An update on global mining land use

Victor Maus; Stefan Giljum; Dieison M. da Silva; Jakob Gutschlhofer; Robson P. da Rosa; Sebastian Luckeneder; Sidnei L. B. Gass; Mirko Lieber; Ian McCallum

doi:10.1038/s41597-022-01547-4

journal article Open Access Jul 22, 2022

An update on global mining land use

Victor Maus

Stefan Giljum

Dieison M. da Silva

Jakob Gutschlhofer

Robson P. da Rosa Sebastian Luckeneder

Sidnei L. B. Gass

Mirko Lieber Ian McCallum

Scientific Data Vol. 9 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/s41597-022-01547-4

Abstract

AbstractThe growing demand for minerals has pushed mining activities into new areas increasingly affecting biodiversity-rich natural biomes. Mapping the land use of the global mining sector is, therefore, a prerequisite for quantifying, understanding and mitigating adverse impacts caused by mineral extraction. This paper updates our previous work mapping mining sites worldwide. Using visual interpretation of Sentinel-2 images for 2019, we inspected more than 34,000 mining locations across the globe. The result is a global-scale dataset containing 44,929 polygon features covering 101,583 km2 of large-scale as well as artisanal and small-scale mining. The increase in coverage is substantial compared to the first version of the dataset, which included 21,060 polygons extending over 57,277 km2. The polygons cover open cuts, tailings dams, waste rock dumps, water ponds, processing plants, and other ground features related to the mining activities. The dataset is available for download from https://doi.org/10.1594/PANGAEA.942325 and visualisation at www.fineprint.global/viewer.

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References

54

[1]

Lenzen, M. et al. Implementing the material footprint to measure progress towards sustainable development goals 8 and 12. Nat. Sustain. 112, 6271 (2021).

[2]

Surge in global metal mining threatens vulnerable ecosystems

Sebastian Luckeneder, Stefan Giljum, Anke Schaffartzik et al.

Global Environmental Change 2021 10.1016/j.gloenvcha.2021.102303

[3]

UN IRP. Global resources outlook 2019: Natural resources for the future we want. https://www.resourcepanel.org/reports/global-resources-outlook (United Nations Environment Programme, Nairobi, 2019).

[4]

OECD. Global Material Resources Outlook to 2060 (OECD, Paris, 2019).

[5]

Bridge, G. Contested Terrain: Mining and the environment. Annu. Rev. Environ. Resour. 29, 205–259 (2004). 10.1146/annurev.energy.28.011503.163434

[6]

Sonter, L. J., Dade, M. C., Watson, J. E. M. & Valenta, R. K. Renewable energy production will exacerbate mining threats to biodiversity. Nat. Commun. 11, 4174 (2020). 10.1038/s41467-020-17928-5

[7]

Murguía, D. I., Bringezu, S. & Schaldach, R. Global direct pressures on biodiversity by large-scale metal mining: Spatial distribution and implications for conservation. J. Eenviron. Manage. 180, 409–420 (2016).

[8]

Kobayashi, H., Watando, H. & Kakimoto, M. A global extent site-level analysis of land cover and protected area overlap with mining activities as an indicator of biodiversity pressure. J. Clean. Prod. 84, 459–468 (2014). 10.1016/j.jclepro.2014.04.049

[9]

Butt, N. et al. Biodiversity risks from fossil fuel extraction. Science 342, 425–426 (2013). 10.1126/science.1237261

[10]

Mining drives extensive deforestation in the Brazilian Amazon

Laura J. Sonter, Diego Herrera, Damian J. Barrett et al.

Nature Communications 2017 10.1038/s41467-017-00557-w

[11]

Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: New approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020). 10.1016/j.oneear.2020.06.007

[12]

Islam, K., Vilaysouk, X. & Murakami, S. Integrating remote sensing and life cycle assessment to quantify the environmental impacts of copper-silver-gold mining: A case study from laos. Resour. Conserv. Recy. 154, 104630 (2020). 10.1016/j.resconrec.2019.104630

[13]

Bringezu, S. Toward science-based and knowledge-based targets for global sustainable resource use. Resources 8 (2019). 10.3390/resources8030140

[14]

Islam, K. & Murakami, S. Global-scale impact analysis of mine tailings dam failures: 1915–2020. Glob. Environ. Change 70, 102361 (2021). 10.1016/j.gloenvcha.2021.102361

[15]

Silva Rotta, L. H. et al. The 2019 brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in brazil. Int. J. Appl. Earth Obs. Geoinf. 90, 102119 (2020).

[16]

Toumbourou, T., Muhdar, M., Werner, T. & Bebbington, A. Political ecologies of the post-mining landscape: Activism, resistance, and legal struggles over kalimantan’s coal mines. Energy Res. Soc. Sci. 65, 101476 (2020). 10.1016/j.erss.2020.101476

[17]

Chen, W., Li, X., He, H. & Wang, L. A review of fine-scale land use and land cover classification in open-pit mining areas by remote sensing techniques. Remote Sensing 10, 15 (2018). 10.3390/rs10010015

[18]

Song, W., Song, W., Gu, H. & Li, F. Progress in the remote sensing monitoring of the ecological environment in mining areas. Int. J. Environ. Res. 17, 1846 (2020).

