Remote Sensing - Lab 5: LiDAR Remote Sensing

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Background & Objectives

The goal of this lab exercise is to gain basic knowledge on LiDAR data structure and processing. LiDAR is a rapidly expanding area within the remote sensing field and there are many new opportunities available to those with applicable experience.

Lab 5 is split into three parts, with the goals of each part outlined below.

Part 1: Point Cloud Visualization in Erdas Imagine

Objective: Load a LAS data file in Erdas Imagine and view the Point Cloud


Part 2: Generate a LAS Dataset and Explore LiDAR Points With ArcGIS

Objective: You are a GIS manager working on a project for the City of Eau Claire, WI, USA. You have acquired LiDAR point cloud in LAS format for a portion of the City of Eau Claire. First, you want to initiate an initial quality check on the data by looking at its area and coverage, and also verify the current classification of the LiDAR.
  • Create a LAS dataset
  • Explore the properties of LAS dataset
  • Visualize the LAS dataset as point cloud in 2D and 3D

Part 3: Generation of LiDAR Derivative Products

Objective: Derive a variety of products from LiDAR point clouds
  • DSM and DTM Products
  • LiDAR Intensity Image

Methods

The methods used to reach the goal in each part of Lab 5 are listed below. All resources used were provided by the instructor and can be seen in the Sources section at the bottom of this post.

Part 1: Point Cloud Visualization in Erdas Imagine

Objective: Load a LAS data file in Erdas Imagine and view the Point Cloud

Open a viewer in Erdas Imagine and add the lidar point cloud files, making sure to change the file type to LAS as Point Cloud (*.las). After all files have loaded, the output should look like Image 1.1 below.

Image 1.1. LiDAR Point Cloud as seen in Erdas Imagine

After the point clouds load, open ArcMap to access the Tile Index File in order to verify which part of the study area each tile covers. The Tile Index File contains two very important pieces of information about the data; (1) the Tile Index and (2) the Metadata.

The Metadata (Image 1.2) tells which map projection and vertical datum is used:
Map Projection: Lambert Conformal Conic (Line 146)
Vertical Datum: North American Vertical Datum of 1988 (Line 174)

Image 1.2. LiDAR metadata viewed in Notepad++

Part 2: Generate a LAS Dataset and Explore LiDAR Points With ArcGIS

Objective: You are a GIS manager working on a project for the City of Eau Claire, WI, USA. You have acquired LiDAR point cloud in LAS format for a portion of the City of Eau Claire. First, you want to initiate an initial quality check on the data by looking at its area and coverage, and also verify the current classification of the LiDAR.

Section 1: Create a LAS dataset
  • Open ArcCatalog and create a new 'LAS Dataset' within the existing lab folder
  • The LAS Dataset Properties window will open. Go to the LAS Files tab and add all of the individual LAS files that are stored in the lab folder.
Section 2: Explore the properties of LAS dataset

After all files have loaded, click the Statistics tab and click Calculate to build the statistics for the data, as seen in Image 2.1. Statistics are used for Quality Assurance/Quality Control (QA/QC) testing of individual .las files as well as the overall LAS dataset.

Image 2.1. Statistics of LAS Dataset

Next, a coordinate system needs to be assigned to the LAS dataset. The metadata that was observed in Part 1 signifies which coordinate system the data was initially captured in.
  • Click the XY Coordinate System tab and choose the NAD_1983_HARN_WISCRS_EauClaire_County_Feet coordinate system (Image 2.2), as this is the closest match to what was reported in the metadata
Image 2.2. XY Coordinate System for LAS Dataset
  • Repeat this for the Z Coordinate System and select the NAVD88_height_(ftUS) coordinate system
  • Click on the General tab to see more information about the dataset, including the number of files contained, total number of points, and the extent and range of the X, Y, and Z data
Section 3: Visualize the LAS dataset as point cloud in 2D and 3D

After configuring the dataset in ArcCatalog, it can now be visualized in ArcMap.
  • Add the LAS dataset to the map. When viewed at the full extent, it will appear as red tiles. This is because there is so much data within each tile, that it can't be visualized until zoomed in to a smaller scale.
  • Add a shapefile of the study area to confirm that the projected LAS data matches with where it is expected to be located.
Upon zooming to the tiles, the point cloud begins to appear. The default point display is according to elevation. The Data Percentage field in the Table of Contents (highlighted in Image 2.3) refers to the percent of data points being used in the display. This percentage rises as the zoom increases on the image.

Image 2.3. Point Cloud display of Eau Claire with 5.3% of points used in display

Changing the map Filters in the LAS Dataset Toolbar, highlighted at the top of Image 2.4, allows the map to display in a wide variety of ways. Elevation, surface class, slope aspect, slope angle, contours, ground returns, and first returns are just some of the ways that the data can be filtered and displayed.

Image 2.4. LAS Dataset filtered by Slope Angle and Ground Return Only

Another way to visualize the LAS dataset is with the LAS Dataset Profile View tool (Image 2.5). 

Image 2.5. LAS Dataset Profile View tool

This tool displays a profile view of the selected surface. Image 2.6 is a profile view of the Phoenix Park footbridge.

