I-40, I-59, I-64, I-65, I-66, I-70, I-71, I-75, I-76, I-77, I-78, I-79, I-80, I-81, I-83, I-84, I-87, I-88, and I-90. The Ohio, Susquehanna, Allegheny, Tennessee, Coosa, Delaware, New, Potomac, and Scioto Rivers and their tributaries drain the area. The province boundary was drawn to include the geologic structures generally considered to be in or bounding the Appalachian Basin.
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| U S Geological Survey |
2004 |
The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) is a nationwide network of precipitation monitoring sites. The network is a cooperative effort between many different groups, including the State Agricultural Experiment Stations, U.S. Geological Survey, U.S. Department of Agriculture, and numerous other governmental and private entities. The NADP/NTN has grown from 22 stations at the end of 1978, our first year, to over 250 sites spanning the continental United States, Alaska, and Puerto Rico, and the Virgin Islands.
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| U S Geological Survey |
2005 |
The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) is a nationwide network of precipitation monitoring sites. The network is a cooperative effort between many different groups, including the State Agricultural Experiment Stations, U.S. Geological Survey, U.S. Department of Agriculture, and numerous other governmental and private entities. The NADP/NTN has grown from 22 stations at the end of 1978, our first year, to over 250 sites spanning the continental United States, Alaska, and Puerto Rico, and the Virgin Islands.
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| U S Geological Survey |
1999 |
The U.S. Geological Survey has developed a National Elevation Database (NED).The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to a consistent datum, edge-match, fill slivers of missing data at quadrangle seams, recast the data to a consistent geographic projection and convert all elevation values to decimal meters as a consistent unit of measure.
NED has a resolution of one-third arc-second (approximately 10 meters) for much of the conterminous United States, Hawaii and Puerto Rico in a NAD83 datum. There is a resolution of two arc-seconds for Alaska and the datum is NAD27.
NED at 10 meters is created using the same methods outlined above with the source data being mostly the 10m DEMs. DEMs at 5 meters, 1/3 arc-second, and 1/9 arc-second maps are also used where available. In some cases, the 10m NED is resampled from LIDAR or created using aerial photography.
One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. Artifact removal greatly improves the quality of the slope, shaded-relief, and synthetic drainage information that can be derived from the elevation data. Geospatial elevation data are used by the scientific and resource management communities for global change research, hydrologic modeling, resource monitoring, mapping, and visualization applications.
NRCS has elected to ONLY serve NED 10 which is 10 meter or better and not NED 10 which was resampled from 30 meter. NRCS also serves the maps in a UTM projection. These two facts differentiate the maps from those served at http://seamless.usgs.gov/.
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| U S Geological Survey |
1999 |
The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. NED files are available on CD from the EROS data center as 1x1 degree tiles. For online distribution the files on PASDA have been aggregated by county and projected into the Albers Equal Area projection.
Data incomplete, areas not mapped when screened at small scales during low
level radioactive waste siting analysis.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 for mapping zone 47 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 was created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 47 encompasses whole or portions of several states including the states of Kentucky, Indiana, Ohio, Tennessee, and Missouri. Questions about the NLCD mapping zone 47 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 tree canopy layer for mapping zone 47 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 was created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 47 encompasses whole or portions of several states including the states of Kentucky, Indiana, Ohio, Tennessee, and Missouri. Questions about the NLCD mapping zone 47 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 tree canopy layer for mapping zone 47 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 was created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 47 encompasses whole or portions of several states including the states of Kentucky, Indiana, Ohio, Tennessee, and Missouri. Questions about the NLCD mapping zone 47 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 for mapping zone 47 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 was created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 47 encompasses whole or portions of several states including the states of Kentucky, Indiana, Ohio, Tennessee, and Missouri. Questions about the NLCD mapping zone 47 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 tree canopy layer for mapping zone 47 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 was created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 47 encompasses whole or portions of several states including the states of Kentucky, Indiana, Ohio, Tennessee, and Missouri. Questions about the NLCD mapping zone 47 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. This landcover map and all documents pertaining to it are considered "provisional" until a formal accuracy assessment can be conducted. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2004) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 is created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 60 encompasses whole or portions of several states, including the states of New Jersey, Delaware, Maryland, Pennsylvania, Virginia, and the District of Columbia. Questions about the NLCD mapping zone 60 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. This landcover map and all documents pertaining to it are considered "provisional" until a formal accuracy assessment can be conducted. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2004) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 is created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 60 encompasses whole or portions of several states, including the states of New Jersey, Delaware, Maryland, Pennsylvania, Virginia, and the District of Columbia. Questions about the NLCD mapping zone 60 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2003 |
The National Land Cover Database 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. This landcover map and all documents pertaining to it are considered "provisional" until a formal accuracy assessment can be conducted. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2004) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 is created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 60 encompasses whole or portions of several states, including the states of New Jersey, Delaware, Maryland, Pennsylvania, Virginia, and the District of Columbia. Questions about the NLCD mapping zone 60 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2004 |
The National Land Cover Database 2001 tree canopy layer for mapping zone 47 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp.
The NLCD 2001 was created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 47 encompasses whole or portions of several states including the states of Kentucky, Indiana, Ohio, Tennessee, and Missouri. Questions about the NLCD mapping zone 47 can be directed to the NLCD 2001 land cover mapping team at the National Center, EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
1999 |
These data can be used in a geographic information system (GIS) for any
number of purposes such as assessing wildlife habitat, water quality,
pesticide runoff, land use change, etc. The State data sets are provided
with a 300 meter buffer beyond the State border to facilitate combining
the State files into larger regions.
The user must have a firm understanding of how the datasets were compiled
and the resulting limitations of these data. The National Land Cover Dataset
was compiled from Landsat satellite TM imagery (circa 1992) with a spatial
resolution of 30 meters and supplemented by various ancillary data (where
available). The analysis and interpretation of the satellite imagery was
conducted using very large, sometimes multi-state image mosaics (i.e. up to 18
Landsat scenes). Using a relatively small number of aerial photographs for
'ground truth', the thematic interpretations were necessarily conducted from a
spatially-broad perspective. Furthermore, the accuracy assessments (see below)
correspond to 'federal regions' which are groupings of contiguous states. Thus,
the reliability of the data is greatest at the state or multi-state level. The
statistical accuracy of the data is known only for the region.
Important Caution Advisory
With this in mind, users are cautioned to carefully scrutinize the data to
see if they are of sufficient reliability before attempting to use the
dataset for larger-scale or local analyses. This evaluation must be made
remembering that the NLCD represents conditions in the early 1990s.
The Pennsylvania portion of the NLCD was created as part of land cover
mapping activities for Federal Region III that includes the States of
Maryland, Delaware, Pennsylvania, Virginia, West Virginia, and the
District of Columbia. The NLCD classification contains 21 different
land cover categories with a spatial resolution of 30 meters. The NLCD
was produced as a cooperative effort between the U.S. Geological Survey
(USGS) and the U.S. Environmental Protection Agency (US EPA) to produce
a consistent, land cover data layer for the conterminous U.S. using
early 1990s Landsat thematic mapper (TM) data purchased by the
Multi-resolution Land Characterization (MRLC) Consortium. The MRLC
Consortium is a partnership of federal agencies that produce or use land
cover data. Partners include the USGS (National Mapping, Biological
Resources, and Water Resources Divisions), US EPA, the U.S. Forest Service,
and the National Oceanic and Atmospheric Administration.
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| U S Geological Survey |
1999 |
These data can be used in a geographic information system (GIS) for any
number of purposes such as assessing wildlife habitat, water quality,
pesticide runoff, land use change, etc. The State data sets are provided
with a 300 meter buffer beyond the State border to facilitate combining
the State files into larger regions.
The user must have a firm understanding of how the datasets were compiled
and the resulting limitations of these data. The National Land Cover Dataset
was compiled from Landsat satellite TM imagery (circa 1992) with a spatial
resolution of 30 meters and supplemented by various ancillary data (where
available). The analysis and interpretation of the satellite imagery was
conducted using very large, sometimes multi-state image mosaics (i.e. up to 18
Landsat scenes). Using a relatively small number of aerial photographs for
'ground truth', the thematic interpretations were necessarily conducted from a
spatially-broad perspective. Furthermore, the accuracy assessments (see below)
correspond to 'federal regions' which are groupings of contiguous states. Thus,
the reliability of the data is greatest at the state or multi-state level. The
statistical accuracy of the data is known only for the region.
Important Caution Advisory
With this in mind, users are cautioned to carefully scrutinize the data to
see if they are of sufficient reliability before attempting to use the
dataset for larger-scale or local analyses. This evaluation must be made
remembering that the NLCD represents conditions in the early 1990s.
The Pennsylvania portion of the NLCD was created as part of land cover
mapping activities for Federal Region III that includes the States of
Maryland, Delaware, Pennsylvania, Virginia, West Virginia, and the
District of Columbia. The NLCD classification contains 21 different
land cover categories with a spatial resolution of 30 meters. The NLCD
was produced as a cooperative effort between the U.S. Geological Survey
(USGS) and the U.S. Environmental Protection Agency (US EPA) to produce
a consistent, land cover data layer for the conterminous U.S. using
early 1990s Landsat thematic mapper (TM) data purchased by the
Multi-resolution Land Characterization (MRLC) Consortium. The MRLC
Consortium is a partnership of federal agencies that produce or use land
cover data. Partners include the USGS (National Mapping, Biological
Resources, and Water Resources Divisions), US EPA, the U.S. Forest Service,
and the National Oceanic and Atmospheric Administration.
