Skip to main content

Advertisement

SpringerLink
Log in

Impact of urbanisation on urban heat island intensity during summer and winter over Indian metropolitan cities

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Industrial and economic development along with the rapid growth in urban population over the global cities is generating a warmer inner core in comparison to the surrounding natural landscape, the common feature of the phenomena of urban heat island (UHI). In the earlier part of the study, the spatial relationship of the changes in the land cover types and land surface temperature (LST) is analysed for metropolitan cities of India during winter season using remote sensing techniques. In the present study, an attempt has been made to estimate the UHI intensities over the metropolitan cities of India during summer season. The LST estimated using the Landsat 7 ETM+ images reveals the presence of a unique form of surface UHIs in the dense built-up areas of different cities. The intensities of UHIs over these cities during summer season are noticed to be in the range of 10.5–14 °C. Delhi possesses the highest UHI intensity in the range of 13.4–14.0 °C, and Kolkata possessed the lowest UHI intensity in the range of 10.5–11.7 °C. Higher-temperature zones (hotspots) are noticed to be increasing within the built-up areas and barren lands. The temperature of hotspots for the northern cities Delhi and Jaipur is observed to be in the range of 45–50 °C and is highest among the cities under consideration. On comparison with the winter results, the LST ranges, UHI intensities and the hotspot ranges for summer season are found be noticeably higher. The LST distribution and the UHI pattern of these cities are found to different during summer season in comparison to the winter season.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abutaleb, K., Ngie, A., Darwish, A., Ahmed, M., Arafat, S., & Ahmed, F. (2015). Assessment of urban heat island using remotely sensed imagery over Greater Cairo, Egypt. Advances in Remote Sensing, 4, 35–47. https://doi.org/10.4236/ars.2015.41004.

    Article  Google Scholar 

  • Aniello, C., Morgan, K., Busbey, A., & Newland, L. (1995). Mapping micro-urban heat islands using Landsat TM and a GIS. Computers and Geosciences, 21, 965–969.

    Google Scholar 

  • Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2), 161–173.

    Google Scholar 

  • Bokaiea, M., Zarkesha, M. K., Arastehb, P. D., & Ali, H. (2016). Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustainable Cities and Society, 23, 94–104.

    Google Scholar 

  • Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252.

    Google Scholar 

  • Chakraborty, S. D., Kant, Y., & Bharath, B. D. (2014). Study of land surface temperature in Delhi City to managing the thermal effect on urban developments. International Journal of Advanced Scientific and Technical Research, 4(1), 439–450.

    Google Scholar 

  • Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104, 133–146.

    Google Scholar 

  • Coseo, P., & Larsen, L. (2014). How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain urban heat islands in Chicago. Landscape and Urban Planning, 125, 117–129.

    Google Scholar 

  • Dash, P., Gottsche, F. M., Olesen, F. S., & Fischer, H. (2002). Land surface temperature and emissivity estimation from passive sensor data: theory and practice—current trends. International Journal of Remote Sensing, 23(13), 2563–2594.

    Google Scholar 

  • Estoque, R. C., Murayama, Y., & Myint, S. W. (2017). Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Science of the Total Environment, 577, 349–359.

    CAS  Google Scholar 

  • Goldreich, Y. (1992). Urban climate studies in Johannesburg, a sub-tropical city located on a ridge—a review. Atmospheric Environment, 26, 407–420.

    Google Scholar 

  • Grimmond, C. S. B. (2007). Urbanization and global environmental change: local effects of urban warming. The Geographical Journal, 173, 83–88. https://doi.org/10.1111/j.1475-4959.2007.232_3.x.

    Article  Google Scholar 

  • Grimmond, C. S. B., Oke, T. R., & Cleugh, H. A. (1993). The role of “rural” in comparisons of observed suburban and rural flux differences. IAHS Publication, 212, 165–174.

    Google Scholar 

  • Grimmond, C. S. B., Ward, H. C., & Kotthaus, S. (2015). How is urbanization altering local and regional climate? In K. C. Seto, W. D. Solecki, & C. A. Griffith (Eds.), The Routledge handbook of urbanization and global environmental change (pp. 169–187). London: Routledge, Taylor & Francis Group ISBN 9780415732260.

