A comparative study on the changes in heat island effect in Chinese and foreign megacities

WANG Meiya; XU Hanqiu

doi:10.6046/zrzyyg.2020393

2021 Vol. 33, No. 4

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2021 > 33(4): 200-208

Next Article Previous Article

WANG Meiya, XU Hanqiu. 2021. A comparative study on the changes in heat island effect in Chinese and foreign megacities. Remote Sensing for Natural Resources, 33(4): 200-208. doi: 10.6046/zrzyyg.2020393

Citation:

WANG Meiya, XU Hanqiu. 2021. A comparative study on the changes in heat island effect in Chinese and foreign megacities. Remote Sensing for Natural Resources, 33(4): 200-208. doi: 10.6046/zrzyyg.2020393

A comparative study on the changes in heat island effect in Chinese and foreign megacities

WANG Meiya¹,
XU Hanqiu²

1. School of History and Geography, Minnan Normal University, Zhangzhou 363000, China；
2. College of Environment and Resources, Institute of Remote Sensing Information Engineering, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou University, Fuzhou 350116, China

Received Date: 08 December 2020

Publish Date: 15 December 2021

Abstract

Abstract

Megacities have formed due to rapid urbanization. As a result, the surface cover has rapidly changed, which changes the heat balance of Earth's surface and induces drastic changes in the thermal environment in megacities. With six typical megacities (Beijing, Shanghai, Guangzhou, London, New York, and Tokyo) across the world as study objects and multi-temporal Landsat remote-sensing images of the 1990s, the 2000s, and 2015 as the main data sources, this study compares the changes in the thermal environment among the six megacities and analyzes their causes. For each of the megacities, the surface temperature was determined through reversion using the universal single-channel algorithm and the urban heat island ratio index (URI) was calculated to quantitatively compare the spatial-temporal changes in the heat island effect during the study period. The results are as follows. From the 1990s to 2015, the URI values of Beijing, Shanghai, and Tokyo showed an overall upward trend, and while that of Guangzhou, London, and New York showed an overall downward trend. In 2015, Tokyo suffered the most serious urban heat island effect (URI=0.630), followed by Beijing, Shanghai, New York, and Guangzhou successively, of which the URI values were 0.617, 0.594, 0.555, and 0.530, respectively. In contrast, London had the smallest URI of 0.433. The megacities such as Beijing, Shanghai, Guangzhou, and Tokyo all considerably expanded throughout the study period. In these cities, the built-up areas and impervious surfaces increased by more than 500 km² and more than 370 km² on average, respectively in terms of area. They continuously spread outwards and occupied ecological land. Furthermore, green belts can not be formed between urban clusters. All these caused a significant increase in urban surface temperature and especially the significant aggravation of the heat island effect in new urban areas. In comparison, the thermal environment in the old urban areas was significantly improved through urban reconstruction. London and New York were not significantly expanded, where the surface temperature slightly changed. Therefore, it is necessary to pay attention to ecological philosophy, optimize the pattern of urban surface space, and improve the efficiency of ecological land in future urban construction.
- megacities /
- urban heat environment /
- urban heat island ratio index /
- remote sensing /
- surface landscape pattern /

FullText(HTML)

