2001—2021年“三北”工程区植被韧性分布特征及其驱动因素

doi:10.11707/j.1001-7488.LYKX20240683

摘要/Abstract

摘要：

目的: 探讨2001—2021年间“三北”工程区内不同类型植被韧性的分布特征及其主要驱动因素，为在气候变化背景下提升“三北”工程区植被生态服务功能的可持续性提供科学依据。方法: 采用21年（2001—2021年）核归一化植被指数（kNDVI）的滞后1时间自相关系数（AC1）来衡量植被韧性，分析“三北”工程区植被韧性的分布特征。同时，运用可解释的机器学习算法解析生物和环境因素对植被韧性的调控机制。结果: 在“三北”工程区内，森林的韧性最高，其次是灌木，草地韧性最低；从空间分布来看，内蒙古高原地区植被韧性最低，而西北地区则表现出较高的植被韧性。不同植被类型的韧性受到各驱动因素的影响程度存在差异，但总体而言，年平均气温（MAT）和年平均降水量（MAP）等环境因素对植被韧性的影响显著高于生物因素。此外，植被韧性受到植被覆盖度（FVC）与MAP之间交互作用的显著影响。在干旱地区，应特别关注水资源承载力的限制，合理控制森林FVC，以避免因水分竞争导致的韧性下降；而草地FVC与韧性呈正相关关系，FVC的增加有助于提升草地韧性。在半干旱和半湿润地区，森林FVC与韧性呈正相关，高FVC有助于增强森林韧性，植被种植与管理应根据当地水资源可用情况进行调整。结论: “三北”工程区植被韧性的变化主要受环境因素驱动。针对不同类型的植被，应结合区域生态可利用水条件实施差异化的经营管理策略，以增强生态韧性。在全球气候变化的背景下，本研究不仅有助于揭示“三北”工程区植被的韧性，还为未来的造林规划和植被种植管理提供了重要的科学依据和理论指导。

关键词: "三北"工程, 水资源, 植被韧性, 机器学习, 沙普利加性解释

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

中图分类号:

S718.55

袁泽雨,许行,任怡,许杨,庞建壮,吴小云,张翰遥,张志强. 2001—2021年“三北”工程区植被韧性分布特征及其驱动因素[J]. 林业科学, 2025, 61(7): 182-191.

Zeyu Yuan,Hang Xu,Yi Ren,Yang Xu,Jianzhuang Pang,Xiaoyun Wu,Hanyao Zhang,Zhiqiang Zhang. Distribution Characteristics of Vegetation Resilience and its Driving Factors in the Three-North Shelterbelt Forest Program Region from 2001 to 2021[J]. Scientia Silvae Sinicae, 2025, 61(7): 182-191.

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