Remote Sensing Lab #5: Lidar

Goals and Background

The goal of this lab is to obtain a basic understanding of Lidar data and processing.  In this lab we will be using Lidar point clouds in LAS file format to create various models of the earths surface.  Lidar has had a significantly growth in the remote sensing field, creating many jobs.  Understanding this information will give me an additional tool as my career advances.

Lidar is a active remote sensing system.  The system sends a laser pulse from an aircraft towards the ground and then a sensor mounted on the aircraft receives the return pulse from the ground (Left, Fig. 1).  From this data the system produces point cloud data.  From this data we are able to calculate location and elevation.  The return data is broken down into return heights (Right, Fig. 1). 

(Fig. 1) (Left) Depiction of Lidar system on an aircraft. (Right) Example of return levels of lidar. (https://ic.arc.losrios.edu/~veiszep/28fall2012/Fancher/G350_ZFancher.html)

Methods

The first section of the lab had us importing Lidar point cloud files into Erdas Imagine for a visual understanding (Fig. 2).  Additionally, we inspected the Tile Index to help located where specific tiles lie within the study area (Fig. 3).

(Fig. 2)  LAS point cloud files of a portion of Eau Claire County displayed in Erdas Imagine.

To gain better understanding of where the study area was, I opened the tile index in ArcMap.  The next step was to open the same LAS files from the first step in ArcMap.  The LAS files opened were only just a small portion of the full tile index (Fig. 2)  Next, I was able to calculate and inspect the statistics in the properties window.  Analyzing the statistics revealed the elevation (z-values) to be higher than the actual elevation of the study area (Fig. 4).  The elevation for the study area is just a little shy of 1000 ft, so the z-value of 1800 is an anomaly.  I will examine and determine why the z-value is so high later in this lab.

(Fig. 3) The tile index with the LAS files overlayed in ArcMap.

(Fig. 4) Statistics of the LAS dataset.

The majority of older Lidar data is delivered to the analysis without having a coordinate system defined in the dataset.  The information is available within the metadata but as the analyst I have to the define the coordinate systems before use.  Switching to the XY Coordiante System tab in the LAS Dataset Properties I defined the dataset to the appropriate projection for both the horizontal and the vertical coordinate system.

With the point cloud dataset open in ArcMap I examined the surface data in four different methods/conversion tools on the LAS Dataset Toolbar:  Elevation, Aspect, Slope, Contour.  I found the elevation to be the most useful for multiple forms of analysis.  Aspect, slope, and contour have there uses but are more limited.

(Fig.5) Point cloud data displayed in ArcMap.

The area is (Fig. 6) which appear to be a mountain is actually in the middle of the river.  This has to do with the interpolation method used to generate the display.  Inspecting (Fig. 4) you can see there is no real data within the river area.  The elevation to essentially just makes an educated guess as to the topology of the area.

(Fig. 6) The elevation conversion of the point cloud data in ArcMap.

I will be examining the first returns of the elevation point cloud image utilizing the LAS Dataset Profile View tool with in ArcMap.  Using the information from the statistics tables I zoomed in to the grid square with the highest elevation to attempt to locate the cause of the anomaly of the high elevation value.  After a little searching and the use of the 3D View I was able to locate the points with the profile view (Fig. 7).  My guess is the feature well above the majority of the points is some form of communication tower.

(Fig. 7) Profile view with in ArcMap of Lidar point cloud data.

One of the great aspects of Lidar is the ability to derive 3 dimensional images from the data.  These images have a multitude of uses.  For this lab I will be creating a digital surface model (DSM) with the first return data at a spectral resolution of 2 meters.  I will also be creating a digital terrain model (DTM) and a hillshade model of both the DSM and DTM.

Before creating the images I had to set the display parameters in ArcMap correctly.  I set the layer to display the points by elevation and only utilized the first returns.  Using LAS Dataset to Raster tool in ArcMap I set the specifications as follows: Value Field=Elevation, Cell Type=Maximum, Void Filling=Natural Neighbors, Cell Size= 6.56168 (approximately 2 meters).  The tool take a few minutes to run, but once it is done you have visual image of the elevation of the study area (Fig. 8).  The image leaves a little to be desired as far as visual clarity.  To enhance the detail of the image I will created a hillshade of the DSM (Fig. 9).

(Fig. 8)  DSM model created within ArcMap.

(Fig. 9) DSM model with hillshade enhancements.

Next using the same tool I will create a DTM or in a simpler term a "bare earth" raster image.  The image will display only the ground elevation and none of the buildings or trees.  This is a great tool to get a good understanding of the terrain when working in an area.

I set the filter to Ground returns and left the point elevation.  I used the LAS Dataset to Raster tool again with the same settings.  The result is an image of the same area from the DSM but without all the above surface features (Fig. 10)

(Fig. 10) DTM of the same study area as above.

The final transformation I conducted was to create a Intensity image based on the Lidar point cloud information.  The intensity image is collected in the first return.  Before running the tool I set the filter to first returns.  Using the LAS Dataset to Raster tool I left all of the settings the same as before except the Value Field which I changed to INTENSITY.  After the tool was complete the image was very dark and not very detailed or visable.  I could have adjusted the setting with in ArcMap to alter the view but a simpler method was to open the image in Erdas Imagine (Fig. 11).  Erdas enhances the display of the image on the fly.

(Fig. 11) Intensity image created in ArcMap, viewed in Erdas Imagine.

Results

I now have a solid base understanding of how Lidar works and the data which results.  The operations I preformed above are just the basics in the utilization of Lidar data.  To become proficient at Lidar one would have to take multiple classes to fully understand all of the process associated with production and use of the data.  I look forward to future exploration of Lidar processes.

Sources

Lidar point cloud and Tile Index obtained from Eau Claire County, 2013.
Eau Claire County Shapefile is from Mastering ArcGIS 6th Edition data by Margaret Price, 2014.