Terrestrial laser scanning technology has developed rapidly, and substantial data have been accumulated in dynamic forest monitoring. Point cloud data of standing trees not only provide tree parameters but also show three-dimensional tree structure. Selection of the key parameters from the point cloud data is a prerequisite for volume estimation of standing trees. Our study collected three phases of data over 5 years from Liriodendron chinense plantation forest. A series of the height-related characteristic parameters were extracted from the scanned points of each tree stems, including a proposed new parameter and the height cumulative percentage (Hz % ). The upper diameter accuracy obtained by multi-station scanning is high, and the correlation coefficient with manually measured data is 0.9864. The shape of the upper tree trunk extracted by the point cloud is equivalent to that of the sample trees (height of 10 to 20 m) with points at H25 % and H50 % of the height. These two parameters also show a high correlation with volume. Results show that H25 % can better associated with tree volume, with R2 at 0.951, 0.957, and 0.901 at three stages, respectively. The volume dynamic change calculated by model 2 is linearly correlated with the rate in point cloud restoration, the intercept is −0.081, and the slope is 1.14. Compared with previous researches, the volume model established based on point cloud hierarchical parameters in this study could be used for monitoring the dynamic volume changes in Liriodendron forest. The Hz value extracted from multi-station scanning point cloud data could be used to represent the dynamic forest structure. The results of this study contribute to further development of terrestrial laser scanning-based modeling and estimation methods for individual tree and forest growth, thereby improving the accuracy of forest inventories estimation and providing better tools for decision-making processes.

中文翻译：

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用于生成异速生长茎体积模型的基于多站 LiDAR 扫描的分层特征

地面激光扫描技术发展迅速，在动态森林监测方面积累了大量数据。立树的点云数据不仅提供了树木参数，还展示了三维树结构。从点云数据中选择关键参数是进行立树体积估计的先决条件。我们的研究从鹅掌楸人工林中收集了超过 5 年的三个阶段的数据。从每个树干的扫描点中提取一系列与高度相关的特征参数，包括提出的新参数和高度累积百分比（Hz % ）。多工位扫描得到的上径精度高，与人工测量数据的相关系数为0.9864。点云提取的上部树干的形状与样本树（高度为10至20 m）的形状相同，点在高度的H25%和H50%处。这两个参数也显示出与体积的高度相关性。结果表明，H25% 可以更好地与树木体积相关联，三个阶段的 R2 分别为 0.951、0.957 和 0.901。模型2计算的体积动态变化与点云恢复率线性相关，截距为-0.081，斜率为1.14。与以往研究相比，本研究基于点云分层参数建立的体积模型可用于鹅掌楸森林动态体积变化监测。从多站扫描点云数据中提取的Hz值可用于表示动态森林结构。