Exercise: LiDAR products

DEM, DSM, CHM

In this exersice we want to calculate three of the most common LiDAR products: the Digital Elevation Model, the Digital Surface Model, and the Canopy Height Model.

DEM, DSM, CHM source: https://www.earthdatascience.org/images/courses/earth-analytics/lidar-raster-data-r

We already learned that it is possible to derive a DEM from LiDAR data by filtering out the ground points. What attribute of the LAS file could we use to create the DSM?

Of course, we eventually need some sort of grid to rasterize the pointcloud. Of course lidR provides the functions for these tasks. Have a look at rasterize_terrain() and rasterize_canopy().

Tasks:

  • Calculate a DEM, DSM and CHM with a 1 m resolution
  • Save the CHM as a tif file

Mean vegetation height

The Sentinel grid topography will now be the basis for the calculation of the mean vegetation height, which we already encountered in Baessler et al. 2010.

Tasks:

  • Load the first layer of the cropped Lahntal Sentinel-2 scene
  • Crop the layer with the extent of the LiDAR pointcloud
  • Calculate a DEM with the Sentinel tile as a grid template with rasterize_terrain()
  • Normalize the pointcloud with normalize_height(). What happened?
  • Calculate the mean vegetation height for each pixel
  • Save the mean vegetation height as a tif file
  • Plot the mean vegetation height as overlay to the Lahntal sentinel scene

Updated: