Mining (especially surface) is one of the major causes of land and environmental degradation globally [1
]. Environmental impacts such as deforestation, landscape degradation, alteration of stream and river morphology, widespread environmental pollution, siltation of water bodies, biodiversity loss, etc., have been noted to be associated with mining [1
]. Despite these environmental impacts, mining significantly contributes to the economies of most developing countries. In Guinea and South Africa, for example, Aryee [5
] reported that mining contributes 25% and 5.9%, respectively of their Gross Domestic Product (GDP).
The mining sector in Ghana has always been an important contributor to the economic development of the country. Mining is among the top foreign direct investment earners for Ghana [6
]. From 2011 to 2013, the sector contributed between 8.4 and 9.8% to the country’s GDP [8
]. Together with cocoa, the two commodities accounted for 53% of total export receipts in 2014 [9
]. Despite its contribution to the formal economy of the country, local communities and indigenes of mining areas receive minimal benefits but suffer the negative impacts of forced relocation, takeover of their farmlands by large mining companies, reduced agricultural productivity and unprecedented environmental pollution [10
]. This situation has led to an abuse of the country’s small-scale mining law, where unregistered and unregulated artisanal miners (galamsey) resort to the use of heavy machinery/equipment for mining along river bodies, causing excessive destruction of the vegetative cover and widespread pollution of water bodies [13
]. Although artisanal mining in Ghana dates back to the 15th century [14
], the introduction of heavy machinery and a sudden influx of foreign nationals (especially Chinese) [16
] has attracted significant attention to small-scale mining and the environmental degradation perpetuated by these miners (legal or illegal).
In response, the government of Ghana set up the Inter-Ministerial Committee on Illegal Mining (IMCIM) to develop and roll out a comprehensive policy framework to regularize small-scale mining across the country [17
]. In developing the policy framework, the committee recommended the use of geospatial technologies and tools as part of the licensing procedure and monitoring of small-scale mining activities. Specifically, they recommended the use of Unmanned Aerial Vehicles (UAVs or drones) to survey mining concessions prior to licensing and to monitor illegal mining activities [18
]. Whereas drones are useful, especially in the detailed mapping of mining concessions, they are not particularly suitable for effectively monitoring mining activities over large areas and on a regular basis. Several drones would have to be deployed to monitor activities over a large area, which has cost implications. In addition, tremendous human effort will be required to operate the drones on a regular basis and process the resulting images.
Remotely sensed data from satellite sensors are better (economically and human resource-wise) in monitoring mining-induced degraded areas and other activities over large areas. Compared to UAVs, satellite sensors have larger footprints (cover large areas), are increasingly becoming free and can acquire images on near-real time basis. Previous studies conducted in southern Ghana and elsewhere used freely available Landsat images to study land use/land cover changes and inferred the presence of mining activities from the conversion of broad land use and land cover (LULC) classes such as savanna areas to bare ground and settlement [19
]. Recently, Snapir et al. [21
] improved on these previous efforts by mapping the expansion of illegal mining sites in a large area of southern Ghana using multi-temporal optical remote sensing data from UK-DMC2 (United Kingdom—Disaster Monitoring Constellation 2). They noted the availability of cloud free optical images as a major challenge in mapping and monitoring illegal mining activities in southern Ghana. Persistent cloud cover in the region often makes it nearly impossible to obtain usable optical images throughout the year. In the case of Snapir et al. [21
], analysis of the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud fraction product [22
] for the year 2015 revealed an average cloud cover of 83% in southern Ghana, with a minimum of 53% in December, which is actually a dry season month. Consequently, there was no usable Landsat image for the period they analyzed. The use of multi-date composited products such as MOD09Q1 (eight days) or MOD13Q1 (16 days) can minimize the challenge of persistent cloud cover. However, their coarse spatial resolution will not permit an explicit delineation of illegal mining activities, especially along river channels where illegal small-scale mines are concentrated.
