Shan et al. (2025) Monitoring Long-Term Vegetation Dynamics in the Hulun Lake Basin of Northeastern China Through Greening and Browning Speeds from 1982 to 2015

Identification

• Journal: Plants
• Year: 2025
• Date: 2025-11-05
• Authors: Nan Shan, Tie Wang, Qian Zhang, Jinqi Gong, Mingzhu He, Xiaokang Zhang, Xuehe Lu, Feng Qiu
• DOI: 10.3390/plants14213394

Research Groups

• Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People’s Republic of China
• Inner Mongolia Hulun Lake (Wetland) Comprehensive Monitoring Station for Ecological Quality
• School of Geomatics Science and Technology, Nanjing Tech University
• College of Geodesy and Geomatics, Shandong University of Science and Technology
• Chinese Academy of Surveying & Mapping
• School of National Safety and Emergency Management, Beijing Normal University
• School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology

Short Summary

This study investigated long-term vegetation dynamics in the Hulun Lake Basin (HLB) from 1982 to 2015 using NDVI and a novel Vegetation NDVI Change Rate (VNDVI) metric, revealing an overall greening trend, accelerated spring greening, and delayed autumn browning driven by distinct seasonal climatic factors.

Objective

• To analyze the spatiotemporal patterns and trends of vegetation greenness (NDVI) and greening/browning speeds (VNDVI) in the Hulun Lake Basin from 1982 to 2015.
• To identify the dominant climatic drivers (temperature, precipitation, solar radiation) influencing these vegetation dynamics across different phenological phases.

Study Configuration

• Spatial Scale: Hulun Lake Basin (HLB), Northeastern Inner Mongolia, China, focusing on a grassland–cropland–woodland transition area covering 84,530 square kilometers.
• Temporal Scale: 1982 to 2015 (34 years), with biweekly observations.

Methodology and Data

• Models used: Linear regression analysis, partial correlation analysis.
• Data sources:
  • GIMMS NDVI3g dataset (biweekly, 0.083° spatial resolution, 1982–2015).
  • High-resolution meteorological forcing dataset for China (air temperature, precipitation, solar radiation; 3-hour temporal resolution, 0.1° spatial resolution, 1982–2015) from the National Tibetan Plateau Data Center.

Main Results

• The NDVI showed an overall significant upward trend of +0.0028 per year (p < 0.05) across more than 70% of the basin, indicating a persistent greening tendency.
• The VNDVI revealed an accelerated spring greening rate of +0.8% per year (p < 0.05) and a slowed autumn browning rate of −0.6% per year (p < 0.05), reflecting an extended growing season.
• Spatial correlation analysis indicated distinct seasonal shifts in dominant climatic drivers:
  • Temperature primarily influenced spring greening (partial r = 0.52).
  • Precipitation governed summer growth (partial r = 0.64).
  • Solar radiation modulated autumn senescence (partial r = 0.38).
• Compared to the NDVI, the VNDVI was more sensitive to both climatic fluctuations and anthropogenic disturbances, highlighting its utility in capturing process-level vegetation dynamics.
• Late-summer declines in VNDVI spatially coincided with known grazing zones, suggesting modulation of process rates by human activity.

Contributions

• Introduced and validated the Vegetation NDVI Change Rate (VNDVI) as a mechanistic alternative to traditional NDVI for monitoring vegetation activity, providing process-level insights into greening and browning speeds.
• Quantified the spatiotemporal dynamics of greening and browning speeds in the Hulun Lake Basin, a vulnerable grassland–wetland–forest ecotone.
• Identified the distinct seasonal and spatial shifts in dominant climatic drivers (temperature, precipitation, solar radiation) influencing vegetation growth rates.
• Demonstrated that VNDVI is more sensitive to climatic fluctuations and anthropogenic disturbances than NDVI, offering a more robust framework for disentangling their impacts.
• Provided scientific support for ecological conservation and management in North China’s grassland–forest ecotone by improving the detection of seasonal driver transitions and facilitating the interpretation of ecosystem carbon flux seasonality.

Funding

• National Natural Science Foundation of China (grant number 42071050)
• Ecological Security Investigation and Assessment Project of Hulun Lake (HSZCS-G-F-210059)
• Taishan Scholar Project (TSQN202306210)

Citation

@article{Shan2025Monitoring,
  author = {Shan, Nan and Wang, Tie and Zhang, Qian and Gong, Jinqi and He, Mingzhu and Zhang, Xiaokang and Lu, Xuehe and Qiu, Feng},
  title = {Monitoring Long-Term Vegetation Dynamics in the Hulun Lake Basin of Northeastern China Through Greening and Browning Speeds from 1982 to 2015},
  journal = {Plants},
  year = {2025},
  doi = {10.3390/plants14213394},
  url = {https://doi.org/10.3390/plants14213394}
}