Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast

Detalhes bibliográficos
Autor(a) principal: Dias, Helen Cristina
Data de Publicação: 2021
Outros Autores: Gramani, Marcelo Fischer, Grohmann, Carlos Henrique, Bateira, Carlos, Vieira, Bianca Carvalho
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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spelling 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
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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
repository.mail.fl_str_mv
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