A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery - Ratansingh.B.Parmar; Kapil Aggarwal; Dr.K.Himabindu (International Journal of Satellite Remote Sensing, 2026)
Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection models, and data fusion frameworks for early-warning systems. Studies demonstrate that AI enhances deforestation mapping accuracy, enables real- time detection through UAV and IoT integration, and supports automated alert systems. Moreover, there is a growing exploration of advanced machine learning architectures, such as transformer-based models and hybrid deep learning frameworks, to enhance feature extraction and classification performance in various forest ecosystems. These methods allow for a more exact identification of subtle changes in land use and illicit activities. Furthermore, challenges such as data availability, model generalizability across ecosystems, and computational costs are identified. To guarantee these technologies are deployed responsibly, ethical aspects are addressed. These include data privacy, governance policies, and the necessity of transparent AI models. The paper concludes that AI-enabled remote sensing represents a paradigm shift in forest governance and climate resilience, with future opportunities in multi-sensor fusion, explainable AI, and cross-border monitoring frameworks.
Keywords: Deep Learning; Illegal Logging; artificial intelligence; deforestation; remote sensing; satellite imagery