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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (1) : 221-231     DOI: 10.6046/zrzyyg.2023226
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Responses of vegetation growth conditions to meteorological drought in Shanxi Province from 2000 to 2020
ZHAO Fu(), WANG Li(), MA Yuang, JIANG Ruixia
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
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Abstract  

Based on the standardized precipitation index (SPI) and vegetation condition index (VCI) from 2000 to 2020, this study analyzed the trends in meteorological drought across different vegetation types in Shanxi Province using methods such as variational mode decomposition (VMD), Mann-Kendall trend analysis, and Pearson correlation coefficient. Accordingly, this study quantified the response time of vegetation growth conditions to meteorological drought. The results indicate that from the beginning of the 2000s, the overall meteorological drought in Shanxi Province has gradually eased. However, on a seasonal scale, areas with increasingly aggravated drought continuously expand from spring to winter. Meteorological drought has alleviated across various vegetation types, with the alleviation becoming increasingly significant with an increase in the time scale. In contrast, on a seasonal scale, the drought relief gradually weakens from spring to winter, during which drought aggravation progressively strengthens. Vegetation growth conditions are significantly influenced by meteorological drought. On the annual scale, there is a predominantly positive correlation between both. On the seasonal scale, areas with a strong correlation between both gradually contract from spring to winter, when such areas are dominated by the northwestern and northeastern areas of the province. Additionally, the response time of vegetation to drought is longer in spring and winter compared to autumn and summer. Across different vegetation types, the responses of vegetation growth conditions to meteorological drought prove the most rapid during the summer, and cultivated lands are identified as the most sensitive land type to meteorological drought.

Keywords SPI      VCI      meteorological drought      response relationship      Shanxi Province     
ZTFLH:  TP79  
  P461  
Issue Date: 17 February 2025
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Fu ZHAO
Li WANG
Yuang MA
Ruixia JIANG
Cite this article:   
Fu ZHAO,Li WANG,Yuang MA, et al. Responses of vegetation growth conditions to meteorological drought in Shanxi Province from 2000 to 2020[J]. Remote Sensing for Natural Resources, 2025, 37(1): 221-231.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023226     OR     https://www.gtzyyg.com/EN/Y2025/V37/I1/221
Fig.1  Distribution of terrain, vegetation types and meteorological stations in Shanxi Province
Fig.2  Variation trend of SPI at the interannual and monthly scales
Fig.3  Variation trend of SPI at the seasonal scale
土地类型 类别 SPI1 SPI3 SPI12 春季 夏季 秋季 冬季
耕地 z 0.604 0.609 1.693* 2.420** 1.419 0.635 -0.544 -0.974
s 0.044 0.001 0.002 0.003 0.046 0.033 -0.025 -0.031
草地 z 2.477** 1.075 2.818*** 6.555*** 1.359 1.178 0.030 0.616
s 0.082 0.001 0.003 0.006 0.049 0.048 0.002 -0.021
林地 z 0.967 0.407 1.439 2.461** 1.571 0.574 -0.574 -0.974
s 0.034 0.000 0.001 0.002 0.050 0.033 -0.025 -0.033
无植被区 z 1.298 0.753 2.141** 3.661*** 1.480 1.238 -0.513 -0.876
s 0.051 0.001 0.002 0.003 0.040 0.050 -0.027 -0.035
Tab.1  Variation trend of SPI at different scales under different vegetation types
Fig.4-1  VMD decomposition of grassland SPI in spring
Fig.4-2  VMD decomposition of grassland SPI in spring
Fig.5  Spatial distribution of correlation coefficient and significance test between interannual VCI and SPI12
Fig.6  Spatial distribution of maximum correlation coefficient between seasonal VCI and SPI at different time scales
Fig.7  Response time of seasonal VCI to SPI
Fig.8  The average value of the correlation coefficient between VCI and SPI with a time scale of 1 to 24 months
土地
类型
4月 5月 6月 7月 8月 9月 10月
R 响应时
/
R 响应时
/
R 响应时
/
R 响应时
/
R 响应时
/
R 响应时
/
R 响应时
/
耕地 0.38 8 0.41 2 0.47 5 0.49 6 0.44 7 0.37 16 0.24 9
草地 0.49 15 0.47 16 0.46 5 0.50 6 0.49 19 0.46 16 0.43 9
林地 0.33 24 0.49 3 0.37 5 0.25 18 0.28 7 0.24 16 0.37 12
无植被区 0.32 7 0.29 8 0.41 5 0.38 6 0.27 10 0.21 16 0.16 10
Tab.2  Maximum correlation coefficients and corresponding time scales of growing seasons under different vegetation types
[1] Varela V, Vlachogiannis D, Sfetsos A, et al. Projection of forest fire danger due to climate change in the French Mediterranean region[J]. Sustainability, 2019, 11(16):4284.
