Researchers have developed an artificial intelligence model that produces high-resolution canopy height maps using only standard RGB imagery, achieving near-lidar accuracy for monitoring forest biomass and carbon storage. This innovation addresses the challenge of balancing cost, precision, and scalability in forest monitoring, which is essential for understanding global carbon cycles and managing plantation resources.
A joint research team from Beijing Forestry University, Manchester Metropolitan University, and Tsinghua University created a framework combining large vision foundation models with self-supervised learning. Published in the Journal of Remote Sensing on October 20, 2025, the study details a model that achieved a mean absolute error of only 0.09 meters and an R² of 0.78 when compared with airborne lidar measurements. The research is available at https://spj.science.org/doi/10.34133/remotesensing.0880.
The model consists 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. This approach enables over 90% accuracy in single-tree detection and strong correlations with measured above-ground biomass. 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.
Tested in Beijing's Fangshan District with fragmented plantations of Populus tomentosa, Pinus tabulaeformis, and Ginkgo biloba, the model used one-meter-resolution Google Earth imagery and lidar-derived references. It significantly outperformed global canopy height model products, capturing subtle variations in tree crown structure that existing models often missed. The generated maps supported individual-tree segmentation and plantation-level biomass estimation with R² values exceeding 0.9 for key species.
Dr. Xin Zhang, corresponding author at Manchester Metropolitan University, stated that the model demonstrates how large vision foundation models can fundamentally transform forestry monitoring. By combining global image pretraining with local self-supervised enhancement, the team achieved lidar-level precision using ordinary RGB imagery, drastically reducing costs and expanding 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. When applied to a geographically distinct forest in Saihanba, the network maintained robust accuracy, confirming its cross-regional adaptability.
This technology bridges the gap between expensive lidar surveys and low-resolution optical methods, enabling detailed forest assessment with minimal data requirements. 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. 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.
