Abstract
Since the rise of the gold price in 2000, artisanal and small-scale gold mining (ASGM) is a growing economic activity in developing countries. It represents a source of income for several millions of people in West Africa. Exploitation techniques have evolved from traditional gold panning to mechanization and use of chemical products that are harmful for the environment. Government strategies to control and regulate this activity are impeded by the difficulties to collect spatial information, due to the remote location and the mobile and informal natural of ASGM. Here we present and discuss the value of remote sensing techniques to complement the knowledge on artisanal mining impacts, including for detection of illegal sites, the evaluation of the degradation of soils and waters, the deforestation and the monitoring of expansion of ASGM with time. However, these techniques are blind regarding gender issues, labor relations, mobility, migration, and insecurity and need to be considered with knowledges from other disciplines. Remote sensing is also instilled with various powers accruing to those enabled to produce and interpret these data. Remote sensing should be therefore used in a reflexive manner that accounts for the social, ethical and political implications of ASGM governance informed by space observations.
Article Highlights
-
Artisanal and small-scale gold mining is an important economic activity in developing countries with gold resources
-
Observations from space can be used to detect mining sites and evaluate their impact on the environments
-
Remote sensing should be used in a reflexive and critical manner that accounts for the social, ethical and political implications of ASGM governance informed by space observations
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
Artisanal and small-scale gold mining (ASGM)—low-tech mineral, labor intensive extraction and processing—is a very important economic activity in developing countries with gold resources. In West Africa, for instance, thousands of artisanal gold mines provide employment for more than 2 millions of people, and livelihoods for at least three times more (IGF 2017). The number of gold miners and proportion of the population living from this sector vary from one country to another among the West African countries: Burkina (1,000,000 in 2017), Niger (450,000 in 2011), Côte d’Ivoire (503,100 in 2017), Ghana (1,100,000 in 2011), Guinea (300,000 in 2011), Mali (400,000 in 2016), Senegal (15,000 in 2014) and Sierra Leone (300,000 in 2011) (source: https://delvedatabase.org/data). These numbers are approximate, not recent, and most likely represent underestimation of present employment directly working in ASGM. These gold mines are frequented by men, women and children who rely on the income from artisanal mining and related activities to escape poverty or complement farming incomes, especially during the dry season (Cartier and Bürge 2011; Hilson and Garforth 2012). This activity is generally unlicensed and partakes in the predominantly informal economy that unfolds outside the legal domain of formal recognition by the State. In consequence, ASGM tends to be described as illegal or clandestine, by contrasts with the large-scale industrial mines (LSM). However, the distinction between industrial and artisanal mining should not be view as rigid or sharp, both because ASGM and LSM often interact throughout the mine cycle, and because ASGM is increasingly capitalized and mechanized. Recent studies indeed suggest that legal small-scale mining entrepreneurship is developing when the national framework promotes such activity (Hilson et al. 2018). Besides, formality and informality should not be equated to the presence or absence of rules. While artisanal mining occurs mainly outside the legal framework of the State, artisanal miners have developed complex and efficient modes of organization. In contrast, while industrial mining is regulated by the legal system of the State, such laws may sometimes be questioned for failing to retain a sufficient share of the mining rent, to protect the environment, or to downplay the rights of local populations in favor of those of foreign investors. Large-scale mining and artisanal mining are both responsible for environmental degradation in gold mining areas, but in different ways, as the development and exploitation techniques of artisanal mining sites are essentially uncontrolled. Together with rural and urban poverty (Hilson and Garforth 2012), the rise in the price of gold since 2000 is the main cause of the proliferation of these artisanal mines, with spikes subsequent to global crises, such as following 2009 global downturn and more recently the COVID pandemic. Technological factors, such as the development of metal detectors, have contributed to the proliferation of ASGM. Despite the fact that it represents a source of income for the local population, it also causes damage to the environment, potentially affecting directly the health of gold miners and people living in their vicinity.
Faced with the proliferation of these artisanal mines, governments have sometimes tried to ban artisanal mining, but more generally adopt various strategies to control or regulate this sector of activity, which can take different forms. When the strategy is to regulate the activity through formalization, gold miners are expected to access mining permits, which in practice is often hindered by important financial and social barriers and administrative obstacles (Hilson 2020; Tschakert 2009). However, this activity is mobile by nature, depending on new discoveries, as well as frequent expulsions from the mining sites. Some gold mining sites have been exploited for one or two centuries and are tolerated, and yet most remain in the realm of informality. The deployment of this activity in space and time, and its regulation, is therefore a complex issue involving many actors: the state and its representatives at various levels, the local authorities (e.g., heads of districts, mayors), the customary authorities (administrative heads of villages), investors from the political–economical elite, the landowners, as well as the villagers and immigrants from neighboring areas and countries.
The regulation of ASGM is accompanied by missions on the ground by the authorities, involving controls, the seizure or destruction of equipment, and so-called clearing operations of illegal sites. Conflicts with artisanal gold miners also occur in the case of attribution of exploration or exploitation permits to large/international mining companies. This regulatory effort is also accompanied by training of artisanal gold miners (e.g., training camps in Côte d'Ivoire) aimed at developing environmentally more friendly practices and the development of an activity within a legal framework. However, these efforts are strongly undermined by the costs and difficulties to monitor activities, which usually take place at large distance of major urban centers or capitals. Field observations (including aerial and unmanned aerial vehicles—UAVs) may not be conducted on a regular basis, expulsions are sometimes followed by the re-settlement of clandestine mining sites, whereas the boundaries of allocated corridors may not be observed by gold miners, especially when they are, or become, unproductive.
Therefore, to fill gaps related to the lack of geographic, spatial and temporal information on artisanal mining sites, researchers and governments, in different regions of the world, have used satellite data to identify, monitor and map artisanal mining sites and analyze/assess their environmental impacts. The objective of this article is to present an overview of the contributions of remote sensing approaches to the monitoring of ASGM and their environmental impact in developing countries. The focus is placed on West Africa, where solutions based on Earth Observations are generally under-exploited. Whereas applied research and exploration of mineral resources in West African received considerable attention over the last decades, research on the impact of mining activities and on mining environments has drawn the attention of international donors only recently, as illustrated with the recent installation of several Center of Excellences in this domain in Côte d’Ivoire, Niger and Guinée, with funding from the World Bank. This article also discusses the social aspects of the use of remote sensing techniques, including the compilation and presentation of data and facts on maps to the different actors (objectivity), as well as the risks associated with these techniques. The recent and future deployment of satellites orbiting the Earth with sensors with enhanced spectral capabilities and resolutions also offers new avenues for both research and support to public policies in this sector. These prospective aspects are discussed in the last section of this article.
The use of remote sensing to study and monitor areas degraded by gold mining activity began in the early 2000s in Brazil. The first study carried out was by Almeida-Filho and Shimabukuro (2000) and Almeida-Filho (2002). It focused on soil degradation. Our bibliographic synthesis therefore covers the period 2000–2021. Since this first study, the theme of "remote sensing and artisanal mining" has become more diversified. Some studies focus on the identification of exploitation areas (legal/illegal) (Gond et al. 2004; Laperche et al. 2008; Elmes et al. 2014; Gallwey et al. 2020; Asner and Tupayachi 2016; Almeida-Filho and Shimabukuro 2000; Lobo et al. 2018; LaJeunesse Connette et al. 2016; Le Tourneau and Albert 2005; Telmer and Stapper 2007). Other applications include the evaluation of deforestation (Rahm et al. 2014; Schueler et al. 2011; Poudori et al. 2001; Caballero Espejo et al. 2018; Diringer et al. 2020; Asner and Tupayachi 2016; Asner et al. 2009; Swenson et al. 2011), the measure of water turbidity related to ASGM (Lobo 2015; Gallay et al. 2018; Linares 2019; Lobo et al. 2018), and the analysis of gold panning expansion over time (Kusimi 2008; Manu et al. 2004; Obodai et al. 2019; Snapir et al. 2017; LaJeunesse Connette et al. 2016; Isidro et al. 2017). Following the work of Almeida-Filho (2002), the analysis of land degradation by ASGM has continued in many other regions of the world.
