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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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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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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
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| Registros recuperados : 8 | |
| 2. |  | ESPÍRITO-SANTO, F. D. B.; KELLER, M. M.; LINDER, E.; OLIVEIRA JUNIOR, R. C. de; PEREIRA, C.; OLIVEIRA, C. G. Gap formation and carbon cycling in the Brazilian Amazon: measurement using high-resolution optical remote sensing and studies in large forest plots. Plant Ecology & Diversity, v. 7, n. 1/2, p. 305-318, 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Amazônia Oriental; Embrapa Territorial. |
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| 3. | | LEFSKY, M. A.; HARDING, D. J.; KELLER, M.; COHEN, W. B.; CARABAJAL, C. C.; ESPIRITO-SANTO, F. D. B.; HUNTER, M. O.; OLIVEIRA JUNIOR, R. de. Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, v. 32, n. 22, L22S02, Nov. 2005.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Internacional - A |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 4. | | SMITH, C. C.; ESPÍRITO-SANTO, F. D. B.; HEALEY, J. R.; YOUNG, P. J.; LENNOX, G. D.; FERREIRA, J. N.; BARLOW, J. Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon. Global Change Biology, v. 26, n. 12, p. 7006-7020, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 5. | | WITHEY, K.; BERENGUER, E.; PALMEIRA, A. F.; ESPÍRITO-SANTO, F. D. B.; LENNOX, G. D.; SILVA, C. V. J.; ARAGÃO, L. E. O. C.; FERREIRA, J. N.; FRANÇA, F.; MALHI, Y.; ROSSI, L. C.; BARLOW, J. Quantifying immediate carbon emissions from El Ninõ-mediated wildfires in humid tropical forests. Philosophical Transactions of the Royal Society B, v. 373, n. 1760, p. 1-11, Nov. 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 6. | | ESPÍRITO-SANTO, F. D. B.; GLOOR, M.; KELLER, M.; MALHI, Y.; SAATCHI, S.; NELSON, B.; OLIVEIRA JUNIOR, R. C.; PEREIRA, C.; LLOYD, J.; FROLKING, S.; PALACE, M.; SHIMABUKURO, Y. E.; DUARTE, V.; MONTEAGUDO MENDOZA, A.; LÓPEZ-GONZÁLEZ, G.; BAKER, T. R.; FELDPAUSCH, T. R.; BRIENEN, R. J. W.; ASNER, G. P.; BOYD, D. S.; PHILLIPS, O. L. Size and frequency of natural forest disturbances and the Amazon forest carbon balance. Nature Communications, v. 5, art. n. 3434, 18 Mar. 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Amazônia Oriental; Embrapa Territorial. |
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| 7. | | ESPÍRITO-SANTO, F. D. B.; GLOOR, M.; KELLER, M.; MALHI, Y.; SAATCHI, S.; NELSON, B.; OLIVEIRA JUNIOR, R.; PEREIRA, C.; LLOYD, J.; FROLKING, S.; PALACE, M.; SHIMABUKURO, Y.; DUARTE, V.; MENDONZA, A.; LOPEZ-GONZALEZ, G.; BAKER, T. R.; FELDPAUSCH, T.; ASNER, G.; BOYD, D.; PHILLIPS, O. The spectrum of natural forest disturbances and the Amazon forest carbon balance. In: AGU FALL MEETING, 2014, San Francisco. [Proceedings]. [San Francisco]: AGU, 2014.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 8. | | 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. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sensing, v. 12, n. 17, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Cerrados. |
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| Registros recuperados : 8 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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