Reliability of summer crop masks derived from second order polynomial equations.
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/951407 |
Resumo: | Remote Sensing techniques are useful to determine planted areas, especially of commodities like maize and soybeans (summer cultures). The vegetation indexes as NDVI (Normalized Difference Vegetation Index) has been used to map agricultural areas due to its low cost and accessibility. Therefore the objective of this study was to develop a new methodology to generate masks of summer crops based on fitting second order polynomial equations to temporal NDVI profiles. The results showed that selecting polynomial equations with r2 fitting above 0.75, a Kappa index of 0.86 and a global accuracy of 93% is obtained. This is slightly higher than the results obtained when using the maximum-minimum NDVI technique, with a Kappa index of 0.82 and a global accuracy of 91%. However, while quantifying the areas under study, one verified that the mask used by the proposed methodology, was closer to the official IBGE data, with a difference of -10.25%, followed by the vector technique with 23% and by the maximum-minimum NDVI, with a difference of 42.8%. |
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Reliability of summer crop masks derived from second order polynomial equations.Índice de vegetaçãoSojaMilhoSensoriamento RemotoSoybeansVegetationRemote sensingCornRemote Sensing techniques are useful to determine planted areas, especially of commodities like maize and soybeans (summer cultures). The vegetation indexes as NDVI (Normalized Difference Vegetation Index) has been used to map agricultural areas due to its low cost and accessibility. Therefore the objective of this study was to develop a new methodology to generate masks of summer crops based on fitting second order polynomial equations to temporal NDVI profiles. The results showed that selecting polynomial equations with r2 fitting above 0.75, a Kappa index of 0.86 and a global accuracy of 93% is obtained. This is slightly higher than the results obtained when using the maximum-minimum NDVI technique, with a Kappa index of 0.82 and a global accuracy of 91%. However, while quantifying the areas under study, one verified that the mask used by the proposed methodology, was closer to the official IBGE data, with a difference of -10.25%, followed by the vector technique with 23% and by the maximum-minimum NDVI, with a difference of 42.8%.CHRISTIANO L. ARRAES, Unicamp; RUBENS A. C. LAMPARELLI, Unicamp; JANSLE V. ROCHA, Unicamp; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; PABLO SALVADOR, University of Valladolid; JAVIER RODRÍGUEZ, University of Valladolid; JOSÉ-LUIS CASANOVA ROQUE, University of Valladolid; JULIA SANZ JUSTO, University of Valladolid; BEATRIZ G. BENATTI, IMECC/Unicamp.ARRAES, C. L.LAMPARELLI, R. A. C.ROCHA, J. V.ESQUERDO, J. C. D. M.SALVADOR, P.RODRÍGUES, J.ROQUE, J.-L. C.JUSTO, J. S.BENATTI, B. G.2013-02-27T11:11:11Z2013-02-27T11:11:11Z2013-02-2720132013-09-12T11:11:11Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleJournal of Agricultural Science, Toronto, v. 5, n. 3, p. 63-75, 2013.http://www.alice.cnptia.embrapa.br/alice/handle/doc/951407enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2017-08-15T23:37:33Zoai:www.alice.cnptia.embrapa.br:doc/951407Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542017-08-15T23:37:33falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542017-08-15T23:37:33Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Reliability of summer crop masks derived from second order polynomial equations. |
title |
Reliability of summer crop masks derived from second order polynomial equations. |
spellingShingle |
Reliability of summer crop masks derived from second order polynomial equations. ARRAES, C. L. Índice de vegetação Soja Milho Sensoriamento Remoto Soybeans Vegetation Remote sensing Corn |
title_short |
Reliability of summer crop masks derived from second order polynomial equations. |
title_full |
Reliability of summer crop masks derived from second order polynomial equations. |
title_fullStr |
Reliability of summer crop masks derived from second order polynomial equations. |
title_full_unstemmed |
Reliability of summer crop masks derived from second order polynomial equations. |
title_sort |
Reliability of summer crop masks derived from second order polynomial equations. |
author |
ARRAES, C. L. |
author_facet |
ARRAES, C. L. LAMPARELLI, R. A. C. ROCHA, J. V. ESQUERDO, J. C. D. M. SALVADOR, P. RODRÍGUES, J. ROQUE, J.-L. C. JUSTO, J. S. BENATTI, B. G. |
author_role |
author |
author2 |
LAMPARELLI, R. A. C. ROCHA, J. V. ESQUERDO, J. C. D. M. SALVADOR, P. RODRÍGUES, J. ROQUE, J.-L. C. JUSTO, J. S. BENATTI, B. G. |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
CHRISTIANO L. ARRAES, Unicamp; RUBENS A. C. LAMPARELLI, Unicamp; JANSLE V. ROCHA, Unicamp; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; PABLO SALVADOR, University of Valladolid; JAVIER RODRÍGUEZ, University of Valladolid; JOSÉ-LUIS CASANOVA ROQUE, University of Valladolid; JULIA SANZ JUSTO, University of Valladolid; BEATRIZ G. BENATTI, IMECC/Unicamp. |
dc.contributor.author.fl_str_mv |
ARRAES, C. L. LAMPARELLI, R. A. C. ROCHA, J. V. ESQUERDO, J. C. D. M. SALVADOR, P. RODRÍGUES, J. ROQUE, J.-L. C. JUSTO, J. S. BENATTI, B. G. |
dc.subject.por.fl_str_mv |
Índice de vegetação Soja Milho Sensoriamento Remoto Soybeans Vegetation Remote sensing Corn |
topic |
Índice de vegetação Soja Milho Sensoriamento Remoto Soybeans Vegetation Remote sensing Corn |
description |
Remote Sensing techniques are useful to determine planted areas, especially of commodities like maize and soybeans (summer cultures). The vegetation indexes as NDVI (Normalized Difference Vegetation Index) has been used to map agricultural areas due to its low cost and accessibility. Therefore the objective of this study was to develop a new methodology to generate masks of summer crops based on fitting second order polynomial equations to temporal NDVI profiles. The results showed that selecting polynomial equations with r2 fitting above 0.75, a Kappa index of 0.86 and a global accuracy of 93% is obtained. This is slightly higher than the results obtained when using the maximum-minimum NDVI technique, with a Kappa index of 0.82 and a global accuracy of 91%. However, while quantifying the areas under study, one verified that the mask used by the proposed methodology, was closer to the official IBGE data, with a difference of -10.25%, followed by the vector technique with 23% and by the maximum-minimum NDVI, with a difference of 42.8%. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-02-27T11:11:11Z 2013-02-27T11:11:11Z 2013-02-27 2013 2013-09-12T11:11:11Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Journal of Agricultural Science, Toronto, v. 5, n. 3, p. 63-75, 2013. http://www.alice.cnptia.embrapa.br/alice/handle/doc/951407 |
identifier_str_mv |
Journal of Agricultural Science, Toronto, v. 5, n. 3, p. 63-75, 2013. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/951407 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503375865249792 |