Spatial distribution of artisanal goldmining in Ghana: Using machine learning and Google Earth Engine to quantify conversion of vegetation to gold mines

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Abigail Barenblitt, Amanda Payton, David Lagomasino, Temilola Fatoyinbo, Kofi Asare, Kenneth Aidoo, Hugo Pigott, Charles Som, Omar Seidu, Laurent Smeets, al. ,Earth and Space Science Open Archive (2020)

DOI: 10.1002/essoar.10505321.1

Gold mining has played a significant role in Ghana’s economy for centuries. Regulation of this industry has varied over time and while large-scale mining is prevalent in the country, prevalence of artisanal mining, or Galamsey has escalated throughout Ghana in recent years. These mines are not only harmful to human health due to the use of Mercury in the amalgamation process, but also leave a significant footprint on terrestrial ecosystems, degrading and destroying forested ecosystems in the region. This study used machine learning and Google Earth Engine to quantify the footprint of artisanal gold mines in Ghana and understand how conversion of forested regions to mining has changed from 2002-2019. We used Landsat imagery and a random forest classification to classify areas of anomalous NDVI loss during this time period and used WorldView image collections to assess the accuracy of the model. We then used a 3-year moving average to calculate the year of maximum derivative NDVI values. We used this calculation to identify the year of conversion to mining. Within the study area of Southwestern Ghana, our analysis showed that approximately 35,000 ha of vegetation were converted to mining. The majority of this mining occurred between 2014 and 2017. Additionally, around 700 ha ha of mining occurred within protected areas defined by the World Database on Protected Areas. Often, artisanal mining appears to be co-located with rivers such as the Orin and Ankobra Rivers, demonstrating the potential of these mines to affect access to clean drinking water. Through the process of gold extraction, these mines leave a distinct footprint with a series of ponds following these major rivers. However, while the footprints of these ponds are spatially distinct, our model does not distinguish between active and inactive ponds if no remediation actions are taken following inactivity. Future research should work towards distinguishing between active and inactive mining sites to better understand current levels of mining activity in Ghana.