Affiliation:
1. College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2. Key Laboratory of Safety Engineering and Technology Research of Zhejiang Province, Hangzhou 310012, China
3. Key Laboratory of Western China’s Mineral Resources and Geological Engineering, Ministry of Education, Chang’an University, Xi’an 710054, China
4. College of Construction Engineering, Jilin University, Changchun 130026, China
Abstract
Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km2) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region.
Funder
the National Key Research and Development Program of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference49 articles.
1. Brabb, E.E. (1985, January 23–31). Innovative approaches to landslide hazard and risk mapping. Proceedings of the International Landslide Symposium Proceedings, Toronto, ON, Canada.
2. Increasing landslide activity in the Taxkorgan River Basin (eastern Pamirs Plateau, China) driven by climate change;Pei;Catena,2023
3. Zhan, J.W., Yu, Z.Y., Lv, Y., Peng, J.B., Song, S.Y., and Yao, Z.W. (2022). Rockfall Hazard Assessment in the Taihang Grand Canyon Scenic Area Integrating Regional-Scale Identification of Potential Rockfall Sources. Remote Sens., 14.
4. Modelling of spatial variability of soil undrained shear strength by conditional random fields for slope reliability analysis;Jiang;Appl. Math. Model.,2018
5. Investigating landslide susceptibility procedures in Greece;Kavoura;Landslides,2019
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