Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Pesquisa Agropecuária Brasileira (Online) |
Texto Completo: | https://seer.sct.embrapa.br/index.php/pab/article/view/3129 |
Resumo: | The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating oybean areas. |
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Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, BrazilEstimativa de áreas de soja usando superfícies espectro-temporais derivadas de imagens MODIS em Mato Grosso, BrasilGlycine max; accuracy; agricultural statistics; classification; remote sensing; thematic mapGlycine max; acurácia; estatísticas agrícolas; classificação; sensoriamento remoto; mapa temáticoThe objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating oybean areas.O objetivo deste trabalho foi avaliar a aplicação do método de classificação por superfícies de resposta espectro-temporal (STRS) em imagens do sensor Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) para estimar áreas de plantio de soja no Estado de Mato Grosso, Brasil. A classificação foi realizada usando o algoritmo de máxima verossimilhança (MLA) adaptado ao algoritmo STRS. Trinta segmentos de 30x30 km foram escolhidos ao longo das principais regiões agrícolas do estado, com dados da safra de verão de 2005/2006 (outubro a março), e mapeados com base em dados de campo e de imagens orbitais TM/Landsat-5 e CCD/CBERS-2. Cinco classes temáticas foram consideradas: Soja, Floresta, Cerrado, Pastagem e Solos Expostos. A classificação pelo método das STRS foi feita com base em uma área interseccionada por um subconjunto de segmentos de 30x30 km. O STRS superestimou os valores de referência em 21,31% em regiões com predomínio da cultura da soja e em 132,37% em regiões nas quais a soja era menos predominante. A exatidão global da classificação foi de 80%. As imagens MODIS e o algoritmo STRS mostraram-se promissores para a classificação da soja em regiões com predominância de grandes fazendas. Entretanto, os resultados para áreas fragmentadas em fazendas menores foram menos eficientes, superestimando as áreas de soja.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraFapesp, CNPqEpiphanio, Rui Dalla ValleFormaggio, Antonio RobertoRudorff, Bernardo Friedrich TheodorMaeda, Eduardo EijiLuiz, Alfredo José Barreto2010-12-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/3129Pesquisa Agropecuaria Brasileira; v.45, n.1, jan. 2010; 72-80Pesquisa Agropecuária Brasileira; v.45, n.1, jan. 2010; 72-801678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAenghttps://seer.sct.embrapa.br/index.php/pab/article/view/3129/5937https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/3129/2677info:eu-repo/semantics/openAccess2014-10-15T20:44:30Zoai:ojs.seer.sct.embrapa.br:article/3129Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2014-10-15T20:44:30Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil Estimativa de áreas de soja usando superfícies espectro-temporais derivadas de imagens MODIS em Mato Grosso, Brasil |
title |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil |
spellingShingle |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil Epiphanio, Rui Dalla Valle Glycine max; accuracy; agricultural statistics; classification; remote sensing; thematic map Glycine max; acurácia; estatísticas agrícolas; classificação; sensoriamento remoto; mapa temático |
title_short |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil |
title_full |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil |
title_fullStr |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil |
title_full_unstemmed |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil |
title_sort |
Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil |
author |
Epiphanio, Rui Dalla Valle |
author_facet |
Epiphanio, Rui Dalla Valle Formaggio, Antonio Roberto Rudorff, Bernardo Friedrich Theodor Maeda, Eduardo Eiji Luiz, Alfredo José Barreto |
author_role |
author |
author2 |
Formaggio, Antonio Roberto Rudorff, Bernardo Friedrich Theodor Maeda, Eduardo Eiji Luiz, Alfredo José Barreto |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Fapesp, CNPq |
dc.contributor.author.fl_str_mv |
Epiphanio, Rui Dalla Valle Formaggio, Antonio Roberto Rudorff, Bernardo Friedrich Theodor Maeda, Eduardo Eiji Luiz, Alfredo José Barreto |
dc.subject.por.fl_str_mv |
Glycine max; accuracy; agricultural statistics; classification; remote sensing; thematic map Glycine max; acurácia; estatísticas agrícolas; classificação; sensoriamento remoto; mapa temático |
topic |
Glycine max; accuracy; agricultural statistics; classification; remote sensing; thematic map Glycine max; acurácia; estatísticas agrícolas; classificação; sensoriamento remoto; mapa temático |
description |
The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating oybean areas. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-12-28 |
dc.type.none.fl_str_mv |
|
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://seer.sct.embrapa.br/index.php/pab/article/view/3129 |
url |
https://seer.sct.embrapa.br/index.php/pab/article/view/3129 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://seer.sct.embrapa.br/index.php/pab/article/view/3129/5937 https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/3129/2677 |
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 |
Pesquisa Agropecuaria Brasileira Pesquisa Agropecuária Brasileira |
publisher.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira Pesquisa Agropecuária Brasileira |
dc.source.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira; v.45, n.1, jan. 2010; 72-80 Pesquisa Agropecuária Brasileira; v.45, n.1, jan. 2010; 72-80 1678-3921 0100-104x reponame:Pesquisa Agropecuária Brasileira (Online) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Pesquisa Agropecuária Brasileira (Online) |
collection |
Pesquisa Agropecuária Brasileira (Online) |
repository.name.fl_str_mv |
Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
pab@sct.embrapa.br || sct.pab@embrapa.br |
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1793416664265523200 |