The basic motivation for these different studies is that human activity in the broad sense around mining sites (exploration, extraction, storage and processing of ore) and the natural degradation of abandoned mining sites, including the formation of acid mine drainage, have physical properties (usually optical) that are distinct from other types of land use. Once these properties are known, usually through field studies, the specific spectral signature of the gold mining sites is analyzed, in time and/or space. Analysis of the changes detected in these areas from space allows quantification of the environmental impacts (deforestation, increase in water sediment load, soil degradation), and the expansion of ASGM. Different approaches have been pursued depending on the objectives; they differ in the type of data used, the method of site identification, and/or the method of detecting changes over time. Concerning the data, Landsat images have been the most frequently used over the period 2000–2018, their main assets being their accessibility (free), a large global coverage, and long time series. More recently, authors of this type of work have used higher resolution optical data (SPOT, Sentinel-2, UK-DMC2, MODIS—moderate resolution imaging spectrometer—, Google Earth and UAVs), while others have chosen to work with radar data (Sentinel-1, JERS1, PALSAR, ENVISAT) to avoid constraints related to cloud cover, particularly in tropical areas. Sentinel-1 radar images have been efficiently used to monitor deforestation in the tropical forest of French Guiana (Ballère et al. 2021; Bouvet et al. 2018), and these methods identify the fraction of deforestation related to gold mining. The contribution of data from hyperspectral satellites (Hyperion) and data from airborne sensors (Hymap, AVIRIS) have also been explored to know the impact of mining discharges and abandoned mining sites on the environment. Studies have focused in particular on the impact of acid mine drainage (AMD), defined as acidification of surface water due to the weathering of minerals exposed to the air following mining operations (Choe et al. 2008; Buzzi et al. 2014; Riaza and Müller 2010; Riaza et al. 2015, 2012b; Swayze et al. 2000; Pfitzner et al. 2018; Percival et al. 2014; Kemper and Sommer 2002; Jeong et al. 2018; Jackisch et al. 2018; Davies and Calvin 2017). Some of these studies are very simple and qualitative: photo-interpretation, thresholding, and manual digitization of sites, while other more complex and powerful approaches are developing (use of artificial intelligence and statistical methods) and allow for more comprehensive, automated, and recurrent treatment of regions affected by this activity. In all cases, prior knowledge of the terrain, i.e., the characteristics of the gold washing sites and the techniques used to extract and process the ore, is necessary to conduct these studies. The characteristics of the gold mining sites vary according to the geology of the region (nature of the deposits), thus influencing the ore extraction techniques. These techniques vary widely from one site to another, ranging from shafts and tunnels up to a few tens of meters deep to reach vein deposit, or to open shafts or the panning of riverbeds for placer deposits. The morphology of the sites is also dependent on the type and geometry of the deposit (vein or placer type). The spatial organization of the extraction and processing sites also varies, in particular according to the presence or absence of nearby water sources (see paragraph describing the study sites). Our synthesis is structured around the following applications of remote sensing techniques focusing on capabilities and limitations for each of them: detection and identification of ASGM sites, soil degradation, deforestation and analysis of the temporal expansion of ASGM, and ASGM and water pollution as seen from space. This organization is justified by the fact that the first information is the occurrence of a mining site (detection), whereas use of remote sensing data has then focused on the consequences on different compartments of the natural environment: soil, vegetation and waters. Naturally, these compartments are open systems and communicate with each other. This section also includes a subsection focusing on research and applied prospective, based on the new capabilities of future satellites equipped with enhanced spectral capabilities and/or higher imaging resolutions. These sections are followed by a discussion on societal and ethical aspects of the use of space-based information for the governance of the mining sector. The discussion also emphasizes the use of research findings by various actors in the artisanal mining sector, and in particular in the operational applications of gold mining regulations that have developed in some countries as a result of academic work.
2 Remote Sensing Observation of ASGM
2.1 Detection of ASGM Sites
The most ubiquitous application of remote sensing techniques to ASGM is to address the question of the mapping mining sites, which may develop clandestinely in remote places. In French Guiana, following the government's plan to regulate and control ASGM, a feasibility study of the detection of gold panning sites by satellite imagery was conducted. This study, carried out by Gond and Brognoly (2005), provided information on the spectral and optical properties of gold mining areas. A detection algorithm was developed using the visible and infrared bands of a SPOT 4 image (20 m resolution) and Landsat 5 and 7 images (30 m resolution). The detection method was based on the contrasts between the observed object and its environment (Gond et al. 2004). These studies concluded that Satellite imagery is reliable for detection of mining sites despite the frequently dense cloud cover typical of equatorial regions. In the case of gold mining site detection by satellite imagery in the tropics, the observed object (the mining site) is distinguished from its environment (the tropical rainforest) by a strong contrast between the exposed ground and the surrounding vegetation (Fig. 1). Figure 2 illustrates the ASGM seen at different scales (from ground to space) in tropical areas (with an example in Côte d’Ivoire). This strong contrast will be revealed in a variable way on the spectral bands of the satellite data used. The contrast is more or less marked depending on the observation wavelength or the filter used. The major difficulty highlighted in this study is the similarity, from a spectral point of view, between the different types of bare soil: gold mining, deforestation for timber exploitation, cultivated areas, inhabited areas, etc..
The authors of the study in French Guiana therefore attempted to amplify this contrast by using spectral indices, which made it possible to identify threshold values to distinguish surfaces exploited for gold from other types of surfaces. A field campaign was conducted to validate these results. Once the gold mining sites were geolocated using this method, the information was collected in a geospatial database. Based on this study, the Guyanese government initiated remote sensing monitoring of gold mining activities to determine the location of illegal operations and to assess the expansion of the activity (Laperche et al. 2008). The government also created the “Observatoire de l'Activité Minière” (OAM) in 2008. The OAM currently monitors gold mining with the development of several processing lines that are used to monitor water turbidity and deforestation (Linares 2019). In 2019 during the presentation of the OAM 10-year review, it was reported that more than 2000 images from different sensors (Landsat 5 and 7 in 2006, SPOT 2–4 and 5 between 2007 and 2014, Landsat 8 between 2013 and 2017, Sentinel-2 since 2016) have been processed, which has allowed the detection of 15,439 sites and the digitization of 12,288 ha. However, the monitoring of the evolution of mining sites by optical satellite imagery faced problems related to cloud cover, which is frequent in tropical areas, and the temporal repeatability of Landsat and SPOT data did not allow for a sufficient number of cloud-free images, unlike what is now possible with Sentinel-2 data (Landsat-8 has a 16-day cycles, to be compared with the 5-day revisit frequency of the Sentinel-2a and b constellation). In Myanmar, LaJeunesse Connette et al. (2016) identified and mapped the extent and current expansion of artisanal mining activities using Landsat 8 satellite imagery and Google Earth imagery, producing the first public database on artisanal mining in that country. An approach based on photo-interpretation and comparison of satellite images at different dates was used in this study. The authors point out the constraint related to the resolution of Landsat data to detect gold panning sites, which are very small, and the similarity at the spectral level, which leads to confusion, hence the choice of photo-interpretation, which is more qualitative and subjective, using Google Earth.
In Ghana, Gallwey et al. (2020) applied deep learning to gold panning site detection. This study applied a convolutional neural network model to Sentinel-2 data. Results validated with field observations (test sites) showed less than 8% error, justifying the effectiveness of their approach. The model was also used to quantify the extent of deforestation related to illegal mining in the protected forests of Ghana and to estimate the effect of governance. The ASM land-use category increased by 15,000 ha over the period 2015–2019. However, this study also shows the effectiveness of the Ghanaian government's efforts to regulate gold mining in 2017 with a decrease of 6000 ha of impacted land. The deep learning approach also quantified the extent of illegal mining-related deforestation within Ghana's protected forests, measured at over 3500 ha, with 2400 of these lost since 2015. The results obtained with deep learning are much more accurate compared to those obtained using other machine learning methods such as random forest or multilayer perceptron (MLP). However, additional training data are needed in different climatic zones and land-use types to extend its application.
