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| Registros recuperados : 17 | |
| 11. |  | GEROLD, G.; JUNGKUNST, H. F.; WANTZEN, K. M.; SCHÖNENBERG, R.; AMORIM, R. S. S.; COUTO, E. G.; MADARI, B.; HOHNWALD, S. (ed.). Interdisciplinary analysis carbon-optimized land management strategies for Southern Amazonia. Göttingen: Universitätsverlag Göttingen, 2014. 174 p. Carbocial Status Conference, 2013, Cuiabá.| Biblioteca(s): Embrapa Acre. |
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| 13. | | GEROLD, G.; COUTO, E. G.; MADARI, B. E.; JUNGKUNST, H. F.; AMORIM, R. S. S.; HOHNWALD, S.; KLINGLER, M.; MACHADO, P. L. O. de A.; SCHÖNENBERG, R.; NENDEL, C. Carbon-optimised land management strategies for southern Amazonia. Regional Environmental Change, v. 18, n. 1, p. 1-9, 2018.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 14. | | MURGA-ORRILLO, H.; ABANTO-RODRIGUEZ, C.; DIONISIO, L. F. S.; CHU-KOO, F. W.; SCHWARTZ, G.; BUSTAMANTE, E. N.; STEWART, P. M.; AMORIM, R. S. S.; VOURLITIS, G. L.; LOBO, F. de A.; BARDALES-LOZANO, R. M. Tara (Caesalpinia spinosa) in natural and agroforestry systems under an altitudinal gradient in the Peruvian Andes: responses to soil and climate variation. Agronomy, v. 13, n. 2, Article 282, 2023.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 15. | | AIRES, U. R. V.; SILVA, D. D. da; FERNANDES FILHO, E. I.; RODRIGUES, L. N.; ULIANA, E. M.; AMORIM, R. S. S.; RIBEIRO, C. B. de M.; CAMPOS, J. A. Machine learning-based modeling of surface sediment concentration in Doce river basin. Journal of Hydrology, v. 619, 2023. e129320.| Biblioteca(s): Embrapa Cerrados. |
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| 16. | | SILVA, L. de C. M. da; PEIXOTO, D. S.; GOMES, J. B. V.; AVANZI, J. C.; AMORIM, R. S. S.; BORGHI, E.; RESENDE, A. V. de; SILVA, B. M.; MANCINI, M.; CURI, N. Mineralogy and pore size distribution of clayey Oxisols with granular structure and the effect of management systems. Soil & Tillage Research, v. 223, 105479, 2022.| Biblioteca(s): Embrapa Florestas; Embrapa Milho e Sorgo. |
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| 17. | | AIRES, U. R. V.; SILVA, D. D. da; FERNANDES FILHO, E. I.; RODRIGUES, L. N.; ULIANA, E. M.; AMORIM, R. S. S.; RIBEIRO, C. B. de M.; CAMPOS, J. A. Modeling of surface sediment concentration in the Doce River basin using satellite remote sensing. Journal of Environmental Management, v. 323, 2022. 15 p.| Biblioteca(s): Embrapa Cerrados. |
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| Registros recuperados : 17 | |
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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. |
Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
26/10/2022 |
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Data da última atualização: |
01/11/2022 |
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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: |
AIRES, U. R. V.; SILVA, D. D. da; FERNANDES FILHO, E. I.; RODRIGUES, L. N.; ULIANA, E. M.; AMORIM, R. S. S.; RIBEIRO, C. B. de M.; CAMPOS, J. A. |
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Afiliação: |
UILSON RICARDO VENANCIO AIRES; DEMETRIUS DAVID DA SILVA; ELPÍDIO INACIO FERNANDES FILHO; LINEU NEIVA RODRIGUES, CPAC; EDUARDO MORGAN ULIANA; RICARDO SANTOS SILVA AMORIM; CELSO BANDEIRA DE MELO RIBEIRO; JASMINE ALVES CAMPOS. |
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Título: |
Modeling of surface sediment concentration in the Doce River basin using satellite remote sensing. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Journal of Environmental Management, v. 323, 2022. |
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Páginas: |
15 p. |
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DOI: |
https://doi.org/10.1016/j.jenvman.2022.116207 |
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Idioma: |
Inglês |
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Conteúdo: |
A B S T R A C T Surface sediment concentration (SSC) is linked to several problems related to water quality and its monitoring is costly because of the required fieldwork and laboratory analyses. Thus, sediment measurements are often sporadic, punctual, and performed during a short period. Orbital remote sensing allows the monitoring of SSC along the river channel permitting continuous and spatial information. This work had two objectives: (1) to model the surface concentration of sediments in the main channel of the Doce river using data from Multispectral Instrument (MSI)/Sentinel 2 and Operational Land Imager (OLI)/Landsat 8 satellite sensors; and (2) to compare different linear modeling approaches to select the best variables for SSC monitoring. For comparison with actual field data, we used mean SSC measurements in 14 sediment gauge stations from 2013 to 2020. Reflectance