Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir

Detalhes bibliográficos
Autor(a) principal: Soares, Jefferson Francisco
Data de Publicação: 2019
Outros Autores: Ramirez, Gláucia Miranda, Carvalho, Mirléia Aparecida de, Alves, Marcelo de Carvalho, Sarmiento, Christiany Mattioli, Marin, Diego Bedin
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/40880
Resumo: The maintenance of riparian forests is considered one of the main vegetative practices for mitigating the degradation of water resources and is mandatory by law. However, in Brazil there is still a progressive and constant decharacterization of these areas. Facing this reality, it is necessary to broaden researches that identify the occurring changes and provide efficient solutions at a fast pace and low cost. Remote sensing techniques show great application potential in characterizing natural resources. The objective of this work was to map, to characterize the land use and occupation and to verify the best method of high spatial resolution image classification of the Permanent Preservation Areas of the Funil Hydroelectric Power Plant reservoir, located between the municipalities of Lavras, Perdões, Bom Sucesso, Ibituruna, Ijací and Itumirim, in the state of Minas Gerais. The methods used to classify the high spatial resolution image from the Quickbird satellite were visual, object-oriented and pixel-by-pixel. Results showed the best method for mapping land use and occupation of the study area was object-oriented classification using the K-nearest neighbor algorithm, with kappa coefficient of 0.88 and global accuracy of 91.40%.
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spelling Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoirComparação de classificadores supervisionados na discriminação de áreas de preservação em reservatório hidrelétricoRemote sensingRiparian forestsKappa coefficientOverall accuracySensoriamento remotoMatas ciliaresCoeficiente kappaExatidão globalThe maintenance of riparian forests is considered one of the main vegetative practices for mitigating the degradation of water resources and is mandatory by law. However, in Brazil there is still a progressive and constant decharacterization of these areas. Facing this reality, it is necessary to broaden researches that identify the occurring changes and provide efficient solutions at a fast pace and low cost. Remote sensing techniques show great application potential in characterizing natural resources. The objective of this work was to map, to characterize the land use and occupation and to verify the best method of high spatial resolution image classification of the Permanent Preservation Areas of the Funil Hydroelectric Power Plant reservoir, located between the municipalities of Lavras, Perdões, Bom Sucesso, Ibituruna, Ijací and Itumirim, in the state of Minas Gerais. The methods used to classify the high spatial resolution image from the Quickbird satellite were visual, object-oriented and pixel-by-pixel. Results showed the best method for mapping land use and occupation of the study area was object-oriented classification using the K-nearest neighbor algorithm, with kappa coefficient of 0.88 and global accuracy of 91.40%.A manutenção de matas ciliares, considerada uma das práticas vegetativas de mitigação da degradação dos recursos hídricos, é exigida por lei. Contudo, no Brasil, ainda há uma progressiva e constante descaracterização dessas áreas. Diante de tal realidade, torna-se necessário ampliar pesquisas que identifiquem as mudanças ocorridas e forneçam soluções eficientes com rapidez e baixo custo. Técnicas de sensoriamento remoto demonstram grande potencial de aplicação na caracterização dos recursos naturais. O objetivo deste trabalho foi mapear, caracterizar o uso e a ocupação do solo e verificar o melhor método de classificação de imagem de alta resolução espacial das Áreas de Preservação Permanente do reservatório da Usina Hidrelétrica de Funil, localizada entre os municípios de Lavras, Perdões, Bom Sucesso, Ibituruna, Ijaci e Itumirim no Estado de Minas Gerais. Os métodos utilizados para classificação da imagem de alta resolução espacial do Satélite Quickbird foram: visual, orientada a objetos e pixel a pixel. Os resultados demonstraram que o melhor método para o mapeamento de uso e ocupação do solo da área de estudo foi a classificação orientada a objetos, utilizando o algoritmo K-nearest neighbor, com coeficiente kappa de 0,88 e exatidão global de 91,40%.Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais - IFSULDEMINAS2020-05-13T17:39:57Z2020-05-13T17:39:57Z2019-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/pdfSOARES, J. F. et al. Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir. Revista Agrogeoambiental, Pouso Alegre, v. 11, n. 3, p. 150-165, set. 2019.http://repositorio.ufla.br/jspui/handle/1/40880Revista Agrogeoambientalreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSoares, Jefferson FranciscoRamirez, Gláucia MirandaCarvalho, Mirléia Aparecida deAlves, Marcelo de CarvalhoSarmiento, Christiany MattioliMarin, Diego Bedineng2023-05-03T11:13:59Zoai:localhost:1/40880Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T11:13:59Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
Comparação de classificadores supervisionados na discriminação de áreas de preservação em reservatório hidrelétrico
title Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
spellingShingle Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
Soares, Jefferson Francisco
Remote sensing
Riparian forests
Kappa coefficient
Overall accuracy
Sensoriamento remoto
Matas ciliares
Coeficiente kappa
Exatidão global
title_short Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
title_full Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
title_fullStr Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
title_full_unstemmed Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
title_sort Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir
author Soares, Jefferson Francisco
author_facet Soares, Jefferson Francisco
Ramirez, Gláucia Miranda
Carvalho, Mirléia Aparecida de
Alves, Marcelo de Carvalho
Sarmiento, Christiany Mattioli
Marin, Diego Bedin
author_role author
author2 Ramirez, Gláucia Miranda
Carvalho, Mirléia Aparecida de
Alves, Marcelo de Carvalho
Sarmiento, Christiany Mattioli
Marin, Diego Bedin
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Soares, Jefferson Francisco
Ramirez, Gláucia Miranda
Carvalho, Mirléia Aparecida de
Alves, Marcelo de Carvalho
Sarmiento, Christiany Mattioli
Marin, Diego Bedin
dc.subject.por.fl_str_mv Remote sensing
Riparian forests
Kappa coefficient
Overall accuracy
Sensoriamento remoto
Matas ciliares
Coeficiente kappa
Exatidão global
topic Remote sensing
Riparian forests
Kappa coefficient
Overall accuracy
Sensoriamento remoto
Matas ciliares
Coeficiente kappa
Exatidão global
description The maintenance of riparian forests is considered one of the main vegetative practices for mitigating the degradation of water resources and is mandatory by law. However, in Brazil there is still a progressive and constant decharacterization of these areas. Facing this reality, it is necessary to broaden researches that identify the occurring changes and provide efficient solutions at a fast pace and low cost. Remote sensing techniques show great application potential in characterizing natural resources. The objective of this work was to map, to characterize the land use and occupation and to verify the best method of high spatial resolution image classification of the Permanent Preservation Areas of the Funil Hydroelectric Power Plant reservoir, located between the municipalities of Lavras, Perdões, Bom Sucesso, Ibituruna, Ijací and Itumirim, in the state of Minas Gerais. The methods used to classify the high spatial resolution image from the Quickbird satellite were visual, object-oriented and pixel-by-pixel. Results showed the best method for mapping land use and occupation of the study area was object-oriented classification using the K-nearest neighbor algorithm, with kappa coefficient of 0.88 and global accuracy of 91.40%.
publishDate 2019
dc.date.none.fl_str_mv 2019-09
2020-05-13T17:39:57Z
2020-05-13T17:39:57Z
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 SOARES, J. F. et al. Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir. Revista Agrogeoambiental, Pouso Alegre, v. 11, n. 3, p. 150-165, set. 2019.
http://repositorio.ufla.br/jspui/handle/1/40880
identifier_str_mv SOARES, J. F. et al. Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir. Revista Agrogeoambiental, Pouso Alegre, v. 11, n. 3, p. 150-165, set. 2019.
url http://repositorio.ufla.br/jspui/handle/1/40880
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais - IFSULDEMINAS
publisher.none.fl_str_mv Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais - IFSULDEMINAS
dc.source.none.fl_str_mv Revista Agrogeoambiental
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
_version_ 1784550157274054656

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