Different approaches to use morphometric attributes in landslide susceptibility mapping based on meso-scale spatial units: A case study in Rio de Janeiro (Brazil)

Landslide susceptibility maps are widely used in landslide hazard management. Although many models have been proposed, mapping unit definition is a matter that still needs to be fully examined. In the literature, the most reported mapping units are pixels and slope units, while in this work, developed in the Rio de Janeiro region (Brazil), the use of drainage basins as a mapping unit is examined; even if their use leads to the definition of maps with a coarser spatial resolution than pixels-based maps, they convey information that can be easily and rapidly handled by civil defense organizations. However, for the morphometrical characterization of entire basins, a standardized procedure does not exist, and the susceptibility results may be sensitive to the approach used. To investigate this issue, a random forest model was used to assess landslide susceptibility, using 12 independent variables: four categorical (land use, soil type, lithology and slope orientation) and eight numerical variables (slope gradient, elevation, slope curvature, profile curvature, planar curvature, flow accumulation, topographic wetness index, stream power index). For each basin, the numerical variables were aggregated according to different approaches, which, in turn, were used to set up four different model configurations: i) maximum values, ii) mean values, iii) standard deviation values, iv) joint use of all the above. The resulting maps showed noticeable differences and a quantitative validation procedure showed that the best configurations were the ones based on mean values of independent variables, and the one based on the combination of all the values of the numerical variables. The main outcomes of this work consist of a landslide susceptibility map of the study area, to be used in operational procedures of risk management and in some insights on the best approaches to aggregate raster cell data into wider spatial units.