artisanal and small-scale gold mining; Earth observations; land cover change; land degradation; Landsat; Sentinels; sustainable land management; Artisanal and small-scale gold mining; Gold mining; Land degradation; Land-cover change; LANDSAT; Mining activities; Sentinel; Small scale; Sustainable land managements; Earth and Planetary Sciences (all); General Earth and Planetary Sciences
Abstract :
[en] This paper discusses opportunities to use remote sensing (RS) technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining (ASGM) sector. The paper uses a systematic literature review to identify, analyze, and synthesize various uses of RS on the detection and monitoring of ASGM activities across the globe. The study covers the use of spaceborne sensors and available opportunities for data access and processing and emphasizes the important role that freely-available data has played in understanding ASGM activities. It discusses applications and opportunities offered in assessing the geospatial and temporal characteristics of ASGM and its impacts on the surrounding environment. Furthermore, it examines different indicators for the detection of ASGM in the landscape. Finally, technological capabilities described in the study are illustrated with case studies in the Democratic Republic of Congo and in Colombia using cloud computing with the Open Data Cube. The case studies demonstrate the identification and quantification of impacts of ASGM activities on land degradation and water turbidity in remote areas and results are dissiminated using the MapX platform. This facilitates policy development, implementation, and evaluation in the ASGM context.
Precision for document type :
Review article
Disciplines :
Earth sciences & physical geography
Author, co-author :
Moomen, Abdul-Wadood ; School of Mines and Built Environment, University of Energy and Natural Resources, Sunyani, Ghana
Lacroix, Pierre ; GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland ; EnviroSPACE Lab, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
Benvenuti, Antonio; GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
Planque, Marion; GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
Piller, Thomas; GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
Davis, Kenneth; Secretariat of the Minamata Convention on Mercury, United Nations Environment Programme, Geneva, Switzerland
Miranda, Manoela; Secretariat of the Minamata Convention on Mercury, United Nations Environment Programme, Geneva, Switzerland
Ibrahim, Elsy ; Université de Liège - ULiège > Département ArGEnCo > Géoressources minérales & Imagerie géologique
Giuliani, Gregory ; GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland ; EnviroSPACE Lab, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
Language :
English
Title :
Assessing the Applications of Earth Observation Data for Monitoring Artisanal and Small-Scale Gold Mining (ASGM) in Developing Countries
Tsang, V.W.L.; Lockhart, K.; Spiegel, S.J.; Yassi, A. Occupational Health Programs for Artisanal and Small-Scale Gold Mining: A Systematic Review for the WHO Global Plan of Action for Workers’ Health. Ann. Glob. Health 2019, 85, 128. https://doi.org/10.5334/aogh.2592.
United Nations Department of Economic and Social Affairs, Resources and Transport Division. Small-Scale Mining in the Developing Countries; United Nations: New York, NY, USA, 1972.
Labonne, B. Seminar on artisanal and small-scale mining in Africa. Identifying best practices and building the sustainable livelihoods of communities. In Recommendations: Yaounde Vision Statement; CRC Press: Yaounde, Cameroun, 2002.
Africa Mining Vision; UNECA: Addis Ababa, Ethiopia, 2009.
Franks, D.M.; Ngonze, C.; Pakoun, L.; Hailu, D. Voices of Artisanal and Small-Scale Mining, Visions of the Future: Report from the International Conference on Artisanal and Small-Scale Mining and Quarrying. Extr. Ind. Soc. 2020, 7, 505–511. https://doi.org/10.1016/j.exis.2020.01.011.
World Bank. 2020 State of the Artisanal and Small-Scale Mining Sector; World Bank: Washington, DC, USA, 2020.
Rajaee, M.; Obiri, S.; Green, A.; Long, R.; Cobbina, S.J.; Nartey, V.; Buck, D.; Antwi, E.; Basu, N. Integrated Assessment of Artisanal and Small-Scale Gold Mining in Ghana—Part 2: Natural Sciences Review. Int. J. Environ. Res. Public Health 2015, 12, 8971–9011. https://doi.org/10.3390/ijerph120808971.
