LiDAR

Goal:

The goal of this lab was to gain a basic knowledge of how LiDAR data are structured, and how to process them. This was done by creating a number of surface models and an intensity image.

Methods:

In order to view the LiDAR as a point cloud I created a Las Dataset using ArcMap (Figure 1).  Next, I created a Digital Terrain Model (DTM) showing just the LiDAR points classified as "ground". This was done by filtering the Las dataset to show just "ground" points, then using the "LAS Dataset to Raster" tool to output a 2m surface raster (Figure 1). After the DTM was created, I generated a hillshade to emphasize the relief (Figure 1). Next, I created a Digital Surface Model (DSM) by filtering the Las dataset to just show the "First Return" points, and using the "LAS Dataset to Raster" tool to output a 2m raster (Figure 1). I made a hillshade of the DSM, in order to emphasize the surface's texture (Figure 1). The next stage was to create an intensity image, which was done using the "LAS Dataset to Raster", with "Intensity" as the raster value (Figure 1).

Figure 1: The data flow model

Results:

The Las dataset allowed for visual interpretation of numerous features that aren't observable from satellite remote sensing. This is due to the Las dataset's 3D visualization (Figure 2).

Figure 2: The Las dataset allows for observation of the bridge's structure,
something not possible with traditional remote sensing methods.

The DTM and its hillshade allowed for the ground surface to be seen without the distraction of buildings or vegetation. The small pixel size allowed for the interpretation of small fluvial features, such as the lower section of Otter Creek (Figure 3).

Figure 3: Otter Creek

The DSM and its hillshade aided visual interpretation of buildings and vegetation features (Figure 4).

Figure 4: The DSM with Hillshade

The intensity image proved to be a fantastic tool for visual interpretation, as vegetation features and buildings have very high contrast when compared to the ground surface (Figure 5).

Figure 5: The intensity image of UW - Eau Claire's campus

Conclusion:

LiDAR is an extremely useful type of data, as it has an extremely vast range of applications across disciplines. Also, the high density at which it is captured allows for its derivatives to be produced at extremely high spatial resolutions.