I enjoy working with LiDAR data whenever I can because of its remarkable topographic detail and unique characteristics. More often then not, I work with LiDAR data interpolated onto a raster grid. Lately, however, I’ve been working with terrestrial laser scanner data and having a means of quickly visualizing the point data itself has become important to my workflows. Did you know that as of the latest release of Whitebox GAT (v. 3.2.1) there is now native support for displaying LAS files, the commonly used standard format for storing LiDAR point clouds, in the map area?
The LAS point cloud will be added to the Whitebox map area in the same way that you can overlay other vector or raster geospatial data. Here’s an example of a LAS dataset overlaid on top of a raster hillshade image.
The display properties, including the point size, colour palette, and display value ranges can be easily manipulated.
You can even render the point cloud based on elevation, intensity, class value, scan angle, or the GPS time (This is a newly added feature that will be present in the next public release).
Here’s an example of a LAS point cloud rendered using its intensity data. It’s interesting how much it resembles a fine-resolution orthophotograph, but this image is actually made up of millions of individual points. You need to zoom in to see them all.
Visualizing the GPS time and scan angle can be very useful for identifying individual flight lines in airborne LiDAR datasets. Here’s an example of rendering the LAS file displayed above using the GPS time as the display attribute:
It may not be much to look at (unless you really like orange), but the edges of the various overlapping flight lines are quite apparent.
I’ve also recently added a new LiDAR Histogram tool that will allow you to visualize the statistical distribution of elevation, intensity, or scan angle within a LAS dataset, including outputting a table of the percentiles.
Whitebox has certainly become a first-rate open-source platform for manipulating and analyzing LiDAR data. And it’s getting better with every release! Leave your comments below and, as always, best wishes and happy geoprocessing.
If you enjoyed this blog, you may also enjoy “Working with LiDAR data in Whitebox GAT“.