The use of Synthetic Aperture Radar (SAR) data is a viable alternative to optical data. In fact, the IMCIM has acknowledged the usefulness of SAR data in monitoring mining activities [23
]. Previous studies have demonstrated the potential of SAR data in detecting areas disturbed by gold mining activities. The sensitivity of radar systems to topography, surface roughness and dielectric properties of materials make them suitable for distinguishing between disturbed and non-disturbed areas. For example, Almeida-Filho and Shimabukuro [24
] calculated the normalized difference index from JERS-1 SAR images for three years (1993, 1994, 1996) and found an increase in disturbed areas from 1993 to 1994 and from 1993 to 1996. Telmer and Stapper [25
] reviewed the use of optical and SAR data for monitoring small-scale mining in Indonesia and Brazil and noted promising results with the use of SAR. A change detection analysis based on a time-series of ALOS PALSAR data (12.5 m spatial resolution) over Galangan, Indonesia, was conducted to reveal artisanal small-scale mining (ASM) areas between June and September 2006. The results showed different colors representing various degrees of change in topography and certain land covers (e.g., vegetation, cleared land, etc.). Validation of the map with ground truth and aerial photographs acquired in November 2006 showed that the SAR data reasonably identified the ASM areas.
This study aims at contributing to existing knowledge of the use of multi-date satellite SAR imagery to monitor small-scale illegal mining in Ghana. The analysis is based on time series imagery (2015–2019) of the Sentinel-1 (S-1) satellites, which are operated by the European Space Agency (ESA).
Access to this data is free and open, which makes them an ideal data source for data poor regions such as West Africa. The C-Band data of S-1 are suited for the characterization of spatiotemporal dynamics, i.e., identification of land cover changes over time, as they offer a large spatial coverage with a single acquisition and a temporal revisit of usually 12 days [26
]. Time series data of S-1 were analyzed for a study area south of the city of Kumasi. The overall scope is to map and monitor the extent of small-scale mining activities along the Ankobra, Ofin, Oda, and Pra rivers in Ghana. Specifically, the study sought to answer the following research questions: (i) what is the suitability of C-Band Sentinel-1 data in monitoring illegal mining activities in South-Western Ghana, and (ii) what has been the temporal dynamics in the extent of illegal mining areas in South-Western Ghana between 2015 and 2019.
4.1. Susceptibility of Sentinel-1 data to Cloud Cover
The analysis revealed that a significant portion of the S-1 data were affected by atmospheric effects, typical for SAR acquisitions taken over cloud-prone regions [37
]. Affected areas were characterized by a strong local decrease in the backscatter intensity of single scenes compared to the long-term mean intensity of the time-series. It is assumed that the strong lowering of the backscatter was caused by large convection cells and heavy rainfall events that are typical of the study region. However, this effect is usually more frequently observed at short wavelengths (e.g., X-Band) [37
], while longer wavelength such as C- and L-Bands are usually less affected due to the larger penetration depth. Nevertheless, Alpers et al. [38
] pointed out that high precipitation amounts of several tens of millimeters per hour can cause a significant lowering of the backscattering intensity, even at C-Band. The S-1 scenes acquired in March or October (i.e., during the rainy seasons) were more frequently affected than scenes acquired in other months (Figure 12
). The first (major) season begins in March, while the second (minor) usually starts in September [39
]. Especially at the beginning of the first season and the end of the second season (i.e., the months of March/April and October/November) convective rainfalls, such as strong local rainstorms and squall lines (i.e., line of thunderstorms), are frequently observed in the area. These events are characterized by intensive and short-lasting rainfalls, which can reach precipitation amounts of more than 30 mm/h within the first half hour [39
]. Friesen [40
] noted that rainstorms are short lasting (1–2 h) and stationary systems that usually affect rather small areas of approx. 20–50 km2
; a size that fits well with the average extent of the affected areas. Further, the rainstorm events occur most frequently in the evening due to their convective nature and this is also the time at which the S-1 images are acquired.
4.2. Change Detection and Monitoring of Illegal Mining
This study employed a change detection method that was based on the generation of binary changed/unchanged masks using single thresholds, estimated via histogram analysis and the Otsu algorithm. The analyses were based on S-1 time-series features (i.e., minimum, mean and maximum backscattering intensity observed within one year), as they offered the following advantages compared to the analysis of single scenes: (i) speckle is reduced due to the temporal averaging, (ii) seasonal effects are averaged and compensated and thus do not appear as change areas in the classification result, (iii) the temporal features still preserve most important information on the backscatter characteristics and therefore on potential LULC changes. Due to the high signal stability of S-1 [32
] it was expected that significant changes in the average backscattering intensities (approx. > ±1 dB) between the time series features of two temporal periods can be attributed to changes in land cover.
