The Ecuadorian Amazon, one of the world's most biodiverse ecosystems, is increasingly threatened by mining activities that encroach on protected areas and Indigenous territories. These conflicts are exacerbated by inadequate regulatory frameworks, resulting in significant environmental and social concerns. This study evaluates the effectiveness of Ecuador's mining laws and regulations over the past fifteen years (2008–2023) by analysing land use and land cover (LULC) changes using multitemporal satellite imagery from the MapBiomas-Ecuador project, processed with Google Earth Engine. Spatial analysis techniques, including Kernel Density Estimation (KDE) with a 500 m cell size and a 2500 m radius, were employed to assess the spatial distribution and intensity of mining activities. The results reveal a notable increase in mining land cover, particularly in critical areas such as water bodies, conservation units, and Indigenous territories. This expansion suggests that the current mining legislation and its enforcement have not been effective in curbing the spread of extractive activities. The combination of remote sensing, detailed LULC data, and non-parametric spatial statistics offers an innovative and comprehensive framework for assessing land-use changes and mining impacts. The findings provide valuable insights for the design of more effective public policies, environmental monitoring, and territorial planning in ecologically sensitive regions of the Ecuadorian Amazon. This research underscores the pressing need for robust regulatory measures and ongoing monitoring to ensure the long-term sustainability of the region’s natural resources.
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Geospatial Tools and Mining Laws: Analysis of Regulatory Efficiency in the Ecuadorian Amazon
Published:
02 September 2025
by MDPI
in The 2nd International Electronic Conference on Land
session Land Use Dynamics and Socio-Ecological Systems: Modeling Across Scales
Abstract:
Keywords: Ecuadorian Amazon; Mining; Kernel Density Estimation; Conservation Units; Indigenous Territories
