1. Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored.
2. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type.
3. We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively.
4. LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open‐source tool with an end‐user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge.

中文翻译：

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LeWoS：一种通用的木材分类方法，可使用陆地LiDAR简化大型热带树木的3D建模

1. 地面LiDAR数据中的叶木分离是非破坏性评估生物物理森林特性（如原木体积和叶面积分布）的前提。当前的方法尚未在热带树木上广泛应用和测试。此外，很少探讨它们对后续木材体积取回精度的影响。
2. 我们介绍LeWoS，这是一种新的全自动工具，仅基于样地和单个树比例尺上的几何信息，即可自动分离树叶和木材成分。这种数据驱动的方法利用了递归点云分割和正则化程序。只需要一个参数，这使我们的方法可以轻松，通用地应用于任何LiDAR技术和林类型的数据。
3. 我们对61种热带树木的广泛数据集进行了LeWoS方法的双重评估。我们首先评估了按点分类的准确性，平均得分为0.91±0.03。其次，我们通过将木材体积和树枝长度的估计值与基于人工分离的木材点的估计值进行交叉比较，评估了该方法对3D树模型的影响。该比较显示了相似的结果，相对偏差在体积和长度上分别小于9％和21％。
4. 当结合3D建模方法时，LeWoS允许自动处理链进行无损树木体积和生物量估算。笔记本电脑的平均处理时间为90秒钟，可处理100万个积分。我们提供LeWoS作为具有最终用户界面的开源工具，以及来自对比森林结构的带有标签的3D点云的大型数据集。这项研究填补了在热带森林中进行林分体积建模的空白，在这些森林中，叶片和木材的分离仍然是至关重要的挑战。