| Date | Title | Provider |
| 2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2017 |
Tile Index - Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2017 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2025 |
The purpose of this project was to conduct an assessment of tree canopy change that occurred between 2015 and 2020 utiliing LiDAR data and a previously prepared canopy dataset. Comprehensive canopy change statistics are provided for various geographies down to the parcel-level scale. Tree canopy was extracted from LiDAR data collected in the fall of 2019 and spring of 2020 in ArcMap. A digital surface model (DSM) was created by interpolating the maximum values of the first returns of each laser pulse across a 3-foot grid surface (raster). A speckled output was created because some pulses can entirely or partially pass-through tree canopy before detecting a return, so maximum focal statistics in a 3 by 3 rectangular grid window were applied to the DSM to create a smooth surface. Another raster representing the elevations of solid surfaces which LiDAR does not penetrate - usually ground and buildings, but occasionally dense evergreens as well, was created by interpolating the minimum values of the last returns (which are also the first return in instances of single return). Mean focal statistics in a 3 by 3 cell window were applied to this raster. The last return raster was subtracted from the first return raster, creating a canopy height model (CHM) – a representation of the heights of objects with complex return structures above the ground. In addition to trees, this includes built structures such as power lines, poles, transmission towers, gantries, etc. The edges of buildings also appeared in the CHM as a result of different cell assignment and focal statistics types applied to the first and last return rasters. The heights of dense evergreens were underestimated due to the inability of LiDAR to penetrate to the ground for a proper base for height. A constant raster of CHM cells with a height greater than 15 feet was created. Holes less than 500 square feet were filled to eliminate dubious small gaps while preserving discernable canopy gaps. This raster was then shrunk by 2 cells and expanded back by 2 cells. This process eliminated narrow or small features such as building edges, power lines, and poles. This raster was then converted into a vector polygon format for editing.
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| Allegheny County |
| 2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2015 |
Lidar Data Products for the Allegheny County, PA collection area including a 6ft DEM, hydrogrpahic breakines, and tiled 2ft Contours. The lidar dataset was collected to be utilized for the creation of a digital elevation model, hydrographic breaklines, and 2ft contours. Other uses expected.
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| Allegheny County |
| 2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
| 2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
| 2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
| 2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
| 2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
| 2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
| 2008 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2008. DEMs were generated from the raw data.
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| City of Philadelphia |
| 2010 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2010. DEMs were generated from the raw data.
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| City of Philadelphia |
| 2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
| 2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
| 2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. The GIS Services Group at OIT generated these 10ft Contours for the 2015 1ft Contours.
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| City of Philadelphia |
| 2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. Lidar Data Products for the Philadelphia, PA collection area including a 5ft Digital Elevation Model (DEM), and tiled1ft Contours.
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| City of Philadelphia |
| 2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. The GIS Services Group at OIT generated these 2ft Contours from the 2015 1ft Contours.
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| City of Philadelphia |
| 2018 |
DEM/Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
| 2018 |
DEM/Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEM/Hillshade was generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
| 2018 |
Planimetric Coverage containing the delineation of topographic contours at ten foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels
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| City of Philadelphia |
| 2018 |
Planimetric Coverage containing the delineation of topographic contours at two foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
| 2022 |
Contours 10ft - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
| 2022 |
DEM - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
| 2022 |
Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
| 2022 |
Topographic Contours 1ft - Planimetric Coverage containing the delineation of topographic contours at one foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
| 2008 |
High resolution land cover dataset for Philadelphia. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at ten square feet. The primary sources used to derive this land cover layer were 2008 Orthophotography and 2008 LiDAR LAS data. Ancillary data sources included GIS data (building footprints, road polygons, and hydrography) provided by City of Philadelphia. This land cover dataset is considered current as of 2008. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30700 corrections were made to the classification.
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| City of Philadelphia |
| 2018 |
High resolution land cover dataset for Philadelphia,Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2018 LiDAR data and 2017 NAIP imagery. Ancillary data sources included GIS data provided by Philadelphia,Pennsylvania or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3500 and all observable errors were corrected.
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| City of Philadelphia |
| 2008 |
LiDAR data collection performed over the City of Philadelphia, PA in April of 2008. Products generated include Breaklines, 10ft DEM and 5ft DEM.
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| City of Philadelphia |
| 2010 |
LiDAR data collection performed over the City of Philadelphia, PA in April of 2010. Products generated include Breaklines, 10ft DEM and 5ft DEM.