Several other studies have demonstrated the capacity of satellite imagery in the identification and detection of ASGM sites (e.g., Simionato et al. 2021), but several difficulties arise. These difficulties are related to the optical spectral signature of the sites and their spatial footprints. Depending on the objectives and the study area, the approach to be adopted must take these difficulties into account. Regarding the spectral signature, ASGM sites are often confused with other types of bare soil. Spatially, the size of the sites makes their detectability with medium-resolution data difficult, if not impossible. In Senegal, Ngom et al. (2020) proposed a method for detecting and mapping ASGM sites with the minimal confusion possible using Sentinel-2 data and the Google Earth Engine. The method is based on the study of spectral properties over time of different landcover types such as bare soil, water, vegetation, settlement and area used for ASGM. These authors used principal component analysis (PCA) to determine the optimal period of the year for mapping ASGM area, separability and threshold (SEaTH) to determine the optimal bands or spectral indices to discriminate ASGM from other land use. The most relevant bands served as band sets for automatic classification and mapping of the scenes achieved with support vector machine (SVM) classifier. This proposed method has been validated with an accuracy of more than 80% showing the effectiveness of Sentinel-2 data for mapping gold panning sites. Beyond detection of mining sites, Earth Observation from space can also be used to quantify the consequences of ASGM as a function of time and in different compartment of our natural environment, including soil, vegetation and rivers. These topics are addressed in the next section.
2.2 Soil Degradation
This section focuses on studies whose objectives are of documenting from space, as a function of time, the state of soil degradation as a result of ASGM. Soil degradation is defined as a change in soil health that results in a decrease in the capacity of the ecosystem to provide goods and services for its beneficiaries. Soil degradation generally refers to its acidification, erosion or compaction, the modification or destruction of the humus in relation to changes in the vegetation, the decrease of the amount of organic matter, and the pollution by toxic metals (Fig. 3). A pioneering study in this domain (Almeida-Filho 2002) used a 12-year Landsat-Thematic Mapper (TM) image time series (1987–1999) at 30-m resolution to analyze areas degraded by gold miners. The approach, based on a principal component analysis of the images, highlights areas of bare soil in relation to the vegetation of the surrounding savannah. It is followed by a post-classification that consists of image segmentation and classification of the segmented regions. The results of this study showed that degraded areas increased from 94.4 ha in 1987 to 404.4 ha in 1994. Then, following the cessation of ASGM, a re-vegetation of the land was observed, decreasing the bare areas from 311.2 ha in 1995 to 246.2 ha in 1999. However, this study showed that the areas that were intensively exploited revegetated more slowly than in other cases of de-vegetation, thus demonstrating the particularly negative impacts of ASGM on soil fertility. Following this study, studies have been initiated in other regions of the world, such as Indonesia and Brazil (Telmer and Stapper 2007; Lobo et al. 2016), with improved approaches but also based on the detection of changes in optical signatures. In Niger, Abass Saley et al. (2021) were able to map the evolution of the mining site of Koma Bangou during four decades using Landsat data. This study relied on spectral indices designed to be sensitive to minerals present in mining waste (such as oxides and hydrated minerals) and took advantage of the scarcity of the vegetation in this region. The authors of this work were able to clearly distinguish the expansion of mining waste, as well as the introduction and expansion of cyanidation (hydrometallurgical technique for extracting gold by converting the metal to a water-soluble coordination complex using an aqueous solution of cyanide) during the last decade.
Several studies of land degradation caused by mining activities in general (not just ASGM) have also focused on acid mine drainage, which can, in addition to the acidification water, contribute to the release of toxic metals from mining waste in the soils. Hyperspectral and multispectral data have been able to detect and map this phenomenon in Pennsylvania (Williams et al. 2002), Iran (Seifi et al. 2019; Shahriari et al. 2013), California (Montero et al. 2005) and South Africa (Mielke et al. 2014). They are often combined with airborne data to understand contamination in acidic water basins in mining areas. Hyperspectral data can provide early indications of the status of soil contamination generated by waste from sulfide-rich mines, allowing authorities to activate mitigation procedures. Riaza et al. (2012a, b) used Hyperion data (30 m resolution, demonstrator) and Hymap airborne data (5 m resolution) to map mine waste from massive sulfide deposits in the Iberian area. This study showed that mine dams, mill tailings and mine dumps in a variable state of pyrite oxidation are recognizable on these types of data. Acidic water pools at mine sites are spectrally remarkable and can be mapped with simple image processing procedures, including Minimum Noise Fraction Transform, Pixel Purity Index, N-Dimensional Visualizer, or Spectral Angle Mapper. In addition, acidic waters of different chemical composition that are responsible for soil degradation can be mapped using hyperspectral data, whether at high or low spatial resolution (Quental et al. 2013; Riaza et al. 2015).
2.3 Deforestation
Deforestation is one of the best known and most documented impacts of gold mining. It has been measured from space by several authors in many different regions. It is generally defined (in the remote sensing and gold mining theme) by a conversion of forest into mining sites, the transformation being particularly visible on satellite images (Fig. 4). In this section, we include both destruction of natural forests and degradation of areas previously used for agroforestry (e.g., cocoa growing). Taking advantage of the availability of more than 30 years of satellite imagery, it has been possible to determine deforestation and expansion in areas where these activities were known for decades. In the Amazonian Park of French Guiana, the WWF (World Wildlife Fund) raised the alarm bells on the catastrophic situation caused by gold washing. Aerial overflights conducted three times a year to survey the sites showed record pressure from illegal gold mining in this protected area. Impacted areas had increased by 24% in 2016 and 139 active sites were identified (WWF 2016). One of the OAM processing chains mentioned in the previous paragraph is used to track areas impacted by ASGM in French Guiana and thus quantify deforestation. With this chain, monitoring is done on a weekly basis with Sentinel-2 data. The operating mode of the process is presented in Fig. 5. Thanks to this processing chain, several illegal operations have been detected and interrupted. One example is the destruction of the Grande Bagot creek detected by the “Office National des Forêts (ONF)” on the Sentinel-2 image of 15 May 2019. The gendarmerie intervened in 2019 on this site to put an end to illegal activities.
In the Peruvian Amazon in the department of Madre de Dios, a study by Swenson et al. (2011) used Landsat 5 data acquired between the period of 2003–2009 to quantify deforestation through an ISODATA unsupervised classification approach in the Guacamayo and Colorado-Piquiri mining areas. This study showed that, from 2003 to 2009, mining at both sites converted 6600 ha of primary rainforest and wetlands into large areas of ponds and tailings. This destruction of the Peruvian forest has been strongly correlated with the increase in the price of gold (~ 18% per year). It increased from 292 ha/year between 2003 and 2006 to 1915 ha/year between 2006 and 2009, a sixfold increase. A further study by Caballero Espejo et al. (2018) using CLASlite (Asner et al. 2009) and Global Forest Change (Hansen et al. 2013) algorithms applied to Landsat data in the same area showed that ASGM caused deforestation quantified at 100,000 ha over a 34-year period (1984–2017) in the Peruvian forest.
In southern Ghana, Snapir et al. (2017) applied an unsupervised classification approach on UK—Disaster Monitoring Constellation-2 (UK-DMC2) satellite data at 22 m resolution to analyze the destruction of cocoa growing areas due to illegal gold mining between 2011 and 2015. This study showed that ASGM expanded very rapidly. The exploited areas tripled over the study period along the Offin, Ankobra, Birim, Anum, and Tano rivers. Despite the positive results of this study, the authors noted the difficulties of working with optical imagery due to cloud cover over the area, which poses a major challenge for mapping and monitoring illegal mining activities in southern Ghana. The study by Forkuor et al. (2020) therefore explores the contribution of Sentinel-1 radar imagery for change identification and detection. A change detection approach, based on three time-series characteristics—minimum, average, maximum—was used to calculate an appropriate backscatter threshold for identifying/detecting mining-induced land-cover changes in the study area. This study showed a downward trend in the evolution of ASGM in southern Ghana from 2015 to 2019 illustrating the impacts of the efforts of the Ghanaian government in the fight against illegal gold panning. It should be noted that even radar data have limitations in tropical regions, as Forkuor et al. (2020) reported that 25% of the Sentinel-1 data were unusable due to atmospheric effects from high intensity rainfall events.
2.4 Water Turbidity
Remote sensing is used to analyze the impact of gold mining on rivers. Mining is most often carried out along streams, resulting in significant sediment discharge into rivers (Fig. 6). In this case, the turbidity of the water is due to suspended matter following discharge of mining waste into the rivers.
