基于树木相对位置关系的多时相机载激光雷达人工林点云匹配

人工林资源的精准监测是提升人工林培养质量、可持续经营管理水平及准确估测人工林碳储量的前提。使用机载激光雷达可获取高精度的森林冠层结构信息，然而，只有在同一森林区域获取多期激光雷达点云并进行精准匹配的基础上，才能有效实现人工林资源的动态监测。针对人工林树种单一、排列规整，缺乏纹理特征等特点，基于树木的相对空间关系，创建了一种高鲁棒性的多期机载激光雷达人工林点云匹配算法。首先，利用地面点进行z轴配准，并对两期点云进行单木分割，获取树位置和高度信息，并根据树木水平及垂直方向的相对关系提取单木匹配特征；其次，建立合适的相似度函数，结合单木匹配特征构造加权二分图模型，并使用最大权匹配算法得到两期树木对应关系；最后，使用奇异值分解求解最优变换矩阵，完成配准。通过在江苏省沿海典型人工林研究区（主要树种为杨树和水杉）进行试验验证，结果表明：该匹配算法在水杉和杨树的典型样地中配准效果均较好，其中水杉样地（配准后RMSE=42.5 cm）配准结果优于杨树样地（配准后RMSE=58.8 cm）。该算法能够有效提升多期机载激光雷达人工林点云的匹配精度，并为人工林的动态监测（特别是单木尺度的砍伐和生长等信息获取）提供了技术前提。

Registration of Multi- phase ALS Point Clouds in Planted Forests based on Relative Spatial Relationship of Trees

Accurate monitoring of plantation resources is a prerequisite for improving the cultivation quality of plantation forests， the level of sustainable operation and management， and accurately estimating the effect of increasing carbon storage of plantation forests. The use of airborne lidar can obtain high-precision forest canopy structure information. However， the dynamic monitoring of plantation resources can only be effectively realized on the basis of obtaining multi-phase lidar point clouds in the same forest area and accurately matching them. Aiming at the characteristics of single tree species， regular arrangement， and lack of necessary texture features， this study created a highly robust multi-phase airborne lidar plantation point cloud matching algorithm based on the relative spatial relationship of the trees： First， Use ground points for rough registration， and perform individual tree segmentation on the two-phase point cloud to obtain tree position and height information， and extract individual tree matching features according to the relative relationship between the horizontal and vertical directions of the trees； secondly， establish a suitable similarity function， Combined with individual tree matching features to construct a weighted bipartite graph model， and use the Kuhn-Munkres algorithm to obtain the corresponding relationship between the two periods of plantation； finally， use singular value decomposition to solve the optimal transformation matrix to complete the registration. Tests were conducted in the typical coastal plantation research area of Jiangsu Province （the main tree species are poplar and metasequoia）. The results show that the matching algorithm created has a good registration effect in the typical sample plots of Metasequoia and poplar. Among them， the metasequoia plots （RMSE=42.5 cm， after registration） The result of registration is better than poplar plots （RMSE=58.8 cm，after registration）. This algorithm can effectively improve the matching accuracy and efficiency of multi-phase airborne lidar plantation point clouds， and provides a technical prerequisite for the dynamic monitoring of plantation individual tree （such as felling， growth， etc.）.