As pointed out, mining activities in the study area usually cause a removal of vegetation and the creation of dug-outs. The analysis pointed out that LULC changes associated with mining activities were indicated by changes in both VV and VH intensities of +1.65 dB or higher (i.e., threshold estimated for VH minimum via histogram intersection), this compares well with the expected direction and magnitude of a land cover change from vegetation to water/bare soil. On the other hand, unchanged areas showed average values around ±0 dB and standard deviations less than 0.9 dB for both polarizations and for all time series features. This observation is in line with results proposed by References [31
]. Note in this context that the design of the approach inferred that only new sites could be detected, e.g., no information on the activity of already established sites or their abandonment was provided.
The analysis of the distance metrics (SIM and DIST), as well as the accuracy assessments conducted for Period 1 and for a smaller subset of the study area, showed that VV and VH polarization perform similar in change detection. However, the VH polarization achieved better classification accuracies, which might be attributed to the higher sensitivity of VH polarization to volume scattering processes. This sensitivity in turn leads to a better separation of water and vegetation classes in the feature space, compared to the VV polarization. Scattering from water is characterized by very low volume scattering contribution, whereas the presence of vegetation causes significantly higher volume scattering contributions and intensities at C-Band [41
Among the investigated time series features, the minimum feature performed better than the mean or the maximum feature. This is reasonable, as the minimum feature is sensitive to any changes in backscattering within the period, as just the lowest intensity value observed is displayed in this feature. Due to this fact, it was of high importance to screen and remove datasets that are affected by atmospheric conditions (i.e., clouds), as they might cause false detections. Conversely, the mean feature is dependent on the time at which the changes occur, as all intensity values of the entire period are averaged, therefore changes that occur at the end of the observation period are more difficult to detect.
It is clear that the temporal integration and the analysis of time series features has drawbacks. First, no information on the exact timing of the change is provided beside the year/period. Second, the approach is not suited to the dynamic detection of new illegal mining areas, as the approach essentially requires longer observation periods to generate the time series features. These limitations also infer that small and short-lasting changes are unlikely to be detected, also considering the spatial resolution of the S-1 data. Since this study focused on detecting inter-annual changes in rather large-scale illegal mining activities in the past, it is expected that the stated drawbacks did not have a significant effect on our results.
In this study, the thresholds for generating the binary changed/unchanged masks were estimated using a supervised (histogram intersection) and unsupervised (Otsu algorithm) approach. Both approaches showed acceptable to good classification accuracies, which were ca. 68% (Prod. Acc.) and 83% (User Acc.) for the threshold estimated via the Otsu algorithm and ca. 84% (Prod. Acc.) and 72% (User. Acc.) for the threshold estimated via the histogram intersection. Higher thresholds values were generally observed via the Otsu algorithm, which resulted in higher user but lower producer accuracies. This means that higher threshold values result in more strict and conservative estimates and have fewer false alarms at the expense of more missed detections. Higher user accuracy in turn might be a prerequisite for actual ground validation and legal actions and thus desired in an operational context.
A challenge of this study was the quantity and extent of the reference data. As pointed out, the results were validated for a smaller region using high-resolution Google Earth Imagery. Mining activities can be detected on these imagery with a high level of confidence. However, due to persistent cloud cover, only few images were available that (i) covered a sufficiently large area affected by mining activities and (ii) the period of acquisition corresponded to the S-1 observation periods making them suitable for validation. Consequently, only few images provided a useful and reliable reference that was suited to assess the classification accuracies provided by the unsupervised and supervised binary classifications. In this regard, upcoming studies should compile more suited high-resolution optical space-borne imagery, also from commercial sources, e.g., the archive of the Planet satellites (planet.com). Having more high-quality reference data will allow for a more comprehensive classification and referencing, e.g., also for the most recent observation periods. On the other hand, future studies can embark on an extensive field campaign (including historical land use interviews) to generate more reference data for validation. Considering the increased observation frequency of optical space-borne systems (e.g., due to the operation of Sentinel-2), it might also be possible to fuse SAR and optical data in the classification process in order to enhance the classification accuracies, to increase the sensitivity of the detection and the spatial resolution of the classification result.
