留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing

LI Yuanzheng WANG Lan ZHANG Liping LIU Min ZHAO Guosong

LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. 中国地理科学, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9
引用本文: LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. 中国地理科学, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9
LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. Chinese Geographical Science, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9
Citation: LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. Chinese Geographical Science, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9

Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing

doi: 10.1007/s11769-019-1080-9
基金项目: 

Under the auspices of National Natural Science Foundation of China (No. 41901238, 41701501), Social Science Fund of China (General Projects) (No.17BJL065), Key Scientific and Technological Project of Henan Province (No. 192102310003), Edu-cational Commission of Henan Province (No. 2019-ZZJH-094)

Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing

Funds: 

Under the auspices of National Natural Science Foundation of China (No. 41901238, 41701501), Social Science Fund of China (General Projects) (No.17BJL065), Key Scientific and Technological Project of Henan Province (No. 192102310003), Edu-cational Commission of Henan Province (No. 2019-ZZJH-094)

  • 摘要: This study aimed to accurately study the intra-annual spatiotemporal variation in the surface urban heat island intensities (SUHIIs) in 1449 cities in China. First, China was divided into five environmental regions. Then, the SUHIIs were accurately calculated based on the modified definitions of the city extents and their corresponding nearby rural areas. Finally, we explored the spatiotemporal variation of the mean, maximum, and minimum values, and ranges of SUHIIs from several aspects. The results showed that larger annual mean daytime SUHIIs occurred in hot-humid South China and cold-humid northeastern China, and the smallest occurred in arid and semiarid west China. The seasonal order of the SUHIIs was summer > spring > autumn > winter in all the temperate regions except west China. The SUHIIs were obviously larger during the rainy season than the dry season in the tropical region. Nevertheless, significant differences were not observed between the two seasons within the rainy or dry periods. During the daytime, the maximum SUHIIs mostly occurred in summer in each region, while the minimum occurred in winter. A few cold island phenomena existed during the nighttime. The maximum SUHIIs were generally significantly positively correlated with the minimum SUHIIs during the daytime, nighttime and all-day in all environmental regions throughout the year and the four seasons. Moreover, significant correlation scarcely existed between the daytime and nighttime ranges of the SUHIIs. In addition, the daytime SUHIIs were also insignificantly correlated with the nighttime SUHIIs in half of the cases.
  • [1] Chen L, Zhang M G, Zhu J et al., 2018. Modeling impacts of urbanization and urban heat island mitigation on boundary layer meteorology and air quality in Beijing under different weather conditions. Journal of Geophysical Research-Atmospheres, 123(8):4323-4344. doi:10.1002/2017 jd027501
    [2] Clinton N, Gong P, 2013. MODIS detected surface urban heat islands and sinks:Global locations and controls. Remote Sensing of Environment, 134:294-304. doi:10.1016/j.rse. 2013.03.008
    [3] Fallmann J, Forkel R, Emeis S, 2015. Secondary effects of urban heat island mitigation measures on air quality. Atmospheric Environment, 125(Part A):199-211. doi:10.1016/j.atmosenv. 2015.10.094 doi
    [4] Filho W L, Icaza L E, Emanche V O et al., 2017. An evi-dence-based review of impacts, strategies and tools to mitigate urban heat islands. International Journal of Environmental Research & Public Health, 14(12):1600. doi:10.3390/ijerph 14121600