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| City of Philadelphia |
| 2015 |
This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
| 2018 |
2018 LiDAR – 8ppm – Classified. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. Key attribute field names and descriptions: LiDAR Classification Categories: 0 Created Not Classified 1 Unclassified 2 Ground 3 Low vegetation 4 Vegetation 5 High vegetation 6 Building 9 Water 17 Bridge Deck
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| City of Philadelphia |
| 2022 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
| 2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
| 2010 |
(2010) Pennsylvania area natural color seamless orthoimagery acquired by Aerials Express at a 45cm pixel-resolution. Flight operations began on 12/11/09 and ended on 04/10/10 using an (DMC) camera with an approximate forward overlap of 60% and side overlap of 30% with an approximate Ground Sample Distance of (44 cm). The dataset is projected as Universal Transverse Mercator (UTM) 17 on the North American Datum of 1983. The PAMAP Program LiDAR Data of Pennsylvania; West Virginia Statewide Digital Elevation Models; USGS National Elevation Dataset (NED) - (Used in the respective sequential order) were utilized as the Digital Elevation Model in ortho-processing.
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| DCNR PAMAP Program |
| 2010 |
TILE INDEX North - (2010) Pennsylvania area natural color seamless orthoimagery acquired by Aerials Express at a 45cm pixel-resolution. Flight operations began on 12/11/09 and ended on 04/10/10 using an (DMC) camera with an approximate forward overlap of 60% and side overlap of 30% with an approximate Ground Sample Distance of (44 cm). The dataset is projected as Universal Transverse Mercator (UTM) 17 on the North American Datum of 1983. The PAMAP Program LiDAR Data of Pennsylvania; West Virginia Statewide Digital Elevation Models; USGS National Elevation Dataset (NED) - (Used in the respective sequential order) were utilized as the Digital Elevation Model in ortho-processing.
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| DCNR PAMAP Program |
| 2010 |
TILE INDEX South - (2010) Pennsylvania area natural color seamless orthoimagery acquired by Aerials Express at a 45cm pixel-resolution. Flight operations began on 12/11/09 and ended on 04/10/10 using an (DMC) camera with an approximate forward overlap of 60% and side overlap of 30% with an approximate Ground Sample Distance of (44 cm). The dataset is projected as Universal Transverse Mercator (UTM) 17 on the North American Datum of 1983. The PAMAP Program LiDAR Data of Pennsylvania; West Virginia Statewide Digital Elevation Models; USGS National Elevation Dataset (NED) - (Used in the respective sequential order) were utilized as the Digital Elevation Model in ortho-processing.
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| DCNR PAMAP Program |
| 2006 - 2008 |
Mosaics of PAMAP DEMs by PAMAP Lidar Delivery Zones -
This dataset, produced by the PAMAP Program, consists of a raster digital elevation model with a horizontal ground resolution of 3.2 feet. The model was constructed from PAMAP LiDAR (Light Detection and Ranging) elevation points. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
| 2006 - 2008 |
This dataset, produced by the PAMAP Program, consists of topographic contours mapped at an interval of 2 feet. Contours were derived from a bare-earth digital elevation model constructed from PAMAP LiDAR (Light Detection and Ranging) elevation points. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
| 2006 - 2008 |
This dataset, produced by the PAMAP Program, consists of a raster digital elevation model with a horizontal ground resolution of 3.2 feet. The model was constructed from PAMAP LiDAR (Light Detection and Ranging) elevation points. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
| 2006 - 2008 |
This dataset consists of classified LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.
PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
| 2005 |
PAMAP 10,000 feet x 10,000 feet tile index covering counties in the southern State Plane zone of Pennsylvania. This version has been updated to include additional tiles within a 5000 feet buffer of the Pennsylvania border. Also, the one tile overlap along the border between the north-south State Plane zones has been removed.
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| DCNR PAMAP Program |
| 2005 |
PAMAP 10,000 feet x 10,000 feet tile index covering counties in the southern State Plane zone of Pennsylvania. This version has been updated to include additional tiles within a 5000 feet buffer of the Pennsylvania border. Also, the one tile overlap along the border between the north-south State Plane zones has been removed.
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| DCNR PAMAP Program |
| 2006 - 2008 |
This dataset, provided by the PAMAP Program, consists of vectors used to classify LiDAR points and aesthetically enhance contour lines. The vectors are commonly delineated along features such as road edges, railroads, bridge decks, double line hydro (20' wide and greater), lakes and ponds, swamps and marshes, and extreme terrain breaks (cliffs, retaining walls, etc.). PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).
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| DCNR PAMAP Program |
| 2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
| 2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
| 2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
| 2023 |
An intermediate product of the Pennsylvania Hydrography Dataset (PAHD) generation. This product is the result of a conflation study with existing hydrography originated by the Allegheny County Division of Computer Services Geographic Information Systems Group. This product is not intended to be a finalized component of the Pennsylvania Hydrography Dataset (PAHD): these are provisional data that have undergone no manual refinement. The Modeled_PAHD_Flowpath geometries represent an intermediate product that was created from a workflow that was examining, among other things, the application of conflation steps, monotonicity, and Topographic Positioning Index (TPI) products toward an automated elevation-derived hydrography (EDH) workflow.
