GIS5100 Module 2: Forestry and LiDAR

    This week in Applications in GIS focused on LiDAR (Light Detection and Ranging) and how to handle LiDAR data in ArcGIS Pro to calculate forest height and forest biomass.

Using the decompressed version of the provided LiDAR data of the study area in Shenandoah, Virginia (Fig. 1), we created a Digital Elevation Model (DEM) and Digital Surface Model (DSM) to determine the tree canopy height of the study area.

Fig. 1: LiDAR image of Shenandoah, Virginia study area

Creating DEM

Using the LiDAR .las file, right click in Contents -> Filters -> LAS Points = Ground

The LAS Dataset to Raster geoprocessing tool was used to convert the LiDAR data into a raster format. This made a Digital Elevation Model (DEM). See Fig. 2 for this map output.

Creating a DSM

Using the LiDAR .las file, right click in Contents -> Filters -> LAS Points = Non-Ground

The LAS Dataset to Raster geoprocessing tool was used to convert the LiDAR data into a raster format. This made a Digital Surface Model (DSM).

  

Making a Height Raster

Using the Minus tool, I subtracted the DEM and DSM to create a Height raster. See Fig. 2 for this map output.

Calculating Biomass Density

We needed to create MultiPoint files of the .las file for both the Ground and Vegetation – I used the LAS to MultiPoint geoprocessing tool.

The “Average Point Spacing” was determined from our Point File Information feature class that was created from our .las file.   

I then converted the MultiPoint files that were just made into rasters using the Point to Raster tool on both the Ground MultiPoint and Vegetation MultiPoint files.

The cell size value was chosen based off the Average Point Spacing value (1.005159) that was found in the Point File Information layer attribute table – per lab, “the ideal cell size is 3 times the determine average point spacing”, so the value for the cell size in this instance was determined to be (3).

The IS NULL tool was then used to create a binary file (“Ground_Null”, “Veg_Null”) – this binary file assigned a 1 to all the values that weren’t null – this was done for the newly created Ground and Vegetation raster files from the previous step.

From there, I ran the Con tool on the binary files so that if a 1 is encountered, it will pull information from the original raster, but if it encounters a 0, it will only accept it as a true value. The Input true raster or constant value is set to (0) and Input false raster or constant value in this tool is set to the Raster file for each type respectively (ground or vegetation). The input is the binary files (outputs from the IS NULL tool) for each respective type. The outputs for the Con tool are “Count_Ground” and “Count_Veg” files.

Next, the Plus tool was used to combine the “Count_Ground” and “Count_Veg” files – this will start the process of determining overall density. This output results in an integer type, but we want it to be a float type- to do that, I used the Float tool to convert into float type.

To calculate the density, I used the Divide tool – the resulted (tree) Canopy Density raster. See Fig. 2 for this map output, as well as finished product.

Fig. 2: Digital Elevation Model (DEM), Canopy Density map, Canopy Height map, and Tree Canopy Height Distribution graph of the Shenandoah, Virginia study area