Thermokarst landslides susceptibility evaluation across the permafrost region of the central Qinghai-Tibet Plateau: Integrating a machine learning model with InSAR technology

Thermokarst landslides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-layer detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tibet Plateau (QTP) permafrost area. TLs induce many environmental problems and threaten the safety of infrastructure. A landslide susceptibility map is crucial to prevent the negative impacts of these landslides. However, traditional thermokarst landslides susceptibility (TLS) evaluations do not consider real-time surface deformation information. Therefore, we propose a novel method that integrates a conventional machine learning model with ground surface deformation. Nine influencing factors, including slope, normalized difference vegetation index, elevation, precipitation, thawing degree days, soil content, active layer thickness, water content, and vegetation type were selected based on the q-index detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accuracy of the InSAR results was validated through comparison with data from two field monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribution matrix, and confirmed the fusion model's higher rationality and accuracy through comparison with the IM. Furthermore, we examined the impact of the quadtree segmentation method on atmospheric correction and validated the TLS results obtained using the ITM with high-resolution optical remote sensing imagery from GF6. Finally, the influencing factors and distribution characteristics of TLS was analyzed, and the results indicated that climatic conditions are the primary factors affecting the distribution of TLS.