中国地表城市热岛驱动因素及其空间异质性  

China’s surface urban heat island drivers and its spatial heterogeneity

作　　者：牛陆 张正峰[1] 彭中 姜亚珍[2,3] 刘萌 周孝明 唐荣林 NIU Lu;ZHANG Zheng-feng;PENG Zhong;JIANG Ya-zhen;LIU Meng;ZHOU Xiao-min;TANG Rong-lin(School of Public Administration and Policy,Renmin University of China,Beijing 100872,China;State Key Laboratory of Resources and Environment Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture and Rural Affairs,Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China;School of Civil Engineering,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]中国人民大学公共管理学院,北京100872 [2]中国科学院地理科学与资源研究所,资源与环境信息系统国家重点实验室,北京100101 [3]中国科学院大学,北京100049 [4]中国农业科学院农业资源与农业区划研究所,农业农村部农业遥感重点实验室,北京100081 [5]兰州理工大学土木工程学院,甘肃兰州730050

出　　处：《中国环境科学》2022年第2期945-953,共9页China Environmental Science

基　　金：国家自然科学基金资助项目(71874196,42077433);中国人民大学2021年度拔尖创新人才培育资助计划成果。

摘　　要：基于MODIS卫星遥感数据,计算了中国284个地级市2018年的年平均地表城市热岛强度,分析了中国地表城市热岛的空间分布规律和空间集聚模式.结合多元遥感数据、气象数据和社会经济统计数据,利用地理加权回归模型分析了日间和夜间地表城市热岛强度主要驱动因素的空间异质性.结果表明,中国地表城市热岛强度的空间分布存在明显的空间自相关性;地理加权模型相比传统的普通最小二乘模型,极大地提高了驱动因素的解释程度,日间和夜间的决定系数分别由最小二乘模型的0.659和0.189提高到了0.876和0.651并且具有更低的残差平方和以及赤池信息准则值,从驱动因素来看,除日间的植被因素对地表城市热岛强度的影响显著为负外,其余因素的影响方向均会随着地理位置的改变存在结构性的差异.总体来讲,日间的地表城市热岛强度受城乡植被差异的影响最大,而在夜间则更容易受社会经济因素的影响.Based on satellite remote sensing data acquired through Moderate Resolution Imaging Spectrometer(MODIS),not only was the annual mean surface urban heat island intensity of 284prefecture-level cities in 2018 figured out,but spatial distribution patterns and spatial agglomeration models of surface urban heat islands in China were analyzed.Combining multivariate remote sensing data,meteorological data and socioeconomic statistics,a geographically weighted regression model was utilized to analyze spatial heterogeneity in main drivers for surface urban heat island intensity during daytime and nighttime.As demonstrated by relevant results,an obvious spatial autocorrelation existed in spatial distribution of China’s surface urban heat island intensity.Compared with the traditional global ordinary least squares(OLS)model,interpretation of the drivers was significantly improved according to the geographically weighted regression model.Moreover,determination coefficients for daytime and nighttime increased from 0.651 and 0.189 in the OLS model to 0.876 and 0.659 respectively.In addition,both the residual sum of squares and the Akaike information criterion were calculated to be lower by the geographically weighted regression model.In terms of the drivers,vegetation placed a significantly negative influence on surface urban heat island intensity during the daytime,while structural differences were proved to exist in directions of influence that was applied by other factors along with geographic position changes.On the whole,surface urban heat island intensity was most significantly affected by differences in urban and rural vegetation in daytime;but at night,it was susceptible to socio-economic factors.

关 键 词：城市热岛 城市环境 热红外遥感 土地利用 MODIS 空间异质性 地理加权回归 驱动因素 

分 类 号：X87[环境科学与工程—环境工程]