The Spatiotemporal Pattern and Driving Factors of Vegetation in Zhejiang Province over the Past 20 Years
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摘要:
利用中分辨率MOD13Q1影像计算归一化植被指数（NDVI），应用地理信息系统技术提取了浙江省2000—2019年的NDVI数据，并运用趋势分析法对NDVI的时空格局、变化趋势及其驱动因子进行了研究。结果表明，2000—2019年浙江省NDVI在空间上存在显著差异，时间上呈波动上升趋势，平均增长率为0.022/10年。季节上，平均NDVI大小顺序为夏季 > 秋季 > 春季 > 冬季，呈现单峰单谷分布，峰值和谷值分别出现在7月和2月。总体来看，NDVI有所提高，改善和显著改善面积分别占全省总面积的15.81%和55.85%，退化面积占22.02%。多元线性回归模型结果显示，月最高气温、月平均相对湿度、月累计降水量、道路密度、河流密度、铁路密度、坡度和海拔等8个因子对NDVI有显著影响。模型的均方根误差（RMSE）、均方绝对百分比误差（MAPE）和变异解释量（R2）分别为0.050、5.68%和0.572，这表明多元线性回归模型在估测浙江省NDVI方面具有一定的可靠性。研究结果揭示了浙江省NDVI的时空格局及其驱动因子，为浙江省植被恢复提供了科学依据。
Abstract:This study utilized medium-resolution MOD13Q1 imagery to calculate the Normalized Difference Vegetation Index (NDVI). Using geographic information system (GIS) technology, NDVI data for Zhejiang Province from 2000 to 2019 was extracted. Trend analysis was then employed to investigate the spatiotemporal patterns, changing trends, and driving factors of NDVI in Zhejiang Province. The results show significant spatial differences in NDVI across Zhejiang Province from 2000 to 2019, with a fluctuating upward trend over time and an average growth rate of 0.022 per decade. Seasonally, the average NDVI ranked as follows: summer > autumn > spring > winter, exhibiting a single peak and single valley distribution, with the peak in July and the valley in February. Overall, NDVI improved with areas of improvement and significant improvement accounting for 15.81% and 55.85% of the province's total area, respectively, while degraded areas accounted for 22.02%. Results from a multiple linear regression model indicate that eight factors significantly influence NDVI: monthly maximum temperature, monthly average relative humidity, monthly cumulative precipitation, road density, river density, railway density, slope, and altitude. The model’s root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 0.050, 5.68%, and 0.572, respectively, indicating a certain level of reliability in estimating NDVI in Zhejiang Province. The study's findings reveal the spatiotemporal patterns and driving factors of NDVI in Zhejiang Province, providing a scientific basis for vegetation restoration in the region.
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Keywords:
- Zhejiang /
- NDVI /
- Spatial and temporal patterns /
- trend analysis /
- driving factors
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图 2 2000—2019年浙江省平均NDVI月变化
Figure 2. Monthly variation of average NDVI in Zhejiang Province from 2000 to 2019
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图 3 2000—2019年浙江省平均NDVI年变化
Figure 3. Annual variation of average NDVI in Zhejiang Province from 2000 to 2019
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图 5 2000—2019年浙江省NDVI时空变化
Figure 5. Spatial changes of NDVI in Zhejiang from 2000 to 2019
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图 8 浙江省不同地类的NDVI年变化
Figure 8. Annual changes of NDVI in different land types in Zhejiang Province
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图 10 多元线性回归模型观测值和预测值线性拟合图
Figure 10. Plots of the observed and the predicted values calculated by MLR model for the final model
表 1 多元线性回归模型因子筛选
Table 1 Factor screening for multiple linear regression models
模型变量 P值最小值 P值最大值 样本显著性数 VIF 解释度/% 月日照时数 0.036 0.396 1 21.76 2.41 月平均气温 0.264 0.871 0 13.3 3.35 月最高气温 <0.001 <0.001 5 4.93 6.85 月最低气温 0.047 0.845 1 9.28 9.37 月平均相对湿 <0.001 <0.001 5 2.1 10.2 月最小相对湿 0.639 0.949 0 8.36 0.13 月极大风速 0.581 0.945 1 8.36 0.23 月累计降水 <0.001 0.125 4 1.24 1.45 居民点密度 0.521 0.740 0 1.4 2.94 POI点密度 0.058 0.574 0 1.84 9.19 道路密度 <0.001 <0.001 5 3.1 22.39 河流密度 <0.001 <0.001 5 3.46 5.42 铁路密度 <0.001 0.212 4 1.91 1.87 人口密度 0.045 0.946 1 1.77 0.99 GDP 0.194 0.762 0 1.74 0.82 坡向 0.676 0.925 0 1.11 0.63 坡度 <0.001 0.078 4 1.66 7.87 海拔 <0.001 0.002 5 2 13.88 
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表 2 最终多元线性回归模型
Table 2 The final Multiple Linear Regression models
变量 估计系数 标准误 显著性水平 常量 0.623 40 0.013 23 / 月最高气温 0.000 18 0.000 04 <0.000 1 月平均相对湿 −0.001 20 0.000 11 <0.000 1 月累计降水 0.000 05 0.000 01 0.041 2 道路密度 −2.745 00 0.250 10 <0.000 1 河流密度 −2.088 00 0.472 50 <0.000 1 铁路密度 −0.372 80 0.451 20 0.040 9 坡度 0.003 51 0.001 75 0.045 1 海拔 0.000 07 0.000 01 <0.000 1
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