Image 2.6. Profile view of Phoenix Park footbridge in Downtown Eau Claire

Part 3: Generation of LiDAR Derivative Products

Objective: Derive a variety of products from LiDAR point clouds

Section 1: DSM and DTM Products

 DSM:

 Before deriving products from the point cloud, it is important to estimate the average Nominal Pulse Spacing (NPS) that the point cloud was collected at. The NPS will be used to determine the spatial resolution that the derivative products should be produced at. The Point Spacing for each LAS file can be seen in the LAS Dataset Properties (Image 3.1). An estimate of the average NPS for this dataset is 1.5

Image 3.1. Point Spacing in LAS Dataset Properties 

The next step in creating derivative products is to create a geoprocessing workspace by going to Geoprocessing > Environments > Workspace. 

 In ArcToolbox, access the LAS Dataset to Raster tool by going to Conversion Tools > To Raster > LAS Dataset to Raster. Enter the input LAS dataset and selecting the Binning Interpolation Type as seen in Image 3.2. Click OK to run the process, which takes 6 - 10 minutes.

Image 3.2. LAS Dataset to Raster Dialog - DSM

After the model is loaded, use the Hillshade tool in ArcToolbox to enhance the DSM (3D Analyst Tools > Raster Surface > Hillshade). Enter the DSM just created as the input and click OK to run the tool.


DTM:

Set the LAS Dataset filters to show the Ground Return and the Point Tool shown by colored elevation.

Run the LAS Dataset to Raster tool again to create a raster output of the bare Earth. The screen setup should look like Image 3.3.

Image 3.3. LAS Dataset to Raster Dialog - DTM

After the model is loaded, use the Hillshade tool in ArcToolbox to enhance the DTM.

It is possible to see the difference between the DSM and DTM layers by using the Swipe function in the Effects Toolbar. Make sure the DSM layer is above DTM in the Table of Contents.


Section 2: LiDAR Intensity Image

 Using a procedure similar to the DSM and DTM images, generate a LiDAR Intensity Image.

Set the LAS Dataset filter to First Return.

Run the LAS Dataset to Raster tool again to create a raster output of the Intensity of the image. The screen setup should look like Image 3.4.

Image 3.4. LAS Dataset to Raster Dialog - Intensity Image

Results

The goal of this lab exercise was to explore LAS data and learn about the different applications of LiDAR data. This includes visualization of Point Cloud data as well as the generation of LiDAR derived products, such as DSM and DTM.

Explore LiDAR Point Cloud Data Visualizations With ArcGIS

Point Cloud:

The displayed Point Cloud is a representation of all of the data points that were recorded by the LiDAR sensor. Filtering the data by the return number can help to visualize the kinds of surface features that are on the Earth's surface. Filter by First Return to see the highest elevation of all surface features, or filter by Ground Return to see the elevation of the land below all of those surface features. Image 4.1 shows a point cloud display of the Downtown Eau Claire area, filtered by First Return. Notice the Chippewa and Eau Claire rivers running joining near the center of the image, as well as the East Hill neighborhood at higher elevation in the Southeast corner of the image.

Image 4.1. Point Cloud Elevation display of Downtown Eau Claire

Elevation:

Filtering by the TIN surface layer instead of the data points can help to show a more clear picture of ground elevation. If the area of interest is an urban area or has dense vegetation, it can be difficult to see the ground elevation when filtered by First Return. Filtering by Ground Return seems to be the better option when it comes to viewing the TIN elevation layer. For reference, Image 4.2 displays the same area of interest as Image 4.1.

Image 4.2. TIN Elevation display of Downtown Eau Claire

Profile View:

Using the Profile View tool allows the user to select an area of interest and view the horizontal profile of the surface feature. This can be very useful while identifying features, like the Phoenix Park Footbridge seen in Image 4.3.

Image 4.3. Profile View of Phoenix Park Footbridge in Downtown Eau Claire


Generation of LiDAR Derivative Products

DSM Hillshade:

Because the first return was used in this model (Image 4.4), the trees and vegetation stand out significantly. This makes it harder to determine the actual slope of the land. Water features are also very noticeable because the Nominal Pulse Spacing (NPS) is much higher than on land, which makes for some pyramidal shapes over the water when the raster image connects the points.

Image 4.4. DSM Hillshade display of Eau Claire


DTM Hillshade:

In the DTM output (Image 4.5), it is much easier to see the slopes of the land below the vegetation and other surface features. Homes and other buildings are still visible on the output image because the LiDAR lasers used do not penetrate through the buildings due to privacy laws, so they are considered the Ground Return. Much like the DSM, water features still return a pyramidal shape due to the wide point spacing of return signals.

Image 4.5. DTM Hillshade display of Eau Claire

When using the swipe tool to see the difference between the DSM and the DTM output images, the biggest impact from removing vegetation and buildings is that it is now possible to clearly see the land surface and notice elevation changes within the area of interest.

LiDAR Intensity Image:

This output (Image 4.6) creates a panchromatic image that can be very useful in surface feature identification.

Image 4.6. LiDAR Intensity Image of Eau Claire

Sources

LiDAR Point Cloud and Tile Index are from Eau Claire County:

Eau Claire County (2013). LiDAR Point Cloud and Tile Index. Retrieved on April 9, 2018.


Eau Claire County Shapefile is from Mastering ArcGIS Dataset:

Price, Maribeth (2016). Mastering ArcGIS 7th Edition Dataset. McGraw Hill. Retrieved on April 9, 2018.

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