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| U S Geological Survey |
1999 |
These data can be used in a geographic
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| U S Geological Survey |
2005 |
New York state boundary includes offshore boundaries (oceans, bays, and great lakes). This map layer portrays the State boundaries of the United States, and the boundaries of Puerto Rico and the U.S. Virgin Islands. The map layer was created by extracting the State boundary polygons from the individual
1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced
by the U.S.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
1999 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that comprise the nation's surface water drainage system. Medium resolution NHD is based on the content of the U.S. Geological Survey 1:100,000-scale Digital Line Graph (DLG) hydrography data, integrated with reach-related information from the U.S. Environmental Protection Agency Reach File Version 3.0 (RF3). More specifically, it contains reach codes for networked features and isolated lakes, flow direction, names, stream level, and centerline representations for areal water bodies. Reaches are also defined to represent water bodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
1999 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that comprise the nation's surface water drainage system. Medium resolution NHD is based on the content of the U.S. Geological Survey 1:100,000-scale Digital Line Graph (DLG) hydrography data, integrated with reach-related information from the U.S. Environmental Protection Agency Reach File Version 3.0 (RF3). More specifically, it contains reach codes for networked features and isolated lakes, flow direction, names, stream level, and centerline representations for areal water bodies. Reaches are also defined to represent water bodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2004 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
1999 |
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that comprise the nation's surface water drainage system. Medium resolution NHD is based on the content of the U.S. Geological Survey 1:100,000-scale Digital Line Graph (DLG) hydrography data, integrated with reach-related information from the U.S. Environmental Protection Agency Reach File Version 3.0 (RF3). More specifically, it contains reach codes for networked features and isolated lakes, flow direction, names, stream level, and centerline representations for areal water bodies. Reaches are also defined to represent water bodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
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| U S Geological Survey |
2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
2014 |
The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). The success of NLCD over nearly two decades is credited to the continuing collaborative spirit of the agencies that make up the MRLC. NLCD 2011 is the most up-to-date iteration of the National Land Cover Database, the definitive Landsat-based, 30-meter resolution land cover database for the Nation. The data in NLCD 2011 are completely integrated with NLCD 2001 (2011 Edition, amended 2014) and NLCD 2006 (2011 Edition, amended 2014).
For NLCD 2011, there are 5 primary data products:
1) NLCD 2011 Land Cover
2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class
3) NLCD 2011 Percent Developed Imperviousness
4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels
5) NLCD 2011 Tree Canopy Cover provided by an MRLC partner - the USDA Forest Service Remote Sensing Applications Center.
In addition, ancillary metadata includes the NLCD 2011 Path/Row Index shapefile showing the footprint of Landsat scenes and change analysis pairs used to derive 2006/2011 spectral change. All Landsat scene acquisition dates are included in the shapefile's attribute table. As part of the NLCD 2011 project, NLCD 2001 and 2006 land cover and impervious data products were revised and reissued (2011 Edition, amended 2014) to provide full compatibility with the new NLCD 2011 products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes.
NLCD Tree Canopy Cover was created using MRLC mapping zones from NLCD 2001 (see Tree Canopy Cover metadata for additional detail). All other NLCD 2011 products were created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2011 land cover product can be directed to the NLCD 2011 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2014 |
The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture -Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). The success of NLCD over nearly two decades is credited to the continuing collaborative spirit of the agencies that make up the MRLC. NLCD 2011 is the definitive Landsat-based, 30-meter resolution land cover database for the Nation. The data in NLCD 2011 are completely integrated with NLCD 2001 (2011 Edition, amended 2014) and NLCD 2006 (2011 Edition, amended 2014).
For NLCD 2011, there are 5 primary data products:
1) NLCD 2011 Land Cover;
2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class;
3) NLCD 2011 Percent Developed Imperviousness;
4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels; and
5) NLCD 2011 Tree Canopy Cover provided by an MRLC partner - the USDA Forest Service Remote Sensing Applications Center.
In addition, ancillary metadata includes the NLCD 2011 Path/Row Index shapefile showing the footprint of Landsat scenes and change analysis pairs used to derive 2006/2011 spectral change. All Landsat scene acquisition dates are included in the shapefile's attribute table. As part of the NLCD 2011 project, NLCD 2001 and 2006 land cover and impervious data products have been revised and reissued (2011 Edition, amended 2014) to provide full compatibility with the new NLCD 2011 products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes.
NLCD Tree Canopy Cover was created using MRLC mapping zones from NLCD 2001 (see Tree Canopy Cover metadata for additional detail). All other NLCD 2011 products were created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2011 products can be directed to the NLCD 2011 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2019 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four
National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011.
These products provide spatially explicit and reliable information on the Nation’s land cover and land cover
change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s
land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016
design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land
cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was
conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing
Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development
and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land
cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for
generating land cover and change products; a continuous fields biophysical parameters modeling method; and
an automated scripted operational system for the NLCD 2016 production. The performance of the developed
strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous
U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was
achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive
and highly automated procedure for NLCD 2016 operational mapping.
Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2019 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/rows throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested areas and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2014 |
The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture -Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). The success of NLCD over nearly two decades is credited to the continuing collaborative spirit of the agencies that make up the MRLC. NLCD 2011 is the definitive Landsat-based, 30-meter resolution land cover database for the Nation. The data in NLCD 2011 are completely integrated with NLCD 2001 (2011 Edition, amended 2014) and NLCD 2006 (2011 Edition, amended 2014). For NLCD 2011, there are 5 primary data products: 1) NLCD 2011 Land Cover; 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class; 3) NLCD 2011 Percent Developed Imperviousness; 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels; and 5) NLCD 2011 Tree Canopy Cover provided by an MRLC partner - the USDA Forest Service Remote Sensing Applications Center. In addition, ancillary metadata includes the NLCD 2011 Path/Row Index shapefile showing the footprint of Landsat scenes and change analysis pairs used to derive 2006/2011 spectral change. All Landsat scene acquisition dates are included in the shapefile's attribute table. As part of the NLCD 2011 project, NLCD 2001 and 2006 land cover and impervious data products have been revised and reissued (2011 Edition, amended 2014) to provide full compatibility with the new NLCD 2011 products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes. NLCD Tree Canopy Cover was created using MRLC mapping zones from NLCD 2001 (see Tree Canopy Cover metadata for additional detail). All other NLCD 2011 products were created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2011 products can be directed to the NLCD 2011 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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| U S Geological Survey |
2021 |
The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries.The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements.This 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products.The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics.In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
2002 |
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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| U S Geological Survey |
2002 |
Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002.
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| U S Geological Survey |
1996 |
Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals. The normal orientation of data is by columns and rows. Each column contains a series of elevations ordered from south to north with the order of the columns from west to east. The DEM is formatted as one ASCII header record (A-record), followed by a series of profile records (B-records) each of which include a short B-record header followed by a series of ASCII integer elevations per each profile. The last physical record of the DEM is an accuracy record (C-record). 7.5-minute DEM (30- by 30-meter data spacing, cast on Universal Transverse Mercator (UTM) projection). Provides coverage in 7.5- by 7.5-minute blocks. Each product provides the same coverage as a standard USGS 7.5-minute quadrangle without over edge. Coverage is for the Contiguous United States, Hawaii, and Puerto Rico.
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| U S Geological Survey |
1996 |
Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals. The normal orientation of data is by columns and rows. Each column contains a series of elevations ordered from south to north with the order of the columns from west to east. The DEM is formatted as one ASCII header record (A-record), followed by a series of profile records (B-records) each of which include a short B-record header followed by a series of ASCII integer elevations per each profile. The last physical record of the DEM is an accuracy record (C-record). 7.5-minute DEM (30- by 30-meter data spacing, cast on Universal Transverse Mercator (UTM) projection). Provides coverage in 7.5- by 7.5-minute blocks. Each product provides the same coverage as a standard USGS 7.5-minute quadrangle without over edge. Coverage is for the Contiguous United States, Hawaii, and Puerto Rico.
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL1 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 95 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 31 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL1 Intensity Imagery.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 13813 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 34 counties in Pennsylvania, covering approximately 5621 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
County Mosaics - These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2002 |
Quadrangle Boundaries of Pennsylvania
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| U S Geological Survey |
2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL1 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Pennsylvania Western Lidar 2020 QL1; Classified Point Cloud
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| U S Geological Survey |
2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
PA_WesternPA_2019_D20 Lidar QL1 Intensity Imagery.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Pennsylvania Western Lidar 2020 QL2; Classified Point Cloud
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| U S Geological Survey |
2020 |
These lidar data are processed Classified LAS 1.4 files, formatted to 2684 individual 10,000 ft x 10,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 2684 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Pennsylvania Western Lidar 2020 QL2; Intensity Imagery
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| U S Geological Survey |
2020 |
PA_WesternPA_2019_D20 Lidar QL2 Intensity Imagery.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2003 |
This map layer contains the shallowest principal aquifers of the
conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin
Islands, portrayed as polygons. The map layer was developed as part of
the effort to produce the maps published at 1:2,500,000 in the printed
series "Ground Water Atlas of the United States". The published maps
contain base and cultural features not included in these data. This is a
replacement for the July 1998 map layer called Principal Aquifers of the
48 Conterminous United States.
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| U S Geological Survey |
2002 |
Quadrangle Boundaries for the continental United States and Hawaii
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| U S Geological Survey |
2017 |
Contours with a 2-foot interval in Esri file shapefile format.
