Abstract

We propose a semi-automatic approach to detect, map and classify rainfall-induced shallow landslides. Theapproach combines the classification of a post-event multispectral satellite image with information on themorphometric signature of landslides in a Bayesian framework. We apply the approach in two steps. First,we detect and map the rainfall-induced landslides separating the stable ground from the failed areas. Next,we classify internally the landslides separating the source from the run out areas. We obtain the prior prob-ability from the Mahalanobis discriminant function used to classify the satellite image, and the likelihoodfrom the frequency distribution of terrain slope and cross section convexity in the pre-existing shallow land-slides. We tested the approach in southern Taiwan, in a catchment where Typhoon Morakot caused abundantlandslides in August 2009. Using the semi-automatic approach, we obtained a detailed event landslide inven-tory map that we compared to an inventory obtained through the visual interpretation of post-eventortho-photographs taken a few days after the landslide triggering rainfall event. Quantitative comparisonin a Geographical Information System revealed a degree of matching between the two event inventories ex-ceeding 90%. The approach is general and flexible, and can be used with different satellite imagery and topo-graphic data. Best suited in landscapes where shallow landslides leave distinct radiometric and topographicsignatures, the approach is expected to facilitate the production of event landslide inventory maps with pos-itive consequences for geomorphological investigations, landslide hazard and risk modeling, and for postevent recovery efforts.

Autore Pugliese

• Pasquariello Guido

Tutti gli autori

• A.C. Mondini; I. Marchesini; M. Rossi; K.-T. Chang;G. Pasquariello; F. Guzzetti

Titolo volume/Rivista

Geomorphology

Anno di pubblicazione

2013

ISSN

0169-555X

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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