Lab 5: LiDAR Remote Sensing

Krista Emery
Aug 14, 2019
3 min read

Updated: Apr 28, 2023

Goals and Background

Basic background and exposure to LiDAR data structure and processing procedures
Creating and processing digital terrain models and digital surface models from laser (LAS) collected point cloud data
Processing and formation of hillshade and intensity images from LAS point cloud.

The purpose of this lab is to demonstrate how LiDAR is a highly useful tool and is becoming more exposed in different applications in remote sensing.

Methods

Part 1: Point Cloud Visualization in Erdas Imagine

This section has the user copy the data from the professor's folder on the Q drive. After examining each LAS file and determining that they do overlap, we add the LiDAR point cloud and change the file type from LAS to Point Cloud (*.las.) Then, the images are all brought into Erdas Imagine to create a more complete picture of the area. In order to maintain accuracy and spatial context, you must have the Tile Index and Metadata for the dataset. Once opened in ArcMap, open quarter sections 1 shapefile to locate the tile index shapefile location. We will continue to work in ArcMap for a large portion of this lab, as it deals with LiDAR point clouds more accurately and simplistic than Erdas.

Part 2: Generate a LAS dataset and Explore LiDAR Point Clouds with ArcGIS

Tasks:

Create a LAS dataset
Explore the properties of LAS dataset
Visualize the LAS dataset as point cloud in 2D and 3D

Section 1: Create Folder Connection

After enabling the LAS Dataset toolbar on ArcMap, connect to lab 5 folder. In the folder containing the copied dataset from the Q drive, right click the LAS folder and select New > LAS Dataset. Rename to Eau_Claire_City and select properties. Upload all the imagery from the Q drive to the LAS Dataset properties window's LAS Files tab. In the Statistics tab, Calculate the statistics for the group of images. Statistics are helpful in the instance of quality assurance and quality control of your project files. A good indicator of quality is comparing the Min and Max Z values to known elevations of the study range. If it is within a reasonable range, the data will be used. Establish a coordinate system for the LAS dataset by clicking the LAS Dataset properties 'XY Coordinate System' tab. The corresponding PCS that represents our study area best is the NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet) coordinate system. For the 'Z Coordinate System' select the 'NAVD 1988 US feet' vertical coordinate system. Add the Eau Claire Ct shapefile to ArcMap to define the coordinate system of the lidar data noted in the metadata.

Part 3: Generation of LiDAR Derivative Products

Section 1: Deriving DSM and DTM Products from Point Clouds

The NPS, or average nominal pulse spacing, is estimated from the point clouds from which they are collected and are used to determine the spatial resolution that the products of these processes. We will be using the LAS Dataset to Raster tool in ArcMap to create the products in this portion of the lab.

Hillshade (DTM) - study area with all above ground features included

Value: Elevation Interpolation Type: Binning Cell Assignment Type: Maximum Void Fill: Natural Neighbor Sampling Type/Cell Size: 6.56168 (see figure 1) Hillshade (DSM) - study area without any above ground features

Value: Elevation Interpolation Type: Binning Cell Assignment Type: Minimum Void Fill: Natural Neighbor Sampling Type/Cell Size: 6.56168 (see figure 2)

Section 2: Deriving LiDAR Intensity Image from Point Cloud

This section describes how to create a lidar intensity image using the first return point echoes in a manner similar to the DSM and DTM procedures. We used the LAS Dataset to Raster Tool to create the intensity image. Inputted the following values: Intensity Image Value: Intensity Interpolation Type: Binning Cell Assignment Type: Average Void Fill: Natural Neighbor Sampling Type/Cell Size: 6.56168

In ArcMap, the intensity image is dominated by black across the study area with little specks of white here and there. (See figure 3.) In order to see a clearer visual of the intensity image, we open it in ERDAS Imagine. (See figure 4.)