Geographic Extent: 13 counties in Pennsylvania, covering approximately 6,602 total
square miles. Dataset Description: The South Central Pennsylvania 2017 QL2 LiDAR
project called for the planning, acquisition, processing, and derivative products of
lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project
specifications are based on the U.S. Geological Survey National Geospatial Program
Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal
projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD
1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4
files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled
intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x
1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase
format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on
the ground and rivers were at or below normal levels. In order to post process the
LiDAR data to meet task order specifications and meet ASPRS vertical accuracy
guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that
were used to calibrate the LiDAR to known ground locations established throughout
the project area. An additional 245 independent accuracy checkpoints, 142 in Bare
Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA
points), were used to assess the vertical accuracy of the data. These checkpoints
were not used to calibrate or post process the data.
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| U S Geological Survey |
2000 |
A scope of work was developed in response to a request by the U. S. Army Corps
of Engineers, Philadelphia District. The request was to perform a topographic
change grid analysis for the Frankford 7.5-minute quadrangle, 1:24,000-scale
topographic map, which includes the Wissinoming neighborhood, and the Germantown 7.5-minute quadrangle, which includes the Logan and Feltonville neighborhoods of the City of Philadelphia. The following tasks were performed under this scope of work: A GPS-corrected GIS grid analysis for each quadrangle was completed and is accompanied by documentation that describes procedures and provides metadata of the informational content of the GIS. A high-resolution global positioning system (GPS) survey was conducted for each topographic quadrangle in order to evaluate and correct systematic discrepancies in elevation between the modern and historic surveys. Prior to release, the fully documented GPS-corrected GIS grid analysis for each quadrangle was reviewed for (1) com-pleteness of documentation and for (2) appropriate analysis and discussion of uncertainties.
The following report is in fulfillment of the tasks outlined in this scope of work and was performed by the U. S. Geological Survey for the U. S. Army Corps of Engineers, Philadelphia District under MIPR agreement number: W25PHS93358288.
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| U S Geological Survey |
1996 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
1996 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
1996 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
1996 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
1996 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
1996 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
1996 |
A Digital Raster Graphic (DRG) is a raster image of a scanned USGS
topographic or planimetric map including the collar information, georeferenced to
the UTM grid.
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| U S Geological Survey |
2002 |
The Total Petroleum System is used in the National Assessment Project and incorporates the Assessment Unit, which is the fundamental geologic unit used for the assessment of undiscovered oil and gas resources. The Total Petroleum System is shown here as a geographic boundary defined and mapped by the geologist responsible for the province and incorporates not only the set of known or postulated oil and (or) gas accumulations, but also the geologic interpretation of the essential elements and processes within the petroleum system that relate to source, generation, migration, accumulation, and trapping of the discovered and undiscovered petroleum resource(s).
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| U S Geological Survey |
2009 |
One foot GSD, natural color (RGB), 8-bit digital orthophotography for the City of Erie, Pennsylvania. The imagery was collected using the Leica Geosystems ADS40 sensor between April 27th and June 6th, 2009 at an average altitude of 9,600 feet above ground level. The National Elevation Dataset (NED) was used as vertical control. Airborne GPS/IMU data was used as horizontal control. The orthophotography is georeferenced to UTM Zone 17 North, meter units, NAD83, NAVD88. The imagery was produced by Pixxures, Inc. under contract for DigitalGlobe, Inc." Data received at EROS were reprojected from 1-foot Pennsylvania 3-band, State Plane to 3-band, 0.30 meter UTM Zone 17 and resampled to align to the USNG using the USGS Seamless system. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map Seamless Server at Chip-level metadata are provided in XML format.
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| U S Geological Survey |
2009 |
TILE INDEX - One foot GSD, natural color (RGB), 8-bit digital orthophotography for the City of Erie, Pennsylvania. The imagery was collected using the Leica Geosystems ADS40 sensor between April 27th and June 6th, 2009 at an average altitude of 9,600 feet above ground level. The National Elevation Dataset (NED) was used as vertical control. Airborne GPS/IMU data was used as horizontal control. The orthophotography is georeferenced to UTM Zone 17 North, meter units, NAD83, NAVD88. The imagery was produced by Pixxures, Inc. under contract for DigitalGlobe, Inc." Data received at EROS were reprojected from 1-foot Pennsylvania 3-band, State Plane to 3-band, 0.30 meter UTM Zone 17 and resampled to align to the USNG using the USGS Seamless system. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map Seamless Server at Chip-level metadata are provided in XML format.
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| U S Geological Survey |
2009 |
One foot GSD, natural color (RGB), 8-bit digital orthophotography for the City of PIttsburgh, Pennsylvania. The imagery was collected using the Leica Geosystems ADS40 sensor between April 26th and July 8th, 2009 at an average altitude of 9,600 feet above ground level. The National Elevation Dataset (NED) was used as vertical control. Airborne GPS/IMU data was used as horizontal control. The orthophotography is georeferenced to UTM Zone 17 North, meter units, NAD83, NAVD88. The imagery was produced by Pixxures, Inc. under contract for DigitalGlobe, Inc." Data received at EROS were reprojected from 1-foot Pennsylvania 3-band, State Plane to 3-band, 0.30 meter UTM Zone 17 and resampled to align to the USNG using the USGS Seamless system. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map Seamless Server at Chip-level metadata are provided in XML format.
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| U S Geological Survey |
2012 |
"This task order consists of digital orthophoto production covering the Pittsburgh Area, Pennsylvania." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files.
Data received at Earth Resources Observation and Science Center (EROS) were verified as:
Projection: NAD_1983_UTM_Zone_17N
Resolution: 0.3000 m
Type: Natural Color
and resampled to align to the U.S. National Grid (USNG) using The National Map. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map at http://nationalmap.gov/viewer.html Chip-level metadata are provided in HTML and XML format. Data were compressed utilizing IAS software. The compression was JPEG2000 Lossy Compressed. The file format created was .jp2.
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| U S Geological Survey |
2009 |
TILE INDEX - One foot GSD, natural color (RGB), 8-bit digital orthophotography for the City of PIttsburgh, Pennsylvania. The imagery was collected using the Leica Geosystems ADS40 sensor between April 26th and July 8th, 2009 at an average altitude of 9,600 feet above ground level. The National Elevation Dataset (NED) was used as vertical control. Airborne GPS/IMU data was used as horizontal control. The orthophotography is georeferenced to UTM Zone 17 North, meter units, NAD83, NAVD88. The imagery was produced by Pixxures, Inc. under contract for DigitalGlobe, Inc." Data received at EROS were reprojected from 1-foot Pennsylvania 3-band, State Plane to 3-band, 0.30 meter UTM Zone 17 and resampled to align to the USNG using the USGS Seamless system. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map Seamless Server at Chip-level metadata are provided in XML format.
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| U S Geological Survey |
2012 |
TILE INDEX -"This task order consists of digital orthophoto production covering the Pittsburgh Area, Pennsylvania." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files.
Data received at Earth Resources Observation and Science Center (EROS) were verified as:
Projection: NAD_1983_UTM_Zone_17N
Resolution: 0.3000 m
Type: Natural Color
and resampled to align to the U.S. National Grid (USNG) using The National Map. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map at http://nationalmap.gov/viewer.html Chip-level metadata are provided in HTML and XML format. Data were compressed utilizing IAS software. The compression was JPEG2000 Lossy Compressed. The file format created was .jp2.
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| U S Geological Survey |
2009 |
The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.
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GeoJSON
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| U S Geological Survey |
2009 |
The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.
Metadata
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KMZ
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Spreadsheet
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GeoJSON
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Feature
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| U S Geological Survey |
2009 |
The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.
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KMZ
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| U S Geological Survey |
2024 |
This dataset is Elevation-derived hydrography (EDH) for the 140G0223F0100 PA_Northeast_Susquehanna_D23_H project covering HU 02050301. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from 1m light detection and ranging (lidar) Digital Elevation Models. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD).
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| U S Geological Survey |
2024 |
This dataset is Elevation-derived hydrography (EDH) for the 140G0223F0100 PA_Northeast_Susquehanna_D23_H project covering HU 02050301. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from 1m light detection and ranging (lidar) Digital Elevation Models. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD).
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| U S Geological Survey |
2024 |
This dataset is Elevation-derived hydrography (EDH) for the 140G0223F0100 PA_Northeast_Susquehanna_D23_H project covering HU 02050301. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from 1m light detection and ranging (lidar) Digital Elevation Models. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD).
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| U S Geological Survey |
2023 |
This dataset is Elevation-derived hydrography (EDH) for the 140G00221F0093-PA_EDHL_Raystown_2021_D21 project covering HU 02050302 - Upper Juniata Watershed. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from light detection and ranging (lidar) derived Digital Elevation Model of 1m, flown as part of 3 different projects between November 2017 and March 2020. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD). The EDH product should be suitable for pre-conflation to the NHD.
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| U S Geological Survey |
2023 |
This dataset is Elevation-derived hydrography (EDH) for the 140G00221F0093-PA_EDHL_Raystown_2021_D21 project covering HU 02050303 - Raystown Watershed. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from light detection and ranging (lidar) derived Digital Elevation Model of 1m, flown as part of 3 different projects between November 2017 and March 2020. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD). The EDH product should be suitable for pre-conflation to the NHD.
