Leaves’ pores explain longstanding mystery of uneven tree growth in a carbon-enriched world
The mechanics of how water and carbon dioxide move in and out of plants greatly affects how trees grow in a carbon-dioxide-enriched environments.
Duke University
The basics of photosynthesis are something that every student learns in school: carbon dioxide, water and light in; oxygen and sugar for growth out. In a world where atmospheric carbon dioxide levels are rising, it is plausible to think that trees and other plant life growth will rise in lockstep.
But that is not what observations have borne out. As global levels of carbon dioxide have risen, measurements of tree growth—and how much carbon they are storing for the long-term—have varied greatly. How much of that variance can be attributed to carbon dioxide levels has long been unknown.
In a paper published online on December 1 in the journal Nature Climate Change, researchers led by Duke University and Wuhan University describe a model that answers many of these questions. By looking at the tradeoffs between taking in more carbon dioxide to grow and losing water to evaporation, they show how an engineer’s view of this delicate balance in the pores of a tree’s leaves can explain and predict its growth over decades and centuries.
“There used to be a common assumption that higher levels of carbon dioxide will cause trees to grow more and store more carbon,” said Gaby Katul, the George Pearsall Distinguished Professor of Civil and Environmental Engineering at Duke. “But benchmark experiments showed that while this may be true in isolation, other environmental factors also play a large role. We have now uncovered some of the underlying mechanisms at work.”
The benchmark experiments Katul is referring to took place at Duke University and ETH Zurich to investigate how much carbon the world’s forests might capture in a future carbon-rich atmosphere. Over the course of 16 years, the Duke site fed groups of trees excess carbon dioxide while the ETH Zurich site increased their local humidity levels. By closely measuring tree growth and carbon sequestration, and monitoring many other variables, researchers showed that trees in general would not take in nearly as much carbon as previously conjectured.
But the reasons why were still not fully understood. To help explain these results, and dozens of others from around the world, Katul and his collaborators turned to an engineer’s view of water movement in a tree.
For a tree to take in carbon dioxide, it must open pores on its leaves called stomata. With more carbon dioxide in the atmosphere, the working assumption has been that proportionally more carbon dioxide would enter these pores.
However, in warmer and drier environments, water evaporates from these pores into the atmosphere more quickly. To keep their internal water systems balanced, trees compensate by making their stomatal pores smaller, which in turn leads to them absorbing less carbon dioxide.
This dynamic causes a direct tradeoff between gathering more carbon dioxide to grow and losing water needed to survive. And to complicate matters further, there is a delicate balance of water tension held throughout a tree’s roots, trunk and limbs that risks disruption if too much water is lost too quickly, especially as trees reach their mature heights.
“Stomata are like valves that control how much water is drawn up into the leaves and released into the air,” said Katul.
Looking at the interplay between stomatal opening, carbon levels and water loss as an optimization problem is a new approach to complement physiological theories, Katul explained. But it has proven accurate in describing results from the benchmark experiments at Duke and ETH Zurich.
During those studies, researchers captured incredibly rich data about stomatal activity. By encapsulating individual leaves and tightly controlling and monitoring variables such as temperature, humidity, carbon dioxide, stomatal size and more, the long-term experiments gave Katul’s team all the ammunition they needed to build their model.
Once finished, the researchers then used their approach to analyze dozens of reports of tropical tree growth that showed large amounts of variability. Despite levels of carbon dioxide rising in the atmosphere for the past half-century, some studies found increases, some found no change at all and some even found decreases. Using their new model, the researchers were able to finally offer an accurate explanation as to why.
There are, of course, plenty of other mitigating factors that can be added to the new model to increase its accuracy. Soil nutrients, water availability, surrounding plant and animal life, and changing seasonal patterns all come to mind. And while this model can describe behavior on a tree-by-tree basis, work must be done to incorporate these findings into large-scale regional climate models.
“There is a lot of value in looking at these environmental and biological questions from an engineering perspective,” Katul said. “Figuring out how best to ameliorate climate change using nature-based green technology in the decades to come is going to take contributions from many disciplines.”
