LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

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 two-fold evaluation of the LeWoS method on an extensive data set of 61 tropical trees. We first assessed the point-wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Secondly, and for the first time, 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 data set 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.1. 地面激光雷达（terrestrial LiDAR）数据中的叶木分离任务，是无损估算森林生物物理属性（如立木材积与叶面积分布）的前置条件。现有方法尚未在热带树木上开展大规模应用与测试，且其对后续木材体积反演精度的影响也鲜有探讨。 2. 本文提出LeWoS——一款仅依赖样地与单木尺度几何信息的全新全自动叶木分离工具。该数据驱动方法采用递归点云分割与正则化流程，仅需单一参数，可便捷、通用地适配任意激光雷达技术与森林类型的点云数据。 3. 我们基于包含61棵热带树木的大规模数据集，对LeWoS方法开展了双重评估：首先评估逐点分类精度，平均得分为0.91±0.03；其次，我们首次通过交叉对比——将基于LeWoS的木材体积与枝条长度估算结果，与基于人工分离木材点云的估算结果进行对比，评估了该方法对三维树木模型的影响。对比结果显示二者一致性良好，体积与长度的相对偏差分别低于9%与21%。 4. 结合三维建模方法后，LeWoS可构建用于无损获取树木材积与生物量的自动化处理流程。在普通笔记本电脑上，处理100万点云数据的平均耗时为90秒。我们将LeWoS作为带终端用户界面的开源工具发布，并同步提供一套涵盖不同森林结构的标注三维点云数据集。本研究填补了热带森林林分材积建模领域的空白——叶木分离在此领域始终是一项关键挑战。

搜集汇总

背景与挑战

背景概述

该数据集介绍了LeWoS方法，这是一种基于几何信息的全自动叶片-木材分类工具，专为地面LiDAR数据设计，特别适用于热带树木，仅需一个参数即可通用应用。评估结果显示，点分类准确率平均达0.91±0.03，且对木材体积和分支长度估计的偏差较小（相对偏差分别低于9%和21%），处理速度快（每百万点约90秒），并作为开源工具提供，以支持非破坏性树木体积和生物量估计。