A global-scale data set of mining areas

被引：157

作者：

Maus, Victor ^{[1
,2
]}

Giljum, Stefan ^{[1
]}

Gutschlhofer, Jakob ^{[1
]}

da Silva, Dieison M. ^{[3
]}

Probst, Michael ^{[1
]}

Gass, Sidnei L. B. ^{[3
]}

Luckeneder, Sebastian ^{[1
]}

Lieber, Mirko ^{[1
]}

McCallum, Ian ^{[2
]}

机构：

[1] Vienna Univ Econ & Business WU, Inst Ecol Econ, Vienna, Austria

[2] Int Inst Appl Syst Anal IIASA, Ecosyst Serv & Management, Laxenburg, Austria

[3] Fed Univ Pampa UNIPAMPA, Itaqui, Brazil

来源：

SCIENTIFIC DATA | 2020年 / 7卷 / 01期

基金：

欧洲研究理事会;

关键词：

GEOGRAPHICALLY WEIGHTED REGRESSION; LAND-COVER; BIODIVERSITY; PATTERNS; DEPLETION; ACCURACY; ATACAMA;

D O I：

10.1038/s41597-020-00624-w

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 10km 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 km(2). 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 10.1594/PANGAEA.910894 and visualization at www.fineprint.global/viewer.

引用

页数：13

共 65 条

[1] A suite of global, cross-scale topographic variables for environmental and biodiversity modeling [J].

Amatulli, Giuseppe ;

Domisch, Sami ;

Tuanmu, Mao-Ning ;

Parmentier, Benoit ;

Ranipeta, Ajay ;

Malczyk, Jeremy ;

Jetz, Walter .

SCIENTIFIC DATA, 2018, 5

[2]

[Anonymous], **DATA OBJECT**, DOI DOI 10.5281/ZENODO.3691743

[3]

[Anonymous], **DATA OBJECT**

[4]

[Anonymous], 2019, GNU GEN PUBL LIC VER

[5] Random forest in remote sensing: A review of applications and future directions [J].

Belgiu, Mariana ;

Dragut, Lucian .

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 114 :24-31

[6] Restoration of mined lands - using natural processes [J].

Bradshaw, A .

ECOLOGICAL ENGINEERING, 1997, 8 (04) :255-269

[7] Measuring telecouplings in the global land system: A review and comparative evaluation of land footprint accounting methods [J].

Bruckner, Martin ;

Fischer, Guenther ;

Tramberend, Sylvia ;

Giljum, Stefan .

ECOLOGICAL ECONOMICS, 2015, 114 :11-21

[8] Geographically weighted regression - modelling spatial non-stationarity [J].

Brunsdon, C ;

Fotheringham, S ;

Charlton, M .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN, 1998, 47 :431-443

[9] Geographically weighted regression: A method for exploring spatial nonstationarity [J].

Brunsdon, C ;

Fotheringham, AS ;

Charlton, ME .

GEOGRAPHICAL ANALYSIS, 1996, 28 (04) :281-298

[10] Biodiversity Risks from Fossil Fuel Extraction [J].

Butt, N. ;

Beyer, H. L. ;

Bennett, J. R. ;

Biggs, D. ;

Maggini, R. ;

Mills, M. ;

Renwick, A. R. ;

Seabrook, L. M. ;

Possingham, H. P. .

SCIENCE, 2013, 342 (6157) :425-426

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