Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/97118 |
Resumo: | Estimations of crop areaweremade based on the temporal profiles of the EnhancedVegetation Index (EVI) obtained frommoderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm(MCDA) to estimate soybean crop areas was performed for fields in theMato Grosso state, Brazil. Using theMCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R² = 0.97 and RMSD= 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year.The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters,MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state. |
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Gusso, AníbalArvor, DamienDucati, Jorge RicardoVeronez, Maurício RobertoSilveira Junior, Luiz Gonzaga da2014-07-02T02:06:52Z20141537-744Xhttp://hdl.handle.net/10183/97118000918394Estimations of crop areaweremade based on the temporal profiles of the EnhancedVegetation Index (EVI) obtained frommoderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm(MCDA) to estimate soybean crop areas was performed for fields in theMato Grosso state, Brazil. Using theMCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R² = 0.97 and RMSD= 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year.The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters,MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state.application/pdfengThe scientific world journal. Newbury, UK. Vol. 2014 (2014), ID 863141, 9 p.Sensoriamento remotoEstatística agrícolaSojaMato GrossoAssessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, BrazilEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000918394.pdf000918394.pdfTexto completo (inglês)application/pdf2135914http://www.lume.ufrgs.br/bitstream/10183/97118/1/000918394.pdfffc2ad8848e026424484dfce4dcdf872MD51TEXT000918394.pdf.txt000918394.pdf.txtExtracted Texttext/plain40788http://www.lume.ufrgs.br/bitstream/10183/97118/2/000918394.pdf.txt2eb2e05c5dca2a73d125ad4c8cc25807MD52THUMBNAIL000918394.pdf.jpg000918394.pdf.jpgGenerated Thumbnailimage/jpeg1841http://www.lume.ufrgs.br/bitstream/10183/97118/3/000918394.pdf.jpg8ec7a093fbe5a99b33e3ba8f97f15512MD5310183/971182019-06-07 02:35:08.055316oai:www.lume.ufrgs.br:10183/97118Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2019-06-07T05:35:08Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil |
title |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil |
spellingShingle |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil Gusso, Aníbal Sensoriamento remoto Estatística agrícola Soja Mato Grosso |
title_short |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil |
title_full |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil |
title_fullStr |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil |
title_full_unstemmed |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil |
title_sort |
Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso State, Brazil |
author |
Gusso, Aníbal |
author_facet |
Gusso, Aníbal Arvor, Damien Ducati, Jorge Ricardo Veronez, Maurício Roberto Silveira Junior, Luiz Gonzaga da |
author_role |
author |
author2 |
Arvor, Damien Ducati, Jorge Ricardo Veronez, Maurício Roberto Silveira Junior, Luiz Gonzaga da |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Gusso, Aníbal Arvor, Damien Ducati, Jorge Ricardo Veronez, Maurício Roberto Silveira Junior, Luiz Gonzaga da |
dc.subject.por.fl_str_mv |
Sensoriamento remoto Estatística agrícola Soja Mato Grosso |
topic |
Sensoriamento remoto Estatística agrícola Soja Mato Grosso |
description |
Estimations of crop areaweremade based on the temporal profiles of the EnhancedVegetation Index (EVI) obtained frommoderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm(MCDA) to estimate soybean crop areas was performed for fields in theMato Grosso state, Brazil. Using theMCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R² = 0.97 and RMSD= 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year.The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters,MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state. |
publishDate |
2014 |
dc.date.accessioned.fl_str_mv |
2014-07-02T02:06:52Z |
dc.date.issued.fl_str_mv |
2014 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
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article |
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1537-744X |
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000918394 |
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http://hdl.handle.net/10183/97118 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
The scientific world journal. Newbury, UK. Vol. 2014 (2014), ID 863141, 9 p. |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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