Counting marmots…
<p>This dataset represents a 1/3 m resolution vegetation canopy height model for the upper East River Watershed in Western Colorado. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm. The file "UER_canopy_ht_33cm_v2.tif" contains gridded canopy height data, with canopy height measured in meters. </p><p>This dataset is distinct from, but covers overlapping geographic areas as, LiDAR datasets collected as part of the 2018 NEON Aerial Observation Platform campaign in the Upper East River (Goulden T ; Hass B ; Brodie E ; Chadwick K D ; Falco N ; Maher K ; Wainwright H ; Williams K (2020): NEON AOP Survey of Upper East River CO Watersheds: LAZ Files, LiDAR Surface Elevation, Terrain Elevation, and Canopy Height Rasters. Watershed Function SFA. doi:10.15485/1617203), and the 2015 LiDAR scan contracted by the Department of Energy for the East River and Washington Gulch (Wainwright H ; Williams K (2017): LiDAR collection in August 2015 over the East River Watershed, Colorado, USA. Watershed Function SFA. doi:10.21952/WTR/1412542). The current dataset has a higher point density (9.4 points / square meter) than the 2018 NEON AOP data, and covers a larger geographic area than the 2015 DOE data.</p><p>The original point cloud data, processing scripts, and other supporting documents can be found in the ESS-DIVE repository (following the DOI link).</p>
Knowledge graph centered on A composite high resolution canopy height map for with 3 nodes and 3 connections. Top connected: canopy surface area, Forest inventory plot sampling.
Items connected by shared entities, co-authorship, citations, or semantic similarity.