In 2017, the French public organisation BRGM developed a telemetry system for water turbidity in French Guiana (Linares 2019). This system consisted in transforming the reflectance of turbid waters, measured from Sentinel-2 band 4 into a turbidity value expressed in nephelometric turbidity unit (NTU). It was first applied by the Amazonian Park of French Guiana for a turbidity observatory of the Maroni. The method was consolidated a few years later with approaches conducted by Gallay et al. (2018). In this study, the authors combined field data (water flow, precipitation, suspended sediment concentration, suspended sediment load estimates) and satellite data (MODIS) to monitor the evolution of suspended sediment fluxes in the Maroni and Oyapock rivers in French Guiana. The objective was to assess whether changes in sediment loads were associated with deforestation and removal of soil layers caused by the development of mining activities in the two rivers. Results showed that 15 years of intensive mining activities have altered the concentration of suspended sediment in the Maroni River, significantly increasing sediment loads and yields since 2009. This increase in sediment discharge is also correlated with deforestation along with the development of the mining activity (Gallay et al. 2018). This approach, also tested in Burkina Faso, has proven to be very effective for monitoring water turbidity (Robert et al. 2016). However, the resolution of MODIS data (250 m) does not allow detection of turbid waters of small areas. Recently, this processing chain has been adapted to Sentinel-2 data and the operationality of this tool will allow monitoring of pollution of waterways related to mining activities. The work of Lobo et al. (2018) conducted on the Tapajos River in the Amazon quantified, particularly from Sentinel-2 data, the effects of suspended solids derived from mining activities on optical properties. In this study, the aim was to relate the reflectance of satellite data to the total amount of suspended solids in the water. The authors concluded that the inorganic nature of the mine tailings affected the underwater scalar irradiance in the Tapajós River basin. For tributaries with little or no influence of mine tailings, the waters are relatively more absorbent.
2.5 Future Capabilities, Persistent Limitations and Avenues for Interdisciplinary Research
With increasing temporal, spatial and spectral resolution, the future sensors and satellites imaging the surface of the Earth will offer new capabilities for monitoring the development of ASGM and assessing their consequences on the environment. In particular, hyperspectral imaging, which provide the capabilities to map the concentration of certain types of minerals, will be useful for mapping waste, cyanidation areas and acid mining drainage in detail. A resolution of a few meters to a few tens of meters per pixel will be required for such applications. For this purpose, it is possible to mention the PRISMA (PRecursore IperSpettrale della Missione Applicativa) and HySIS (HyperSpectral Imaging Satellite) satellites put into orbit in recent years and the future HSI (HyperSpectral Imager, Mücke et al. 2019) and HISUI (Hyperspectral Imager SUIte, Tachikawa et al. 2012) satellite missions. The spatial resolution of this new generation of satellite (30 m) will remain a possible limitation for mapping sparse and disseminated mining waste. Studies using hyperspectral satellite data will develop with these new generation of satellites, knowing that Hyperion was a demonstrator of this technology and that the spatial and temporal coverage of Hyperion data remain limited. For example, no Hyperion data are available for the Kharakhéna and Bantakokouta sites considered in the recent study of Ngom et al. (2020), while only one scene has been acquired at the Kokumbo site in Côte d'Ivoire, but this one has a high cloud cover. This example illustrates the fact that cloud coverage will limit the capabilities of hyperspectral sensors as it does for satellite imagery. Only high revisit frequency may overcome this obstacle in tropical regions, during the rainy season. The recent elaboration of new techniques to monitor deforestation (Reiche et al. 2021; Ballère et al. 2021; Bouvet et al. 2018) on the weekly basis from Sentinel imagery may provide additional relevant data with comparable or higher performance than optical imagery to detect artisanal mining sites in their early phases of development.
It should be finally noted here that all these techniques remain (and will likely remain) blind regarding gender issues, labor relations, insecurity on mining sites, and migrations related to gold panning, which all need to be considered in the governance of this sector. It is therefore clear that the governance of ASGM informed by space-based observations must include knowledge from other disciplines in an interdisciplinary approach. They are many possible connections between these issues and observations that can be inferred from space, which offers avenues for interdisciplinary research in geology, environment, social, political, economic and health sciences. The next section of this manuscript therefore addresses the social and ethical aspects of a governance of ASGM informed by remote sensing observations and lists some of these outstanding questions that may be addressed by interdisciplinary research.
3 Governance of ASGM Informed by Remote Sensing Observations: Social and Ethical Aspects
In many ways, remote sensing observations promise to complement the knowledge on artisanal mining impacts in the areas where it occurs, and which are often remote and hardly accessible for the agents of the concerned governments' branches. More broadly conceived, remote sensing observations complement the available tools (such as census, mapping, agents' delegation on site, episodic reporting, etc.) to a state to properly "see" the territory it is assumed to govern (Scott 2008) by objectifying and reducing it in legible formats. As any technology of the sight, remote sensing is also necessarily parceled in what it enables to see, and inevitably instilled with various powers accruing to those enabled to produce, make accessible (or not), and interpret through different frames and political agendas "objectified" representations of the landscape (Witjes and Olbrich 2017; Litfin 2002; Hall 2017; Davis et al. 2021). This final section is thus a call to the actors of this dynamic field to use remote sensing in a reflexive manner that accounts for the social and political implications of ASGM governance informed by space observations and that fosters access to information and enhance knowledge across levels beyond government. Taking stock of the prolific social science scholarship on artisanal and small-scale mining, we briefly point to some of the major issues that these considerations raise.
3.1 Earth Observation for an Inclusive Formalization Policy
As an instrument of governance, Earth Observation (EO) from space (or remote sensing) cannot be disentangled from current major regulatory trends. Spurred by major donors such as the World Bank and various UN agencies, the push toward the formalization of the artisanal mining sector has become a central piece of the development strategy of several countries in West Africa (Hilson 2017). Yet, after 15 years, dominant top-down formalization strategies (e.g., ASGM licenses, corridors, etc.) have overall had very limited results (Van Bockstael 2019; Hilson 2017; Geenen 2012) when not merely counterproductive (Marshall and Veiga 2017; Hilson and Maconachie 2017; Banchirigah 2006). The passage from tolerance to formality (by dedicating specific sections to ASGM in the last round of mining codes' revision) has de facto placed most miners in the realm of illegality (Van Bockstael 2014; Hilson 2013), notwithstanding “vulnerabilizing” (Fisher 2007), “marginalizing” (Tschakert and Singha 2007; Sauerwein 2020), or “criminalizing” (Crawford and Botchwey 2018) informal actors. Indeed, while ASGM relies on ephemeral and moving sites, constantly reconfigured by artisanal miners’ mobility, formalization relies on the contrary on a fixation of the activity and the definition of specific spaces where it is legal (or not) to exploit. From the grounded perspective of the artisanal miners, the fraught equation between informality and illegality has then mainly translated in the multiplication of bans and (often violent) evictions (Bolay 2017; Dessertine 2019; Dessertine and Noûs 2021; Werthmann 2006; Hilson 2017; Wireko-Gyebi et al. 2020), putting at risk the livelihoods of thousands of miners and their dependents. It is therefore crucial that remote sensing instruments support a truly inclusive formalization policy (e.g., Hilson 2020), rather than merely implementing a surveillance apparatus of control and repression.
3.2 The Importance of an Open-Access EO Information and Products
Despite its mainly informal nature, artisanal mining is highly regulated by various forms of non-state “practical norms of real governance” (De Herdt and Olivier de Sardan 2015). Local actors such as “customary” village authorities or mine authorities (as the vigilante groups called tombolomaw in Mande populations, for example) enforce the everyday real governance of mining sites (collection of “informal” taxes, resolution of conflicts, allocation of plots, etc.) (Brottem and Ba 2019; Persaud et al. 2017; Grätz 2004, 2009; Sangaré et al. 2017), whose revenues tangibly contribute to local development (e.g., Teschner 2014; Bazillier and Girard 2018). The relative discrepancy between state and non-state actors and norms urgently raises the question of access to information generated by remotely sensed imagery. While remote sensing information can expectedly help governments in decision making and monitoring artisanal mining activities at a distance, i.e., identify new "rushes", deforestation issues, or informal settlements, it is still vital that information flows extend beyond government offices and get accessed by local level actors who are often considered more legitimate both by miners and local populations. This is even more so regarding environmental deleterious impacts, whose "slow violence" (Nixon 2011)—i.e., its long-term consequences being often synchronically imperceptible—can be made more legible through longitudinal mapping.
