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Registro Completo |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
05/10/2011 |
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Data da última atualização: |
03/05/2019 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
VICENTE, L. E.; VICTORIA, D. de C.; BOLFE, E. L.; ANDRADE, R. G. |
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Afiliação: |
LUIZ EDUARDO VICENTE, CNPM; DANIEL DE CASTRO VICTORIA, CNPM; EDSON LUIS BOLFE, CNPM; RICARDO GUIMARAES ANDRADE, CNPM. |
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Título: |
Estimativa de propriedades biofísicas no mapeamento de pastagens utilizando espectroscopia de imageamento e dados do sensor EO1-Hyperion. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE, 2011. |
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Páginas: |
p. 8575-8582. |
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Idioma: |
Português |
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Conteúdo: |
Much attention has been devoted recently to the matter of pasture degradation and the identification of such areas. The main interests in degraded pastures are related to the fact that the recovery of such areas could be used to increase beef cattle production and thus reduce the need for the establishment of new agricultural fields, lowering deforestation pressure. Another topic of interest related to degraded pastures is the Brazilian National Climate Change Policy and the Low Carbon Agriculture Program (Programa ABC ? Agricultura de Baixo Carbono) which aims to reduce national carbon dioxide emissions. The recovery of degraded pasture is one of the objectives of this program, which increases the amount of carbon stored in the soil, acting as a carbon sink. However, the identification of degraded pastures through the use of remote sensing is still in development. Here we present a method based on the use of hyperespectral classification and images from the EO1-Hyperion hyperspectral sensor in order to map the occurrence of pasture areas among the cerrado region. The method is based on a linear spectral unmixing model that can be linked to vegetation characteristics and was capable of discriminating the signals of natural savanna vegetation from pasture and bare soil. The procedure used was able to map pasture areas in the Brazilian Pantanal region and estimate biophysical parameters associated to non-photosynthetic vegetation (ANPV ? dry matter). As future research, spectral mixture analysis approache similar to the ones obtained from Hyperion will be developed based on different orbital sensors, in order to evaluate pasture areas in larger regions. MenosMuch attention has been devoted recently to the matter of pasture degradation and the identification of such areas. The main interests in degraded pastures are related to the fact that the recovery of such areas could be used to increase beef cattle production and thus reduce the need for the establishment of new agricultural fields, lowering deforestation pressure. Another topic of interest related to degraded pastures is the Brazilian National Climate Change Policy and the Low Carbon Agriculture Program (Programa ABC ? Agricultura de Baixo Carbono) which aims to reduce national carbon dioxide emissions. The recovery of degraded pasture is one of the objectives of this program, which increases the amount of carbon stored in the soil, acting as a carbon sink. However, the identification of degraded pastures through the use of remote sensing is still in development. Here we present a method based on the use of hyperespectral classification and images from the EO1-Hyperion hyperspectral sensor in order to map the occurrence of pasture areas among the cerrado region. The method is based on a linear spectral unmixing model that can be linked to vegetation characteristics and was capable of discriminating the signals of natural savanna vegetation from pasture and bare soil. The procedure used was able to map pasture areas in the Brazilian Pantanal region and estimate biophysical parameters associated to non-photosynthetic vegetation (ANPV ? dry matter). As future research, spectr... Mostrar Tudo |
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Palavras-Chave: |
Agricultura de baixo carbono; Espectroscopia de imageamento; Modelo linear de mistura. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/42877/1/Vicente1-SBSR.pdf
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Marc: |
LEADER 02391nam a2200193 a 4500
001 1902389
005 2019-05-03
008 2011 bl uuuu u00u1 u #d
100 1 $aVICENTE, L. E.
245 $aEstimativa de propriedades biofísicas no mapeamento de pastagens utilizando espectroscopia de imageamento e dados do sensor EO1-Hyperion.
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE$c2011
300 $ap. 8575-8582.
520 $aMuch attention has been devoted recently to the matter of pasture degradation and the identification of such areas. The main interests in degraded pastures are related to the fact that the recovery of such areas could be used to increase beef cattle production and thus reduce the need for the establishment of new agricultural fields, lowering deforestation pressure. Another topic of interest related to degraded pastures is the Brazilian National Climate Change Policy and the Low Carbon Agriculture Program (Programa ABC ? Agricultura de Baixo Carbono) which aims to reduce national carbon dioxide emissions. The recovery of degraded pasture is one of the objectives of this program, which increases the amount of carbon stored in the soil, acting as a carbon sink. However, the identification of degraded pastures through the use of remote sensing is still in development. Here we present a method based on the use of hyperespectral classification and images from the EO1-Hyperion hyperspectral sensor in order to map the occurrence of pasture areas among the cerrado region. The method is based on a linear spectral unmixing model that can be linked to vegetation characteristics and was capable of discriminating the signals of natural savanna vegetation from pasture and bare soil. The procedure used was able to map pasture areas in the Brazilian Pantanal region and estimate biophysical parameters associated to non-photosynthetic vegetation (ANPV ? dry matter). As future research, spectral mixture analysis approache similar to the ones obtained from Hyperion will be developed based on different orbital sensors, in order to evaluate pasture areas in larger regions.
653 $aAgricultura de baixo carbono
653 $aEspectroscopia de imageamento
653 $aModelo linear de mistura
700 1 $aVICTORIA, D. de C.
700 1 $aBOLFE, E. L.
700 1 $aANDRADE, R. G.
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Registro original: |
Embrapa Territorial (CNPM) |
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| Registros recuperados : 311 | |
| 82. |  | LEIVAS, J. F.; TEIXEIRA, A. H. de C.; ANDRADE, R. G.; VICTORIA, D. de C. Estimativa da evapotranspiração, biomassa e produtividade da água em municípios produtores de trigo, no Rio Grande do Sul. In: SEMINÁRIO DA REDE AGROHIDRO, 2., 2014, Campinas. Impactos da agricultura e das mudanças climáticas nos recursos hídricos: anais... Campinas: Embrapa Informática Agropecuária, 2014. p. 126-130.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Territorial. |
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| 91. | | NOGUEIRA, S. F.; SILVA, G. B. S. da; ANDRADE, R. G.; VICENTE, L. E. Geotecnologias para o monitoramento dos níveis de degradação das pastagens no Brasil. MundoGEO, Curitiba, PR, v. 15, n. 72, p. 60-64, 2013 p. 60-64.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 4 |
| Biblioteca(s): Embrapa Territorial. |
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| 99. | | LEIVAS, J. F.; ANDRADE, R. G.; VICTORIA, D. de C.; TORRESAN, F. E.; BOLFE, E. L. Índice de vegetação padronizado a partir de imagens do SPOT-Vegetation na detecção de estiagens na região sul do Brasil. In: CONGRESSO BRASILEIRO DE ECOLOGIA DE PAISAGENS, 2.; SIMPÓSIO SCGIS-BR, 2., 2012, Salvador, BA. Anais... Salvador, BA: IALE-BR, 2012. 1 p.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Territorial. |
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| 100. | | ALVAREZ, I. A.; PEREIRA, L. A.; SANTOS, S. M.; ANDRADE, R. G. Modificações na paisagem da região de Petrolina/Juazeiro do Rio São Francisco. In: CONGRESSO BRASILEIRO DE ECOLOGIA DE PAISAGENS, 2.; SIMPÓSIO SCGIS-BR, 2., 2012, Salvador, BA. Anais... Salvador, BA: IALE-BR, 2012. 1 p.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Territorial. |
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| Registros recuperados : 311 | |
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