Modeling spatial landslide susceptibility in volcanic terrains through continuous neighborhood spatial analysis and multiple logistic regression in La Cienega watershed, Nevado de Toluca, Mexico

Little study has been done on the effect of the pixel neighborhood information when modeling landslide susceptibility using multiple logistic regression (MLR). The present research uses in situ and neighborhood cartographic information to evaluate how the size of the neighboring area to be sampled affects the precision and accuracy of the MLR landslide susceptibility model. Two landslide susceptibility models are used: MLR-in situ, calibrated and validated by using variables that are collected at the site of the sampling point, and MLR in combination with continuous neighborhood spatial analysis (CNSA) to incorporate a search radius to extract pixel values for each cartographic variable based on a distance ratio. La Cienega watershed on the eastern flank of the volcano Nevado de Toluca is selected as a study area. Its climate, topography, geomorphology, and geology predispose it to episodic landslides. The resulting susceptibility maps are validated in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC), and they are compared with an inventory map in a contingency table; the MLR-CNSA model yields the better spatial prediction and representation of landslide susceptibility. The AUC evaluation indicates a predictive capability for the MLR-CNSA model of 0.969.

Automated summary

Little research has been done on the impact of pixel neighborhood information in modeling landslide susceptibility using multiple logistic regression (MLR). This study evaluates the precision and accuracy of the MLR landslide susceptibility model by incorporating in situ and neighborhood cartographic information. The MLR-CNSA model, which combines MLR with continuous neighborhood spatial analysis, provides a better spatial prediction of landslide susceptibility.