Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101202 |
Resumo: | Abstract This research study was developed in the Ribeirão dos Macacos basin at the district of Nova Lima, Minas Gerais state, Brazil. The information value statistical method was applied in the construction of the landslide susceptibility map at the 1:25,000 scale. Different partitions of the inventory were tested, as well as different landslide predisposing factors. In the construction of the landslide inventory, the south, southeast and south-west slopes generally present a higher quality in aerial / orbital images due to the position of the sun (lighting direction), which emphasizes the surface structures and it may omit old landslides on slopes facing north, northeast, and northwest. This condition can generate misleading models when using the slope aspect. Another verification was that the models with better Area Under the Curve index will not always restrict the high susceptibility class in smaller areas. This incongruence occurs due to the different curve conformations, since a smaller index curve can present more restrictive results than a larger index curve. The results showed that the model has a high capacity of adjustment to the input data and high landslide predictive capacity. |
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Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazilnatural disasterslandslide susceptibility mapstatistical methodsinformation valueterritorial planningAbstract This research study was developed in the Ribeirão dos Macacos basin at the district of Nova Lima, Minas Gerais state, Brazil. The information value statistical method was applied in the construction of the landslide susceptibility map at the 1:25,000 scale. Different partitions of the inventory were tested, as well as different landslide predisposing factors. In the construction of the landslide inventory, the south, southeast and south-west slopes generally present a higher quality in aerial / orbital images due to the position of the sun (lighting direction), which emphasizes the surface structures and it may omit old landslides on slopes facing north, northeast, and northwest. This condition can generate misleading models when using the slope aspect. Another verification was that the models with better Area Under the Curve index will not always restrict the high susceptibility class in smaller areas. This incongruence occurs due to the different curve conformations, since a smaller index curve can present more restrictive results than a larger index curve. The results showed that the model has a high capacity of adjustment to the input data and high landslide predictive capacity.Academia Brasileira de Ciências2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101202Anais da Academia Brasileira de Ciências v.93 n.1 2021reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202120180897info:eu-repo/semantics/openAccessROSA,MATEUS L.SOBREIRA,FREDERICO G.BARELLA,CESAR F.eng2021-01-13T00:00:00Zoai:scielo:S0001-37652021000101202Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2021-01-13T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil |
title |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil |
spellingShingle |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil ROSA,MATEUS L. natural disasters landslide susceptibility map statistical methods information value territorial planning |
title_short |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil |
title_full |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil |
title_fullStr |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil |
title_full_unstemmed |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil |
title_sort |
Landslide susceptibility mapping using the statistical method of Information Value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil |
author |
ROSA,MATEUS L. |
author_facet |
ROSA,MATEUS L. SOBREIRA,FREDERICO G. BARELLA,CESAR F. |
author_role |
author |
author2 |
SOBREIRA,FREDERICO G. BARELLA,CESAR F. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
ROSA,MATEUS L. SOBREIRA,FREDERICO G. BARELLA,CESAR F. |
dc.subject.por.fl_str_mv |
natural disasters landslide susceptibility map statistical methods information value territorial planning |
topic |
natural disasters landslide susceptibility map statistical methods information value territorial planning |
description |
Abstract This research study was developed in the Ribeirão dos Macacos basin at the district of Nova Lima, Minas Gerais state, Brazil. The information value statistical method was applied in the construction of the landslide susceptibility map at the 1:25,000 scale. Different partitions of the inventory were tested, as well as different landslide predisposing factors. In the construction of the landslide inventory, the south, southeast and south-west slopes generally present a higher quality in aerial / orbital images due to the position of the sun (lighting direction), which emphasizes the surface structures and it may omit old landslides on slopes facing north, northeast, and northwest. This condition can generate misleading models when using the slope aspect. Another verification was that the models with better Area Under the Curve index will not always restrict the high susceptibility class in smaller areas. This incongruence occurs due to the different curve conformations, since a smaller index curve can present more restrictive results than a larger index curve. The results showed that the model has a high capacity of adjustment to the input data and high landslide predictive capacity. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101202 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101202 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202120180897 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.93 n.1 2021 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
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1754302869698248704 |