十大孔兑流域植被覆盖度时空变化特征及其驱动力

doi:10.12118/j.issn.1000-6060.2025.668

Abstract

Abstract:

Understanding the dynamics of vegetation cover and their driving mechanisms in arid and semiarid regions are crucial for ecological restoration, land management, and sustainable development, as vegetation in these areas is highly sensitive to both natural environmental changes and human disturbances. This study focuses on the Ten Tributaries region in the Jiziwan area of the Yellow River Basin, investigating long-term changes in vegetation cover and their driving factors from 2001 to 2022 using MODIS 13Q1 normalized difference vegetation index (NDVI) data. By integrating trend analysis with the Geodetector model, we examined the spatiotemporal dynamics of vegetation cover across three ecological zones: The upper silty gully region, the middle desert-crossing region, and the lower plain region. We selected a range of natural factors (temperature, precipitation, digital elevation model, slope, soil moisture, soil type, and land-use type) and human activity factors (gross regional production, population density, and nighttime light intensity) to assess the mechanisms behind vegetation changes, evaluating their individual explanatory power and interactive effects on NDVI variation. The results reveal the following: (1) NDVI in the Ten Tributaries region exhibited clear interannual fluctuations but exhibited an overall increasing trend during the study period, with an average annual growth rate of 0.0085·a^-1, indicating a general improvement in vegetation cover. Among the ecological zones, the middle desert-crossing region experienced the most significant recovery, achieving the highest annual increase rate of 0.012·a^-1, even though NDVI values there were lower than in the lower plain region, which maintained relatively high and stable vegetation cover. (2) Single-factor Geodetector analysis indicated that land-use type and soil type were the dominant factors influencing NDVI variation, with explanatory powers (q values) of 0.612 and 0.569, respectively, highlighting their critical role in regulating vegetation dynamics in the Ten Tributaries region. By contrast, human activity factors exhibited relatively weaker explanatory power, although land-use type had a stronger influence than other anthropogenic indicators, reflecting the effects of human-induced land-use changes on vegetation patterns. (3) Interaction analysis revealed that the explanatory power of most factor combinations was enhanced compared to individual factors, suggesting significant synergistic effects between natural and human drivers. Notably, the interaction between soil type and land-use type had the highest explanatory power (q=0.732), with the enhancement effect of factor interactions particularly pronounced in the middle desert-crossing region. Overall, changes in vegetation cover in the Ten Tributaries region were primarily driven by natural environmental factors, with human activities acting as secondary regulators. The dominant driving mechanisms varied among ecological zones, highlighting the importance of region-specific strategies for vegetation restoration and ecological management.

Key words: Ten Tributaries region, vegetation coverage, spatiotemporal variation, Geodetector, driving factors

YUAN Weicheng, ZHANG Huilan, GAO Huiru, GU Fangzheng. Spatiotemporal variations and driving forces of vegetation coverage in the Ten Tributaries region[J].Arid Land Geography, 2026, 49(10): 2094-2106.

Figures/Tables 11

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数据	数据来源	分辨率/m	年份
归一化植被指数	美国国家航空航天局（MODIS 13Q数据）（https://ladsweb.modaps.eosdis.nasa.gov）	250	2001—2022
年均降水量	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）	1000	2001—2022
年均气温	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）	1000	2001—2022
数字高程模型（DEM）	地理空间数据云（https://www.gscloud.cn/）	30	2022
坡度	地理空间数据云（https://www.gscloud.cn/）	30	2022
土壤湿度	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）	250	2022
土壤类型	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）	250	2022
土地利用类型	美国地质勘探局官网（https://earthexplorer.usgs.gov/）	30	2001—2022
地区生产总值	https://github.com/thestarlab/ChinaGDP	1000	2014—2020
人口密度	美国能源部橡树岭国家实验室（https://landscan.ornl.gov）	1000	2001—2022
夜间灯光指数	Harvard Dataverse（https://dataverse.harvard.edu/）	500	2001—2022

判据	交互作用
$\mathit{q}({\mathit{X}}_{1}\bigcap {\mathit{X}}_{2})<\mathrm{m}\mathrm{i}\mathrm{n}\left[\mathit{q}\right({\mathit{X}}_{1}),\mathit{q}({\mathit{X}}_{2}\left)\right]$	非线性减弱
$\mathrm{m}\mathrm{i}\mathrm{n}\left[\mathit{q}\left({\mathit{X}}_{1}\right),\mathit{q}\left({\mathit{X}}_{2}\right)\right]<\mathit{q}\left({\mathit{X}}_{1}\bigcap {\mathit{X}}_{2}\right)<\mathrm{m}\mathrm{a}\mathrm{x}\left[\mathit{q}\right({\mathit{X}}_{1}),\mathit{q}({\mathit{X}}_{2}\left)\right]$	单因子非线性减弱
$\mathit{q}({\mathit{X}}_{1}\bigcap {\mathit{X}}_{2})>\mathrm{m}\mathrm{a}\mathrm{x}\left[\mathit{q}\right({\mathit{X}}_{1}),\mathit{q}({\mathit{X}}_{2}\left)\right]$	双因子增强
$\mathit{q}({\mathit{X}}_{1}\bigcap {\mathit{X}}_{2})=\mathit{q}\left({\mathit{X}}_{1}\right)+\mathit{q}\left({\mathit{X}}_{2}\right)$	独立
$\mathit{q}({\mathit{X}}_{1}\bigcap {\mathit{X}}_{2})>\mathit{q}\left({\mathit{X}}_{1}\right)+\mathit{q}\left({\mathit{X}}_{2}\right)$	非线性增强

因子类型	驱动因子	代表符号	因子类型	驱动因子	代表符号
自然因子	年均降水量	X₁	自然因子	土壤类型	X₆
	年均气温	X₂	人类活动因子	土地利用类型	X₇
	DEM	X₃		地区生产总值	X₈
	坡度	X₄		人口密度	X₉
	土壤湿度	X₅		夜间灯光指数	X₁₀

Spatiotemporal variations and driving forces of vegetation coverage in the Ten Tributaries region

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