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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
02/02/2016 |
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Data da última atualização: |
03/02/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ANTUNES, J. F. G.; ESQUERDO, J. C. D. M. |
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Afiliação: |
JOÃO FRANCISCO GONÇALVES ANTUNES, CNPTIA; JÚLIO CÉSAR DALLA MORA ESQUERDO, CNPTIA. |
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Título: |
Quantification of flooded areas of Pantanal by sub-pixel classification of modis time-series data. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Geografia, Rio Claro, v. 40, p. 39-53, ago. 2015. |
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Idioma: |
Inglês |
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Notas: |
Número especial. |
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Conteúdo: |
Floods 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. |
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Palavras-Chave: |
Áreas úmidas; Lógica difusa; Neuro-fuzzy networks; Pattern recognition; Processamento de imagem; Reconhecimento de padrões; Redes neurais; Redes neuro-fuzzy. |
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Thesagro: |
Sensoriamento remoto. |
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Thesaurus Nal: |
Fuzzy logic; Image analysis; Neural networks; Remote sensing; Wetlands. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/138274/1/Geografia-Quantification-Antunes.pdf
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Marc: |
LEADER 02255naa a2200313 a 4500
001 2035883
005 2016-02-03
008 2015 bl uuuu u00u1 u #d
100 1 $aANTUNES, J. F. G.
245 $aQuantification of flooded areas of Pantanal by sub-pixel classification of modis time-series data.$h[electronic resource]
260 $c2015
500 $aNúmero especial.
520 $aFloods 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.
650 $aFuzzy logic
650 $aImage analysis
650 $aNeural networks
650 $aRemote sensing
650 $aWetlands
650 $aSensoriamento remoto
653 $aÁreas úmidas
653 $aLógica difusa
653 $aNeuro-fuzzy networks
653 $aPattern recognition
653 $aProcessamento de imagem
653 $aReconhecimento de padrões
653 $aRedes neurais
653 $aRedes neuro-fuzzy
700 1 $aESQUERDO, J. C. D. M.
773 $tGeografia, Rio Claro$gv. 40, p. 39-53, ago. 2015.
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Embrapa Agricultura Digital (CNPTIA) |
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