Background and Aims In addition to terrestrial laser scanning (TLS), mobile laser scanning (MLS) is increasingly arousing interest as a technique which provides valuable 3-D data for various applications in forest research. Using mobile platforms, the 3-D recording of large forest areas is carried out within a short space of time. Vegetation structure is described by millions of 3-D points which show an accuracy in the millimetre range and offer a powerful basis for automated vegetation modelling. The successful extraction of single trees from the point cloud is essential for further evaluations and modelling at the individual-tree level, such as volume determination, quantitative structure modelling or local neighbourhood analyses. However, high-precision automated tree segmentation is challenging, and has so far mostly been performed using elaborate interactive segmentation methods. Methods Here, we present a novel segmentation algorithm to automatically segment trees in MLS point clouds, applying distance adaptivity as a function of trajectory. In addition, tree parameters are determined simultaneously. In our validation study, we used a total of 825 trees from ten sample plots to compare the data of trees segmented from MLS data with manual inventory parameters and parameters derived from semi-automatic TLS segmentation. Key Results The tree detection rate reached 96 % on average for trees with distances up to 45 m from the trajectory. Trees were almost completely segmented up to a distance of about 30 m from the MLS trajectory. The accuracy of tree parameters was similar for MLS-segmented and TLS-segmented trees. Conclusions Besides plot characteristics, the detection rate of trees in MLS data strongly depends on the distance to the travelled track. The algorithm presented here facilitates the acquisition of important tree parameters from MLS data, as an area-wide automated derivation can be accomplished in a very short time.

中文翻译：

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从移动激光扫描森林点云中自动提取和测量单棵树

背景和目标除了地面激光扫描 (TLS) 之外，移动激光扫描 (MLS) 作为一种为森林研究中的各种应用提供有价值的 3-D 数据的技术，正日益引起人们的兴趣。使用移动平台，可在短时间内对大片森林区域进行 3D 记录。植被结构由数百万个 3-D 点描述，这些点显示了毫米范围内的精度，并为自动植被建模提供了强大的基础。从点云中成功提取单棵树对于在单棵树级别进行进一步评估和建模至关重要，例如体积确定、定量结构建模或局部邻域分析。然而，高精度的自动树分割具有挑战性，到目前为止，主要是使用精细的交互式分割方法来执行的。方法 在这里，我们提出了一种新的分割算法来自动分割 MLS 点云中的树，应用距离自适应作为轨迹的函数。此外，树参数是同时确定的。在我们的验证研究中，我们使用来自 10 个样本地块的总共 825 棵树来比较从 MLS 数据分割的树木数据与手动库存参数和从半自动 TLS 分割得到的参数。主要结果 对于距离轨迹长达 45 m 的树木，树木检测率平均达到 96%。树木几乎完全被分割到距 MLS 轨迹约 30 m 的距离。对于 MLS 分割树和 TLS 分割树，树参数的准确性相似。结论 除了地块特征外，MLS 数据中树木的检测率在很大程度上取决于到行进轨迹的距离。这里介绍的算法有助于从 MLS 数据中获取重要的树参数，因为可以在很短的时间内完成区域范围的自动推导。