Algorithm for soybean classification using medium resolution satellite images
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/116208 |
Resumo: | An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales. |
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Gusso, AníbalDucati, Jorge Ricardo2015-05-14T02:00:52Z20122072-4292http://hdl.handle.net/10183/116208000897497An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales.application/pdfengRemote Sensing. Basel. Vol. 4, n. 10 (Oct. 2012), p. 3127-3142Processamento de imagensSensoriamento remotoReflexibilidaderemote sensingClassificationCrop areaReflectanceAlgorithm for soybean classification using medium resolution satellite imagesEstrangeiroinfo: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:UFRGSORIGINAL000897497.pdf000897497.pdfTexto completo (inglês)application/pdf895602http://www.lume.ufrgs.br/bitstream/10183/116208/1/000897497.pdfb249228ba2c4c17046898cc1118006c0MD51TEXT000897497.pdf.txt000897497.pdf.txtExtracted Texttext/plain37431http://www.lume.ufrgs.br/bitstream/10183/116208/2/000897497.pdf.txt19f164de42bb47c5152ed4c2043f44ccMD52THUMBNAIL000897497.pdf.jpg000897497.pdf.jpgGenerated Thumbnailimage/jpeg2053http://www.lume.ufrgs.br/bitstream/10183/116208/3/000897497.pdf.jpgc6915912c2b27131f7777939eedb632aMD5310183/1162082018-10-22 08:05:46.848oai:www.lume.ufrgs.br:10183/116208Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2018-10-22T11:05:46Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Algorithm for soybean classification using medium resolution satellite images |
title |
Algorithm for soybean classification using medium resolution satellite images |
spellingShingle |
Algorithm for soybean classification using medium resolution satellite images Gusso, Aníbal Processamento de imagens Sensoriamento remoto Reflexibilidade remote sensing Classification Crop area Reflectance |
title_short |
Algorithm for soybean classification using medium resolution satellite images |
title_full |
Algorithm for soybean classification using medium resolution satellite images |
title_fullStr |
Algorithm for soybean classification using medium resolution satellite images |
title_full_unstemmed |
Algorithm for soybean classification using medium resolution satellite images |
title_sort |
Algorithm for soybean classification using medium resolution satellite images |
author |
Gusso, Aníbal |
author_facet |
Gusso, Aníbal Ducati, Jorge Ricardo |
author_role |
author |
author2 |
Ducati, Jorge Ricardo |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Gusso, Aníbal Ducati, Jorge Ricardo |
dc.subject.por.fl_str_mv |
Processamento de imagens Sensoriamento remoto Reflexibilidade |
topic |
Processamento de imagens Sensoriamento remoto Reflexibilidade remote sensing Classification Crop area Reflectance |
dc.subject.eng.fl_str_mv |
remote sensing Classification Crop area Reflectance |
description |
An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales. |
publishDate |
2012 |
dc.date.issued.fl_str_mv |
2012 |
dc.date.accessioned.fl_str_mv |
2015-05-14T02:00:52Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/116208 |
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2072-4292 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000897497 |
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2072-4292 000897497 |
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http://hdl.handle.net/10183/116208 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Remote Sensing. Basel. Vol. 4, n. 10 (Oct. 2012), p. 3127-3142 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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