Monitoring and forecasting forest responses to accelerating climate change is essential for sustaining ecosystem services, enhancing carbon sequestration, and conserving biodiversity. This review synthesises recent advances in high‑resolution remote sensing, process‑based ecosystem modelling, and data assimilation, and outlines pathways toward operational “digital‑twin” forest systems that can inform adaptive management and policy. We evaluate sensing platforms ranging from CubeSat constellations and spaceborne LiDAR (light detection and ranging) to unmanned aerial vehicle (UAV)‑mounted hyperspectral and thermal systems, highlighting how canopy structural metrics, foliar biochemical traits, and disturbance indicators can be translated into measures of biomass, hydrological regulation, and habitat quality. Modelling approaches—including process‑based, dynamic global vegetation, individual‑based, and hybrid machine‑learning frameworks—are assessed for their capacity to simulate coupled carbon, water, and nutrient cycles and to project forest resilience under diverse climatic and management regimes. Data assimilation methods, such as Ensemble Kalman Filters, Particle Filters, and variational techniques, are examined for their ability to reduce predictive uncertainty by integrating multi‑temporal observations with model states. Case studies from tropical, temperate, and coastal forests demonstrate how integrated observation–modelling frameworks can support biodiversity conservation, wildfire mitigation, and climate‑adaptive planning. Persistent challenges include observation gaps, spatio‑temporal mismatches, computational demands, and structural model biases. Future priorities emphasise the development of low‑cost hyperspectral CubeSats, quantum LiDAR, physics‑informed machine learning, and FAIR (Findable, Accessible, Interoperable, Reusable)‑compliant cloud‑native workflows to build transparent, interoperable forest monitoring systems. Such advances will strengthen climate resilience, safeguard ecosystem services, and provide actionable insights for sustainable forest governance.
Niamjit Das (Fri,) studied this question.