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, low-cost monitoring of forest biomass and carbon storage over large areas. This innovation addresses the long-standing challenge of balancing cost, precision, and scalability in forest monitoring, offering a promising tool for managing plantations and tracking carbon sequestration under initiatives such as China's Certified Emission Reduction program.
The joint research team from Beijing Forestry University, Manchester Metropolitan University, and Tsinghua University 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. Published in the Journal of Remote Sensing on October 20, 2025, the study introduces a novel framework that combines large vision foundation models with self-supervised learning. The research is documented at https://spj.science.org/doi/10.34133/remotesensing.0880.
The model achieved a mean absolute error of only 0.09 meters and an R² of 0.78 when compared with airborne lidar measurements, outperforming traditional CNN and transformer-based methods. It also enabled over 90% accuracy in single-tree detection and strong correlations with measured above-ground biomass. Beyond its accuracy, the model demonstrated strong generalization across forest types, making it suitable for both regional and national-scale carbon accounting. Testing in the Fangshan District of Beijing, an area with fragmented plantations primarily composed of Populus tomentosa, Pinus tabulaeformis, and Ginkgo biloba, showed the AI model produced canopy height maps closely matching ground truth data using one-meter-resolution Google Earth imagery and lidar-derived references.
The model 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. When applied to a geographically distinct forest in Saihanba, the network maintained robust accuracy, confirming its cross-regional adaptability. 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.
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 to achieve 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 team employed an end-to-end deep-learning framework combining pre-trained LVFM features with a self-supervised enhancement process, using high-resolution Google Earth imagery from 2013–2020 as input and UAV-based lidar data as reference for training and validation.
The AI-based mapping framework offers a powerful and affordable approach for tracking forest growth, optimizing plantation management, and verifying carbon credits. Its adaptability across ecosystems makes it suitable for global afforestation and reforestation monitoring programs. 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.
