Zheng et al. (2026) Climate change weakened the productivity benefits from the forestry ecological engineering projects-induced greening in China

Identification

• Journal: International Journal of Applied Earth Observation and Geoinformation
• Year: 2026
• Date: 2026-01-10
• Authors: Liang Zheng, Hao Wu, Anqi Lin, J. Lu
• DOI: 10.1016/j.jag.2025.105052

Research Groups

• College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China
• Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan, China
• State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

Short Summary

This study investigated the long-term effectiveness of China's Forestry Ecological Engineering Projects (FEEPs) on vegetation greenness and productivity from 1982 to 2018. It found that while FEEPs significantly increased vegetation greenness, climate change, particularly extreme events, weakened the corresponding productivity benefits, indicating a decoupling and vulnerability of terrestrial carbon sequestration.

Objective

• Analyze long-term vegetation change trends in the FEEP regions.
• Assess whether greening driven by FEEPs translates into enhanced terrestrial carbon sequestration.
• Quantify the contributions of FEEPs to vegetation greenness and carbon sequestration.

Study Configuration

• Spatial Scale: Eight regional-scale Forestry Ecological Engineering Projects (FEEPs) across China, including the Three North Shelter Program (TNSP), Afforestation Program for Taihang Mountain (APTM), Shelterbelt Program for Upper and Middle Reaches of the Yangtze River (SPUMRYR), Coastal Shelterbelt Program (CSP), Shelterbelt Program for Middle Reaches of the Yellow River (SPMRYR), Shelterbelt Program for Huaihe River and Taihu Lake (SPHRTL), Shelterbelt Program for Pearl River (SPPR), and Shelterbelt Program for Liaohe River (SPLR).
• Temporal Scale: 1982 to 2018 (37 years).

Methodology and Data

• Models used:
  • Ensemble Empirical Mode Decomposition (EEMD) for time series analysis.
  • Modified residual trend analysis (multiple linear regression) to distinguish climate and FEEP effects, using specific FEEP initiation dates.
  • Partial correlation analysis to examine relationships between vegetation indices and climatic factors.
  • Z-score method for data standardization.
  • Penman-Monteith (PM) equation for evapotranspiration calculation in SPEI.
• Data sources:
  • NDVI data: Monthly Advanced Very High-Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) product (0.05° spatial resolution) from NOAA Climate Data Record (CDR) of AVHRR NDVI, Version 5 (1982–2018).
  • GPP data: Global Land Surface Satellite (GLASS) Gross Primary Productivity (GPP) product (0.05° spatial resolution, 8-day interval) (1982–2018), merging results from eight light-use efficiency models.
  • Climate data: Monthly temperature, precipitation, and insolation data from 613 meteorological stations across China (1982–2018), obtained from the Resource and Environmental Science Data Platform.
  • Ancillary data:
    • FEEP scope and distribution from the Resource and Environment Science Data Centre of the Chinese Academy of Sciences.
    • Land cover data from MODIS MCD12Q1 Version 061 product (500 m spatial resolution) (2001 onwards), classified by the International Geosphere–Biosphere Programme (IGBP) land cover scheme.
    • Standardized Precipitation Evapotranspiration Index (SPEI-12) for drought monitoring.

Main Results

• Both NDVI and GPP showed overall increasing trends across the FEEP regions from 1982 to 2018, indicating improvements in vegetation greenness and productivity.
• GPP exhibited lower magnitude and stability in its increase (monotonically increasing in approximately 35% of pixels) compared to NDVI (monotonically increasing in approximately 54% of pixels).
• GPP was more sensitive to environmental changes (temperature, precipitation, and sunlight) than NDVI, with higher proportions of pixels significantly correlated with these factors.
• NDVI trend turning points were closely related to FEEP implementation timing, suggesting FEEPs were the primary driver of greenness increases and effectively mitigated negative climate impacts on vegetation greenness.
• GPP trend turning points showed weaker correlations with FEEP implementation and were mainly controlled by climate, with engineering measures proving insufficient to offset the long-term negative effects of extreme climate events.
• A significant decoupling was observed: the pronounced greening in FEEP regions did not translate into a proportional increase in regional carbon sequestration (GPP).

Contributions

• This study is the first to combine Ensemble Empirical Mode Decomposition (EEMD) with a modified residual trend analysis to quantify the distinct contributions of Forestry Ecological Engineering Projects (FEEPs) to vegetation greenness and productivity in China.
• It highlights the complex and asynchronous responses of vegetation greenness (structural indicator) and productivity (process-based indicator) to both FEEP implementation and climate change.
• The research emphasizes the potential vulnerability of ecosystem carbon sequestration under ongoing climate change, even in regions experiencing significant greening due to ecological projects.
• It introduces an improved methodology for residual trend analysis by aligning it with the specific initiation dates of individual FEEPs, enhancing the accuracy of attributing drivers of vegetation dynamics.

Funding

• National Natural Science Foundation of China [U23A2020, 4250013224, 42371367, 42271354]
• China Postdoctoral Science Foundation [2024M761098]
• Department of Human Resources and Social Security of the Hubei Province Foundation [2024HBBHJD051, 2025HBBSHCXB091]

Citation

@article{Zheng2026Climate,
  author = {Zheng, Liang and Wu, Hao and Lin, Anqi and Lu, J. and Chen, Xiaoling},
  title = {Climate change weakened the productivity benefits from the forestry ecological engineering projects-induced greening in China},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  year = {2026},
  doi = {10.1016/j.jag.2025.105052},
  url = {https://doi.org/10.1016/j.jag.2025.105052}
}