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Scientia Silvae Sinicae ›› 2025, Vol. 61 ›› Issue (7): 182-191.doi: 10.11707/j.1001-7488.LYKX20240683

• Research papers • Previous Articles    

Distribution Characteristics of Vegetation Resilience and its Driving Factors in the Three-North Shelterbelt Forest Program Region from 2001 to 2021

Zeyu Yuan1,Hang Xu1,*(),Yi Ren2,Yang Xu1,Jianzhuang Pang1,Xiaoyun Wu1,Hanyao Zhang1,Zhiqiang Zhang1,*()   

  1. 1. School of Soil and Water Conservation, Beijing Forestry University Beijing 100083
    2. Academy of Inventory and Planning, National Forestry and Grassland Administration Beijing 100714
  • Received:2024-11-15 Online:2025-07-20 Published:2025-07-25
  • Contact: Hang Xu,Zhiqiang Zhang E-mail:hangxu@bjfu.edu.cn;zhqzhang@bjfu.edu.cn

Abstract:

Objective: This study aims to comprehensively investigate the distribution characteristics of vegetation resilience across different types of vegetation in the Three-North Shelterbelt Forest Program (TNSFP) region from 2001 to 2021, and its key driving factors, providing scientific foundations for enhancing the sustainability of vegetation ecological services in the TNSFP region under the context of climate change. Method: The lag-1 autocorrelation coefficient (AC1) of the kernel Normalized Difference Vegetation Index (kNDVI) over a 21-year period (2001–2021) was used to assess vegetation resilience, and analyze the distribution characteristics of vegetation resilience in the TNSFP region. Additionally, interpretable machine learning algorithms were employed to elucidate the regulatory mechanisms of biological and environmental factors on vegetation resilience. Result: This study revealed that foress had the highest resilience, followed by shrublands, with grasslands exhibiting the lowest resilience in the TNSFP region. Spatially, the vegetation in Inner Mongolia Plateau region had the lowest resilience, whereas that in northwest regions exhibited relatively higher resilience. Although the impact of various driving factors on resilience differed among vegetation types, environmental factors such as mean annual temperature (MAT) and mean annual precipitation (MAP) significantly outweighed biological factors overall. Additionally, vegetation resilience was significantly affected by the interaction between fractional vegetation coverage (FVC) and MAP. In arid regions, particular attention should be paid to the limitations imposed by water resource carrying capacity, and forest FVC should be managed carefully to avoid resilience reduction caused by competition for water resources. In contrast, grassland FVC showed a positive correlation with increased resilience, and increasing FVC helps enhance grassland resilience. In semi-arid and semi-humid regions, forest FVC exhibited a positive correlation with increased resilience, where higher FVC contributed to enhancing forest resilience. Vegetation planting and management should be adjusted based on local water resource availability. Conclusion: The variations in vegetation resilience in the TNSFP region are predominantly driven by environmental factors. Differentiated management strategies should be implemented for different vegetation types, considering regional ecological water availability, to enhance ecological resilience. In the context of global climate change, this study not only deepens the understanding of vegetation resilience in the TNSFP region but also offers a critical scientific foundation and theoretical framework for future afforestation planning and vegetation management practices.

Key words: three-north shelterbelt forest program, water resources, vegetation resilience, machine learning, SHAP

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