Urbanization influenced SUHI Of 41 megacities of the world using big geospatial data assisted with google earth engine

被引:12
|
作者
Ul Moazzam, Muhammad Farhan [1 ]
Lee, Byung Gul [1 ]
机构
[1] Jeju Natl Univ, Coll Ocean Sci, Dept Civil Engn, 102 Jejudaehakro, Jeju 63243, South Korea
基金
新加坡国家研究基金会;
关键词
Megacities; Urbanization; Land Surface Temperature (LST); SUHI; Driving factors; URBAN HEAT-ISLAND; CITIES; PATTERNS; CIRCULATIONS; TEMPERATURE;
D O I
10.1016/j.scs.2023.105095
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The rapid pace of urbanization with climate change pushed megacities into the forefront of human habitation, emphasizing the urgent need to unravel the relationship between urban expansion and environmental dynamics. This study examines the Surface Urban Heat Island (SUHI) within the 41 megacities, to interpret the complex relationship that emerges from the nexus of urbanization and climatic influence. Through a comprehensive analysis, it has been observed that daytime and nighttime temperature is increasing particularly in Asian regions. An interesting SUHI intensity emerges in all megacities of the world. The SUHI does vary greatly across the megacities of the world. Similarly, the average daytime ocean/coastal cities SUHI (1.19 degrees C) was higher than inland cities (0.77 degrees C) but at nighttime inland cities had a higher average SUHI (0.95 degrees C) than ocean/coastal cities (0.93 degrees C). Notably, the findings illuminate the complex relationship between city size, elevation, and location with inland and ocean/coastal cities. An inverse yet substantively significant relationship emerges between, city size, elevation, and SUHI magnitude. Our study delivers a positive but moderate correlation between daytime and nighttime SUHI and population density (r(2) = 0.3/r(2) = 0.5, p<0.05). In summary, this study elevates our understanding of SUHI phenomena. These insights are for well-informed urban policies and sustainable development.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine
    Al Saim, Abdullah
    Aly, Mohamed H.
    ATMOSPHERIC POLLUTION RESEARCH, 2024, 15 (09)
  • [32] Automatic Flood Monitoring Method with SAR and Optical Data Using Google Earth Engine
    Peng, Xiaoran
    Chen, Shengbo
    Miao, Zhengwei
    Xu, Yucheng
    Ye, Mengying
    Lu, Peng
    WATER, 2025, 17 (02)
  • [33] Cropland data fusion and correction using spatial analysis techniques and the Google Earth Engine
    Li, Kewei
    Xu, Erqi
    GISCIENCE & REMOTE SENSING, 2020, 57 (08) : 1026 - 1045
  • [34] Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data
    Sazib, Nazmus
    Mladenova, Iliana
    Bolten, John
    REMOTE SENSING, 2018, 10 (08)
  • [35] A Study of Design with Spatial Rule-Based Engine Using GeoSpatial Big-Data
    Kim, Sang-Su
    Song, Kwaun-Sik
    Go, Jun-Hee
    2018 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC), 2018, : 1273 - 1275
  • [36] Monitoring winter wheat in ShanDong province using Sentinel data and Google Earth Engine platform
    Yang, Aixia
    Zhong, Bo
    Wu, Jinhua
    2019 10TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTITEMP), 2019,
  • [37] Remote sensing and big data: Google Earth Engine data to assist calibration processes in hydro-sediment modeling on large scales
    Rossoni, Renata Barao
    Laipelt, Leonardo
    de Paiva, Rodrigo Cauduro Dias
    Fan, Fernando Mainardi
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2024, 36
  • [38] Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine
    Inman, Victoria L.
    Lyons, Mitchell B.
    REMOTE SENSING, 2020, 12 (08)
  • [39] Urban Growth Modeling using Historical Landsat Satellite Data Archive on Google Earth Engine
    Miyazaki, Hiroyuki
    Bhushan, Himanshu
    Wakiya, Kotone
    2019 FIRST INTERNATIONAL CONFERENCE ON SMART TECHNOLOGY & URBAN DEVELOPMENT (STUD), 2019, : 61 - 65
  • [40] Temporal analysis of the vineyard phenology from remote sensing data using Google Earth Engine
    Jesus, J.
    Santos, F.
    Gomes, A.
    Teodoro, A. C.
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XXII, 2020, 11528