References

[1]	匡文慧, 杨天荣, 刘爱琳, 等. 城市地表覆盖结构组分与热环境调控模型(EcoCity)研究——以北京城市为例[J]. 中国科学(地球科学), 2017,47(7):847-859. Google Scholar
[2]	Kuang W H, Yang T R, Liu A L, et al. An EcoCity model for regulating urban land cover structure and thermal environment:Taking Beijing as an example[J]. Science China Earth Sciences, 2017,47(7):847-859. Google Scholar
[3]	Xu H Q, Wang M Y, Shi T T, et al. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index(RSEI)[J]. Ecological Indicators, 2018,93:730-740. Google Scholar
[4]	Croft-White M V, Cvetkovic M, Rokitnicki-Wojcik D, et al. A shoreline divided:Twelve-year water quality and land cover trends in Lake Ontario coastal wetlands[J]. Journal of Great Lakes Research, 2017,43(6):1005-1015. Google Scholar
[5]	United Nations. State of the world population 2014-the power of 1.8 billion:Adolescents,youth and the transformation of the future[EB/OL]. https://reliefweb.int/report/world/state-world-population-2014-power-18-billion-adolescents-youth-and-transformation. Google Scholar
[6]	Meng F, Shan B Y, Liu M. Remote-sensing evaluation of the relationship between urban heat islands and urban biophysical descriptors in Jinan,China[J]. Journal of Applied Remote Sensing, 2014,8(1):083693. Google Scholar
[7]	葛荣凤, 王京丽, 张力小, 等. 北京市城市化进程中热环境响应[J]. 生态学报, 2016,36(19):6040-6049. Google Scholar
[8]	Ge R F, Wang J L, Zhang L X, et al. Impacts of urbanization on the urban thermal environment in Beijing[J]. Acta Ecologica Sinica, 2016,36(19):6040-6049. Google Scholar
[9]	Feyisa G L, Meilby H, Jenerette G D, et al. Locally optimized separability enhancement indices for urban land cover mapping:Exploring thermal environmental consequences of rapid urbanization in Addis Ababa,Ethiopia[J]. Remote Sensing of Environment, 2016,175:14-31. Google Scholar
[10]	Weng Q H, Lu D S. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis,United States[J]. International Journal of Applied Earth Observation and Geoinformation, 2008,10(1):68-83. Google Scholar
[11]	Zheng B J, Myint S W, Fan C. Spatial configuration of anthropogenic land cover impacts on urban warming[J]. Landscape and Urban Planning, 2014,130:104-111. Google Scholar
[12]	Chen Y J, Yu S X. Impacts of urban landscape patterns on urban thermal variations in Guangzhou,China[J]. International Journal of Applied Earth Observation and Geoinformation, 2017,54:65-71. Google Scholar
[13]	Kotharkar R, Bagade A. Evaluating urban heat island in the critical local climate zones of an Indian city[J]. Landscape and Urban Planning, 2018,169:92-104. Google Scholar
[14]	中国国家统计局. 中国统计年鉴(2015年)[M]. 北京: 中国统计出版社, 2016. Google Scholar
[15]	National Bureau of Statistics of China. China statistical yearbook[M]. Beijing: China Statistics Press, 2016. Google Scholar
[16]	United Nations. World population prospects:The 2018 revision[EB/OL]. https://population.un.org/wup/Publications/. Google Scholar
[17]	中国住房和城乡建设部. 中国城市建设统计年鉴2014[M]. 北京: 中国统计出版社, 2015. Google Scholar
[18]	Ministry of Housing and Urban-Rural Development of the People's Republic of China. China urban construction statistical yearbook[M]. Beijing: China Statistics Press, 2015. Google Scholar
[19]	Van de Voorde T, Jacquet W, Canters F. Mapping form and function in urban areas:An approach based on urban metrics and continuous impervious surface data[J]. Landscape and Urban Planning, 2011,102(3):143-155. Google Scholar
[20]	Chander G, Markham B L, Helder D L. Summary of current radiometric calibration coefficients for Landsat MSS,TM,ETM+,and EO-1 ALI sensors[J]. Remote Sensing of Environment, 2009,113(5):893-903. Google Scholar
[21]	Charvz Jr P S. Image-based atmospheric corrections-revisited and revised[J]. Photogrammetric Engineering and Remote Sensing, 1996,62(9):1025-1036. Google Scholar
[22]	Jiménez-Muñoz J C, Cristobal J, Sobrino J A, et al. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009,47(1):339-349. Google Scholar
[23]	Jiménez-Muñoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research Atmospheres, 2003,108(D22):4688. Google Scholar
[24]	Jiménez-Muñoz J C, Sobrino J A, Skoković D, et al. Land surface temperature retrieval methods from Landsat8 thermal infrared sensor data[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(10):1840-1843. Google Scholar
[25]	徐涵秋, 陈本清. 不同时相的遥感热红外图像在研究城市热岛变化中的处理方法[J]. 遥感技术与应用, 2003,18(3):129-133. Google Scholar
[26]	Xu H Q, Chen B Q. An image processing technique for the study of urban heat island changes using different seasonal remote sensing data[J]. Remote Sensing Technology and Application, 2003,18(3):129-133. Google Scholar
[27]	国家环保部. 生态环境状况评价技术规范(发布稿)[S]. 北京: 中国标准出版社, 2015. Google Scholar
[28]	Ministry of Ecology and Environment the People's Republic of China. Technical criterion for ecosystem status evaluation[S]. Beijing: Standards Press of China, 2015. Google Scholar
[29]	住房城乡建设部. 城市生态建设环境绩效评估导则(试行)[M]. 北京: 中国建筑工业出版社, 2015. Google Scholar
[30]	Ministry of Housing and Urban-Rural Development of the People's Republic of China. Guidelines for performance assessment of urban ecological construction[M]. Beijing: China Architecture and Building Press, 2015. Google Scholar
[31]	樊智宇, 詹庆明, 刘慧民, 等. 武汉市夏季城市热岛与不透水面增温强度时空分布[J]. 地球信息科学学报, 2019,21(2):226-235. Google Scholar
[32]	Fan Z Y, Zhan Q M, Liu H M, et al. Spatial-temporal distribution of urban heat island and the heating effect of impervious surface in summer in Wuhan[J]. Journal of Geo-Information Science, 2019,21(2):226-235. Google Scholar
[33]	Breiman L. Random forests[J]. Machine Learning, 2001,45(1):5-32. Google Scholar
[34]	Du P J, Samat A, Waske B, et al. Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,105:38-53. Google Scholar
[35]	Padmanaban R, Bhowmik A K, Cabral P. A remote sensing approach to environmental monitoring in a reclaimed mine area[J]. ISPRS International Journal of Geo-Information, 2017,6(12):401. Google Scholar
[36]	Schneider A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach[J]. Remote Sensing of Environment, 2012,124:689-704.