| Citation: |
WANG Xiuying，YANG Hongjuan，JIA Yifan，et al. Landslide susceptibility evaluation in Fengjie County based on slope units extracted using the MIA-HSU method[J]. The Chinese Journal of Geological Hazard and Control，2025，36(2): 1-10. DOI: 10.16031/j.cnki.issn.1003-8035.202403016
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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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