Mining poses danger to the climate and biodiversity
For the first time, Earth observation satellite images allow researchers to make a comprehensive assessment of the global impact of mining activities. Victor Maus from WU’s Institute for Ecological Economics led the mapping of 100,000 km² of mining areas around the world. The result: Land of high value for biodiversity conservation and climate stability is most impacted by mining: This applies to 29% (29,171 km²) of the global mining area. The researchers also found that tropical and subtropical forests correspond to 60% (8,533 km²) of the area of forest removed for mining between 2000 to 2019.
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The global mining sector plays a dual role in sustainable development: On the one hand, mineral extraction produces the most significant human-made waste flows, pollutes and destroys natural habitats across the entire globe, and contributes to biodiversity loss and climate change. At the same time, tackling the current environmental crisis requires a transition to renewable energy, for which mineral extraction will play a critical role. As it stands, renewable energy sources will require more mining in the future.
The study demonstrates that information extracted from satellite data can enhance environmental transparency in the growing mining industry. It also highlights the need to increase awareness of the impact of mining for everyone involved in global supply chains.
Until now, data on global mining activities did not include any information on the land area used for mineral extraction. This study fills this gap by presenting a new dataset on the extent of mining activities, obtained through the visual interpretation of satellite imagery. Based on the approximate geographic coordinates of more than 34,000 mining sites around the world, the method mapped out mining areas within a 10 km radius. The result is a global dataset consisting of 45,000 polygons with a total area of 100,000 km². The polygons include all surface mining features identified in the satellite imagery, including open pit mines, spoil piles, waste rock piles, water ponds, and processing infrastructure.
Victor Maus came to WU’s Institute for Ecological Economics in 2018. He has also worked as a research scholar at the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria, since 2016. His research contributes to methodological developments in geoinformatics and spatial data science for sustainable development, particularly related to land-use changes. His main early achievements include the development of a pattern recognition algorithm called Time-Weighted Dynamic Time Warping (TWDTW) to classify satellite image time series in data-scarce conditions and the production of the first global map of mining activities. Maus has published his research in leading international journals such as Global Environmental Change, Nature Scientific Data, and the Journal of Statistical Software.
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