地基激光雷达单木树冠体积提取球坐标积分法

A spherical coordinate integration method for extracting crown volumes of individual trees based on the TLS point clouds

树冠体积是地表生态监测研究的关键因子,激光点云可精细描述单木空间形态,为树冠体积提取提供了数据基础,但现有的激光点云单木树冠提取方法存在参数敏感、自动化程度不高等不足。文章在分析单木三维空间形态结构的基础上,提出了一种基于地基激光点云数据的单木树冠体积提取球坐标积分法。首先,根据激光点云高程分布特性,采用可视高程阈值分割得到树冠点;然后,将激光点云投影至三维球坐标空间并进行四棱锥形微元分割;最后,利用三维球坐标积分确定树冠体积。选择6种不同类型的单木激光点云数据进行试验,结果表明:文章方法能较好地顾及树冠形态、点云密度等因素,单木树冠体积提取绝对误差绝对值最大值为2.33 m^(3),相对误差最大值为3.40%,相比已有方法具有树冠提取精度高、稳定性好的优势。该研究对激光点云林木参数提取具有重要的参考价值。

Crown volumes serve as a crucial factor for surface ecological monitoring.Laser point clouds can characterize the fine-scale spatial morphologies of individual trees,providing a data basis for crown volume extraction.However,existing laser point cloud-based methods for extracting crown volumes of individual trees are sensitive to parameters and exhibit low degrees of automation.Based on the analysis of the three-dimensional morphological structures of individual trees,this study proposed a spherical coordinate integration method for extracting crown volumes of individual trees based on the terrestrial laser scanning(TLS)point clouds.First,the crown points were obtained through visual elevation threshold-based segmentation according to the elevation distributions of TLS point clouds.Then,the TLS point clouds were projected onto the spherical coordinate space for infinitesimal segmentation into triangular pyramids.Finally,the crown volumes were determined through the three-dimensional spherical coordinate integration.Six types of TLS point cloud data for individual trees were selected for tests.As indicated by the test results,the proposed method effectively considers factors like crown morphology and point cloud density,achieving a maximum absolute error of 2.33 m^(3)and a maximum relative error of 3.40%in the crown volume extraction of individual trees.It manifests higher extraction accuracy and stability compared to the existing methods.Therefore,this study holds significant reference value for extracting tree parameters based on TLS point clouds.