Lab 5 : LIDAR and Remote Sensing

Lab 5 : LIDAR and Remote Sensing

Introduction

Lab five introduced us to hands on Lidar techniques while forcing us to combine what we learned in lecture to the methods at hand.  The goal of this lab was to gain the basic knowledge we need to understand Lidar data structure and processing.  The first of two specific processes examined in this lab was the retrieval of various surface and terrain models.  The second process examined in this lab was the creation of intensity images and other derivative products from a point cloud file type (Lidar data).  Lidar is a new and expanding field of remote sensing involving data collection through laser technology.  

Methods

Generating an LAS Dataset and Lidar Point Clouds with ArcGIS

In this part of the lab I am picturing my role as a GIS manager working on a project for the city of Eau Claire, Wisconsin.  The first step was to create a folder extension to my personal folder containing the necessary LAS data.  After making the folder connection, I added all of the LAS files to ArcMap and clicked on the statistics tab to build statistics for the LAS dataset. After the statistics were completed loading, I examined them under the LAS files tab.  These statistics are useful because they can be used for quality assurance.  The header data did not contain the coordinate system specification, therefore I had to look at the metadata information to figure out coordinate information. The horizontal coordinate system assigned was D_North_American_1983 and the vertical coordinate system North American Vertical Datum of 1988. The next step was to display the LAS data set in ArcMap to explore the point cloud.   The point cloud was color coded based on elevation upon first examination.  The point clouds were being sorted according to elevation, while the other options of sorting the points were aspect, slope, and contour. Clicking the layer properties, there are several different ways of displaying contours including changing the contour interval under Symbology or adjusting returns under Filter. Next, I examined the point cloud based on class, return, and profile. I set the filter to first return and then used the LAS Dataset Profile View tool to create a profile of a bridge point cloud by clicking and dragging. A new profile window pops up showing the profile of the bridge in 2D, while you can also view it in an interactive 3D view.  

Generation of Lidar derivative Products 

The first step in this part of the lab was to create a digital terrain model (DTM) and a Digital Surface Model (DSM) from the Lidar data. First I needed to obtain the nominal pulse spacing of the data set by looking at the point spacing field in the LAS data set properties. I also needed to make sure my workspace had my personal lab 5 folder as the output so my Lidar derivative products would automatically be saved there.  I used the LAS Dataset to Raster tool to properly configure the LAS data.  The interpolation type was Binning. Once that processing was complete, the derivative DSM product could be viewed in the viewer.  The DSM looks like an elevation model with higher elevations indicated by lighter shades and lower elevations indicated by darker shades. It is important to note that the 3D analyst extension is enabled in order to create a hillshade derived raster.  Using the Hillshade tool (3D analyst tools > Raster Surface > Hillshade), I inputted my DSM file and the output raster for Hillshade can be seen in results as Figure 1. Next step was to create a digital terrain model (DTM) from the Lidar point cloud.  I am again using the LAS data set to raster tool, only this time I am setting the filter to ground.  All of the parameters are the same except for the cell assignment type, which is now set to minimum instead of maximum earlier.  After the DTM output is created, I created another hillshade raster.  This is also known as a bare Earth Product because it only shows the features on the ground instead of higher returns like the one used earlier to created the DSM file.  After this raster was created, I then explored the differences between the two by utilizing the effects toolbar.  With both hillshade files selected, I used the swipe function located on the effects toolbar to easily see the differences between both hillshade files.  

Deriving Lidar Intensity Image from Point Cloud 

The final portion of this lab involved generating a Lidar intensity image.  Again, we are using the LAS data set to Raster tool.  The parameters were the same for the DTM and DSM except for the cell assignment type, which was set to average. After processing, the output intensity image was created and displayed in Erdas Imagine.  The intensity raster is pictured in results as Figure 3.  An intensity image from Lidar data shows the return strength of laser pulses based on the reflectivity of the object struck by the laser pulse.  Intensity images are used for feature detection and sometimes as a substitute for aerial imagery when availability is a problem.  

Results 

Figure 1

Figure 1 shows a hillshade derived raster from a DSM model

Figure 2 

Figure 2 is a close up of Figure 1

Figure 3

Figure 3 shows an intensity image derived from a Lidar point cloud.  An intensity image from Lidar data shows the return strength of laser pulses based on the reflectivity of the object struck by the laser pulse.  Intensity images are used for feature detection and sometimes as a substitute for aerial imagery when availability is a problem.  

Data Sources

Lidar point cloud and Tile Index are from Eau Claire County, 2013 

Eau Claire County Shapefile is from Mastering ArcGIS 6th Edition data by Margaret Price, 2014