Lab 5 LiDAR Remote Sensing

Nathan Sylte
Lab 5

LiDAR Remote Sensing

Goals and Background:

    Lab five involved taking on the roll of a GIS manager where the goal was to work on a project for the City of Eau Claire, Wisconsin. Some important aspects of the project involved obtaining LiDAR point cloud information in LAS formation for the City of Eau Claire. Also, there was a quality check that occurred to ensure data quality.

    The overall objective of lab five was to become more knowledgeable about the structure and processing of LiDAR data. Specifically, lab five involved the processing and retrieval of surface and terrain models. Another important aspect of the lab was the processing and creation of intensity images and other results from point cloud.

    The lab was divided into three parts. Part one involved point cloud visualization in Erdas Imagine. Part two resulted in the generation of an LAS dataset, and part three involved the generation of LiDAR derivative products. 

Methods:

    Part one involved point cloud visualization in Erdas Imagine. Initially, the LiDAR point cloud files were saved in the (.las) format. This allowed them to be displayed in Erdas Imagine. ArcMap was then opened and a shape file of the AOI (area of interest) was added. The Erdas image was then compared with the ArcMap image.

    In part two, a new folder was created in arc catalog in which a new LAS dataset was to be stored. The LAS files where then converted into a LAS dataset. Statistics were also calculated for analysis purposes. Next, x, y, and z coordinate systems where assigned to the dataset. For the x and y coordinate system, the NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet) coordinate system was used. For the z (vertical) coordinate system the NAVD 1988 system was used.

    Part three (generation of LiDAR derivative products) involved the LAS dataset to raster tool. This tool was used to generate a DSM (digital surface model) of Eau Claire that showed elevation. The parameters used are shown in figure one below. The hill shade 3D analyst tool was then used to enhance the image. The LAS dataset to raster tool was used again to generate a DTM (digital terrain model) of Eau Claire. However, different parameters were used. The cell assignment type was switched to Minimum. Last in section two of part three, a LiDAR intensity image was generated. The LAS dataset to raster tool was used again. The changed parameters included the value field reading INTENSITY, and the binning cell assignment type was changed to Average.

   

   

Figure 1. Shows the parameters used for the digital surface model.

Results:

Figure 2. Figure two shows the digital surface model from part three with the hill shade tool applied.

Figure 3. Figure three shows the digital terrain model from part three with the hill shade tool applied. Many surface features have been removed for analysis of terrain features.  

Figure 4. Figure four shows the intensity image displayed in Erdas Imagine for better viewing purposes.

Sources:

Eau Claire County. (2013). Lidar point cloud and Tile Index.

 

 Lab 5