<p>In mountainous-forested soil mantled landscapes all around the world, rainfall-induced shallow landslides are one of the most common hydro-geomorphic hazards, which frequently impact the environment and human lives and properties. In order to produce shallow landslide susceptibility maps, several models have been proposed in the last decade, combining simplified steady state topography- based hydrological models with the infinite slope scheme, in a GIS framework. In the present paper, two of the still open issues are investigated: the assessment of the validity of slope stability models and the inclusion of root cohesion values. In such a perspective the “Stability INdex MAPping” has been applied to a small forested pre-Alpine catchment, adopting different calibrating approaches and target indexes. The Single and the Multiple Calibration Regions modality and three quantitative target indexes – the common Success Rate (SR), the Modified Success Rate (MSR), and a Weighted Modified Success Rate (WMSR) herein introduced – are considered. The results obtained show that the target index can 34 003_Bischetti(569)_23 1-12-2010 9:48 Pagina 34 significantly affect the values of a model’s parameters and lead to different proportions of stable/unstable areas, both for the Single and the Multiple Calibration Regions approach. The use of SR as the target index leads to an over-prediction of the unstable areas, whereas the use of MSR and WMSR, seems to allow a better discrimination between stable and unstable areas. The Multiple Calibration Regions approach should be preferred, using information on space distribution of vegetation to define the Regions. The use of field-based estimation of root cohesion and sliding depth allows the implementation of slope stability models (SINMAP in our case) also without the data needed for calibration. To maximize the inclusion of such parameters into SINMAP, however, the assumption of a uniform distribution of probability of the parameters must be overtaken. In small and steep catchments where there is an intrinsic susceptibility to instability phenomena, moreover, an additional class of low probability of instability (0.81)&lt;1.0) has been proposed to better discriminate the areas classified as unstable.</p>