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| Pennsylvania Department of Conservation and Natural Resources |
| 2024 |
Hydrography layers for the study area covered by the Surficial Geologic Map of Bessemer, New Castle South, Portersville, and the Pennsylvania portion of the New Middletown quads. The vector data herein were derived from the most recently available Quality Level 2 (QL2) lidar data using geomorphon analysis and least-cost analysis. These data are a subset of the larger Pennsylvania Hydrography Dataset (PAHD), which is in the process of being generated. They were produced using QL2 lidar deliverables and most will have a minimum horizontal accuracy of 1 meter and a minimum vertical accuracy of 0.5 meter at a 1:2,400 scale. This geodatabase contains a dataset of hydrography features of the Bessemer, New Castle South, Portersville, and the Pennsylvania portion of the New Middletown quadrangles
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| Pennsylvania Department of Conservation and Natural Resources |
| 2024 |
Hydrography layers for the study area covered by Map 24–09.0: Hydrography map showing automated stream permanence identification for the Catawissa 7.5-minute quadrangle, Columbia County, Pennsylvania. The vector data herein were derived from the most recently available Quality Level 2 (QL2) lidar data using geomorphon analysis and least-cost analysis. These data are a subset of the larger Pennsylvania Hydrography Dataset (PAHD), which is in the process of being generated. They were produced using QL2 lidar deliverables and most will have a minimum horizontal accuracy of 1 meter and a minimum vertical accuracy of 0.5 meter at a 1:2,400 scale. This geodatabase contains a dataset of derived hydrography features of the Catawissa quadrangle as well as field validation points collected in the study area.
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| Pennsylvania Department of Conservation and Natural Resources |
| 2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
| 2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
| 2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
| 2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
| 2017 |
The National Guard Bureau (NGB) required high accruacy classified LiDAR data in combination with raster digital elevation models and hydrographic breaklines. For this effort, Continental Mapping Consultants (Continental) will collect and process high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models.The National Guard Bureau (NGB) requires the collection and processing of high accuracy classified LiDAR data in .LAS format as well as a combination of raster digital elevation models and additional hydrographic breaklines. The data is to be acquired during the Spring 2017 timeframe, during leaf-off conditions. The acquired LiDAR data will be used for various planning, design, research and mapping purposes. The NGB requires this data collection for Fort Indiantown Gap near Lebanon, Pennsylvania.
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| Pennsylvania Department of Military Veterans Affairs |
| 2019 |
Pennsylvania North Central Lidar 2019 - Tile Indexes. Includes: LiDAR 2019 PA North 10K QL2, PA North 5K QL1, PA North 5K QL1, North 5K QL2, PA South 10K QL2, PA South 5K QL2
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| Pennsylvania North Central Lidar 2019 - Tile Indexes |
| 2020 |
Pennsylvania Western Lidar 2020 - Tile Indexes. Includes: LiDAR 2020 QL1 5K SP North, QL2 10K SP North, QL2 10K SP South, QL2 5K SP North, QL2 5K SP South
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| Pennsylvania North Central Lidar 2019 - Tile Indexes |
| 2018 |
GIS raster datasets displaying Topographic Wetness Index (TWI) for Adams, Cumberland, Dauphin, Franklin, Lancaster, and York Counties, PA. The TWI rasters were derived from 2016 LiDAR for Dauphin County, 2015 LiDAR for Lancaster and York Counties, and 2006-08 LiDAR for Adams, Cumberland, and Franklin Counties. The TWI rasters were derived from 2015 LiDAR for Lancaster and York Counties and 2006-08 LiDAR for Adams and Franklin Counties. The TauDEM extension (D-Infinity tools) for ArcMap was used to create flow direction, slope, and contributing area rasters. TWI was then calculated using the following equation: Ln (Contributing Area/Slope). The methodology was described by Cody Fink in his 2013 thesis entitled Dynamic Soil Property Change in Response to Natural Gas Development in Pennsylvania. TWI results in a dimensionless raster and should be displayed using a red (low values representing no flow) to blue (high, representing high probability flowpaths) color gradient. TWI results vary depending on raster size and analysis options so value thresholds for probability-based overland flowpaths for water should be field verified.
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| Susquehanna River Basin Commission SRBC |
| 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 |
| 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 |
| 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 |
| 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 |
| 2025 |
This dataset is a compilation of airborne lidar derived digital elevation models for Pennsylvania, organized by HUC8 watershed boundaries and sampled at 3 m resolution. Source data was downloaded from the US Geological Survey National Map, primarily from the 2019 Pennsylvania 3D Elevation Program lidar survey, but supplemented with other available datasets where needed to ensure continuous coverage. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
| 2025 |
This dataset represents the upstream flow accumulation, or contributing drainage area, in units of square meters, calculated from the dataset “Pennsylvania 3m lidar digital elevation models 2019”, organized by HUC8 watershed. Flow accumulation was calculated using the “carve” approach in TopoToolbox (Schwanghart and Scherler, 2014). Flow accumulation does not account for incoming flow from outside HUC8 regions, and so should be used with caution when interpreting large trunk streams that cross HUC 8 regions. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025. Schwanghart, W., Scherler, D., 2014. TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences. Earth Surface Dynamics, 2, 1-7. https://doi.org/10.5194/esurf-2-1-2014
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| The Pennsylvania State University |
| 2025 |
Geomorphons (geomorphologic phonotypes) are landforms computed from a digital elevation model based on the principle of pattern recognition and the concept of computer line of sight (Jasiewicz and Stepinski 2013). This geomorphon map was calculated using ArcGIS Pro following the same approach as the dataset “Pennsylvania Geomorphon Landform Maps 2021”, but using the topographic dataset “Pennsylvania 3m lidar digital elevation models 2019”.
