This module followed the use of LiDAR data in forestry applications. Specifically, how to manipulate a LiDAR point cloud to provide detailed maps and information for forest DEM/DSM's, canopy height, and forest biomass.
For this lab assignment, a .las LiDAR point cloud file was provided for a specific grid area in Shenandoah Forest, Virginia. Using this data in a local scene in ArcGIS Pro, I was instructed to follow steps to achieve the aforementioned maps and information.
To calculate forest height, I first ran the .las file through the Point File Information tool to summarize the file based on number of points, average point spacing, and minimum/maximum z-values. Next, I filtered the LiDAR layer by ground points and ran the layer through the LAS Dataset to Raster tool to return an elevation value. The result was a DEM of the area. I followed these same steps, instead filtering for non-ground points on the LiDAR file, to return a DSM. Using the Minus tool and subtracting the DEM from the DSM, I was able to achieve a raster that displayed forest height ranging from a minimum to maximum value. I will provide a map of the result here:
The next step in the assignment was to calculate biomass density for the area. I began this process by running the .las file through the LAS to MultiPoint tool, setting the average spacing value as the value from the result of the Point File Information tool from earlier. I did this process twice, using a Class Code of 2 to represent bare earth and a code of 1to represent vegetation. I converted both of the results to rasters using the Point to Raster tool. Using the IS NULL tool I assigned a constant to values in the rasters that were not null. I also used the Con tool on both rasters so that if a value of 0 was encountered, it would be accepted as a true value and if the value was equal to the set constant it would pull from the original raster. Next, the Plus tool was employed to combine the ground and vegetation count rasters. The result was run through the Float tool to change the values from integers to more accurate values. Finally, I used the Divide tool using the vegetation count raster and the Float tool result to produce a raster for canopy density. I will provide the map created from this result here:
It is important to note how man-made features, such as roads, affect vegetation. There is little to no density in these areas. Canopy density information such as this is useful to foresters in so many ways, mainly for forest management and operational activities.
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