With the advancement of 3D information collection technology, such as LiDAR scanning, information regarding the trees growing on large, complex landscapes can be obtained increasingly more efficiently. Such forestry data can play a key role in the cultivation, monitoring, and utilization of artificially planted forests. Studying the tree growth of artificially planted trees during the leafy period is an important part of forestry and ecology research; the extraction of tree feature parameters from the point clouds of leafy trees, obtained via terrestrial laser scanning (TLS), is an important area of research. The separation of foliage and stem point clouds is an important step in extracting tree feature parameters from data collected via TLS. By modeling the separated stem point clouds, we can obtain parameters such as a tree's diameter at breast height (DBH), the number of branches, and the relationship between these and other parameters. However, there are always problems with the collected foliated tree point clouds; it is difficult to separate the point clouds into foliage and stems, yielding poor separation results. To address this challenge, the current study uses a deep learning-based method to train a mixture of non-foliated and foliated point clouds from artificially planted trees to semantically segment the foliage labels from the stem labels of these trees. And this study focused on a Chinese white poplar (Populus tomentosa Carr.) plantation stand. At the same time, the method of this study greatly reduces the workload of labeling foliated point clouds and training models; an overall segmentation accuracy of 0.839 was achieved for the foliated Populus tomentosa point clouds. By building the Quantitative Susceptibility Mapping (QSM) model of the segmented point clouds, a mean value of 0.125 m for the tree diameter at breast height, and a mean value of 14.498 m for the height of the trees was obtained for the test set. The residual sum of squares for the diameter at breast height was 0.003 m, which was achieved by comparing the calculated value with the measured value. This study employed a semantic segmentation method that is applicable to the foliated point clouds of Populus tomentosa trees, which solves the difficulties of labeling and training models for the point clouds and improves the segmentation precision of stem-based point clouds. It offers an efficient and reliable way to obtain the characteristic parameters and stem analyses of Populus tomentosa trees.

The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and ranging (LiDAR) scanning, the stand information of trees in large areas and complex terrain can be obtained more efficiently. However, due to the diversity of the forest floor, the morphological diversity of the trees, and the fact that forestry is often planted as large-scale plantations, efficiently segmenting the point cloud of artificially planted forests and extracting standing wood feature parameters remains a considerable challenge. An effective method based on energy segmentation and PointCNN is proposed in this work to address this issue. The network is enhanced for learning point cloud features by geometric feature balance model (GFBM), enabling the efficient segmentation of tree point clouds from forestry point cloud data collected by terrestrial laser scanning (TLS) in outdoor environments. The 3D Forest software is then used to obtain single wood point cloud after semantic segmentation, and the extracted single wood point cloud is finally employed to extract standing wood feature parameters using TreeQSM. The point cloud semantic segmentation method is the most important part of our research. According to our findings, this method can segment datasets of two different artificially planted woodland point clouds with an overall accuracy of 0.95 and a tree segmentation accuracy of 0.93. When compared with the manual measurements, the root-mean-square error (RMSE) for tree height in the two datasets are 0.30272 and 0.21015 m, and the RMSEs for the diameter at breast height are 0.01436 and 0.01222 m, respectively. Our method is a robust framework based on deep learning that is applicable to forestry for extracting the feature parameters of artificially planted trees. It solves the problem of segmenting tree point clouds in artificially planted trees and provides a reliable data processing method for tree information extraction, trunk shape analysis, etc.