Lab 11: Lidar Remote Sensing

Goals

Lidar is one of the most rapidly expanding areas of remote sensing which is also causing a great deal of growth in the job market. The main goal of this lab exercise was to use Lidar data for various aspects of remote sensing. The specific objectives include the processing of surface and terrain models, creating intensity images and other derivative products from point clouds and the use of Lidar derivative products as ancillary data in order to improve optical remotely sensed data image classification. 

Methods

For this particular lab exercise I was placed in a real-world scenario in order to apply my conceptual knowledge of Lidar data to a portion of the City of Eau Claire. This scenario states that I am to act as a GIS manager working on a project for the City of Eau Claire where I have acquired Lidar point could in LAS format for a portion of the city. I need to first initiate a quality check on the data by viewing its coverage and area while also studying the current classification of the Lidar data. My tasks are as follows: create an LAS database, explore the properties of LAS datasets and visualize the LAS dataset as point clouds in both 2D and 3D formats. For majority of this lab I will use ArcMap rather than ERDAS Imagine. 

To start, I created a new LAS dataset within my Lab 11 folder. After this dataset was created I opened the properties in order to add files to this dataset. After adding all the files provided to me for this lab I selected the "statistics" tab within the properties and selected the option to "calculate" which will build statistics for the LAS dataset. Once the statistics were added, I could then look at the statistics for each individual LAS file. These statistics can be used for OA/OC (quality assurance/ quality control of the individual LAS files as well as the dataset as a whole. An easy way to check the OA/ OC is to compare the Max Z and Min Z values which are the known elevations within the range of the Eau Claire study area. The next step is to assign the coordinate information to the LAS dataset. To do this I clicked on the “XY Coordinate System” tab. Since the data had no assigned coordinate system I had to look at the metadata to determine the horizontal and vertical coordinate systems for the data (Fig. 2). Once I applied the coordinate system to the LAS dataset, I opened it in ArcMap. I then added a shapefile of Eau Claire County to the file in order make sure that the data is spatially located correctly. Next I zoomed into the tiles in order to visualize the point clouds in elevation form (Fig. 3).

(Fig. 1) The red tiled area is the region of Eau Claire county where I will be working with Lidar data.

(Fig. 2) The metadata for the various LAS data files can be used to determine the horizontal and vertical coordinate systems used for these images.

(Fig. 3) This image shows a zoomed in view of the red tiled shown in Fig. 1 and shows the Lidar data.

Digital surface models (DSMs) produce Lidar data which can be used as ancillary data to improve on classification within an expert system classifier. I then could add contour lines to the data by selecting the symbology tab within the layer properties. I then could change the index factor in order to experiment with how the values effected the contours within the display. 

I could then explore the point clouds according to class, return and profile. To do this I zoomed out to the full extent of the study area and set the points to "elevation" and the filter to "first return". Using this  method I could drag a line over a bridge feature on the map and see a 2D illustration of the feature's elevation. 

The next objective for this lab was to generate Lidar derivative products. The first step in this process was to derive DSM and DTM products from the point clouds. In order to figure out what the spatial resolution which the derivative products should be produced at I had to estimate the average NPS (nominal pulse spacing) at which the point clouds where initially collected at. This information can be found in the LAS dataset properties menu under the "point spacing" region of the LAS file information. 

I then set up a geoprocessing workspace in order to create raster derivative products at a spatial resolution of 2 meters. The next step was to open the toolbox and select: "conversion tools> to raster> LAS dataset to raster". After inputting the LAS dataset I set the value field to "elevation". I then used the binning interpolation method and set the cell type to maximum and void filling to natural neighbor. Once the tool is finished running I opened the DSM result into ArcMap. The DSM file can then be used as ancillary data which can be used to classify buildings and forest, both of which are structures above the ground surface. Using the 3D analyst tool hillshade, the derived raster was added to my map. 

(Fig. 4) The output image shown above is the derivative product, DSM result.

Next I derived a DTM (digital terrain model) from the lidar point cloud. I used the LAS dataset toolbar, setting the filter to ground in order to make sure the point tool shows the points which are colored based on elevation. I set the interpolation to binning, cell assignment type to minimum, void fill method to natural neighbor and sampling type as cellsize. After the tool was run I opened it in ArcMap and can view the derivative product which resulted from the tool.

I then derived a Lidar intensity image from the point cloud which requires a similar process to creating the DSM and DTMs which were explained above. This time however, the value field will be set to intensity, the binning cell assignment type to average, and void fill natural neighbor. Once this tool finished running I opened the output image in ERDAS Imagine.

(Fig. 5) Lidar intensity image produced from the original point cloud image.

Results

Throughout this lab exercise I learned how to utilize Lidar data in remote sensing. The output images produced from this lab can be seen in the above method section. I both processed surface and terrain models and used Lidar derivative products as ancillary data in order to improve optical remotely sensed data image classification.

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.