[19]

Werner, T. T. et al. Global-scale remote sensing of mine areas and analysis of factors explaining their extent. Glob. Environ. Change 60 (2020). 10.1016/j.gloenvcha.2019.102007

[20]

Liang, T., Werner, T. T., Heping, X., Jingsong, Y. & Zeming, S. A global-scale spatial assessment and geodatabase of mine areas. Glob. Planet. Change 204, 103578 (2021). 10.1016/j.gloplacha.2021.103578

[21]

Maus, V. et al. A global-scale data set of mining areas. Sci. Data 7, 289 (2020). 10.1038/s41597-020-00624-w

[22]

Tost, M. et al. Ecosystem services costs of metal mining and pressures on biomes. Extr. Ind. Soc. 7, 79–86 (2020).

[23]

Maus, V. et al. Global-scale mining polygons (version 1). PANGAEA https://doi.org/10.1594/PANGAEA.910894 (2020). 10.1594/pangaea.910894

[24]

S&P Global Market Intelligence. SNL metals and mining database. https://www.spglobal.com/marketintelligence/en/campaigns/metals-mining (2018).

[25]

EOX IT Services GmbH. Sentinel-2 cloudless (contains modified copernicus sentinel data 2019). https://s2maps.eu (2020).

[26]

Lesiv, M. et al. Characterizing the spatial and temporal availability of very high resolution satellite imagery in google earth and microsoft bing maps as a source of reference data. Land 7 (2018). 10.3390/land7040118

[27]

Gutschlhofer, J. & Maus, V. Web application for mining area polygonization version 1.2. Zenodo https://doi.org/10.5281/zenodo.3691743 (2020). 10.5281/zenodo.3691743

[28]

Montibeller, B., Kmoch, A., Virro, H., Mander, U. & Uuemaa, E. Increasing fragmentation of forest cover in brazil’s legal amazon from 2001 to 2017. Sci. Rep. 10, 5803 (2020). 10.1038/s41598-020-62591-x

[29]

Simple Features for R: Standardized Support for Spatial Vector Data

Edzer Pebesma

The R Journal 2018 10.32614/rj-2018-009

[30]

Dunnington, D., Pebesma, E. & Rubak, E. s2: Spherical geometry operators using the s2 geometry library, version 1.0.7. The Comprehensive R Archive Network https://CRAN.R-project.org/package=s2 (2021).

[31]

EUROSTAT. Countries, 2016 - administrative units - dataset (generalised dataset derived from eurogeographics and un-fao gi data). https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/countries/ (2018).

[32]

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling

Giuseppe Amatulli, Sami Domisch, Mao-Ning Tuanmu et al.

Scientific Data 2018 10.1038/sdata.2018.40

[33]

Maus, V. et al. Global-scale mining polygons (version 2). PANGAEA https://doi.org/10.1594/PANGAEA.942325 (2022). 10.1594/pangaea.942325

[34]

Asner, G. P., Llactayo, W., Tupayachi, R. & Luna, E. R. Elevated rates of gold mining in the amazon revealed through high-resolution monitoring. PNAS 110, 18454–18459 (2013). 10.1073/pnas.1318271110

[35]

Nyamekye, C., Ghansah, B., Agyapong, E. & Kwofie, S. Mapping changes in artisanal and small-scale mining (asm) landscape using machine and deep learning algorithms. - a proxy evaluation of the 2017 ban on asm in ghana. Env. Challenges 3, 100053 (2021). 10.1016/j.envc.2021.100053

[36]

Shen, L. & Gunson, A. J. The role of artisanal and small-scale mining in china’s economy. J. Clean. Prod. 14, 427–435 (2006). 10.1016/j.jclepro.2004.08.006

[37]

Shen, L., Dai, T. & Gunson, A. J. Small-scale mining in china: Assessing recent advances in the policy and regulatory framework. Resour. Policy 34, 150–157 (2009). 10.1016/j.resourpol.2009.01.003

[38]

Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014). 10.1016/j.rse.2014.02.015

[39]

Goutte, C. & Gaussier, E. A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. Lect. Notes Comput. Sci. 3408, 345–359 (2005). 10.1007/978-3-540-31865-1_25

[40]

Chicco, D. & Jurman, G. The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genet. 21, 1–13 (2020).

[41]

An introduction to ROC analysis

Tom Fawcett

Pattern Recognition Letters 2006 10.1016/j.patrec.2005.10.010

[42]

Pontius, R. G. & Millones, M. Death to kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 32, 4407–4429 (2011). 10.1080/01431161.2011.552923

[43]

OGC – Open Geospatial Consortium. GeoPackage encoding standard. https://www.geopackage.org/ (2005).

[44]

OGC – Open Geospatial Consortium. Geographic tagged image file format (GeoTIFF). https://www.ogc.org/standards/geotiff (2019).

[45]

The Internet Society. RFC 4180: Common format and MIME type for comma-separated values (CSV). https://tools.ietf.org/html/rfc4180 (2005).

[46]

QGIS Development Team. QGIS geographic information system, version 3.12.0. Open Source Geospatial Foundation https://www.qgis.org (2020).

[47]

R Core Team. R: A language and environment for statistical computing, version 3.6.1. Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org (2019).

[48]

Python Core Team. Python: A dynamic, open source programming language, version 2.7.17. Python Software Foundation https://www.python.org (2019).

[49]

OGC – Open Geospatial Consortium. Web map service interface standard (WMS). https://www.ogc.org/standards/wms (2020).

[50]

GNU general public license, version 3. Free Software Foundation https://www.gnu.org/licenses/gpl-3.0.en.html (2019).

Showing 50 of 54 references

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Published: Jul 22, 2022
Vol/Issue: 9(1)
License: View

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Cite This Article

Victor Maus, Stefan Giljum, Dieison M. da Silva, et al. (2022). An update on global mining land use. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01547-4

An update on global mining land use

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