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DATE: 2021-04-20
AUTHOR: John L. Godlee
I've been trying to extract tree canopy complexity statistics from my terrestrial LiDAR data. As part of this I have been creating Canopy Height Models (CHMs) for each of my savanna plots. This involves "pit-filling" to remove low points where the LiDAR didn't penetrate all the way to the top of the canopy. Without pit-filling, the canopy surface appears pock-marked and jaggedy, while I want to approximate the top of the canopy as an even surface. So basically, in order to pick the best pit-filling and canopy height model algorithms, I have been making lots of maps and 3D surfaces of the tree canopy surface. I'm not going to get into the maths behind what I did, I just wanted to share some pretty pictures that came out of this process. Note, all these images are from the same plot, in Bicuar National Park, southwest Angola.
99th percentile of height from raw point cloud data, following noise removal and voxelisation
Predicted values from a generalised additive model of canopy height
Canopy height after pit-filling
Topographic roughness index, using a more coarse 1 m^2 resolution
