Avaliação da resolução espacial e espectral de imagens de satélite na identificação das mudanças de uso e ocupação em áreas de rompimento da barragem de rejeitos B1, na sub-bacia do Ribeirão Ferro-Carvão, em Brumadinho, MG

Abstract

Tailings dam failure causes several socioenvironmental impacts, and these substances may be toxic to the ecosystem and the general population, changing the use and cover of areas. There were two dam failures in Minas Gerais state, Brazil, in 5 years, the first in 2015 in the city of Mariana and the other in 2019 in the municipality of Brumadinho. Thus, knowing the changes in land use and cover after these disasters is essential to repair or mitigate environmental degradation. This study aimed to diagnose the changes in land cover after the failure of dam B1 in Brumadinho in the Ferro-Carvão Stream watershed. Landsat (30 m), Sentinel-2 (10 m) and PlanetScope Dove (4.77 m) images collected between 2018 and 2021 were processed on the Google Earth Engine platform. The Random Forest machine learning algorithm was used to construct the space-time scenarios, classifying their respective accuracy and identifying the morphological changes resulting from the failure. Thus, the present study investigates remote sensing accuracy associated with learning machine models to identify changes in land cover caused by tailings dam failure under different spatial and spectral resolutions. The results showed that the spatial resolution of the images affects classification accuracy and that all the satellites assessed were able to accurately classify land use and cover in a watershed with dam failure.