Lab 7: Discerning Indian Burial Mounds using LiDAR data.

Introduction: On November 10, 2018, the University of Wisconsin-Eau Claire Geospatial Field Methods class ventured out to a field survey site on the lower Chippewa River (figure 1) to watch a drone survey of Indian burial mounds. The drone’s obtained picture data up to two centimeters of resolution, which can be used to discern where Indian burial mounds are. With the drone data, our plan was to process the data through Pix4D to create a 3D representation of our study area. However, the data was too large for UWEC computers to process. Because the drone data was too large to process, we were unable to create a 3D representation of our study area. Despite our misfortune, we were able to discern Indian burial mounds using LiDAR other methods in ArcMap, which is the focus of this lab.

Problem/Statement: To understand the best mean neighborhood value to use in determining Indian burial mounds using ArcMap.

Data Collection: In collecting data, we used Eau Claire County LiDAR data to generate a DEM of our study area.

Data Processing: Despite the fact we couldn’t create a 3D model using Pix4D, we were able to generate a Digital Elevation Model (DEM) of our study area using Pix4D. Once we created a DEM, we imported the DEM into ArcMap, and used model builder to filter out low and high points (figure 2). Using the raster clip function, we clipped the DEM to our study area, then used the focal statistics function to calculate the mean DEM for the raster. We calculated the mean DEM four separate times, one for 5, 10, 20, and 30 meter mean neighborhood values. After, we used the map algebra tool to subtract the focal statistics DEM from the clipped DEM, which gave us our final output. 

Figure 1. Study Area Map. 

Figure 2. Example of Model Builder. This was the general flow model of this lab, highlighted in the data processing section of the report. 

Results: In processing the data, I produced figure 3, figure 4, figure 5, and figure 6. Figure 3 represents 5-meter neighborhood value; figure 4 represents 10-meter neighborhood value; figure 5 represents 20-meter neighborhood value, and figure 6 represents a 30-meter neighborhood value. 

Figure 3. 5-meter neighborhood value.  

Figure 4. 10-meter neighborhood value. 

Figure 5. 20-meter neighborhood value. 

Figure 6. 30-meter neighborhood value. 

Discussion: In comparing figures 3-6, it seems that the 30- and 20-meter resolution produce the best representation of the Indian burial mounds. The Indian burial mounds are the dots above the plaid looking landscape in the center part of the map (figure 7). In comparing all the neighborhood values, it seems both the 5-meter and 10-meter shows too many high and low values. Both the 20-meter resolution and the 30-meter resolutions accurately show the location of the burial mounds without showing too many unnecessary values.  

Figure 7. Example of where the Indian burial mounds are located, represented by the red circle.