河北地质大学 河北省农业干旱遥感监测国际联合研究中心/土地科学与空间规划学院, 河北 石家庄 050031
赵琛旭(1999—),男(汉族),江苏省南京市人,硕士研究生,研究方向为资源与环境遥感。Email:cxzhaonj@163.com。
刘兴冉(1988—),女(汉族),河北省石家庄人,博士,副教授,主要从事农业水文与水资源,遥感水文研究。Email:xrliu@sjziam.ac.cn。
收稿:2024-07-18,
修回:2025-03-11,
纸质出版:2025-06-10
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赵琛旭, 刘兴冉, 朱骥, 等.2013—2022年江苏省植被覆盖度时空变化趋势及其驱动因素[J].水土保持通报,2025,45(3):397-406.
Zhao Chenxu, Liu Xingran, Zhu Ji, et al. Spatiotemporal variation trends and driving factors of fractional vegetation cover in Jiangsu Province from 2013 to 2022 [J]. Bulletin of Soil and Water Conservation,2025,45(3):397-406.
赵琛旭, 刘兴冉, 朱骥, 等.2013—2022年江苏省植被覆盖度时空变化趋势及其驱动因素[J].水土保持通报,2025,45(3):397-406. DOI: 10.13961/j.cnki.stbctb.2025.03.037. CSTR: 32312.14.stbctb.2025.03.037..
Zhao Chenxu, Liu Xingran, Zhu Ji, et al. Spatiotemporal variation trends and driving factors of fractional vegetation cover in Jiangsu Province from 2013 to 2022 [J]. Bulletin of Soil and Water Conservation,2025,45(3):397-406. DOI: 10.13961/j.cnki.stbctb.2025.03.037. CSTR: 32312.14.stbctb.2025.03.037..
目的
2
探究江苏省2013—2022年植被覆盖度(fractional vegetation cover, FVC)的时空变化趋势及其驱动力,为全面掌握该省植被覆盖情况,宏观调控相关政策提供科学参考。
方法
2
基于GEE(Google Earth Engine),应用像元二分模型估算FVC,采用Sen趋势分析、Mann-Kendall显著性检验、变异系数、Hurst指数等方法,系统分析FVC的时空变化趋势与特征,并以FVC和夜间灯光指数(night light index,NLI)为变量构建一元线性回归模型,通过残差分析量化FVC变化的驱动因素及其贡献率。
结果
2
2013—2022年江苏省年均FVC为0.648,尽管年际变化呈波动下降趋势,但仍以极高和高FVC类型为主。FVC呈减少趋势的面积比例为51.85%,呈增加趋势的面积比例为45.91%;变异系数平均值为0.16,Hurst指数平均值为0.56。气候变化和人类活动共同作用FVC的区域占58%,人类活动对江苏省FVC变化呈正向贡献的区域比例为86.53%,气候变化对江苏省FVC变化呈正向贡献的区域比例为71.47%。
结论
2
2013—2022年江苏省整体植被覆盖良好,下降趋势趋于平缓,整体波动幅度较低,变化趋势以弱持续性为主、弱反持续性次之,交错分布,退化与改善并存。苏北和苏中的植被覆盖度显著高于苏南,但植被退化程度也更为严重。变化的主要驱动力以气候变化和人类活动的共同作用为主,人类活动对FVC变化的贡献总体上比气候变化的贡献更大。
Objective
2
The spatiotemporal variation trends and driving factors of fractional vegetation cover (FVC) in Jiangsu Province from 2013 to 2022 were investigated, in order to provide a scientific reference for comprehensively understanding vegetation coverage and guiding macro-level policy adjustments in the province.
Methods
2
Based on the Google Earth Engine platform, the pixel dichotomy model was applied to estimate FVC. Sen’s trend analysis, Mann-Kendall significance test, the coefficient of variation, and the Hurst index were employed to systematically analyze the spatiotemporal trends and characteristics of FVC. A univariate linear regression model was constructed using FVC and night light index as variables, followed by residual analysis to quantify the driving factors of FVC changes and their contribution rates.
Results
2
From 2013 to 2022, the annual average FVC in Jiangsu Province was 0.648, with year-to-year variation showing a fluctuating downward trend but still predominantly consisting of very high and high FVC types. Areas with a decreasing FVC trend accounted for 51.85% of the study area, whereas areas with an increasing trend accounted for 45.91%. The average coefficient of variation was 0.16, and the average Hurst index was 0.56. Climate change and human activities jointly drove FVC changes in 58% of the total area. Human activities made a positive contribution to FVC changes in 86.53% of the regions, whereas climate change had a positive impact in 71.47% of the regions.
Conclusion
2
From 2013 to 2022, Jiangsu Province generally exhibited good vegetation coverage, with a gradually stabilizing downward trend and low overall fluctuation. The change trends were primarily weakly persistent, followed by weakly anti-persistent trends, showing an alternating distribution with coexisting degradation and improvement. Northern and central Jiangsu had significantly higher FVC than southern Jiangsu but also experienced more severe vegetation degradation. The primary drivers of FVC changes were the combined effects of climate change and human activities, with human activities generally contributing more than climate change.
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