Likewise, the increasingly open-access feature of EO makes it subject of political disputes along what term the measurement–commensuration–diffusion continuum (Noucher et al. 2021), that is the phases though which environmental governance quantifies, categorizes, and communicates environmental problems. At each of these stages, EO data may be embedded in and support different political agendas. In French Guiana, these authors show how the same reality of illegal artisanal gold mining is differently measured and interpreted by different actors within and outside the State. While the French State relied on its own EO data and was reluctant to make it available to the public, the WWF launched a competing mapping project through open access EO and participatory, crowed sourced, mapping. Not only did the quantification of the activity vary, but also its interpretation and the political measures that were communicated. Such discrepancies also manifested within state structures, with armed forces being more concerned by the legal status of operations and parks and conservation with the stage and intensity of the activity. WWF, instead, made no such difference at all as the NGO's aim was rather to challenge the State to increase disclosure on environmental impacts, and alert the public on deforestation. All in all, beyond claims for objectivity, EO provides information that can serve contradictory goals such as support decision-making or legitimize decisions, establish forms of control or contest them. This calls for attention to the inherent political uses and to the discrepancies in accessing, interpreting, and diffusing information gathered from EO, for such information may both serve coercive aims as well as play a role in informing public debate in the context of a more concerted and inclusive regulation of artisanal mining.
The fact that the vast majority of countries facing ASGM do not produce satellite data themselves implies that these states may—temporarily—remain depend of open-access space data initiatives from countries with well-developed space programs. However, the more intensive use of earth observation products contributes to the development of a community of researchers and end-users, which will be ready to use their own satellite data in the future, mentioning, for instance, the recent launch of African satellites and the emergence of new space programs in several African countries (Pović et al. 2018).
3.3 Integration of Space-Based Results within Interdisciplinary Research Results into Governance Measure
Finally, as demonstrated in this review of remote sensing uses in mining and environmental governance, visualization from space can indeed inform on a series of environmental impacts such as pollution of rivers and water basins, deforestation, but also urbanization/new settlements (e.g., Gagnol et al. 2019). However, they are of little help to understand social questions that equally partake in sustainability issues, such as gender (Buss et al. 2019; Huggins et al. 2017; Hinton et al. 2017), labor relations (Lanzano and Arnaldi di Balme 2021; Bolay 2017; Hilson 2010), mobility and migration related to artisanal mining (Dessertine 2016; Werthmann 2010; Bolay 2014, 2016), or insecurity (Ros-Tonen et al. 2021; Lanzano et al. 2021), all while they underlie much of miners' practices and their potentially environmentally unsound consequences. As argued by Fisher et al. (2021), plural knowledges are necessary for addressing the complexity of sustainability in gold mining. As one among other forms of knowledge, remote sensing will probably need to be cross fertilized with other knowledges; scientific and formal certainly, but also more grounded forms of local (ecological) knowledge (e.g., Duff and Downs 2019; Hennessy 2015). This is both a conceptual challenge regarding how to actually do interdisciplinary research, and an operational one requiring grassroots approaches as traditionally devised in ethnographic methods or collaborative approaches potentially implicating crowed sourced data that have proven to be efficient, for instance in water governance (Buytaert et al. 2014).
Considering that the actual governance of mining sites, such as where, when, and by whom are gold deposit exploited, usually bypass government structures, information generated by remote sensing technologies can increase the local knowledge about mining and its impacts, provided that it is made accessible to the local actors of the “real governance” of artisanal mining. Yet, as argued by Poncian (2020) in a study on the role of Information and Communication Technologies (ICT) in the citizen engagement of extractive resources governance in Tanzania, while government bodies can indeed increase the flow of information through conventional and new media channels (in particular social medias), this does not necessarily translate into more citizen engagement. To actually increase engagement and the use of information by the local actors of governance, communication should, according to this author, not be limited to unidirectional, top-down, channels. This can be achieved through the usual apparatus of onsite workshops and trainings, as well as through a more interactive use of media platforms throughout which qualitative information can flow up to governments. For instance, McFarlane and Rebecca (2019) study of artisanal mining in Myanmar uses USGS Landsat observations and Google Earth data to assess the growth of illegal suction pump mining over the last twenty years. To understand these changes, it builds upon human centered design workshops, field visits, household surveys and interviews to interpret the social, environmental and economic implications of this activity from the perspective of the workers and local population, upon which policy measures are proposed. Thus, as argued by Davis et al. (2021) more collaborative approaches to remote sensing that includes local stakeholders and researchers across data acquisition, analysis and dissemination in “active collaboration and knowledge exchange can help balance existing power structures” and ensure that local concerns and perceptions of sustainability are integrated in governance measures.
4 Conclusions
The article was conceived as an entry point in the already vast literature about remote sensing characterization of artisanal and small-scale gold mining sites. It is primarily intended either for students and researchers of the academic domain willing to develop further research in this domain. The present-day capabilities of satellites orbiting the Earth and their sensors offer various applications, including identification of ASGM site, mapping degradation of soil, deforestation, characterization of sediments discharge in rivers, and short- or long-term mapping of the expansion of artisanal gold mines and their waste. The development of a new generation of satellites with hyperspectral capabilities will open new avenues for research, as detailed characterization of minerals present in mining waste will become possible. This article also emphasizes that the complex issues associated with the regulation of mining sites cannot be solved without interdisciplinary and collaborative approaches combining different sources of knowledge, and involving the different actors, including researchers and local stakeholders. Within this framework, remote sensing data and their scientific interpretations provide maps, quantified interpretations, and reconstruction of the past evolution of the landscape. These data and products should be considered as pedagogic tools of great values that can be made easily accessible to the different actors via on-line platforms and dedicated applications, following a pioneering example being proposed in Senegal (Ngom and Mbaye 2021). Remote sensing data should be also used in a reflexive approach, that accounts for the economic, social, and ethical implications of ASGM regulations.
References
Abass Saley A, Baratoux D, Baratoux L, Ahoussi KE, Yao KA, Kouamé KJ (2021) Evolution of the Koma Bangou gold panning site (Niger) from 1984 to 2020 using Landsat imagery. Earth Space Sci 8:e2021EA001879. https://doi.org/10.1029/2021EA001879