Gari, S.R.; Ortiz Guerrero, C.E.; A-Uribe, B.; Icely, J.D.; Newton, A. A DPSIR-Analysis of Water Uses and Related Water Quality Issues in the Colombian Alto and Medio Dagua Community Council. Water Sci. 2018, 32, 318–337. https://doi.org/10.1016/j.wsj.2018.06.001.
Esdaile, L.J.; Chalker, J.M. The Mercury Problem in Artisanal and Small-Scale Gold Mining. Chem. Eur. J. 2018, 24, 6905–6916. https://doi.org/10.1002/chem.201704840.
UNEP. Global Mercury Assessment 2018; UNEP: Nairobi, Kenya, 2019.
Rettberg, A.; Ortiz-Riomalo, J.F. Golden Opportunity, or a New Twist on the Resource–Conflict Relationship: Links between the Drug Trade and Illegal Gold Mining in Colombia. World Dev. 2016, 84, 82–96. https://doi.org/10.1016/j.worlddev.2016.03.020.
Li, Y.; Zhao, H.; Fan, J. Application of Remote Sensing Technology in Mine Environment Monitoring. MATEC Web Conf. 2015, 22, 04008. https://doi.org/10.1051/matecconf/20152204008.
Barenblitt, A.; Payton, A.; Lagomasino, D.; Fatoyinbo, L.; Asare, K.; Aidoo, K.; Pigott, H.; Som, C.K.; Smeets, L.; Seidu, O.; et al. The Large Footprint of Small-Scale Artisanal Gold Mining in Ghana. Sci. Total Environ. 2021, 781, 146644. https://doi.org/10.1016/j.scitotenv.2021.146644.
Bruno, D.E.; Ruban, D.A.; Tiess, G.; Pirrone, N.; Perrotta, P.; Mikhailenko, A.V.; Ermolaev, V.A.; Yashalova, N.N. Artisanal and Small-Scale Gold Mining, Meandering Tropical Rivers, and Geological Heritage: Evidence from Brazil and Indonesia. Sci. Total Environ. 2020, 715, 136907. https://doi.org/10.1016/j.scitotenv.2020.136907.
Isidro, C.; Mcintyre, N.; Lechner, A.; Callow, I. Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region. Remote Sens. 2017, 9, 945. https://doi.org/10.3390/rs9090945.
Ibrahim, E.; Jiang, J.; Lema, L.; Barnabé, P.; Giuliani, G.; Lacroix, P.; Pirard, E. Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery. Remote Sens. 2021, 13, 736. https://doi.org/10.3390/rs13040736.
Lobo, F.D.L.; Costa, M.; Novo, E.M.L.M.; Telmer, K. Distribution of Artisanal and Small-Scale Gold Mining in the Tapajós River Basin (Brazilian Amazon) over the Past 40 Years and Relationship with Water Siltation. Remote Sens. 2016, 8, 579. https://doi.org/10.3390/rs8070579.
Lobo, F.; Novo, E.; Barbosa, C.; Vasconcelos, V. Monitoring Water Siltation Caused by Small-Scale Gold Mining in Amazonian Rivers Using Multi-Satellite Images. In Limnology: Some New Aspects of Inland Water Ecology; IntechOpen: London, UK, 2018; ISBN 978-1-83880-788-7.
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. Environ. Chall. 2021, 3, 100053. https://doi.org/10.1016/j.envc.2021.100053.
Telmer, K.; Stapper, D. Evaluating and Monitoring Small Scale Gold Mining and Mercury Use: Building a Knowledge-Base with Satellite Imagery and Field Work; United Nations Industrial Development Organization: Victoria, BC, Canada, 2007.
Forkuor, G.; Ullmann, T.; Griesbeck, M. Mapping and Monitoring Small-Scale Mining Activities in Ghana Using Sentinel-1 Time Series (2015–2019). Remote Sens. 2020, 12, 911. https://doi.org/10.3390/rs12060911.
Adler Miserendino, R.; Bergquist, B.A.; Adler, S.E.; Guimarães, J.R.D.; Lees, P.S.J.; Niquen, W.; Velasquez-López, P.C.; Veiga, M.M. Challenges to Measuring, Monitoring, and Addressing the Cumulative Impacts of Artisanal and Small-Scale Gold Mining in Ecuador. Resour. Policy 2013, 38, 713–722. https://doi.org/10.1016/j.resourpol.2013.03.007.
