Correlating the Impact of Mining-Related Deforestation on Surface Temperature and Precipitation Patterns in the Amazon Rainforest

The large-scale issues of mining and deforestation in the Brazilian Amazon Rainforest have emerged as critical global concerns, particularly due to their impacts on weather patterns and surface temperatures. This study aims to investigate the correlations among mining activities, deforestation area, local and regional surface temperatures and precipitation patterns. We employed the Earthrise Enterprise mining detector model, a lightweight convolutional neural network (CNN), which was trained using hand-labeled data from Sentinel-2 satellite imagery spanning the years 2017 to 2023. This trained model effectively differentiates mining areas from other topographical features with an accuracy of 99.11% at a threshold value of 0.9. Other numerical climate data for the Brazilian Amazonas region from 2017 to 2023 were extracted, including: 1) surface temperature, 2) deforestation area, and 3) precipitation. These data were sourced from Hansan Global Land Discovery and Analysis and ECMWF Reanalysis v5 (ERA5) imagery, utilizing Google Earth Engine for processing. We employed the Pearson correlation analysis to examine the relationships among these variables. Our findings indicate a strong positive correlation between mining and deforestation areas (r = 0.81), a weak correlation between deforestation areas and Earth's surface temperature (r = 0.27), and a negative correlation between surface temperature and precipitation amount (r = -0.52). This study demonstrates that CNNs can be effective tools for analyzing satellite images to explore mining activities. The correlation between mining and deforestation illustrates the role of mining plays in the deforestation of the Amazon Rainforest. The weak correlation between deforestation and temperature indicates that surface temperature changes may be dynamic and multifactorial. Finally, the negative correlation between surface temperature and precipitation suggests that surface temperature increase can be an important drought-predicting factor. We will also discuss the limitations of this study due to the limited number of data points used for the correlation study, which impacts the strength and accuracy of correlations.