Decision-Level Fusion of PS-InSAR and Optical Data for Landslide Susceptibility Mapping Using Wavelet Transform and MAMBA

Landslides remain a critical geohazard in mountainous regions, where intensified extreme rainfall and rapid land-use changes exacerbate slope instability, challenging the reliability of traditional single-sensor susceptibility assessments. To overcome the limitations of data heterogeneity and noise, this study presents a decision-level fusion strategy integrating Permanent Scatterer InSAR (PS-InSAR) deformation dynamics with multi-source optical remote sensing indicators via a Wavelet Transform (WT) enhanced Multi-source Additive Model Based on Bayesian Analysis (MAMBA). San Martín del Castañar (Spain), a region characterized by rugged terrain and active deformation, served as the study area. We utilized Sentinel-1A C-band datasets (January 2020–February 2025) as the primary source for continuous monitoring, complemented by L-band ALOS-2 observations to ensure coherence in vegetated zones, yielding 24,102 high-quality persistent scatterers. The WT-based multi-scale enhancement improved the signal-to-noise ratio by 23.5% and increased deformation anomaly detection by 18.7% across 24,102 validated persistent scatterers. Bayesian fusion within MAMBA produced high-resolution susceptibility maps, indicating that very-high and high susceptibility zones occupy 24.0% of the study area while capturing 84.5% of the inventoried landslides. Quantitative validation against 1247 landslide events (2020–2025) achieved an AUC of 0.912, an overall accuracy of 87.3%, and a recall of 84.5%, outperforming Random Forest, Logistic Regression, and Frequency Ratio models by 6.8%, 10.8%, and 14.3%, respectively (p < 0.001). Statistical analysis further demonstrates a strong geo-ecological coupling, with landslide susceptibility significantly correlated with ecological vulnerability (r = 0.72, p < 0.01), while SHapley Additive exPlanations identify land-use type, rainfall, and slope as the dominant controlling factors.