基于无人机载LiDAR的输电线路树障单木识别

树障是高压输电线路在复杂山区植被茂密区域运行所面临的主要安全威胁之一,不同树种生长周期不同,一定时间内的树障风险也不同。为了大范围准确识别林区的树木种类,文中提出了一种基于机载雷达测量技术的树木种类快速识别方法。首先利用机载雷达对输电线路地区地面进行快速点云数据获取,并且预处理数据得到单棵树木的冠层点云;随后建立冠层的空间属性点云特征量,包括树冠高度、树冠体积、树冠点云密度、冠层激光反射强度以及树冠形貌特征;最后根据树木的空间点云特征建立树木的种类K均值聚类识别模型。结果表明：对于该地区生长的树木,5种空间点云特征具有良好的识别效果,最终建立的树木种类K均值聚类识别模型对于验证数据的准确率达到了85.9%,Kappa系数0.812。输电线路下方植被种类的快速识别对于树障风险评估和预警具有重要意义。

Recognition of Tree Barriers on Transmission Lines Based on LIDAR

Tree barriers are one of the main safety threats faced by high-voltage transmission lines operating in dense mountainous areas with dense vegetation. Different tree species have different growth cycles and different risks of tree barriers within a certain period of time. In order to accurately identify the types of trees in the forest area on a large scale, this paper proposes a rapid identification method for tree types based on airborne radar measurement technology. First, use airborne radar to quickly obtain point cloud data on the ground of the transmission line area, and preprocess the data to obtain the canopy point cloud of a single tree; then establish the canopy spatial attribute point cloud feature quantities, including canopy height, canopy volume, Canopy point cloud density, canopy laser reflection intensity, and canopy topography features; finally, a tree-type K-means clustering recognition model is established based on the trees spatial point cloud characteristics. The results show that for the trees growing in this area, the five spatial point cloud features have a good recognition effect. The final establishment of the K-means clustering recognition model for trees has an accuracy rate of 89% and a kappa of 0.812 for the verification data. Rapid identification is of great significance to the risk assessment and early warning of tree barriers.