LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR
LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR
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DOI：
10.1111/2041-210x.13342
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发表时间：
2020-01
影响因子：
6.6
通讯作者：
Di Wang;S. Momo Takoudjou;E. Casella
中科院分区：
文献类型：
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作者：
Di Wang;S. Momo Takoudjou;E. Casella
陆地LiDAR数据中的叶木分离是非破坏性估计生物物理森林属性（如立木体积和叶面积分布）的先决条件。目前的方法尚未在热带树木上广泛应用和测试。此外，它们对随后的木材体积检索的准确性的影响很少探讨。我们提出了LeWoS，一个新的全自动工具，自动分离的树叶和木材成分，仅基于几何信息在情节和个别树尺度。这种数据驱动的方法利用递归点云分割和正则化程序。只需要一个参数，这使得我们的方法很容易和普遍适用于任何激光雷达技术和森林类型的数据。我们对61棵热带树木的广泛数据集进行了LeWoS方法的双重评估。我们首先评估了逐点分类准确性，平均得分为0.91 ± 0.03。其次，我们通过将木材体积和分支长度的估计值与基于手动分离的木材点的估计值进行交叉比较，评估了所提出的方法对3D树木模型的影响。该比较显示了相似的结果，体积和长度的相对偏差分别小于9%和21%。LeWoS结合3D建模方法，可实现非破坏性树木体积和生物量估算的自动化处理链。笔记本电脑上的平均处理时间为90秒，为100万个点。我们提供LeWoS作为一个开源工具，具有最终用户界面，以及来自对比森林结构的标记3D点云的大型数据集。这项研究关闭了差距林分体积建模在热带森林中，叶和木材分离仍然是一个关键的挑战。
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. 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. 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. 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.