Determining the Boundary and Probability of Surface Urban Heat Island Footprint Based on a Logistic Model

被引:72
作者
Qiao, Zhi [1 ]
Wu, Chen [1 ]
Zhao, Dongqi [2 ]
Xu, Xinliang [3 ]
Yang, Jilin [4 ,5 ]
Feng, Li [6 ]
Sun, Zongyao [2 ]
Liu, Luo [7 ]
机构
[1] Tianjin Univ, Sch Environm Sci & Engn, Key Lab Indoor Air Environm Qual Control, Tianjin 300350, Peoples R China
[2] Tianjin Univ, Sch Architecture, Tianjin 300272, Peoples R China
[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
[4] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
[5] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[6] Hohai Univ, Sch Geog Sci & Engn, Nanjing 211100, Jiangsu, Peoples R China
[7] South China Agr Univ, Coll Nat Resources & Environm, Guangdong Prov Key Lab Land Use & Consolidat, Guangzhou 510642, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
surface urban heat island; footprint; threshold; boundary; logistic model; MODIS; THERMAL ENVIRONMENT; VEGETATION PHENOLOGY; ECOSYSTEM SERVICES; TEMPORAL TRENDS; LAND-COVER; CLIMATE; CHINA; PATTERN; IMPACTS; TEMPERATURE;
D O I
10.3390/rs11111368
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Studies of the spatial extent of surface urban heat island (SUHI or UHISurf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical limitations on the ability of their algorithms to accurately determine the impacts of the shape, topography, and landscape heterogeneity of the city. The key to determining the FP boundary is identifying background temperatures in reference rural regions. Due to the instability of remote sensing data, these background temperatures should be determined automatically rather than manually, to eliminate artificial bias. To address this need, we developed an algorithm that adequately represents the decay of land surface temperature (LST) from the urban center to surrounding rural regions, and automatically calculates thresholds for reference rural LSTs in all directions based on a logistic curve. In this study, we applied this algorithm with data from the Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS) 8-day level 3 (L3) LST global grid product to delineate precise SUHI FPs for the Beijing metropolitan area during the summers of 2004-2018 and determine the interannual and diurnal variations in FP boundaries and their relationship with SUHI intensity.
引用
收藏
页数:20
相关论文
共 50 条
  • [21] Influence of Urbanization Factors on Surface Urban Heat Island Intensity: A Comparison of Countries at Different Developmental Phases
    Cui, Yaoping
    Xu, Xinliang
    Dong, Jinwei
    Qin, Yaochen
    [J]. SUSTAINABILITY, 2016, 8 (08):
  • [22] Interannual variations in surface urban heat island intensity and associated drivers in China
    Yao, Rui
    Wang, Lunche
    Huang, Xin
    Zhang, Wenwen
    Li, Junli
    Niu, Zigeng
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2018, 222 : 86 - 94
  • [23] Spatial patterns and temporal variations of footprint and intensity of surface urban heat island in 141 China cities
    Hu, Jia
    Yang, Yingbao
    Zhou, Yuyu
    Zhang, Tao
    Ma, Zhangfeng
    Meng, Xiangjin
    [J]. SUSTAINABLE CITIES AND SOCIETY, 2022, 77
  • [24] MODIS-based climatology of the Surface Urban Heat Island at country scale (Romania)
    Cheval, Sorin
    Dumitrescu, Alexandru
    Irasoc, Adrian
    Paraschiv, Monica-Gabriela
    Perry, Michael
    Ghent, Darren
    [J]. URBAN CLIMATE, 2022, 41
  • [25] Quantifying the effects of urban development intensity on the surface urban heat island across building climate zones
    He, Tianxing
    Zhou, Rui
    Ma, Qun
    Li, Chunlin
    Liu, Dan
    Fang, Xuening
    Hu, Yina
    Gao, Jun
    [J]. APPLIED GEOGRAPHY, 2023, 158
  • [26] Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
    Yuan, Debao
    Zhang, Liuya
    Fan, Yuqing
    Sun, Wenbin
    Fan, Deqin
    Zhao, Xurui
    [J]. APPLIED SCIENCES-BASEL, 2024, 14 (12):
  • [27] Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration
    Du, Hongyu
    Wang, Duoduo
    Wang, Yuanyuan
    Zhao, Xiaolei
    Qin, Fei
    Jiang, Hong
    Cai, Yongli
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 571 : 461 - 470
  • [28] Recognizing surface urban heat 'island' effect and its urbanization association in terms of intensity, footprint, and capacity: A case study with multi-dimensional analysis in Northern China
    Yao, Lei
    Sun, Shuo
    Song, Chaoxue
    Wang, Yixu
    Xu, Ying
    [J]. JOURNAL OF CLEANER PRODUCTION, 2022, 372
  • [29] Modelling inter-pixel spatial variation of surface urban heat island intensity
    Chen, Yanhua
    Chen, Wendy Y.
    Giannico, Vincenzo
    Lafortezza, Raffaele
    [J]. LANDSCAPE ECOLOGY, 2022, 37 (08) : 2179 - 2194
  • [30] Reconciling Debates on the Controls on Surface Urban Heat Island Intensity: Effects of Scale and Sampling
    Lai, Jiameng
    Zhan, Wenfeng
    Quan, Jinling
    Liu, Zihan
    Li, Long
    Huang, Fan
    Hong, Falu
    Liao, Weilin
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2021, 48 (19)