    Google Scholar 

  • Grover, A., & Singh, R. B. (2015). Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): a comparative study of Delhi and Mumbai. Environments, 2, 125–138.

    Google Scholar 

  • GWI–Jaipur. (2013). Government of India, Ministry of Water Resources, Central Ground Water Board. Ground Water Information, Jaipur District, Rajasthan. http://cgwb.gov.in/District_Profile/Rajasthan/Jaipur.pdf. Accessed 28 Sep 2018.

  • Haashemi, S., Weng, Q., Darvishi, A., & Alavipanah, S. K. (2016). Seasonal variations of the surface urban heat island in a semi-arid city. MDPI – Remote Sensing, 8(4), 352. https://doi.org/10.3390/rs8040352.

    Article  Google Scholar 

  • Hu, L., & Brunsell, N. A. (2013). The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring. Remote Sensing of Environment, 134, 162–174.

    Google Scholar 

  • Huang, L., Li, J., Zhao, D., & Zhu, J. (2008). A fieldwork study on the diurnal changes of urban microclimate in four types of ground cover and urban heat island of Nanjing, China. Building and Environment, 43, 7–17.

    Google Scholar 

  • Hung, T., Uchihama, D., Ochi, S., & Yasuoka, Y. (2006). Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8, 34–48.

    Google Scholar 

  • Kant, Y., Bharath, B. D., Mallick, J., Atzberger, C., & Kerle, N. (2009). Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi, India. Journal of the Indian Society of Remote Sensing, 37, 201–214.

    Google Scholar 

  • Kato, S., & Yamaguchi, Y. (2005). Analysis of urban heat-island effect using ASTER and ETM+ data: separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sensing of Environment, 99, 44–54.

    Google Scholar 

  • Kotharkar, R., & Surawar, M. (2016). Land use, land cover, and population density impact on the formation of canopy urban heat islands through traverse survey in the Nagpur urban area. India. Journal of Urban Planning and Development, 142(1).

  • Lazzarini, M., Marpu, P. R., & Ghedira, H. (2013). Temperature-land cover interactions: the inversion of urban heat island phenomenon in desert city areas. Remote Sensing of Environment, 130, 136–152.

    Google Scholar 

  • Li, Q., Zhang, H., Liu, X., & Huang, J. (2004). Urban heat island effect on annual mean temperature during the last 50 years in China. Theoretical and Applied Climatology, 79, 165–174.

    Google Scholar 

  • Li, J. J., Wang, X. R., Wang, X. J., Ma, W. C., & Zhang, H. (2009). Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecological Complexity, 6, 413–420.

    Google Scholar 

  • Li, Y., Zhang, H., & Kainz, W. (2012). Monitoring patterns of urban heat islands of the fast growing Shanghai metropolis, China: using time-series of Landsat TM/ETM+ data. International Journal of Applied Earth Observation and Geoinformation, 19, 127–138.

    Google Scholar 

  • Lo, C. P., & Quattrochi, D. A. (2003). Land-use and land-cover change, urban heat island phenomenon, and health implications: a remote sensing approach. Photogrammetric Engineering and Remote Sensing, 69(9), 1053–1063.

    Google Scholar 

  • Lo, C. P., Quattrochi, D. A., & Luvall, J. C. (1997). Application of high resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International Journal of Remote Sensing, 18, 287–304.

    Google Scholar 

  • Ma, Y., Kuang, Y., & Huang, N. (2010). Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery. International Journal of Applied Earth Observation and Geoinformation, 12, 110–118.

    Google Scholar 

  • Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM. The Journal of Indian Geophysical Union, 12, 131–140.

    Google Scholar 

  • Mallick, J., Singh, C. K., Shashtri, S., Rahman, A., & Mukherjee, S. (2012). Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi City. International Journal of Applied Earth Observation and Geoinformation, 19, 348–358.

    Google Scholar 

  • Manzar, A. (2013). Government of India, Ministry of Water Resources, Central Ground Water Board. Ground Water Information, Nagpur District, Maharashtra. http://cgwb.gov.in/District_Profile/Maharashtra/Nagpur.pdf. Accessed 28 Sep 2018.

