Characterization of individual tree crowns using three-dimensional shape signatures derived from LiDAR data

Three-dimensional (3D) shape signatures based on the distance distribution of random point pairs are introduced and the effectiveness evaluated using computer simulations and samples of oak and Douglas fir crowns selected from Light Detection and Ranging (LiDAR) point clouds and Digital Surface Models (DSMs). The results suggest that comparison of 3D crown shapes can be effectively reduced to the comparison of frequency distributions of distances between random points, and that it is more computationally efficient when shape signatures are derived from raster surfaces. The results also suggest that the statistically based 3D shape signatures are relatively insensitive to noise and other small local variations, which is important for crown shape analysis in real-world environments.