Landform classification: a high-performing mapping unit partitioning tool for landslide susceptibility assessment—a test in the Imera River basin (northern Sicily, Italy)

In landslide susceptibility studies, the type of mapping unit adopted affects the obtained models and maps in terms of accuracy, robustness, spatial resolution and geomorphological adequacy. To evaluate the optimal selection of these units, a test has been carried out in an important catchment of northern Sicily (the Imera River basin), where the spatial relationships between a set of predictors and an inventory of 1608 rotational/translational landslides were analysed using the multivariate adaptive regression splines (MARS) method. In particular, landslide susceptibility models were prepared and compared by adopting four different types of mapping units: the largely adopted grid cells (PX), the typical contributing area–controlled slope units (5000_SLU), the recently optimized parameter-free multiscale slope units (PF_SLU) and a new type (LCL_SLU) of slope unit obtained by crossing classic hydrological partitioning with landform classification. At the same time, once a pixel-based model was prepared, four different SLU modelling strategies were applied to each of the obtained slope unit layers, including two different types of pixel score zoning, a pixel score re-modelling and a factor-based SLU re-modelling. According to the achieved results, LCL_SLUs produced the highest performance and reliability, offering an optimal compromise between the high-performing but scattered and the smoothed but lower-performing prediction images that were obtained from pixel-based and hydrologic SLU–based modelling, respectively. Additionally, among the four adopted SLU modelling strategies, the new proposed procedure, which uses the zoned pixel–based score deciles as the LCL_SLU predictors for a new regression, resulted in the best outstanding performance (ROC_AUC = 0.95).