[2] Zhou Z Q, Shi H Y, Fu Q, et al. Assessing spatiotemporal characteristics of drought and its effects on climate-induced yield of maize in Northeast China[J]. Journal of Hydrology, 2020,588:125097.
[3] Heim R R Jr. A review of twentieth-century drought indices used in the United States[J]. Bulletin of the American Meteorological Society, 2002, 83(8):1149-1166.
[4] Li J, Wu C, Xia C A, et al. Assessing the responses of hydrological drought to meteorological drought in the Huai River basin,China[J]. Theoretical and Applied Climatology, 2021, 144(3):1043-1057.
[5] 吴燕锋, 巴特尔·巴克, 李维, 等. 基于综合气象干旱指数的1961—2012年阿勒泰地区干旱时空演变特征[J]. 应用生态学报, 2015, 26(2):512-520.
[5] Wu Y F, Bake B, Li W, et al. Spatio-temporal variation of drought condition during 1961 to 2012 based on composite index of meteorological drought in Altay region,China[J]. Chinese Journal of Applied Ecology,2015, 26(2):512-520.
[6] 刘永佳, 黄生志, 方伟, 等. 不同季节气象干旱向水文干旱的传播及其动态变化[J]. 水利学报, 2021, 52(1):93-102.
[6] Liu Y J, Huang S Z, Fang W, et al. Propagation and dynamic change of meteorological drought to hydrological drought in different seasons[J]. Journal of Hydraulic Engineering, 2021, 52(1):93-102.
[7] 靖娟利, 王永锋, 和彩霞. 滇黔桂地区NDVI变化及其对SPEI的响应特征[J]. 长江流域资源与环境, 2022, 31(8):1763-1775.
[7] Jing J L, Wang Y F, He C X. Spatial and temporal variations of NDVI and its response to SPEI in Yunnan-Guizhou-Guangxi region[J]. Resources and Environment in the Yangtze Basin, 2022, 31(8):1763-1775.
[8] 张更喜, 粟晓玲, 郝丽娜, 等. 基于NDVI和scPDSI研究1982—2015年中国植被对干旱的响应[J]. 农业工程学报, 2019, 35(20):145-151.
[8] Zhang G X, Su X L, Hao L N, et al. Response of vegetation to drought based on NDVI and scPDSI data sets from 1982 to 2015 across China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(20):145-151.
[9] 李家誉, 佘敦先, 张利平, 等. 黄土高原植被变化对气象干旱多尺度响应特征与机制[J]. 水土保持学报, 2022, 36(6):280-289.
[9] Li J Y, She D X, Zhang L P, et al. Multi-scale response characteristics and mechanism of vegetation to meteorological drought on the Loess Plateau[J]. Journal of Soil and Water Conservation, 2022, 36(6):280-289.
[10] 李明, 葛晨昊, 邓宇莹, 等. 黄土高原气象干旱和农业干旱特征及其相互关系研究[J]. 地理科学, 2020, 40(12):2105-2114.
doi: 10.13249/j.cnki.sgs.2020.12.017
[10] Li M, Ge C H, Deng Y Y, et al. Meteorological and agricultural drought characteristics and their relationship across the Loess Plateau[J]. Scientia Geographica Sinica, 2020, 40(12):2105-2114.
doi: 10.13249/j.cnki.sgs.2020.12.017
[11] 张华, 徐存刚, 王浩. 2001—2018年西北地区植被变化对气象干旱的响应[J]. 地理科学, 2020, 40(6):1029-1038.
doi: 10.13249/j.cnki.sgs.2020.06.019
[11] Zhang H, Xu C G, Wang H. Response of vegetation change to meteorological drought in Northwest China from 2001 to 2018[J]. Scientia Geographica Sinica, 2020, 40(6):1029-1038.
doi: 10.13249/j.cnki.sgs.2020.06.019
[12] Dai M, Huang S, Huang Q, et al. Propagation characteristics and mechanism from meteorological to agricultural drought in various seasons[J]. Journal of Hydrology, 2022,610:127897.
[13] 吕振涛, 李生宇, 彭中敏, 等. 蒙古国植被对干旱响应的敏感性研究[J]. 地理研究, 2021, 40(11):3016-3028.
doi: 10.11821/dlyj020210105
[13] Lyu Z T, Li S Y, Peng Z M, et al. Sensitivity of vegetation responses to drought in Mongolia[J]. Geographical Research, 2021, 40(11):3016-3028.
