Stand-density optimization could substantially increase aboveground biomass in China’s planted forests without land expansion

Expanding forest cover is a cornerstone of land-based climate mitigation, yet its future contribution is increasingly limited by competition for land and by broader policy and social constraints. Making better use of the forests already planted, therefore, offers a mitigation opportunity that requires no additional land. Here we show how much aboveground biomass China’s planted forests could gain by optimizing stand density, drawing on nationwide unmanned aerial vehicle (UAV) and satellite LiDAR, tree density maps, and multiple biomass datasets. Across 6226 paired UAV-satellite observations, aboveground biomass exhibits a hump-shaped response to tree density, mediated in part by forest canopy complexity. Matching density to local environmental conditions could raise the biomass these forests hold by roughly 8.84 Pg, achieved by planting in sparse stands and thinning in crowded ones with little net change in total tree numbers. Under future climate, the most productive densities shift geographically, underscoring the need for adaptive management. Optimizing stand density through targeted planting and thinning could increase aboveground biomass in China’s planted forests by approximately 8.8 Pg without additional land, according to analyses of paired UAV and satellite LiDAR observations, tree density maps, and multiple biomass datasets.

Authors

Institutions

Publication Details

Journal
Communications Earth & Environment
Published
2026-09-21
DOI
https://doi.org/10.1038/s43247-026-04064-z
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Stand-density optimization could substantially increase aboveground biomass in China’s planted forests without land expansion

Qinghua Guo, Yu Ren, Yu Li, Peirong Lin et al.
Communications Earth & Environment
Remote Sensing and LiDAR Applications
article

Stand-density optimization could substantially increase aboveground biomass in China’s planted forests without land expansion

Qinghua Guo, Yu Ren, Yu Li, Peirong Lin, Kai Cheng, Yanjun Su, Zekun Yang, Haitao Yang, Yixuan Zhang, Keping Ma, Mengxi Chen, Zhiyong Qi, Guangcai Xu, Jinyan Tian, Ang Chen, Junmin Zhang
article en

Abstract

Expanding forest cover is a cornerstone of land-based climate mitigation, yet its future contribution is increasingly limited by competition for land and by broader policy and social constraints. Making better use of the forests already planted, therefore, offers a mitigation opportunity that requires no additional land. Here we show how much aboveground biomass China’s planted forests could gain by optimizing stand density, drawing on nationwide unmanned aerial vehicle (UAV) and satellite LiDAR, tree density maps, and multiple biomass datasets. Across 6226 paired UAV-satellite observations, aboveground biomass exhibits a hump-shaped response to tree density, mediated in part by forest canopy complexity. Matching density to local environmental conditions could raise the biomass these forests hold by roughly 8.84 Pg, achieved by planting in sparse stands and thinning in crowded ones with little net change in total tree numbers. Under future climate, the most productive densities shift geographically, underscoring the need for adaptive management. Optimizing stand density through targeted planting and thinning could increase aboveground biomass in China’s planted forests by approximately 8.8 Pg without additional land, according to analyses of paired UAV and satellite LiDAR observations, tree density maps, and multiple biomass datasets.

Communications Earth & Environment
Chinese Academy of Sciences (CN), Peking University (CN), Hainan University (CN), Chengdu University of Technology (CN), Institute of Tibetan Plateau Research (CN), Institute of Botany (CN), University of Chinese Academy of Sciences (CN), Northeast Forestry University (CN), Capital Normal University (CN)
Climate action
Openalex Percentile: Top 18%
Remote Sensing and LiDAR Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.