Enhanced Detection of Human-Driven Forest Alterations using Echo State Networks

Forest monitoring is crucial for understanding ecosystem dynamics, detecting changes, and implementing effective conservation strategies. In this work, we propose a novel approach for automated detection of human-induced changes in woodlands using Echo State Networks (ESNs) and satellite imagery. The utilization of ESNs offers a promising solution for analyzing time-series data and identifying deviations indicative of forest alterations, particularly those caused by human activities such as deforestation and logging. The proposed experimental setup leverages satellite imagery to capture temporal variations in the Normalized Difference Vegetation Index (NDVI), and involves the training and evaluation of ESN models using extensive datasets from Chile's central region, encompassing diverse woodland environments and human-induced disturbances. Our initial experiments demonstrate the effectiveness of ESNs in predicting NDVI values and detecting deviations indicative of human-related changes in woodlands, even in the presence of climate-induced changes like drought and browning. Our work contributes to forest monitoring by offering a scalable and efficient solution for automated change detection in woodland environments. Integrating ESNs with satellite imagery analysis provides valuable insights into human impacts on forest ecosystems, facilitating informed decision-making for sustainable land management and biodiversity conservation.