近20年太行山-燕山植被生态指数时空动态及其驱动因素
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河北省社会科学基金项目(HB23YJ008)


Spatio-temporal dynamics of vegetation ecological index and its driving factors in Taihangshan-Yanshan Mountains in the last 20 years
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    摘要:

    为掌握太行山-燕山综合区植被生态状况,创新性地构建了既能反映植被生产力又能反映植被覆盖度的植被生态指数 (Vegetation Ecology Index,VEI)。选取2003-2021年的GEE云平台(Google Earth Engine)数据,采用Slope趋势分析、变异系数和相关性分析等方法,研究了植被VEI时空变化特征,分析了气温、降水等气候因素对植被VEI变化的影响。结果表明:(1)2003-2021年太行山-燕山植被VEI多年平均值为0.526,整体呈现自北部向其他方向蔓延减少的趋势,其分布规律与海拔分布具有一定的相关性;(2)2003-2021年,研究区VEI整体呈下降趋势,速率为0.0017/a,增加区域仅为36.71%;(3)研究年限内植被VEI呈现稳定性变化的区域约为76.96%,整体处于较稳定状况;(4)年均气温较年均降水来说对研究区VEI的影响更显著。研究结果将对太行山-燕山生态环境状况评估、资源合理配置、区域环境与社会经济发展战略的制定等具有重要的理论价值和现实意义。

    Abstract:

    In order to gain a comprehensive understanding of the vegetation ecological status of the Taihangshan-Yanshan Mountains Integrated Area in China, this study constructed an innovative Vegetation Ecology Index (VEI) that reflects both vegetation productivity and vegetation cover. This index has immeasurable guiding significance for measuring the superior and inferior status of the ecological environment. In this study, data from the Google Earth Engine (GEE) cloud platform was selected for analysis, covering the period from 2003 to 2021. This was done in order to gain a deeper understanding of the spatial and temporal change characteristics of the Vegetation Ecology Index (VEI). In order to study the changing law of the vegetation ecological index, a number of analytical techniques were applied, including slope trend analysis, coefficient of variation analysis and correlation analysis. These methods facilitate the revelation of the temporal and spatial patterns of vegetation VEI change. It was found that the vegetation VEI exhibits certain temporal and spatial change patterns within the Taihangshan-Yanshan Mountains Integrated Area. On this basis, the effects of climatic factors such as temperature and precipitation on the changes of vegetation VEI were further analysed. The results show that: (1) Between 2003 and 2021, the multi-year average value of the vegetation ecological index in the Taihangshan-Yanshan Mountains region of China reached 0.526. In general, the vegetation ecological index shows a trend of gradual decrease from the north to other directions. This distribution pattern is correlated with the altitude distribution. (2) During the period from 2003 to 2021, the vegetation ecological index of the study area showed an overall decreasing trend, with an average annual decrease rate of approximately 0.0017 per year. During this period, only 36.71% of the areas exhibited an increase in the vegetation ecological index. (3) During the study years, approximately 76.96% of the areas exhibited a stable change in the vegetation ecological index, with the majority of the areas demonstrating this pattern. The vegetation growth conditions remained relatively stable throughout the study period. (4) The analysis of a large amount of data revealed that the effect of the average annual temperature on the vegetation ecological index of the study area was more significant than the average annual precipitation. The findings of this study not only provide a valuable theoretical foundation, but also have significant implications for the assessment of ecological and environmental conditions, the rational allocation of resources, and the formulation of regional environmental and socio-economic development strategies in the Taihangshan-Yanshan Mountains.