The final product includes 10 most common landforms: flat (FL - 1), peak (PK- 2), ridge (RI - 3), shoulder (SH - 4), spur (SP - 5), slope (SL -6), hollow (HL - 7), footslope (FS - 8), valley (VL - 9), and pit (PT - 10). This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
| 2025 |
This dataset represents local slope angle in units of degrees, calculated from the dataset “Pennsylvania 3m lidar digital elevation models 2019”, organized by HUC8 watershed. Slope was calculated using the Spatial Analyst Slope tool in ArcGIS Pro. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
| 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 |
| 2025 |
This dataset contains polygon shapefiles of the stream network with contributing area larger than 1 square kilometer in Pennsylvania, organized by HUC8 watershed. Flow paths are derived from the dataset “Pennsylvania 3m lidar flow accumulation 2019”, and stream width is modeled using the hydraulic geometry scaling of width and drainage area from Hack (1957). This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025..
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| The Pennsylvania State University |
| 2025 |
This dataset contains polyline shapefiles of the intersection between stream channels and valley walls, as calculated from the datasets “Pennsylvania Stream Polygons 2019” and “Pennsylvania Valley Bottom Polygons 2019”. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
| 2025 |
This dataset contains polygon shapefiles of valley bottoms derived from the dataset “Pennsylvania Geomorphon LandformMaps 2019”. Valley bottoms are defined by geomorphon types Flat (FL-1), Footslope (FS-8), Valley (VL-9), and Pit (PT-10) within a buffer of 300 m on either side of the stream network in the dataset “Pennsylvania Stream Polygons 2019”. Final polygon boundaries were cleaned using iterative boundary expanding and shrinking, and regions smaller than 10,000 square meters were eliminated. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
| 2025 |
This dataset contains polygon shapefiles of segmented valley bottoms derived from the dataset “Pennsylvania Valley Bottom Polygons 2019”. Valley bottoms were segmented at 200 m intervals, and for each segment the following parameters were calculated and included: DrArea: Upstream drainage area (square meters) ValLength: Valley length (meters) StrLength: Total length of streams (meters) ValWidth: Average valley width, calculated as area divided by valley length (meters) StrWidth: Average total stream width, calculated as stream area divided by stream length (meters) AreaRatio: Stream area divided by area of valley (dimensionless) LngthRatio: Stream length divided by length of valley (dimensionless) Cnfinement: Stream-valley confinement ratio, a confinement index calculated as the length of the stream contact with valley walls divided by the total valley wall length (dimensionless) The calculations in this dataset derive from the datasets “Pennsylvania Valley Bottom Polygons 2019”, “Pennsylvania Stream Polygons 2019”, “Pennsylvania 3m lidar flow accumulation 2019”, and “Pennsylvania Stream Valley Intersections 2019”. To clean the dataset, valley segments were removed where: the valley length or stream length was less than 100 m; where the stream length to valley length ratio was less than 0.75, or where valley wall length divided by valley length was greater than 3. This project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. April 2025.
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| The Pennsylvania State University |
| 2020 |
GIS raster datasets displaying Topographic Wetness Index (TWI) for Pennsylvania by County. TWI raster datasets were derived from 2006-2008 LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum: NAD83, units: feet); blocks in the southern half are in Pennsylvania State Plane South. Raster spatial resolution is 9.6 ft (approximately 3 m).
The TWI, also called Compound Topographic Index (CTI) or Topographic Convergence Index (TCI), is a hydrological-based topographic index that describes the tendency of a cell or area to accumulate and retain water under steady-state conditions. TWI is defined as Ln(Contributing Area/Slope angle). It balances contributing areas capturing the tendency to receive water versus slope angles capturing the tendency to evacuate water. An automated procedure was developed in ArcMap® for the TWI computation. Contributing areas (i.e. cumulative contributing area per unit contour length) were determined based on the D-Infinity model proposed by Tarboton, D. (1997). Lengths were measured considering cell size and whether the direction is adjacent or diagonal. Land surface slope was computed using the Horn’s method.
The project was funded by the Pennsylvania Department of Environmental Protection (PADEP) through its Water Program. August 2020.
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| The Pennsylvania State University |
| 2020 |
Lidar, Hyperspectral Imagery, Orthoimagery for The Pennsylvania State University Stone Valley Experimental Forest.
