Lab 7: LiDAR remote sensing

 

Shelby Short

Goal and Background

The goal of this lab exercise was to gain introductory knowledge on LiDAR data structure and processing. Specific objectives revolve around processing and retrieval of various surface and terrain models, and then processing and creating intense images and other derivative products from point cloud. LiDAR is one of the recently expanding areas of remote sensing with significant growth in new job creation. To successfully complete this lab, students will work with Lidar point clouds in LAS file format.

Part 1: Point cloud visualization in Erdas Imagine 

To start this lab we added the LiDAR .las files to our image viewer in ERDAS. After examining our image, we looked at the density of the points. The back-scattered points have x,y, and z coordinates. Bodies of water have low point density because backscattering is only 5%, and the other energy is absorbed which is shown in Figure 1. 

Figure 1: Backscattering of surface features by the footbridge on UWEC's campus.

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

To start this part of the lab we generated a new LAS dataset (Eau_Claire_city.lasd) in ArcMap in our LAS folder that we copied over from the sharedrive. Then, we calculated statistics for our .las files and set the correct coordinate system and units in order to generate our.lasd file correctly. We are now ready to visualize our profile!

Figure 2 : LiDAR image files in arcMap with a visualized profile of a bridge. 

Part 3: : Generation of Lidar derivative products 

In this part of the lab our goal was to create digital surface model (DSM) with first return data more specifically a digital terrain model (DTM) , a hillshade of our DSM  and a hillshade of our  DTM. The fist tool we used converted our .las first return point values to a raster image. After we created our raster image we used that to show the hill shade. This allowed us to see vegetation, bridges, buildings, and water, long with all of the other surface features and topography.  Now were generating an image based off of the ground returns, which are the minimum number of returns. This gives us smooth image without the interruption of vegetation and buildings. 

After that we generated an intensity image which we compared to the Landsat image. LiDAR is an active imaging and Landsat is passive so the images differ slightly from one another. 

Results and Knowledge Gained:

After completing this lab I better understood how LiDAR data collection works, what the importance is between return points, and how these data can be used to create different images. 

Data Source:

Wilson, C. (2020) LiDAR Remote Sensing, LAB 7, GEOG 338: Remote sensing of the Environment (pp. 1-24). 

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

 Eau Claire County Shapefile is from Mastering ArcGIS 7 th Edition data by Margaret Price, 2016.