Remote Sensing Capabilities of Detecting Spatio-Temporal Dynamics in Unregulated Gold Mining Hotspots in Ecuador

Abstract. Degradation of the Amazon rainforest is increasing by expanding human activities, especially unregulated gold mining. These pressures have intensified over the past decade due to rising global gold prices and policy shifts. Given the sensitivity of the topic and the need for transparent and reproducible information, this study assesses the suitability of remote sensing datasets, including Sentinel-1 (S-1) Synthetic Aperture Radar data, PlanetScope (PS) optical imagery, as well as the Satellite Embedding Dataset V1 (SED), for detecting unregulated mining and investigating the spatio-temporal dynamics of mining expansion. All datasets are processed mainly in Google Earth Engine with dataset-specific methodologies applied. Supervised quantitative classification approaches were used for the SED and PS imagery, covering the period from 2017 to 2024. For S-1 data, a Sequential Change Detection (SCD) approach was implemented. The analysis focuses on three mining hotspots in eastern Ecuador where unregulated activities have been reported. Results show a pronounced increase in mining extent and associated deforestation across all study areas, with particularly strong expansion during 2023 and 2024. Comparison of classification results indicates that persistent cloud cover and temporal inconsistencies limit the effectiveness of optical PS data, whereas the SED dataset provides a reliable and efficient alternative for annual assessments with minimal preprocessing requirements. The SCD analysis revealed detailed expansion dynamics, demonstrating that mining typically initiates along major rivers and progressively expands toward tributaries and surrounding forest areas. The multi-method approach further enables cross-validation of results, which are consistent with independent reports documenting similar spatial patterns and trends. The severe environmental consequences of unregulated mining and threats to communities emphasize the importance of systematic and transferable remote sensing-based monitoring frameworks to support environmental protection and enable timely, accessible reporting for environmental governance and decision-making.