Almeida-Filho R (2002) Digital processing of a Landsat-TM time series for mapping and monitoring degraded areas caused by independent gold miners, Roraima State. Braz Amaz Remote Sens Environ 79(1):42–50. https://doi.org/10.1016/S0034-4257(01)00237-1
Almeida-Filho R, Shimabukuro YE (2000) Detecting areas disturbed by gold mining activities through JERS-1 SAR images, Roraima State, Brazilian Amazon. Int J Remote Sens 21(17):3357–3362. https://doi.org/10.1080/014311600750019967
Asner GP, Tupayachi R (2016) Accelerated losses of protected forests from gold mining in the Peruvian Amazon. Environ Res Lett 12(9):094004. https://doi.org/10.1088/1748-9326/aa7dab
Asner GP, Knapp DE, Balaji A, Paez-Acosta G (2009) Automated mapping of tropical deforestation and forest degradation: CLASlite. J Appl Remote Sens 3(1):033543. https://doi.org/10.1117/1.3223675
Ballère M, Bouvet A, Mermoz S, Le Toan T, Koleck T, Bedeau C, André M, Forestier E, Frison P-L, Lardeux C (2021) SAR Data for tropical forest disturbance alerts in French Guiana: benefit over optical imagery. Remote Sens Environ 252(janvier):112159. https://doi.org/10.1016/j.rse.2020.112159
Bazillier R, Girard V (2018) The gold digger and the machine: evidence on the distributive effect of the artisanal and industrial Gold Rushes in Burkina Faso. AFD research papers series, July, 77
Bolay M (2014) When miners become “Foreigners”: competing categorizations within gold mining spaces in Guinea. Resour Policy 40:117–127. https://doi.org/10.1016/j.resourpol.2014.02.001
Bolay M (2016) Il faut être là où l’or sort ! De l’itinérance temporaire au maintien d’un mode de vie mobile chez les orpailleurs de Haute Guinée. Vienna J Afr Stud 16:111–135. https://doi.org/10.25365/phaidra.266_06
Bolay M. (2017) Gold journeys: expulsion-induced mobility and the making of artisanal mining spaces in West Africa. An ethnography of Itinerant Labour at the Bottom of the Gold Supply Chain. Ph.D. Thesis, Université de Neuchâtel, Faculté des lettres et sciences humaines, 408 p
Bouvet A, Mermoz S, Ballère M, Koleck T, Le Toan T (2018) Use of the SAR shadowing effect for deforestation detection with sentinel-1 time series. Remote Sens 10(8):1250. https://doi.org/10.3390/rs10081250
Brottem LV, Ba L (2019) Gendered livelihoods and land tenure: the case of artisanal gold miners in Mali, West Africa. Geoforum 105:54–62. https://doi.org/10.1016/j.geoforum.2019.07.005
Buss D et al (2019) Gender and artisanal and small-scale mining: implications for formalization. Extr Ind Soc 6(4):1101–1112. https://doi.org/10.1016/j.exis.2019.10.010
Buytaert W et al (2014) Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development. Front Earth Sci 2:26. https://doi.org/10.3389/feart.2014.00026
Buzzi J, Riaza A, García-Meléndez E, Weide S, Bachmann M (2014) Mapping changes in a recovering mine site with hyperspectral airborne HyMap imagery (Sotiel, SW Spain). Minerals 4(2):313–329. https://doi.org/10.3390/min4020313
Caballero Espejo J, Messinger M, Román-Dañobeytia F, Ascorra C, Fernandez L, Silman M (2018) Deforestation and forest degradation due to gold mining in the Peruvian Amazon: a 34-year perspective. Remote Sens 10(12):1903. https://doi.org/10.3390/rs10121903
Cartier LE, Bürge M (2011) Agriculture and artisanal gold mining in Sierra Leone: alternatives or complement? J Int Dev 23(8):1080–1099. https://doi.org/10.1002/jid.1833
Choe E et al (2008) Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: a case study of the Rodalquilar mining area, SE Spain. Remote Sens Environ 112(7):3222–3233. https://doi.org/10.1016/j.rse.2008.03.017
Crawford G, Botchwey G (2018) Militarisation and criminalisation of artisanal and small-scale gold mining: the state and the so-called ‘galamsey menace’ in Ghana. Rev Afr Polit Econ 45(156):267–334
Davies GE, Calvin WM (2017) Mapping acidic mine waste with seasonal airborne hyperspectral imagery at varying spatial scales. Environ Earth Sci 76(12):432. https://doi.org/10.1007/s12665-017-6763-x
Davis DS et al (2021) The aerial panopticon and the ethics of archaeological remote sensing in sacred cultural spaces. Archaeol Prospect 28(3):305–320. https://doi.org/10.1002/arp.1819
De Herdt T, de Sardan JPO (2015) Real governance and practical norms in Sub-Saharan Africa: the game of the rules, 1st edn. Routledge, London. https://doi.org/10.4324/9781315723365
Dessertine A (2016) From pickaxes to metal detectors: gold mining mobility and space in Upper Guinea, Guinea Conakry. Extr Ind Soc 3(2):435–441. https://doi.org/10.1016/j.exis.2016.02.010
Dessertine A (2019) Une justice foncièrement autre? Pouvoir et foncier en contexte minier aurifère (Guinée). Revue Int Etud Dév 238(2):141. https://doi.org/10.3917/ried.238.0141
Dessertine A, Noûs C (2021) Hybrid territorialisation: a reconfiguration of rural spaces through gold mining in Upper Guinea. Polit Geogr 86:102348. https://doi.org/10.1016/j.polgeo.2021.102348
Diringer SE et al (2020) Deforestation due to artisanal and small-scale gold mining exacerbates soil and mercury mobilization in Madre de Dios, Peru. Environ Sci Technol 11:286–296
Duff PM, Downs TJ (2019) Frontline narratives on sustainable development challenges/opportunities in the ‘illegal’ gold mining region of Madre de Dios, Peru: informing an integrative collaborative response. Extr Ind Soc 6(2):552–561. https://doi.org/10.1016/j.exis.2019.01.005
Elmes A et al (2014) Mapping licit and illicit mining activity in the Madre de Dios Region of Peru. Remote Sens Lett 5(10):882–891. https://doi.org/10.1080/2150704X.2014.973080
Fisher E (2007) Occupying the margins: labour integration and social exclusion in artisanal mining in Tanzania. Dev Change 38(4):735–760. https://doi.org/10.1111/j.1467-7660.2007.00431.x
Fisher E et al (2021) Transforming matters: sustaining gold lifeways in artisanal and small-scale mining. Curr Opin Environ Sustain 49:190–200. https://doi.org/10.1016/j.cosust.2021.06.010
Forkuor G, Ullmann T, Griesbeck M (2020) Mapping and monitoring small-scale mining activities in Ghana using Sentinel-1 time series (2015–2019). Remote Sens 12(6):911. https://doi.org/10.3390/rs12060911
Gagnol L, Magrin G, Chevrillon-Guibert R (2019) Une trajectoire urbaine insolite en Mauritanie. L’Espace Polit. https://doi.org/10.4000/espacepolitique.6562
Gallay M et al (2018) Impact of land degradation from mining activities on the sediment fluxes in two large rivers of French Guiana. Land Degrad Dev 29(12):4323–4336. https://doi.org/10.1002/ldr.3150
Gallwey J, Robiati C, Coggan J, Vogt D, Eyre M (2020) 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 248:111970. https://doi.org/10.1016/j.rse.2020.111970
Geenen S (2012) A dangerous bet: the challenges of formalizing artisanal mining in the Democratic Republic of Congo. Resour Policy 37(3):322–330. https://doi.org/10.1016/j.resourpol.2012.02.004
Gond V, Brognoly C (2005) Télédétection et aménagement du territoire: localisation et identification des sites d’orpaillage en Guyane française. Bois Forêts Trop 286(4):5–13
Gond V, Bartholomé E, Ouattara F, Nonguierma A, Bado L (2004) Surveillance et cartographie des plans d’eau et des zones humides et inondables en régions arides avec l’instrument VEGETATION embarqué sur SPOT-4. Int J Remote Sens 25(5):987–1004. https://doi.org/10.1080/0143116031000139908
Grätz T (2004) Les frontières de l’orpaillage en Afrique occidentale. Autrepart 30(2):135. https://doi.org/10.3917/autr.030.0135
Grätz T (2009) Moralities, risk and rules in West African artisanal gold mining communities: a case study of Northern Benin. Resour Policy 34(1–2):12–17. https://doi.org/10.1016/j.resourpol.2008.11.002
Hall R (2017) Surveillance and public space. In: Oxford research encyclopedia of communication. Oxford University Press, Oxford. https://doi.org/10.1093/acrefore/9780190228613.013.145
Hansen MC et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853. https://doi.org/10.1126/science.1244693
Hennesy L (2015) Where there is no company: indigenous peoples, sustainability, and the challenges of mid-stream mining reforms in Guyana’s small-scale gold sector. New Polit Econ 20(1):126–153. https://doi.org/10.1080/13563467.2014.914158
