Identification of potential geohazards in mountainous towns based on "Space-Air-Ground-Underground" approach: A case study of key towns in Xide County, Sichuan Province
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摘要:
为了更好地适应山区城镇地质灾害隐患精细识别的需求，实现中、小型以及高隐蔽性地灾隐患的有效判识，本次以川西南喜德县重点乡镇为例，综合采用了光学遥感、InSAR、无人机载LiDAR、地面精细调查、高密度电法等方法，从不同精度和角度对研究区系统开展了地质灾害隐患判识。结果表明，不同手段具有很好的互补性，综合识别效果较好，共识别地灾隐患80处，包括新增识别29处，此外，识别潜在危险源131处；孕灾条件差异制约着不同识别方法的有效性，其中光学遥感在研究区西北部构造变形强烈、坚硬岩组分布区具有更好的识别效果，无人机摄影测量配合地面斜坡详查及物探探查等手段更适合东部米市宽缓向斜红层分布区的地灾识别，机载LiDAR高清三维摄影对重点城镇后山斜坡普遍发育的“簸箕形”平面形态滑坡和“栎叶状”流域平面形态泥石流识别效果较好；易崩易滑工程地质岩组与顺向型斜坡结构的优势组合是研究区地灾孕育的关键，而深部物探对控灾结构面探查是地质灾害隐患判识的重要佐证。
Abstract:In order to better meet the needs of geohazard identification in mountainous towns and effectively detect small-sized, medium-sized, and hidden geohazards, a case study was conducted in several typical towns in Xide County, southwest Sichuan. The study employed a comprehensive suite of techniques, including optical remote sensing, InSAR, LiDAR, detailed slope investigation, and high-density resistivity methods, to identify geohazards from various perspectives and levels of precision. The results show that these methods complement each other well and are effective in geohazard identification. A total of 80 occurrences of geohazards were identified, including 29 new identifications, along with 131 potential geohazard dangers. The differences in disaster-inducing factors in the study area constrain the effectiveness of different methods. Optical remote sensing proved more effective in areas characterized by strong structural deformation and hard rock formations. In contrast, unmanned aerial vehicle (UAV) photogrammetry, combined with detailed ground surveys and geophysical exploration, was more suitable for identifying geohazards in the red layer distribution areas of the Mishi wide gentle syncline. Airborne LiDAR high-definition 3D photography was particularly effective for identifying "dustpan-shaped" landslides and "oak leaf-shaped" debris flows, which are common on the slopes of key towns. The combination of easily collapsible and slidable engineering geological rock groups and dip-slope structures is the key to the formation of geohazards in the study area. Geophysical exploration targeting disaster-controlling structures is an important support for geohazard identification.
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表 1 地质灾害及隐患综合遥感识别统计表
Table 1. Comprehensive remote sensing identification of geohazards and their interpretation signs
地质灾害及
隐患类型识别方法 识别数量/处 主要识别标志 辅助识别标志 滑坡 光学遥感 10 地表地形破碎，周界清晰，
可见下错台坎等。坡面冲沟发育，植被分布差异等。 InSAR 1 “煎蛋状”环形形变干涉图，自外环往内变形增强等。 LiDAR 12 “簸箕形”平面形态，
滑坡壁、滑坡台阶、滑坡舌、滑坡裂缝、滑坡鼓丘等地形。不平整的坡体特征，后缘陡坎植被稀疏，地表裸露等。 崩塌 光学遥感 7 浅色调倒锥状碎石堆积体，
陡坎地形等。岩浆岩及碳酸盐岩等坚硬岩组分布区，前缘开挖等人类活动强烈等。 InSAR 1 单一型形变干涉图，图斑
颜色变化较为单一等。LiDAR 0 泥石流 光学遥感 22 栎叶形、瓢形、桃叶形沟
谷地貌，谷坡松散物源分布，沟口扇状地貌等。堆积扇呈套叠现象，谷坡植被
破坏，谷坡区大量修建消防通道的路渣等。InSAR 0 LiDAR 3 “栎叶状”流域形态，沟道及其两侧斜坡发育崩滑物源和沟道物源，沟口扇状地貌等。 沟床内流水冲刷，块石裸露，
零星灌木发育等。
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