摘要
随着铁路网络向复杂山区延伸,植被覆盖使人工调查、光学遥感等地质灾害调查方法失效,导致隐蔽性灾害难以识别。为此,提出一种基于机载激光雷达(LiDAR)的地质灾害识别方法。以某山区运营铁路为研究对象,采用优化点云数据采集密度、地形增强显示算法、三维解译环境构建以及解译标志提炼等技术,研发微地貌特征自动提取和地质灾害识别方法,揭示植被覆盖条件下地质灾害的遥感特征。主要结论:基于点云抽稀及植被郁闭度反演方法,系统评估不同点密度对地形重建与地质灾害识别的影响;通过地形可视化增强显示和数据融合建模方法,提升地质灾害的识别精度。在模型分辨率为0.5 m的条件下,岩溶落水洞最小识别直径达1 m;研究成果可为山区铁路灾害筛查与防护设计提供参考。
As the railway network extends to complex mountainous areas,vegetation coverage renders geological hazard investigation methods such as manual surveys and optical remote sensing ineffective,making it difficult to identify hidden hazards.Therefore,a geological hazard identification method based on airborne LiDAR is proposed.Taking the operation of a railway in a mountainous area as the research object,this study adopts technologies such as optimizing point cloud data collection density,terrain enhancement display algorithm,3D interpretation environment construction,and interpretation marker extraction to develop automatic extraction of micro landform features and geological hazard identification methods,revealing the remote sensing characteristics of geological hazards under vegetation cover conditions.The main research conclusions are:Based on point cloud thinning and vegetation canopy closure inversion methods,the impact of different point densities on terrain reconstruction and geological hazard identification was systematically evaluated.The recognition accuracy of geological hazards has been improved through terrain visualization enhancement and data fusion modeling methods.Under the condition of a model resolution of 0.5 m,the minimum identification diameter of karst water drop tunnels is 1 m.The research results can provide reference for the screening and protection design of railway disasters in mountainous areas.
作者
张璇钰
Zhang Xuanyu(Geological Investigation and Design Institute,China Railway Design Corporation,Tianjin 300251,China)
出处
《铁道技术标准(中英文)》
2025年第7期23-33,共11页
Railway Technical Standard(Chinese & English)
关键词
山区铁路
植被覆盖
地质灾害
机载激光雷达
遥感判识
mountainous railway
vegetation coverage
geological hazards
airborne liDAR
remote sensing identification
