Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/940360
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dc.contributor.authorVICTORIA, D. de C.pt_BR
dc.contributor.authorPAZ, A. R. DApt_BR
dc.contributor.authorCOUTINHO, A. C.pt_BR
dc.contributor.authorKASTENS, J.pt_BR
dc.contributor.authorBROWN, J. C.pt_BR
dc.date.accessioned2012-11-23T11:11:11Zpt_BR
dc.date.available2012-11-23T11:11:11Zpt_BR
dc.date.created2012-11-23pt_BR
dc.date.issued2012pt_BR
dc.identifier.citationPesquisa Agropecuária Brasileira, Brasilia, DF, v. 47, n. 9, p. 1270-1278, set. 2012.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/940360pt_BR
dc.descriptionThe objective of this work was to evaluate a simple, semi‑automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R2 = 0.89), but poor agreement in municipalities with less than 5% crop cover (R2 = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectCropland maskspt_BR
dc.subjectCultivated areapt_BR
dc.subjectFourierpt_BR
dc.titleCropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2012-11-23T11:11:11Zpt_BR
dc.subject.nalthesaurusVegetation indexpt_BR
riaa.ainfo.id940360pt_BR
riaa.ainfo.lastupdate2012-11-23pt_BR
dc.contributor.institutionDANIEL DE CASTRO VICTORIA, CNPM; ADRIANO ROLIM DA PAZ, UNIVERSIDADE FEDERAL DA PARAÍBA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JUDE KASTENS, KANSAS APPLIED REMOTE SENSING; J. CHRISTOPHER BROWN, UNIVERSITY OF KANSAS.pt_BR
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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