ISSN 1003-8035 CN 11-2852/P

    基于MIA-HSU方法划分斜坡单元的奉节县滑坡易发性评价

    Landslide susceptibility evaluation in Fengjie County based on slope units extracted using the MIA-HSU method

    • 摘要: 栅格单元难以表征斜坡的形态与边界,以其为制图单元的滑坡易发性评价结果,无法精细化描述自然斜坡的滑坡易发程度。由于形态图像分析-均匀坡度单元(morphological image analysis-homogeneous slope unit,MIA-HSU)方法提取的斜坡单元可以表征斜坡的形态与边界,并能克服传统方法提取的斜坡单元存在坡度突变的缺陷,文章使用MIA-HSU为滑坡易发性评价提供制图单元。以重庆市奉节县为研究区,选取高程、坡度、坡向、归一化植被指数、归一化建筑指数、起伏度、距河流距离、距道路距离、岩性、剖面曲率、土地利用、地形湿度指数、水流功率指数、泥沙输移指数、地形位置指数等15个指标,采用信息量法评价奉节县的滑坡易发性程度。评价结果表明,滑坡易发性越高的区域灾害点密度越大,1950—2015年参加训练的滑坡点落在极高易发区和高易发区中的比例为 94.13%,成功率曲线法对滑坡易发性评价结果的测试精度为0.764,表明评价结果与实际滑坡分布情况基本吻合;2018年以后发生的未参与模型训练的滑坡点中超过90%落在高易发区和极高易发区,说明易发性评价结果具有较高的泛化性。研究结果可为研究区滑坡隐患点识别和灾害防治提供科学参考。

       

      Abstract: Grid units have limitations in accurately delineating the morphology and boundaries of slopes, and when used as mapping units in landslide susceptibility evaluation, they cannot accurately describe the landslide susceptibility of natural slopes. Investigations have shown that the morphological image analysis-homogeneous slope unit(MIA-HSU) method provides slope units that are more homogenous in slope angle and aspect, addressing the deficiencies of traditional methods. In this study, MIA-HSU was applied to provide mapping units for landslide susceptibility evaluation. Taking Fengjie County, Chongqing as the study area, 15 factors including elevation, slope angle, slope aspect, normalized difference vegetation index (NDVI), normalized difference built-up index(NDBI), topographic relief, distance from rivers, distance from roads, lithology, profile curvature, land use, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), and topographic position index(TPI) were selected to evaluate landslide susceptibility using the information value method. The evaluation results indicated that areas with higher landslide susceptibility exhibited a greater density of disaster points. During the 1950 to 2015 period, 94.13% of the landslide points used for training fell within the extremely high and high susceptibility zones. The accuracy of landslide susceptibility evaluation was further verified using the success rate curve method. The accuracy of the verification set was 0.764, indicating that the evaluation results were generally consistent with the actual landslide distribution. Over 90% of the landslide points occurring after 2018 (which were not used in training) were located in the high and extremely high susceptibility zones, demonstrating the model’s high generalization ability. The findings provide a scientific basis for identifying potential landslide hazards and for landslide prevention and mitigation in the study area.

       

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