Lidar (light detection and ranging) has been widely utilized for estimating the structural parameters of plants, such as tree height, leaf inclination angle, and biomass. However, individual trees have been primarily manually extracted from three-dimensional (3D) point cloud images. Automatically detecting each tree and analyzing its structural parameters is desirable. In this study, we propose a method to (1) detect each tree from 3D point cloud images obtained from ground-based lidar, (2) estimate the number of trees and diameter at breast height (DBH) from the detected 3D point cloud images of trees, and (3) segment each tree canopy. First, we focused on point clouds whose height ranged from 0.5 to 1.5 m and detected each cluster of tree trunks. Then, the clusters were expanded by classifying other points to the clusters that are located near the points and then repeating this process. The process assigns the points in the 3D point cloud image to each tree in the upward direction and separates not only tree trunks but also tree canopies. As a result, the trees in 3D point cloud images were detected with high accuracy, and the number of trees and DBH was estimated. Moreover, each tree canopy was segmented.

中文翻译：

---

从地面激光雷达获得的三维点云图像中自动检测个体树木并进行冠层分割

激光雷达（光探测和测距）已被广泛用于估计植物的结构参数，例如树高、叶倾角和生物量。然而，个别树木主要是从三维（3D）点云图像中手动提取的。自动检测每棵树并分析其结构参数是可取的。在这项研究中，我们提出了一种方法：（1）从从地面激光雷达获得的 3D 点云图像中检测每棵树，（2）从检测到的 3D 点云图像中估计树木的数量和胸高（DBH）的直径树，和（3）分割每个树冠。首先，我们专注于高度在 0.5 到 1.5 m 之间的点云，并检测每个树干簇。然后，通过将其他点分类到位于这些点附近的集群，然后重复此过程，来扩展集群。该过程将 3D 点云图像中的点向上分配给每棵树，不仅分离树干，还分离树冠。结果，3D点云图像中的树木被高精度检测，并估计了树木的数量和DBH。此外，每个树冠都被分割了。