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| U S Geological Survey |
2023 |
This dataset is Elevation-derived hydrography (EDH) for the 140G00221F0093-PA_EDHL_Raystown_2021_D21 project covering HU 02050304 - Lower Juniata Watershed. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from light detection and ranging (lidar) derived Digital Elevation Model of 1m, flown as part of 3 different projects between November 2017 and March 2020. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD). The EDH product should be suitable for pre-conflation to the NHD.
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| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| U S Geological Survey |
2014 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
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| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
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| U S Geological Survey |
2014 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
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| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
Metadata
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| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
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| U S Geological Survey |
2014 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
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| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
Metadata
|
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| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
Download
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| U S Geological Survey |
2014 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
Metadata
|
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| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
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| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
Metadata
|
Download
| More Options...
| U S Geological Survey |
2014 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| U S Geological Survey |
2020 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
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| U S Geological Survey |
2011 |
GeoPDF 30 x 60 Minute Quadrangle Map for Pennsylvania
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| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
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| U S Geological Survey |
2011 |
The USGS Historical Quadrangle Scanning Project (HQSP) is scanning all scales and all editions of topographic maps published by the U.S. Geological Survey (USGS) since the inception of the topographic mapping program in 1884. This map is provided as a general purpose map in GeoPDF for users who are not GIS experts.
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| U S Geological Survey |
2011 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| U S Geological Survey |
2005 |
This data set consists of 0.3-meter pixel resolution (approximately 1-foot), natural color orthoimages covering Mercer County, Pennsylvania. An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The design accuracy is estimated not to exceed 3-meter diagonal RMSE (2.12m RMSE in X or Y).
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| U S Geological Survey |
2005 |
This data set consists of 0.3-meter pixel resolution (approximately 1-foot), natural color orthoimages covering Mercer County, Pennsylvania. An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The design accuracy is estimated not to exceed 3-meter diagonal RMSE (2.12m RMSE in X or Y).
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| U S Geological Survey |
2005 |
This data set consists of 0.3-meter pixel resolution (approximately 1-foot), natural color orthoimages covering the Pittsburgh, PA Urban Area (Allegheny and Beaver Counties. An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The design accuracy is estimated not to exceed 3-meter diagonal RMSE (2.12m RMSE in X or Y).
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| U S Geological Survey |
2005 |
Tile Index - This data set consists of 0.3-meter pixel resolution (approximately 1-foot), natural color orthoimages covering the Pittsburgh, PA Urban Area (Allegheny and Beaver Counties. An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The design accuracy is estimated not to exceed 3-meter diagonal RMSE (2.12m RMSE in X or Y).
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| U S Geological Survey |
2011 |
"This task order consists of digital orthophoto production covering the Pittsburgh Area, Pennsylvania." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files.
Data received at Earth Resources Observation and Science Center (EROS) were verified as:
Projection: NAD_1983_UTM_Zone_17N
Resolution: 0.3000 m
Type: Natural Color
and resampled to align to the U.S. National Grid (USNG) using The National Map. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map at http://nationalmap.gov/viewer.html Chip-level metadata are provided in HTML and XML format. Data were compressed utilizing IAS software. The compression was JPEG2000 Lossy Compressed. The file format created was .jp2.
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| U S Geological Survey |
2011 |
TILE INDEX -"This task order consists of digital orthophoto production covering the Pittsburgh Area, Pennsylvania." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files.
Data received at Earth Resources Observation and Science Center (EROS) were verified as:
Projection: NAD_1983_UTM_Zone_17N
Resolution: 0.3000 m
Type: Natural Color
and resampled to align to the U.S. National Grid (USNG) using The National Map. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map at http://nationalmap.gov/viewer.html Chip-level metadata are provided in HTML and XML format. Data were compressed utilizing IAS software. The compression was JPEG2000 Lossy Compressed. The file format created was .jp2.
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| U S Geological Survey |
2012 |
"This task order consists of digital orthophoto production covering the Pittsburgh Area, Pennsylvania." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files.
Data received at Earth Resources Observation and Science Center (EROS) were verified as:
Projection: NAD_1983_UTM_Zone_17N
Resolution: 0.3000 m
Type: Natural Color
and resampled to align to the U.S. National Grid (USNG) using The National Map. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map at http://nationalmap.gov/viewer.html Chip-level metadata are provided in HTML and XML format. Data were compressed utilizing IAS software. The compression was JPEG2000 Lossy Compressed. The file format created was .jp2.
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| U S Geological Survey |
2012 |
TILE INDEX -"This task order consists of digital orthophoto production covering the Pittsburgh Area, Pennsylvania." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files.
Data received at Earth Resources Observation and Science Center (EROS) were verified as:
Projection: NAD_1983_UTM_Zone_17N
Resolution: 0.3000 m
Type: Natural Color
and resampled to align to the U.S. National Grid (USNG) using The National Map. The naming convention is based on the U.S. National Grid (USNG), taking the coordinates of the SW corner of the orthoimage. The metadata were imported and updated for display through The National Map at http://nationalmap.gov/viewer.html Chip-level metadata are provided in HTML and XML format. Data were compressed utilizing IAS software. The compression was JPEG2000 Lossy Compressed. The file format created was .jp2.
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| U S Geological Survey |
2009 |
One foot GSD, natural color (RGB), 8-bit digital orthophotography for the City of New Castle, Pennsylvania. The imagery was collected using the Leica Geosystems ADS40 sensor between October 19th and November 2nd, 2009 at an average altitude of 9,600 feet above ground level. The National Elevation Dataset (NED) was used as vertical control. Airborne GPS/IMU data was used as horizontal control. The orthophotography is georeferenced to UTM Zone 17 North, meter units, NAD83, NAVD88. The imagery was produced by Pixxures, Inc. under contract for DigitalGlobe, Inc." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files. Data received at EROS as: Projection: NAD_1983_UTM_Zone_17N Resolution: 0.3 meter Type: Natural Color and chipped to the Standard Product as: Standard Product Projection: NAD_1983_UTM_Zone_17N Standard Product Resolution: 0.3000 m Rows: 5,000 Columns: 5,000.
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| U S Geological Survey |
2009 |
TILE INDEX - One foot GSD, natural color (RGB), 8-bit digital orthophotography for the City of New Castle, Pennsylvania. The imagery was collected using the Leica Geosystems ADS40 sensor between October 19th and November 2nd, 2009 at an average altitude of 9,600 feet above ground level. The National Elevation Dataset (NED) was used as vertical control. Airborne GPS/IMU data was used as horizontal control. The orthophotography is georeferenced to UTM Zone 17 North, meter units, NAD83, NAVD88. The imagery was produced by Pixxures, Inc. under contract for DigitalGlobe, Inc." An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. There is no image overlap between adjacent files. Data received at EROS as: Projection: NAD_1983_UTM_Zone_17N Resolution: 0.3 meter Type: Natural Color and chipped to the Standard Product as: Standard Product Projection: NAD_1983_UTM_Zone_17N Standard Product Resolution: 0.3000 m Rows: 5,000 Columns: 5,000.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles in NAD83(2011) State Plane Pennsylvania North FIPS 3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US; used to create intensity images, 3D breaklines and hydro-flattened DEMs as necessary.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data
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| U S Geological Survey |
2022 |
Geospatial data includes structures and other selected map features. It is a general purpose dataset for users who are not GIS experts. The geospatial data are from selected National Map data holdings and other government sources.
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| U S Geological Survey |
2022 |
Layers of geospatial data include roads, airports, trails, and railroads.
The geospatial data are from selected National Map data holdings and other government sources.
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the WVSA, PA 2017 Lidar project project. Breaklines are reviewed against lidar intensity imagery to verify completeness of capture. The compilation procedure included use of lidar intensity, bare earth surface model, point cloud data, and open source imagery in an effort to manually compile hydrologic features in a 2-d environment. Following the compilation phase, a separate process was used to adjust the breakline data to best match the water level at the time of the lidar collection. Any ponds and/or lakes were adjusted to be at or just below the bank and to be at a constant elevation. Any streams were adjusted to be at or just below the bank and to be monotonic. Manual QAQC and peer-based QC review was performed on all delineated data to ensure horizontal placement quality and on all adjusted data to ensure vertical placement quality. Bridge breaklines were also compiled in efforts to generate an accurate DEM product. The final hydrologic and bridge breakline product was delivered in ESRI geodatabase format and was also used in the processing of the DEM deliverableGeographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for WVSA PA 2017 Impervious Surface Lidar task as part of the required deliverables for WVSA PA 2017 Impervious Surface project. Class 2 (ground) lidar points in conjunction with the hydro breaklines and bridge breaklines were used to create a 1 foot hydro-flattened Raster DEM.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for WVSA PA 2017 Impervious Surface Lidar task as part of the required deliverables for WVSA PA 2017 Impervious Surface project. Class 2 (ground) lidar points in conjunction with the hydro breaklines and bridge breaklines were used to create a 1 foot hydro-flattened Raster DEM.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2016 |
Tile Indexes - Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
This dataset depicts geographic features on the surface of the earth. It is a general purpose dataset for users who are not GISexperts. The geospatial data in this dataset are from selected National Map data holdings and other government sources.
