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四川盆地植被EVI动态变化及其驱动机制 被引量:1

Dynamic variation of vegetation EVI and its driving mechanism in the Sichuan Basin
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摘要 以四川盆地为研究区,基于MOD13A3EVI时间序列、气象数据、土地利用类型数据和夜间灯光数据,利用Theil-Sen Median趋势分析、相关性分析、多重共线性检验(VIF)、残差分析和相对作用分析等方法,探究2000~2020年四川盆地植被EVI时空变化及其变化的驱动机制.结果表明:2000~2020年四川盆地83.81%的植被EVI呈上升趋势,集中分布于四川盆地中部和东部;呈下降趋势的面积仅占16.19%,集中分布于成都城市圈和重庆城市圈,其他地区零星分布.城市尺度上,除成都市植被EVI呈下降趋势外,四川盆地各地级市植被EVI均呈上升趋势.四川盆地植被EVI变化受不同气候因子的调控,且表现出明显的地域差异.整体上,四川盆地植被EVI与最低气温和降水呈正相关,与气压呈负相关,且植被EVI与降水的相关性最高.最低气温和降水对四川盆地植被生长呈促进作用的面积大于呈抑制作用的面积,而气压对四川盆地植被生长呈抑制作用的面积大于呈促进作用的面积.四川盆地植被EVI变化受最低气温、降水和气压弱驱动的影响大于其他气候驱动类型.人类活动对植被EVI变化的影响具有双重作用,且积极作用大于消极作用.城市扩张所引起的建设用地的大量转入和现代化农业快速发展引起的林地向耕地的大量转入是导致四川盆地植被EVI下降的主要原因,而四川盆地生态工程推进所引起的林地的大量转入和农林业综合发展所引起的草地向耕地的大量转入是导致植被EVI上升的主要原因.由相对作用分析结果可知,气候变化和人类活动的共同驱动是四川盆地植被EVI上升的主要原因.气候变化对植被EVI上升的影响要大于人类活动,而人类活动对植被EVI下降的影响大于气候变化.研究结果可为四川盆地植被动态监测和生态环境质量评估等提供理论参考. This study took the Sichuan Basin as the study area.MOD13A3EVI time series,climate data,land use type data,and nighttime light data were obtained,and Thile-Sen Median analysis,correlation analysis,multi-collinearity test,residual analysis,and relative analysis were utilized to explore the spatial-temporal variation of vegetation EVI and the driving mechanism of vegetation EVI in the Sichuan Basin from 2000 to 2020.Results showed that the vegetation EVI in 83.81%of the Sichuan Basin showed an upward trend from 2000 to 2020,which were concentrated in the central and eastern of the Sichuan Basin.Accounting for 16.19%of the vegetation EVI in the Sichuan Basin exhibited downward trends,which were mainly located in the Chengdu and Chongqing metropolitan areas and sparsely distributed in other regions.At the city scale,except for a downward trend of vegetation EVI observed in Chengdu,the vegetation EVI of all cities in the Sichuan Basin showed an upward trend.Vegetation EVI variation in the Sichuan Basin was dual-regulated by climate change and the effects of climate change to vegetation EVI showed obvious regional differences.At the regional scale,vegetation EVI was positively correlated with minimum temperature and precipitation,but negatively correlated with atmosphere pressure in the Sichuan Basin.In addition,vegetation EVI had the highest correlation with precipitation.The area where minimum temperature and precipitation promoted vegetation growth in the Sichuan Basin was greater than that with inhibitory effect,while the area where air pressure had an inhibitory effect on vegetation growth in the Sichuan Basin was greater than the area with promoting effect.The combined impact of precipitation,minimum temperature,and atmosphere pressure on vegetation EVI variation was greater that other climatical-driving types.Human activities exhibited dual effect on vegetation EVI variation,and the positive effect was greater than the negative effect.Urban expansion and population increase leading to the increase of construction land and the massive transfer of forest land to cultivated land,respectively,had degraded the vegetation EVI.Meanwhile,the development of agriculture and forestry and implementation of ecological engineering leading to the increase of forest land and the massive transfer of grassland to cultivated land,respectively,had improved the vegetation EVI.Vegetation EVI increase was mainly driven by the combined effect of climate change and human activities.Furthermore,the impact of climate change on vegetation EVI increase was greater than that of human activities,and the impact of human activities on vegetation EVI decrease was greater than that of climate change.The research result can provide a theoretical reference for dynamic vegetation monitoring and eco-environmental quality assessment in the Sichuan Basin.
作者 戴强玉 徐勇 赵纯 卢云贵 黄雯婷 DAI Qiang-yu;XU Yong;ZHAO Chun;LU Yun-gui;HUANG Wen-ting(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541006,China)
出处 《中国环境科学》 EI CAS CSCD 北大核心 2023年第8期4292-4304,共13页 China Environmental Science
基金 广西自然科学基金资助项目(2020GXNSFBA297160) 广西科技基地和人才专项(桂科AD21220133)。
关键词 四川盆地 植被EVI 多重共线性检验 相对作用分析 驱动机制 the Sichuan Basin enhanced vegetation index multi-collinearity test relative analysis driving mechanism
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