    [5] Haashemi S, Weng Q, Darvishi A et al., 2016. Seasonal variations of the surface urban heat island in a semi-arid city. Remote Sensing, 8(4):352. doi: 10.3390/rs8040352
    [6] Howard L, 1833. Climate of London Deduced from Metrological Observations (3rd edition). London:Harvey and Dorton Press.
    [7] Imhoff M L, Zhang P, Wolfe R E et al., 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3):504-513. doi: 10.1016/j.rse.2009.10.008
    [8] Lai L W, Cheng W L, 2009. Air quality influenced by urban heat island coupled with synoptic weather patterns. Science of the Total Environment, 407(8):2724-2733. doi: 10.1016/j.scitotenv.2008.12.002
    [9] Lazzarini M, Marpu P R, Ghedira H, 2013. Temperature-land cover interactions:the inversion of urban heat island phenom-enon in desert city areas. Remote Sensing of Environment, 130:136-152. doi: 10.1016/j.rse.2012.11.007
    [10] Lee Y Y, Din M F M, Ponraj M et al., 2017. Overview of urban heat island (UHI) phenomenon towards human thermal com-fort. Environmental Engineering and Management Journal, 16(9):2097-2111. doi: 10.30638/eemj.2017.217
    [11] Li Y, Wang L, Zhang L et al., 2019. Monitoring the interannual spatiotemporal changes in the land surface thermal environment in both urban and rural regions from 2003 to 2013 in China based on remote sensing. Advances in Meteorology, 2019:8347659. doi: 10.1155/2019/8347659
    [12] Li Yuanzheng, Yin Ke, Wang Yanting et al., 2017. Studies on influence factors of surface urban heat island:a review. World Sci Tech R & D, 39(1):56-66. (in Chinese)
    [13] Li Yuanzheng, Yin Ke, Zhou Hongxuan et al., 2016. Progress in urban heat island monitoring by remote sensing. Progress in Geography, 35(9):1062-1074. doi: 10.18306/dlkxjz.2016.09.002
    [14] Liu J, Kuang W, Zhang Z et al., 2014. Spatiotemporal characteris-tics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences, 24(2):195-210. doi: 10.1007/s11442-014-1082-6
    [15] Liu J, Liu M, Tian H et al., 2005. Spatial and temporal patterns of China's cropland during 1990-2000:an analysis based on Landsat TM data. Remote Sensing of Environment, 98(4):442-456. doi: 10.1016/j.rse.2005.08.012
    [16] Liu X, Hu G, Ai B et al., 2015. A normalized urban areas compo-site index (NUACI) based on combination of DMSP-OLS and MODIS for mapping impervious surface area. Remote Sensing, 7(12):17168-17189. doi: 10.3390/rs71215863
    [17] Memon R A, Leung D Y C, Liu C H, 2009. An investigation of urban heat island intensity (UHII) as an indicator of urban heating. Atmospheric Research, 94(3):491-500. doi: 10.1016/j.atmosres.2009.07.006
    [18] Mostovoy G V, King R L, Reddy K R et al., 2006. Statistical es-timation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi. GIScience & Remote Sensing, 43(1):78-110. doi: 10.2747/1548-1603.43.1.78
    [19] United Nations, Department of Economic and Social Affairs, Pop-ulation Division (UN DESA PD), 2014. World Urbanization Prospects:The 2014 Revision, Highlights. Department of Eco-nomic and Social Affairs. Population Division, United Nations.
    [20] Peng S, Piao S, Ciais P et al., 2011. Surface urban heat island across 419 global big cities. Environmental Science & Tech-nology, 46(2):696-703. doi: 10.1021/es2030438
    [21] Ren Guoyu, Guo Jun, Xu Mingzhi et al., 2005. Climate changes of China's mainland over the past half century. Acta Meteorologica Sinica, 63(6):942-956. (in Chinese)
    [22] Richards D R, Edwards P J, 2018. Using water management in-frastructure to address both flood risk and the urban heat island. International Journal of Water Resources Development, 34(4):490-498. doi: 10.1080/07900627.2017.1357538
    [23] Schwarz N, Lautenbach S, Seppelt R, 2011. Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sensing of En-vironment, 115(12):3175-3186. doi: 10.1016/j.rse.2011.07.003
    [24] Schwarz N, Schlink U, Franck U et al., 2012. Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators-an application for the city of Leipzig (Germany). Ecological Indicators, 18:693-704. doi: 10.1016/j.ecolind.2012.01.001