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| U S Geological Survey |
2008 |
The concern caused by the explosive spread of the zebra mussel, (Dreissena
polymorpha), within the United States resulted in passage of the
Nonindigenous Aquatic Nuisance Prevention and Control Act of 1990 (P.L.
101-646). The impact of this biofouling pest on the economy and
ecological processes in the U.S. and Canada has required prompt action on
a large scale to prevent further infestations and minimize ecological
degradation. This map layer is a compilation of reports of confirmed
zebra mussel sightings in the United States from 1988 to January 2008.
It provides geographical and historical information to show distribution
over time. The reports came from a variety of Federal, State, and
municipal agencies, public utilities, universities, engineering and
private consultant firms. The locations of confirmed sightings were
registered at 1:100,000-scale on EPA Reach File Version 3.0 and are
maintained as an ArcInfo export file. This is a revised version of the
June 2005 map layer.
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KMZ
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| U S Geological Survey |
2025 |
Current Land Use
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KMZ
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| Union County |
2025 |
Facility Sites
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| Union County |
2025 |
FEMA Flood Zone
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KMZ
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| Union County |
2025 |
Municipal Boundary
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| Union County |
2025 |
Open Space Conservancy
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| Union County |
2025 |
Tax Parcels
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| Union County |
2025 |
Polling Places
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| Union County |
2025 |
Railroads
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| Union County |
2025 |
Road Centerline
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| Union County |
2025 |
Site Address Point
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| Union County |
2025 |
Voting Precincts
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KMZ
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| Union County |
2025 |
Waterbodies
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KMZ
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| Union County |
2025 |
Waterlines
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KMZ
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| Union County |
2025 |
Zoning District
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| Union County |
2013 |
This layer was created as part of a Flood Inundation Map Library developed for display within the NOAA National Weather Service's Advanced Hydrologic Prediction Services (AHPS), the SRBC Susquehanna Inundation Map Viewer (SIMV), and the USGS Flood Inundation Mapper (FIM). This data represents the potential flood extent for a stage of 11-ft to 37-ft as recorded at the Harrisburg (Susquehanna River at Harrisburg, PA; USGS ID 01570500) river gage. This data is part of a series of inundation layers meant to correlate observations and forecasts from the river gage with a visual representation of the areas impacted by high water. The data set of flood inundation areas was created from flood scenarios generated by HEC-RAS runs provided by USACE-Baltimore and LiDAR data from PASDA processed to extract bare earth points. A shapefile of inundation area for each stage was created and subsequently merged to form continuous datasets for the main-stem Susquehanna River and backwater areas on its tributaries.This data was developed to assist the public and emergency officials with planning and response to high water episodes at or near a defined National Weather Service river forecast point.
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| United States Army Corps of Engineers USACE |
2020 |
Area of Interest: Allegheny River, 15 miles, north of Clarion County, in Forest and Warren counties These files contain rasterized topobathy lidar elevations generated from data collected by the Coastal Zone Mapping and Imaging Lidar (CZMIL) system and topographic lidar elevations generated from data collected using a Teledyne ALTM Galaxy PRIME sensor. CZMIL integrates a lidar sensor with simultaneous topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. Native lidar data is not generally in a format accessible to most Geographic Information Systems (GIS). Specialized in-house and commercial software packages are used to process the native lidar data into 3-dimensional positions that can be imported into GIS software for visualization and further analysis. Horizontal positions, provided in decimal degrees of latitude and longitude, are referenced to the North American Datum of 1983 National Adjustment of 2011 (NAD83 (2011). Vertical positions are referenced to the NAD83 (2011) ellipsoid and provided in meters. The National Geodetic Survey's (NGS) GEOID12B model is used to transform the vertical positions from ellipsoid to orthometric heights referenced to the North American Vertical Datum of 1988 (NAVD88). The 3-D position data are sub-divided into a series of LAS files, which are tiled into 1-km by 1-km boxes defined by the Military Grid Reference System. The LAS file index is provided by the shape files, "MGRS_1km_17T.shp ", and the numbers used to identify files are in the "Box" field of the shape file. The data file naming convention is based on the year, effort, area, "Box" number and data product type. An example file name is "2020_ERDC_PA_17TPF2793_1mGrid.tif", where 2020 is the year of data collection, ERDC is the effort under which data were collected, PA is the area of data collection, 17TPF2793 is the "Box" number and 1mGrid is the data product type
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| United States Army Corps of Engineers USACE |
2014 |
High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
2014 |
High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Commonwealth of Pennsylvania. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in the Chesapeake Bay Watershed and Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the Pennsylvania portion of the Chesapeake Bay Watershed
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Delaware River Basin. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
Clipped to Counties - Mapping Area polygon - High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
2016 |
High-resolution land cover dataset for the Commonwealth of Pennsylvania. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in the Chesapeake Bay Watershed and Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the Pennsylvania portion of the Chesapeake Bay Watershed
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| University of Vermont Spatial Analysis Laboratory |
2018 |
High-resolution land cover dataset for the Commonwealth of Pennsylvania. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in Commonwealth of Pennsylvannia. At the time of its publication, it represented the most accurate and detailed land cover map for the state.
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| University of Vermont Spatial Analysis Laboratory |
2017 |
High-resolution land cover dataset for the State of New Jersey, Delaware River Basin. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2013 leaf-on orthoimagery, 2015 leaf-off orthoimagery, and leaf-off LiDAR acquired across a series of dates during the period 2006-2015. Ancillary data sources such as road centerlines and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in the Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the New Jersey portion of the Delaware River Basin.
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| University of Vermont Spatial Analysis Laboratory |
2008 |
High resolution land cover dataset for The Abingtons (five miles north of the City of Scranton, Pennsylvania). Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
2007 |
High resolution land cover dataset for Baltimore County/metro area, MD.
Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
2010 |
High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
2010 |
High resolution land cover dataset for Lancaster County, Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
2011 |
High resolution land cover dataset for Prince Georges County. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 0.5 ft square . The primary sources used to derive this land cover layer were 2009 LiDAR and 2009 Color Infrared Imagery (3 band). Ancillary data sources included GIS data (building footprints, impervious surfaces, roads, railroads, water) provided by M-NCPPC. This land cover dataset is considered current as of 2009. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 26497 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
2006 |
High resolution land cover dataset for State College, Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
2014 |
High-resolution land cover dataset for the State of Delaware. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Paved Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.https://docs.google.com/presentation/d/1lgOyFO0lCBl8skDGDZusthLNRVBwQs6b5nrAi05EohY/edit?usp=sharingThe primary sources used to derive this land cover layer were 2014 leaf-off LiDAR data, 2012 leaf-off imagery, and 2013 leaf-on imagery. Ancillary data sources such as roads centerlines, hydrology polygons, and parcel boundaries were obtained for the State of Delaware and used to augment the land cover mapping. This land cover dataset is considered current based on the LiDAR date of acquisition. Land cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
2013 |
Change in the District of Columbia’s tree canopy estimated from 2011 leaf-on imagery and 2006 leaf-on imagery in conjunction with 2008 leaf-off LiDAR. This layer consists of three classes of tree canopy: 1) no change, 2) loss, and 3) gain. No change indicates that the tree canopy has not changed substantially from 2006 to 2011. Loss indicates that tree canopy was removed from 2006 to 2011. Gain indicates that new tree canopy was established between 2006 and 2011.The method for producing this layer was based on the object fate analysis technique developed by Schopfer and Lang. The principal goal was to insure that estimates of tree canopy change were due to actual change and not due to differences in the 2006 and 2011 imagery. As such tree canopy mapping was done at the individual tree level. A combination of automated and manual techniques were employed using 2006 Quickbird imagery, 2008 LiDAR, and 2011 National Agricultural Imagery Program (NAIP) data. Extensive quality assurance and quality control methods were employed. This dataset has been independently reviewed by two separate organizations.
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| University of Vermont Spatial Analysis Laboratory |
2013 |
This dataset was developed to support land-cover mapping and modeling initiatives in the Commonwealth of Pennsylvania.
High-resolution wetlands dataset for Pennsylvlania. Primary wetlands classes were mapped, plus water:EmergentScrub\ShrubForestedWaterThe primary sources used to derive this modeled wetlands layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, 2013 high-resolution land-cover data, and moderate-resolution predictive wetlands maps incorporating topography, hydrological flow potential, and climate data. This dataset is considered current based on the 2013 land-cover map.Wetlands classes were mapped using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties. Using this technique, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were used to ensure that the end product was both accurate and cartographically coherent.This dataset was developed to support land-cover mapping and modeling initiatives in Pennsylvania.
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| University of Vermont Spatial Analysis Laboratory |
2013 |
This dataset was developed to support land-cover mapping and modeling initiatives in the Commonwealth of Pennsylvania.
High-resolution dataset depicting restorable wetlands in Pennsylvania. It includes agricultural fields that have topographic, hydrological flow, and climate characteristics indicative of wetlands. Theoretically, these features could be restored as wetlands if different land uses were practiced at each site.The primary sources used to derive this restorable wetlands layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, 2013 high-resolution land-cover data, and moderate-resolution predictive wetlands maps incorporating topography, hydrological flow potential, and climate data. This dataset is considered current based on the 2013 land-cover map.Restorable wetlands were mapped using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account land-cover boundaries imposed by the 2013 land-cover map. Using this technique, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product was both accurate and cartographically coherent.This dataset was developed to support land-cover mapping and modeling initiatives in Pennsylvania.This vector version was derived from the original 1-meter raster layer.
