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.

Ioannilli, M., Paregiani, A. (2008). Automated unsupervised geomorphometric classification of earth surface for landslide susceptibility assessment. In Computational science and its applications - ICCSA 2008, PT 1, Proceedings (pp.268-283). BERLIN : SPRINGER-VERLAG BERLIN [10.1007/978-3-540-69839-5_21].

Automated unsupervised geomorphometric classification of earth surface for landslide susceptibility assessment

IOANNILLI, MARIA;
2008-01-01

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.
International conference on computational science and its applications, ICCSA 2008
Perugia, ITALY
2008
University of Perugia;University of Calgary;Innovative Computational Science Applications (ICSA);MASTER-UP;University of Calgary, SPARCS Laboratory;OptimaNumerics
Rilevanza internazionale
contributo
2-lug-2008
Settore ICAR/20 - Tecnica e Pianificazione Urbanistica
English
Geomorphometry; hazard analysis; landslide; spatial analysis; susceptibility; terrain classification
Intervento a convegno
Ioannilli, M., Paregiani, A. (2008). Automated unsupervised geomorphometric classification of earth surface for landslide susceptibility assessment. In Computational science and its applications - ICCSA 2008, PT 1, Proceedings (pp.268-283). BERLIN : SPRINGER-VERLAG BERLIN [10.1007/978-3-540-69839-5_21].
Ioannilli, M; Paregiani, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/31830
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