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| The Pennsylvania State University |
| 2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
| 2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
| 2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
| 1999 |
The U.S. Geological Survey has developed a National Elevation Database (NED).The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to a consistent datum, edge-match, fill slivers of missing data at quadrangle seams, recast the data to a consistent geographic projection and convert all elevation values to decimal meters as a consistent unit of measure.
NED has a resolution of one-third arc-second (approximately 10 meters) for much of the conterminous United States, Hawaii and Puerto Rico in a NAD83 datum. There is a resolution of two arc-seconds for Alaska and the datum is NAD27.
NED at 10 meters is created using the same methods outlined above with the source data being mostly the 10m DEMs. DEMs at 5 meters, 1/3 arc-second, and 1/9 arc-second maps are also used where available. In some cases, the 10m NED is resampled from LIDAR or created using aerial photography.
One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. Artifact removal greatly improves the quality of the slope, shaded-relief, and synthetic drainage information that can be derived from the elevation data. Geospatial elevation data are used by the scientific and resource management communities for global change research, hydrologic modeling, resource monitoring, mapping, and visualization applications.
NRCS has elected to ONLY serve NED 10 which is 10 meter or better and not NED 10 which was resampled from 30 meter. NRCS also serves the maps in a UTM projection. These two facts differentiate the maps from those served at http://seamless.usgs.gov/.
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| U S Geological Survey |
| 2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
| 2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
| 2015 |
This data will assist in the evaluation of coastal storm damage impacts; aid in post-event reconstruction and mitigation planning for future events and collect LiDAR for counties heavily impacted by storm and flooding for which data is incomplete or inadequate to conduct proper analysis, as part of USGS Hurricane Sandy response.
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| U S Geological Survey |
| 2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL1 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 95 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 31 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Pennsylvania North Central Lidar QL1 Intensity Imagery.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 13813 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 34 counties in Pennsylvania, covering approximately 5621 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
County Mosaics - These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL1 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Pennsylvania Western Lidar 2020 QL1; Classified Point Cloud
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| U S Geological Survey |
| 2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
PA_WesternPA_2019_D20 Lidar QL1 Intensity Imagery.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Pennsylvania Western Lidar 2020 QL2; Classified Point Cloud
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| U S Geological Survey |
| 2020 |
These lidar data are processed Classified LAS 1.4 files, formatted to 2684 individual 10,000 ft x 10,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 2684 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2020 |
Pennsylvania Western Lidar 2020 QL2; Intensity Imagery
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| U S Geological Survey |
| 2020 |
PA_WesternPA_2019_D20 Lidar QL2 Intensity Imagery.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
Contours with a 2-foot interval in Esri file shapefile format.
Geographic Extent: 13 counties in Pennsylvania, covering approximately 6,602 total
square miles. Dataset Description: The South Central Pennsylvania 2017 QL2 LiDAR
project called for the planning, acquisition, processing, and derivative products of
lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project
specifications are based on the U.S. Geological Survey National Geospatial Program
Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal
projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD
1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4
files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled
intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x
1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase
format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on
the ground and rivers were at or below normal levels. In order to post process the
LiDAR data to meet task order specifications and meet ASPRS vertical accuracy
guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that
were used to calibrate the LiDAR to known ground locations established throughout
the project area. An additional 245 independent accuracy checkpoints, 142 in Bare
Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA
points), were used to assess the vertical accuracy of the data. These checkpoints
were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2024 |
This dataset is Elevation-derived hydrography (EDH) for the 140G0223F0100 PA_Northeast_Susquehanna_D23_H project covering HU 02050301. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from 1m light detection and ranging (lidar) Digital Elevation Models. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD).
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| U S Geological Survey |
| 2024 |
This dataset is Elevation-derived hydrography (EDH) for the 140G0223F0100 PA_Northeast_Susquehanna_D23_H project covering HU 02050301. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from 1m light detection and ranging (lidar) Digital Elevation Models. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD).
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| U S Geological Survey |
| 2024 |
This dataset is Elevation-derived hydrography (EDH) for the 140G0223F0100 PA_Northeast_Susquehanna_D23_H project covering HU 02050301. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from 1m light detection and ranging (lidar) Digital Elevation Models. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD).
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| U S Geological Survey |
| 2023 |
This dataset is Elevation-derived hydrography (EDH) for the 140G00221F0093-PA_EDHL_Raystown_2021_D21 project covering HU 02050302 - Upper Juniata Watershed. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from light detection and ranging (lidar) derived Digital Elevation Model of 1m, flown as part of 3 different projects between November 2017 and March 2020. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD). The EDH product should be suitable for pre-conflation to the NHD.
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| U S Geological Survey |
| 2023 |
This dataset is Elevation-derived hydrography (EDH) for the 140G00221F0093-PA_EDHL_Raystown_2021_D21 project covering HU 02050303 - Raystown Watershed. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from light detection and ranging (lidar) derived Digital Elevation Model of 1m, flown as part of 3 different projects between November 2017 and March 2020. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD). The EDH product should be suitable for pre-conflation to the NHD.
