基于无人机激光雷达点云的树干提取  

Extraction of tree trunks based on UAV lidar point clouds

作　　者：周理想 曹明兰[2,3] 郎博 李爱国[4] 王强 ZHOU Lixiang;CAO Minglan;LANG Bo;LI Aiguo;WANG Qiang(Henan Geology Mineral College,Zhengzhou 451464,Henan,China;Beijing Polytechnic College,Beijing 100042,China;Beijing Key Laboratory of Urban Spatial Information Engineering,Beijing 100042,China;School of Surveying&Land Information Engineering,Zhenghzou 454000,Henan,China;Stecol Corporation,Tianjin 300384,China)

机构地区：[1]河南地矿职业学院,河南郑州451464 [2]北京工业职业技术学院,北京100042 [3]北京市城市空间信息工程重点实验室,北京100042 [4]河南理工大学测绘与国土信息工程学院,河南焦作454000 [5]中国电建市政建设集团有限公司,天津300384

出　　处：《中南林业科技大学学报》2024年第11期22-28,共7页Journal of Central South University of Forestry & Technology

基　　金：北京市教育委员会科研计划一般项目(KM202110853001);北京工业职业技术学院重点课题(BGY2024KY-11Z,BGY2024KY-10Z)。

摘　　要：【目的】无人机搭载激光雷达技术具有灵活性高、监测周期短、具有穿透力等优势。在实际应用中,由于树干间的遮挡,易导致激光雷达获取的林冠下点云数据密度不足。而且从激光点云提取树干的算法尚不成熟,存在数据处理量大、提取精度低等挑战。该研究旨在提高从无人机激光雷达数据中提取树干的精度和效率,为森林监测和林业管理提供准确高效的技术支持。【方法】无人机搭载激光雷达从不同高度多次飞行,以获取覆盖林冠和林下的完整点云。采用基于回波类型的分类方法,将点云数据分为地面点和植被点,并利用地面点云生成数字地形模型。根据树干和树叶的反射强度差异,通过阈值和半监督支持向量机算法进行分类,并基于区域生长法将点群分割成连通区域。计算连通区域与水平面的交点,整合同一高度的交点信息,重建树干模型。采用假设检验法判断横截面的连续性和半径变化,识别并构建完整的树干网格模型。【结果】采用多旋翼无人机搭载激光雷达,设定50 m航高、90%航向重叠率、75%旁向重叠率、云台镜头角度90°,以4 m/s的飞行速度采集了林冠点云数据。同时利用相同设备从1.5~2.5 m高度,以0°、45°和-45°的云台角度,获取林冠下更精细的点云信息。经过处理,去除了植被和地面点云,精确提取了树干点云。利用区域生长法分割点云并生成二维距离图像,对图像进行噪声过滤和颜色分割后进一步提升了数据质量。切割线集合和RANSAC算法的应用,有效估计了主干截面形状,并构建了精确的树干模型。在联想Think Station图形工作站测试表明,树干点群提取仅需15.9 s,树干网格模型生成也仅需71.5 s。该方法的乔木树干胸径提取平均精度为0.958、树高平均提取精度达到0.964。【结论】文章应用多旋翼无人机搭载激光雷达,采用多架次、多航高、多姿态�【Objective】Unmanned aerial vehicle(UAV)mounted LiDAR technology offers advantages such as high flexibility,short monitoring cycles,and the ability to penetrate through obstacles.In practical applications,due to the occlusion between tree trunks,it is easy to result in insufficient point cloud data density for the canopy obtained by LiDAR.Moreover,the algorithms for extracting tree trunks from LiDAR point clouds are still immature,with challenges such as large data processing volumes and low extraction accuracy.This study aims to improve the accuracy and efficiency of extracting tree trunks from UAV LiDAR data to provide accurate and efficient technical support for forest monitoring and forestry management.【Method】The UAV carried LiDAR to fly multiple times at different altitudes to obtain complete point clouds covering the canopy and the understory. A classification method based on echo types was used to divide the point cloud data into ground points and vegetation points, and a digital terrain model was generated using the ground point cloud. Based on the difference in reflection intensity between tree trunks and leaves, classification was performed using thresholds and semi-supervised support vector machine algorithms, and point clusters were segmented into connected areas using the region growing method. The intersections of the connected areas with the horizontal plane were calculated, and the intersection information at the same height was integrated to reconstruct the tree trunk model. Hypothesis testing was used to judge the continuity of the cross-section and the variation in radius, to identify and construct a complete tree trunk mesh model.【Result】A multi-rotor UAV equipped with LiDAR was used to collect canopy point cloud data at an altitude of 50 m, with a 90% flight direction overlap rate, a 75% lateral overlap rate, and a gimbal lens angle of 90°, at a flight speed of 4 m/s. At the same time, the same equipment was used to obtain more refined point cloud information under the canopy at

关 键 词：无人机 激光雷达 树干提取 乔木树种 

分 类 号：S758.5[农业科学—森林经理学]