Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing

Detalhes bibliográficos
Autor(a) principal: Lamounier Machado, Marley
Data de Publicação: 2011
Outros Autores: Ramos Alves, Helena Maria, Grossi Chquiloff Vieira, Tatiana, Inácio Fernandes Filho, Elpídio, Pinto Coelho Lacerda, Marilusa
Tipo de documento: Artigo
Idioma: por
Título da fonte: Coffee Science (Online)
Texto Completo: https://coffeescience.ufla.br/index.php/Coffeescience/article/view/334
Resumo: The aim of this work was to map coffee lands in the Zona da Mata region, in Minas Gerais state, using non-conventional aerial photographs and satellite images. A pilot area, representative of the regional coffee lands, was chosen. A non-conventional aerophotogrammetric survey of the study area was carried out (scale 1:10000) and an ETM+Landsat7 satellite image was acquired. This image was registered and transformed into surface reflectance data. Photointerpretation of the limits of land use classes was done over a digital mosaic. These limits were overlaid onto the image, providing reflectance sampling of each land use type for statistical analysis and assessment of the vegetation’s spectral response. Statistical analysis showed that bands 3, 4, 5 and 7 were the most representative in the discrimination of vegetation canopies. Although statistical analysis showed a significant difference between the bands for the different land use/land cover types, the classifications did not provide good target discrimination due to shading, to the region’s very steep landscape and to the spectral signature similarity between coffee and forest. The mapping accuracy between the classified image and photointerpretation was considered regular to weak and the best results were obtained through a combination of bands. The use of ETM/Landsat7 images to map coffee lands presented limitations, despite the few types of land use. This is due to the shading of the images, owing to the steep topography, and to the fragmentation of most of the coffee lands into small fields.
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spelling Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensingMapeamento de áreas cafeeiras (Coffea arabica L.) da zona da mata mineira usando sensoriamento remotoCoffea arabicageotechnologysatellite imageaerophotogrammetrydigital image processingCoffea arábicageotecnologiaimagem de satéliteaerofotogrametriaprocessamento digital de imagensThe aim of this work was to map coffee lands in the Zona da Mata region, in Minas Gerais state, using non-conventional aerial photographs and satellite images. A pilot area, representative of the regional coffee lands, was chosen. A non-conventional aerophotogrammetric survey of the study area was carried out (scale 1:10000) and an ETM+Landsat7 satellite image was acquired. This image was registered and transformed into surface reflectance data. Photointerpretation of the limits of land use classes was done over a digital mosaic. These limits were overlaid onto the image, providing reflectance sampling of each land use type for statistical analysis and assessment of the vegetation’s spectral response. Statistical analysis showed that bands 3, 4, 5 and 7 were the most representative in the discrimination of vegetation canopies. Although statistical analysis showed a significant difference between the bands for the different land use/land cover types, the classifications did not provide good target discrimination due to shading, to the region’s very steep landscape and to the spectral signature similarity between coffee and forest. The mapping accuracy between the classified image and photointerpretation was considered regular to weak and the best results were obtained through a combination of bands. The use of ETM/Landsat7 images to map coffee lands presented limitations, despite the few types of land use. This is due to the shading of the images, owing to the steep topography, and to the fragmentation of most of the coffee lands into small fields. Objetivou-se, neste trabalho, estabelecer uma metodologia para o mapeamento de áreas cafeeiras da Zona da Mata mineira por meio do sensoriamento remoto, usando imagens de satélite e fotografias aéreas digitais não convencionais. Uma área piloto representativa da cafeicultura da região foi selecionada. O levantamento aerofotogramétrico não convencional da área de estudo, em escala 1:10000, foi realizado e uma imagem orbital ETM+Landsat7 foi adquirida. Essa imagem foi registrada e transformada para dados de reflectância de superfície. Limites das classes de uso da terra foram interpretados sobre o mosaico digital e sobrepostos à imagem, possibilitando a amostragem de cada cultura para fins estatísticos e verificação do comportamento espectral da vegetação. A análise estatística comprovou que as bandas 3, 4, 5 e 7 foram as mais representativas para a discriminação das coberturas vegetais. Apesar de a análise estatística ter indicado diferença significativa entre as bandas para os diferentes tipos de uso, as classificações não permitiram boa discriminação dos alvos devido ao efeito do sombreamento, ao relevo muito montanhoso da região e à similaridade espectral das coberturas, principalmente entre as classes de uso café e mata. A exatidão de mapeamento entre a imagem classificada e a fotointerpretação foi considerada de regular a fraca, sendo os melhores resultados obtidos por combinação de bandas. O uso de imagens orbitais ETM/Landsat7 para mapeamento das áreas cafeeiras na Zona da Mata indicou limitações, apesar dos poucos tipos de classe de uso. Tal fato resultou do