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| U S Geological Survey |
| 2023 |
This dataset is Elevation-derived hydrography (EDH) for the 140G00221F0093-PA_EDHL_Raystown_2021_D21 project covering HU 02050304 - Lower Juniata Watershed. The hydrography layer contains line features representing stream rivers and polygons representing waterbody. The EDH was derived from light detection and ranging (lidar) derived Digital Elevation Model of 1m, flown as part of 3 different projects between November 2017 and March 2020. This dataset was created to meet the requirements of the USGS Elevation-derived hydrography specification, https://www.usgs.gov/core-science-systems/ngp/ss/elevation-derived-hydrography-specifications. The line features contain Elevation class (EClass) codes useful for hydro-enforcement, including culvert identification. Feature Class (FCLASS) and Feature codes (FCodes) are hydrography codes compatible with the National Hydrography Dataset (NHD). The EDH product should be suitable for pre-conflation to the NHD.
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| U S Geological Survey |
| 2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles in NAD83(2011) State Plane Pennsylvania North FIPS 3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US; used to create intensity images, 3D breaklines and hydro-flattened DEMs as necessary.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data
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| U S Geological Survey |
| 2019 |
Breakline data is used to hydroflatten the DEMs created for the WVSA, PA 2017 Lidar project project. Breaklines are reviewed against lidar intensity imagery to verify completeness of capture. The compilation procedure included use of lidar intensity, bare earth surface model, point cloud data, and open source imagery in an effort to manually compile hydrologic features in a 2-d environment. Following the compilation phase, a separate process was used to adjust the breakline data to best match the water level at the time of the lidar collection. Any ponds and/or lakes were adjusted to be at or just below the bank and to be at a constant elevation. Any streams were adjusted to be at or just below the bank and to be monotonic. Manual QAQC and peer-based QC review was performed on all delineated data to ensure horizontal placement quality and on all adjusted data to ensure vertical placement quality. Bridge breaklines were also compiled in efforts to generate an accurate DEM product. The final hydrologic and bridge breakline product was delivered in ESRI geodatabase format and was also used in the processing of the DEM deliverableGeographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
These are Digital Elevation Model (DEM) data for WVSA PA 2017 Impervious Surface Lidar task as part of the required deliverables for WVSA PA 2017 Impervious Surface project. Class 2 (ground) lidar points in conjunction with the hydro breaklines and bridge breaklines were used to create a 1 foot hydro-flattened Raster DEM.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2019 |
These are Digital Elevation Model (DEM) data for WVSA PA 2017 Impervious Surface Lidar task as part of the required deliverables for WVSA PA 2017 Impervious Surface project. Class 2 (ground) lidar points in conjunction with the hydro breaklines and bridge breaklines were used to create a 1 foot hydro-flattened Raster DEM.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2016 |
Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2016 |
Tile Indexes - Allentown, Pennsylvania, covering approximately 20 square miles in eastern Pennsylvania. Dataset Description: Allentown, Pennsylvania 2016 QL1 LiDAR project called for the planning, acquisition, processing, and production of products derivative of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet and vertical datum of NAVD1988 (GEOID 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 129 individual 2,500-foot X 2,500-foot tiles, 1-foot hydro-flattened bare-earth raster DEMs in ERDAS .IMG format and intensity images in GeoTIFF format, tiled to the same 2,500-foot X 2,500-foot tile schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. A mosaic of the hydro-flattened bare-earth raster DEMs was produced in ERDAS .IMG format. Ground Conditions: LiDAR was collected in spring of 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, A total of 18 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 20 NVA points and 5 VVA points (25 total QC checkpoints).
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
| 2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
| 2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
| 2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
| 2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
| 2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
| 2016 |
The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2017 |
The South Central Pennsylvania 2017 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 245 independent accuracy checkpoints, 142 in Bare Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
| 2013 |
This layer was created as part of a Flood Inundation Map Library developed for display within the NOAA National Weather Service's Advanced Hydrologic Prediction Services (AHPS), the SRBC Susquehanna Inundation Map Viewer (SIMV), and the USGS Flood Inundation Mapper (FIM). This data represents the potential flood extent for a stage of 11-ft to 37-ft as recorded at the Harrisburg (Susquehanna River at Harrisburg, PA; USGS ID 01570500) river gage. This data is part of a series of inundation layers meant to correlate observations and forecasts from the river gage with a visual representation of the areas impacted by high water. The data set of flood inundation areas was created from flood scenarios generated by HEC-RAS runs provided by USACE-Baltimore and LiDAR data from PASDA processed to extract bare earth points. A shapefile of inundation area for each stage was created and subsequently merged to form continuous datasets for the main-stem Susquehanna River and backwater areas on its tributaries.This data was developed to assist the public and emergency officials with planning and response to high water episodes at or near a defined National Weather Service river forecast point.
