1. With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood–leaf separation methods adopt the point-wise classification strategy, which is not efficient for processing large-volume TLS datasets acquired at the forest plot level.
2. In this study, we proposed a segment-wise classification strategy to improve the efficiency of the wood–leaf separation from large-volume TLS point cloud datasets collected at the forest plot. The proposed method first decomposes the point cloud into three parts based on the threshold values of its local curvature. Then, the first two parts with lower local curvatures were segmented respectively by a connected component labelling algorithm. Finally, the segmented point clouds were classified into wood or leaf segments according to the segment-wise geometric features of each segment. We tested our method on both needleleaf and broadleaf forest plots in temperate and tropical forests. We also compared our method with two other state-of-the-art wood–leaf separation methods, that is, the CANUPO and LeWoS.
3. The results showed that our method was more than 10 times faster than the compared methods while maintaining comparable and even higher accuracy.
4. Our study demonstrates that the segment-wise classification strategy applies to the large-volume TLS datasets and can greatly improve the efficiency of the classification. The proposed method is simple, fast and universally applicable to the TLS data from various tree species and forest types at the plot level, which may facilitate the adoption of TLS technology by forest ecologists in their studies.

中文翻译：

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一种新型有效的森林地块级地面激光扫描点云木叶分离方法

1. 随着陆地激光扫描（TLS）技术在森林生态领域的应用越来越广泛，基于TLS点云数据的木叶分离研究开展了大量研究。然而，大多数木叶分离方法采用逐点分类策略，这对于处理在森林地块级别获取的大容量 TLS 数据集效率不高。
2. 在这项研究中，我们提出了一种分段分类策略，以提高从森林图收集的大容量 TLS 点云数据集进行木叶分离的效率。所提出的方法首先根据其局部曲率的阈值将点云分解为三部分。然后，具有较低局部曲率的前两个部分分别通过连通分量标记算法进行分割。最后，根据每个段的分段几何特征将分割的点云分为木段或叶段。我们在温带和热带森林的针叶林和阔叶林地块上测试了我们的方法。我们还将我们的方法与其他两种最先进的木叶分离方法进行了比较，即 CANUPO 和 LeWoS。
3. 结果表明，我们的方法比比较方法快 10 倍以上，同时保持了可比性甚至更高的准确性。
4. 我们的研究表明，分段分类策略适用于大容量 TLS 数据集，可以大大提高分类效率。所提出的方法简单、快速且普遍适用于地块级别的各种树种和森林类型的 TLS 数据，这可能有助于森林生态学家在他们的研究中采用 TLS 技术。