Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil

Detalhes bibliográficos
Autor(a) principal: Denise de Fátima Santos da Silva
Data de Publicação: 2021
Outros Autores: Rosyelle Cristina Corteletti, Roberto Almeida Cunha Filgueiras, Allan Erlikhman Medeiros Santos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.20502/rbg.v22i2.1937
http://hdl.handle.net/1843/54398
https://orcid.org/0000-0002-9695-2449
https://orcid.org/0000-0001-6006-2877
https://orcid.org/0000-0003-4391-9415
https://orcid.org/0000-0003-4302-3897
Resumo: The remote sensing has been widely used in studies involving records and monitoring landslides, also, this tool is widely used in mapping areas of geological risk. In this paper, two remote sensing methodologies were used to identify, characterize and catalog scars generated along the Estrada de Ferro Vitória-Minas (EFVM). The first methodology allowed to estimate the area and volume of landslides scars of EFVM through the images available in Google Earth Pro. The second methodology used a drone to capture the parameters in thefield of landslides scars. From the obtained parameters it was possible to compare the estimates of areas and volumes of landslides by drone image with the data obtained by images of Google Earth Pro and to test the applicability of both methods. The results obtained showed estimates scars areas with correlation of 0.92 for the two methodologies. The volume estimates presented a correlation of 0.8; with underestimation of the results obtained by Google Earth Pro.
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spelling 2023-06-02T19:40:23Z2023-06-02T19:40:23Z2021-04-01222297314https://doi.org/10.20502/rbg.v22i2.19372236-5664http://hdl.handle.net/1843/54398https://orcid.org/0000-0002-9695-2449https://orcid.org/0000-0001-6006-2877https://orcid.org/0000-0003-4391-9415https://orcid.org/0000-0003-4302-3897The remote sensing has been widely used in studies involving records and monitoring landslides, also, this tool is widely used in mapping areas of geological risk. In this paper, two remote sensing methodologies were used to identify, characterize and catalog scars generated along the Estrada de Ferro Vitória-Minas (EFVM). The first methodology allowed to estimate the area and volume of landslides scars of EFVM through the images available in Google Earth Pro. The second methodology used a drone to capture the parameters in thefield of landslides scars. From the obtained parameters it was possible to compare the estimates of areas and volumes of landslides by drone image with the data obtained by images of Google Earth Pro and to test the applicability of both methods. The results obtained showed estimates scars areas with correlation of 0.92 for the two methodologies. The volume estimates presented a correlation of 0.8; with underestimation of the results obtained by Google Earth Pro.O sensoriamento remoto tem sido largamente aplicado em estudos que envolvem registros e monitoramento de movimentos de massa, além disso, essas ferramentas são muito empregadas na geração de mapas de áreas de risco geológico. Neste artigo utilizou-se duas metodologias de sensoriamento remoto para identificação, caracterização e catalogação das cicatrizes geradas por deslizamento ao longo da Estrada de Ferro Vitória-Minas (EFVM). A primeira metodologia permitiu estimar a área e o volume das cicatrizes de deslizamento da EFVM por meio das imagens disponíveis no Google Earth Pro. A segunda metodologia utilizou-se de um drone para captação dos parâmetros em campo das cicatrizes de deslizamento. A partir dos parâmetros obtidos pode-se comparar as estimativas de áreas e volumes de deslizamentos pelo imageamento com drone com os dados obtidos a partir das imagens do Google Earth Pro e testar a aplicabilidade de ambos os métodos. Os resultados obtidos apontam estimativas de áreas de cicatrizes com correlação de 0,92 para as duas metodologias. Já as estimativas de volume apresentaram correlação 0,8; com subestimativa dos resultados obtidos por meio do Google Earth Pro.Outra AgênciaengUniversidade Federal de Minas GeraisUFMGBrasilIGC - DEPARTAMENTO DE GEOLOGIARevista Brasileira de GeomorfologiaDeslizamentoGeociências - Sensoriamento remotoImagens de sensoriamento remotoLandslide scarsGoogle Earth ProDroneCorrelations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern BrazilCorrelações entre parâmetros de cicatrizes de deslizamento utilizando sensoriamento remoto na estrada de ferro Vitória-Minas, sudeste do Brasilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://rbgeomorfologia.org.br/rbg/article/view/1937Denise