Lidar-derived rasters of point density, elevation, and geomorphological features for 2013, 2015, and 2022 for the Greater Raleigh Area, North Carolina
Dates
Publication Date
2023-04-11
Start Date
2013
End Date
2022
Citation
Gurley, L.N., Hopkins, K.G., and Stillwell, C.C., 2023, Lidar-derived rasters of point density, elevation, and geomorphological features for 2013, 2015, and 2022 for the Greater Raleigh Area, North Carolina: U.S. Geological Survey data release, https://doi.org/10.5066/P9V1J754.
Summary
As part of a collaborative study with the City of Raleigh, North Carolina, the U.S. Geological Survey developed a suite of high-resolution lidar-derived raster datasets for the Greater Raleigh Area, North Carolina, using repeat lidar data from the years 2013, 2015, and 2022. These datasets include raster representations of digital elevation models (DEMs), DEM of difference, the ten most common geomorphons (i.e. geomorphologic feature), lidar point density, and positive topographic openness. Raster footprints vary by year based on extent of lidar data collection. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded. These metrics have been developed to pair [...]
Summary
As part of a collaborative study with the City of Raleigh, North Carolina, the U.S. Geological Survey developed a suite of high-resolution lidar-derived raster datasets for the Greater Raleigh Area, North Carolina, using repeat lidar data from the years 2013, 2015, and 2022. These datasets include raster representations of digital elevation models (DEMs), DEM of difference, the ten most common geomorphons (i.e. geomorphologic feature), lidar point density, and positive topographic openness. Raster footprints vary by year based on extent of lidar data collection. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded. These metrics have been developed to pair with field geomorphic assessments for use in the development of a model that can remotely predict streambank erosion potential along streams in the Greater Raleigh, NC Area, however, they have the potential to be used in numerous applications.
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Purpose
These datasets were developed as part of a collaborative study between the U.S. Geological Survey and City of Raleigh, North Carolina, to help identify stream reaches throughout the Greater Raleigh, NC Area that may be most susceptible to streambank erosion.