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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
16/10/2006 |
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Data da última atualização: |
17/10/2017 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
INAMASU, R. Y.; PAYTON, S.; JOHNSON, S.; FRANCIS, D. D.; SCHLEMMER, M.; SHANAHAN, J. F.; LUCHIARI JUNIOR, A.; CALDWELL, R. M.; SCHEPERS, J. S. |
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Afiliação: |
RICARDO YASSUSHI INAMASU, CNPDIA; STEVE PAYTON, USDA/ARS; SVEN JOHNSON, USDA/ARS; DENNIS D. FRANCIS, USDA/ARS; MICHAEL SCHLEMMER, USDA/ARS; JOHN F. SHANAHAN, USDA/ARS; ARIOVALDO LUCHIARI JUNIOR, CNPTIA; ROBERT M. CALDWELL, UNIVERSITY OF NEBRASKA; JAMES S. SCHEPERS, USDA/ARS. |
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Título: |
Controlador VRT com sensoriamento de alta resolução: uma ferramenta para desenvolvimento de algoritmos. |
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Ano de publicação: |
2004 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA- CONBEA, 33., 2004, São Pedro, SP. Anais... Campinas : UNICAMP, Faculdade de Engenharia Agrícola: Embrapa Informática Agropecuária, 2004. 1 CD-ROM. |
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Descrição Física: |
1 CD ROM |
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Idioma: |
Português |
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Thesagro: |
Agricultura de Precisão; Fertilizante; Método de Aplicação; Sensoriamento Remoto. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/135586/1/2004AA004.PDF
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Marc: |
LEADER 00940nam a2200253 a 4500
001 1015146
005 2017-10-17
008 2004 bl uuuu u00u1 u #d
100 1 $aINAMASU, R. Y.
245 $aControlador VRT com sensoriamento de alta resolução$buma ferramenta para desenvolvimento de algoritmos.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA- CONBEA, 33., 2004, São Pedro, SP. Anais... Campinas : UNICAMP, Faculdade de Engenharia Agrícola: Embrapa Informática Agropecuária, 2004. 1 CD-ROM.$c2004
300 $c1 CD ROM
650 $aAgricultura de Precisão
650 $aFertilizante
650 $aMétodo de Aplicação
650 $aSensoriamento Remoto
700 1 $aPAYTON, S.
700 1 $aJOHNSON, S.
700 1 $aFRANCIS, D. D.
700 1 $aSCHLEMMER, M.
700 1 $aSHANAHAN, J. F.
700 1 $aLUCHIARI JUNIOR, A.
700 1 $aCALDWELL, R. M.
700 1 $aSCHEPERS, J. S.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
06/02/2018 |
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Data da última atualização: |
02/05/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
WU, J.; KOBAYASHI, H.; STARK, S. C.; MENG, R.; GUAN, K.; TRAN, N. N.; GAO, S.; YANG, W.; RESTREPO-COUPE, N.; MIURA, T.; OLIVEIRA JUNIOR, R. C. de; ROGERS, A.; DYE, D. G.; NELSON, B. W.; SERBIN, S. P.; HUETE, A. R.; SALESKA, S. R. |
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Afiliação: |
Jin Wu, University of Arizona / Brookhaven National Laboratory; Hideki Kobayashi, Japan Agency for Marine-Earth Science and Technology; Scott C. Stark, Michigan State University; Ran Meng, Brookhaven National Laboratory; Kaiyu Guan, University of Illinois at Urbana Champaign; Ngoc Nguyen Tran, University of Technology Sydney; Sicong Gao, University of Technology Sydney; Wei Yang, Chiba University; Natalia Restrepo-Coupe, University of Arizona; Tomoaki Miura, University of Havaii; RAIMUNDO COSME DE OLIVEIRA JUNIOR, CPATU; Alistair Rogers, Brookhaven National Laboratory; Dennis G. Dye, Northern Arizona University; Bruce W. Nelson, INPA; Shawn P. Serbin, Brookhaven National Laboratory; Alfredo R. Huete, University of Technology Sydney; Scott R. Saleska, University of Arizona. |
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Título: |
Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
New Phytologist, v. 217, n. 4, p. 1507-1520, Mar. 2018. |
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DOI: |
10.1111/nph.14939 |
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Idioma: |
Inglês |
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Conteúdo: |
Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun?sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate?phenology relationships in the tropics. MenosSatellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun?sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite... Mostrar Tudo |
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Palavras-Chave: |
Sazonalidade. |
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Thesagro: |
Fenologia; Floresta tropical. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02563naa a2200361 a 4500
001 2087194
005 2018-05-02
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1111/nph.14939$2DOI
100 1 $aWU, J.
245 $aBiological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest.$h[electronic resource]
260 $c2018
520 $aSatellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun?sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate?phenology relationships in the tropics.
650 $aFenologia
650 $aFloresta tropical
653 $aSazonalidade
700 1 $aKOBAYASHI, H.
700 1 $aSTARK, S. C.
700 1 $aMENG, R.
700 1 $aGUAN, K.
700 1 $aTRAN, N. N.
700 1 $aGAO, S.
700 1 $aYANG, W.
700 1 $aRESTREPO-COUPE, N.
700 1 $aMIURA, T.
700 1 $aOLIVEIRA JUNIOR, R. C. de
700 1 $aROGERS, A.
700 1 $aDYE, D. G.
700 1 $aNELSON, B. W.
700 1 $aSERBIN, S. P.
700 1 $aHUETE, A. R.
700 1 $aSALESKA, S. R.
773 $tNew Phytologist$gv. 217, n. 4, p. 1507-1520, Mar. 2018.
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