Forest digital twin via big data integration: Current advances and future perspectives

Forest ecosystems face increasing pressures from climate change, biodiversity loss, and human activities, necessitating innovative tools for management and prediction. Forest digital twin is emerging as a transformative tool for monitoring, simulating, and managing forest ecosystems. By integrating multi-source big data – satellite imagery, airborne and terrestrial LiDAR, Internet of Things (IoT) sensors, and process-based ecological models – the forest digital twin can provide dynamic, high-resolution representations of forest structure and function. This review integrates and analyses recent advances reported across 56 scholarly works and finds several gaps in current research, including limited real-time integration of IoT and remote-sensing data, weak interoperability between modelling platforms, and insufficient multi-scale ecological representation. This review addresses these shortcomings by synthesising the technological, ecological, and computational components required for a functional forest digital twin. Overall, the review provides a coherent conceptual framework, identifies critical research priorities, and demonstrates how digital twin technologies can transform forest monitoring and management through adaptive, data-driven decision support.