Examining the Relationship between Urban Sprawl and Heat Island Intensification Through a Sustainable Urban Management Approach

Document Type : Research Paper

Authors

1 Department of GIS, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

Urban sprawl is recognized as one of the irregular patterns of city growth and development. This phenomenon has detrimental consequences in the economic, social, and environmental domains. The present study investigates the relationship between the expansion of built-up areas and the increase in urban heat islands, which is one of the consequences of urban sprawl. To this end, Landsat satellite images were used to generate land surface temperature (LST) maps for Houston, United States, over the period from 2017 to 2023. Additionally, land cover maps of the study area were produced using a machine learning algorithm with an accuracy of 94.31%. Normalized Shannon entropy values of 0.9271 for 2017 and 0.9314 for 2023 indicate the high dispersion of built-up areas and the growth of urban sprawl during this period. The comparison of land cover and LST maps reveals a 3.84°C increase in the temperature of built-up areas over the study period. Furthermore, the temperature difference of 4.41°C between built-up areas and adjacent non-built areas highlights the intensity of heat islands caused by construction growth. The significant temperature rise due to construction and the impact of built-up areas on the formation of urban heat islands underscore the importance of effective management and planning to address the challenges of urban sprawl and promote sustainable development.

Keywords

Main Subjects

References

عموشاهی، س.؛ سلمان‌ماهینی، ع.؛ مرادی، ح.؛ میکائیلی تبریزی، ع. و گالان، ک. (1402). تحلیل اهمیت شاخص‌‌های توسعة پایدار شهری در ایران و مقایسة آن با شاخص‌های جهانی. آمایش سرزمین, 15(1)، 53 ـ 71.
Amoushahi, S., Salman Mahini, A., Moradi, H., Mikaeili Tabrizi, A., & Ghalan, K. (2023). Analysis of the Importance of Urban Sustainable Development Indicators in Iran and Comparison with Global Indicators. Journal of "Town and Country Planning", 15(1), 53-71. (in Persian)
Avdan, U. & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of sensors, 2016(1), 1480307.
Barman, S., Roy, D., Chandra Sarkar, B., Almohamad, H., & Abdo, H. G. (2024). Assessment of urban growth in relation to urban sprawl using landscape metrics and Shannon’s entropy model in Jalpaiguri urban agglomeration, West Bengal, India. Geocarto International, 39(1), 2306258.
Bendib, A., Dridi, H., & Kalla, M. I. (2017). Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, Eastern Algeria. Geocarto International, 32(5), 503-513.
Biney, E. & Boakye, E. (2021). Urban sprawl and its impact on land use land cover dynamics of Sekondi-Takoradi metropolitan assembly, Ghana. Environmental Challenges, 4, 100168.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Carneiro, E., Lopes, W., & Espindola, G. (2021). Linking urban sprawl and surface urban heat island in the Teresina–Timon conurbation area in Brazil. Land, 10(5), 516.
Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M. P., & Obrist, M. K. (2015). Impacts of urbanisation on biodiversity: the role of species mobility, degree of specialisation and spatial scale. Oikos, 124(12), 1571-1582.
Ding, Y., Jia, L., Wang, C., & Wang, P. (2024). Urban sprawl and its effects on water competition between building industry and residents: Evidence from 31 provinces in China. Water-Energy Nexus, 7, 26-38.
Estoque, R. C. & Murayama, Y. (2015). Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological indicators, 56, 205-217.
Ewing, R. (1997). Is Los Angeles-style sprawl desirable?. Journal of the American planning association, 63(1), 107-126.
Feng, Z., Wang, X., Yuan, J., Zhang, Y., & Yu, M. (2023). Changes in air pollution, land surface temperature, and urban heat islands during the COVID-19 lockdown in three Chinese urban agglomerations. Science of the Total Environment, 892, 164496.
Fu, S., Wang, L., Khalil, U., Cheema, A. H., Ullah, I., Aslam, B., Tariq, A., Aslam, M., & Alarifi, S. S. (2024). Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment. Physics and Chemistry of the Earth, Parts a/b/c, 135, 103682.
Hajilou, M., Meshkini, A., Mirehei, M., & Ghaedrahmati, S. (2023). Urban sprawl: Do its financial and economic benefits outweigh its costs for local governments?. GeoJournal, 88(2), 2325-2343.
Hanif, I. (2018). Impact of fossil fuels energy consumption, energy policies, and urban sprawl on carbon emissions in East Asia and the Pacific: A panel investigation. Energy strategy reviews, 21, 16-24.
Hislop, S., Jones, S., Soto-Berelov, M., Skidmore, A., Haywood, A., & Nguyen, T. H. (2018). Using landsat spectral indices in time-series to assess wildfire disturbance and recovery. Remote Sensing, 10(3), 460.
Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International journal of Applied earth Observation and Geoinformation, 10(1), 26-43.
Kamboj, S. & Ali, S. (2021). Urban sprawl of Kota city: A case study of urban heat island linked with electric consumption. Materials Today: Proceedings, 46, 5304-5314.
Kim, S. W. & Brown, R. D. (2021). Urban heat island (UHI) intensity and magnitude estimations: A systematic literature review. Science of the Total Environment, 779, 146389.
Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean journal of anesthesiology, 70(1), 22-26.
Ma, X. & Peng, S. (2021). Assessing the quantitative relationships between the impervious surface area and surface heat island effect during urban expansion. PeerJ, 9, e11854.
Nechyba, T. J. & Walsh, R. P. (2002). Urban sprawl. Journal of economic perspectives, 18 (4), 177-200.
Nuruzzaman, M.  (2015). Urban heat island: causes, effects and mitigation measures-a review. International Journal of Environmental Monitoring and Analysis, 3(2), 67-73.
Powers, D. M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061.
Rana, B., Bandyopadhyay, J., & Halder, B. (2024). Investigating the relationship between urban sprawl and urban heat island using remote sensing and machine learning approaches. Theoretical and Applied Climatology, 155(5), 4161-4188.
Rodríguez, M. C., Dupont-Courtade, L., & Oueslati, W. (2016). Air pollution and urban structure linkages: Evidence from European cities. Renewable and Sustainable Energy Reviews, 53, 1-9.
Shao, Z., Sumari, N. S., Portnov, A., Ujoh, F., Musakwa, W., & Mandela, P. J. (2021). Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data. Geo-Spatial Information Science, 24(2), 241-255.
Singh, R. & Kalota, D. (2019). Urban sprawl and its impact on generation of urban heat island: A case study of Ludhiana city. Journal of the Indian Society of Remote Sensing, 47(9), 1567-1576.
Syarif, I., Prugel-Bennett, A., & Wills, G. (2016). SVM parameter optimization using grid search and genetic algorithm to improve classification performance. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(4), 1502-1509.
Tatsumi, K., Yamashiki, Y., Torres, M. A. C., & Taipe, C. L. R. (2015). Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Computers and Electronics in Agriculture, 115, 171-179.
Youssef, A. (1999). Image downsampling and upsampling methods. National Institute of Standards and Technology.
 
 
 
Town and Country Planning
Volume 17, Issue 1
Spring & Summer
April 2025
Pages 1-17

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History

  • Receive Date: 23 October 2024
  • Revise Date: 30 November 2024
  • Accept Date: 07 January 2025

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