Hilson G (2010) Child labour in African artisanal mining communities: experiences from Northern Ghana: child labour in artisanal mining communities (Northern Ghana). Dev Change 41(3):445–473. https://doi.org/10.1111/j.1467-7660.2010.01646.x
Hilson G (2013) “Creating” rural informality. SAIS Rev Int Aff 33(1):51–64
Hilson G (2017) Shootings and burning excavators: some rapid reflections on the Government of Ghana’s handling of the informal Galamsey mining ‘menace.’ Resour Policy 54:109–116. https://doi.org/10.1016/j.resourpol.2017.09.009
Hilson G (2020) The ‘Zambia Model’: a blueprint for formalizing artisanal and small-scale mining in Sub-Saharan Africa? Resour Policy 68:101765. https://doi.org/10.1016/j.resourpol.2020.101765
Hilson G, Garforth C (2012) Agricultural poverty’ and the expansion of artisanal mining in Sub-Saharan Africa: experiences from Southwest Mali and Southeast Ghana. Popul Res Policy Rev 31(3):435–464. https://doi.org/10.1007/s11113-012-9229-6
Hilson G, Maconachie R (2017) Formalising artisanal and small-scale mining: insights. Contest Clarif Area 49(4):443–451. https://doi.org/10.1111/area.12328
Hilson G, Hilson A, Maconachie R (2018) Opportunity or necessity? Conceptualizing entrepreneurship at African small-scale mines. Technol Forecast Soc Change 131:286–302. https://doi.org/10.1016/j.techfore.2017.12.008
Hinton J, Hinton B, Veiga, MM (2017) Women in artisanal and small-scale mining in Africa: pit women and others. In: Women miners in developing countries: pit women and others, Kuntala Lahiri-Dutt and Martha Macintyre, 1 edn. Routledge, London. https://doi.org/10.4324/9781315233734
Huggins C, Buss D, Rutherford B (2017) A ‘cartography of concern’: place-making practices and gender in the artisanal mining sector in Africa. Geoforum 83:142–152. https://doi.org/10.1016/j.geoforum.2016.09.009
IGF (2017) Intergovernmental forum on mining, minerals, metals and sustainable development (IGF). Global trends in artisanal and small-scale mining (ASM): a review of key numbers and issues. IISD, Winnipeg
Isidro CM, McIntyre N, Lechner AM, Callow I (2017) Applicability of earth observation for identifying small-scale mining footprints in a wet tropical region. Remote Sens 9(9):945. https://doi.org/10.3390/rs9090945
Jackisch R, Loren S, Zimmermann R, Möckel R, Gloaguen R (2018) Drone-borne hyperspectral monitoring of acid mine drainage: an example from the Sokolov lignite district. Remote Sens 10(3):385. https://doi.org/10.3390/rs10030385
Jeong Y, Yu J, Wang L, Shin J (2018) Spectral responses of As and Pb contamination in tailings of a hydrothermal ore deposit: a case study of Samgwang mine, South Korea. Remote Sens 10(11):1830. https://doi.org/10.3390/rs10111830
Kemper T, Sommer S (2002) Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ Sci Technol 36(12):2742–2747. https://doi.org/10.1021/es015747j
Kusimi JM (2008) Assessing land use and land cover change in the Wassa West District of Ghana using remote sensing. GeoJournal 71(4):249–259. https://doi.org/10.1007/s10708-008-9172-6
LaJeunesse CK et al (2016) Assessment of mining extent and expansion in Myanmar based on freely-available satellite imagery. Remote Sens 8(11):912. https://doi.org/10.3390/rs8110912
Lanzano C, di Balme LA (2021) Who owns the mud? Valuable leftovers, sociotechnical innovation and changing relations of production in artisanal gold mining (Burkina Faso). J Agrar Change 21(3):433–458. https://doi.org/10.1111/joac.12412
Lanzano C, Luning S, Ouédraogo A (2021). Insécurité au Burkina Faso—au-delà des minerais de conflit les liens complexes entre l’exploitation aurifere artisanale et la violence. The Nordic Africa Institute. NAI Policy Notes 2021:4
Laperche V, Nontanovanh M, Thomassin JF (2008) Synthèse critique des connaissances sur les conséquences environnementales de l’orpaillage en Guyanes. BRGM/RP-56652-FR. http://infoterre.brgm.fr/rapports/RP-56652-FR.pdf
Le Tourneau F-M, Albert B (2005) 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). Télédétection 4(4):335–371
Linares S (2019) Observatoire de l’orpaillage illégal: 10 ans de télédétection au service de l’action. J Natl Géonumér l’AFIGEO DECRYPTAGEO, Artois Expo, Arras, Hauts de France
Litfin KT (2002) Public eyes: satellite imagery, the globalization of transparency, and new networks of surveillance. In: Rosneau JN, Singh JP (eds) Information technologies and global politics: the changing scope of power and governance. State of New York University Press, New York, pp 65–88
Lobo F (2015) Spatial and temporal analysis of water siltation caused by artisanal small-scale gold mining in the Tapajós Water Basin, Brazilian Amazon: an optics and remote sensing approach. Ph.D., University of Victoria. https://doi.org/10.13140/RG.2.1.4455.6247
Lobo FL, Costa M, Novo E, Telmer K (2016) 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 8(7):579. https://doi.org/10.3390/rs8070579
Lobo FL, Novo EMLM, Barbosa CCF, de Vasconcelos VHF (2018) Monitoring water siltation caused by small-scale gold mining in Amazonian rivers using multi-satellite images. In: Gökçe D (ed) Limnology—some new aspects of inland water ecology. https://doi.org/10.5772/intechopen.79725
Manu A, Twumasi YA, Coleman TL (2004) Application of remote sensing and GIS technologies to assess the impact of surface mining at Tarkwa, Ghana. In: IEEE international geoscience and remote sensing symposium, 2004. IGARSS ’04, vol 1. Anchorage, AK, USA, pp 572–574. https://doi.org/10.1109/IGARSS.2004.1369091
Marshall BG, Veiga MM (2017) Formalization of artisanal miners: stop the train, we need to get off! Extr Ind Soc 4(2):300–303. https://doi.org/10.1016/j.exis.2017.02.004
Mücke M, Sang B, Heider B, Honold H-P, Sornig M, Fischer S (2019) EnMAP: hyperspectral Imager (HSI) for Earth observation: current status. In International Conference on Space Optics — ICSO 2018, édité par Nikos Karafolas, Zoran Sodnik, et Bruno Cugny, 222. Chania, Greece: SPIE. https://doi.org/10.1117/12.2536142.
McFarlane WD, Rebecca V (2019) The state of artisanal mining in Myanmar, 27
Mielke C, Boesche N, Christian R, Kaufmann H, Gauert C, de Wit M (2014) Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern push-broom missions: hyperion/OLI and EnMAP/sentinel-2. Remote Sens 6(8):6790–6816. https://doi.org/10.3390/rs6086790
Mohammed Banchirigah S (2006) How have reforms fuelled the expansion of artisanal mining? Evidence from Sub-Saharan Africa. Resourc Policy 31(3):165–171. https://doi.org/10.1016/j.resourpol.2006.12.001
Montero S, Irene C, Brimhall GH, Alpers CN, Swayze GA (2005) Characterization of waste rock associated with acid drainage at the Penn Mine, California, by ground-based visible to short-wave infrared reflectance spectroscopy assisted by digital mapping. Chem Geol 215(1–4):453–472. https://doi.org/10.1016/j.chemgeo.2004.06.045
Ngom M, Mbaye M (2021) Mapping artisanal small-scale gold mining in Senegal. Application. https://maramengom.users.earthengine.app/view/goldminingsenegal
Ngom NM, Mbaye M, Baratoux D, Baratoux L, Catry T, Dessay N, Faye G, Sow EH, Delaitre E (2020) Mapping artisanal and small-scale gold mining in Senegal using sentinel-2 data. GeoHealth. https://doi.org/10.1029/2020GH000310
Nixon R (2011) Slow violence and the environmentalism of the poor. Harvard University Press, Cambridge
Noucher M, Le Tourneau, FM, Gautreau P (2021). Data-driven remote governance of sparsely populated areas: measurement and commensuration of wildcat gold mining in French Guiana. GeoJournal 1–24
Obodai J, Adjei KA, Odai SN, Lumor M (2019) Land use/land cover dynamics using landsat data in a gold mining basin-the Ankobra, Ghana. Remote Sens Appl Soc Environ 13:247–256. https://doi.org/10.1016/j.rsase.2018.10.007
Percival JB, White HP, Goodwin TA, Parsons MB, Smith PK (2014) Mineralogy and spectral reflectance of soils and tailings from historical gold mines, Nova Scotia. Geochem Explor Environ Anal 14(1):3–16. https://doi.org/10.1144/geochem2011-071
Persaud AW, Telmer KH, Costa M, Moore ML (2017) Artisanal and small-scale gold mining in Senegal: livelihoods, customary authority, and formalization. Soc Nat Resour 30(8):980–993. https://doi.org/10.1080/08941920.2016.1273417
Pfitzner KS, Harford AJ, Whiteside TG, Bartolo RE (2018) Mapping magnesium sulfate salts from saline mine discharge with airborne hyperspectral data. Sci Total Environ 640–641:1259–1271. https://doi.org/10.1016/j.scitotenv.2018.05.396
