Refined landslide susceptibility analysis based on InSAR technology and UAV multi-source data

Landslide susceptibility analysis at the regional scale is the focus of landslide risk management. To obtain more accurate and guiding significance results of landslide susceptibility, it is necessary to conduct a refined analysis on the basis of regional scale. Therefore, we proposed a refined method for landslide susceptibility assessment. This method comprehensively considered the geological and dynamic surface deformation information, and was applied in some areas of Maoxian County. We selected twelve influencing factors (elevation, slope, relief amplitude, curvature, aspect, engineering geological rock group, distance to fault, distance to river, land type, vegetation type, and topographic wetness index, rainfall) and used the traditional machine learning method for landslide susceptibility. Then, the Interferometric Synthetic Aperture Radar (InSAR) technology was introduced to modify the unsuitable zoning of the traditional landslide susceptibility. Based on the improved landslide susceptibility mapping, field investigation and UAV models were used to verify. The results showed that the introduction of InSAR technology and UAV multi-source data can rationalize the inappropriate zoning in traditional landslide susceptibility, and landslides (L1 and L7) with very high susceptibility were identified. The field investigation and spatial-temporal evolution characteristics of typical landslides indicated that both L1 and L7 were severely damaged and in the deformation stage. L1 showed significant deformation during road construction. However, the deformation of L1 reacted on the pavement, resulting in many tensile cracks. The deformation of L7 was mainly affected by rainfall and presented the characteristics of seasonal variation, and the deformation was accelerated in the rainy season. Therefore, the proposed method had a better performance in landslide susceptibility and improved the accuracy. It can realize the refined analysis of landslide susceptibility in a large area and provide technical support for geological hazard susceptibility assessment and reference for disaster prevention and mitigation.