http://www.cnr.it/ontology/cnr/individuo/prodotto/ID275429
Landslide morphometric signature (Abstract/Poster in convegno)
- Type
- Label
- Landslide morphometric signature (Abstract/Poster in convegno) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Alternative label
Mauro Rossi (a,b), Alessandro C. Mondini (a), Ivan Marchesini (a), Michele Santangelo (a,b), Francesco Bucci (a), Fausto Guzzetti (a) (2013)
Landslide morphometric signature
in 8th IAG/AIG International Conference on Geomorphology Geomorphology and Sustainability, Paris, France, 27-31 August 2013
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Mauro Rossi (a,b), Alessandro C. Mondini (a), Ivan Marchesini (a), Michele Santangelo (a,b), Francesco Bucci (a), Fausto Guzzetti (a) (literal)
- Note
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- a) Consiglio Nazionale delle Ricerche, Istituto per la Protezione Idrogeologica, Perugia, Italy
b) Dipartimento di Scienze della Terra, Università degli Studi di Perugia, Perugia, Italy (literal)
- Titolo
- Landslide morphometric signature (literal)
- Abstract
- Morphometric parameters are widely used in landslides mapping and modeling. Morphological information is used by
geomorphologists to map landslides in the field or during aerial photo-interpretation and using remote sensing techniques. Landslide
models can explicitly consider this information (e.g. slope in physically-based infinite slope modeling), or assess its importance through
a statistical inference (e.g. morphometric variables used in statistical multivariate landslide susceptibility models). Investigators have
attempted to quantify morphological changes produced by landslides locally or in small areas, but at present no common criteria, or set
of variables or analysis tools exist to compare globally these changes. Here we present a framework to analyze the morphological
fingerprints of landslides in a territory. We also define a method to group them in categories based on different triggers and
environmental settings. For this purpose we identify a set of morphometric variables and a procedure to distinguish different
morphological landslide signatures. Further we provide a web processing service to allow external user to apply the proposed procedure
in specific areas. Our intent is to create a library of the landslide morphological signatures as much as possible complete. Results will
be helpful to improve: (a) the ability to detect landslide on the surface, (b) the modeling capabilities, and (c) the knowledge of landslide
processes. (literal)
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