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.
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Abstract. Flooding is an ongoing and complex problem in Italy. Very large floods caused inundation of the closest areas to the city centre in Rome in 1937, 1976, 1992, 2005 and most recently in 2008. Rome is located at the bottom of the Tiber River catchment, which cover an area of 16 000 km2. Intense precipitations struck the Tyrrhenian Sea side of the peninsula inducing a flood event on the Tiber and Aniene's (Tiber's tributary) basins – which captured the attention of the national and international media. Actually there is no validated model in operation for real-time flood forecasting. This research aims at comparing the Cellular Model CAESAR (Cellular Automation Evolutionary Slope And River) application on a reach of the Aniene River with Earth Observation Systems. The main result expected is the prediction of future channel dynamics on short and medium time scale.
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