The presented approach does not require a comprehensive processing and therefore it can be repeated by using the Ground Range Detected (GRD) products of Sentinel-1. These products require less disk space, are already partially radiometrically corrected and the processing of sigma or gamma nought requires less processing steps, compared to the SLC products. Further, it is suggested to run the approach using cloud-processing facilities such as the Google Earth Engine (GEE) (earthengine.google.com
]. GEE already offers the necessary infrastructure to assess the S-1 archive (i.e., the GRD products are available) and to process time series features from a stack of S-1 images. This in turn will save the download and processing of large amounts of S-1 data and will provide analyses ready data. However, the issue that S-1 data are affected by atmospheric conditions (i.e., clouds) must be addressed, as patches of low backscattering will manifest in the minimum time series feature. Therefore, other metrics, e.g., such as the analyses of lower quantile values instead of the minimum, should be investigated as a cloud-based processing strategy is not suited to the manual selection and removal of individual scenes.
4.3. Trend in the Extent of Illegal Mining Areas between 2015 and 2019
Our results showed a decreasing trend in the extent of illegal mining in the study area from 102 km2
in Period 1 (2015/2016–2016/2017) to 60 km2
and 33 km2
in periods 2 (2016/2017–2017/2018) and 3 (2017/2018–2018/2019), respectively. Despite its widespread practice and resultant degradation and pollution, very few studies have used remote sensing data to quantify changes (over a period) in the extent of illegal mining activities in the country. This is principally attributable to excessive cloud cover in the southern part of Ghana where illegal mining mostly takes place, and the difficulty in obtaining usable optical images. Owusu-Nimo et al. [44
] mapped and analyzed the spatial distribution patterns of artisanal small-scale gold mining in the Western region of Ghana, but did not use satellite imagery nor quantified the extent/area of the activity in the region. They generally mapped galamsey sites as points using field-based surveys and found a total of 911 galamsey sightings comprising of 7470 individual operations in 312 communities across 11 municipal and district assemblies in Western region. Out of 11 districts, they found three as the galamsey hotspot districts: Tarkwa Nsuaem (294 sightings), Amenfi East (223 sightings) and Prestea Huni Valley Districts (156 sightings). Two out of these three hotspot districts fall within our study area. Awotwi et al. [45
] conducted a general land use and land cover mapping in the Pra river basin using a time-series of Landsat images acquired between 1986 and 2016. They noted an increase of 10.62% in the extent of mining areas between 2004 and 2008, and 304% between 2004 and 2016. Other studies also inferred increases in the extent of mining areas based on general land use and land cover analysis based on satellite imagery [20
Snapir et al. [21
] is the only study we have found that had a specific focus on assessing the extent of illegal mining sites between 2011 and 2015 using optical satellite imagery. For a subset of their study area (change area), they found that the extent of illegal mining areas tripled between 2011 and 2015, increasing from 109.07 km2
in 2011 to 232.82 km2
and 366.96 km2
in 2013 and 2015, respectively. Although the area and period investigated are not the same as that of this study’s, a note-worthy difference in their results compared to ours is the trend in the extent of illegal mining areas, i.e., increasing trend in Snapir et al. [21
] and decreasing trend in this study. Two possible reasons for this variance can be discussed. First, as alluded by Snapir et al. [21
], their approach did not differentiate between active and abandoned pits or mining areas. Thus, the area detected in, for example 2013, could be a summation of abandoned pits from 2011 and active/new pits that opened in 2013. Similarly, active/new pits in 2015 would add up to those of 2011 and 2013 to indicate a much larger extent compared to previous years. The approach adopted in this study, however, detects mining-induced changes from one period to the other. Thus, the detected changes at Period 2, relative to Period 1, can be considered as new illegal mining areas that opened in the period under analysis. In this sense, when areas mapped in, for example, Period 1 are added to the area of detected change in Period 2, there would be an overall increase in the extent of illegal mining areas at Period 2. When analyzed this way, our results will indicate an increasing trend in the extent of illegal mining areas as 102 km2
, 162 km2
and 195 km2
for Periods 1, 2 and 3, respectively.
