Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping

Abstract

Remote sensing and geographic information systems (GIS) are widely used for landslide susceptibility mapping (LSM) to support planning authorities to plan, prepare and mitigate the consequences of future hazards. In this study, we compared the traditional per-pixel models of data-driven frequency ratio (FR) and expert-based multi-criteria assessment, i.e. analytical hierarchical process (AHP), with an object-based model that uses homogenous regions (‘geon’). The geon approach allows for transforming continuous spatial information into discrete objects. We used ten landslide conditioning factors for the four models to produce landslide susceptibility maps: elevation, slope angle, slope aspect, rainfall, lithology, geology, land use, distance to roads, distance to drainage, and distance to faults. Existing national landslide inventory data were divided into training (70%) and validation data (30%). The spatial correlation between landslide locations and the conditioning factors were identified using GIS-based statistical models. Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the resulting susceptibility maps. The area under the curve (AUC) was used to obtain the following values from ROC for the per-pixel based FR approach (0.894) and the AHP (0.886) compared with the object-based geon FR approach (0.905) and the geon AHP (0.896). The object-based geon aggregation yielded a higher accuracy than both per-pixel based weightings (FR and AHP). We proved that the object-based geon approach creates meaningful regional units that are beneficial for regional planning and hazard mitigation.