Assessing inclination angles of tree branches from terrestrial laser scan data using a skeleton extraction method

Abstract Assessing the inclination angles (IAs) of tree branches is essential to evaluate their rainfall interception ability. However, depicting the IA distribution within a forest canopy is still a challenge. This study developed the WoodSKE method to extract skeletons from discrete point clouds of tree branches collected by terrestrial laser scanners (TLSs) for assessing their IAs. A TLS point cloud of tree branches was firstly contracted according to the pointwise local point distribution pattern to extract its coarse skeleton. Then, the coarse skeleton would be thinned and optimized by a noise filtering method. The IAs of tree branches were estimated based on their skeleton distribution. For each point cloud that has the reference skeleton, the average and root mean squared error (RMSE) of offset distance for its WoodSKE-extracted skeleton was less than 0.011 m and less than 0.019 m, respectively. Furthermore, the WoodSKE method was robust to process point clouds with noise. Comparing to the measured IAs at sample points selected from tested point clouds, the mean absolute error and RMSE of their skeleton-assessed IAs were 7˚ and 11.7˚, respectively. Over 86% of the number of sample points in each tested point cloud with IA assessment error lower than 15˚. Extracting skeletons from TLS point clouds of tree branches provides a base for assessing the distribution of IA-related structure features within a forest canopy.