Remote Sensing and Machine Learning Approaches for Assessing Environmental Dynamics in the Southeastern Watersheds of Madre de Dios, Peru

This study investigates the dynamics of environmental transformation in the southeastern basins of Madre de Dios, Peru, by integrating multi-spectral remote sensing data with advanced machine learning methodologies. To capture and quantify land surface changes over time, satellite imagery from Landsat and Sentinel missions was utilized to derive key spectral indices—specifically, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). These indices provided critical insights into vegetation health and surface water distribution. To manage the high dimensionality of the spectral data, Principal Component Analysis (PCA) was applied, enabling more efficient data interpretation and visualization. Subsequently, unsupervised K-means clustering was employed to classify land cover types and detect spatial patterns of change without prior labeling. The analysis revealed a significant decline in dense vegetative cover, accompanied by a notable expansion of bare soil and surface water areas. These findings point to accelerating environmental degradation in the region, likely driven by both natural and anthropogenic pressures. The methodological framework adopted in this study demonstrates strong potential for scalable, data-driven environmental monitoring and offers a replicable model for assessing land cover dynamics in other ecologically sensitive regions.