Cheng et al. (2026) Stand-density optimization could substantially increase aboveground biomass in China’s planted forests without land expansion

Identification

• Journal: Communications Earth & Environment
• Year: 2026
• Date: 2026-09-21
• Authors: Kai Cheng, Ang Chen, Yu Ren, Mengxi Chen, Peirong Lin, Yanjun Su, Yixuan Zhang, Haitao Yang, Yu Li, Zhiyong Qi, Zekun Yang, Junmin Zhang, Jinyan Tian, Guangcai Xu, Keping Ma, Qinghua Guo
• DOI: 10.1038/s43247-026-04064-z

Research Groups

• Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, China.
• Institute of Tibetan Plateau, Peking University, Beijing, China.
• State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China.
• University of Chinese Academy of Sciences, Beijing, China.
• School of Ecology, Hainan University, Haikou, China.
• College of Earth Sciences, Chengdu University of Technology, Chengdu, China.
• Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China.
• Beijing GreenValley Technology Co., Ltd, Haidian District, Beijing, China.
• Northeast Asia Biodiversity Research Center, Northeast Forestry University, Harbin, China.

Short Summary

This study investigates the potential for increasing aboveground biomass in China's planted forests by optimizing stand density. The results show that a 8.84 Pg increase in biomass is possible through targeted thinning and enrichment planting, without expanding forest area.

Objective

• To investigate the relationship between stand density and aboveground biomass in China's planted forests.
• To quantify the potential for increasing aboveground biomass through stand-density optimization.

Study Configuration

• Spatial Scale: National scale, with a focus on China's planted forests.
• Temporal Scale: Long-term, with projections to 2100 under different climate scenarios.

Methodology and Data

• Models used: Quantile regression forests (QRF) models were used to estimate the upper envelope of aboveground biomass and identify pixel-level optimal tree densities. Generalized additive models (GAMs) were used to examine the relationships among tree density, canopy structural parameters, and aboveground biomass.
• Data sources: Nationwide UAV LiDAR observations, GEDI spaceborne LiDAR, tree density maps, multisource AGB products, and environmental data.

Main Results

• The study found a consistent inverted-U relationship between stand density and aboveground biomass across 6226 paired UAV-GEDI footprints.
• The optimal density for aboveground biomass was found to be around 100-200 trees per 25-m GEDI footprint.
• The study estimated that the potential AGB ceiling of China's planted forests could increase by 8.84 ± 0.11 Pg through stand-density optimization.

Contributions

• This study provides a spatially explicit assessment of a land-neutral pathway for enhancing forest carbon storage in China's planted forests.
• The results challenge the common assumption that forest carbon gains scale primarily with the number of trees planted or the area afforested.

Funding

• This research was supported by various projects and programs, including:
  • [Insert funding information]

Citation

@article{Cheng2026Standdensity,
  author = {Cheng, Kai and Chen, Ang and Ren, Yu and Chen, Mengxi and Lin, Peirong and Su, Yanjun and Zhang, Yixuan and Yang, Haitao and Li, Yu and Qi, Zhiyong and Yang, Zekun and Zhang, Junmin and Tian, Jinyan and Xu, Guangcai and Ma, Keping and Guo, Qinghua},
  title = {Stand-density optimization could substantially increase aboveground biomass in China’s planted forests without land expansion},
  journal = {Communications Earth & Environment},
  year = {2026},
  doi = {10.1038/s43247-026-04064-z},
  url = {https://doi.org/10.1038/s43247-026-04064-z}
}