摘要
针对滑坡变形监测中GNSS站址选择因人工踏勘主观性强、易受植被、山体遮挡造成严重多路径效应,影响滑坡监测可靠性与稳定性的问题,文中提出一种基于无人机激光点云数据与多准则决策方法的GNSS智能化选址方法。首先,利用无人机激光雷达获取滑坡区域高密度激光点云,经滤波分离地面与非地面点后,计算单元网格的环境遮挡指数、植被覆盖率、坡度及地表粗糙度等关键选址因子;随后,构建层次分析法与熵权法的加权融合模型对各因子进行决策融合,智能化评价滑坡区域选址适宜性并计算最优候选点位;最后,通过分析实验区域不同遮挡环境中共计11个测站的GNSS数据质量,适宜性值从空旷环境的0.8195降至两边严重遮挡的0.5039时,对应测站在E、N、U方向的RMS由0.243 m、0.050 m、0.139 m增大至0.367 m、0.406 m、0.930 m。表明文中方法能够有效识别并优选GNSS数据质量较高的站址,所构建的选址适宜性值与GNSS数据质量之间存在相关关系,可为提升滑坡变形监测的可靠性与精度提供有力支撑。
This paper proposes a GNSS intelligent site selection method based on unmanned aerial vehicle(UAV)laser point cloud data and multi-criteria decision making method(MCDM)to address the issues of strong subjectivity in manual survey and severe multi-path effects caused by vegetation and mountain obstruction in landslide deformation monitoring,which affect the reliability and stability of landslide monitoring.Firstly,the high-density laser point cloud in the landslide area is obtained by using UAV LiDAR.After filtering and separating ground and non-ground points,key site selection factors such as environmental occlusion index,vegetation coverage,slope,and surface roughness of the unit grid are calculated.Secondly,a weighted fusion model of Analytic Hierarchy Process(AHP)and Entropy Weight Method(EWM)is constructed to perform decision fusion on various factors,intelligently evaluates the suitability of landslide area site selection,and calculates the optimal candidate points.Finally,by analyzing the GNSS data quality of a total of 11 stations in different occlusion environments in the experimental area,the suitability value decreases from 0.8195 in an open environment to 0.5039 in severe occlusion on both sides.The corresponding RMS of the stations in the E,N,and U directions increases from 0.243 m,0.050 m,and 0.139 m to 0.367 m,0.406 m,and 0.930 m.This indicates that the method proposed in this article can effectively identify and select sites with high GNSS data quality.There is a correlation between the constructed site suitability value and GNSS data quality,which can provide strong support for improving the reliability and accuracy of landslide deformation monitoring.
作者
刘冰山
王利
舒宝
许豪
安君毅
张勤
LIU Bingshan;WANG Li;SHU Bao;XU Hao;AN Junyi;ZHANG Qin(College of Geological Engineering and Geomatics,Chang’an University,Xi’an 710054 China;National Key Laboratory of Loess Science,Xi’an 710054,China;Key Laboratory of Ecological Geology and Disaster Prevention,Ministry of Natural Resources,Xi’an 710054,China)
出处
《测绘工程》
2025年第6期18-26,39,共10页
Engineering of Surveying and Mapping
基金
国家重点研发计划项目(2024YFC3012603)
陕西省科技创新团队项目(2021TD-51)
陕西省地学大数据与地质灾害防治创新团队项目(2022)。
关键词
滑坡监测
点云数据
多准则决策
智能化选址
landslide monitoring
point cloud data
MCDM
intelligent site selection
