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

干旱区地理 ›› 2026, Vol. 49 ›› Issue (10): 2094-2106.doi: 10.12118/j.issn.1000-6060.2025.668 cstr: 32274.14.ALG2025668

十大孔兑流域植被覆盖度时空变化特征及其驱动力

袁伟诚¹(), 张会兰¹^,²(), 高慧茹¹, 谷方正¹

¹ 北京林业大学水土保持学院，北京 100083
² 北京林业大学重庆缙云三峡库区森林生态系统国家定位观测研究站，北京 100083

收稿日期:2025-10-23 修回日期:2025-12-19 出版日期:2026-10-25 发布日期:2026-10-26
通讯作者: 张会兰（1984-），女，博士，教授，主要从事水文与水沙动力过程等方面的研究. E-mail: zhanghl@bjfu.edu.cn
作者简介:袁伟诚（2001-），男，硕士研究生，主要从事水土保持等方面的研究. E-mail: WeichengYuan2001@bjfu.edu.cn
作者贡献
论文构思：袁伟诚、张会兰；数据管理：袁伟诚；规范分析：袁伟诚、张会兰；获取资助：张会兰；调查研究：袁伟诚、高慧茹；方法设计：袁伟诚、高慧茹、张会兰；项目管理：张会兰；提供资源：张会兰；软件编程：袁伟诚；监督指导：张会兰，谷方正；验证复核：袁伟诚；可视化：袁伟诚；初稿撰写：袁伟诚；修订编辑：袁伟诚、张会兰。
基金资助:
内蒙古自治区揭榜挂帅项目(2024JBGS0023)

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

YUAN Weicheng¹(), ZHANG Huilan¹^,²(), GAO Huiru¹, GU Fangzheng¹

¹ School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
² Three Gorges Reservoir Area Forest Ecosystem Research Station in Jinyun of Chongqing, Beijing Forestry University, Beijing 100083, China

Received:2025-10-23 Revised:2025-12-19 Published:2026-10-25 Online:2026-10-26

摘要/Abstract

摘要：

干旱半干旱地区植被覆盖变化及其驱动力研究对于生态修复与可持续发展具有重要意义。以黄河“几字弯”十大孔兑流域为研究区，基于2001—2022年MODIS 13Q1归一化植被指数（NDVI）数据，结合趋势分析法和地理探测器方法，探讨不同生态分区（上游覆土丘陵沟壑区、中游沙漠穿越区、下游平原区）植被覆盖的时空演变特征及其驱动机制。从自然（年均气温、年均降水量、数字高程模型、坡度、土壤湿度、土壤类型）和人类活动（土地利用类型、地区生产总值、人口密度、夜间灯光指数）关键因素，单因子和多因子交互作用2方面进行分析。结果表明：（1）研究区NDVI整体呈波动上升趋势，年均增幅为0.0085·a^-1，沙漠穿越区的植被恢复最为显著，年均增长率达到0.012·a^-1。不同生态分区之间植被覆盖度差异显著，下游平原区的NDVI值高于其他区域。（2）单因子分析显示，土地利用类型与土壤类型对NDVI空间分异的解释力最强（q值分别为0.612和0.569），是十大孔兑流域植被变化的主要驱动因子，除土地利用类型外的人类活动因子解释力较低，表明在流域内植被覆盖变化仍以自然因子为主导，人类活动为次要调节因素。（3）因子交互作用多呈增强效应，土壤类型与土地利用类型的交互作用解释力最高（q=0.732）；中游沙漠穿越区驱动力交互作用增强最明显。研究表明，十大孔兑流域植被变化主要受自然因子驱动，且在不同生态分区驱动机制存在差异。

关键词: 十大孔兑, 植被覆盖度, 时空变化, 地理探测器, 驱动因子

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

袁伟诚, 张会兰, 高慧茹, 谷方正. 十大孔兑流域植被覆盖度时空变化特征及其驱动力[J]. 干旱区地理, 2026, 49(10): 2094-2106.

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.

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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

十大孔兑流域植被覆盖度时空变化特征及其驱动力

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

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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

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