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Spatiotemporal variation of vegetation and its influencing factors in the Hulunbuir typical steppe based on MODIS data

  • Kexin LI ,
  • Hairong TANG ,
  • Peng ZHANG
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  • 1.Key Laboratory of Technology in Geo-Spatial Information Processing and Application System,Chinese Academy of Sciences,Beijing 100190,China
    2.School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China

Received date: 2024-09-30

  Revised date: 2025-02-21

  Online published: 2025-03-26

Abstract

As a green ecological barrier in northern China, changes in the ecological environment of Hulunbuir steppe have a profound impact on both the regional ecological balance and, to some extent, the broader ecological equilibrium of the entire country. Based on the long-term MOD13Q1 vegetation index dataset and meteorological data, this study developed a dynamically adaptable vegetation cover index, gsNDVI, which is sensitive to the active vegetation season. The vegetation change trend and future development of the Hulunbuir typical steppe from 2000 to 2022 were analyzed using the Theil-Sen regression, the Mann-Kendall method, and the Hurst exponent. Additionally, partial correlation analysis was employed to investigate the impact of climate factors on vegetation change. The results show that the multi-year average gsNDVI value for the study area is 0.39, with a spatial distribution characterized by higher values in the northeast and lower values in the southwest. Overall, the mean gsNDVI value exhibits a slow growth trend, though periodic fluctuations are observed. Vegetation coverage has increased in most areas, though noticeable degradation is evident in urban areas. The partial correlation analysis results indicate that precipitation is the primary factor influencing vegetation cover change in the study area, with grasslands being the most sensitive to changes in precipitation. Furthermore, based on the combined results of trend analysis and the Hurst exponent, the vegetation trend in certain areas of Hulunbuir is expected to reverse in the future, which may pose significant challenges to ecological and environmental protection in the region.

Cite this article

Kexin LI , Hairong TANG , Peng ZHANG . Spatiotemporal variation of vegetation and its influencing factors in the Hulunbuir typical steppe based on MODIS data[J]. Journal of University of Chinese Academy of Sciences, 2026 , 43(4) : 541 -552 . DOI: 10.7523/j.ucas.2025.004

