Data/hora: 14/06/2024 04:40:42

Biblioteca(s): 	Embrapa Cerrados.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	A - 1	
Data corrente: 	26/10/2022	
Data da última atualização: 	01/11/2022	
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.	
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.	
Título: 	Modeling of surface sediment concentration in the Doce River basin using satellite remote sensing.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	Journal of Environmental Management, v. 323, 2022.	
Páginas: 	15 p.	
DOI: 	https://doi.org/10.1016/j.jenvman.2022.116207	
Idioma: 	Inglês	
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.	
Thesagro: 	Barragem; Satélite; Sedimento.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Cerrados (CPAC)	
Exemplares: 	CPAC - (37406-1) - UPC - AP - DD - DIGITAL -  - DIGITAL - DIS