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Registro Completo |
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Biblioteca(s): |
Embrapa Solos; Embrapa Unidades Centrais. |
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Data corrente: |
22/06/2021 |
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Data da última atualização: |
08/11/2021 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
PEREIRA, M. de A.; TAVARES, S. C. C. de H.; GOIS, S. L. L. de. |
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Afiliação: |
MARIANA DE ARAGAO PEREIRA, CNPGC; SELMA CAVALCANTI CRUZ DE H TAVARES, CNPS; SUSANA LENA LINS DE GOIS, SIN. |
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Título: |
SDG 17 in the world, in Brazil and within Embrapa. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
In: GOIS, S. L. L. de; PEREIRA, M. de A.; MELO, P. E. de; TAVARES, S. C. C. de H.; DRUMOND, P. M. (ed.). Partnerships for the goals: contributions of Embrapa. Brasília, DF: Embrapa, 2021. cap. 1, p. 13-20. (Sustainable development goal, 17). |
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Idioma: |
Inglês |
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Conteúdo: |
Among Brazilian state-owned companies, Embrapa stands out for the countless initiatives presented below. They reveal partnerships established globally, nationally and within Embrapa which contribute to pursuing SDG 17. |
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Thesagro: |
Agricultura; Desenvolvimento Sustentável; Parceria; Transferência de Tecnologia. |
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Thesaurus Nal: |
General partnership; Sustainable development; Technology transfer; Tropical agriculture. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01127naa a2200241 a 4500
001 2132465
005 2021-11-08
008 2021 bl uuuu u00u1 u #d
100 1 $aPEREIRA, M. de A.
245 $aSDG 17 in the world, in Brazil and within Embrapa.$h[electronic resource]
260 $c2021
520 $aAmong Brazilian state-owned companies, Embrapa stands out for the countless initiatives presented below. They reveal partnerships established globally, nationally and within Embrapa which contribute to pursuing SDG 17.
650 $aGeneral partnership
650 $aSustainable development
650 $aTechnology transfer
650 $aTropical agriculture
650 $aAgricultura
650 $aDesenvolvimento Sustentável
650 $aParceria
650 $aTransferência de Tecnologia
700 1 $aTAVARES, S. C. C. de H.
700 1 $aGOIS, S. L. L. de
773 $tIn: GOIS, S. L. L. de; PEREIRA, M. de A.; MELO, P. E. de; TAVARES, S. C. C. de H.; DRUMOND, P. M. (ed.). Partnerships for the goals: contributions of Embrapa. Brasília, DF: Embrapa, 2021. cap. 1, p. 13-20. (Sustainable development goal, 17).
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Registro original: |
Embrapa Solos (CNPS) |
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Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
14/12/2020 |
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Data da última atualização: |
14/12/2020 |
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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: |
BISPO, P. da C.; RODRÍGUEZ-VEIGA, P.; ZIMBRES, B.; MIRANDA, S. do C. de; CEZARE, C. H. G.; FLEMING, S.; BALDACCHINO, F.; LOUIS, V.; RAINS, D.; GARCIA, M.; ESPIRITO-SANTO, F. D. B.; ROITMAN, I.; PACHECO-PASCAGAZA, A. M.; GOU, Y.; ROBERTS, J.; BARRETT, K.; FERREIRA, L. G.; SHIMBO, J. Z.; ALENCAR, A.; BUSTAMANTE, M.; WOODHOUSE, I. H.; SANO, E. E.; OMETTO, J. P.; TANSEY, K.; BALZTER, H. |
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Afiliação: |
EDSON EYJI SANO, CPAC. |
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Título: |
Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 17, 2020. |
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Idioma: |
Português |
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Conteúdo: |
The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1. View Full-Text |
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Thesagro: |
Biomassa; Carbono; Cerrado; Sensoriamento Remoto. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219145/1/SANO-WOODY-ABOVEGROUND-BIOMASS-MAPPING-OF-THE-BRAZILIAN-SAVANNA.pdf
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Marc: |
LEADER 02683naa a2200457 a 4500
001 2128070
005 2020-12-14
008 2020 bl uuuu u00u1 u #d
100 1 $aBISPO, P. da C.
245 $aWoody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.$h[electronic resource]
260 $c2020
520 $aThe tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1. View Full-Text
650 $aBiomassa
650 $aCarbono
650 $aCerrado
650 $aSensoriamento Remoto
700 1 $aRODRÍGUEZ-VEIGA, P.
700 1 $aZIMBRES, B.
700 1 $aMIRANDA, S. do C. de
700 1 $aCEZARE, C. H. G.
700 1 $aFLEMING, S.
700 1 $aBALDACCHINO, F.
700 1 $aLOUIS, V.
700 1 $aRAINS, D.
700 1 $aGARCIA, M.
700 1 $aESPIRITO-SANTO, F. D. B.
700 1 $aROITMAN, I.
700 1 $aPACHECO-PASCAGAZA, A. M.
700 1 $aGOU, Y.
700 1 $aROBERTS, J.
700 1 $aBARRETT, K.
700 1 $aFERREIRA, L. G.
700 1 $aSHIMBO, J. Z.
700 1 $aALENCAR, A.
700 1 $aBUSTAMANTE, M.
700 1 $aWOODHOUSE, I. H.
700 1 $aSANO, E. E.
700 1 $aOMETTO, J. P.
700 1 $aTANSEY, K.
700 1 $aBALZTER, H.
773 $tRemote Sensing$gv. 12, n. 17, 2020.
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Registro original: |
Embrapa Cerrados (CPAC) |
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