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| University of Vermont Spatial Analysis Laboratory |
2015 |
This dataset maps tree canopy for the entirety of Pennsylvania at a resolution of 1m, making it 900 times more detailed than the National Land Cover Dataset (NLCD)! With our landscapes becoming increasingly fragmented and heterogeneous high-resolution datasets add precision and accuracy to any analysis.
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| University of Vermont Spatial Analysis Laboratory |
2023 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| USGS Geopdf's for Pennsylvania |
2023 |
Tax Parcels
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| Venango County |
2023 |
Street Centerlines
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| Venango County |
2020 |
Tax Parcels
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| Warren County |
2024 |
Washington County address points generated by the Department of Public Safety
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| Washington County |
2022 |
Washington County amenities maintained by the Washington County GIS Department
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| Washington County |
2023 |
Washington County building outlines generated by the Department of Tax Revenue
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| Washington County |
2024 |
Washington County County Owned Roads
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| Washington County |
2022 |
Washington County election precincts maintained by the Washington County Elections and GIS Departments
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| Washington County |
2022 |
Washington County Magisterial Districts
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| Washington County |
2024 |
Washington County Magistrate Offices
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| Washington County |
2022 |
Washington County municipal outlines generated by the Department of Tax Revenue
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| Washington County |
2022 |
Washington County Municipal Offices
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| Washington County |
2023 |
Parcel data developed by the Department of Tax Revenue
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| Washington County |
2023 |
Washington County parks maintained by the Planning Department
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| Washington County |
2022 |
Washington County polling locations maintained by the Washington County Elections and GIS Departments
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| Washington County |
2024 |
Washington County Road Centerlines generated by the Department of Public Safety
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| Washington County |
2021 |
Washington County School Districts
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| Washington County |
2024 |
An ambitious effort is underway to protect and restore the Delaware River Basin’s water quality and overall ecological health. Kick-started by a $35 million commitment by the William Penn Foundation, the Delaware River Watershed Initiative (DRWI) is targeting eight “clusters” within the basin for conservation investment. More than 50 leading nonprofits have joined together, aligning priorities for land protection and restoration projects and assessing water quality impacts using standardized methods. Partners are focusing on reducing agricultural runoff and urban stormwater in areas of lesser water quality, and they are protecting headwaters, forests, and groundwater reserves where water quality is high.
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| WeConservePA |
2024 |
Land Trusts working within the Delaware River Basin.
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| WeConservePA |
2024 |
Delaware Basin NGOs and local governments, armed with greater knowledge and informed with improved land conservation, conservation finance, land use, and other practices, will increase the volume and quality of their work, achieving better water resource protection outcomes in the Basin. Improvements in water quality will also follow from improvements in public policy, which in turn will follow from higher engagement by NGO staff and volunteers in public policy advocacy that is stimulated by the grant-funded work. Purpose: The purpose of this map is to combine multi-state conserved land datasets into a singular form with common attribute fields that follow that of the Protected Areas Database of the United States (PAD-US) for the area that is the Delaware River Basin. Protected land categories follow that of the PA Conserved Lands map. This dataset can be used in the analysis of potential water quality improvements; alternative approaches to stewardship; riparian buffer development; and prioritization of land protection prospects within the Delaware River Watershed.
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| WeConservePA |
2024 |
Watershed Associations working within the Delaware River Basin.
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| WeConservePA |
2024 |
Environmental Advisory Councils working within the Delaware River Basin.
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| WeConservePA |
2023 |
Trails on land trust preserves and in local parks collected by a WeConservePA intern over two years. Last updated 13 July 2023.
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| WeConservePA |
2023 |
The National Hydrography Dataset Plus High Resolution (NHDPlus HR) is a geospatial model for the flow of water across the landscape and through the stream network. The dataset is built using data at 1:24,000 scale or better. The dataset expands on the NHD with additional attribute data. This layer uses the NHDPlus HR to create a Pennsylvania-wide Strahler stream order database. The layer includes unique identifiers for stream reaches (Reach Code) to allow joins with other stream datasets built off the NHD. PA Department of Environmental Protection’s non-attaining, impaired, streams layer (202301) was joined in this manner. The Stream Integrated List is provided as two separate layers determined if the stream is attaining or not attaining its designated uses. DEP Streams Integrated List layer is maintained by the PADEP Office of Water Management, Bureau of Water Supply & Wastewater Management, Water Quality Assessment and Standards Division. The layer is based on the High Resolution National Hydrography Dataset (NHD). Additional update information is provided by Bureau of Watershed Management, Water Use Planning Division.
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| WeConservePA |
2023 |
OpenStreet Map Trails filtered to named trails and footpaths including hiking and cycling last downloaded from https://www.openstreetmap.org/ on 27 June 2023. To download the latest data, navigate to Export > Geofabrik Downloads > Pennsylvania [https://download.geofabrik.de/north-america/us/pennsylvania.html]. Format description PDF found here [https://download.geofabrik.de/osm-data-in-gis-formats-free.pdf].
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| WeConservePA |
2023 |
Agricultural Security Areas - A unit of 250 or more acres of land used for the agricultural production of crops, livestock and livestock products under the ownership of one or more persons and designated as such by the procedures set forth in this act or designated as such pursuant to the act of January 19, 1968 (1967 P.L. 992, No. 442), entitled “An act authorizing the Commonwealth of Pennsylvania and the counties thereof to preserve, acquire or hold land for open space uses,” prior to the effective date of this amendatory act, by the governing body of the county or governing body of the municipality in which such agricultural land is located on the basis of criteria and procedures which predate the effective date of this amendatory act. – From Agricultural Area Security Law (6/24/13).
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| WeConservePA |
2025 |
The conservation easement is a tool for conserving land in the public interest that doesn’t rely on government regulations and that allows private landowners to maintain ownership and control of their land. A conservation easement is established by mutual agreement between a landowner and a private land trust or government. The easement limits certain uses of the land in order to achieve particular conservation objectives while keeping the land in the owner’s control. The owner may continue to use the land as the owner wishes—within the constraints agreed to when establishing the easement. A conservation easement does not create a right for the public to access a property, unless the owner explicitly establishes that right. The conservation easement, which is an interest in real property, continues in force no matter who owns the land in the future. Practically all conservation easements are designed to be perpetual; for nearly all land trusts and circumstances, this is non-negotiable. For more information, search “Conservation Easement” at https://conservationtools.org/.
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| WeConservePA |
2025 |
This dataset contains farmland preservation easements. Most of these easements are funded through the Pennsylvania Agricultural Conservation Easement Purchase Program. The program is a partnership between all levels of government and non-profit organizations - with a common goal of saving prime farmland. For more information, visit https://www.pa.gov/agencies/pda/plants-land-water/farmland-preservation.html
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| WeConservePA |
2025 |
This dataset contains land owned by the federal government and managed by federal government agencies (includes US Forest Service, US Fish & Wildlife Service, National Park Service, Department of Defense, and Army Corps of Engineers). Notable recreational areas include: Allegheny National Forest, Raystown Recreation Area, the Delaware Water Gap, and the Appalchian Trail.
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| WeConservePA |
2025 |
This dataset contains county- or municipal-owned local parks and open space.
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| WeConservePA |
2025 |
This dataset contains privately conserved land and nature preserves owned in fee by land trusts. Find a land trust in your area by visiting https://weconservepa.org/groups/ or https://weconservepa.org/gis/ and navigating to the Land Trust service area web application.
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| WeConservePA |
2025 |
This dataset contains land owned by the state and managed by state government agencies (includes state parks, state forests, game lands, Historic & Museum Commission properties, and Fish & Boat Commission properties).
Pennslvania has 20 state forests with over 2.5 million acres; 124 state parks over 300,000 acres that includes three new state parks established in 2022 – Big Elk Creek in Chester, Susquehanna Riverlands in York, and Vosburg Neck in Wyoming County (temporary names); and 314 State Game Lands.
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| WeConservePA |
2023 |
This data comes directly from USGS National Watershed Boundary Dataset (see below) and is clipped to the PA State boundary and projected to PA State Plane in US Feet. A link to download the nationwide dataset is provided below.
Source acquisition date: September 2021
Data Source: USGS - Watershed Boundary Dataset (WBD) https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset?qt-science_support_page_related_con=4#qt-science_support_page_related_con https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Hydrography/WBD/National/GDB/
Nationwide MapServer for Watershed Boundary Dataset: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/
Download the WBD from the NRCS Geospatial Data Gateway Watershed Boundaries can also be downloaded from the Natural Resources Conservation Service. Data on the Geospatial Data Gateway are refreshed every six months (March and September).
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| WeConservePA |
2023 |
This data comes directly from USGS National Watershed Boundary Dataset (see below) and is clipped to the PA State boundary and projected to PA State Plane in US Feet. A link to download the nationwide dataset is provided below.
Source acquisition date: September 2021
Data Source: USGS - Watershed Boundary Dataset (WBD) https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset?qt-science_support_page_related_con=4#qt-science_support_page_related_con https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Hydrography/WBD/National/GDB/
Nationwide MapServer for Watershed Boundary Dataset: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/
Download the WBD from the NRCS Geospatial Data Gateway Watershed Boundaries can also be downloaded from the Natural Resources Conservation Service. Data on the Geospatial Data Gateway are refreshed every six months (March and September).
