Analysis of the impact on vegetation caused by abrupt deforestation via orbital sensor in the environmental disaster of Mariana, Brazil

Carlos Antonio da Silva Junior, Andressa Dias Coutinho, José Francisco de Oliveira-Júnior, Paulo Eduardo Teodoro, Mendelson Lima, Muhammad Shakir, Givanildo de Gois and Jerry Adriani Johann

Land Use Policy, 2018, vol. 76, issue C, 10-20

Abstract: The failure of the Fundão Dam in Mariana, more precisely in the subdistrict of Bento Rodrigues, state of Minas Gerais (Brazil) on November 5th, 2015, is considered to be "the biggest environmental tragedy in the country's history." About thirty-four million cubic meters of tailings were dumped into the river where, another 16 million continued to reach the Atlantic ocean. This disaster seriously affected the flora, fauna, economic activities and people's lives, including the loss of human lives. Remote sensing allows mapping the variability of terrain properties, such as vegetation, water and geology, both in space and time, offering a synoptic view and useful environmental information in future decision making. In this way, this research aims to analyze the impacts of the failure of the Fundão Dam in the municipality of Mariana-MG on the vegetation cover, by means of remote sensing techniques and analysis of digital processing of orbital optical images. In order to analyze the soil cover, Unmixing Espectral Linear Model (UELM) was used in order to separate soil, shade and vegetation classes. Subsequently, Artificial Neural Network (ANN) classification method was applied, followed by Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI). The results showed a loss of 13.02% of the vegetation, about 1289 ha, and a reduction of 68.57% of shade (water), approximately 1347 ha. The UELM showed to be effective in the separation of each image-fraction, being an important stage for the success of the classification. The EVI was the index that best described the vegetation deficit in the affected areas spilling the sludge from waste.

Keywords: Digital image; Environmental disaster; Green biomass variation; Artificial neural network; Mining activity; Tailings (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:lauspo:v:76:y:2018:i:c:p:10-20

DOI: 10.1016/j.landusepol.2018.04.019

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