M2 - Biomass Density Analysis

This is the first of two weeks working with Light Detection and Ranging (LiDAR). This week we are working with data acquired from the Virginia Geographic Information Network (VGIN). A LiDAR point cloud was acquired for one of the park and valley areas in the Shenandoah National Park.

With the singular point cloud several different products and transformations were made to derive the biomass density map below. The point cloud itself (seen in the second image) is a 3d feature layer as height is involved with each point in the cloud. The primary transformation involved deriving ground and elevation data to generate a Digital Elevation Model (DEM), and a Digital Surface Model (DSM).

Interestingly, there is quite a sequence of tool use to generate these deliverables.
- LAS to Multipoint > Point to Raster > Is Null > Con > Plus > Float > Divide

Note that this sequence is either transforming the data type, as in the LAS to point or point to raster. Or it is an adjustment to the cell values in the case of the remainder of the string. The Divide tool is different as it is a combination of the ground and surface data which provides for our final output below. 

The biomass density map above shows the cumulative height by pixel for the entire scene. The DSM and DEM scenes have been averaged together to give each cell a 0 – 1 value. This allows the higher values to show denser vegetation and the lower values to show less height or less dense areas. This is helpful to forresters because it can indicate areas of highest / densest brush. From the image here you can see that these areas follow the contours of the valley in the north / north east portion of the scene. The scene can also highlight the difference between lower scrub compared to the high trees, areas where plains may be compared to tree thickets. 

As described in the map above you can see the LiDAR point cloud which was then used to transform into the raster based DEM on the left. While all of the images above are the exact same area, they are transformations or translations of this point cloud. 

This was an interesting lab with significant tool usage, but it is overall interesting to see how it can be transformed from raw data to a useable product. Thank you.

v/r

Brandon