Manually monitoring and documenting trees is labour intensive. Lidar provides a possible solution for automatic tree‐inventory generation. Existing approaches for segmenting trees from original point cloud data lack scalable and efficient methods that separate individual trees sampled by different laser‐scanning systems with sufficient quality under all circumstances. In this study a new algorithm for efficient individual tree delineation from lidar point clouds is presented and validated. The proposed algorithm first resamples the points using cuboid (modified voxel) cells. Consecutively connected cells are accumulated by vertically traversing cell layers. Trees in close proximity are identified, based on a novel cell‐adjacency analysis. The scalable performance of this algorithm is validated on airborne, mobile and terrestrial laser‐scanning point clouds. Validation against ground truth demonstrates an improvement from 89% to 94% relative to a state‐of‐the‐art method while computation time is similar.

中文翻译：

---

3D点云中的可伸缩单个树轮廓

手动监视和记录树木需要大量劳动。激光雷达为自动生成树库存提供了一种可能的解决方案。现有的从原始点云数据中分割树木的方法缺乏可扩展且高效的方法，这些方法无法在任何情况下以足够的质量将由不同激光扫描系统采样的单个树木分开。在这项研究中，提出并验证了一种有效的从激光雷达点云中划定单个树的新算法。提出的算法首先使用长方体（修改的体素）单元对点进行重新采样。连续连接的单元格通过垂直遍历单元格层而积累。基于新颖的细胞邻接分析，可以确定附近的树木。该算法的可扩展性能已在机载上验证，移动和地面激光扫描点云。相对于最先进的方法，针对地面真实性的验证表明从89％到94％的改进，而计算时间相似。