Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1035217
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dc.contributor.authorEBERHARDT, I. D. R.pt_BR
dc.contributor.authorLUIZ, A. J. B.pt_BR
dc.contributor.authorFORMAGGIO, A. R.pt_BR
dc.contributor.authorSANCHES, I. D.pt_BR
dc.contributor.authorSCHULTZ, B.pt_BR
dc.contributor.authorTRABAQUINI, K.pt_BR
dc.date.accessioned2016-01-26T11:11:11Zpt_BR
dc.date.available2016-01-26T11:11:11Zpt_BR
dc.date.created2016-01-26pt_BR
dc.date.issued2015pt_BR
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 5650-5657.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1035217pt_BR
dc.descriptionAbstract: Nowadays the challenge in agricultural estimates using remote sensing is to produce the estimates and data across the crop season, in near real-time. The aim of this paper is to build an approach capable to produce the crop maps of soybean+maize in near real-time, for Rio Grande do Sul state, using MODIS images. To generate the near real-time crop maps we used the MODIS 16 days composites vegetation index (VI) images of NDVI and EVI. This new approach was called Near Real-Time Crop Fields Detection (DATQuaR). The MODIS VIs images were aggregated in bimonthly periods using different ways: average, maximum, minimum and median of registered values. After that, the image of the previous period was subtracted from the image of the monitored period, generating the DATQuaR images. These images were classified by slice using as limit the occupied area estimate with soybean+maize produced by random sampling over Landsat image and visual interpretation. The DATQuaR maps were submitted to 3x3 pixel window mode filter. The results showed that the best approach was to aggregate the maximum registered MODIS IVs value in the monitored period and the minimum value registered in the previous period. In this case the EVI images and the 3x3 pixel window mode filterwere used. Using this approach the DATQuaR method achieved over 81% (in the worst period, January/February of 2014) of agreement with random sampling Landsat pixels classified by visual interpretation.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectMapas de cultivo de verãopt_BR
dc.subjectMODISpt_BR
dc.subjectDATQuaRpt_BR
dc.titleDetecção de áreas agrícolas em tempo quase real (DATQuaR).pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2016-01-26T11:11:11Zpt_BR
dc.subject.thesagroSensoriamento remotopt_BR
dc.subject.thesagroEstatística agrícolapt_BR
dc.subject.nalthesaurusRemote sensingpt_BR
dc.subject.nalthesaurusStatisticspt_BR
riaa.ainfo.id1035217pt_BR
riaa.ainfo.lastupdate2016-01-26pt_BR
dc.contributor.institutionISAQUE DANIEL ROCHA EBERHARDT, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; ANTONIO ROBERTO FORMAGGIO, INPE; IEDA DEL'ARCO SANCHES, INPE; BRUNO SCHULTZ, INPE; KLEBER TRABAQUINI, INPE.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPMA)

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