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 | Acesso ao texto completo restrito à biblioteca da Embrapa Territorial. Para informações adicionais entre em contato com cnpm.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
19/02/2013 |
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Data da última atualização: |
10/06/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
STARK, S. C.; LEITOLD, V.; WU, J. L.; HUNTER, M. O.; CASTILHO, C. V. de; COSTA, F. R. C.; MCMAHON, S. M.; PARKER, G. G.; SHIMABUKURO, M. T.; LEFSKY, M. A.; KELLER, M.; ALVES, L. F.; SCHIETTI, J.; SHIMABUKURO, Y. E.; BRANDÃO, D. O.; WOODCOCK, T. K.; HIGUCHI, N.; CAMARGO, P. B. DE; OLIVEIRA JUNIOR, R. C. de; SALESKA, S. R. |
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Afiliação: |
SCOTT C. STARK, UNIVERSITY OF ARIZONA; VERONIKA LEITOLD, UNIVERSITY OF ARIZONA; JIN L. WU, UNIVERSITY OF ARIZONA; MARIA O. HUNTER, UNIVERSITY OF NEW HAMPSHIRE; CAROLINA VOLKMER DE CASTILHO, CPAF-RR; FLÁVIA R. C. COSTA, INPA; SEAN M. MCMAHON, SMITHSONIAN TROPICAL RESEARCH INSTITUTE; GEOFFREY G. PARKER, SMITHSONIAN ENVIRONMENTAL RESEARCH CENTER; MÔNICA TAKAKO SIMABUKURO, INPE; MICHAEL A. LEFSKY, COLORADO STATE UNIVERSITY; MICHAEL KELLER, USDA FOREST SERVICE/EMBRAPA MONITORAMENTO POR SATÉLITE; LUCIANA F. ALVES, INSTITUTO DE BOTÂNICA; JULIANA SCHIETTI, INPA; YOSIO EDEMIR SHIMABUKURO, INPE; DIEGO O. BRANDÃO, INPA; TARA K. WOODCOCK, UNIVERSITY OF ARIZONA; NIRO HIGUCHI, INPA; PLÍNIO B. DE CAMARGO, CENA/USP; RAIMUNDO COSME DE OLIVEIRA JUNIOR, CPATU; SCOTT R. SALESKA, UNIVERSITY OF ARIZONA. |
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Título: |
Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Ecology Letters, v. 15, n. 12, dez. 2012. |
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Páginas: |
p. 1406-1414. |
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DOI: |
10.1111/j.1461-0248.2012.0186.x |
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Idioma: |
Inglês |
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Notas: |
Artigo publicado por Pesquisador Visitante da Embrapa Monitoramento por Satélite. |
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Conteúdo: |
Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) ? remotely estimated from LiDAR ? control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function. |
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Palavras-Chave: |
Biomass growth; Carbon balance; Gap fraction; Leaf area profiles; Remote sensing of canopy structure. |
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Thesaurus Nal: |
LiDAR. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02422naa a2200457 a 4500
001 1949933
005 2014-06-10
008 2012 bl uuuu u00u1 u #d
024 7 $a10.1111/j.1461-0248.2012.0186.x$2DOI
100 1 $aSTARK, S. C.
245 $aAmazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment.$h[electronic resource]
260 $c2012
300 $ap. 1406-1414.
500 $aArtigo publicado por Pesquisador Visitante da Embrapa Monitoramento por Satélite.
520 $aTropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) ? remotely estimated from LiDAR ? control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.
650 $aLiDAR
653 $aBiomass growth
653 $aCarbon balance
653 $aGap fraction
653 $aLeaf area profiles
653 $aRemote sensing of canopy structure
700 1 $aLEITOLD, V.
700 1 $aWU, J. L.
700 1 $aHUNTER, M. O.
700 1 $aCASTILHO, C. V. de
700 1 $aCOSTA, F. R. C.
700 1 $aMCMAHON, S. M.
700 1 $aPARKER, G. G.
700 1 $aSHIMABUKURO, M. T.
700 1 $aLEFSKY, M. A.
700 1 $aKELLER, M.
700 1 $aALVES, L. F.
700 1 $aSCHIETTI, J.
700 1 $aSHIMABUKURO, Y. E.
700 1 $aBRANDÃO, D. O.
700 1 $aWOODCOCK, T. K.
700 1 $aHIGUCHI, N.
700 1 $aCAMARGO, P. B. DE
700 1 $aOLIVEIRA JUNIOR, R. C. de
700 1 $aSALESKA, S. R.
773 $tEcology Letters$gv. 15, n. 12, dez. 2012.
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Embrapa Territorial (CNPM) |
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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: |
21/09/2020 |
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Data da última atualização: |
14/12/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 2 |
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Autoria: |
REIS, A. A. dos; SILVA, B. C.; WERNER, J. P. S.; SILVA, Y. F.; ROCHA, J. V.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C; MAGALHÃES, P. S. G. |
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Afiliação: |
Feagri, Nipe/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; Nipe/Unicamp; Nipe/Unicamp. |
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Título: |
Exploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42-3, W12, p. 419-424, 2020. |
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DOI: |
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020 |
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Idioma: |
Inglês |
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Notas: |
Publicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020. |
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Conteúdo: |
ABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring. MenosABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promisi... Mostrar Tudo |
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Palavras-Chave: |
Aprendizado de máquina; Dove satellites; Floresta aleatória; Índice de vegetação; Integração lavoura-pecuária; Integrated crop-livestock system; Machine Learning; Nano-Satellites; Pastureland; Random Forest; Vegetation Indices. |
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Thesagro: |
Biomassa; Pastagem. |
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Thesaurus NAL: |
Biomass; Pasture management; Vegetation index. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03286naa a2200457 a 4500
001 2125045
005 2021-12-14
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020$2DOI
100 1 $aREIS, A. A. dos
245 $aExploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system.$h[electronic resource]
260 $c2020
500 $aPublicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020.
520 $aABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.
650 $aBiomass
650 $aPasture management
650 $aVegetation index
650 $aBiomassa
650 $aPastagem
653 $aAprendizado de máquina
653 $aDove satellites
653 $aFloresta aleatória
653 $aÍndice de vegetação
653 $aIntegração lavoura-pecuária
653 $aIntegrated crop-livestock system
653 $aMachine Learning
653 $aNano-Satellites
653 $aPastureland
653 $aRandom Forest
653 $aVegetation Indices
700 1 $aSILVA, B. C.
700 1 $aWERNER, J. P. S.
700 1 $aSILVA, Y. F.
700 1 $aROCHA, J. V.
700 1 $aFIGUEIREDO, G. K. D. A.
700 1 $aANTUNES, J. F. G.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C
700 1 $aMAGALHÃES, P. S. G.
773 $tThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. 42-3, W12, p. 419-424, 2020.
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