Deforestation & Land-Use Change: Artificial Intelligence Approaches for Detection, Attribution, and Predictive Monitoring Of Global Forest Loss

Abstract Deforestation and land-use change remain among the most consequential drivers of biodiversity loss, carbon emissions, and hydrological disruption worldwide. Traditional monitoring approaches, which rely on periodic satellite surveys and manual interpretation, are often too slow to support timely conservation intervention. This article reviews the application of Artificial Intelligence (AI) including machine learning, deep learning, and multi-modal data fusion to the detection, attribution, and prediction of deforestation. We synthesize current methodologies spanning random forests, convolutional neural networks (CNNs), U-Net segmentation, and vision transformers, applied to optical and synthetic aperture radar (SAR) satellite imagery. Comparative performance data, regional case studies, and driver-attribution frameworks are presented alongside supporting tables and infographics. The review finds that fusion models combining optical and radar data currently achieve the highest reported accuracy (>96%) and that explainable AI (XAI) integration remains an underexplored but increasingly critical requirement for policy adoption. We conclude with a discussion of current limitations including data scarcity in the Global South, cloud interference, and selective-logging detection and propose directions for future research, including transfer learning for low-resource regions and carbon-stock-linked monitoring pipelines.