Skip to main content
Springer Nature Link
Log in

Study on the Differences of Urban Thermal Environment Under the Framework of Local Climate Zones

  • Conference paper
  • First Online:
Spatial Data and Intelligence (SpatialDI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13614))

Included in the following conference series:

  • 424 Accesses

Abstract

The urbanization process causes the urban heat island effect and affects the urban climate change. The urban heat island intensity is often used to describe the urban thermal environment, but how to objectively and scientifically obtain the urban heat island intensity is a research difficulty. This paper uses remote sensing data, OpenStreetMap data, climate data and other data, based on the local climate zones framework, combined with surface temperature retrieval, GIS spatial analysis and other methods, to study the urban thermal environment differences in Wuhan. The research shows that the rapid acquisition method of the area of interest based on OpenStreetMap data can better realize the local climate classification. The overall accuracy is 77.1% and Kappa coefficient is 0.75, which meets the accuracy requirements. On this basis, by superimposing the results of surface temperature retrieval from remote sensing images, the thermal characteristics of ground objects of different local climate types in the city can be obtained, thereby comprehensively reflecting the differences in the thermal environment in the city.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. World Urbanization Prospects: The 2018 Revision (2018)

    Google Scholar 

  2. Manley, G.: On the frequency of snowfall in metropolitan England. Q. J. Roy. Meteorol. Soc. 84, 70–72 (1958)

    Article  Google Scholar 

  3. X, A., J, T., X, Z.: The synergistic effect of urban heat and heatwave in Shanghai and its influencing island factor. J. Geogr. Sci. 14 (2019)

    Google Scholar 

  4. Ward, K., Lauf, S., Kleinschmit, B., Endlicher, W.: Heat waves and urban heat islands in Europe: a review of relevant drivers. Sci. Total Environ. 569–570, 527–539 (2016)

    Article  Google Scholar 

  5. Yang, X., et al.: Contribution of urbanization to the increase of extreme heat events in an urban agglomeration in east China. Geophys. Res. Lett. 44, 6940–6950 (2017)

    Article  Google Scholar 

  6. Georgescu, M., Moustaoui, M., Mahalov, A., Dudhia, J.: Summer-time climate impacts of projected megapolitan expansion in Arizona. Nat. Clim. Change 3, 37–41 (2012)

    Article  Google Scholar 

  7. Zhou, D.C., Zhao, S.Q., Liu, S.G., Zhang, L.X.: Zhu: Surface urban heat island in China’s 32 major cities: spatial patterns and drivers. Remote Sens. Environ. 2014(152), 51–61 (2014)

    Article  Google Scholar 

  8. Zhao, L., Smith, R.B., Oleson, K.: Strong contributions of local background climate to urban heat islands. Nature 511, 216–219 (2014)

    Article  Google Scholar 

  9. Kolokotroni, M., Ren, X., Davies, M., Mavrogianni, A.: London’s urban heat island: Impact on current and future energy consumption in office buildings. Energy Build. 47, 302–311 (2012)

    Article  Google Scholar 

  10. Tomlinson, C.J., Chapman, L., Thornes, J.E., Ba Ker, C.J.: Including the urban heat island in spatial heat health risk assessment strategies: a case study for Birmingham, UK. Int. J. Health Geogr. 10, 1–14 (2011)

    Article  Google Scholar 

  11. Yan, Z., Shepherd, J.M.: Atlanta’s urban heat island under extreme heat conditions and potential mitigation strategies. Nat. Hazards 52, 639–668 (2010)

    Article  Google Scholar 

  12. Stewart, I.D., Oke, T.R.: Local climate for urban temperature studies. Bull. Am. Meteorol. Soc. 93, 1879–1900 (2012)

    Article  Google Scholar 

  13. Chung, S.C., Chao, R.: Outdoor thermal comfort in different urban settings of sub-tropical high-density cities: an approach of adopting local climate zone (LCZ) classification. Build. Environ. 154, 227–238 (2019)

    Article  Google Scholar 

  14. Kotharkar, R., Bagade, A., Ramesh, A.: Assessing urban drivers of canopy layer urban heat island: a numerical modeling approach. Landsc. Urban Plann. 190, 103586 (2019)

    Article  Google Scholar 

  15. Ran, W.A., Meng, C.A., Chao, R., Bb, D., Yong, X.E., Ena, B.: Detecting multi-temporal land cover change and land surface temperature in Pearl River Delta by adopting local climate zone. Urban Clim. 28, 100455 (2019)

    Article  Google Scholar 

  16. Wang, C., Middel, A., Myint, S.W., Kaplan, S., Brazel, A.J., Lukasczyk, J.: Assessing local climate in arid cities: the case of Phoenix, Arizona and Las Vegas, Nevada. ISPRS J. Photogram. Remote Sens. 141, 59–71 (2018)