Elsayed Zeinelabdein, K.A.; El-Nadi, A.H.H.; Babiker, I.S. Prospecting for Gold Mineralization with the Use of Remote Sensing and GIS Technology in North Kordofan State, Central Sudan. Sci. Afr. 2020, 10, e00627. https://doi.org/10.1016/j.sciaf.2020.e00627.
The Environment Protection Agency of Sierra Leone. The ASGM Overview of Sierra Leone; 685; Environment Protection Agency: Freetown, Sierra Leone, 2019.
UNECA. Minerals and Africa’s Development; UNECA: Addis Ababa, Ethiopia, 2011.
Pullin, A.S.; Stewart, G.B. Guidelines for Systematic Review in Conservation and Environmental Management. Conserv. Biol. 2006, 20, 1647–1656. https://doi.org/10.1111/j.1523-1739.2006.00485.x.
Collaboration for Environmental Evidence. Guidelines for Systematic Review in Environmental Management; Wiley: Hoboken, NJ, USA, 2013.
Plummer, R.; de Loë, R.; Armitage, D. A Systematic Review of Water Vulnerability Assessment Tools. Water Resour. Manag. 2012, 26, 4327–4346. https://doi.org/10.1007/s11269-012-0147-5.
Lillesand, T.; Kiefer, R.W.; Chipman, J. Remote Sensing and Image Interpretation, 7th ed.; Wiley: Hoboken, NJ, USA, 2015.
Russell, E.W. Aerial Photography and Remote Sensing for Soil Survey. Exp. Agric. 1978, 14, 400. https://doi.org/10.1017/S001447970000908X.
Mhangara, P.; Tsoeleng, L.T.; Mapurisa, W. Monitoring the Development of Artisanal Mines in South Africa. J. S. Afr. Inst. Min. Metall. 2020, 120, 299–306. https://doi.org/10.17159/2411-9717/938/2020.
Soudani, K.; François, C.; le Maire, G.; Le Dantec, V.; Dufrêne, E. Comparative Analysis of IKONOS, SPOT, and ETM+ Data for Leaf Area Index Estimation in Temperate Coniferous and Deciduous Forest Stands. Remote Sens. Environ. 2006, 102, 161–175. https://doi.org/10.1016/j.rse.2006.02.004.
Du, Y.; Zhang, Y.; Ling, F.; Wang, Q.; Li, W.; Li, X. Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sens. 2016, 8, 354. https://doi.org/10.3390/rs8040354.
Maxwell, A.E.; Warner, T.A.; Fang, F. Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review. Int. J. Remote Sens. 2018, 39, 2784–2817. https://doi.org/10.1080/01431161.2018.1433343.
Alloghani, M.; Al-Jumeily, D.; Mustafina, J.; Hussain, A.; Aljaaf, A.J. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In Supervised and Unsupervised Learning for Data Science; Berry, M.W., Mohamed, A., Yap, B.W., Eds.; Unsupervised and Semi-Supervised Learning; Springer International Publishing: Cham, Switzerland, 2020; pp. 3–21; ISBN 978-3-030-22475-2.
Bivand, R.S. Progress in the R Ecosystem for Representing and Handling Spatial Data. J. Geogr. Syst. 2021, 23, 515–546. https://doi.org/10.1007/s10109-020-00336-0.
Gomes, V.C.F.; Queiroz, G.R.; Ferreira, K.R. An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sens. 2020, 12, 1253. https://doi.org/10.3390/rs12081253.
Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.; Queiroz Feitosa, R.; van der Meer, F.; van der Werff, H.; van Coillie, F.; et al. Geographic Object-Based Image Analysis—Towards a New Paradigm. ISPRS J. Photogramm. Remote Sens. 2014, 87, 180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014.
Ibrahim, E.; Lema, L.; Barnabé, P.; Lacroix, P.; Pirard, E. Small-Scale Surface Mining of Gold Placers: Detection, Mapping, and Temporal Analysis through the Use of Free Satellite Imagery. Int. J. Appl. Earth Obs. Geoinf. 2020, 93, 102194. https://doi.org/10.1016/j.jag.2020.102194.
