Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/48922 |
Resumo: | Statistical susceptibility assessment is a common approach applied worldwide for shallow landslide studies. Identification of morphological and geological conditions is essential and still incipient to evaluate the susceptibility of landslide events in the Brazilian territory. This study aimed to develop and compare shallow landslide susceptibility scenarios based on a bivariate statistical evaluation of geological (lithology and structures) and morphological (curvature, elevation, slope, and aspect) factors in Caraguatatuba, northern coast of São Paulo State in Brazil. A compilation of geological factors from published maps was made, and morphological maps were created based on Shuttle Radar Topography Mission (30 m). A bivariate statistical application by the informative value method was used to create four susceptibility scenarios, and the validation was achieved using the area under the curve (AUC). The results indicated that lithology was the more relevant conditioning factor, followed by elevation and slope. The methodology used to determine the susceptibility was efficient (AUC values between 0.809 and 0.841). The susceptibility scenario comparison identified that conditioning factors with the highest informational value generated the most accurate mapping. This indicates that using several conditioning factors does not necessarily generate a better map. This study contributes to shallow landslides research from a methodological perspective, as it is the first analysis of its kind in Serra do Mar Paulista, which are continuously affected by mass movements. Open-source data were chosen to be used, focusing on methodological applicability in other regions of the country, since resources for landslide studies in Brazil are low. |
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Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coastInformative valueMass movementSerra do MarStatistical index methodAUCStatistical susceptibility assessment is a common approach applied worldwide for shallow landslide studies. Identification of morphological and geological conditions is essential and still incipient to evaluate the susceptibility of landslide events in the Brazilian territory. This study aimed to develop and compare shallow landslide susceptibility scenarios based on a bivariate statistical evaluation of geological (lithology and structures) and morphological (curvature, elevation, slope, and aspect) factors in Caraguatatuba, northern coast of São Paulo State in Brazil. A compilation of geological factors from published maps was made, and morphological maps were created based on Shuttle Radar Topography Mission (30 m). A bivariate statistical application by the informative value method was used to create four susceptibility scenarios, and the validation was achieved using the area under the curve (AUC). The results indicated that lithology was the more relevant conditioning factor, followed by elevation and slope. The methodology used to determine the susceptibility was efficient (AUC values between 0.809 and 0.841). The susceptibility scenario comparison identified that conditioning factors with the highest informational value generated the most accurate mapping. This indicates that using several conditioning factors does not necessarily generate a better map. This study contributes to shallow landslides research from a methodological perspective, as it is the first analysis of its kind in Serra do Mar Paulista, which are continuously affected by mass movements. Open-source data were chosen to be used, focusing on methodological applicability in other regions of the country, since resources for landslide studies in Brazil are low.SpringerRepositório da Universidade de LisboaDias, Helen CristinaGramani, Marcelo FischerGrohmann, Carlos HenriqueBateira, CarlosVieira, Bianca Carvalho2021-07-14T10:58:52Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/48922engDias, H. C., Gramani, M.F., Grohmann, C.H., Bateira, C. & Vieira, B. C. (2021) [Early Access]. Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast. Natural Hazards. https://doi.org/10.1007/s11069-021-04676-y0921-030X10.1007/s11069-021-04676-y1573-0840metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:52:31Zoai:repositorio.ul.pt:10451/48922Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:00:42.096605Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast |
title |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast |
spellingShingle |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast Dias, Helen Cristina Informative value Mass movement Serra do Mar Statistical index method AUC |
title_short |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast |
title_full |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast |
title_fullStr |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast |
title_full_unstemmed |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast |
title_sort |
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast |
author |
Dias, Helen Cristina |
author_facet |
Dias, Helen Cristina Gramani, Marcelo Fischer Grohmann, Carlos Henrique Bateira, Carlos Vieira, Bianca Carvalho |
author_role |
author |
author2 |
Gramani, Marcelo Fischer Grohmann, Carlos Henrique Bateira, Carlos Vieira, Bianca Carvalho |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Dias, Helen Cristina Gramani, Marcelo Fischer Grohmann, Carlos Henrique Bateira, Carlos Vieira, Bianca Carvalho |
dc.subject.por.fl_str_mv |
Informative value Mass movement Serra do Mar Statistical index method AUC |
topic |
Informative value Mass movement Serra do Mar Statistical index method AUC |
description |
Statistical susceptibility assessment is a common approach applied worldwide for shallow landslide studies. Identification of morphological and geological conditions is essential and still incipient to evaluate the susceptibility of landslide events in the Brazilian territory. This study aimed to develop and compare shallow landslide susceptibility scenarios based on a bivariate statistical evaluation of geological (lithology and structures) and morphological (curvature, elevation, slope, and aspect) factors in Caraguatatuba, northern coast of São Paulo State in Brazil. A compilation of geological factors from published maps was made, and morphological maps were created based on Shuttle Radar Topography Mission (30 m). A bivariate statistical application by the informative value method was used to create four susceptibility scenarios, and the validation was achieved using the area under the curve (AUC). The results indicated that lithology was the more relevant conditioning factor, followed by elevation and slope. The methodology used to determine the susceptibility was efficient (AUC values between 0.809 and 0.841). The susceptibility scenario comparison identified that conditioning factors with the highest informational value generated the most accurate mapping. This indicates that using several conditioning factors does not necessarily generate a better map. This study contributes to shallow landslides research from a methodological perspective, as it is the first analysis of its kind in Serra do Mar Paulista, which are continuously affected by mass movements. Open-source data were chosen to be used, focusing on methodological applicability in other regions of the country, since resources for landslide studies in Brazil are low. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-14T10:58:52Z 2021 2021-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/48922 |
url |
http://hdl.handle.net/10451/48922 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Dias, H. C., Gramani, M.F., Grohmann, C.H., Bateira, C. & Vieira, B. C. (2021) [Early Access]. Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast. Natural Hazards. https://doi.org/10.1007/s11069-021-04676-y 0921-030X 10.1007/s11069-021-04676-y 1573-0840 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134555592982528 |