  • Oke, T. R. (1973). City size and the urban heat island. Atmospheric Environment, 7, 769–779.

    Google Scholar 

  • Oke, T. R., & East, C. (1971). The urban boundary layer in Montreal. Boundary-Layer Meteorology, 1, 411–437.

    Google Scholar 

  • Oke, T. R., Johnson, G. T., Steyn, D. G., & Watson, I. D. (1991). Simulation of surface urban heat islands under ‘ideal’ conditions at night. Part 2: diagnosis of causation. Boundary-Layer Meteorology, 56, 339–358.

    Google Scholar 

  • Omran, E. E. (2012). Detection of land-use and surface temperature change at different resolutions. Journal of Geographic Information System, 4, 189–203. https://doi.org/10.4236/jgis.2012.43024.

    Article  Google Scholar 

  • Pan, J. (2015). Area delineation and spatial-temporal dynamics of urban heat island in Lanzhou City, China using remote sensing imagery. Journal of the Indian Society of Remote Sensing, 44(1), 111–127. https://doi.org/10.1007/s12524-015-0477-x.

    Article  Google Scholar 

  • Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 4(2), 439–473.

    Google Scholar 

  • Purevdorj, T. S., Tateishi, R., Ishiyama, T., & Honda, Y. (1998). Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 19(18), 3519–3535.

    Google Scholar 

  • Qiao, Z., Tian, G., & Xiao, L. (2013). Diurnal and seasonal impacts of urbanization on the urban thermal environment: a case study of Beijing using MODIS data. ISPRS Journal of Photogrammetry and Remote Sensing, 85, 93–101.

    Google Scholar 

  • Roth, M. (2013). Urban heat islands. Chapter 11. In H. J. Fernando (Ed.), Handbook of environmental fluid dynamics (Vol. 2, pp. 143–159). Boca Raton: CRC, Taylor & Francis Group ISBN: 978-1-4665-5601-0.

    Google Scholar 

  • Santamouris, M. (2015). Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions. Science of the Total Environment, 512–513, 582–598.

    Google Scholar 

  • Sharma, R., & Joshi, P. K. (2013). Monitoring urban landscape dynamics over Delhi (India) using remote sensing (1998–2011) inputs. Journal of the Indian Society of Remote Sensing, 41(3), 641–650.

    Google Scholar 

  • Sharma, R., & Joshi, P. K. (2014). Identifying seasonal heat islands in urban settings of Delhi (India) using remotely sensed data—an anomaly based approach. Urban Climate, 9, 19–34.

    Google Scholar 

  • Sharma, R., & Joshi, P. K. (2016). Mapping environmental impacts of rapid urbanization in the National Capital Region of India using remote sensing inputs. Urban Climate, 15(70), 82.

    Google Scholar 

  • Sharma, R., Chakraborty, A., & Joshi, P. K. (2015). Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India). Environmental Monitoring and Assessment, 187(1), 4206.

    Google Scholar 

  • Sidiqui P., Huete A., & Devadas R. (2016). Spatio-temporal mapping and monitoring of urban heat island patterns over Sydney, Australia using MODIS and Landsat-8. Fourth International Workshop on Earth Observation and Remote Sensing Applications.

  • Singh, R. B., & Grover, A. (2015). Spatial correlations of changing land use, surface temperature (UHI) and NDVI in Delhi using Landsat satellite images. In R. B. Singh (Ed.), Urban development challenges, risks and resilience in Asian megacities (pp. 83–98). Tokyo: Springer.

    Google Scholar 

  • Sobrino, J. A., Oltra-Carrió, R., Sòria, G., Bianchi, R., & Paganini, M. (2012). Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sensing of Environment, 117, 50–56.

    Google Scholar 

  • Streutker, D. R. (2002). A remote sensing study of the urban heat island of Houston, Texas. International Journal of Remote Sensing, 23(13), 2595–2608.

    Google Scholar 

  • Sultana, S., & Satyanarayana, A. N. V. (2018). Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization. International Journal of Remote Sensing, 39, 6692–6730. https://doi.org/10.1080/01431161.2018.1466072.