de Fátima Santos da SilvaRosyelle Cristina CortelettiRoberto Almeida Cunha FilgueirasAllan Erlikhman Medeiros Santosapplication/pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/54398/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALCorrelations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil.pdfCorrelations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil.pdfapplication/pdf1403639https://repositorio.ufmg.br/bitstream/1843/54398/2/Correlations%20between%20landslide%20scars%20parameters%20using%20remote%20sensing%20methods%20in%20the%20estrada%20de%20ferro%20Vit%c3%b3ria-Minas%2c%20southeastern%20Brazil.pdff05d689986f9a67c990ef3b35ce339d6MD521843/543982023-06-02 16:40:23.913oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-06-02T19:40:23Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
dc.title.alternative.pt_BR.fl_str_mv Correlações entre parâmetros de cicatrizes de deslizamento utilizando sensoriamento remoto na estrada de ferro Vitória-Minas, sudeste do Brasil
title Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
spellingShingle Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
Denise de Fátima Santos da Silva
Landslide scars
Google Earth Pro
Drone
Deslizamento
Geociências - Sensoriamento remoto
Imagens de sensoriamento remoto
title_short Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
title_full Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
title_fullStr Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
title_full_unstemmed Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
title_sort Correlations between landslide scars parameters using remote sensing methods in the estrada de ferro Vitória-Minas, southeastern Brazil
author Denise de Fátima Santos da Silva
author_facet Denise de Fátima Santos da Silva
Rosyelle Cristina Corteletti
Roberto Almeida Cunha Filgueiras
Allan Erlikhman Medeiros Santos
author_role author
author2 Rosyelle Cristina Corteletti
Roberto Almeida Cunha Filgueiras
Allan Erlikhman Medeiros Santos
author2_role author
author
author
dc.contributor.author.fl_str_mv Denise de Fátima Santos da Silva
Rosyelle Cristina Corteletti
Roberto Almeida Cunha Filgueiras
Allan Erlikhman Medeiros Santos
dc.subject.por.fl_str_mv Landslide scars
Google Earth Pro
Drone
topic Landslide scars
Google Earth Pro
Drone
Deslizamento
Geociências - Sensoriamento remoto
Imagens de sensoriamento remoto
dc.subject.other.pt_BR.fl_str_mv Deslizamento
Geociências - Sensoriamento remoto
Imagens de sensoriamento remoto
description The remote sensing has been widely used in studies involving records and monitoring landslides, also, this tool is widely used in mapping areas of geological risk. In this paper, two remote sensing methodologies were used to identify, characterize and catalog scars generated along the Estrada de Ferro Vitória-Minas (EFVM). The first methodology allowed to estimate the area and volume of landslides scars of EFVM through the images available in Google Earth Pro. The second methodology used a drone to capture the parameters in thefield of landslides scars. From the obtained parameters it was possible to compare the estimates of areas and volumes of landslides by drone image with the data obtained by images of Google Earth Pro and to test the applicability of both methods. The results obtained showed estimates scars areas with correlation of 0.92 for the two methodologies. The volume estimates presented a correlation of 0.8; with underestimation of the results obtained by Google Earth Pro.
publishDate 2021
dc.date.issued.fl_str_mv 2021-04-01
dc.date.accessioned.fl_str_mv 2023-06-02T19:40:23Z
dc.date.available.fl_str_mv 2023-06-02T19:40:23Z
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/1843/54398
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.20502/rbg.v22i2.1937
dc.identifier.issn.pt_BR.fl_str_mv 2236-5664
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0002-9695-2449
https://orcid.org/0000-0001-6006-2877
https://orcid.org/0000-0003-4391-9415
https://orcid.org/0000-0003-4302-3897
url https://doi.org/10.20502/rbg.v22i2.1937
http://hdl.handle.net/1843/54398
https://orcid.org/0000-0002-9695-2449
https://orcid.org/0000-0001-6006-2877
https://orcid.org/0000-0003-4391-9415
https://orcid.org/0000-0003-4302-3897
identifier_str_mv 2236-5664
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Revista Brasileira de Geomorfologia
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv IGC - DEPARTAMENTO DE GEOLOGIA
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
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