The Accuracy of Land Use and Cover Mapping across Time in Environmental Disaster Zones: The Case of the B1 Tailings Dam Rupture in Brumadinho, Brazil - Filho, Carlos Roberto Mangussi; do Valle Junior, Renato Farias; de Melo Silva, Maytê Maria Abreu Pires; Mendes, Rafaella Gouveia; de Souza Rolim, Glauco [UNESP]; Pissarra, Teresa Cristina Tarlé [UNESP]; de Melo, Marília Carvalho; Valera, Carlos Alberto; Pacheco, Fernando António Leal [UNESP]; Fernandes, Luís Filipe Sanches (MDPI, 2023)
The rupture of a tailings dam causes several social, economic, and environmental impacts because people can die, the devastation caused by the debris and mud waves is expressive and the released substances may be toxic to the ecosystem and humans. There were two major dam failures in the Minas Gerais state, Brazil, in the last decade. The first was in 2015 in the city of Mariana and the second was in 2019 in the municipality of Brumadinho. The extent of land use and cover changes derived from those collapses were an expression of their impacts. Thus, knowing the changes to land use and cover after these disasters is essential to help repair or mitigate environmental degradation. This study aimed to diagnose the changes to land cover that occurred after the failure of dam B1 in Brumadinho that affected the Ferro-Carvão stream watershed. In addition to the environmental objective, there was the intention of investigating the impact of image preparation, as well as the spatial and spectral resolution on the classification’s accuracy. To accomplish the goals, visible and near-infrared bands from 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 Pixel Reduction to Median tool was used to prepare the record of images, and then the random forest algorithm was used to detect the changes in land cover caused by the tailings dam failure under the different spatial and spectral resolutions and to provide the corresponding measures of accuracy. The results showed that the spatial resolution of the images affects the accuracy, but also that the selected algorithm and images were all capable of accurately classifying land use and cover in the Ferro-Carvão watershed and their changes over time. After the failure, mining/tailings areas increased in the impacted zone of the Ferro-Carvão stream, while native forest, pasture, and agricultural lands declined, exposing the environmental deterioration. The environment recovered in subsequent years (2020–2021) due to tailings removal and mobilization.
Keywords: environmental degradation; Google Earth Engine; random forest classifier; remote sensing; socio-environmental impacts; soil cover change