地基与无人机激光雷达结合提取单木参数

激光雷达（Light Detection And Ranging，LiDAR）在森林空间结构测量方面具有无可比拟的优势，但单独利用地基或无人机LiDAR难以完整描述森林垂直结构。为此，该研究提出了地基和无人机LiDAR点云相结合的单木参数提取方法，采用相对最短路径算法（Comparative Shortest-Path algorithm，CSP）和点云区域生长算法分别从地基和无人机LiDAR点云中识别单木，根据地基和无人机LiDAR的单木位置与地面实测单木位置进行点云粗匹配，然后采用迭代最近点（Iterative Closest Point，ICP）进行点云精匹配，采用最高值和基于密度的噪声应用空间聚类（Density-Based Spatial Clustering of Applications with Noise，DBSCAN）提取单木树高和胸径，并通过地面实测样地数据对地基、无人机和融合点云的单木参数提取精度进行评价。结果表明：基于地基和融合点云的单木检出率一致，简单、中等、复杂样地的单木检出率分别为98%、94%、91%，基于无人机点云的单木检出率较低；基于地基与融合点云的胸径提取精度基本一致，三种样地胸径提取值的决定系数R2均在0.96以上，均方根误差（Root Mean Square Error，RMSE）在1.2～1.6 cm之间；基于融合点云的树高提取精度最优，简单、中等、复杂样地树高提取值的R2分别为0.98、0.94和0.73，RMSE在1.38～4.19 m之间；融合点云对中等样地树高提取精度提升较大，融合后RMSE相较地基点云降低了0.34 m，R2提高了3%，对简单、复杂样地提升较小；所研究的单木中，杉木的胸径和树高提取精度最高，R2最高分别为0.99、0.89，RMSE最低分别为1.35 cm、1.96 m。地基和无人机LiDAR融合点云可以更精细地测量森林空间结构，更好地满足森林资源调查业务应用。

Extraction of individual tree parameters by combining terrestrial and UAV LiDAR

Abstract: Forests can provide all kinds of necessary resources for the production and life of human society. The distribution of forests can be timely and accurately identified for the protection and utilization of natural resources. Alternatively, a Lightning Detection and Ranging (LiDAR) can be expected to measure the forest''s spatial structure. It is probable to detect the position structure inside the vegetation. However, the information in the upper part of the forest tends to lose using the ground-based LiDAR alone, whereas, that in the middle and lower part of the forest using UAV LiDAR alone. It is a high demand to completely describe the vertical structure of the forest. In this study, the point clouds of terrestrial and UAV LiDAR were fused to extract the individual tree parameters, fully considering the different LiDAR data. The Comparative Shortest-Path (CSP) and the point cloud region growing algorithm were firstly used to identify the individual tree from the terrestrial and UAV LiDAR point clouds, respectively. The point cloud was then roughly matched, according to the tree position of terrestrial and UAV LiDAR, as well as the ground measured. The ground point cloud was converted into the UAV point cloud coordinate system. Then, the Iterative Closest Point (ICP) algorithm was used to perform the fine matching of point clouds. The highest value and Density-based spatial clustering of applications with noise (DBSCAN) algorithm were used to extract the tree height and the Diameter at Breast Height (DBH). The extraction accuracy of individual tree parameters was evaluated in the terrestrial, UAV, and fusion point clouds. The results show that the consistent detection rate of individual trees was achieved using the terrestrial and fusion point clouds. The detection rates of individual trees in easy, medium, and difficult plots were 98%, 94%, and 91%, respectively. There was a low detection rate of individual trees using the UAV point cloud, where the overall detection rate was 42%, and the highest detection rate was only 58% in the three types of plots. There was all the same in the DBH extraction accuracy using the terrestrial and fusion point cloud. Specifically, the R2 values of easy, medium, and difficult plots were around 0.98, 0.97, and 0.96, respectively, and the Root Mean Square Error (RMSE) were 1.2, 1.46, and 1.5 cm, respectively. The highest accuracy was achieved in the tree height extraction using a fusion point cloud, where the R2 values of easy, medium, and difficult plots were 0.98, 0.94, and 0.73, respectively, and the RMSE was between 1.38 and 4.19 m. The fusion point cloud greatly improved the extraction accuracy of tree height in the medium plots, where the RMSE was reduced by 0.34 m, compared with the terrestrial point cloud, and the R2 value increased by 3%. Nevertheless, there was a small improvement for the easy and difficult plots. Among them, the accuracy of parameter extraction was higher for the Cunninghamia lanceolata than that for the eucalyptus, where the R2 values of DBH were 0.99 and 0.96, respectively, and the RMSEs were 1.35 and 1.49 cm, respectively. The R2 values of tree height were 0.89 and 0.73, and the RMSEs were 1.96 and 3.47 m, respectively. The extraction accuracy of tree parameters decreased gradually, as the plot was much more complex. There was a more significant influence of tree height on the complex plots, with the R2 decreased by about 0.25, and the RMSE increased by about 2.8 m in the difficult plot, compared with the easy one. In general, the fusion point cloud of terrestrial and UAV LiDAR can be applied to more precisely measure the forest spatial structure, in order to improve the parameter extraction accuracy of the easy and medium plots for the better application of forest resources.