[14] Zhao A, Yu Q, Feng L, et al. Evaluating the cumulative and time-lag effects of drought on grassland vegetation:A case study in the Chinese Loess Plateau[J]. Journal of Environmental Management, 2020,261:110214.
[15] Han Z, Huang Q, Huang S, et al. Spatial-temporal dynamics of agricultural drought in the Loess Plateau under a changing environment:Characteristics and potential influencing factors[J]. Agricultural Water Management, 2021,244:106540.
[16] 刘立文, 段永红, 徐立帅, 等. 山西省农业干旱时空变化特征[J]. 灌溉排水学报, 2020, 39(2):114-121.
[16] Liu L W, Duan Y H, Xu L S, et al. Characteristics of drought spatio-temporal transformation in Shanxi Province[J]. Journal of Irrigation and Drainage, 2020, 39(2):114-121.
[17] 袁瑞强, 龙西亭, 王鹏, 等. 山西省降水量时空变化及预测[J]. 自然资源学报, 2015, 30(4):651-663.
[17] Yuan R Q, Long X T, Wang P, et al. Tempo-spatial variation and forecast of precipitation in Shanxi Province[J]. Journal of Natural Resources, 2015, 30(4):651-663.
doi: 10.11849/zrzyxb.2015.04.011
[18] Holben B N. Characteristics of maximum-value composite images from temporal AVHRR data[J]. International Journal of Remote Sensing, 1986, 7(11):1417-1434.
[19] Cao S, Zhang L, He Y, et al. Effects and contributions of meteorological drought on agricultural drought under different climatic zones and vegetation types in Northwest China[J]. Science of the Total Environment, 2022,821:153270.
[20] 刘远, 周买春. 3种IGBP分类系统的土地覆盖数据在韩江流域的对比分析[J]. 遥感技术与应用, 2017, 32(3):575-584.
doi: 10.11873/j.issn.1004-0323.2017.3.0575
[20] Liu Y, Zhou M C. Comparative analysis on three land cover datasets based on IGBP classification system over Hanjiang River basin[J]. Remote Sensing Technology and Application, 2017, 32(3):575-584.
[21] McKee T B, Doesken N J, Kleist J. The relationship of drought frequency and duration to time scales[C]// Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society,1993:179-183.
[22] Svoboda M, Fuchs B A. Handbook of drought indicators and indices[M]. Switzerland: World Meteorological Organization Geneva,2016:13-14.
[23] Ma L, Huang Q, Huang S, et al. Propagation dynamics and causes of hydrological drought in response to meteorological drought at seasonal timescales[J]. Hydrology Research, 2022, 53(1):193-205.
[24] Zhou Z, Liu S, Ding Y, et al. Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis[J]. Journal of Environmental Management, 2022,311:114879.
[25] Han Z, Huang S, Huang Q, et al. Propagation dynamics from meteorological to groundwater drought and their possible influence factors[J]. Journal of Hydrology, 2019,578:124102.
[26] 吕纯月. 基于SPI指数的中国夏季干旱区域性特征及环流异常研究[D]. 南京: 南京信息工程大学, 2021.
[26] Lyu C Y. Study on regional characteristics and circulation anomalies of summer drought in China based on SPI index[D]. Nanjing: Nanjing University of Information Science & Technology, 2021.
[27] 马景钊, 郝璐. 基于SPI和SPEI指数的锡林郭勒草原干旱时空变化特征[J]. 草业科学, 2021, 38(12):2327-2339.
[27] Ma J Z, Hao L. Temporal and spatial variation of drought in the Xilingol grassland based on the standardized precipitation index and standardized precipitation evapotranspiration index[J]. Pratacultural Science, 2021, 38(12):2327-2339.
[28] Kogan F N. Application of vegetation index and brightness temperature for drought detection[J]. Advances in Space Research, 1995, 15(11):91-100.
[29] Li W, Wang Y, Yang J. Cloudy region drought index (CRDI) based on long-time-series cloud optical thickness (COT) and vegetation conditions index (VCI):A case study in Guangdong,south eastern China[J]. Remote Sensing, 2020, 12(21):3641.
[30] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3):531-544.
[31] 李文武, 石强, 王凯, 等. 基于变分模态分解和深度门控网络的径流预测[J]. 水力发电学报, 2020, 39(3):34-44.
[31] Li W W, Shi Q, Wang K, et al. Runoff prediction based on variational mode decomposition and deep gated network[J]. Journal of Hydroelectric Engineering, 2020, 39(3):34-44.
[32] 吴文轩, 王志坚, 张纪平, 等. 基于峭度的VMD分解中k值的确定方法研究[J]. 机械传动, 2018, 42(8):153-157.
doi: 10.16578/j.issn.1004.2539.2018.08.030
[32] Wu W X, Wang Z J, Zhang J P, et al. Research of the method of determining k value in VMD based on kurtosis[J]. Journal of Mechanical Transmission, 2018, 42(8):153-157.