This work was supported by the National Natural Science Foundation of China (42371035), the European Research Council (242955), and the EC projects ISONET EVK2-CT-2002-00147 and Millennium FP6-2004-GLOBAL-017008-2.
CITATION: Zhang, Q., Zhang, J., Adams, M.A. et al. Increased efficiency of water use does not stimulate tree productivity. Nat. Clim. Chang. (2025). https://doi.org/10.1038/s41558-025-02504-w
Journal
Nature Climate Change
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Increased efficiency of water use does not stimulate tree productivity
Next-generation vision model maps tree growth at sub-meter precision
image:
RGB imagery of the study area. The position of 3 plots with lidar reference is marked by yellow rectangle. The center of 1,436 plantations with species and age labels is represented by red dots.
view moreCredit: Journal of Remote Sensing
Forests and plantations play a vital role in carbon sequestration, yet accurately monitoring their growth remains costly and labor-intensive. Researchers have developed an advanced artificial intelligence (AI) model that produces high-resolution canopy height maps using only standard RGB imagery. By integrating a large vision foundation model with self-supervised enhancement, this method achieves near-lidar accuracy, enabling precise, low-cost monitoring of forest biomass and carbon storage over large areas.
Monitoring forest canopy structure is essential for understanding global carbon cycles, assessing tree growth, and managing plantation resources. 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. Deep learning methods have improved canopy estimation but still demand massive labeled datasets and often lose fine spatial details. Moreover, global models struggle to adapt to fragmented plantation landscapes with uniform tree structures. Due to these challenges, developing a cost-effective, high-resolution, and generalizable approach for mapping canopy height and biomass has become an urgent research priority.
A joint research team from Beijing Forestry University, Manchester Metropolitan University, and Tsinghua University has developed a new artificial intelligence (AI)-driven vision model that delivers sub-meter accuracy in estimating tree heights from RGB satellite images. Published (DOI: 10.34133/remotesensing.0880) in the Journal of Remote Sensing on October 20, 2025, the study introduces a novel framework that combines large vision foundation models (LVFMs) with self-supervised learning. The approach addresses the long-standing problem 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 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. The model achieved a mean absolute error of only 0.09 m 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 (AGB). Beyond its accuracy, the model demonstrated strong generalization across forest types, making it suitable for both regional and national-scale carbon accounting.
The model was tested in the Fangshan District of Beijing, an area with fragmented plantations primarily composed of Populus tomentosa, Pinus tabulaeformis, and Ginkgo biloba. Using one-meter-resolution Google Earth imagery and lidar-derived references, the AI model produced canopy height maps closely matching ground truth data. It significantly outperformed global CHM 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. Moreover, 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. This innovation bridges the gap between expensive lidar surveys and low-resolution optical methods, enabling detailed forest assessment with minimal data requirements.
“Our model demonstrates that large vision foundation models (LVFMs) can fundamentally transform forestry monitoring,” said Dr. Xin Zhang, corresponding author at Manchester Metropolitan University. “By combining global image pretraining with local self-supervised enhancement, we 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 team employed an end-to-end deep-learning framework combining pre-trained LVFM features with a self-supervised enhancement process. High-resolution Google Earth imagery (2013–2020) was used as input, and UAV-based lidar data served as reference for training and validation. The model was implemented in PyTorch and trained using the fastai framework on an NVIDIA RTX A6000 GPU. Comparative experiments with conventional networks (U-Net and DPT) and global CHM datasets confirmed superior accuracy and efficiency, validating the model’s potential for scalable canopy height mapping and biomass estimation.
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. Future research will extend this method to natural and mixed forests, integrate automated species classification, and support real-time carbon monitoring platforms. 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.
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References
DOI
Original Source URL
https://spj.science.org/doi/10.34133/remotesensing.0880
Funding Information
This study is supported by the National Science Foundation of China (72140005) and the Natural Science Foundation of Beijing, China (grant no. 3252016) and partly by BBSRC (BB/R019983/1, BB/S020969/), EPSRC (EP/X013707/1), and the Key Research and Development Program of Shaanxi Province (program no. 2024NC-YBXM-220).
About Journal of Remote Sensing
The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
A Novel Large Vision Foundation Model-Based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management
