Global extraction of minerals grew at an unprecedented pace in the past decades, causing a wide range of social and environmental impacts around the world. Growing demand for essential minerals and declining quality of ores lead to larger volumes of unused material extracted and disposed, increasing appropriation of land. The direct land used by mining is a crucial indicator of environmental pressure, which is closely associated with a range of negative impacts, including fragmentation and degradation of ecosystems and biodiversity loss. Such an indicator supports the implementation and monitoring of several Sustainable Development Goals (SDGs), as mining impacts on biodiversity and ecosystem services can be reduced by limiting mining areas. Data on land use of mining is also important to further develop land footprint indicators that inform about land required along global supply chains to satisfy final consumption of products. Yet, to date information about mining areas worldwide is not available.
These researchers contribute to filling this knowledge gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. Their data set covers more than six thousand mining sites distributed across the entire globe. These mining sites have reported mineral extraction or activities between the years 2000 and 2017, according to the SNL Metals and Mining database. Within these regions, Giljum et al delineated the mining areas (i.e., drew polygons) by visual interpretation of several satellite data sources, including Google Satellite, Microsoft Bing Imagery and Sentinel-2 cloudless. As a result, they derived a set of 21,060 polygons globally, covering a total area of 57,277 km2. The data set is available for download from https://doi.org/10.1594/PANGAEA.910894 and visualisation at www.fineprint.global/viewer.
This novel data set can help improving environmental impact assessments of the global mining sector, for example, regarding mining-induced deforestation or fragmentation and degradation of ecosystems. It can also serve as a benchmark for further monitoring the temporal evolution of mining sites around the world and as training and validation data to support automated classification of mines using satellite images.
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