| Date | Title | Provider |
| 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.
Purpose:
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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 |
| 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 |
| 2024 |
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 |
| 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 |
| 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 |