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 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
21/09/2018 |
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Data da última atualização: |
02/05/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BRITO, J. L. S.; ARANTES, A. E.; FERREIRA, L. G.; SANO, E. E. |
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Afiliação: |
JORGE LUÍS SILVA BRITO, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; ARIELLE ELIAS ARANTES, UNIVERSIDADE FEDERAL DE GOIÁS; LAERTE GUIMARÃES FERREIRA, UNIVERSIDADE FEDERAL DE GOIÁS; EDSON EYJI SANO, CPAC. |
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Título: |
MODIS estimates of pastures productivity in the Cerrado based on ground and Landsat-8 data extrapolations. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Journal of Applied Remote Sensing, v. 12, n. 2, 2018. |
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Idioma: |
Inglês |
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Conteúdo: |
With ∼175 Mha (38% of which is in the Cerrado biome), pastures are the main land use forms in Brazil. Aiming at improving pasture utilization, in this study we combined field and satellite data to assess the biophysical characteristics and productivity patterns of the Cerrado pasturelands. From October 2013 to September 2014, field data of total and green biomass and percent green cover (%GC) were collected in two study areas located in the municipality of Uberlândia, Minas Gerais State. These data were extrapolated to the entire Cerrado biome through regression equations involving normalized vegetation index values derived from Landsat-8 Operational Land Imager (OLI) and Terra MODIS sensors for the year 2014. Total green biomass in the growing season was used to estimate potential livestock intensifi- cation in terms of animal unit (AU) per hectare. Average green biomass values ranged from 1482 kg ha−1 in August (dry season peak) to 2878 kg ha−1 in March (end of the rainy season), whereas %GC ranged from 49% in August to 66% in January and April 2014. Considering the estimated availability of green forage, cattle stocking rate in the Cerrado could increase, on aver- age, from 1.11 to 2.56 AU ha−1. |
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Palavras-Chave: |
Imagem biofísica; Índice vegetativo; Monitoramento de pastagem. |
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Thesagro: |
Biomassa; Cerrado; Pastagem; Sensoriamento Remoto. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01924naa a2200241 a 4500
001 2096120
005 2024-05-02
008 2018 bl uuuu u00u1 u #d
100 1 $aBRITO, J. L. S.
245 $aMODIS estimates of pastures productivity in the Cerrado based on ground and Landsat-8 data extrapolations.
260 $c2018
520 $aWith ∼175 Mha (38% of which is in the Cerrado biome), pastures are the main land use forms in Brazil. Aiming at improving pasture utilization, in this study we combined field and satellite data to assess the biophysical characteristics and productivity patterns of the Cerrado pasturelands. From October 2013 to September 2014, field data of total and green biomass and percent green cover (%GC) were collected in two study areas located in the municipality of Uberlândia, Minas Gerais State. These data were extrapolated to the entire Cerrado biome through regression equations involving normalized vegetation index values derived from Landsat-8 Operational Land Imager (OLI) and Terra MODIS sensors for the year 2014. Total green biomass in the growing season was used to estimate potential livestock intensifi- cation in terms of animal unit (AU) per hectare. Average green biomass values ranged from 1482 kg ha−1 in August (dry season peak) to 2878 kg ha−1 in March (end of the rainy season), whereas %GC ranged from 49% in August to 66% in January and April 2014. Considering the estimated availability of green forage, cattle stocking rate in the Cerrado could increase, on aver- age, from 1.11 to 2.56 AU ha−1.
650 $aBiomassa
650 $aCerrado
650 $aPastagem
650 $aSensoriamento Remoto
653 $aImagem biofísica
653 $aÍndice vegetativo
653 $aMonitoramento de pastagem
700 1 $aARANTES, A. E.
700 1 $aFERREIRA, L. G.
700 1 $aSANO, E. E.
773 $tJournal of Applied Remote Sensing$gv. 12, n. 2, 2018.
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Embrapa Cerrados (CPAC) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
14/12/2021 |
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Data da última atualização: |
14/12/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
ALMEIDA, H. S. L.; REIS, A. A. dos; WERNER, J. P. S.; ANTUNES, J. F. G.; ZHONG, L.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. |
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Afiliação: |
HENRIQUE S. L. ALMEIDA, UNICAMP; ALINY APARECIDA DOS REIS, UNICAMP; JOÃO PAULO SAMPAIO WERNER, UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LIHENG ZHONG, Ant Group, World Financial Center, Beijing; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; RUBENS AUGUSTO CAMARGO LAMPARELLI, UNICAMP; PAULO S. G. MAGALHÃES, UNICAMP. |
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Título: |
Deep neural networks for mapping integrated crop-livestock systems using PlanetScope time series. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE, 2021. |
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Páginas: |
p. 4224-4227. |
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ISBN: |
978-1-6654-0369-6 |
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DOI: |
10.1109/IGARSS47720.2021.9554500 |
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Idioma: |
Inglês |
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Notas: |
IGARSS 2021. Paper WE2.MM-8.3. |
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Conteúdo: |
Abstract: Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area. |
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Palavras-Chave: |
Aprendizado profundo; Convolutional Neural Networks; Deep learning; EVI; Nano-Satellites; Nanossatélites; NDVI; Redes neurais; Redes neurais convolucionais; Redes neurais profundas; Séries temporais; Sistemas de integração lavoura-pecuária. |
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Thesaurus NAL: |
Neural networks; Time series analysis. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02288nam a2200433 a 4500
001 2137800
005 2021-12-14
008 2021 bl uuuu u00u1 u #d
020 $a978-1-6654-0369-6
024 7 $a10.1109/IGARSS47720.2021.9554500$2DOI
100 1 $aALMEIDA, H. S. L.
245 $aDeep neural networks for mapping integrated crop-livestock systems using PlanetScope time series.$h[electronic resource]
260 $aIEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE$c2021
300 $ap. 4224-4227.
500 $aIGARSS 2021. Paper WE2.MM-8.3.
520 $aAbstract: Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area.
650 $aNeural networks
650 $aTime series analysis
653 $aAprendizado profundo
653 $aConvolutional Neural Networks
653 $aDeep learning
653 $aEVI
653 $aNano-Satellites
653 $aNanossatélites
653 $aNDVI
653 $aRedes neurais
653 $aRedes neurais convolucionais
653 $aRedes neurais profundas
653 $aSéries temporais
653 $aSistemas de integração lavoura-pecuária
700 1 $aREIS, A. A. dos
700 1 $aWERNER, J. P. S.
700 1 $aANTUNES, J. F. G.
700 1 $aZHONG, L.
700 1 $aFIGUEIREDO, G. K. D. A.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C.
700 1 $aMAGALHÃES, P. S. G.
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