Lab 5: Interpretation of Lidar Data

The learning outcomes for this lab was to become familiar with the manipulation of Lidar Data. The first objective was the introduction of some of the tasks associated with editing raw Lidar data so as to be able to display it within a GIS. Once the data was displayed I learned several simple viewing and conversion techniques to be able to symbolize and understand the data. Finally I was tasked with exporting the data as a raster file. 

Objective 1: Formatting data

At the beginning of this lab I was provided with a point cloud data set which I was able to view in both Erdas Imagine and ESRI ArcMap. Most of the data manipulation was done with ArcMap seeing as it has a slightly better interface than Erdas. But in order to get ArcMap to properly project the data I had to sift through the included metadata file information to find its original datum and unit of measurement for both the horizontal XY axis and Vertical Z axis. After defining this information for the point cloud data by using ArcCatalog I was able to display it within ArcMap.

Objective 2: Viewing the data

Once the data was formatted properly for display in ArcMap I was able to symbolize several aspects of the data. Four common displays are shown below in figure 1, a four panel map showing the same extent some aspects of the University of Wisconsin Eau Claire. The data included all returns with the exception of aspect, which used only the ground returns so as to simplify the data display.

Figure 1. Lidar data symbolized for Elevation, Aspect, Slope, and Contour. 

Elevation is the display of height of the data, in this map the “hotter” colors (reds oranges) are higher than the cooler colors (blues and greens). Slope shows the steepest angles of features as a vibrant red (building walls or trees) and flatter features (rooftops or fields) as green. Aspect is a continuation of Slope but instead of symbolizing the degree of slope it show the direction of the slope. Contour is a display of elevation changes by the use of user defined isoclines. In this case the contour interval was set to five feet. With the index contours set for every five lines.

To help see the how the Lidar interacts with a surface and to display the 3d qualities of a dataset ESRI integrated several featuers into their software, such as the abilitie to take cross sections of an area to see its profile or to even render an area in three dimensions. See the images below (Figure 2-4).

Figure 2. Profile of a railroad bridge using the first return.

Figure 3. Profile view of the South side of Phillips Science Hall on the UWEC campus using the first return. One can see a surprising amount of detail in this image. Note the observatory on the roof of the building and the sport utility vehicle parked several floors directly below it. 

Figure 4. Three dimensional rendering of features of the University of Wisconsin Eau Claire Campus.

Objective 3: Rasterizing Data

For this objective I was tasked with converting the point data from the LAS file to a raster file type. From that raster file I was to make a hill shade image using ESRI software. It was a pretty straight forward process that only involved the use of two tools: “LAS to Raster” and “Hillshade.” The LAS to raster required some parameters to be filled in to work properly but the hill shade tool did not require any special inputs. Below (Figure 5) is a collaborated image of the original LAS file, and the two derived files: raster and hill shade.

Figure 5. A progression of data manipulation from the original LAS point data (first return), to raster, and finally to a hill shade.

Using the same process above I performed a raster conversion and created a hill shade of the same LAS data file as before. The only difference being that I used ground return points instead of first return points (Figure 6).

Figure 6. LAS ground return data converted to raster then hillshaded.

The final part of this lab was to generate an intensity image from the LAS file (Figure 7).

Figure 7. Intensity image produced from LAS data file.

This image was created in a similar fashion as the hill shades above. The difference being that instead of defining the elevation aspect to be converted to raster the intensity strength was used. This shows the most reflective features in the image with lighter tones while the more absorptive features appear darker.