Multi-scale spatial validation and probability calibration of pixel-based landslide susceptibility modeling in the northern Peruvian Andes

Abstract. Landslides are recurrent geohazards in Andean regions, causing significant impacts on infrastructure and local communities. In spatially structured terrains, model reliability hinges on the definition of pseudo-absence samples and the treatment of spatial dependence during validation. This study evaluates pixel-based rotational landslide susceptibility in the province of Huancabamba (Piura, northern Peru) using a Random Forest classifier and seven conditioning factors derived from a photogrammetric digital elevation model and lithological data at 10 m resolution. The landslide inventory consists of 25 field-mapped rotational landslides compiled from geomorphological surveys and high-resolution photogrammetric products. Pseudo-absence samples were selected outside mapped polygons using a buffered exclusion zone to reduce label uncertainty, and a balanced sampling scheme (1:1) was adopted. To obtain spatially realistic performance estimates, model evaluation was conducted using spatial block cross-validation with block sizes ranging from 600 to 1500 m. This provides a clear view of how spatial partitioning affects discrimination and calibration, alongside the model's stability throughout the validation folds. Results show that discrimination performance decreases systematically as spatial block size increases, indicating that conventional random validation may overestimate predictive capacity due to spatial autocorrelation. A block size of 900 m provided a compromise between spatial independence and fold stability. Permutation importance computed under spatially independent folds identified lithology and elevation as the dominant predictors of rotational landslide occurrence, followed by aspect and topographic wetness index. Calibration metrics (Brier score and Expected Calibration Error) indicated moderate but stable reliability of susceptibility scores across spatial configurations. The resulting susceptibility map shows spatial patterns consistent with the geomorphological setting and the mapped inventory, with high susceptibility concentrated in steep slopes developed over weak lithological units. These findings indicate that integrating spatial validation, calibration, and constrained sampling improves the reliability of pixel-based modelling in this Andean setting.