    Article  Google Scholar 

  • Swain, D., Roberts, G. J., Dash, J., Lekshmi, K., Vinoj, V., & Tripathy, S. (2017). Impact of rapid urbanization on the city of Bhubaneswar, India. Proceedings of the National Academy of Sciences, India, Section A: Physical Sciences, 87, 845. https://doi.org/10.1007/s40010-017-0453-7.

    Article  Google Scholar 

  • Tang, J., Di, L., Xiao, J., Lu, D., & Zhou, Y. (2017). Impacts of land use and socioeconomic patterns on urban heat Island. International Journal of Remote Sensing, 38(11), 3445–3465.

    Google Scholar 

  • United Nations. (2014). World urbanization prospectus: the 2014 revision. Department of Economic and Social Affairs. Accessed 23 Feb 2018. https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.pdf.

  • Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114, 449–465.

    Google Scholar 

  • Voogt, J. A. (2000). Image representations of complete urban surface temperatures. Geocarto International, 15(3), 21–32.

    Google Scholar 

  • Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86, 370–384.

    Google Scholar 

  • Vyas, A., Shastri, B., & Joshi, Y. (2014). Spatio-temporal analysis of UHI using geo-spatial techniques: a case study of Ahmedabad city. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, XL–8, 997–2014 ISPRS Technical Commission VIII Symposium.

    Google Scholar 

  • Wang, W. C., Zeng, Z., & Karl, T. R. (1990). Urban heat islands in China. Geophysical Research Letters, 17, 2377–2380.

    Google Scholar 

  • Wang, J., Meng, B., Fu, D., Pei, T., & Xu, C. (2018). Mapping spatiotemporal patterns and multi-perspective analysis of the surface urban heat islands across 32 major cities in China. ISPRS International Journal of Geo-Information, 7, 207. https://doi.org/10.3390/ijgi7060207.

    Article  Google Scholar 

  • Weng, Q. (2001). A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22, 1999–2014.

    Google Scholar 

  • Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends (review article). ISPRS Journal of Photogrammetry and Remote Sensing, 64, 335–344.

    Google Scholar 

  • Weng, Q., Lub, D., & Schubringa, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 467–483.

    Google Scholar 

  • WHO. (2014). World Health Organisation, global report on Urban Health: equitable, healthier cities for sustainable development. ISBN 978 92 4 156527 1. Accessed 22 Apr 2018. http://www.who.int/gho/urban_health/situation_trends/urban_population_growth_text/en/.

  • Yue, W., Xu, J., Tan, W., & Xu, L. (2007). The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28(15), 3205–3226.

    Google Scholar 

  • Zhang, Y., Odeh, I. O. A., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11, 256–264.

    Google Scholar 

  • Zhang, P., Imhoff, M. L., Wolfe, R. E., & Bounoua, L. (2010). Characterizing urban heat islands of global settlements using MODIS and night-time lights products. Canadian Journal of Remote Sensing, 36(3), 185–196.

    Google Scholar 

  • Zhang, Y., Yiyun, C., Qing, D., & Jiang, P. (2012). Study on urban heat island effect based on normalized difference vegetated index: a case study of Wuhan City. Procedia Environmental Sciences, 8, 574–581.

    Google Scholar 

Download references

Acknowledgements

Sabiha is thankful to the Indian Institute of Technology Kharagpur for facilitating the research as a part of my PhD work and providing the MHRD, Government of India, fellowship. The authors are also grateful to the USGS Earth Resources Observation and Science (EROS) data centre for freely providing Landsat 7 datasets used in the study. We are thankful to the anonymous reviewers for their suggestions in improving the quality of the manuscript.

Author information

Authors and Affiliations

  1. Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology, Kharagpur, 721302, India

    Sabiha Sultana & A. N. V. Satyanarayana

Authors
  1. Sabiha Sultana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. N. V. Satyanarayana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. N. V. Satyanarayana.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Terrestrial and Ocean Dynamics: India Perspective

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sultana, S., Satyanarayana, A.N.V. Impact of urbanisation on urban heat island intensity during summer and winter over Indian metropolitan cities. Environ Monit Assess 191 (Suppl 3), 789 (2019). https://doi.org/10.1007/s10661-019-7692-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-019-7692-9

Keywords

Search

Navigation