    参考文献
    [1] 于璐. 京津风沙源区植被覆盖变化归因及其对地表气温的影响[D].太原:山西大学, 2023.
    [2] 阿妮克孜·肉孜, 张岩, 何远梅, 李镇, 杨松. 基于QuickBird影像的黄土高原植被恢复差异. 干旱区研究, 2016, 33(3): 554-559.
    [3] Wang Q, Zhang Q P, Zhou W. Grassland coverage changes and analysis of the driving forces in Maqu County. Physics Procedia, 2012, 33: 1292-1297.
    [4] 赵明伟, 王妮, 施慧慧, 江岭, 王春. 2001-2015年间我国陆地植被覆盖度时空变化及驱动力分析. 干旱区地理, 2019, 42(2): 324-331.
    [5] 孟琪, 武志涛, 杜自强, 张红. 基于地理探测器的区域植被覆盖度的定量影响--以京津风沙源区为例. 中国环境科学, 2021, 41(2): 826-836.
    [6] Kong D D, Zhang Q, Singh V P, Shi P J. Seasonal vegetation response to climate change in the Northern Hemisphere (1982-2013). Global and Planetary Change, 2017, 148: 1-8.
    [7] 赵安周, 田新乐. 基于GEE平台的1986-2021年黄土高原植被覆盖度时空演变及影响因素. 生态环境学报, 2022, 31(11): 2124-2133.
    [8] Yan X, Li L H. Spatiotemporal characteristics and influencing factors of ecosystem services in Central Asia. Journal of Arid Land, 2023, 15(1): 1-19.
    [9] Guo B, Zang W Q, Yang F, Han B M, Chen S T, Liu Y, Yang X, He T L, Chen X, Liu C T, Gong R. Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai-Tibet Plateau of China from 2000 to 2015. Journal of Arid Land, 2020, 12(1): 1-17.
    [10] Wilson R. Advanced remote sensing: terrestrial information extraction and applications, by Shunlin Liang, Xiaowen Li and Jindi Wang. International Journal of Remote Sensing, 2013, 34(14): 5262-5263.
    [11] Zhang M, Yuan N Q, Lin H, Liu Y, Zhang H Q. Quantitative estimation of the factors impacting spatiotemporal variation in NPP in the Dongting Lake wetlands using Landsat time series data for the last two decades. Ecological Indicators, 2022, 135: 108544.
    [12] 王芳, 汪左, 张运. 2000-2015年安徽省植被净初级生产力时空分布特征及其驱动因素. 生态学报, 2018, 38(8): 2754-2767.
    [13] 刘刚, 孙睿, 肖志强, 崔天翔. 2001-2014年中国植被净初级生产力时空变化及其与气象因素的关系. 生态学报, 2017, 37(15): 4936-4945.
    [14] 朱文泉, 陈云浩, 徐丹, 李京. 陆地植被净初级生产力计算模型研究进展. 生态学杂志, 2005, 24(3): 296-300.
    [15] Gemechu Legesse T, Dong G, Jiang S C, Chen J Y, Dong X B, Alemu Daba N, Muluneh Sorecha E, Qu L P, Tian L, Shao C L. Small precipitation events enhance the Eurasian grassland carbon sink. Ecological Indicators, 2021, 131: 108242.
    [16] 祝聪, 彭文甫, 张丽芳, 罗瑶, 董永波, 王梅芳. 2006-2016年岷江上游植被覆盖度时空变化及驱动力. 生态学报, 2019, 39(5): 1583-1594.
    [17] 吴志杰, 何国金, 王猛猛, 傅娇凤, 邹丹. 南方丘陵区植被覆盖度遥感估算与时空变化研究--以福建省永定县为例. 遥感技术与应用, 2016, 31(6): 1201-1208.
    [18] Wang B, Jia K, Liang S L, Xie X H, Wei X Q, Zhao X, Yao Y J, Zhang X T. Assessment of sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover. Remote Sensing, 2018, 10(12): 1927.
    [19] 王炜. 太行山区植被覆盖时空演变及其驱动力分析[D].邯郸:河北工程大学, 2021.
    [20] 李薇, 谈明洪. 太行山区不同坡度NDVI变化趋势差异分析. 中国生态农业学报, 2017, 25(4): 509-519.
    [21] 丛明旸, 曹迪, 陈国平, 陈宝政, 孙丰宾. 燕山和太行山过渡区植物多样性垂直变化特点. 植物研究, 2017, 37(5): 673-681.
    [22] 李晓荣, 高会, 韩立朴, 刘金铜. 太行山区植被NPP时空变化特征及其驱动力分析. 中国生态农业学报, 2017, 25(4): 498-508.
    [23] 钱拴, 延昊, 吴门新, 曹云, 徐玲玲, 程路. 植被综合生态质量时空变化动态监测评价模型. 生态学报, 2020, 40(18): 6573-6583.
    [24] 张成才, 娄洋, 李颖, 姬兴杰, 董萌佳. 基于像元二分模型的伏牛山地区植被覆盖度变化. 水土保持研究, 2020, 27(3): 301-307.
    [25] 奎国娴, 史常青, 杨建英, 李瑞鹏, 魏广阔, 刘佳琪. 内蒙古草原区植被覆盖度时空演变及其驱动力. 应用生态学报, 2023, 34(10): 2713-2722.
    [26] 李佳洺, 陆大道, 徐成东, 李扬, 陈明星. 胡焕庸线两侧人口的空间分异性及其变化. 地理学报, 2017, 72(1): 148-160.
    [27] 田义超, 黄远林, 张强, 陶进, 张亚丽, 黄鹄, 周国清. 北部湾南流江流域植被净初级生产力时空分布及其驱动因素. 生态学报, 2019, 39(21): 8156-8171.
    [28] 吴炳伦. 深圳市植被覆盖度动态变化及驱动力分析[D]. 湖南:中南林业科技大学, 2020.
    [29] 闫戈丁, 景海涛, 何湜, 李慧, 郭桓超. 太行山区生态环境质量时空变化与演变趋势. 山地学报, 2023, 41(3): 335-347.
    [30] 严恩萍, 林辉, 党永峰, 夏朝宗. 2000-2012年京津风沙源治理区植被覆盖时空演变特征. 生态学报, 2014, 34(17): 5007-5020.
    [31] 王金杰, 赵安周, 胡小枫. 京津冀植被净初级生产力时空分布及自然驱动因子分析. 生态环境学报, 2021, 30(6): 1158-1167.
    [32] 刘亚静, 刘红健. 基于信息量-随机森林模型的地震带地质灾害易发性评价:以松潘-较场地震带为例. 科学技术与工程, 2024, 24(1): 143-154.
    [33] 陈成, 杨栋淏, 王建雄, 李亚强, 辛京达. 滇西南植被覆盖度动态变化特征及其驱动力分析. 水土保持研究, 2022, 29(4): 198-206.
    [34] Hu S, Wang F Y, Zhan C S, Zhao R X, Mo X G, Liu L M Z. Detecting and attributing vegetation changes in Taihang Mountain, China. Journal of Mountain Science, 2019, 16(2): 337-350.
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陈雨琦,闫丰,王澜颖,尹海魁,王文迪,王佳莹,陈亚恒,许皞.近20年太行山-燕山植被生态指数时空动态及其驱动因素.生态学报,2024,44(18):8283~8293

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