基于激光雷达点云数据快速精准地获取城市行道树的结构特征和安全风险状况,对于辅助智慧城市管理具有重要意义。针对行道树参数获取中LiDAR点云数据对于形态特征不明显区域难以分割的问题,研究提出了一种树干中心点圆形索引的行道树单株木提取方法。首先,根据高程信息获取树干层切片点云并基于改进的DBSCAN聚类算法对切片数据进行分割;其次,结合地物形态特征识别树干并获取中心点,基于中心点的圆形索引方法完成单株木结构特征信息提取;最后,结合风险矩阵法对研究区内行道树的稳定性和其对交通影响的安全风险进行评价。结果表明:提出的单株木提取方法能有效提高形态特征不明显区域中行道树单株木分割精度,可以准确获取行道树的数量、形态和位置等结构参数信息;安全风险评价发现,研究区内大部分行道树稳定性和对交通影响风险处于Ⅰ级可忽略风险状态,但部分行道树稳定性和对交通影响风险为Ⅱ、Ⅲ级,这些树木主要分布在研究区行道树密集交错的区域。研究结果可为相关部门及时有效地监测和管理行道树提供相应的决策支持。

Rapid and accurate acquisition of the structural characteristics and safety risk status of urban street trees based on LiDAR point cloud data is of great significance to assist smart city management.In order to solve the problem that LiDAR point cloud data is difficult to segment regions with unclear morphological characteristics in street tree parameter acquisition,a individual tree extraction method based on circular index of trunk center point was proposed.Firstly,it obtained the sliced point cloud of the trunk layer according to the elevation information,then,segmented the sliced data based on the improved DBSCAN clustering algorithm.Secondly,it identified the trunk through the morphological characteristics of the ground features and obtained the central point,so as to complete the extraction through the circular index method based on the central point.Finally,it combined with the risk matrix method to evaluate the safety risk of the stability and traffic impact of the street trees in the study area.The results showed that the proposed individual tree extraction method could effectively improve the segmentation accuracy of individual trees of street trees in areas with unclear morphological characteristics,and accurately obtain the structural parameter information such as the number,shape and position of street trees;The safety risk assessment found that the stability of most street trees and the risk of traffic impact in the study area werein a level I negligible risk state,but there were some street trees whose stability and risk of traffic impact were level II and level III.These trees were mainly distributed in the area with dense and interlaced roadside trees in the study area.The results can provid corresponding decision support for relevant departments to monitor street trees in a timely and effective manner.