LiDAR Remote Sensing

Goal and Background

 LiDAR is a rapidly growing geospatial technology and the increasing market for it has many implications for the future of imagery and image collection. The goal of this lab exercise was to practice basic skills involved in processing LiDAR data, particularly using LiDAR point clouds in LAS file format. Objectives of this lab included processing and retrieval of various surface and terrain models, and processing of intensity images and point cloud-derived products. We were told to imagine this exercise in the context of a GIS manager working on a project for the city of Eau Claire.

Objectives

1. Acquire LiDAR point cloud in LAS format for a portion of the City of Eau Claire.
2. Initiate an initial quality check on the data by looking at its area and coverage, and also verify the current classification of the LiDAR.
3. Create a LAS dataset
4.  Explore the properties of LAS dataset
5. Visualize the LAS dataset as point cloud in 2D and 3D
6. Generate LiDAR derivative products

Methods

In part one, I download point cloud data into Erdas Imagine and accessed the tile index file specified for the lab. It is a point cloud dataset of Eau Claire. I selected the same section in ERDAS and ArcMap for practice.

I performed a QA/QC check by comparing the minimum and maximum Z points to the known elevation. The dataset was displayed in tiles.

Part two focused on generating a LAS dataset and exploring LiDAR point clouds with ArcGIS. I created a new LAS dataset in ArcCatalogue. LAS data comprises a great deal of information in the header files associated with the data and there is also ancillary data that can be accessed as well. Using the external metadata, I assigned the D_North_American_1983 and North American Vertical Datum of 1988 to the dataset. For reference, I added a shapefile of Eau Claire County (see Figure 1), which was deleted afterwards.

Figure 1 The grid highlights the area covered by the pointcloud dataset of the city of Eau Claire as compared to the greater Eau Claire County.

The default display settings are point color codes according to elevation, using all returns. Figure 2 shows the elevation range and a portion of the dataset.

Figure 2 A section of the dataset displaying elevation of all returns.

Below are four different views of the data; Elevation, Aspect, Slope, and Contour (see Figure 3).

Figure 3 Four different views of the same subset of the LAS dataset, each displaying a different element.

Next I explored the point clouds according to Class, Return and profile. In ArcMap there is the option to display selected features in 2D and 3D models for more thorough visualization.

Part three involved the generation of Lidar derivative products. In order to derive DSM and DTM products from point clouds, I estimated the average nominal pulse spacing (NPS) at which the point clouds were collected. The derived image below (Figure 4) is the result of  inputting the LAS dataset into the LAS dataset to Raster tool and using the binning interpolation method with a maximum cell type and a natural neighbor void filling method.

Figure 4 The derived raster converted from the LAS dataset using the LAS dataet to Raster tool in ArcMap.

Using the LAS Dataset to Raster and Hillshade tools, I then created the following derivative products:

• Digital surface model (DSM) with first return
• Digital terrain model (DTM)
• Hillshade of the DSM (see Figure 5)
• Hillshade of the DTM (see Figure 6)

Figure 5 The hillshade results of the first return elevation model performed on the derived raster.

Figure 6 The hillshade results of the terrain model (ground returns only) of the derived raster.

Finally, a lidar intensity image was generated following a similar procedure used to create the DSM and DTMs above. The image was displayed in ERDAS to enhance its appearance automatically.

Results

Through this exercise I learned how to use Lidar data and gained familiarity with some processing toold in ArcMAP. I processed surface and terrain models and used Lidar derivative products as ancillary data in order to improve image classification of the remotely sensed imagery. The final output image was an intensity image of the point cloud data (see Figure 7).

Figure 7 A high intensity image produced with the LAS dataset from the city of Eau Claire.

Data sources

The Lidar point cloud and Tile Index data are from the Eau Claire County 2013 and the Eau Claire County shapefile is from Mastering ArcGIS 6th Edition data by Margaret Price 2014. All data was provided by Dr. Cyril Wilson of the University of Wisconsin- Eau Claire.