Poncian J (2020) ICT, citizen engagement and the governance of extractive resources in Tanzania: documenting the practice and challenges. Extr Ind Soc 7(4):1498–1510. https://doi.org/10.1016/j.exis.2020.09.006
Poudori L, Fotsing JM, Orru JF (2001) Déforestation et orpaillage·apport de la télédétection pour la surveillance de réoccupation du sol en Guyane française, 30
Pović M, Backes M, Baki P et al (2018) Development in astronomy and space science in Africa. Nat Astron 2:507–510. https://doi.org/10.1038/s41550-018-0525-x
Quental L, Sousa AJ, Marsh S, Abreu MM (2013) Identification of materials related to acid mine drainage using multi-source spectra at S. Domingos mine Southeast Portugal. Int J Remote Sens 34(6):1928–1948. https://doi.org/10.1080/01431161.2012.730154
Rahm JB, et al (2014) Monitoring the impact of gold mining on the forest cover and freshwater in the Guiana shield. Reference Year 2015. WWF France, ONF International
Reiche J, Mullissa A, Slagter B, Gou Y, Tsendbazar N-E, Odongo-Braun C, Vollrath A et al (2021) Forest disturbance alerts for the Congo Basin using sentinel-1. Environ Res Lett 16(2):024005. https://doi.org/10.1088/1748-9326/abd0a8
Riaza A, Müller A (2010) Hyperspectral remote sensing monitoring of pyrite mine wastes: a record of climate variability (Pyrite Belt, Spain). Environ Earth Sci 61(3):575–594. https://doi.org/10.1007/s12665-009-0368-y
Riaza A, Buzzi J, García-Meléndez E, Carrère V, Sarmiento A, Müller A (2012a) River acid mine drainage: sediment and water mapping through hyperspectral HyMap data. Int J Remote Sens 33(19):6163–6185. https://doi.org/10.1080/01431161.2012.675454
Riaza A, Buzzi J, García-Meléndez E, Vázquez E, Bellido E, Carrère V, Müller A (2012b) Pyrite mine waste and water mapping using Hymap and Hyperion hyperspectral data. Environ Earth Sci 66(7):1957–1971. https://doi.org/10.1007/s12665-011-1422-0
Riaza A, Buzzi J, García-Meléndez E, Carrère V, Sarmiento A, Müller A (2015) Monitoring acidic water in a polluted river with hyperspectral remote sensing (HyMap). Hydrol Sci J 60(6):1064–1077. https://doi.org/10.1080/02626667.2014.899704
Robert E, Grippa M, Kergoat L, Pinet S, Gal L, Cochonneau G, Martinez JM (2016) Monitoring water turbidity and surface suspended sediment concentration of the Bagre reservoir (Burkina Faso) using MODIS and field reflectance data. Int J Appl Earth Obs Geoinf 52:243–251. https://doi.org/10.1016/j.jag.2016.06.016
Ros-Tonen MAF, Aggrey JJ, Somuah DP, Derkyi M (2021) Human insecurities in gold mining: a systematic review of evidence from Ghana. Extr Ind Soc. https://doi.org/10.1016/j.exis.2021.100951
Sangaré O, Mundler P, Ouedraogo LS (2017) Institutions informelles et gouvernance de proximité dans l’orpaillage artisanal. Un cas d’étude au Burkina Faso. Rev Gouv 13(2):53–73. https://doi.org/10.7202/1039240ar
Sauerwein T (2020) Gold mining and development in Côte d’Ivoire: trajectories, opportunities and oversights. Land Use Policy 91:104323. https://doi.org/10.1016/j.landusepol.2019.104323
Schueler V, Kuemmerle T, Schröder H (2011) Impacts of surface gold mining on land use systems in Western Ghana. Ambio 40(5):528–539. https://doi.org/10.1007/s13280-011-0141-9
Scott JC (2008) Seeing like a state: how certain schemes to improve the human condition have failed. Nachdr. Yale Agrarian studies. Yale University Press, New Haven
Seifi A, Hosseinjanizadeh M, Ranjbar H, Honarmand M (2019) Identification of acid mine drainage potential using sentinel2a imagery and field data. Mine Water Environ 38(4):707–717. https://doi.org/10.1007/s10230-019-00632-2
Shahriari H, Ranjbar H, Honarmand M (2013) Image segmentation for hydrothermal alteration mapping using PCA and concentration-area fractal model. Nat Resour Res 22:191–206. https://doi.org/10.1007/s11053-013-9211-y
Simionato J, Bertani G, Osako LS (2021) Identification of artisanal mining sites in the Amazon rainforest using geographic object-based image analysis (GEOBIA) and data mining techniques. Remote Sens Appl Soc Environ 24:100633. https://doi.org/10.1016/j.rsase.2021.100633
Snapir B, Simms DM, Waine TW (2017) Mapping the expansion of Galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing. Int J Appl Earth Obs Geoinf 58:225–233. https://doi.org/10.1016/j.jag.2017.02.009
Swayze GA et al (2000) Using imaging spectroscopy to map acidic mine waste. Environ Sci Technol 34(1):47–54. https://doi.org/10.1021/es990046w
Swenson JJ, Carter CE, Domec J-C, Delgado CI (2011) Gold Mining in the Peruvian Amazon: global prices, deforestation, and mercury imports. PLoS ONE 6(4):e18875. https://doi.org/10.1371/journal.pone.0018875
Tachikawa T, Kashimura O, Tanii J, Iwasaki A, Matsunaga T, Tsuchida S, Yamamoto H (2012) Outline and prospect of hyperspectral imager suite (HISUI). J Remote Sens Soc Japan 32(5):280–286. https://doi.org/10.11440/rssj.32.280
Telmer K, Stapper D (2007) UNIDO Project EG/GLO/01/G34 Final Report, 49
Teschner BA (2014) “Orpaillage pays for everything”: how artisanal mining supported rural institutions following Mali’s Coup d’état. Futures 62:140–150. https://doi.org/10.1016/j.futures.2014.04.016
Tschakert P (2009) Recognizing and nurturing artisanal mining as a viable livelihood. Resour Policy 34(1–2):24–31. https://doi.org/10.1016/j.resourpol.2008.05.007
Tschakert P, Singha K (2007) Contaminated Identities: mercury and marginalization in Ghana’s artisanal mining sector. Geoforum 38(6):1304–1321. https://doi.org/10.1016/j.geoforum.2007.05.002
Van Bockstael S (2014) The persistence of informality: perspectives on the future of artisanal mining in Liberia. Futures 62:10–20. https://doi.org/10.1016/j.futures.2014.02.004
Van Bockstael S (2019) Land grabbing “from below”? Illicit artisanal gold mining and access to land in post-conflict Côte d’Ivoire. Land Use Policy 81:904–914. https://doi.org/10.1016/j.landusepol.2018.04.045
Werthmann K (2006) Gold diggers, earth priests, and district heads: land rights and gold Mining in Southwestern Burkina Faso. In: Kuba R, Lentz C (eds) Land and the politics of belonging in West Africa, p 119136
Werthmann K (2010) ‘Following the hills’: gold mining camps as heterotopias. In: Freitag U, von Oppen A (eds) Translocality. Brill, pp 111–132
Williams DJ, Bigham JM, Cravotta CA III, Trainaa SJ, Anderson JE, Lyon JG (2002) Assessing mine drainage PH from the color and spectral reflectance of chemical precipitates. Appl Geochem 17(10):1273–1286. https://doi.org/10.1016/S0883-2927(02)00019-7
Wireko-Gyebi RS, Asibey MO, Amponsah O, King RS, Braimah I, Darko G, Lykke AM (2020) Perception of small-scale miners on interventions to eradicate illegal small-scale mining in Ghana. SAGE Open 10(4):215824402096366. https://doi.org/10.1177/2158244020963668
Witjes N, Olbrich P (2017) A fragile transparency: satellite imagery analysis, non-state actors, and visual representations of security. Sci Public Policy 44(4):524–534. https://doi.org/10.1093/scipol/scw079
WWF (2016) Orpaillage illégal: situation catastrophique dans le Parc amazonien de Guyane. https://www.wwf.fr/vous-informer/actualites/orpaillage-illegal-situation-catastrophique-dans-le-parc-amazonien-de-guyane
Acknowledgements
We gratefully acknowledge The French National Research Institute for Development for funding the International Laboratory for Responsible Mining in West Africa (LMI MINERWA, https://miner-wa.org). This research also receives support from the African Center of Excellence “Mines and Mining Environment (CEA-MEM, https:// http://cea-mem.inphb.ci/) hosted at the Institut Polytechnique Houphouët-Boigny, Yamoussoukro, Côte d’Ivoire. D.B. acknowledges the University Felix Houphouët-Boigny (UFHB) for hosting his visit in Côte d’Ivoire since January 2021.
Funding
Open access funding provided by University of Bern.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Ngom, N.M., Baratoux, D., Bolay, M. et al. Artisanal Exploitation of Mineral Resources: Remote Sensing Observations of Environmental Consequences, Social and Ethical Aspects. Surv Geophys 44, 225–247 (2023). https://doi.org/10.1007/s10712-022-09740-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10712-022-09740-1