Ngom, N.M.; Mbaye, M.; Baratoux, D.; Baratoux, L.; Catry, T.; Dessay, N.; Faye, G.; Sow, E.H.; Delaitre, E. Mapping Artisanal and Small-Scale Gold Mining in Senegal Using Sentinel 2 Data. GeoHealth 2020, 4, e2020GH000310. https://doi.org/10.1029/2020GH000310.
Kimijima, S.; Sakakibara, M.; Nagai, M. Detection of Artisanal and Small-Scale Gold Mining Activities and Their Transformation Using Earth Observation, Nighttime Light, and Precipitation Data. Int. J. Environ. Res. Public Health 2021, 18, 10954. https://doi.org/10.3390/ijerph182010954.
Novellino, A.; Fleming, C. Footprint of Mining Sites along the Migori River Using Earth Observation. Available online: http://nora.nerc.ac.uk/id/eprint/529987/(accessed on 11 January 2022).
UNODC. Colombia Explotación de Oro de Aluvión Evidencias a Partir de Percepción Remota 2020; UNODC: Vienna, Austria, 2021.
Gallwey, J.; Robiati, C.; Coggan, J.; Vogt, D.; Eyre, M. A Sentinel-2 Based Multispectral Convolutional Neural Network for Detecting Artisanal Small-Scale Mining in Ghana: Applying Deep Learning to Shallow Mining. Remote Sens. Environ. 2020, 248, 111970. https://doi.org/10.1016/j.rse.2020.111970.
Torres, R.M.; Yuen, P.W.T.; Yuan, C.; Piper, J.; McCullough, C.; Godfree, P. Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications. J. Imaging 2020, 6, 87. https://doi.org/10.3390/jimaging6090087.
Finer, M.; Novoa, M.S.; Saurez, D.; Mamni, N. MAAP #140: Detecting Illegal Gold Mining in Rivers with Specialized Satellites; MAAP: Washington DC, USA, 2021.
Caballero Espejo, J.; Messinger, M.; Román-Dañobeytia, F.; Ascorra, C.; Fernandez, L.E.; Silman, M. Deforestation and Forest Degradation Due to Gold Mining in the Peruvian Amazon: A 34-Year Perspective. Remote Sens. 2018, 10, 1903. https://doi.org/10.3390/rs10121903.
Asner, G.P.; Llactayo, W.; Tupayachi, R.; Luna, E.R. Elevated Rates of Gold Mining in the Amazon Revealed through High-Resolution Monitoring. Proc. Natl. Acad. Sci. USA 2013, 110, 18454–18459. https://doi.org/10.1073/pnas.1318271110.
Asner, G.P.; Tupayachi, R. Accelerated Losses of Protected Forests from Gold Mining in the Peruvian Amazon. Environ. Res. Lett. 2016, 12, 094004. https://doi.org/10.1088/1748-9326/aa7dab.
Ding, Q.; Cheng, G.; Wang, Y.; Zhuang, D. Effects of Natural Factors on the Spatial Distribution of Heavy Metals in Soils Surrounding Mining Regions. Sci. Total Environ. 2017, 578, 577–585. https://doi.org/10.1016/j.scitotenv.2016.11.001.
Dethier, E.N.; Sartain, S.L.; Lutz, D.A. Heightened Levels and Seasonal Inversion of Riverine Suspended Sediment in a Tropical Biodiversity Hot Spot Due to Artisanal Gold Mining. Proc. Natl. Acad. Sci. USA 2019, 116, 23936–23941. https://doi.org/10.1073/pnas.1907842116.
Le Tourneau, F.-M.; Albert, B. Usage de La Télédétection Dans Un Contexte Pluridisciplinaire: Impact de l’orpaillage, Agriculture Amérindienne et Régénération Naturelle Dans Une Région Du Territoire Yanomami (Amazonie Brésilienne). Teledetection 2005, 4, 335–371.
Linarès, S.; Joubert, P.; Gond, V. Contre l’orpaillage clandestin: La télédétection. Espaces Nat. 2008, 23, 32–33.
Brognoli, C. Détection par L’imagerie Satellite des Sites D’orpaillage sur le Territoire de la Guyane Française: Compte-rendu de Mission en Guyane Française, 8 Février–7 Avril 2004. Available online: https://agritrop.cirad.fr/538587/(accessed on 11 January 2022).
