Definition of environmental indicators for a fast estimation of landslide risk at national scale

The purpose of this paper is to propose a new set of environmental indicators for the fast estimation of landslide risk over very wide areas. Using Italy (301,340 km2) as a test case, landslide susceptibility maps and soil sealing/land consumption maps were combined to derive a spatially distributed indicator (LRI—landslide risk index), then an aggregation was performed using Italian municipalities as basic spatial units. Two indicators were defined, namely ALR (averaged landslide risk) and TLR (total landslide risk). All data were processed using GIS programs. Conceptually, landslide susceptibility maps account for landslide hazard while soil sealing maps account for the spatial distribution of anthropic elements exposed to risk (including buildings, infrastructure, and services). The indexes quantify how much the two issues overlap, producing a relevant risk and can be used to evaluate how each municipality has been prudent in planning sustainable urban growth to cope with landslide risk. The proposed indexes are indicators that are simple to understand, can be adapted to various contexts and at various scales, and could be periodically updated, with very low effort, making use of the products of ongoing governmental monitoring programs of Italian environment. Of course, the indicators represent an oversimplification of the complexity of landslide risk, but this is the first time that a landslide risk indicator has been defined in Italy at the national scale, starting from landslide susceptibility maps (although Italy is one of the European countries most affected by hydro-geological hazards) and, more in general, the first time that land consumption maps are integrated into a landslide risk assessment

landslide susceptibility of the prato pistoia lucca provinces tuscany italy

ABSTRACTWe mapped landslide susceptibility in the provinces of Lucca, Pistoia and Prato (central Italy), a 3103 km2 territory that approximately corresponds to the portion of Tuscany principally affected by landslides. We used a methodology based on a treebagger random forest. The input parameters used for the susceptibility assessment are curvature, flow accumulation, topographic wetness index, elevation, profile curvature, planar curvature, slope gradient, aspect, land use and lithology. The map was validated providing satisfactory results (AUC = 0.84). The map classifies the study area into four susceptibility classes that identify areas with different probabilities of being affected by landslides. The Main Map represents a useful instrument to assist land planning, development of mitigation measures and landslide risk management. Moreover, it could be used in further research addressing quantitative hazard and risk assessment