Accurate and efficient extraction of tree parameters from plantations lay foundation for estimating individual wood volume and stand stocking. In this study, we proposed a method of extracting high-precision tree parameters based on airborne LiDAR data. The main process included data pre-processing, ground filtering, individual tree segmentation, and parameter extraction. We collected high-density airborne point cloud data from the large-diameter timber of Fokienia hodginsii plantation in Guanzhuang State Forestry Farm, Shaxian County, Fujian Province, and pre-processed the point cloud data by denoising, resampling and normalization. The vegetation point clouds and ground point clouds were separated by the Cloth Simulation Filter (CSF). The former data were interpolated using the Delaunay triangulation mesh method to generate a digital surface model (DSM), while the latter data were interpolated using the Inverse Distance Weighted to generate a digital elevation model (DEM). After that, we obtained the canopy height model (CHM) through the difference operation between the two, and analyzed the CHM with varying resolutions by the watershed algorithm on the accuracy of individual tree segmentation and parameter extraction. We used the point cloud distance clustering algorithm to segment the normalized vegetation point cloud into individual trees, and analyzed the effects of different distance thresholds on the accuracy of indivi-dual tree segmentation and parameter extraction. The results showed that the watershed algorithm for extracting tree height of 0.3 m resolution CHM had highest comprehensive evaluation index of 91.1% for individual tree segmentation and superior accuracy with R2 of 0.967 and RMSE of 0.890 m. When the spacing threshold of the point cloud segmentation algorithm was the average crown diameter, the highest comprehensive evaluation index of 91.3% for individual tree segmentation, the extraction accuracy of the crown diameter was superior, with R2 of 0.937 and RMSE of 0.418 m. Tree height, crown diameter, tree density, and spatial distribution of trees were estimated. There were 5994 F. hodginsii, with an average tree height of 16.63 m and crown diameter of 3.98 m. Trees with height of 15-20 m were the most numerous (a total of 2661), followed by those between 10-15 m. This method of forest parameter extraction was useful for monitoring and managing plantations.
准确高效地提取人工林林木参数可为估算单木材积、林分蓄积量提供关键信息。本文提出基于机载LiDAR数据的高精度单木参数提取方法,其实现过程包括数据预处理、地面滤波、单木分割和参数提取。以福建省沙县官庄国有林场的福建柏大径材人工林为试验区,采集高密度机载点云数据,对点云进行去噪、重采样等预处理。使用布料滤波算法(CSF)分离出植被点云和地面点云,并采用Delaunay三角网法将植被点云数据插值生成数字表面模型(DSM),采用反距离加权插值法将地面点云数据插值生成数字高程模型(DEM),两者作差运算获得冠层高度模型(CHM)。利用分水岭分割算法分析不同分辨率的CHM对单木分割及参数提取精度的影响。采用点云距离聚类算法对归一化植被点云进行单木分割,分析不同的距离阈值对单木分割及参数提取精度的影响。结果表明: 使用分水岭分割算法处理0.3 m分辨率CHM单木分割调和值最高,达到91.1%,提取的树高精度较优,决定系数(R2)达到0.967,均方根误差(RMSE)为0.890 m;使用间距阈值为平均冠幅的点云分割算法单木分割调和值最高,达到91.3%,提取的冠幅精度较优,R2为0.937,RMSE为0.418 m。估算该试验区的树高、冠幅、株数和树木的空间分布等信息发现: 共有福建柏5994株,平均树高为16.63 m,平均冠幅为3.98 m;树高在15～20 m区间的数量最多,有2661株,其次是10～15 m。本林木参数提取方法可为人工林资源监测和管理提供技术支撑。.
Keywords: airborne LiDAR data; forest parameter extraction; individual tree segmentation; plantation.