Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/32122
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dc.contributor.authorVICTORIA, D. D. C.pt_BR
dc.contributor.authorOLIVEIRA, A. F. D.pt_BR
dc.contributor.authorGREGO, C. R.pt_BR
dc.date.accessioned2020-02-12T18:07:48Z-
dc.date.available2020-02-12T18:07:48Z-
dc.date.created2009-04-24
dc.date.issued2009
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 14., (SBSR), 2009, Natal. Anais... São José dos Campos: INPE, 2009.pt_BR
dc.identifier.isbn978-85-17-00044-7pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/32122-
dc.descriptionThe high temporal resolution information obtained with the Moderate Resolution Imaging Spectroradiometer (MODIS) is of great value when it comes to monitoring changes in Earth surface. Since several land cover types presents a distinguished temporal pattern in it?s spectral response, MODIS high temporal resolution can be used to identify such covers. This is specially true when observing the Normalized Difference Vegetation Index (NDVI) of agricultural land covers. Fourier transformations decomposes any signal represented in time to a frequency domain. Applying this transformation in a NDVI time-series results in parameters that describe how this signal behaves along several time frequencies (annual, semestral, etc). A strong annual signal indicates a land cover with a long growth cycle, such as sugar-cane (1 to 1.5 years) while stronger semestral signals are typical of other agricultural crops (soy, corn, beans). Also, observing the annual and semestral signals, it?s possible to distinguish agricultural areas with one or two crop cycles per year. A computational routine, independent of any commercial remote sensing package, has been developed in order to calculate Fourier amplitude and phase images of a NDVI time series. Applying such analysis over a diverse agricultural region in São Paulo state (Ribeirão Preto) indicates that long and short growth period crops are easily distinguished (sugar-cane and annual crops such as soy, corn, beans). Silvicultural areas are also easily distinguished due to their long growth period (5 years) however, these are confused with natural forests. A longer time series analysis could easily solve this.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectMODISpt_BR
dc.subjectNDVIpt_BR
dc.subjectTime seriespt_BR
dc.subjectFourierpt_BR
dc.subjectSeries temporaispt_BR
dc.titleAnálise harmônica de séries temporais de imagens NDVI/MODIS para discriminação de coberturas vegetais.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2020-02-12T18:07:48Z
dc.format.extent2p. 1589-1596.pt_BR
riaa.ainfo.id32122pt_BR
riaa.ainfo.lastupdate2020-02-12
dc.contributor.institutionDANIEL DE CASTRO VICTORIA, CNPM; ARYEVERTON FORTES DE OLIVEIRA, CNPM; CELIA REGINA GREGO, CNPM.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPM)

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