Automated Unsupervised Geomorphometric Classification of Earth Surface for Landslide Susceptibility Assessment

Abstract

The aim of this work is to define an automated method of terrain classification in order to evaluate the correlation degree between topographic forms of the analyzed territory and registered landslide phenomena with a Landslide Inventory and DEMs as unique input data. A reliable procedure that identifies areas subject to different levels of susceptibility by a geomorphometric approach is presented. The main objective is reached by means of intermediate steps. The first step is the individuation of a set of measures, a geometric signature, that describes topographic form to distinguish among geomorphically different landscapes; the identified parameters are slope gradient, aspect, plan and section curvatures, local convexity and surface texture, computed from a 30x30m square-grid digital elevation model (DEM). The second step is the classification of the analyzed territory in eleven classes using the geometric signature tool. Finally, the eleven classes are statistically correlated with the Landslide Inventory of the analyzed territory. This work represents a useful tool in large-scale landslide susceptibility analysis. In fact, the application of this repeatable and reliable procedure may return the best results in a short time and with low economic resources, providing specific useful information in planning Civil Protection investigations and operations.