A global-scale data set of mining areas

Victor Maus,Sebastian Luckeneder,Stefan Giljum,Ian Mccallum,Jakob Gutschlhofer,Mirko Lieber,Michael Probst,Dieison M Da Silva,Sidnei L B Gass

doi:10.1038/s41597-020-00624-w

Abstract

The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10 km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale data set consisting of 21,060 polygons that add up to 57,277 km2. The polygons cover all mining above-ground features that could be identified from the satellite images, including open cuts, tailings dams, waste rock dumps, water ponds, and processing infrastructure. The data set is available for download from https://doi.org/10.1594/PANGAEA.910894 and visualization at www.fineprint.global/viewer.

Highlights

Background & SummaryGlobal extraction of minerals grew at an unprecedented pace in the past decades, causing a wide range of social and environmental impacts around the world[1,2,3]
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[10,11,12,13,14]. 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[15]
The SNL database reports some mining locations in this region, they do not always spatially intersect the mining areas mapped from the satellite images

Summary

Background & Summary

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[1,2,3]. The overall accuracy, calculated from 1,000 stratified random points is 88.4% (for details see the section on Technical Validation) 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 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

Methods

Findings

Code availability