Spatio-temporal variation of vegetation and its correlation with soil moisture in the Yellow River Basin
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摘要:
黄河流域连接了青藏高原、黄土高原、内蒙古高原、华北平原，是我国重要的生态屏障。开展黄河流域植被时空变化及其与土壤湿度相关性分析，定量揭示土壤湿度对植被生长的影响，有利于干旱监测及生态环境保护。利用MOD13Q1 NDVI产品和全球陆面数据同化系统（global land data assimilation system，GLDAS）土壤湿度数据，采用Sen+Mann-Kendall趋势检验法和相关性分析法，分析了2000—2020年黄河流域植被时空变化特征及土地利用变化对植被生长的影响，并在流域尺度探索了生长季植被归一化植被指数（NDVI）与不同深度土壤湿度的相关性。结果表明：（1）研究区植被NDVI在空间上呈现“南高北低”的特征，沿黄河径流方向，上游右岸区域植被生长状况明显好于左岸，中下游两岸区域植被生长状况无明显差异。2000—2020年NDVI整体呈增加趋势，从2000年的0.356增加到2020年的0.435。（2）不同用地类型的NDVI由大到小依次为：林地>耕地>草地>未利用地，不同季节NDVI由大到小依次为：夏季>秋季>春季>冬季。（3）研究区大部分区域植被生长状况处于改善和稳定的状态，小部分区域处于退化状态，退化区域的主要原因是草地退化、城市扩张导致耕地退化及耕地转为建设用地。（4）NDVI与不同深度的土壤湿度（0~10 cm、10~40 cm、40~100 cm、100~200 cm）整体呈正相关趋势，相关系数分别为0.535，0.647，0.681，0.619；不同土地利用类型的NDVI与不同深度土壤湿度的相关性有差异，耕地、草地和未利用地NDVI与10~40 cm处的土壤湿度正相关面积最大，而林地NDVI与40~100 cm处的土壤湿度正相关面积最大。相关研究成果可为黄河流域高质量发展提供科学依据。
Abstract:Analyzing the temporal and spatial changes of vegetation and its correlation with soil moisture and quantitatively revealing the impact of soil moisture on vegetation growth are of certain significance for drought monitoring and ecological protection. However, previous quantitative researches on soil moisture and vegetation growth are not enough. Based on MOD13Q1 NDVI products and global land data assimilation system (GLDAS) soil moisture data, Sen + Mann Kendall trend test and correlation analysis are used to analyze the temporal and spatial variation characteristics of vegetation and the impact of land use change on vegetation growth in the Yellow River Basin from 2000 to 2020. The correlation between vegetation and soil moisture at different depths in growing season is explored. The results show that (1) the vegetation growth is characterized by “high in the south and low in the north”. Along the runoff direction of the Yellow River, the vegetation growth on the right bank of the upper reaches is significantly better than that on the left bank, and there is no significant difference in the vegetation growth on the two banks of the middle and lower reaches. The NDVI increases by 22.19% from 2000 to 2020, with the highest value of 0.435 and the lowest value of 0.356. (2) The order of NDVI value of different land use types from large to small is woodland>cultivated land>grassland>unused land. The order of NDVI value of different seasons from large to small is summer>autumn>spring>winter. (3) Most of the vegetation is in the state of improvement and stability, and a small part is in the state of degradation. The main reason for the degradation is grassland degradation, and urban expansion leads to the degradation of cultivated land and the conversion of cultivated land to construction land. (4) NDVI is positively correlated with soil moisture at different depths (0−10 cm, 10−40 cm, 40−100 cm, 100−200 cm), with the correlation coefficients of 0.535, 0.647, 0.681 and 0.619, respectively. The correlation between NDVI of different land use types and soil moisture of different depths is different. The positive correlation area between NDVI and soil moisture of cultivated land, grassland and unused land is the largest at the depth of 10−40 cm, while the positive correlation area between NDVI and soil moisture of forest land is the largest at the depth of 40−100 cm.
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Key words:
- NDVI /
- soil moisture /
- trend /
- land use /
- Yellow River Basin
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表 1 Sen+Mann-Kendall 趋势检验法分类标准
Table 1. Classification standard of Sen + Mann Kendall trend test
指标 |Z|＞1.96 |Z|≤1.96 
>0.001显著改善 轻微改善 −0.001≤ 
≤0.001基本稳定 基本稳定 
<−0.001显著退化 轻微退化 
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表 2 研究区2000—2020年植被生长变化趋势面积统计表
Table 2. Statistics of the change trend area from 2000 to 2020
序号 变化趋势 面积/（104 km2） 面积占比/% 1 显著改善 101.95 47.42 2 轻微改善 35.99 16.74 3 基本稳定 63.25 29.42 4 轻微退化 10.75 5.00 5 显著退化 3.03 1.41
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表 3 研究区NDVI与不同深度土壤湿度相关性面积统计表
Table 3. Statistics of the correlation area between NDVI and soil moisture at different depths
相关性 面积占比/% 0～10 cm 10～40 cm 40～100 cm 100～200 cm 平均 强负相关 0.00 0.04 0.07 0.26 0.09 中负相关 3.63 4.04 4.78 10.87 5.83 弱负相关 20.89 13.81 14.09 20.39 17.30 弱正相关 38.77 28.34 27.89 29.20 31.05 中正相关 36.59 51.70 51.46 38.58 44.58 强正相关 0.13 2.05 1.72 0.71 1.15 负相关之和 24.52 17.89 18.94 31.52 23.22 正相关之和 75.49 82.09 81.00 68.49 76.79
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