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NOAA Coastal Topographic Lidar Forest Structure Metrics
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Generate raster products using lidR
Generate Canopy Height Model (CHM), Digital Surface Model (DSM), Digital Terrain Model (DTM), and extract forest canopy metrics from lidar point cloud data based on the lidR package.
Select additional output(s):
Smoothed Canopy Height Model
Normalized lidar point cloud
Canopy Metrics Grid and Tree Crowns Boundary
Select algorithm:
p2r: Point-to-Raster. Local gridding of the maximum elevation.
dsmtin: Triangular irregular network (TIN) interpolation of the first returns.
pitfree: Sequential delaunay triangulations to remove pits.
p2r algorithm settings
(optional)
Radius of the circles (optional)
0 (disabled)
0.1
0.2
0.3
0.4
0.5
1.0
Filling no data gaps (na.fill)
none
type = tin
type = knnidw
Number of k-nearest neighbours k (≥ 1)
Power for inverse-distance weighting p (≥ 0)
pitfree algorithm settings
(optional)
thresholds (comma-separated, optional)
Maximum edge length (1-2m)
Classical triangulation value (optional)
Pit-free algorithm value (optional)
Radius of the circles (optional)
0.0 (disabled)
0.1
0.2
0.3
0.4
0.5
1.0
Additional Products
Canopy Gap Detection
2. Canopy Height Model using PDAL/GDAL
Generate Canopy Height model from differencing DSM and DTM using open-source Python libraries, including PDAL and GDAL
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