Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1035883
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dc.contributor.authorANTUNES, J. F. G.pt_BR
dc.contributor.authorESQUERDO, J. C. D. M.pt_BR
dc.date.accessioned2016-02-02T11:11:11Zpt_BR
dc.date.available2016-02-02T11:11:11Zpt_BR
dc.date.created2016-02-02pt_BR
dc.date.issued2015pt_BR
dc.identifier.citationGeografia, Rio Claro, v. 40, p. 39-53, ago. 2015.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1035883pt_BR
dc.descriptionFloods in the Pantanal affect the fish production and influence the dynamics of vegetation, also changing the meat production. The understanding of floods dynamics is crucial to infer the level of flooding, once it promotes changes in the whole plain. The understanding of floods dynamics is crucial to infer the level of flooding. MODIS (Moderate Resolution Imaging Spectroradiometer) images provide wide coverage of the Earthís surface with high temporal resolution, which are important features for flood monitoring. However, its moderate spatial resolution may cause the spectral mixing of different land cover classes within a single pixel. In this context, the objective of this study was to apply a methodology for sub-pixel classification using MODIS time-series data, in order to quantify the flooded areas in the Pantanal. Data from the mid-infrared channel of MODIS sensor allowed the monitoring of flood prone areas in the Pantanal during the 2008/2009 and 2007/2008 hydrological years. The drought and flood periods are quite variable, occurring from North to South and from East to West. The sub-pixel classification models, generated from Fuzzy ARTMAP neural network, demonstrated excellent suitability for the mapping and quantification of flooded areas of the Pantanal based on the Commitment measure.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectÁreas úmidaspt_BR
dc.subjectProcessamento de imagempt_BR
dc.subjectReconhecimento de padrõespt_BR
dc.subjectRedes neuraispt_BR
dc.subjectLógica difusapt_BR
dc.subjectRedes neuro-fuzzypt_BR
dc.subjectPattern recognitionpt_BR
dc.subjectNeuro-fuzzy networkspt_BR
dc.titleQuantification of flooded areas of Pantanal by sub-pixel classification of modis time-series data.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2016-02-03T11:11:11Zpt_BR
dc.subject.thesagroSensoriamento remotopt_BR
dc.subject.nalthesaurusRemote sensingpt_BR
dc.subject.nalthesaurusImage analysispt_BR
dc.subject.nalthesaurusWetlandspt_BR
dc.subject.nalthesaurusFuzzy logicpt_BR
dc.subject.nalthesaurusNeural networkspt_BR
dc.description.notesNúmero especial.pt_BR
riaa.ainfo.id1035883pt_BR
riaa.ainfo.lastupdate2016-02-03pt_BR
dc.contributor.institutionJOÃO FRANCISCO GONÇALVES ANTUNES, CNPTIA; JÚLIO CÉSAR DALLA MORA ESQUERDO, CNPTIA.pt_BR
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