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Methods:
This LiDAR derivative provides information about vegetation height. The 3-foot resolution raster was produced from the winter, 2019 Quality Level 1 LiDAR point cloud (already ground classified) using Lastools in a multistep process. The processing steps were as follows:
Create Tiles (lastile)
Height Normalize the Point Cloud (lasheight)
Classify Buildings (lasclassify)
Clamp Building to 0 Ft. Normalized Height (las2las)
Thin the Point Cloud (lasthin)
Convert the Point Cloud to a DEM (las2dem)
Uses and Limitations:
The canopy height model provides a raster depiction of the highest vegetation returns for each 3x3 foot raster cell across Marin County. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Marin County fine scale vegetation map. However, this data product was produced based on a rapid, fully automated point cloud classification and was not manually edited. As such, it includes some ‘false positives’ – pixels with a canopy height in the raster that aren’t vegetation. These false positives include noise from water aboveground non-vegetation returns from bridge decks, powerlines, and edges of buildings.
Methods:
This LiDAR derivative provides information about vegetation height. The 3-foot resolution raster was produced from the winter, 2019 Quality Level 1 LiDAR point cloud (already ground classified) using Lastools in a multistep process. The processing steps were as follows:
Create Tiles (lastile)
Height Normalize the Point Cloud (lasheight)
Classify Buildings (lasclassify)
Clamp Building to 0 Ft. Normalized Height (las2las)
Thin the Point Cloud (lasthin)
Convert the Point Cloud to a DEM (las2dem)
Uses and Limitations:
The canopy height model provides a raster depiction of the highest vegetation returns for each 3x3 foot raster cell across Marin County. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Marin County fine scale vegetation map. However, this data product was produced based on a rapid, fully automated point cloud classification and was not manually edited. As such, it includes some ‘false positives’ – pixels with a canopy height in the raster that aren’t vegetation. These false positives include noise from water aboveground non-vegetation returns from bridge decks, powerlines, and edges of buildings.