Danger tree detection and tree number estimation based on UAV LiDAR data

doi:10.7523/j.issn.2095-6134.2020.06.006

Abstract

Abstract: The growth of trees in the transmission corridor has a great impact on the transmission line. It is essential to accurately detect the trees that significantly affected the safety of transmission line. Therefore, a method of danger tree detection and tree number estimation based on UAV LiDAR data was proposed in this paper. Firstly, the power line points, ground points, and vegetation points were accurately extracted by rapidly automatic processing the laser point cloud in the transmission corridor. Secondly, the power line was segmented, and then the location and range of tree barrier area were determined by analyzing the safe distance between power lines and vegetation points. Finally, the point clouds in the tree barrier area were divided into single trees, and the number of single trees was counted to achieve accurate estimation of the number of danger trees. The results indicates that the UAV laser point cloud effectively detect the tree barrier in the transmission channel and accurately estimate the number of trees need to be felled with an overall accuracy of 92.3%, which provides not only remote sensing technology support for the safety of transmission line, but also reliable basis for the grid operation and maintenance units to make plan for tree cutting.

Key words: LiDAR, danger tree detection, danger tree number estimation, individual tree segmentation, power line extraction

CLC Number:

TP751

ZHANG Su, QI Lizhong, HAN Wenjun, ZHU Xiaoxiao, XI Xiaohuan, WANG Cheng, WANG Pu, NIE Sheng. Danger tree detection and tree number estimation based on UAV LiDAR data[J]. , 2020, 37(6): 760-766.

References

[1] 胡彬,周宗国,杨时宽,等.输电线路走廊树障清理相关技术分析[J].民营科技,2018(9):126-127.
[2] 黄维,黄志都,王乐,等.架空输电线路走廊树障隐患动态管理及预警分析[J].广西电力,2017,40(3):39-42.
[3] 张昌赛.机载LiDAR输电线走廊点云数据自动分类和树障预警分析方法研究[D].兰州:兰州交通大学,2018.
[4] 张赓.基于机载LiDAR点云数据的电力线安全距离检测[D].兰州:兰州交通大学,2015.
[5] 郑耀华.架空输电线路走廊树障在线监测关键技术研究[J].机电信息,2014(24):150-151.
[6] 刘正坤,袁炜,王昊.基于可见光影像的架空线路树障测量技术研究[J].地理空间信息,2018,16(7):89-91.
[7] 周小红,李向欢,石蕾,等.无人机倾斜摄影技术在电力巡线树障检测中的实践应用研究[J].电力大数据,2019(8):53-59.
[8] 李亮,王成,李世华,等.基于机载LiDAR数据的建筑屋顶点云提取方法[J].中国科学院大学学报,2016,33(3):537-541.
[9] 杜松,李晓辉,刘照言,等.激光雷达回波强度数据辐射特性分析[J].中国科学院大学学报,2019,36(3):392-400.
[10] Guan H, Yu Y, Li J, et al. Extraction of power-transmission lines from vehicle-borne lidar data[J]. International Journal of Remote Sensing, 2016, 37(1):229-247.
[11] Cheng L, Tong L, Yu W, et al. Extraction of urban power lines from vehicle-borne LiDAR data[J]. Remote Sensing, 2014, 6(4):3302-3320.
[12] 任海成.机载LiDAR树木检测在电力巡线中的应用研究[D].兰州:兰州交通大学,2017.
[13] 徐博,刘正军,王坚.基于激光点云数据电力线的提取及安全检测[J].激光杂志,2017,38(7):48-51.
[14] Chen C, Yang B, Song S, et al. Automatic clearance anomaly detection for transmission line corridors utilizing UAV-borne LIDAR data[J]. Remote Sensing, 2018, 10(4):613.
[15] 毛强.基于机载激光雷达智能测距输电线路树障的方法研究[J].湖北电力,2017,41(2):20-23.
[16] 阮峻,陶雄俊,韦新科,等.基于固定翼无人机激光雷达点云数据的输电线路三维建模与树障分析[J].南方能源建设,2019,6(1):120-124.
[17] 丁薇,黄绪勇,谭向宇,等.基于机载激光点云的输电线路走廊树障自动化检测方法[J].测绘与空间地理信息,2018,41(11):37.
[18] 祝贺,于卓鑫,严俊韬.特高压输电线路树障隐患,预判及仿真分析[J].东北电力大学学报,2018,38(2):21-27.
[19] Lin X, Zhang J. Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments[J]. Remote Sensing, 2014, 6(2):1294-1326.
[20] 王平华,习晓环,王成,等.机载激光雷达数据中电力线的快速提取[J].测绘科学,2017,42(2):154-158.
[21] 朱笑笑,王成,习晓环,等.多级移动曲面拟合的自适应阈值点云滤波方法[J].测绘学报,2018,47(2):153-160.
[22] Mclaughlin R A. Extracting transmission lines from airborne LIDAR data[J]. IEEE Geoscience & Remote Sensing Letters, 2006, 3(2):222-226.
[23] 梁静,张继贤,刘正军.利用机载LiDAR点云数据提取电力线的研究[J].测绘通报,2012(7):17-20.
[24] 林祥国,宁晓刚,段敏燕,等.分层随机抽样的单档电力线LiDAR点云聚类方法[J].测绘科学,2017,42(4):10-16.
[25] 王濮.基于机载LiDAR的森林单木识别研究[D].哈尔滨:东北林业大学,2018.
[26] 王濮,邢艳秋,王成,等.一种基于图割的机载LiDAR单木识别方法[J].中国科学院大学学报,2019,36(3):385-391.