Lab 5: LiDAR Remote Sensing

Background

LiDAR data collection has become very popular due to its ability to be analyzed in many different data analysis.

Goals

• Produce Surface and Terrain Models
• Create intensity image and other raster (DSM and DTM) from a point cloud data set that was in LAS format

Methods

Creation of Surface and Terrain Models

1. Copy the LAS files into a person folder and create a new LAS datasets.
2. Add the LAS Files into the LAS dataset by clicking "Add Files" and make sure to use all of the files from the folder.
3. Use quality control to make sure the dataset makes sense.
4. Add the correct coordinate system to the X,Y system and Z coordinate systems.  This is found in the files properties and under XY Coordinate System and Z Coordinate System.
For this example XY was set to NAD 1983 HARN Wisconsin (US Feet) and the Z-Coordinate was set to NAVD 1988 US feet.

5.  The next step is to add the LAS data to ArcMap where only a grid will appear, and add basemap of the area in quetions to make sure the data is in the correct spot.  This is used to check the coordinate system.
6. Turn on the LAS Dataset tool bar in Arcmap, and turn on the 3-D analyst extension from the Customize dropdown tab to active this tool.
7.This allows us to see the different options in the LAS toolbar to learn about the various types of models that can be used to utilized this type of data.
8. Choose which type of model to create from the surface symbology render options which include elevation , aspect, slope, and contour.  In figure 1 I will be displaying Aspect, Slope, and Contour.
Another import option is the LAS dataset Profile View which allows the user to view the LiDAR point cloud in a 2-D view.  This creates a pop-up window to be used for easy viewing.  The next important option is the LAS dataset 3-D view.  This is used in viewing the Z-Vaules of the aspect image.

Figure 1: Showcasing Aspect, Slope, and Contour symbolizes LAS datafields from a section of Eau Claire, Wisconsin

There is also a button within the LAS Dataset tool bar that allows the user to see what part of the pointcloud is most important.  This is determined by elevation, class, or return which is based on their classification code, or based on the LiDAR pulse return number.

Using the LAS Dataview a Cross section can be used to see the Z-Values of the point cloud seen in Figure 2. There is also the 3-D view that gives a view of the cross section into 3-D.

Figure 2:  Cross section dataview of the point cloud. This is a 2-D view.

Creating an intensity image

This part of the lab was focused on deriving DSM and DTM products from point clouds.  The average nominal pulse spacing is critical to understanding the spatial resolution should be for the DSM and DTM output images. 

Digital surface model (DSM) with First return

To start this the LiDAR points need to be in elevation.  Then use the LAS dataset to RASTER tool to create the DSM.  Imput the Las file being used and then for the value field use elevation.  Set the interpolation type to binning, maximum and nearest neighbor. The sampling value was set to 6.56168 which is about 2m in feet.  This is becuase our data is 2m by 2m.  The rest of the values are set to deafult.

Digital Terrain Model (DTM)

To create the DTM the same first steps were used.  The only changes were to change the maximum to minimum in the interpolation.  All the other settings were the same except for that.

Hillshade of DSM and DTM

Creating a hillshade for the DSM and DTM help to create an elevation of the land surface.  to accomplish this 3D analysis needs to be turned on.  Now, to start the process search for the hillshade (3d analysis) tool.  Just enter the input raster and name the output to get a hilshade of the selected raster.  This is the same for both the DSM and the DTM.

Figure 3:  The left image is the Hillshade output derived from the DSM.  The image on the right was derived from the DTM.  The difference between these images are the DSM is the first return points compared to the image on the right that has the ground return points.  Therefore the right image will be a smoother surface to represent the ground.  

Intensity Image

To create our final maps the Dataset to Raster tool was used to create the intensity image. This was done by changing the data back to point symbology and changing the filter to First Return.  This time in the tool the value field was set to intensity instead of elevation.  The interpolation was also set to average instead of max or minimum.  The sampling value once again stayed the same to get that 2mx2m grid size.

Now after saving this raster we can bring it into ERDAS becuase ArcGIS only really shows a black or dark image.  Once it is brought into ERDAS by saving it as a .TIF the image can be seen to show the intensity.

Figure 4:  The image on the left is the final image in ERDAS where we can see the image has contrast to make out details.  The image on the right was created in ArcGIS which makes just a dark image that is very hard to see much of any details.  

Conclusion

Raster data is very useful when trying to understand LiDAR data.  It can be helpful to understand elevation and to just analyze raster data.  LiDAR is constantly increasing in popularity due to the amount of possibility that it has and potential.  This lab was helpful to get a foot in the door to understand how to manipulate and use point clouds.

Sources:

Eau Claire County. (2013).

Price, M. (2014). Mastering ArcGIS 6th Edition. Mastering ArcGIS 6th Edition Dataset [shapefile]. New York: McGraw Hill.