Substantial contribution of trees outside forests to above-ground carbon across China

Abstract Accurately quantifying canopy height and above-ground carbon across diverse land-cover types is crucial for understanding carbon storage dynamics and guiding climate-mitigation strategies. Yet existing maps often overlook non-forest ecosystems. Here we present a deep learning framework based on a U-Net architecture that combines radar, optical, elevation and slope data to produce a 10 m canopy height map across China. The model is trained with laser measurements from NASA’s GEDI mission validated using unmanned aerial vehicle lidar data ( MAE = 2.39 m ). We then estimate the above-ground biomass and carbon from these heights using a Random Forest model ( MAE = 37.71 Mg ha-1 ). By deriving carbon from canopy height, we take advantage of U-Net’s ability to capture trees in non-forest ecosystems such as croplands, grasslands and urban areas. Our nationwide 30 m carbon map reveals that trees outside forests contribute 20.8-32.9% of China’s above-ground carbon in 2019 (3.62-5.72 Pg C), underscoring their importance.

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Publication Details

Journal
Communications Earth & Environment
Published
2026-02-05
DOI
https://doi.org/10.1038/s43247-025-03150-y
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Substantial contribution of trees outside forests to above-ground carbon across China

Martin Brandt, Xiaowei Tong, Tianqi Shi, Yang Su et al.
Communications Earth & Environment
Remote Sensing and LiDAR Applications
article

Substantial contribution of trees outside forests to above-ground carbon across China

Martin Brandt, Xiaowei Tong, Tianqi Shi, Yang Su, W. Li, Xianglin Zhang, S. X. Du, Alexandre d’Aspremont, Siyu Liu, Qinghua Guo, K Cheng, Philippe Ciais, Ge Han, Songchao Chen, Shilong Piao, Xin Ma, Yidi Xu
article en

Abstract

Abstract Accurately quantifying canopy height and above-ground carbon across diverse land-cover types is crucial for understanding carbon storage dynamics and guiding climate-mitigation strategies. Yet existing maps often overlook non-forest ecosystems. Here we present a deep learning framework based on a U-Net architecture that combines radar, optical, elevation and slope data to produce a 10 m canopy height map across China. The model is trained with laser measurements from NASA’s GEDI mission validated using unmanned aerial vehicle lidar data ( MAE = 2.39 m ). We then estimate the above-ground biomass and carbon from these heights using a Random Forest model ( MAE = 37.71 Mg ha-1 ). By deriving carbon from canopy height, we take advantage of U-Net’s ability to capture trees in non-forest ecosystems such as croplands, grasslands and urban areas. Our nationwide 30 m carbon map reveals that trees outside forests contribute 20.8-32.9% of China’s above-ground carbon in 2019 (3.62-5.72 Pg C), underscoring their importance.

Communications Earth & EnvironmentVol. 7(1)
University of Copenhagen (DK), Centre National de la Recherche Scientifique (FR), Zhejiang University of Science and Technology (CN), Université de Versailles Saint-Quentin-en-Yvelines (FR), Chinese Academy of Sciences (CN), Peking University (CN), AgroParisTech (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), École Normale Supérieure - PSL (FR), Wuhan University (CN), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), École Normale Supérieure (BI), Institute of Subtropical Agriculture (CN), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN), Laboratoire des Sciences du Climat et de l'Environnement (FR), Institute of Tibetan Plateau Research (CN), Zhanjiang Experimental Station (CN), Zhejiang University (CN), Wuhan Institute of Technology (CN), Tsinghua University (CN)
Climate action
Openalex Percentile: Top 69%
Remote Sensing and LiDAR Applications
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