丁永康, 叶婷, 陈康. 基于地理探测器的滹沱河流域植被覆盖时空变化与驱动力分析[J]. 中国生态农业学报 (中英文), 2022, 30(11): 1737−1749. DOI: 10.12357/cjea.20220309
引用本文: 丁永康, 叶婷, 陈康. 基于地理探测器的滹沱河流域植被覆盖时空变化与驱动力分析[J]. 中国生态农业学报 (中英文), 2022, 30(11): 1737−1749. DOI: 10.12357/cjea.20220309
DING Y K, YE T, CHEN K. Analysis of spatio-temporal dynamics and driving forces of vegetation cover in the Hutuo River Basin based on the geographic detector[J]. Chinese Journal of Eco-Agriculture, 2022, 30(11): 1737−1749. DOI: 10.12357/cjea.20220309
Citation: DING Y K, YE T, CHEN K. Analysis of spatio-temporal dynamics and driving forces of vegetation cover in the Hutuo River Basin based on the geographic detector[J]. Chinese Journal of Eco-Agriculture, 2022, 30(11): 1737−1749. DOI: 10.12357/cjea.20220309

基于地理探测器的滹沱河流域植被覆盖时空变化与驱动力分析

Analysis of spatio-temporal dynamics and driving forces of vegetation cover in the Hutuo River Basin based on the geographic detector

  • 摘要: 滹沱河流域位于山西和河北两省境内, 是山西忻州市和河北石家庄市的“母亲河”, 也是海河流域的重要组成部分, 对于区域生态环境和城市发展起到重要作用。本研究基于2000—2020年21年间月尺度MOD13Q1 (250 m)数据集, 在总结区内植被覆盖空间分异特征基础上, 采用一元线性回归方法分析了流域内植被时空变化趋势, 借助皮尔逊相关分析方法讨论了温度、降水量与归一化植被指数(NDVI)之间的相关关系, 利用地理探测器将区内温度、降水量、植被类型、土壤类型、海拔等自然因子, 以及土地利用类型、人口密度、GDP等人为因子进行统计划分, 系统探讨了各驱动因子对NDVI的影响程度, 明确了流域内植被时空变化特征及其驱动因子的驱动力大小, 为生态环境保护以及可持续发展提供了依据。结果表明: 1)近21年来, 区内植被覆盖度总体呈现增加的趋势, 每年5—9月NDVI平均值为0.71, Slope指数平均值为0.0035, 区内植被恢复以轻度改善为主, NDVI改善区域面积占81.00%, 退化区域占10.08%。2)区内NDVI与降水量、温度之间整体均呈现正相关关系, NDVI年际变化同降水量更密切, 但阳泉、石家庄周边地区NDVI同二者呈负相关关系, 可能受人类活动影响显著。3)区内单个驱动因子对NDVI影响程度由大到小排序为: 降水量>温度>土地利用类型>植被类型>土壤类型>人口密度>GDP>海拔, 其中前3个因子的q值均大于0.3, 作为影响区内NDVI的主要驱动因子。4)区内各驱动因子交互组合驱动力显著高于单个驱动因子, 并呈现双因子增强效应, 其中人为因子中土地利用类型同自然因子降水量的交互作用最大, q值为0.74, 明显高于仅有人为因子或仅有自然因子进行的交互作用。总体来看,人类活动对区内植被覆盖时空格局产生了较强的影响,综合考虑气象因素并合理规划土地利用是改善区内植被覆盖的关键因素。

     

    Abstract: The Hutuo River Basin, located in the Shanxi and Hebei Provinces, plays an important role in regional ecological environment and urban development. Analysis of the spatio-temporal dynamics and driving forces of vegetation cover in the area provides an important scientific basis for sustainable social and economic development and ecological environmental protection. Based on the monthly scale MOD13Q1 (250 m) dataset for 21 years from 2000 to 2020, this study analyzed the spatio-temporal variation trend of vegetation using the unary linear regression method and discussed the correlation between temperature, precipitation, and NDVI using the Pearson correlation analysis method. Natural factors such as temperature, precipitation, vegetation type, soil type, and altitude, and human factors such as land use type, population density, and GDP were statistically divided using the geographic detector, and the degree of influence of each driving factor on NDVI was systematically discussed. Particularly in the case of the increasingly close relationship between vegetation cover and human activities, the driving force values of different human factors can be obtained quantitatively to provide a basis for future research and analysis of the influence mechanism of the main driving factors, ecological environment protection, and sustainable development of watershed. The results of this study were as follows: 1) In the past 21 years, vegetation cover in the area had been increasing, and the average NDVI and Slope index, which is used to indicate vegetation changing trend with the positive value meaning increase, from May to September every year were 0.71 and 0.0035, respectively. The vegetation restoration in the area improved slightly, with 81.00% of the area improving in NDVI and 10.08% of the area degrading. 2) There were positive correlations between NDVI and precipitation and temperature in the area, and the interannual variation in NDVI was more closely related to precipitation. The proportions of positive and negative correlation areas between precipitation and NDVI were 87.73% and 12.27%, respectively, among which 35.28% and 6.92% of the positive correlation area passed the significance test of P<0.05 and P<0.01, respectively. However, the NDVI in the surrounding areas of Yangquan and Shijiazhuang cities was negatively correlated with precipitation and temperature, which may be significantly affected by human activities. 3) The degree of influence of a single driving factor on NDVI in the area was ranked from high to low as follows: precipitation > temperature > land use type > vegetation type > soil type > population density > GDP > altitude. Among them, the q (showing impacting strength of factor) values of the first three factors were all greater than 0.3, and they were the main driving factors affecting the NDVI in the area. 4) The driving force of all the driving factors combined in pairs was significantly greater than that of a single driving factor, showing a two-factor enhancement effect. In addition, the interaction between land use type and precipitation, with a q value of 0.74, was the largest, and it was significantly greater than that of interactions between only human factors or only natural factors. In general, human activities have had a strong impact on the spatio-temporal pattern of vegetation cover, and comprehensive consideration of meteorological factors and rational planning of land use are key factors in improving vegetation cover in the area.

     

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