Lidar Remote Sensing

- Introduction -

Lidar is an active remote sensing technology that uses the backscattered time differential of self generated laser pulses to accurately model the earth's surface. Lidar stands for light detection and ranging and typically uses NIR radiation around 1.64 micrometers to detect land surface features. Multiple products can be generated through use of Lidar data due to the volume and nature of the light that is produced by the system. For example, the light can penetrate vegetation cover resulting in multiple returns for the top of the tree canopy, branches, lower vegetation, and ground. These returns can be used to extract different information and create different surfaces. In this lab exercise, Lidar data will be visualized in 2D and 3D and multiple derivative products will be created.

- Methods -

Using ArcMap 10.2.2, a LAS dataset was created and Lidar data for the City of Eau Claire, WI was imported in. In the LAS Dataset Properties window, general information on the whole LAS dataset, information like point count, point spacing, and Z min and max for individual LAS files, statistics, XY coordinate system, and Z coordinate system can be viewed and modified. Using the LAS toolbar, the different returns can be viewed as points (Images 1 - 4) or as a triangulated irregular network (TIN) surfaces representing elevation (Image 5), slope (Image 6), or aspect (Image 7). Contours can also be created and visualized (Image 8). Depending on which return is used, digital surface models (DSM) or digital terrain models (DTM) can be created. Using first returns will generate a DSM that represents the surface of the landscape including surface features like trees and buildings. Using ground returns will generate a DTM that represents the actual elevation of the landscape without any surface features. With the LAS Dataset to Raster tool in ArcMap and using the proper returns, both a DSM and DTM were created for the City of Eau Claire. Hillshades were then created of both the DSM and DTM for visual comparison (Image 10). The last derivative product created was an intensity image (Image 11). Intensity is stored in the first returns and the resulting image can be used as ancillary data in image classification. This is because the light used by the Lidar system is within the NIR channel which can be used to parse out different land covers. Lighter areas in the intensity image are reflecting more NIR radiation, signifying bare ground and some urban features. Darker areas represent thick vegetation and the darkest areas are water.

Image 1: All returns symbolized by elevation

Image 2: First return symbolized by elevation

Image 3: Non ground return symbolized by elevation

Image 4: Last (ground) return symbolized by elevation

Image 5: TIN surface of all returns symbolized by elevation

Image 6: TIN surface of all returns symbolized by slope

Image 7: TIN surface of all returns symbolized by aspect

Image 8: Five foot contours

Image 9: 2D and 3D views of a bridge

- Results -

Image 10: Comparison of the hillshades for the DSM (left) and DTM (right) 

Image 11: Intensity image

- Discussion -

Lidar data can lead to exceedingly highly accurate representations of earths surface and can provide meaningful information on earth surface features. The quality of the Lidar data depends on the amount of points collected. This data had an average point spacing of about 1.5 feet. As seen in the images above, water features are sometimes not modeled very well. This is because of waters ability to absorb the NIR radiation coming from the Lidar system resulting in less points and a less accurate surface. If water features are of primary concern, the light produced by the Lidar system can be changed to a wavelength around 0.53 micrometers, within the blue/green channels. Not only can the intensity of the first return be used as ancillary data for image classification but also sections of elevation can be singled out and used as well. Using first and intermediate returns in vegetated areas can give measures of forest biomass. Road networks can be easily distinguished by using ground returns even when not visible in imagery due to vegetation. The list of applications that Lidar data can be used for goes on and on.

- Conclusion -

The applications of Lidar data are numerous and still being explored. This lab exercise was an introduction to using Lidar data that was already processed and ready for use. Pre-processing of Lidar data can be quite complicated, but once completed, the data is a valuable resource. Elevation, slope, aspect, DSM and DTM surfaces can be generated and by using different combinations of returns, a variety of biophysical, economic, and cultural information can be extracted.

- Sources -

Eau Claire County, 2013. Lidar point cloud and tile index.