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<p>Long-term monitoring of vegetation dynamics is essential for assessing ecosystems resilience and responses to climate change. The China Ecosystem Research Network (CERN) is a national long-term observation network that provides an ecological baseline across China’s major ecosystems. This study analyzed vegetation trends and their driving factors from 2000 to 2024 across 34 CERN field stations and their surrounding areas. An intercomparison of multiple NDVI products revealed substantial inconsistencies. MODIS NDVI exhibited superior temporal stability and spatial coherence and was therefore selected for long-term analysis. The mean NDVI at CERN stations (0.419) was 37.8% higher than the national average (0.304), and showed significantly faster greening trends (0.022/10 a) compared to suburban (0.013/10 a) and rural (0.017/10 a) areas, reflecting effective vegetation restoration and stable ecosystem management. Vegetation changes at CERN field stations were predominantly governed by climatic rather than anthropogenic factors. Among different ecosystem types, air temperature (AT), sunshine duration (SD), and relative humility (RH) were the dominant drivers in farmland, forest, and wetland. For grassland, AT and SD were the main drivers, whereas AT and RH exerted the strongest influence in the desert ecosystem. These findings confirm the representativeness of CERN stations and underscore the predominant role of climatic factors in shaping long-term vegetation trajectories across China’s diverse ecosystems.</p>

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Trajectories of vegetation dynamics and their driving factors across China Ecosystem Research Network: A long-term assessment (2000–2024)

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Shuyi Zhang,
  • Ziwen Jin,
  • Mengyao Ci,
  • Ke Zeng,
  • Yue Wang,
  • Hanxin Tang,
  • Rui Zhang,
  • Chaolin Cai,
  • Xianzhong Cao,
  • Min Liu

摘要

Long-term monitoring of vegetation dynamics is essential for assessing ecosystems resilience and responses to climate change. The China Ecosystem Research Network (CERN) is a national long-term observation network that provides an ecological baseline across China’s major ecosystems. This study analyzed vegetation trends and their driving factors from 2000 to 2024 across 34 CERN field stations and their surrounding areas. An intercomparison of multiple NDVI products revealed substantial inconsistencies. MODIS NDVI exhibited superior temporal stability and spatial coherence and was therefore selected for long-term analysis. The mean NDVI at CERN stations (0.419) was 37.8% higher than the national average (0.304), and showed significantly faster greening trends (0.022/10 a) compared to suburban (0.013/10 a) and rural (0.017/10 a) areas, reflecting effective vegetation restoration and stable ecosystem management. Vegetation changes at CERN field stations were predominantly governed by climatic rather than anthropogenic factors. Among different ecosystem types, air temperature (AT), sunshine duration (SD), and relative humility (RH) were the dominant drivers in farmland, forest, and wetland. For grassland, AT and SD were the main drivers, whereas AT and RH exerted the strongest influence in the desert ecosystem. These findings confirm the representativeness of CERN stations and underscore the predominant role of climatic factors in shaping long-term vegetation trajectories across China’s diverse ecosystems.