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| WeConservePA |
2023 |
This data comes directly from USGS National Watershed Boundary Dataset (see below) and is clipped to the PA State boundary and projected to PA State Plane in US Feet. A link to download the nationwide dataset is provided below.
Source acquisition date: September 2021
Data Source: USGS - Watershed Boundary Dataset (WBD) https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset?qt-science_support_page_related_con=4#qt-science_support_page_related_con https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Hydrography/WBD/National/GDB/
Nationwide MapServer for Watershed Boundary Dataset: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/
Download the WBD from the NRCS Geospatial Data Gateway Watershed Boundaries can also be downloaded from the Natural Resources Conservation Service. Data on the Geospatial Data Gateway are refreshed every six months (March and September).
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| WeConservePA |
2023 |
This data comes directly from USGS National Watershed Boundary Dataset (see below) and is clipped to the PA State boundary and projected to PA State Plane in US Feet. A link to download the nationwide dataset is provided below.
Source acquisition date: September 2021
Data Source: USGS - Watershed Boundary Dataset (WBD) https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset?qt-science_support_page_related_con=4#qt-science_support_page_related_con https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Hydrography/WBD/National/GDB/
Nationwide MapServer for Watershed Boundary Dataset: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/
Download the WBD from the NRCS Geospatial Data Gateway Watershed Boundaries can also be downloaded from the Natural Resources Conservation Service. Data on the Geospatial Data Gateway are refreshed every six months (March and September).
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| WeConservePA |
2023 |
This data comes directly from USGS National Watershed Boundary Dataset (see below) and is clipped to the PA State boundary and projected to PA State Plane in US Feet. A link to download the nationwide dataset is provided below.
Source acquisition date: September 2021
Data Source: USGS - Watershed Boundary Dataset (WBD) https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset?qt-science_support_page_related_con=4#qt-science_support_page_related_con https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Hydrography/WBD/National/GDB/
Nationwide MapServer for Watershed Boundary Dataset: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/
Download the WBD from the NRCS Geospatial Data Gateway Watershed Boundaries can also be downloaded from the Natural Resources Conservation Service. Data on the Geospatial Data Gateway are refreshed every six months (March and September).
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| WeConservePA |
2023 |
This data comes directly from USGS National Watershed Boundary Dataset (see below) and is clipped to the PA State boundary and projected to PA State Plane in US Feet. A link to download the nationwide dataset is provided below.
Source acquisition date: September 2021
Data Source: USGS - Watershed Boundary Dataset (WBD) https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset?qt-science_support_page_related_con=4#qt-science_support_page_related_con https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Hydrography/WBD/National/GDB/
Nationwide MapServer for Watershed Boundary Dataset: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/
Download the WBD from the NRCS Geospatial Data Gateway Watershed Boundaries can also be downloaded from the Natural Resources Conservation Service. Data on the Geospatial Data Gateway are refreshed every six months (March and September).
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| WeConservePA |
2019 |
This dataset identifies and scores potential riparian planting sites across Pennsylvania. Planting sites are based on 100-foot buffers around NHD High Resolution streams, areas, and water bodies. Qualifying areas have at least 0.25 acres of “Low Vegetation” land cover on a single tax parcel. Land cover data came from the 1-m High Resolution Land Cover produced by the Chesapeake Conservancy and University of Vermont. Sites are scored using a 0-3 score that combines Topographic Wetness Index, Sediment Trapping Efficiency, and upslope land cover. This version of the prioritization data converts each planting site polygon to a point. It is best suited for distant, small-scale mapping and analyses.
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| WeConservePA |
2019 |
This dataset identifies and scores potential riparian planting sites across Pennsylvania. Planting sites are based on 100-foot buffers around NHD High Resolution streams, areas, and water bodies. Qualifying areas have at least 0.25 acres of “Low Vegetation” land cover on a single tax parcel. Land cover data came from the 1-m High Resolution Land Cover produced by the Chesapeake Conservancy and University of Vermont. Sites are scored using a 0-3 score that combines Topographic Wetness Index, Sediment Trapping Efficiency, and upslope land cover. This version of the prioritization data includes the polygons for each planting area and is best suited for closeup, large-scale mapping and analyses.
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| WeConservePA |
2021 |
This dataset identifies and scores potential riparian planting sites across Pennsylvania. Planting sites are based on 100-foot buffers around NHD High Resolution streams, areas, and water bodies. Qualifying areas have at least 0.25 acres of “Low Vegetation” land cover on a single tax parcel. Land cover data came from the 2013 1-m High Resolution Land Cover produced by the Chesapeake Conservancy and University of Vermont (source: https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/). Sites are scored using a 0-3 score that combines Topographic Wetness Index, Sediment Trapping Efficiency, and upslope land cover. This version of the prioritization data includes the polygons for each planting area and is best suited for closeup, large-scale mapping and analyses.Additions to this data include buffer zone statistics including area within buffer zones along with percent area. Buffer zones are: 0-15, 16-25, 26-50, 51-75, 76-100, 101-125, 126-150, 151-175, and 176-200 feet. Intersection with conserved lands have been updated for the 2020 PA Conserved Lands dataset update.
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| WeConservePA |
2025 |
Every Pennsylvania municipality has the option to establish an Environmental Advisory Council (EAC) that may advise the municipal government on environmental matters and undertake a variety of projects regarding the protection and use of natural resources. This layer shows Pennsylvania municipalities that have formed EACs. Find an EAC in your area by visiting https://weconservepa.org/groups/ or https://weconservepa.org/gis/ and navigating to the EAC service area web application.
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| WeConservePA |
2025 |
This layer shows service areas for 81 local and regional land trusts in Pennsylvania. These areas represent the parts of the state where land trusts acquire land or conservation easements. Attributes include a link to the organization's website and a link to their profile and contact information on the Pennsylvania Land Trust Association website, conservationtools.org. Find land trusts in your area by visiting https://weconservepa.org/groups/ or https://weconservepa.org/gis/ and navigating to the Land Trust service area web application.
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| WeConservePA |
2025 |
This layer shows service areas for trail groups in Pennsylvania. Trail groups may build or maintain trails, or they may acquire land or hold easements on which trails are built. Their trails may be for walking, bicycling, horse-back riding, etc. Service areas in this layer are based on the counties that each group’s trails pass through. Find trail groups in your area by visiting https://weconservepa.org/groups/ or https://weconservepa.org/gis/ and navigating to the Trail Group service area web application.
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| WeConservePA |
2025 |
This layer shows service areas for watershed associations in Pennsylvania. These areas represent the parts of the state where watershed associations work. A watershed association is a non-governmental organization whose mission focuses on protecting, restoring health to, or monitoring a specific body of water, or educating on how policies and actions in the watershed impact the water body. A watershed association advances its mission at least in part by performing on-the-ground or in-the-water projects such as streamside tree plantings, aquatic habitat improvements, stream cleanups, and water quality monitoring. Find Watershed Associations in your area by visiting https://weconservepa.org/groups/ or https://weconservepa.org/gis/ and navigating to the Watershed Association service area web application.
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| WeConservePA |
2004 |
Aquatic Community Classification Project, prepared by the Western Pennsylvania Conservancy
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| Western Pennsylvania Conservancy |
2004 |
September sampling points for aquatic mollusks around Plummers Island, Montgomery Co., MD.
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| Western Pennsylvania Conservancy |
2004 |
This shapefile is representative of the five zones which Erie Bluffs State Park was split into for the Bioblitzes (24 hour inventory of all living things in the park) of May 14,15 2004 and July 16, 17 2004. These zones are split more or less by the different natural community types within the park.
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| Western Pennsylvania Conservancy |
1993 |
Boundary of Conneaut Lake, Crawford County, Pennsylvania. This polyline does not include the many docks that extend into the lake.
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| Western Pennsylvania Conservancy |
2005 |
County Natural Heritage Inventories focus on areas that are the best examples of ecological resources in a county. Although agricultural lands and open space may be included as part of inventory areas, the emphasis for the designation and delineation of the areas are the ecological values present. Important selection criteria for Natural Heritage Areas are the existence of habitat for plants and animals of special concern, the existence of uncommon or especially important natural communities, and the size and landscape context of a site containing good quality natural features. Large areas and areas that are minimally disturbed by development provide the backbone that links habitats and allows plants and animals to shift and move across sizable portions of the landscape. The polygons in this layer represent two types of conservation areas. A Biological Diversity Area (BDA) is defined as an area containing plants or animals of special concern at state or federal levels, exemplary natural communities, or exceptional native diversity. BDAs include both the immediate habitat and surrounding lands important in the support of these special elements. A Landscape Conservation Area (LCA) is defined as an area a large contiguous area that is important because of its size, open space, habitats, and/or inclusion of one or more Biological Diversity Areas. Although an LCA includes a variety of land uses, it typically has not been heavily disturbed and thus retains much of its natural character.
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| Western Pennsylvania Conservancy |
2004 |
Point locations of conservation easements held by Western Pennsylvania Conservancy.
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| Western Pennsylvania Conservancy |
2004 |
Point locations representing Western Pennsylvania Conservancy's projects that have resulted in the transfer of land to another party.
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| Western Pennsylvania Conservancy |
2005 |
This dataset shows the land covertypes within 100 meters of Licking Creek and its tributary, Big Cove Creek in Fulton County, Pa from the Franklin County Line to US 30 on Licking Creek and US 522 on Big Cove Creek. 2003 color aerial photography from the PA MAP Project were used to digitize the cover types.
