Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery

• Autor(a) principal: Crioni, Pedro L. B. [UNESP]
• Data de Publicação: 2023
• Outros Autores: Teramoto, Elias H. [UNESP], Chang, Hung K. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1590/0001-3765202320220177; http://hdl.handle.net/11449/248783
• topic: Mine tailings; Paraopeba river; Remote Sensing; Sentinel-2; turbidity; water quality

Sudden failure of a mine tailing dam occurred in the municipality of Brumadinho, Minas Gerais, Brazil, on January 25, 2019. Approximately 12 million cubic meters of mine tailings discharged into the Paraopeba River, producing strong environmental and societal impacts, mainly due to a massive increase in turbidity (occasionally exceeding 50,000 Nephelometric Turbidity Units [NTU] (CPRM 2019). Remote sensing is a well-established tool for quantifying spatial patterns of turbidity. However, a few empirical models have been developed to map turbidity in rivers impacted by mine tailings. Thus, this study aimed to develop an empirical model capable of producing turbidity estimates based on images from the Sentinel-2 satellite, using the Paraopeba River as the study area. We found that river turbidity was most strongly correlated with the sensor’s near-infrared band (NIR) (band 8). Thus, we built an empirical single-band model using an exponential function with an (R2 of 0.91) to characterize the spatial-temporal variation of turbidity based on satellite observations of NIR reflectance. Although the role of discharged tailings in the seasonal variation of turbidity is not well understood, the proposed model enabled the monitoring of turbidity variations in the Paraopeba River associated with seasonal resuspension or deposition of mine tailings. Our study shows the capability of single-band models to quantify seasonal variations in turbidity in rivers impacted by mine tailing pollution.