Modeling of surface sediment concentration in the Doce River basin using satellite remote sensing. (1st December 2022)

• Record Type: Journal Article
• Authors: Aires, Uilson Ricardo Venâncio; Silva, Demetrius David da; Fernandes Filho, Elpídio Inácio; Rodrigues, Lineu Neiva; Uliana, Eduardo Morgan; Amorim, Ricardo Santos Silva; Ribeiro, Celso Bandeira de Melo; Campos, Jasmine Alves
• Is Part Of: Journal of environmental management. Volume 323(2022)
• Publication Date: 2022-12-01
• Subjects: MSI/Sentinel 2 and OLI/Landsat 8 satellites -- Fundão tailings dam rupture -- Sediment modeling; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2022.116207
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: 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 Fundão tailings dam rupture may have been transported through sediment resuspension and transport processes. Graphical abstract: Image 1 Highlights: SSC measurements are costly as they require fieldwork and laboratory analyses. Remote sensing allows monitoring continuous and spatial SSC along the river. The near-infrared (NIR) band showed a strong linear relationship with SSC. Sediment flow maps show an SSC reduction in the Doce river channel in more recent years.