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| United States Army Corps of Engineers USACE |
| 2020 |
Area of Interest: Allegheny River, 15 miles, north of Clarion County, in Forest and Warren counties These files contain rasterized topobathy lidar elevations generated from data collected by the Coastal Zone Mapping and Imaging Lidar (CZMIL) system and topographic lidar elevations generated from data collected using a Teledyne ALTM Galaxy PRIME sensor. CZMIL integrates a lidar sensor with simultaneous topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. Native lidar data is not generally in a format accessible to most Geographic Information Systems (GIS). Specialized in-house and commercial software packages are used to process the native lidar data into 3-dimensional positions that can be imported into GIS software for visualization and further analysis. Horizontal positions, provided in decimal degrees of latitude and longitude, are referenced to the North American Datum of 1983 National Adjustment of 2011 (NAD83 (2011). Vertical positions are referenced to the NAD83 (2011) ellipsoid and provided in meters. The National Geodetic Survey's (NGS) GEOID12B model is used to transform the vertical positions from ellipsoid to orthometric heights referenced to the North American Vertical Datum of 1988 (NAVD88). The 3-D position data are sub-divided into a series of LAS files, which are tiled into 1-km by 1-km boxes defined by the Military Grid Reference System. The LAS file index is provided by the shape files, "MGRS_1km_17T.shp ", and the numbers used to identify files are in the "Box" field of the shape file. The data file naming convention is based on the year, effort, area, "Box" number and data product type. An example file name is "2020_ERDC_PA_17TPF2793_1mGrid.tif", where 2020 is the year of data collection, ERDC is the effort under which data were collected, PA is the area of data collection, 17TPF2793 is the "Box" number and 1mGrid is the data product type
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| United States Army Corps of Engineers USACE |
| 2014 |
High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
| 2014 |
High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Commonwealth of Pennsylvania. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in the Chesapeake Bay Watershed and Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the Pennsylvania portion of the Chesapeake Bay Watershed
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Chesapeke Bay Watershed. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Delaware River Basin. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over Roads. The complete class definitions and standards can be viewed at the link below.http://goo.gl/THacgg. The primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
Clipped to Counties - Mapping Area polygon - High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
| 2016 |
High-resolution land cover dataset for the Commonwealth of Pennsylvania. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in the Chesapeake Bay Watershed and Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the Pennsylvania portion of the Chesapeake Bay Watershed
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| University of Vermont Spatial Analysis Laboratory |
| 2018 |
High-resolution land cover dataset for the Commonwealth of Pennsylvania. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, and 2013 leaf-on orthoimagery. Ancillary data sources such as LiDAR-derived breaklines for roads and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in Commonwealth of Pennsylvannia. At the time of its publication, it represented the most accurate and detailed land cover map for the state.
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| University of Vermont Spatial Analysis Laboratory |
| 2017 |
High-resolution land cover dataset for the State of New Jersey, Delaware River Basin. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Impervious Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.http://goo.gl/THacggThe primary sources used to derive this land-cover layer were 2013 leaf-on orthoimagery, 2015 leaf-off orthoimagery, and leaf-off LiDAR acquired across a series of dates during the period 2006-2015. Ancillary data sources such as road centerlines and hydrology were used to augment the land-cover mapping. This land-cover dataset is considered current based on data of acquisition for the leaf-on orthoimagery. Land-cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.This dataset was developed to support land-cover mapping and modeling initiatives in the Delaware River Basin. At the time of its publication, it represented the most accurate and detailed land cover map for the New Jersey portion of the Delaware River Basin.
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| University of Vermont Spatial Analysis Laboratory |
| 2008 |
High resolution land cover dataset for The Abingtons (five miles north of the City of Scranton, Pennsylvania). Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
| 2007 |
High resolution land cover dataset for Baltimore County/metro area, MD.
Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
| 2010 |
High resolution land cover dataset for the Delaware River Basin, an area comprised of parts of six counties in the state of New York and four counties in Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at six square meters. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 - 2011 NAIP imagery. LiDAR coverage was complete for the Pennsylvaia portion of the AOI, however, LiDAR was unavailable for large portions of the New York portion. Where LiDAR was not available, imagery was the primary data source. Ancillary data sources included GIS data (eg. such as hydrology, breakline and buildings) provided by the counties of Lackawana, Monroe, Pike and Wayne, PA, as well as the New York State GIS Clearinghouse. Some of these vector datasets were edited by the UVM Spatial Analysis lab through manual interpretation. Other datasets, such as bare soil, were created by the UVM Spatial Anyslsis Lab in order to assist in landcover creation. This land cover dataset is considered current for Pennsylvania portion of the study area as of summer 2010. The dataset is current as of summer 2011 for the New York counties of Chenango, Delaware, Orange and Sullivan. Broome County, NY, is considered current as of summer 2010. Ulster County, NY, employed data from both summer 2010 and summer 2011, therefore currentness varies throughout the county. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control.