    [25] Sfîcă L, Ichim P, Apostol L et al., 2017. The extent and intensity of the urban heat island in Iaşi city, Romania. Theoretical & Applied Climatology, 134(3-4):777-791. doi: 10.1007/s00704-017-2305-4
    [26] Shastri1 H, Barik1 B, Ghosh S et al., 2017. Flip flop of day-night and summer-winter surface urban heat island intensity in India. Scientific Reports, 7:40178. doi: 10.1038/srep40178
    [27] Shi B, Tang C S, Gao L et al., 2012. Observation and analysis of the urban heat island effect on soil in Nanjing, China. Envi-ronmental Earth Sciences, 67(1):215-229. doi: 10.1007/s12665-011-1501-2
    [28] Tran H, Uchihama D, Ochi S et al., 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8(1):34-48. doi: 10.1016/j.jag.2005.05.003
    [29] Voogt J A, Oke T R, 2003. Thermal remote sensing of urban cli-mates. Remote Sensing of Environment, 86(3):370-384. doi: 10.1016/S0034-4257(03)00079-8
    [30] Wang J, Huang B, Fu D et al., 2015. Spatiotemporal variation in surface urban heat island intensity and associated determinants across major Chinese cities. Remote Sensing, 7:3670-3689. doi: 10.3390/rs70403670
    [31] Zhang P, Imhoff M L, Wolfe R E et al., 2010. Characterizing urban heat islands of global settlements using MODIS and nighttime lights products. Canadian Journal of Remote Sensing, 36(3):185-196. doi: 10.5589/m10-039
    [32] Zhang Z, Wang X, Zhao X et al., 2014. A 2010 update of National Land Use/Cover Database of China at 1:100000 scale using medium spatial resolution satellite images. Remote Sensing of Environment, 149:142-154. doi: 10.1016/j.rse.2014.04.004
    [33] Zhao L, Lee X, Smith R B et al., 2014. Strong contributions of local background climate to urban heat islands. Nature, 511(7508):216-219. doi: 10.1038/nature13462
    [34] Zhou D, Xiao J, Bonafoni S et al., 2019. Satellite remote sensing of surface urban heat islands:progress, challenges, and per-spectives. Remote Sensing, 11(1):48. doi: 10.3390/rs11010048
    [35] Zhou D, Zhang L, Li D et al., 2016. Climate-vegetation control on the diurnal and seasonal variations of surface urban heat islands in China. Environmental Research Letters, 11(7):074009. doi: 10.1088/1748-9326/11/7/074009
    [36] Zhou D, Zhao S, Liu S et al., 2014. Surface urban heat island in China's 32 major cities:Spatial patterns and drivers. Remote Sensing of Environment, 152:51-61. doi: 10.1016/j.rse.2014.05.017
    [37] Zhou D, Zhao S, Zhang L et al., 2015. The footprint of urban heat island effect in China. Scientific Reports, 5:11160. doi: 10.1038/srep11160
    [38] Zhou J, Chen Y, Zhang X et al., 2013. Modelling the diurnal vari-ations of urban heat islands with multi-source satellite data. International Journal for Remote Sensing, 34(21):7568-7588. doi: 10.1080/01431161.2013.821576
    [39] Zinzi M, Carnielo E, Mattoni B, 2018. On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy. Applied Energy, 221:148-160. doi: 10.1016/j.apenergy.2018.03.192
  • [1] LI Jinfeng, XU Haicheng, LIU Wanwan, WANG Dongfang, ZHOU Shuang.  Spatial Pattern Evolution and Influencing Factors of Cold Storage in China . Chinese Geographical Science, 2020, 30(3): 505-515. doi: 10.1007/s11769-020-1124-1
    [2] ZHANG Suwen, LI Chenggu, MA Zuopeng, LI Xin.  Influences of Different Transport Routes and Road Nodes on Industrial Land Conversion: A Case Study of Changchun City of Jilin Province, China . Chinese Geographical Science, 2020, 30(3): 544-556. doi: 10.1007/s11769-020-1126-z
    [3] GAO Wenwen, ZENG Yuan, ZHAO Dan, WU Bingfang, REN Zhiyuan.  Land Cover Changes and Drivers in the Water Source Area of the Middle Route of the South-to-North Water Diversion Project in China from 2000 to 2015 . Chinese Geographical Science, 2020, 30(1): 115-126. doi: 10.1007/s11769-020-1099-y
