Landslide Susceptibility Zoning in Yunnan Province Based on SBAS-InSAR Technology and a Random Forest Model

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 11; p. 2864
• Main Authors: Liu, Meiyu, Xu, Bing, Li, Zhiwei, Mao, Wenxiang, Zhu, Yan, Hou, Jingxin, Liu, Weizheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 31.05.2023
• Subjects: Analysis; China; Data processing; Deformation; Disasters; Electrons; Emergency management; Emergency preparedness; Evaluation; Geological hazards; Geological structures; Geology; Geomorphology; Interferometric synthetic aperture radar; Landslides; Landslides & mudslides; Machine learning; Mitigation; Rainfall; random forest; Ravines; Remote sensing; SBAS-InSAR; Susceptibility; susceptibility assessment; Synthetic aperture radar; Technology; Technology application; Topography; Yunnan Province; Zoning; China; Yunnan China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15112864

Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province.