Giuliani, G.; Chatenoux, B.; Piller, T.; Moser, F.; Lacroix, P. Data Cube on Demand (DCoD): Generating an Earth Observation Data Cube Anywhere in the World. Int. J. Appl. Earth Obs. Geoinf. 2020, 87, 102035. https://doi.org/10.1016/j.jag.2019.102035.
Lacroix, P.; Moser, F.; Benvenuti, A.; Piller, T.; Jensen, D.; Petersen, I.; Planque, M.; Ray, N. MapX: An Open Geospatial Platform to Manage, Analyze and Visualize Data on Natural Resources and the Environment. SoftwareX 2019, 9, 77–84. https://doi.org/10.1016/j.softx.2019.01.002.
Kilosho, J.; Stoop, N.; Verpoorten, M. The Social Minefield of Gold Digging in South-Kivu, DRC. The Case of Kamituga; Universiteit Antwerpen, Institute of Development Policy (IOB): Antwerpen, Belgium, 2015.
Guerschman, J.P.; Scarth, P.F.; McVicar, T.R.; Renzullo, L.J.; Malthus, T.J.; Stewart, J.B.; Rickards, J.E.; Trevithick, R. Assessing the Effects of Site Heterogeneity and Soil Properties When Unmixing Photosynthetic Vegetation, Non-Photosynthetic Vegetation and Bare Soil Fractions from Landsat and MODIS Data. Remote Sens. Environ. 2015, 161, 12–26. https://doi.org/10.1016/j.rse.2015.01.021.
IPIS. Interactive Map of Artisanal Mining Exploitation in Eastern DR Congo—2018 Update; IPIS: Antwerpen, Belgium, 2018.
Lymburner, L.; Botha, E.; Hestir, E.; Anstee, J.; Sagar, S.; Dekker, A.; Malthus, T. Landsat 8: Providing Continuity and Increased Precision for Measuring Multi-Decadal Time Series of Total Suspended Matter. Remote Sens. Environ. 2016, 185, 108–118. https://doi.org/10.1016/j.rse.2016.04.011.
Xing, Q.; Lou, M.; Chen, C.; Shi, P. Using in Situ and Satellite Hyperspectral Data to Estimate the Surface Suspended Sediments Concentrations in the Pearl River Estuary. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 731–738. https://doi.org/10.1109/JSTARS.2013.2238659.
Mueller, N.; Lewis, A.; Roberts, D.; Ring, S.; Melrose, R.; Sixsmith, J.; Lymburner, L.; McIntyre, A.; Tan, P.; Curnow, S.; et al. Water Observations from Space: Mapping Surface Water from 25 Years of Landsat Imagery across Australia. Remote Sens. Environ. 2016, 174, 341–352. https://doi.org/10.1016/j.rse.2015.11.003.
Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sens. Environ. 2017, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031.
Tondapu, G.; Markert, K.; Lindquist, E.J.; Wiell, D.; Díaz, A.S.P.; Johnson, G.; Ashmall, W.; Chishtie, F.; Ate, P.; Tenneson, K.; et al. A SERVIR FAO Open Source Partnership: Co-Development of Open Source Web Technologies Using Earth Observation for Land Cover Mapping. In AGU Fall Meeting Abstracts; American Geophysical Union: Washington, DC, USA, 2018; Volume 21.
Martin, P.G.; Payton, O.D.; Fardoulis, J.S.; Richards, D.A.; Scott, T.B. The Use of Unmanned Aerial Systems for the Mapping of Legacy Uranium Mines. J. Environ. Radioact. 2015, 143, 135–140. https://doi.org/10.1016/j.jenvrad.2015.02.004.
Moomen, A.-W.; Bertolotto, M.; Lacroix, P.; Jensen, D. Inadequate Adaptation of Geospatial Information for Sustainable Mining towards Agenda 2030 Sustainable Development Goals. J. Clean. Prod. 2019, 238, 117954. https://doi.org/10.1016/j.jclepro.2019.117954.
Moomen, A.-W.; Jensen, D.; Lacroix, P.; Bertolotto, M. Assessing the Policy Adoption and Impact of Geoinformation for Enhancing Sustainable Mining in Africa. J. Clean. Prod. 2019, 241, 118361. https://doi.org/10.1016/j.jclepro.2019.118361.
