Researchers have developed an advanced artificial intelligence model that produces high-resolution canopy height maps using only standard RGB imagery, achieving near-lidar accuracy for precise monitoring of forest biomass and carbon storage over large areas. This innovation addresses the urgent need for cost-effective, high-resolution forest monitoring as forests and plantations play a vital role in carbon sequestration, yet accurately monitoring their growth has remained costly and labor-intensive.
The joint research team from Beijing Forestry University, Manchester Metropolitan University, and Tsinghua University published their findings in the Journal of Remote Sensing on October 20, 2025. Their study introduces a novel framework that combines large vision foundation models with self-supervised learning to deliver sub-meter accuracy in estimating tree heights from RGB satellite images. The research is available through the DOI 10.34133/remotesensing.0880 and the original source can be accessed at https://spj.science.org/doi/10.34133/remotesensing.0880.
Traditional lidar systems provide accurate height data but are limited by high costs and technical complexity, while optical remote sensing often lacks the structural precision required for small-scale plantations. The new AI-driven vision model addresses this long-standing problem by balancing cost, precision, and scalability in forest monitoring. The researchers created a canopy height estimation network composed of three modules: a feature extractor powered by the DINOv2 large vision foundation model, a self-supervised feature enhancement unit to retain fine spatial details, and a lightweight convolutional height estimator.
When tested in the Fangshan District of Beijing, the model achieved a mean absolute error of only 0.09 meters and an R² of 0.78 compared with airborne lidar measurements, outperforming traditional CNN and transformer-based methods. It enabled over 90% accuracy in single-tree detection and strong correlations with measured above-ground biomass. The model demonstrated strong generalization across forest types, making it suitable for both regional and national-scale carbon accounting. When applied to a geographically distinct forest in Saihanba, the network maintained robust accuracy, confirming its cross-regional adaptability.
Dr. Xin Zhang, corresponding author at Manchester Metropolitan University, stated that their model demonstrates how large vision foundation models can fundamentally transform forestry monitoring. By combining global image pretraining with local self-supervised enhancement, the researchers achieved lidar-level precision using ordinary RGB imagery. This approach drastically reduces costs and expands access to accurate forest data for carbon accounting and environmental management.
The AI-based mapping framework offers a powerful and affordable approach for tracking forest growth, optimizing plantation management, and verifying carbon credits under initiatives such as China's Certified Emission Reduction program. Its adaptability across ecosystems makes it suitable for global afforestation and reforestation monitoring programs. The ability to reconstruct annual growth trends from archived satellite imagery provides a scalable solution for long-term carbon sink monitoring and precision forestry management.
This innovation bridges the gap between expensive lidar surveys and low-resolution optical methods, enabling detailed forest assessment with minimal data requirements. As the world advances toward net-zero goals, such intelligent, scalable mapping tools could play a central role in achieving sustainable forestry and climate-change mitigation. Future research will extend this method to natural and mixed forests, integrate automated species classification, and support real-time carbon monitoring platforms.