data of the MSI/Sentinel 2 and OLI/Landsat 8 satellites bands and spectral indices related to the monitoring of water resources were used as explanatory variables. Simple and multiple linear regression models (SLR and MLR), least absolute shrinkage and selection operator (LASSO), and Elastic Net regression were used to predict the SSC. The near-infrared band images from both MSI/Sentinel 2 and OLI/Landsat 8 satellites showed a strong linear relationship with the SSC. Multiple linear regression, LASSO and Elastic Net regressions showed good performance for SSC prediction. Sediment flow maps show an SSC reduction in the Doce river channel in recent years, indicating that part of the material from the Fund?ao tailings dam rupture may have been transported through sediment resuspension and transport processes. MenosA B S T R A C T Surface sediment concentration (SSC) is linked to several problems related to water quality and its monitoring is costly because of the required fieldwork and laboratory analyses. Thus, sediment measurements are often sporadic, punctual, and performed during a short period. Orbital remote sensing allows the monitoring of SSC along the river channel permitting continuous and spatial information. This work had two objectives: (1) to model the surface concentration of sediments in the main channel of the Doce river using data from Multispectral Instrument (MSI)/Sentinel 2 and Operational Land Imager (OLI)/Landsat 8 satellite sensors; and (2) to compare different linear modeling approaches to select the best variables for SSC monitoring. For comparison with actual field data, we used mean SSC measurements in 14 sediment gauge stations from 2013 to 2020. Reflectance data of the MSI/Sentinel 2 and OLI/Landsat 8 satellites bands and spectral indices related to the monitoring of water resources were used as explanatory variables. Simple and multiple linear regression models (SLR and MLR), least absolute shrinkage and selection operator (LASSO), and Elastic Net regression were used to predict the SSC. The near-infrared band images from both MSI/Sentinel 2 and OLI/Landsat 8 satellites showed a strong linear relationship with the SSC. Multiple linear regression, LASSO and Elastic Net regressions showed good performance for SSC prediction. Sediment flow maps show an SSC ... Mostrar Tudo |
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Thesagro: |
Barragem; Satélite; Sedimento. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02504naa a2200265 a 4500
001 2147819
005 2022-11-01
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.jenvman.2022.116207$2DOI
100 1 $aAIRES, U. R. V.
245 $aModeling of surface sediment concentration in the Doce River basin using satellite remote sensing.$h[electronic resource]
260 $c2022
300 $a15 p.
520 $aA B S T R A C T Surface sediment concentration (SSC) is linked to several problems related to water quality and its monitoring is costly because of the required fieldwork and laboratory analyses. Thus, sediment measurements are often sporadic, punctual, and performed during a short period. Orbital remote sensing allows the monitoring of SSC along the river channel permitting continuous and spatial information. This work had two objectives: (1) to model the surface concentration of sediments in the main channel of the Doce river using data from Multispectral Instrument (MSI)/Sentinel 2 and Operational Land Imager (OLI)/Landsat 8 satellite sensors; and (2) to compare different linear modeling approaches to select the best variables for SSC monitoring. For comparison with actual field data, we used mean SSC measurements in 14 sediment gauge stations from 2013 to 2020. Reflectance data of the MSI/Sentinel 2 and OLI/Landsat 8 satellites bands and spectral indices related to the monitoring of water resources were used as explanatory variables. Simple and multiple linear regression models (SLR and MLR), least absolute shrinkage and selection operator (LASSO), and Elastic Net regression were used to predict the SSC. The near-infrared band images from both MSI/Sentinel 2 and OLI/Landsat 8 satellites showed a strong linear relationship with the SSC. Multiple linear regression, LASSO and Elastic Net regressions showed good performance for SSC prediction. Sediment flow maps show an SSC reduction in the Doce river channel in recent years, indicating that part of the material from the Fund?ao tailings dam rupture may have been transported through sediment resuspension and transport processes.
650 $aBarragem
650 $aSatélite
650 $aSedimento
700 1 $aSILVA, D. D. da
700 1 $aFERNANDES FILHO, E. I.
700 1 $aRODRIGUES, L. N.
700 1 $aULIANA, E. M.
700 1 $aAMORIM, R. S. S.
700 1 $aRIBEIRO, C. B. de M.
700 1 $aCAMPOS, J. A.
773 $tJournal of Environmental Management$gv. 323, 2022.
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