    [4] WANG Liyan, ANNA Herzberger, ZHANG Liyun, XIAO Yi, WANG Yaqing, XIAO Yang, LIU Jianguo, OUYANG Zhiyun.  Spatial and Temporal Changes of Arable Land Driven by Urbanization and Ecological Restoration in China . Chinese Geographical Science, 2019, 20(5): 809-819. doi: 10.1007/s11769-018-0983-1
    [5] HE Qingsong, TAN Shukui, XIE Peng, LIU Yaolin, LI Jing.  Re-assessing Vegetation Carbon Storage and Emissions from Land Use Change in China Using Surface Area . Chinese Geographical Science, 2019, 20(4): 601-613. doi: 10.1007/s11769-019-1058-7
    [6] SHI Tiange, ZHANG Xiaolei, DU Hongru, SHI Hui.  Urban Water Resource Utilization Efficiency in China . Chinese Geographical Science, 2015, 25(6): 684-697. doi: 10.1007/s11769-015-0773-y
    [7] Leszek SOBKOWIAK, LIU Changming.  Comparative Mountain Hydrology: A Case Study of Wis?ok River in Poland and Chaohe River in China . Chinese Geographical Science, 2015, 25(1): 1-12. doi: 10.1007/s11769-014-0673-6
    [8] YANG Zhenshan, LIANG Jinshe, CAI Jianming.  Urban Economic Cluster Template and Its Dynamics of Beijing, China . Chinese Geographical Science, 2014, 0(6): 740-750. doi: 10.1007/s11769-014-0686-1
    [9] LUO Shanghua, MAO Qizheng, MA Keming.  Comparison on Soil Carbon Stocks Between Urban and Suburban Topsoil in Beijing, China . Chinese Geographical Science, 2014, 0(5): 551-561. doi: 10.1007/s11769-014-0709-y
    [10] LI Taijun, LIU Guobin.  Age-related Changes of Carbon Accumulation and Allocation in Plants and Soil of Black Locust Forest on Loess Plateau in Ansai County, Shaanxi Province of China . Chinese Geographical Science, 2014, 0(4): 414-422. doi: 10.1007/s11769-014-0704-3
    [11] SONG Wei, CHEN Baiming, ZHANG Ying.  Land Use Regionalization of Rural Settlements in China . Chinese Geographical Science, 2013, 23(4): 421-434. doi: 10.1007/s11769-013-0592-y
    [12] DU Jia, SONG Kaishan, WANG Zongming, ZHANG Bai, LIU Dianwei.  Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land (SEBAL) Model in Sanjiang Plain, Northeast China . Chinese Geographical Science, 2013, 23(1): 73-91.
    [13] SONG Wei, CHEN Baiming, ZHANG Ying, WU Jianzhai.  Establishment of Rural Housing Land Standard in China . Chinese Geographical Science, 2012, 22(4): 483-495.
    [14] YANG Xiaohuan, CHENG Chuanzhou, LI Yuejiao.  Effect of Cropland Occupation and Supplement on Light-temperature Potential Productivity in China from 2000 to 2008 . Chinese Geographical Science, 2010, 20(6): 536-544. doi: 10.1007/s11769-010-0429-x
    [15] ZHANG Shumin, ZHANG Baolei, ZHANG Lei, LU Chunxia, CHENG Xiaoling.  Spatiotemporal Evolution of Urban Land Uses in Modern Urbanization of China . Chinese Geographical Science, 2010, 20(2): 132-138. doi: 10.1007/s11769-010-0132-y
    [16] ZHU Peng, LU Chunxia, ZHANG Lei, CHENG Xiaoling.  Urban Fresh Water Resources Consumption of China . Chinese Geographical Science, 2009, 19(3): 219-224. doi: 10.1007/s11769-009-0219-5
    [17] SHI Longyu, SHAO Guofan, CUI Shenghui, LI Xuanqi, LIN Tao, YIN Kai, ZHAO Jingzhu.  Urban Three-dimensional Expansion and Its Driving Forces——A Case Study of Shanghai, China . Chinese Geographical Science, 2009, 19(4): 391-398. doi: 10.1007/s11769-009-0291-x
    [18] LIU Dianwei, WANG Zongming, SONG Kaishan, ZHANG Bai, HU Liangjun, HUANG Ni, ZHANG Sumei, LUO Ling, ZHANG Chunhua, JIANG Guangjia.  Land Use/Cover Changes and Environmental Consequences in Songnen Plain, Northeast China . Chinese Geographical Science, 2009, 19(4): 299-305. doi: 10.1007/s11769-009-0299-2
    [19] LIU Chen, Kuninori OTSUBO, WANG Qinxue, Toshiaki ICHINOSE, Sadao ISHIMURA.  Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000 . Chinese Geographical Science, 2007, 17(2): 99-109. doi: 10.1007/s11769-007-0099-5
    [20] XU Jiangang, LIAO Banggu, SHEN Qing, ZHANG Feng, MEI Anxin.  Urban Spatial Restructuring in Transitional Economy——Changing Land Use Pattern in Shanghai . Chinese Geographical Science, 2007, 17(1): 19-27. doi: 10.1007/s11769-007-0019-8
  • 加载中
计量
  • 文章访问数:  189
  • HTML全文浏览量:  3
  • PDF下载量:  265
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-03-08

Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing

doi: 10.1007/s11769-019-1080-9
    基金项目:

    Under the auspices of National Natural Science Foundation of China (No. 41901238, 41701501), Social Science Fund of China (General Projects) (No.17BJL065), Key Scientific and Technological Project of Henan Province (No. 192102310003), Edu-cational Commission of Henan Province (No. 2019-ZZJH-094)

摘要: This study aimed to accurately study the intra-annual spatiotemporal variation in the surface urban heat island intensities (SUHIIs) in 1449 cities in China. First, China was divided into five environmental regions. Then, the SUHIIs were accurately calculated based on the modified definitions of the city extents and their corresponding nearby rural areas. Finally, we explored the spatiotemporal variation of the mean, maximum, and minimum values, and ranges of SUHIIs from several aspects. The results showed that larger annual mean daytime SUHIIs occurred in hot-humid South China and cold-humid northeastern China, and the smallest occurred in arid and semiarid west China. The seasonal order of the SUHIIs was summer > spring > autumn > winter in all the temperate regions except west China. The SUHIIs were obviously larger during the rainy season than the dry season in the tropical region. Nevertheless, significant differences were not observed between the two seasons within the rainy or dry periods. During the daytime, the maximum SUHIIs mostly occurred in summer in each region, while the minimum occurred in winter. A few cold island phenomena existed during the nighttime. The maximum SUHIIs were generally significantly positively correlated with the minimum SUHIIs during the daytime, nighttime and all-day in all environmental regions throughout the year and the four seasons. Moreover, significant correlation scarcely existed between the daytime and nighttime ranges of the SUHIIs. In addition, the daytime SUHIIs were also insignificantly correlated with the nighttime SUHIIs in half of the cases.

English Abstract

LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. 中国地理科学, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9
引用本文: LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. 中国地理科学, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9
LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. Chinese Geographical Science, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9
Citation: LI Yuanzheng, WANG Lan, ZHANG Liping, LIU Min, ZHAO Guosong. Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing[J]. Chinese Geographical Science, 2019, 29(6): 905-916. doi: 10.1007/s11769-019-1080-9
参考文献 (39)

目录

    /

    返回文章
    返回