Forestry and LiDAR - Module 2

 In this week's lab we were provided with a LiDAR image of Big Meadows Recreational Area in Shenandoah National Park, Virginia. From this, we were tasked with providing several outcomes for commonly encountered queries when dealing with vegetation in ArcGIS Pro. These include downloading and unzipping LiDAR data, calculating forest height and displaying the data via histogram, calculating bimoass density, and 

I first retrieved the LiDAR from ESRI and used their LAS Optimizer to extract the zipped files. These LiDAR images are deemed .las files and are designed for archiving and interchange of LiDAR point cloud data. From that, I extracted a digital elevation model as it would prove necessary when calculationg tree height. Symbology was selected to refelect the relative difference in relief with warm hues indicating the higher regions and green the lower regions. Each pixel on these raster images represents a diffent height value. As you examine the LiDAR scene, you will notice that much of the visible polygonal relief happens to be tree tops. Below is an image of both the LiDAR image and the DEM.  

One obstacle that is frequently encountered when using .las images is that they can take quite amount of time to render (depending on operating device). Additionally, they may need to be displayed on a map layout at relatively obtuse scales in order for appropraite rendering and presentation to occur. As you will see in this map, there are two quadrants missing, one of which was visible before the image was exported. To rectify this, in the future it would be wise to attempt to display the image at a significantly larger geographic scale and see if displaying the entire rendered image is possible.

We were then tasked with calculating the forest height. I extracted the point data the raster pixels and converted it into a point file using the Point File Information tool. This tool provides the simple summary statistics for the points in our .las file and will provide values in the attribute table such as the number of points, max and min values of the points, and the point spacing. This will be used when I am needing to calculate the biomass density.
As stated above, I extracted a digital elevation model (measuring elevation of the ground surface through LiDAR) using the LAS Dataset to Raster tool. I then created a digital surface model using the same tool which extrapolates the height of the highest surface encountered from the LiDAR. The differential will be our forest height. I accomplished the differential using the Minus geoprocessing tool and subtracted the DEM from the DSM. Our result is a raster of forest height. Due to inaccuracies and interuptions with the LiDAR, our values do have some outliers.  The subsequent histogram and map are shown below:

Last, we were required to find the vegetation density of the study area. I used the LAS to Multipoint tool to setting the class code to 2 (for ground readings) and this turned the values in our .las file into point data. I repeated the process setting the class code to 1 which extrapolates the data for vegetation. Now, I can take that multipoint data and convert it to a raster image using the point to raster tool. This converted my point data into displayed cells. I eliminated all values of 0 on both rasters using the IS NULL tool and then the Con tool to pull values from the .las files for values that were not null. using the Plus tool, I then combined the two rasters data sets. Using the Float tool, I turned the data into integers. I then took this result and my vegeation raster and used the Divide tool to give me the density.
Below is the density map: