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.

评估树枝的倾角 (IA) 对评估其降雨拦截能力至关重要。然而，描绘森林冠层内的 IA 分布仍然是一个挑战。本研究开发了 WoodSKE 方法，用于从地面激光扫描仪 (TLS) 收集的树枝的离散点云中提取骨架，以评估其 IA。首先根据逐点局部点分布模式收缩树枝的TLS点云以提取其粗略骨架。然后，粗骨架将通过噪声过滤方法进行细化和优化。树枝的 IA 是根据它们的骨架分布估计的。对于每个具有参考骨架的点云，其 WoodSKE 提取骨架的偏移距离的平均和均方根误差 (RMSE) 分别小于 0.011 m 和小于 0.019 m。此外，WoodSKE 方法对于处理带有噪声的点云具有鲁棒性。与从测试点云中选择的样本点处测量的 IA 相比，其骨架评估的 IA 的平均绝对误差和 RMSE 分别为 7° 和 11.7°。每个测试点云中超过 86% 的样本点数的 IA 评估误差低于 15˚。从树枝的 TLS 点云中提取骨架为评估森林冠层内与 IA 相关的结构特征的分布提供了基础。与从测试点云中选择的样本点处测量的 IA 相比，其骨架评估的 IA 的平均绝对误差和 RMSE 分别为 7° 和 11.7°。每个测试点云中超过 86% 的样本点数的 IA 评估误差低于 15˚。从树枝的 TLS 点云中提取骨架为评估森林冠层内与 IA 相关的结构特征的分布提供了基础。与从测试点云中选择的样本点处测量的 IA 相比，其骨架评估的 IA 的平均绝对误差和 RMSE 分别为 7° 和 11.7°。每个测试点云中超过 86% 的样本点数的 IA 评估误差低于 15˚。从树枝的 TLS 点云中提取骨架为评估森林冠层内与 IA 相关的结构特征的分布提供了基础。