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dc.contributor.authorARRAES, C. L.pt_BR
dc.contributor.authorLAMPARELLI, R. A. C.pt_BR
dc.contributor.authorROCHA, J. V.pt_BR
dc.contributor.authorESQUERDO, J. C. D. M.pt_BR
dc.contributor.authorSALVADOR, P.pt_BR
dc.contributor.authorRODRÍGUES, J.pt_BR
dc.contributor.authorROQUE, J.-L. C.pt_BR
dc.contributor.authorJUSTO, J. S.pt_BR
dc.contributor.authorBENATTI, B. G.pt_BR
dc.date.accessioned2013-02-27T11:11:11Zpt_BR
dc.date.available2013-02-27T11:11:11Zpt_BR
dc.date.created2013-02-27pt_BR
dc.date.issued2013pt_BR
dc.identifier.citationJournal of Agricultural Science, Toronto, v. 5, n. 3, p. 63-75, 2013.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/951407pt_BR
dc.descriptionRemote 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%.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectÍndice de vegetaçãopt_BR
dc.titleReliability of summer crop masks derived from second order polynomial equations.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2013-09-12T11:11:11Zpt_BR
dc.subject.thesagroSojapt_BR
dc.subject.thesagroMilhopt_BR
dc.subject.thesagroSensoriamento Remotopt_BR
dc.subject.nalthesaurusSoybeanspt_BR
dc.subject.nalthesaurusVegetationpt_BR
dc.subject.nalthesaurusRemote sensingpt_BR
dc.subject.nalthesaurusCornpt_BR
riaa.ainfo.id951407pt_BR
riaa.ainfo.lastupdate2013-09-12pt_BR
dc.contributor.institutionCHRISTIANO 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.pt_BR
Aparece en las colecciones:Artigo em periódico indexado (CNPTIA)

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