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1.中国地质调查局 长沙自然资源综合调查中心, 湖南 长沙 410600
2.黄山水土资源
3.野外科学观测研究站, 安徽 黄山 245000
中国地质大学(武汉) 资源学院, 湖北 武汉 430074
4.黑龙江科技大学 建筑工程学院, 黑龙江 哈尔滨 150022
5.兰州大学 资源环境学院, 甘肃 兰州 730000
Received:30 October 2025,
Revised:2025-11-20,
Published:28 April 2026
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陈创, 郭军, 陈炟, 等.基于kNDVI与结构方程模型的洞庭湖区植被覆盖时空变化及驱动机制分析[J].水土保持通报,2026,46(2):146-154.
Chen Chuang, Guo Jun, Chen Da, et al. Spatiotemporal changes and driving mechanisms of vegetation cover in Dongting Lake area based on kNDVI and structural equation model [J]. Bulletin of Soil and Water Conservation,2026,46(2):146-154.
陈创, 郭军, 陈炟, 等.基于kNDVI与结构方程模型的洞庭湖区植被覆盖时空变化及驱动机制分析[J].水土保持通报,2026,46(2):146-154. DOI: 10.13961/j.cnki.stbctb.2026.02.039. CSTR: 32312.14.stbctb.2026.02.039.
Chen Chuang, Guo Jun, Chen Da, et al. Spatiotemporal changes and driving mechanisms of vegetation cover in Dongting Lake area based on kNDVI and structural equation model [J]. Bulletin of Soil and Water Conservation,2026,46(2):146-154. DOI: 10.13961/j.cnki.stbctb.2026.02.039. CSTR: 32312.14.stbctb.2026.02.039.
目的
2
探究2000—2022年洞庭湖区植被覆盖度的时空演变趋势及其驱动机制,识别关键影响因子及其贡献程度,为区域生态恢复和可持续发展提供科学参考。
方法
2
基于Google Earth Engine云平台,利用MOD13A1 V6数据构建核归一化植被指数(kernel normalized difference vegetation index,kNDVI),综合应用Theil-Sen Median趋势分析、Mann-Kendall检验、Hurst指数及结构方程模型(structural equation model,SEM),系统分析洞庭湖区kNDVI的时空变化特征,并综合评估气候因子、人类活动和地形因子的耦合作用机制。
结果
2
①2000—2022年洞庭湖区kNDVI整体呈上升趋势(0.025/10 a,
p
<
0.001),空间呈东、南、西部高而中北部较低的格局。 ②植被覆盖以改善为主,Hurst指数预测持续改善区域占57.87%,持续稳定区域占38.97%,持续退化区域占3.61%。 ③结构方程模型显示,各驱动因子对kNDVI变化的总效应表现为:夜间灯光
>
人口密度
>
高程
>
坡度
>
土地利用
>
土壤含水量
>
气温
>
坡向
>
降水。
结论
2
近23年来洞庭湖区植被覆盖状况总体改善,人类活动是植被变化的最主要负向驱动因素,气候因子起正向促进作用,地形因子的影响相对有限。
Objective
2
The spatiotemporal evolution trends and driving mechanisms of vegetation cover in the Dongting Lake area from 2000 to 2022 were investigated, in order to identify key influencing factors and their contribution levels, and provide scientific references for regional ecological restoration and sustainable development.
Methods
2
Based on the Google Earth Engine cloud platform, the kernel normalized difference vegetation index (kNDVI) was constructed using MOD13A1 V6 data. The Theil-Sen Median trend analysis, Mann-Kendall test, Hurst index, and structural equation model (SEM) were integrated to systematically analyze the spatiotemporal variation characteristics of kNDVI in the Dongting Lake area. The coupling mechanisms among climatic factors, human activities, and topographic factors were comprehensively assessed.
Results
2
① From 200
0 to 2022, the kNDVI in the Dongting Lake area showed an overall increasing trend (0.025/10 a,
p
<
0.001), with a spatial pattern of higher values in the eastern, southern, and western parts and lower values in the central and northern parts. ② Vegetation cover exhibited persistent improvement, with the Hurst index predicting that persistently improved areas accounted for 57.87%, persistently stable areas for 38.97%, and persistently degraded areas for only 3.61%. ③ The SEM demonstrated that the total effects of driving factors on kNDVI changes were ranked as follows: nighttime light
>
population density
>
elevation
>
slope
>
land use
>
soil moisture
>
temperature
>
aspect
>
precipitation.
Conclusion
2
Over the past 23 years, vegetation cover in the Dongting Lake area has shown overall improvement. Human activities are the primary negative driving factor of vegetation change, climatic factors play a positive promoting role, and the effects of topographic factors remain relatively limited.
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