黄土高原植被抗旱性监测及影响因素分析

doi:10.12118/j.issn.1000-6060.2025.083

摘要/Abstract

摘要：

随着气候改变和人类活动的不断加剧，黄土高原干旱频发并且危害日益严重，对区域植被的生长状况构成了严重制约。基于2000—2022年标准化降水蒸散指数、归一化植被指数和总初级生产力数据，通过最大相关系数法建立滞后时间、影响程度和植被恢复力3个植被抗旱性指标，构建植被抗旱性综合监测方法来探究植被在干旱情况下的响应及植被抗旱性大小。结果表明：（1）黄土高原滞时为1个月时表现最为明显，73%区域最大相关系数在0.4以上，随着滞时的增加，相关系数呈现由南向北递减趋势。（2）植被在黄土高原南温带南部与东部和高原气候区北部抗旱性主要为0.0~0.2，在中温带中部抗旱性达到0.2~0.4。总体来看，植被抗旱性基本规律为灌木最大，其次是森林和农田，最后为草地。（3）在线性关系中，相关性最高的3个因子分别为地表温度、降雨量和酸碱度，这与最优参数地理探测器探测结果在各个气候区主要影响因子基本相同。研究植物抗旱性综合监测方法有助于了解植被抗旱性规律，对帮助降低灾害风险具有借鉴意义。

关键词: 相关系数, 植被抗旱性, 干旱指数, 遥感, 黄土高原

Abstract:

With intensifying climate change and human activities, the increasing frequency of droughts and their impacts on the Loess Plateau of China have severely constrained regional vegetation growth. Using data from 2000 to 2022, including standardized precipitation evapotranspiration index, normalized difference vegetation index, and gross primary productivity data, three vegetation drought resistance indices were established, namely lag time, impact degree, and vegetation resilience, using the maximum correlation coefficient method. A comprehensive monitoring framework was then constructed to assess vegetation responses under drought conditions and quantify drought resistance. Results revealed three key insights. (1) The Loess Plateau exhibited the most pronounced lag time at 1 month, with maximum correlation coefficients exceeding 0.4 in 73% areas and correlation coefficients decreasing from south to north as lag increases. (2) Vegetation drought resistance ranged from 0 to 0.2 in the southern and eastern of southern temperate and northern plateau climate zones of the Loess Plateau and 0.2-0.4 in the middle temperate zone. Overall, drought resistance was highest in shrubs, followed by forest and farmland, and lowest in grassland. (3) Land surface temperature, rainfall, and soil pH showed the strongest linear correlations with vegetation drought resistance, consistent with the main influencing factors identified via optimal parameter geographic detector analyses across climate zones. This comprehensive monitoring approach enhances understanding of vegetation drought resistance patterns and provides valuable guidance for reducing ecological risk under future drought conditions.

Key words: correlation coefficient, vegetation drought resistance, drought index, remote sensing, Loess Plateau

程西安, 牛全福, 王刚, 邵东虎, 朱登峰, 王振宇. 黄土高原植被抗旱性监测及影响因素分析[J]. 干旱区地理, 2026, 49(1): 1-12.

CHENG Xi’an, NIU Quanfu, WANG Gang, SHAO Donghu, ZHU Dengfeng, WANG Zhenyu. Monitoring and influencing factors of vegetation drought resistance on the Loess Plateau[J]. Arid Land Geography, 2026, 49(1): 1-12.

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滞时/月	高原气候区				南温带气候区				中温带气候区
滞时/月	农田	森林	灌木	草地	农田	森林	灌木	草地	农田	森林	灌木	草地
1	0.461	0.345	0.377	0.439	0.454	0.352	0.321	0.472	0.456	0.402	0.380	0.479
3	0.332	0.348	0.336	0.348	0.384	0.374	0.339	0.372	0.334	0.331	0.345	0.345
6	0.358	0.349	0.339	0.339	0.369	0.361	0.353	0.322	0.286	0.292	0.332	0.296
9	0.411	0.366	0.374	0.381	0.325	0.336	0.317	0.327	0.295	0.323	0.353	0.295

气候区	农田	森林	灌木	草地
高原气候区	-0.014	0.051	0.037	0.001
南温带气候区	-0.016	0.011	0.023	-0.024
中温带气候区	-0.026	0.004	0.034	-0.036

探测因子	高原气候区		南温带气候区		中温带气候区
探测因子	方法	分类级别	方法	分类级别	方法	分类级别
有效水含量	等间距法	3	几何间隔法	4	几何间隔法	4
土壤类型	自然断点法	5	自然断点法	5	几何间隔法	5
有机碳含量	分位数法	5	分位数法	5	分位数法	5
酸碱度	分位数法	5	等间距法	5	等间距法	4
LST	分位数法	5	自然断点法	5	自然断点法	5
降雨量	标准差法	5	分位数法	5	等间距法	5
PET	分位数法	5	分位数法	4	等间距法	5
夜间灯光指数	几何间隔法	4	标准差法	5	标准差法	5
人口密度	分位数法	5	等间距法	5	自然断点法	5
DEM	几何间隔法	4	标准差法	5	等间距法	5
坡度	几何间隔法	5	等间距法	5	分位数法	5
坡向	标准差法	5	几何间隔法	4	等间距法	5