Module 2: LiDAR (Wetland Delineation)

 Module 2: LiDAR (Wetland Delineation)

This weeks module was about using LiDAR data to create different models including Digital Elevation Model (DEM), Digital Surface Model (DSM), Canopy Density, Vegetation Height, and a LiDAR map. 

The first step was downloading data from a state website. This is important because if you know how to download the data you can use many different websites to get your LiDAR data. This data is shown in .laz file that will need to be converted to las data. To convert your data, you need to open up the geoprocessing tool “Convert LAS” and put in your data. This will create an output LAS dataset that is a .lasd data file. In order to open the data under the insert tab you click the down arrow of the New Map and click on New Local Scene. This will open the 3D dataset of LiDAR.

Before doing the second step, so that you can calculate the data, we need to make sure that we have the 3D analyst and Spatial Analyst extension by clicking Project and then Licensing. If the words are black that means you have access to the extension. Next, we need to calculate the forest height from the LiDAR point cloud. First, we need to get the geoprocessing tool “Point File Information” where we run the .lasd data that we previously created. Make sure that the coordinate system is the same as the .lasd data you got from the website. (It is important to note that many surveyors across the US use State Plane when they collect data). After that we need to create a DEM dataset. First you need to click you data under the contents pane and then click on LAS Dataset Layer. Then click on LAS Points arrow and change the points to Ground. Once you do that you search for the geoprocessing tool “LAS Dataset to Raster”. Next you need to select the .lasd file as the input and change the value field to Elevation. Under interpolation type you need to select Binning with cell assignment as Maximum, and Void Field Method as Natural Neighbor. The Output Data Type is Integer and Sampling Type is Cell Size. Lastly, you need to make the Sampling Value as 6 and Z Factor as 1. This produced the DEM dataset that was back and white. To change the color right click the data and click symbology to what you want it to look like on the map. To create a DSM dataset, you need to change the LAS Dataset Layer to Non-Ground. When using the geoprocessing tool “LAS Dataset to Raster” keep everything the same and it will once again show the data as black and white. After that to get the tree height you want to get the geoprocessing tool “Minus” and set the Input Raster 1 as the DSM file and the Input Raster 2 as the DEM file. This was able to show the height raster data, and you can look at the attribute table to see where the negative and positive values are located. Looking at the data shows the negative values are where the roads and shadows of the trees are located. Where the positive values are where the trees are located and the different heights.

The map below shows both the LiDAR data and the DEM data that was derived from the LiDAR data. This shows that the visible points are in different colors to represent the height of the topography in the area. It looks like the lowest height is a dark blue and starting in the northern portion of the data to a red in the southern portion of the data. Also, you are able to see where the roads are located with the help of the base map being topographic. Lastly, you can see the slight rises and dips within the point cloud data.

The third step was to calculate biomass density. This is done by a lot of different geoprocessing tools. The first geoprocessing tools is “LAS to MultiPoint” where you put the .lasd data from the beginning. To get the metadata that represents the bare earth you need to set the average point spacing from the attribute table from the Point File Information dataset that created as the value and set the class code as 2. To create the metadata for the vegetation it is the same value but the class code is 1. After that you want to convert the MultiPoint files to Raster with the “Point to Raster” geoprocessing tool. You will put the data you named for the bare earth as the input. Set the Value field to OID and set the Cell Assignment to Count. Lastly you use the average point spacing times 3 and round to the nearest whole number. For the vegetation you will do the same thing. Next you want to create a binary file where 1 is assigned to all values that are not null by using the “Is Null” geoprocessing tool for both raster files you previously created. The next geoprocessing tool is “Con” so that if a value of 0 is encountered it will accept it as true value and if it is 1 it pulls from the original raster. You will set the Input from the previous data tool and set Input true raster or constant value to 0. Lastly, se the Input false raster or constant value to the raster dataset. Do the same thing for the vegetation dataset. The “Plus” geoprocessing tool is next that combines the vegetation and bare earth count datasets to derive the overall density returns. To get the Plus result from integer to float you need to use the geoprocessing tool “Float” which will provide the true representation of density from the next tool. Lastly, to calculate the density we need to use the “Divide” geoprocessing tool where you set the Input raster to the count dataset for the vegetation and set input raster 2 as the Float result. Once again it shows up as black and white so you want to change the symbology to what you want.

The image below shows canopy density from the LiDAR data. The information being conveyed in the density map is the areas that have trees and the areas that are not located. This would be helpful to foresters because it shows how good the vegetation is in the area. The higher the number the better the vegetation will be because it is showing a higher reflective value. Also, it could show where roads and houses are located.

The fourth and final step was to create a chart. This is done by clicking on the Height (results from the Minus tool) and go to the Data tab and click Create Chart arrow and click Histogram. For the Histogram to show up you need to click on Band_1 that will show the value and count of the information from the shapefile.

The final image below shows the height of the vegetation and a histogram chart from the LiDAR data. The graph tells you that the highest count of tree height is 63.25 feet tall and it is almost a normal bell curve of the different heights on the landscape. Also, this says the area is growing roughly at the same time and if they needed to cut down trees for money they would be able to get more across the landscape. Lastly, the values tell you that there are some areas that are higher topographically and not just the trees themselves.