Spatial heterogeneity of driving factors-induced impacts for global long-term surface urban heat island

被引:6
作者
Si, Menglin [1 ,2 ]
Li, Zhao-Liang [1 ,2 ]
Tang, Bo-Hui [3 ]
Liu, Xiangyang [1 ]
Nerry, Francoise [2 ]
机构
[1] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China
[2] Univ Strasbourg, CNRS, ICube Lab, UMR 7357, Brant, CS, France
[3] Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Global surface urban heat island; driving factors; spatial heterogeneity; seasonal variation; day-night contrast; GEOGRAPHICALLY WEIGHTED REGRESSION; LOCAL BACKGROUND CLIMATE; TEMPERATURE; URBANIZATION; INDIANAPOLIS; TRENDS; GROWTH; MODIS; MITIGATION; POLLUTION;
D O I
10.1080/01431161.2023.2203343
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (Delta EVI) and surface albedo difference (Delta WSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are -0.50, +0.30, +0.16, +1.31, -0.03, and +0.03, respectively, at daytime, and -0.38, -0.33, -0.39, -0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, Delta EVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of Delta WSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast.
引用
收藏
页码:7139 / 7159
页数:21
相关论文
共 67 条
[1]   Multicollinearity [J].
Alin, Aylin .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (03) :370-374
[2]   ADVANTAGES AND DISADVANTAGES OF THE URBAN HEAT-ISLAND - AN EVALUATION ACCORDING TO THE HYGRO-THERMIC EFFECTS [J].
BRUNDL, W ;
HOPPE, P .
ARCHIVES FOR METEOROLOGY GEOPHYSICS AND BIOCLIMATOLOGY SERIES B-THEORETICAL AND APPLIED CLIMATOLOGY, 1984, 35 (1-2) :55-66
[3]   Geographically weighted regression: A method for exploring spatial nonstationarity [J].
Brunsdon, C ;
Fotheringham, AS ;
Charlton, ME .
GEOGRAPHICAL ANALYSIS, 1996, 28 (04) :281-298
[4]   Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns [J].
Buyantuyev, Alexander ;
Wu, Jianguo .
LANDSCAPE ECOLOGY, 2010, 25 (01) :17-33
[5]   Urban heat islands in China enhanced by haze pollution [J].
Cao, Chang ;
Lee, Xuhui ;
Liu, Shoudong ;
Schultz, Natalie ;
Xiao, Wei ;
Zhang, Mi ;
Zhao, Lei .
NATURE COMMUNICATIONS, 2016, 7
[6]   A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications [J].
Chakraborty, T. ;
Hsu, A. ;
Manya, D. ;
Sheriff, G. .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 168 :74-88
[7]   A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability [J].
Chakraborty, T. ;
Lee, X. .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 74 :269-280
[8]   Surface Heat Island in Shanghai and Its Relationship with Urban Development from 1989 to 2013 [J].
Chen, Liang ;
Jiang, Rong ;
Xiang, Wei-Ning .
ADVANCES IN METEOROLOGY, 2016, 2016
[9]   MODIS detected surface urban heat islands and sinks: Global locations and controls [J].
Clinton, Nicholas ;
Gong, Peng .
REMOTE SENSING OF ENVIRONMENT, 2013, 134 :294-304
[10]   Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures [J].
Deilami, Kaveh ;
Kamruzzaman, Md. ;
Liu, Yan .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2018, 67 :30-42