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| University of Vermont Spatial Analysis Laboratory |
| 2010 |
High resolution land cover dataset for Lancaster County, Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
| 2011 |
High resolution land cover dataset for Prince Georges County. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 0.5 ft square . The primary sources used to derive this land cover layer were 2009 LiDAR and 2009 Color Infrared Imagery (3 band). Ancillary data sources included GIS data (building footprints, impervious surfaces, roads, railroads, water) provided by M-NCPPC. This land cover dataset is considered current as of 2009. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 26497 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
| 2006 |
High resolution land cover dataset for State College, Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces.
The primary sources used to derive this land cover layer were 2009 imagery and LiDAR. Ancillary data sources included planimetric GIS data provided. This land cover dataset is considered current as of summer 2009.
Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.
No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30,000 corrections were made to the classification.
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| University of Vermont Spatial Analysis Laboratory |
| 2014 |
High-resolution land cover dataset for the State of Delaware. Twelve land cover classes were mapped:0 - Background1 - Water2 - Emergent Wetlands3 - Tree Canopy4 - Scrub/Shrub5 - Low Vegetation6 - Barren7 - Structures8 - Other Impervious Surfaces9 - Roads10 - Tree Canopy over Structures11 - Tree Canopy over Other Paved Surfaces12 - Tree Canopy over RoadsThe complete class definitions and standards can be viewed at the link below.https://docs.google.com/presentation/d/1lgOyFO0lCBl8skDGDZusthLNRVBwQs6b5nrAi05EohY/edit?usp=sharingThe primary sources used to derive this land cover layer were 2014 leaf-off LiDAR data, 2012 leaf-off imagery, and 2013 leaf-on imagery. Ancillary data sources such as roads centerlines, hydrology polygons, and parcel boundaries were obtained for the State of Delaware and used to augment the land cover mapping. This land cover dataset is considered current based on the LiDAR date of acquisition. Land cover class assignment was accomplished using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.
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| University of Vermont Spatial Analysis Laboratory |
| 2013 |
Change in the District of Columbia’s tree canopy estimated from 2011 leaf-on imagery and 2006 leaf-on imagery in conjunction with 2008 leaf-off LiDAR. This layer consists of three classes of tree canopy: 1) no change, 2) loss, and 3) gain. No change indicates that the tree canopy has not changed substantially from 2006 to 2011. Loss indicates that tree canopy was removed from 2006 to 2011. Gain indicates that new tree canopy was established between 2006 and 2011.The method for producing this layer was based on the object fate analysis technique developed by Schopfer and Lang. The principal goal was to insure that estimates of tree canopy change were due to actual change and not due to differences in the 2006 and 2011 imagery. As such tree canopy mapping was done at the individual tree level. A combination of automated and manual techniques were employed using 2006 Quickbird imagery, 2008 LiDAR, and 2011 National Agricultural Imagery Program (NAIP) data. Extensive quality assurance and quality control methods were employed. This dataset has been independently reviewed by two separate organizations.
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| University of Vermont Spatial Analysis Laboratory |
| 2013 |
This dataset was developed to support land-cover mapping and modeling initiatives in the Commonwealth of Pennsylvania.
High-resolution wetlands dataset for Pennsylvlania. Primary wetlands classes were mapped, plus water:EmergentScrub\ShrubForestedWaterThe primary sources used to derive this modeled wetlands layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, 2013 high-resolution land-cover data, and moderate-resolution predictive wetlands maps incorporating topography, hydrological flow potential, and climate data. This dataset is considered current based on the 2013 land-cover map.Wetlands classes were mapped using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties. Using this technique, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were used to ensure that the end product was both accurate and cartographically coherent.This dataset was developed to support land-cover mapping and modeling initiatives in Pennsylvania.
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| University of Vermont Spatial Analysis Laboratory |
| 2013 |
This dataset was developed to support land-cover mapping and modeling initiatives in the Commonwealth of Pennsylvania.
High-resolution dataset depicting restorable wetlands in Pennsylvania. It includes agricultural fields that have topographic, hydrological flow, and climate characteristics indicative of wetlands. Theoretically, these features could be restored as wetlands if different land uses were practiced at each site.The primary sources used to derive this restorable wetlands layer were 2006-2008 leaf-off LiDAR data, 2005-2008 leaf-off orthoimagery, 2013 high-resolution land-cover data, and moderate-resolution predictive wetlands maps incorporating topography, hydrological flow potential, and climate data. This dataset is considered current based on the 2013 land-cover map.Restorable wetlands were mapped using a rule-based expert system embedded within an object-based framework. Object-based image analysis techniques (OBIA) work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account land-cover boundaries imposed by the 2013 land-cover map. Using this technique, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product was both accurate and cartographically coherent.This dataset was developed to support land-cover mapping and modeling initiatives in Pennsylvania.This vector version was derived from the original 1-meter raster layer.
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| University of Vermont Spatial Analysis Laboratory |
| 2020 |
York County 2ft Contours. Generated from 2015 LiDAR.
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| York County |