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| Western Pennsylvania Conservancy |
2004 |
A compilation of all medium resolution reach layers from National Hydrography Datasets in or adjacent to Pennsylvania.
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| Western Pennsylvania Conservancy |
2004 |
This layer contains polygons of Western Pennsylvania Conservancy's presently-owned properties.
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| Western Pennsylvania Conservancy |
2005 |
April 2004 freshwater snail sampling points around Plummer's Island, Montgomery County, Maryland.
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| Western Pennsylvania Conservancy |
2005 |
This dataset shows the land covertypes within 100 meters of Tonoloway Creek and Little Tonoloway Creek in Fulton County, Pa from the Maryland State Line to US 522 for Tonoloway Creek and Deneen Gap for Little Tonoloway Creek.
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| Western Pennsylvania Conservancy |
2004 |
All Streams of 4th order or greater in the region surrounding data in the Pennsylvania Aquatic Database (PAD) developed by the Pennsylvania Natural Heritage Program (PNHP).
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| Western Pennsylvania Conservancy |
2004 |
All Streams of 5th order or greater in the region surrounding data in the Pennsylvania Aquatic Database (PAD) developed by the Pennsylvania Natural Heritage Program (PNHP).
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| Western Pennsylvania Conservancy |
2004 |
Point file outlining aquatic sampling locations (fish, chemistry and benthic macroinvertebrates) in the Ohio River Valley Water Sanitation Commission (ORSANCO) dataset. Locations limited to the Ohio, Monongahela, and Allegheny Rivers in Pennsylvania.
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| Western Pennsylvania Conservancy |
2004 |
Drainage area polygons for each RF3 river reach flowing in Pennsylvania.
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| Western Pennsylvania Conservancy |
2004 |
12 digit USGS hydrological unit code (HUC) watersheds within Pennsylvania. Includes percentage land-use information from the National Land Cover Dataset for each HUC 12, average slope for each HUC 12, and percent calcareous geology for each HUC 12.
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| Western Pennsylvania Conservancy |
2004 |
Final export of all aquatic sampling sites from the Pennsylvania Aquatic Database (PAD) at the completion of Phase I of the Pennsylvania Aquatic Community Classification project that had no datarelease restrictions (approximately 14% of all stations). This project was undertaken by the Pennsylvania Natural Heritage Program.
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| Western Pennsylvania Conservancy |
2004 |
Drainage area polygons for each RF3 river reach flowing in Pennsylvania. Includes percentage land-use information from the National Land Cover Dataset for each river reach drainage area.
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| Western Pennsylvania Conservancy |
2021 |
Locations of structures detemined via field varification and attributed with latitude and logitude information based on the local project of NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet. Data for structure points and address information is maintained for use in emergency response 911 purposes. This data is created/updated on a daily basis as needed.
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| Wyoming County |
2024 |
Parcel boundaries for Wyoming County, PA
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| Wyoming County |
2024 |
Roads centerline data for Wyoming County, PA. Data has been developed using the basemap of 2012 aerial imagery from County sources. Road name information is determined from local municipal road naming resolutions. Any road numbering information is acquired from PADOT records. Data is updated on a daily basis as creation/updates occur
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| Wyoming County |
2024 |
Locations of all address point locations in York County. Intended for illustration and demonstration purposes. Intended for illustration and demonstration purposes only. Layer was not intended for use under 1:2400
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| York County |
2022 |
Agricultural security areas are intended to promote more permanent and viable farming operations over the long term by strengthening the farming community's sense of security in land use and the right to farm. Agricultural security areas are created by local municipalities in cooperation with individual landowners who agree to collectively place at least 250 acres in an agricultural security area. This is the Ag Security Easements in York County, Pennsylvania. Intended for illustration and demonstration purposes only.
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| York County |
2022 |
Airports and Heliports within York County and those within a 3 mile buffer outside of York County border called "Area of Influence." Intended for illustration and demonstration purposes only.
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| York County |
2022 |
Bike Corridors and Potential Bike Corridors in York County PA
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| York County |
2022 |
Locations of all buildings point locations in York County. The Building Points are broken down into two categories - Main Living Quarters and Auxiliary Building. This data was last updated in 2008-09. Intended for illustration and demonstration purposes only. Layer was not intended for use under 1:2400
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| York County |
2022 |
College Campuses in York County, Pennsylvania. Showing the dormitories and buildings of York County Colleges
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| York County |
2022 |
The agricultural conservation easement may be granted by a farmland owner to the Commonwealth of Pennsylvania, a county agricultural land preservation program, a local government unit or a local land trust. Easements can be sold or donated. After an easement is sold or donated, the conservation easement restrictions are recorded in the recorder of deeds office in the county where the easement is located.
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| York County |
2020 |
York County 2ft Contours. Generated from 2015 LiDAR.
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| York County |
2022 |
York County Boundary Pennsylvania
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| York County |
2022 |
EMS Stations in York County, Pennsylvania
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| York County |
2022 |
Fire Stations in York County, Pennsylvania
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| York County |
2022 |
Police Stations in York County, Pennsylvania
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| York County |
2022 |
Goverment Offices in York County, Pennsylvania
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| York County |
2022 |
Growth Areas as Defined by the York County Planning Commission
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| York County |
2022 |
Impaired streams of York County, Pennsylvania
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| York County |
2022 |
Lakes and ponds of York County, Pennsylvania
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| York County |
2022 |
Land Joins of York County, Pennsylvania
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| York County |
2022 |
2003 York County Landcover Layer. Aerial Imagery used for creation of this dataset was from PAMAP program in 2003. Other Layers Used in this layer is Parcels (for snapping and Landuse codes), Lakes and Ponds, Parks, Roads buffered to 11 feet on each side, Streams buffered to 6 feet on each side and Railroads buffered to 8 feet on each side. Dataset has been dissvoled except for Road Rightaway which has been dissolved by tiles that are used in the PAMAP program. Oldest oblqiue and google streetview was used as well. Intended for illustration and demonstration purposes only
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| York County |
2022 |
Shows Libraries in York County. For illustration purposes only; Not for scales above 1;24000
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| York County |
2022 |
Magisterial Districts in York County, Pennsylvania
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| York County |
2022 |
Existing medical centers in York County, PA
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| York County |
2022 |
Municipal Boundaries in York County, Pennsylvania
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| York County |
2022 |
This shapefile contains the Core Habitat of Biological Diversity Areas identified through the County Natural Heritage Inventory program of the Pennsylvania Natural Heritage Program. County Natural Heritage Inventories focus on areas that are the best examples of ecological resources in a county. Although agricultural lands and open space may be included as part of inventory areas, the emphasis for the designation and delineation of the areas are the ecological values present. Important selection criteria for Natural Heritage Areas are the existence of habitat for plants and animals of special concern, the existence of uncommon or especially important natural communities, and the size and landscape context of a site containing good quality natural features. Large areas and areas that are minimally disturbed by development provide the backbone that links habitats and allows plants and animals to shift and move across sizable portions of the landscape. Core Habitat areas are intended to identify the essential habitat of the species of concern or natural community that can absorb very little activity or disturbance without substantial impact to the natural features. Polygons are based on aerial photo interpretation, field surveys, and existing PNDI data and were delineated by the ecologists on-screen using ArcView (ESRI, Inc., Version 3.3, 8x, and 9x) with the 1:24,000 scale USGS Digital Raster Graphics and/or Digital Aerial Photography images as a background. For each core habitat polygon, the attribute table contains fields indicating the Site Name and Significance. See individual CNHI reports for further information on methodology, site descriptions, and species or communities found at each site.
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| York County |
2022 |
Nursing home locations of York County, Pennsylvania
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| York County |
2022 |
This layer is all the parcels in York County, PA
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| York County |
2022 |
Park and Rides Operated by Rabbit Transit.
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| York County |
2022 |
York County Parks
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| York County |
2022 |
Shows the Subdivsion Data from YCPC
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| York County |
2022 |
A dataset that shows the polling locations of York County Geocoded to the Correct location in the County. Created in cooperation with the Voting Office of York County.
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| York County |
2022 |
York County Railroads. For illustration purposes only; Not for scales above 1;24000
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| York County |
2018 |
York County Existing and Potential Trails
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| York County |
2024 |
This layer is all the Public Roads in York County, PA
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| York County |
2022 |
York County school district boundaries
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| York County |
2022 |
Point locations of all private/public school buildings in York County.
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| York County |
2022 |
Senate District boundaries of York County, Pennsylvania
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| York County |
2014 |
This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.
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| York County |
2022 |
Streams of York County, Pennsylvania
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| York County |
2022 |
Rabbit Transit Routes in the South Central Pa Area
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| York County |
2022 |
Rabbit Transit Bus Stop locations in York County, Pennsylvania
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| York County |
2022 |
Unique features of York County, Pennsylvania
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| York County |
2022 |
Voting Districts in York County, Pennsylvania
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| York County |
2022 |
Zipcodes of York County
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| York County |
2022 |
Shows the Zoning of York County. Updated when Approved by the Planning Commission board and a resolution is passed through the Municipaltiy that is the effected area is.
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| York County |
2022 |
Shows the Zoning overlays of York County. Updated when Approved by the Planning Commission board and a resolution is passed through the Municipaltiy that is the effected area is.
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| York County |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the YCPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| York County Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the YCPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| York County Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the YCPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| York County Planning Commission |