    Article  Google Scholar 

  17. Mushore, T.D., et al.: Remotely sensed retrieval of local climate and their linkages to land surface temperature in Harare metropolitan city, Zimbabwe. Urban Clim. 27, 259–271 (2018)

    Article  Google Scholar 

  18. Geleti, J., Lehnert, M., Dobrovoln, P.: Land Surface temperature differences within local climate, based on two central European cities. Remote Sens. 8, 788 (2016)

    Article  Google Scholar 

  19. Yuan, S., Ka-Lun, L.K., Chao, R., Edward, N.: Evaluating the local climate zone classification in high-density heterogeneous urban environment using mobile measurement. Urban Clim. 25, 167–186 (2018)

    Article  Google Scholar 

  20. Benjamin, B., et al.: Mapping local climate for a worldwide database of the form and function of cities. Int. J. Geo-Inf. 4, 199–219 (2015)

    Article  Google Scholar 

  21. Wuhan Municipal Bureau of Statistics: Wuhan Statistical Yearbook 2021. China Statistics Press, Beijing (2021)

    Google Scholar 

  22. Demuzere, M., Kittner, J., Bechtel, B.: LCZ generator: a web application to create local climate zone maps. Front. Environ. Sci. 9, 637455 (2021)

    Article  Google Scholar 

  23. Danylo, O., See, L., Bechtel, B., Schepaschenko, D., Fritz, S.: Contributing to WUDAPT: a local climate zone classification of two cities in Ukraine. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 1841–1853 (2017)

    Article  Google Scholar 

  24. Hidalgo, J., Schoetter, R., Petit, G., Bocher, E., Dumas, G.: Comparing WUDAPT level 0 cartography with a more detailed urban database. Some examples for French cities using the MAPUCE database (2017)

    Google Scholar 

  25. See, L., et al.: Generating WUDAPT’s specific scale-dependent urban modeling and activity parameters: collection of level 1 and level 2 data (2015)

    Google Scholar 

  26. Yong, X., Chao, R., Meng, C., Edward, N., Wu, T.: Classification of local climate using ASTER and landsat data for high-density cities. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 10, 1–9 (2017)

    Google Scholar 

  27. Rozenstein, O., Qin, Z., Derimian, Y., Karnieli, A.: Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm. Sensors 14, 5768–5780 (2014)

    Article  Google Scholar 

  28. Jimenez-Munoz, J.C., Sobrino, J.A., Skokovic, D., Mattar, C., Cristobal, J.: Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data. IEEE Geosci. Remote Sens. Lett. 11, 1840–1843 (2014)

    Article  Google Scholar 

  29. Wang, S., He, L., Hu, W.: A temperature and emissivity separation algorithm for landsat-8 thermal infrared sensor data. Remote Sens. 12, 9904–9927 (2015)

    Article  Google Scholar 

  30. Chen, D., Ren, H., Qin, Q., Meng, J., Zhao, S.: A practical split-window algorithm for estimating land surface temperature from landsat 8 data. Remote Sens. 7, 647–665 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. China University of Geosciences, Wuhan, 430074, Hubei, China

    Shuyue Wang, Ziyue Fan & Wenyou Fan

  2. Sun Yat-sen University, Zhuhai, 519000, Guangdong, China

    Ziyue Fan

  3. The Greatmap Company Limited, Beijing, 100085, China

    Wenyou Fan

Authors
  1. Shuyue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziyue Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenyou Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenyou Fan .

Editor information

Editors and Affiliations

  1. Peking University, Wuhan, China

    Huayi Wu

  2. Peking University, Beijing, China

    Yu Liu

  3. Harbin Institute of Technology, Harbin, China

    Jianzhong Li

  4. Department of Computer Science and Information, Renmin University of China, Beijing, China

    Xiaofeng Meng

  5. China University of Geosciences (Wuhan), Wuhan, China

    Qingfeng Guan

  6. Southern University of Science and Technology, Shenzhen, China

    Xuan Song

  7. Jiangxi University of Finance and Economics, Nanchang, China

    Guoqiong Liao

  8. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Guoliang Li

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, S., Fan, Z., Fan, W. (2022). Study on the Differences of Urban Thermal Environment Under the Framework of Local Climate Zones. In: Wu, H., et al. Spatial Data and Intelligence. SpatialDI 2022. Lecture Notes in Computer Science, vol 13614. Springer, Cham. https://doi.org/10.1007/978-3-031-24521-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-24521-3_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24520-6

  • Online ISBN: 978-3-031-24521-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics