Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/949316
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dc.contributor.authorVICTORIA, D. de C.eng
dc.contributor.authorPAZ, A. R. daeng
dc.contributor.authorCOUTINHO, A. C.eng
dc.contributor.authorKASTENS, J.eng
dc.contributor.authorBROWN, J. C.eng
dc.date.accessioned2020-02-19T00:36:53Z-
dc.date.available2020-02-19T00:36:53Z-
dc.date.created2013-02-14
dc.date.issued2012
dc.identifier.citationPesquisa Agropecuária Brasileira, Brasília, DF v. 47, n. 9, p. 1270-1278, set. 2012.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/949316-
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.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectÍndice de vegetaçãoeng
dc.subjectMáscara de culturaseng
dc.subjectÁrea cultivadaeng
dc.subjectTransformada de Fouriereng
dc.subjectCropland maskseng
dc.subjectCultivated areaeng
dc.titleCropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil.eng
dc.typeArtigo de periódicoeng
dc.date.updated2020-02-19T00:36:53Z
dc.subject.nalthesaurusvegetation indexeng
riaa.ainfo.id949316eng
riaa.ainfo.lastupdate2020-02-18
dc.contributor.institutionDANIEL DE CASTRO VICTORIA, CNPM; ADRIANO ROLIM DA PAZ, UFPB; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JUDE KASTENS, Kansas Applied Remote Sensing; J. CHRISTOPHER BROWN, University of Kansas.eng
Aparece nas coleções:Artigo em periódico indexado (CNPTIA)

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