Light Detection and Ranging (LiDAR) sensors emit laser signals to calculate distances based on the time delay of the returned laser pulses. They can generate dense point clouds to map forest structures at a high level of spatial resolution. In this work, we consider the problem of segmenting out individual trees in Airborne Laser Scanning (ALS) point clouds. Several techniques have been proposed for this purpose which generally require time-consuming parameter tuning and intense user interaction. Our goal is to design an automated, intuitive, and robust approach requiring minimal user interaction. To this aim, we define a new segmentation approach based on topological tools, namely on the watershed transform and on persistence-based simplification. The approach follows a divide-and-conquer paradigm, splitting a LiDAR point cloud into regions with uniform densities. Our algorithm is validated on coniferous forests collected in the NEW technologies for a better mountain FORest timber mobilization (NEWFOR) dataset, and deciduous forests collected in the Smithsonian Environmental Research Center (SERC) dataset. When compared to four state-of-the-art tree segmentation algorithms, our method performs best in both ecosystem types. It provides more accurate stem estimations and single tree segmentation results at various of stem and point densities. Also, our method requires only a single (Boolean) parameter, which makes it extremely easy to use and very promising for various forest analysis applications, such as biomass estimation and field inventory surveys.

光探测和测距 (LiDAR) 传感器发射激光信号，根据返回激光脉冲的时间延迟计算距离。它们可以生成密集的点云，以高水平的空间分辨率绘制森林结构图。在这项工作中，我们考虑了在机载激光扫描 (ALS) 点云中分割单个树木的问题。为此目的，已经提出了几种技术，这些技术通常需要耗时的参数调整和密集的用户交互。我们的目标是设计一种自动化、直观且稳健的方法，需要最少的用户交互。为此，我们定义了一种基于拓扑工具的新分割方法，即分水岭变换和基于持久性的简化。该方法遵循分而治之的范式，将 LiDAR 点云拆分为密度均匀的区域。我们的算法在收集的针叶林中得到验证改善山地森林木材动员(NEWFOR) 数据集和史密森尼环境研究中心(SERC) 数据集中收集的落叶林的新技术。与四种最先进的树木分割算法相比，我们的方法在两种生态系统类型中均表现最佳。它在各种茎密度和点密度下提供更准确的茎估计和单树分割结果。此外，我们的方法只需要一个（布尔）参数，这使得它非常易于使用并且非常适合各种森林分析应用，例如生物量估算和野外库存调查。