[33] 邢愿. 基于不同时间尺度的贵州省农业干旱对气象干旱的响应机制[D]. 贵阳: 贵州师范大学, 2021.
[33] Xing Y. Response mechanism of agricultural drought to meteorological drought based on different time scales in Guizhou Province[D]. Guiyang: Guizhou Normal University, 2021.
[34] 丁一汇, 司东, 柳艳菊, 等. 论东亚夏季风的特征、驱动力与年代际变化[J]. 大气科学, 2018, 42(3):533-558.
[34] Ding Y H, Si D, Liu Y J, et al. On the characteristics,driving forces and inter-decadal variability of the East Asian summer monsoon[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(3):533-558.
[35] 晏利斌. 1961—2014年黄土高原气温和降水变化趋势[J]. 地球环境学报, 2015, 6(5):276-282.
[35] Yan L B. Characteristics of temperature and precipitation on the Loess Plateau from 1961 to 2014[J]. Journal of Earth Environment, 2015, 6(5):276-282.
[36] 侯青青, 裴婷婷, 陈英, 等. 1986—2019年黄土高原干旱变化特征及趋势[J]. 应用生态学报, 2021, 32(2):649-660.
doi: 10.13287/j.1001-9332.202102.012
[36] Hou Q Q, Pei T T, Chen Y, et al. Variations of drought and its trend in the Loess Plateau from 1986 to 2019[J]. Chinese Journal of Applied Ecology, 2021, 32(2):649-660.
[37] 安彬, 肖薇薇, 朱妮, 等. 近60 a黄土高原地区降水集中度与集中期时空变化特征[J]. 干旱区研究, 2022, 39(5):1333-1344.
doi: 10.13866/j.azr.2022.05.01
[37] An B, Xiao W W, Zhu N, et al. Temporal and spatial variations of precipitation concentration degree and precipitation concentration period on the Loess Plateau from 1960 to 2019[J]. Arid Zone Research, 2022, 39(5):1333-1344.
doi: 10.13866/j.azr.2022.05.01
[38] 薛少博, 李鹏, 于坤霞, 等. 2002—2020年黄土高原土壤水变化及其相关性分析[J]. 水土保持学报, 2021, 35(5):221-226.
[38] Xue S B, Li P, Yu K X, et al. Soil water change and its correlation analysis in the Loess Plateau from 2002 to 2020[J]. Journal of Soil and Water Conservation, 2021, 35(5):221-226.
[39] 史尚渝, 王飞, 金凯, 等. 黄土高原地区植被指数对干旱变化的响应[J]. 干旱气象, 2020, 38(1):1-13.
[39] Shi S Y, Wang F, Jin K, et al. Response of vegetation index to meteorological drought over loess plateau[J]. Journal of Arid Meteorology, 2020, 38(1):1-13.
[40] 王一, 郝利娜, 许强, 等. 2001—2019年黄土高原植被覆盖度时空演化特征及地理因子解析[J]. 生态学报, 2023, 43(6):2397-2407.
[40] Wang Y, Hao L N, Xu Q, et al. Spatio-temporal variations of vegetation coverage and its geographical factors analysis on the Loess Plateau from 2001 to 2019[J]. Acta Ecologica Sinica, 2023, 43(6):2397-2407.
[41] 付建新. 山西黄河流域不同土地利用类型NDVI时空变化及其对气温、降水的响应[J]. 水土保持研究, 2023, 30(3):364-372.
[41] Fu J X. Temporal and spatial changes of NDVI of different land covers and their responses to temperature and precipitation in the Yellow River basin of Shanxi[J]. Research of Soil and Water Conservation, 2023, 30(3):364-372.
[42] 尉毓姣, 朱琳, 曹鑫宇, 等. 基于转移函数分析的蒙东地区不同类型植被变化对干旱的响应[J]. 地球科学, 2023, 48(9):3539-3551.
[42] Wei Y J, Zhu L, Cao X Y, et al. Response of different types of vegetation changes to drought in eastern inner Mongolia based on transfer function analysis[J]. Earth Science, 2023, 48(9):3539-3551.
[43] 吴林霖, 王思远, 马元旭, 等. 中亚地区植被对气候变化的响应机制初探[J]. 遥感学报, 2022, 26(11):2248-2267.
[43] Wu L L, Wang S Y, Ma Y X, et al. Response of vegetation to climate change in Central Asia with remote sensing and meteorological data[J]. National Remote Sensing Bulletin, 2022, 26(11):2248-2267.
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