Assessing the impact of gold mining on forest cover in the Surinamese Amazon from 1997 to 2019: A semi-automated satellite-based approach

Abstract

The Amazon rainforest has faced extensive deforestation for decades due to urban growth, agricultural expansion, logging and mining. Mining however has been relatively understudied in the Amazon. The objectives of this study were: (1) to develop an effective cloud-based classification approach coupled with an innovative semi-automated reclassification method; (2) to quantify the mining extent and its impacts on forest cover in Suriname from 1997 to 2019; and (3) to evaluate the impact of mining on the structure and health of the forest. Landsat 5 and 8 images were used for the initial land-use/land-cover classification using the classification and regression trees algorithm in Google Earth Engine. The resulting classified maps were reclassified in a semi-automated model to correct misclassifications between mining and urban pixels. The final maps were used to analyse the expansion of mining and its impacts on forest cover, structure (using the fragmentation metric effective mesh size), and forest health (using the phenology metric peak greenness). The combined approach resulted in an improvement in mining detection accuracy from 72% (65% producer, 79% user) to 89.5% (84% producer accuracy, 95% user). The results showed that mining increased from 69.4 km2 in 1997 to 431.6 km2 in 2019, an increase by 522% over 22 years, which led to 421.3 km2 of forest loss, of which 85% was due to artisanal and small-scale mining (ASGM). The loss of forest for ASGM resulted in greater fragmentation, with a decrease in effective mesh size by 122.8 km2 compared to a decrease by 83 km2 caused by industrial mining. Mining also caused a decrease in the health of the surrounding forest, with a larger decrease in peak greenness due to ASGM compared to industrial mining. The results demonstrate the potential of this approach that leverages cloud-based machine learning with a semi-automated reclassification to allow for rapid, accurate, and potentially global mining detection.