A Method for Extracting the Tree Feature Parameters of Populus tomentosa in the Leafy StageXingyu Shen, Qingqing Huang, Xin Wang, Benye Xi
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.