The second possible reason for the observed difference in trends, specifically the decreasing trend in our results (with respect to new illegal mining areas), is the government’s efforts to sanitize the small-scale mining sector and reduce illegal mining activities in the country. Although governmental effort to curb the menace started in 2013 with the establishment of an inter-ministerial task force, efforts from 2017 seem to have been more effective. These efforts include: (i) a six-month ban on all small-scale mining activities (even including licensed operators) and seizure of equipment, (ii) suspension of the issuance of licenses for small-scale mining, (iii) setting up of a Multilateral Mining Integrated Project (MMIP) to enact more stringent mining regulations and enforcement of mining laws, (iv) designation of 14 courts by the then Chief Justice to handle galamsey cases and (v) setting up of a media coalition against galamsey to support government’s efforts. In addition, an Afrobarometer survey of 2017 revealed overwhelming support of Ghanaians for government’s efforts, with 74% indicating that no citizen should be permitted to engage in illegal mining for any reason [47
]. These factors may have accounted for the observed decreasing trend in the extent of new illegal mining areas between 2016/2017–2017/2018 and 2017/2018–2018/2019. Our results indicate that government’s efforts between 2017 and 2019 resulted in lesser areas being degraded post 2017, compared to pre-2017 periods. This notwithstanding, there is still evidence of illegal mining activities in the study area, which must be continually monitored to assess efforts at curbing the menace.
In this study, annual S-1 (Sentinel-1) time-series data from 2015 to 2019, together with reference data from Google Earth and existing land cover data, were analyzed using change detection techniques to map and monitor illegal mining activities on an annual basis in South-Western Ghana. The methodology was premised on the fact that mining-induced land cover changes can be detected based on changes in the scattering characteristics of time-series images acquired with the same acquisition geometry. The sensitivity of three time-series features (i.e., minimum, mean and maximum backscattering intensity observed within one year) to mining-induced changes (e.g., clearing of vegetation) was first conducted, after which analysis was performed to determine a threshold suitable to identify/classify changed and unchanged areas.
The use of time-series features proved useful for detecting inter-annual mining-induced changes along river bodies. Compared to the mean and maximum features, the minimum feature (in both VH and VV polarizations) performed better and was found to be more sensitive to changes in backscattering within the period investigated, including the lowest intensity values observed. A backscatter threshold value of +1.65 dB was found suitable to detect illegal mining activities in the study area. Application of the threshold revealed illegal mining area extents of 102 km2, 60 km2 and 33 km2 for periods 2015/2016–2016/2017, 2016/2017–2017/2018 and 2017/2018–2018/2019, respectively. Our approach permitted the detection of new illegal mining activities on an annual basis, which revealed a decreasing trend in the period investigated. This is probably in response to government of Ghana’s efforts to sanitize the small-scale mining sector in the past couple of years.
Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed reasonable susceptibility of S-1 data to atmospheric conditions due to high intensity rainfall events. Scenes acquired in March and October, representing the beginning and ending of the rainy season, were the worst affected. A total of 29 out of 117 (ca. 25%) scenes could not be used in our analysis due to a strong local decrease in backscatter in the order >−10 dB compared to the long-term mean intensity and the undisturbed surroundings. This could have negatively affected our results considering the methodology employed. Further investigation of this finding in other geographies and climatic regions is required to ascertain the suitability of S-1 data for time-series dependent analysis such as the one employed in this study. Future studies can also benefit from an integration of Sentinel-2 data into the analysis.
The quantity and quality of the reference data was found to be a major challenge of this study. Google Earth Imagery permits a detailed view of changes in illegally mined areas. However, lack of sufficient images (spatially and temporarily) over the area analyzed due to excessive cloud cover limited the amount of reference data generated for this work. Future studies should consider embarking on an extensive field data collection to map changed and unchanged areas to serve as reference data.