基于无人机激光雷达的亚热带人工林单木测量研究

Individual Tree Measurement in Subtropical Plantations Forest Using UAV Laser Scanning

摘要:

利用无人机激光雷达(UAV-LS)进行森林参数提取与林分蓄积估算存在的主要问题是单木分割精度不够以及不能直接获得胸高(DBH)参数。针对这一局限性，本研究利用UAV-LS采集桉树和杉木人工林超高密度点云数据，通过改进均值漂移算法(IMSA)提出一种能够准确获取立木任意高直径方法，从而计算树木蓄积，实现从单木分割角度准确估算林分蓄积。结果表明：基于IMSA能有效处理树干附近密集噪点，检测的准确性显著提升，对树干边缘点确定及拟合的准确率最优，估测1.3、2m处直径的决定系数R2>0.93，平均相对误差分别为2.41%（桉树）、-4.05%（杉木）。模型可以有效估测杉木、桉树人工林的单木任意高直径和蓄积量，估测杉木的性能优于桉树。点云密度显著影响模型估测性能，当使用点云密度为原密度50%及以下进行单木测量时漏检率显著上升，当仅用原密度10%时最大平均绝对百分比误差超86%。本研究为利用UAV-LS及时、准确、高效实现单木尺度的林分蓄积量估测提供技术支持和理论依据，同时为资源有限的条件下进行高精度的森林资源评估提供参考。

Abstract:

The main issues in extracting forest parameters and estimating stand volume using UAV laser scanning (UAV-LS) are insufficient accuracy in individual tree segmentation and the inability to directly obtain diameter at breast height (DBH) parameters. To address this limitation, the UAV-LS was utilized to collect high-density point cloud data from Eucalyptus and Chinese fir plantations. By improving the mean shift algorithm (IMSA), a method capable of accurately obtaining the diameter at any height of standing trees was proposed, thereby calculating tree volume and achieving accurate estimation of stand volume from the perspective of individual tree segmentation. The results showed that the improved mean shift algorithm effectively handled dense noise near the tree trunk, significantly enhancing detection accuracy. The accuracy of determining and fitting the edge points of the trunk was optimal, with coefficients of determination R2>0.93 for estimating diameters at heights of 1.3m and 2m, and average relative errors of 2.41% (Eucalyptus) and -4.05% (Chinese fir). The model can effectively estimate the diameter and volume of individual trees in Eucalyptus and Chinese fir plantations, with performance for Chinese fir being optimized compared with Eucalyptus. Point cloud density significantly affected the estimation performance of the model. When using point cloud densities of 50% or less of the original density for individual tree measurements, the omission rate was increased significantly;when using only 10% of the original density, the maximum absolute error exceeded 86%. The research result can provide technical support and theoretical basis for the timely, accurate, and efficient estimation of stand volume at the individual tree scale by using UAV-LS, while also offering a reference for high-precision forest resource assessment under resource-limited conditions.