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The STUDIA UNIVERSITATIS BABEŞ-BOLYAI issue article summary
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Abstract: Mining is generally considered among the most pollutant industrial activities currently running. However, the modern society needs ever increasing quantities of raw mineral materials that can be extracted from the Earth’s sub- and near-surface. As the easily accessible deposits are depleting, new ore bodies are approached, very often in conditions that are more difficult and with lower grades. The environmental issues are more and more taken into account when opening a new mine, and in many cases, new operations are being rejected by the local communities, due to the failure of complying with the current environmental standards. We have to admit that any mine has a significant impact on the environment, and part of the economic benefit should be used for the environmental restoration/reconstruction of the area, but also creating new economic opportunities for the local community in the post-closure period. Under such circumstances, the environmental monitoring and management are very important. The conventional approach generally consists in periodically performing field measurements in discrete points, taking samples and doing laboratory analysis. This procedure is time-, money- and resources-consuming, although the obtained data are not always reliable and reproducible, and the interpretation lacks relevance in many cases. An accurate and cost-effective monitoring system is essential in the management of the environmental issues in mining areas. A new approach is proposed by the EU-funded project ImpactMin, consisting in combining the satellite remote sensing, and aerial lightweight measurements with the ground sampling for developing new models of environmental monitoring in mining areas. Four test-sites were selected for calibrating and demonstrating the new toolset. Among the benefits of the new approach should be mentioned: a more accurate description of the current state of the environment and changes overtime; future developments in the evolution of the environment can be predicted; reduction of the probability of loss by natural/technological disasters.
Key words: remote sensing; hyperspectral / multispectral images; stress vegetation; fluorescence |
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