Automated Detection of Branch Dimensions in Woody Skeletons of Fruit Tree Canopies

Modeling the 3D canopy structure of trees provides the structural mapping capability on which to assign distributed values of light-driven physiological processes in tree canopies. We evaluate the potential of automatically extracted skeletons from terrestrial lidar data as a basis for modeling canopy structure. The automatic and species independent evaluation method for lidar data of trees is based on the SKELTRE algorithm. The SKELTRE skeleton is a graphical representation of the branch hierarchy. The extraction of the branch hierarchy utilizes a graph splitting procedure to extract the branches from the skeleton. Analyzing the distance between the point cloud points and the skeleton is the key to the branch diameter. Frequency distributions of branch length and diameter were chosen to test the algorithm performance in comparison to manually measured data and resulted in a correlation of up to 0.78 for the branch length and up to 0.99 for the branch diameter.