Remote sensing of hazards: The spatio-temporal evolution of land surface temperature over tailings flows and related drivers

Abstract

Since mining activities began in Brazil some 300 years ago, disasters related to tailings dams have been recorded. In 2019, the B1 dam located at Vale, S.A.'s Córrego do Feijão mine in the municipality of Brumadinho (state of Minas Gerais) collapsed, releasing a vast amount of iron ore tailings into the Ferro-Carvão stream, which spread to the Paraopeba River located 10 km downstream. This tragedy devastated around 294 ha of the Ribeirão Ferro-Carvão watershed, including mining infrastructure, farmland and housing, and is among the biggest environmental disasters ever recorded in Brazil. The torrent of mud and debris and the substances released by the collapse, namely metals (e.g. iron, manganese), in addition to completely flooding the bed and banks of the Ribeirão Ferro-Carvão and remaining on them for several years as a thick blanket, caused significant changes in land surface temperature (LST). The aim of this study was to spatially analyze the LST in the period before and after the dam burst (2018–2021), and to find its relationship with the granulometric and chemical parameters of the deposited materials, as well as with the changes in land use and land cover that occurred with and after the dam burst. Landsat 8 satellite images processed on the Google Earth Engine platform were used to estimate the LST. The results showed a relationship between this parameter and the characteristics of the tailings, with lower temperatures being associated with sectors of the impacted area covered with fine fractions (presumably with greater heat dissipation capacity). A reduction in temperature was also detected over the period analyzed, which was related to the tailings removal and revegetation works in the impacted area. The integrated results indicate that LST is an effective parameter for characterizing disasters involving tailings dams, as well as for monitoring natural recovery or recovery as a result of mitigation measures implemented in the impacted areas. We therefore recommend its use in remote sensing of mining activities in general and tailings dam disasters in particular.