sombreamento das imagens, em função da topografia acidentada, e da fragmentação da maioria das lavouras de café em talhões de pequena extensão.Editora UFLA2011-03-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/334Coffee Science - ISSN 1984-3909; Vol. 5 No. 2 (2010); 113-122Coffee Science; Vol. 5 Núm. 2 (2010); 113-122Coffee Science; v. 5 n. 2 (2010); 113-1221984-3909reponame:Coffee Science (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAporhttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/334/pdfCopyright (c) 2011 Coffee Science - ISSN 1984-3909https://creativecommons.org/info:eu-repo/semantics/openAccessLamounier Machado, MarleyRamos Alves, Helena MariaGrossi Chquiloff Vieira, TatianaInácio Fernandes Filho, ElpídioPinto Coelho Lacerda, Marilusa2013-02-24T13:53:23Zoai:coffeescience.ufla.br:article/334Revistahttps://coffeescience.ufla.br/index.php/CoffeesciencePUBhttps://coffeescience.ufla.br/index.php/Coffeescience/oaicoffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com1984-39091809-6875opendoar:2024-05-21T19:53:37.634238Coffee Science (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
Mapeamento de áreas cafeeiras (Coffea arabica L.) da zona da mata mineira usando sensoriamento remoto
title Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
spellingShingle Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
Lamounier Machado, Marley
Coffea arabica
geotechnology
satellite image
aerophotogrammetry
digital image processing
Coffea arábica
geotecnologia
imagem de satélite
aerofotogrametria
processamento digital de imagens
title_short Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
title_full Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
title_fullStr Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
title_full_unstemmed Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
title_sort Mapping of coffee lands (Coffea arabica L.) in the zona da mata region, Minas Gerais state, using remote sensing
author Lamounier Machado, Marley
author_facet Lamounier Machado, Marley
Ramos Alves, Helena Maria
Grossi Chquiloff Vieira, Tatiana
Inácio Fernandes Filho, Elpídio
Pinto Coelho Lacerda, Marilusa
author_role author
author2 Ramos Alves, Helena Maria
Grossi Chquiloff Vieira, Tatiana
Inácio Fernandes Filho, Elpídio
Pinto Coelho Lacerda, Marilusa
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Lamounier Machado, Marley
Ramos Alves, Helena Maria
Grossi Chquiloff Vieira, Tatiana
Inácio Fernandes Filho, Elpídio
Pinto Coelho Lacerda, Marilusa
dc.subject.por.fl_str_mv Coffea arabica
geotechnology
satellite image
aerophotogrammetry
digital image processing
Coffea arábica
geotecnologia
imagem de satélite
aerofotogrametria
processamento digital de imagens
topic Coffea arabica
geotechnology
satellite image
aerophotogrammetry
digital image processing
Coffea arábica
geotecnologia
imagem de satélite
aerofotogrametria
processamento digital de imagens
description The aim of this work was to map coffee lands in the Zona da Mata region, in Minas Gerais state, using non-conventional aerial photographs and satellite images. A pilot area, representative of the regional coffee lands, was chosen. A non-conventional aerophotogrammetric survey of the study area was carried out (scale 1:10000) and an ETM+Landsat7 satellite image was acquired. This image was registered and transformed into surface reflectance data. Photointerpretation of the limits of land use classes was done over a digital mosaic. These limits were overlaid onto the image, providing reflectance sampling of each land use type for statistical analysis and assessment of the vegetation’s spectral response. Statistical analysis showed that bands 3, 4, 5 and 7 were the most representative in the discrimination of vegetation canopies. Although statistical analysis showed a significant difference between the bands for the different land use/land cover types, the classifications did not provide good target discrimination due to shading, to the region’s very steep landscape and to the spectral signature similarity between coffee and forest. The mapping accuracy between the classified image and photointerpretation was considered regular to weak and the best results were obtained through a combination of bands. The use of ETM/Landsat7 images to map coffee lands presented limitations, despite the few types of land use. This is due to the shading of the images, owing to the steep topography, and to the fragmentation of most of the coffee lands into small fields.
publishDate 2011
dc.date.none.fl_str_mv 2011-03-20
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/334
url https://coffeescience.ufla.br/index.php/Coffeescience/article/view/334
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/334/pdf
dc.rights.driver.fl_str_mv Copyright (c) 2011 Coffee Science - ISSN 1984-3909
https://creativecommons.org/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2011 Coffee Science - ISSN 1984-3909
https://creativecommons.org/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editora UFLA
publisher.none.fl_str_mv Editora UFLA
dc.source.none.fl_str_mv Coffee Science - ISSN 1984-3909; Vol. 5 No. 2 (2010); 113-122
Coffee Science; Vol. 5 Núm. 2 (2010); 113-122
Coffee Science; v. 5 n. 2 (2010); 113-122
1984-3909
reponame:Coffee Science (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Coffee Science (Online)
collection Coffee Science (Online)
repository.name.fl_str_mv Coffee Science (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv coffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com
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