摘要
基于洞庭湖流域2000~2017年97个气象站点的综合气象干旱指数(CI)和MODIS增强型植被指数(EVI)资料,结合植被类型数据,采用最大值合成、相关分析等方法,分析了近18年来洞庭湖流域生长季(4~10月)植被(自然和人工植被)EVI与特旱强度的时空变化特征,探讨了自然植被和人工植被对特旱响应的敏感性。结果表明:在年际变化上,自然植被和人工植被区域的特旱强度最大值和EVI的最小值均出现在2011年;在季节变化上,生长季特旱强度分布为秋>夏>春季,自然植被EVI值明显高于人工植被,季节分布均为夏>春>秋季;比较而言,人工植被对特旱的敏感性高于自然植被,但两类植被对特旱的敏感性均随植被生长阶段而变化,其中两种植被EVI与特旱强度之间的最显著相关性均出现在8月;特旱强度和EVI能够很好地反映2011年春旱和夏秋连旱的时空变化过程。
Based on the comprehensive meteorological drought index(CI) at 97 meteorological stations and MODIS enhanced vegetation index(EVI) data between 2000 and 2017 in Dongting Lake Basin, the maximum value composites and correlation analysis methods were applied to analyze the spatial and temporal patterns of the EVI(natural and artificial vegetation) and extreme drought intensity during growing seasons(from April to October) over the recent 18 years and to examine the sensitivity of natural vegetation and artificial vegetation to extreme drought. The results show that the inter-annually maximum value of extreme drought intensity and the minimum value of EVI for both natural vegetation and artificial vegetation regions appeared in 2011. The extreme drought intensity during growing season was ranked as that in autumn > in summer > in spring, and EVI varied seasonally in an order of that in summer > in spring > in autumn, but the EVI values of natural vegetation were significantly higher than that of artificial vegetation. The sensitivity of artificial vegetation to extreme drought was higher than that of natural vegetation. However, the sensitivity of both vegetation types to extreme drought varied with vegetation growth stage, with the most significant correlation between the EVI and the extreme drought intensity in August. The extreme drought intensity and EVI can depict well the spatio-temporal evolutions of drought in spring and summer-autumn in 2011.
作者
雷倩
章新平
黎祖贤
刘福基
姚天次
尚程鹏
王学界
LEI Qian;ZHANG Xin-ping;LI Zu-xian;LIU Fu-ji;YAO Tian-ci;SHANG Cheng-peng;WANG Xue-jie(College of Resources and Environmental Sciences,Hunan Normal University,Changsha 410081,China;Key Laboratory of Geospatial Big Data Mining and Application,Hunan Province,Hunan Normal University,Changsha 410081,China;Weather Modification Office of Hunan Province,Changsha 410118,China;Wuxi Vocational College of Science and Technology,Wuxi 214028,China)
出处
《长江流域资源与环境》
CAS
CSSCI
CSCD
北大核心
2020年第1期187-199,共13页
Resources and Environment in the Yangtze Basin
基金
国家自然科学基金项目(41571021)
湖南省人工影响天气领导小组办公室自主科研课题(201701)
湖南师范大学一流学科“地理学”(810006)。