Lab-5 LiDAR Remote Sensing

Goal and Background

The goal of this lab is to gain basic LiDAR skills through the use of ArcGIS. Some Specific goals were processing and retrieving surface and terrain models. The creation of an intensity image and other point cloud images. The LiDAR data used is in LAS file format which is easily manipulated through the use of ArcGIS.

Methods

Part 1: Point cloud visualization in Erdas Imagine

The first part of this lab Erdas Imagine was used to input point clouds and visualize the LiDAR imagery. This was done by opening each individual LiDAR point cloud file in .las format and this gives the ability to view the LiDar image as a whole (Figure 1). 

Figure 1: This image shows the importing process of LAS files into Erdas Imagine. 

Next we brought these same files into ArcMap where it is easier to manipulate Lidar imagery. This was done by creating an LAS data file where LAS point cloud imagery was added of Eau Claire county. This data appears in a bunch of tiles (Figure 2) where when zoomed out point data can not be seen, however when zoomed in on 2 or less tiles the point data becomes clear (Figure 3).

Figure 2: This image shows the tiles that contain each Las file. 

Figure 3: This image shows what appears when zoomed in on 2 or less tiles, point data becomes clear.

Part 2: Generate a LAS dataset and explore lidar point clouds with ArcGIS

In this part of the lab we acted as a GIS manager of Eau Claire county GIS data, where LiDAR data has been acquired and the goal is to perform a quality assurance and quality control check on the data, and verify the classification of the data in its current state. This was first done by analyzing the minimum and maximum values of the Z (Height) of the data (Figure 4). This was done through calculating statistics of the data, and ensuring that the elevation minimum and maximum were close to the actual minimum and maximum elevation of the county of Eau Claire.  

Figure 4: This is the statistics of the point data where the z statistic is the important statistic which gives an idea of the lowest and highest elevation in Eau Claire County.

Next coordinate systems were applied to the xy coordinate, and the Z coordinate of the LAS data set that was created in part 1, this was done by analyzing the coded metadata and finding that it was in NAD 1983 (feet) for the xy coordinate system (Figure 5), and for the Z coordinate system it was in NAVD88_height_(ftUS) (Figure 6). after determining the coordinate systems they were then applied to the layer that is being worked in in ArcMap.

Figure 5: This image shows the process of setting the xy coordinate system to NAD 1983(feet).

Figure 6: This image shows the process of setting the z coordinate system to NAVD88 height (USft).

Then the number of classes in the Data set was changed to limit low and high noise points which are caused by non surface features, or poor reflection. 

Part 3: Generation of LiDAR derivative products

Next DSM and DTM images were produced the first was a DSM with first return, then DTM, then a hillshade of the DSM, and a hillshade of DTM. To create the DSM with the first return data the tool LAS Dataset to Raster (Figure 7) is used and it produces a gray scale elevation image of Eau Claire County.

Figure 7: This shows the LAS Dataset to Raster tool in the process of converting point data into a gray scale raster image of Eau Claire county using Elevation data.

Then a DTM was created by removing the surface features of Eau Claire County which was done by using only ground points from the LAS data set, and the same tool was used which produced a gray scale image showing only elevation and terrain change of Eau Claire County.

Then The hillshade tool was used to convert both the DSM and DTM into hillshade images which show greater detail in the surface terrain. 

Then an intensity image was produced from the point cloud data where instead of elevation in the value field Intensity was used this creates a TIFF image (Figure 8). 

Figure 8: This image once again shows the LAS dataset to Raster tool but this time the value field was changed to intensity in order to create an intensity image of Eau Claire County.

Results

Part 1: Point cloud visualization in Erdas Imagine

The first image that was created was in Erdas Imagine where all the LAS point files were loaded to create a LiDar image. This image Shows all the data point returns in a stacked image that when zoomed in different surface features can be identified.

Then when the same LAS data files were brought into ArcMap it produced the tiled image seen above but when zoomed in to two or less tiles features can be seen.

Part 2: Generate a LAS dataset and explore lidar point clouds with ArcGIS

Then different features were explored for viewing this lidar image, elevation was used and shows a more detailed image with points connected which created a not broken image unlike seen in Part 1.

Part 3: Generation of LiDAR derivative products

A DSM was created first which shows a raster image of Eau Claire County and its surface features.

A DTM was created next which removes surface features, and creates a raster of only the ground surface, lacking buildings and vegetation.

Next a Hillshade of both the DSM and DTM were created which show greater detail of the surface with better shading to signify different elevation and different features

Finally the Intensity image was created which is a Band 5 image which uses the near infreared spectrum to produce a gray scale image that shows the greatest detail of the current surface, and all of its features. this image has a greater visual spectrum than a normal optically sensed image from a satellite. 

Sources

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

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