During the last decade a pressing need for more adequate tools to manage the considerable increasing number of hydrogeological emergencies arose among land planning and civil protection authorities. As a consequence, both development and testing of different qualitative and quantitative methods for landslide displacements detection become fundamental in order to provide the best analysis performance in terms of cost-benefit and scientific reliability. Lately quantitative methods to measure deformations of unstable slopes had great advances. In this context, remotely sensed radar techniques, such as PSI (Persistent Scatterers Interferometry), can assist traditional landslide investigations in assessing ground and infrastructure deformations caused by large landslides. The main purpose of this study is exploiting the results of PSI analysis conducted over the Island of Malta to train a Bayesan model for evaluating active landslide susceptibility. This approach has been applied in the NW coast of Malta, where outstanding coastal landslides, such as rock spreads and block slides, have been recognized and mapped. The outcomes of the statistical analysis have been validated through specific field check and GNSS measurements. The results show that the developed susceptibility model predicts an acceptable percentage of landslides and can be considered reliable even if in areas without PSI data.

Landslide susceptibility analysis exploiting Persistent Scatterers data in the northern coast of Malta / Mantovani, Matteo; Piacentini, Daniela; Devoto, Stefano; Prampolini, Mariacristina; Pasuto, Alessandro; Soldati, Mauro. - ELETTRONICO. - (2014), pp. 1-7. ((Intervento presentato al convegno International Conference "Analysis and Management of Changing Risks for Natural Hazards" tenutosi a Padova nel 18-19 novembre 2014.

Landslide susceptibility analysis exploiting Persistent Scatterers data in the northern coast of Malta

PIACENTINI, Daniela;DEVOTO, STEFANO;PRAMPOLINI, MARIACRISTINA;SOLDATI, Mauro
2014

Abstract

During the last decade a pressing need for more adequate tools to manage the considerable increasing number of hydrogeological emergencies arose among land planning and civil protection authorities. As a consequence, both development and testing of different qualitative and quantitative methods for landslide displacements detection become fundamental in order to provide the best analysis performance in terms of cost-benefit and scientific reliability. Lately quantitative methods to measure deformations of unstable slopes had great advances. In this context, remotely sensed radar techniques, such as PSI (Persistent Scatterers Interferometry), can assist traditional landslide investigations in assessing ground and infrastructure deformations caused by large landslides. The main purpose of this study is exploiting the results of PSI analysis conducted over the Island of Malta to train a Bayesan model for evaluating active landslide susceptibility. This approach has been applied in the NW coast of Malta, where outstanding coastal landslides, such as rock spreads and block slides, have been recognized and mapped. The outcomes of the statistical analysis have been validated through specific field check and GNSS measurements. The results show that the developed susceptibility model predicts an acceptable percentage of landslides and can be considered reliable even if in areas without PSI data.
International Conference "Analysis and Management of Changing Risks for Natural Hazards"
Padova
18-19 novembre 2014
1
7
Mantovani, Matteo; Piacentini, Daniela; Devoto, Stefano; Prampolini, Mariacristina; Pasuto, Alessandro; Soldati, Mauro
Landslide susceptibility analysis exploiting Persistent Scatterers data in the northern coast of Malta / Mantovani, Matteo; Piacentini, Daniela; Devoto, Stefano; Prampolini, Mariacristina; Pasuto, Alessandro; Soldati, Mauro. - ELETTRONICO. - (2014), pp. 1-7. ((Intervento presentato al convegno International Conference "Analysis and Management of Changing Risks for Natural Hazards" tenutosi a Padova nel 18-19 novembre 2014.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1064670
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