References

[1] Guo D, Song X N, Hu R H, et al. Grassland type-dependent spatiotemporal characteristics of productivity in Inner Mongolia and its response to climate factors[J]. Science of the Total Environment2021775: 145644. DOI:10.1016/j.scitotenv.2021.145644 .
[2] Bhuyan M, Singh B, Vid S, et al. Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020[J]. Environmental Monitoring and Assessment2022195(1): 94. DOI:10.1007/s10661-022-10685-6 .
[3] 刘慧文, 刘欢, 胡鹏, 等. 基于可解释机器学习的青藏高原草地物候变化多因素影响分析[J]. 环境科学202445(6): 3375-3388. DOI:10.13227/j.hjkx.202306185 .
[4] 赵鹏, 陈桃, 王茜, 等. 气候变化和人类活动对新疆草地生态系统NPP影响的定量分析[J]. 中国科学院大学学报202037(1): 51-62. DOI:10.7523/j.issn.2095-6134.2020.01.007 .
[5] 冯莉莉, 何贞铭, 刘学锋, 等. 基于MODIS-NDVI数据的吉林省植被覆盖度及其时空动态变化[J]. 中国科学院大学学报201431(4): 492-499, 536. DOI:10.7523/j.issn.2095-6134.2014.04.008 .
[6] 张佳华, 胡小夏, 刘学锋, 等. 基于MODIS数据提取华北典型区冬小麦种植面积[J]. 中国科学院大学学报201330(5): 637-643. DOI:10.7523/j.issn.2095-6134.2013.05.010 .
[7] 李舒婷, 周艺, 王世新, 等. 2001—2015年内蒙古NDVI时空变化及其对降水和气温的响应[J]. 中国科学院大学学报201936(1): 48-55. DOI:10.7523/j.issn.2095-6134.2019.01.008 .
[8] 田庆久, 闵祥军. 植被指数研究进展[J]. 地球科学进展199813(4): 327-333. DOI:10.3321/j.issn:1001-8166.1998.04.002 .
[9] 秦格霞, 芦倩, 孟治元, 等. 1982—2015年中国北方草地NDVI时空动态及其对气候变化的响应[J]. 水土保持研究202128(1): 101-108, 117. DOI:10.13869/j.cnki.rswc.2021.01.013 .
[10] 陈以恒, 玉素甫江·如素力, 阿卜杜热合曼·吾斯曼. 2001—2020年天山新疆段草地植被覆盖度时空变化及驱动因素分析[J]. 植物生态学报202448(5): 561-576. DOI:10.17521/cjpe.2023.0155 .
[11] 黄菲, 彭建, 李刚勇, 等. 利用NDVI产草量模型浅探新疆叶城县天然草地退化状况[J]. 草食家畜2021(1): 51-57. DOI:10.16863/j.cnki.1003-6377.2021.01.009 .
[12] Liu S L, Cheng F Y, Dong S K, et al. Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI[J]. Scientific Reports20177: 4182. DOI:10.1038/s41598-017-04038-4 .
[13] 莫宇, 鲍雅静, 李政海, 等. 呼伦贝尔草原退化对植被碳库的影响[J]. 内蒙古大学学报(自然科学版)202354(1): 61-68. DOI:10.13484/j.nmgdxxbzk.20230108 .
[14] 张宏斌, 杨桂霞, 黄青, 等. 呼伦贝尔草甸草原景观格局时空演变分析:以海拉尔及周边地区为例[J]. 草业学报200918(1): 134-143. DOI:10.3321/j.issn:1004-5759.2009.01.020 .
[15] 叶佳琦, 李政海, 乌云其其格, 等. 呼伦贝尔草原、湿地和森林植被对气候变化的响应[J]. 中国草地学报201840(6): 26-32. DOI:10.16742/j.zgcdxb.2018-06-04 .
[16] 李晶, 刘乾龙, 刘鹏宇. 1998—2018年呼伦贝尔市植被覆盖度时空变化及驱动力分析[J]. 生态学报202242(1): 220-235. DOI:10.5846/stxb202003270710 .
[17] 张戈丽, 徐兴良, 周才平, 等. 近30年来呼伦贝尔地区草地植被变化对气候变化的响应[J]. 地理学报201166(1): 47-58. DOI:10.11821/xb201101005 .
[18] 范凯凯, 李淑贞, 陈金强, 等. 呼伦贝尔草原土壤呼吸作用空间异质性分析[J]. 草地学报202230(1): 205-211. DOI:10.11733/j.issn.1007-0435.2022.01.025 .
[19] Peng S Z, Ding Y X, Liu W Z, et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017[J]. Earth System Science Data201911(4): 1931-1946. DOI:10.5194/essd-11-1931-2019 .
[20] Yang J, Huang X. 30 m annual land cover and its dynamics in China from 1990 to 2019 [J]. Earth System Science Data Discussions20212021: 1-29. DOI:10.5194/ESSD-2021-7 .
[21] 郭岚瑄, 王贺封, 沈石凯, 等. 京津冀地区kNDVI时空变化及对气候因子的响应[J]. 西南农业学报202437(8): 1837-1849. DOI:10.16213/j.cnki.scjas.2024.8.020 .
[22] 余梦晨, 李迎, 李月芬. 2005—2018年浙江省植被生长季NDVI时空变化及其对气候因子的响应[J]. 湖北农业科学202261(21): 32-38, 56. DOI:10.14088/j.cnki.issn0439-8114.2022.21.006 .
[23] 王雅婷, 朱长明, 张涛, 等. 2002—2020年秦岭—黄淮平原交界带植被物候特征遥感监测分析[J]. 自然资源遥感202234(4): 225-234.
[24] Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geoscience and Remote Sensing200240(8): 1824-1832. DOI:10.1109/TGRS.2002.802519 .
[25] Liu C Y, Dong X F, Liu Y Y. Changes of NPP and their relationship to climate factors based on the transformation of different scales in Gansu, China[J]. Catena2015125: 190-199. DOI:10.1016/j.catena.2014.10.027 .
[26] Hu X Q, Feng H H, Tang Y Y, et al. Detection of the contribution of vegetation change to global net primary productivity: a satellite perspective[J]. Remote Sensing202416(24): 4692. DOI:10.3390/rs16244692 .
[27] Camps-Valls G, Campos-Taberner M, Moreno-Martínez á, et al. A unified vegetation index for quantifying the terrestrial biosphere[J]. Science Advances20217(9): eabc7447. DOI:10.1126/sciadv.abc7447 .
[28] Liu X F, Zhang J S, Zhu X F, et al. Spatiotemporal changes in vegetation coverage and its driving factors in the Three-River Headwaters Region during 2000–2011[J]. Journal of Geographical Sciences201424(2): 288-302. DOI:10.1007/s11442-014-1088-0 .
[29] Ma Y R, Guan Q Y, Sun Y F, et al. Three-dimensional dynamic characteristics of vegetation and its response to climatic factors in the Qilian Mountains[J]. Catena202208: 105694. DOI:10.1016/j.catena.2021.105694 .
[30] Liu Y, Li L H, Chen X, et al. Temporal-spatial variations and influencing factors of vegetation cover in Xinjiang from 1982 to 2013 based on GIMMS-NDVI3g[J]. Global and Planetary Change2018169: 145-155. DOI:10.1016/j.gloplacha.2018.06.005 .
[31] Sen P K. Estimates of the regression coefficient based on Kendall’s tau[J]. Journal of the American Statistical Association196863(324): 1379-1389. DOI:10.1080/01621459.1968.10480934 .
[32] Ren Z G, Tian Z H, Wei H T, et al. Spatiotemporal evolution and driving mechanisms of vegetation in the Yellow River Basin, China during 2000-2020[J]. Ecological Indicators2022138: 108832. DOI:10.1016/j.ecolind.2022.108832 .
[33] Kendall M G. A new measure of rank correlation[J]. Biometrika193830(1/2): 81-93. DOI:10.2307/2332226 .
[34] Mann H B. Nonparametric tests against trend[J]. Econometrica194513(3): 245-259. DOI:10.2307/1907187 .
[35] Chen S Y, Ji L Y, Li K X, et al. The spatio-temporal variation of vegetation and its driving factors during the recent 20 years in Beijing[J]. Remote Sensing202416(5): 851. DOI:10.3390/rs16050851 .
[36] 宗慧琳, 张晓伦, 袁希平, 等. 利用GEE进行1990—2022年小江流域生态环境质量时空格局与演变趋势分析[J]. 环境科学202445(7): 4122-4136. DOI:10.13227/j.hjkx.202308061 .
[37] 奥勇, 汪雅, 王晓峰, 等. 近二十年郑州市生态环境质量时空变化及驱动因素分析[J]. 自然资源遥感202537(1): 102-112. DOI:10.6046/zrzyyg.2023203 .
[38] Hurst H E. Long-term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineers1951116(1): 770-799. DOI:10.1061/taceat.0006518 .
[39] 马晓倩, 钟瑞森, 吴彬, 等. 塔里木河干流植被NDVI时空动态变化及影响因素探测[J]. 环境科学202546(9):5813-5824. DOI:10.13227/j.hjkx.202409115 .
[40] 孟庆俊, 冯启言, 张淇翔, 等. 高寒地区露天煤矿生态修复区生物多样性评估[J]. 能源环境保护201832(3): 44-49. DOI:10.3969/j.issn.1006-8759.2018.03.013 .
[41] 邹兰兰, 冯启言, 郝明, 等. 灵泉露天矿生态修复效果及驱动因素分析[J]. 中国矿业202433(3): 43-50. DOI:10.12075/j.issn.1004-4051.20230100 .
[42] 胡娇娇,宇广.新右旗3397户牧民获草原生态保护补助奖[N].呼伦贝尔日报,2022-11-07(003). DOI:10.28351/n.cnki.nhlbe.2022.001771 .
[43] 李梦茹, 邓泽坤, 杜佳梦. 新巴尔虎右旗植被覆盖变化研究[J]. 草原与草业202133(1): 29-34. DOI:10.3969/j.issn.2095-5952.2021.01.006 .
[44] 邹洪坤. 湖北省植被覆盖度动态变化及其对气候变化的响应[D]. 武汉: 武汉大学, 2018.
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