Approximation of surface urban heat island (SUHI) effect over four populated cities of Andhra Pradesh state of India using ground and satellite data

Authors

  • Jagadish Kumar Mogaraju International Union for Conservation of Nature Commission on Ecosystem Management, Agro-ecosystems, India Author

Keywords:

SUHI, Urban heat island, Climate, Temperature, Population

Abstract

This work reports the probable effect of population ingress in generating urban heat islands over four cities of Andhra Pradesh state of India. We identified twenty-six UHIs and chose four UHIs based on the population data. The period from 1961 to 1990 was selected as the reference period, and 2003 to 2020 was selected as the study period to determine the deviations in mean temperatures. We framed a methodology to filter and select the UHIs to know the effect of SUHI using online resources for data and plots that are free of cost and user-friendly. One out of four UHIs exhibited a stronger deviation in night-time temperatures than in day-time temperatures. We observed that the rise in population equally contributes to the temperature deviations over the long term in the selected areas. We conclude that the population ingress may influence land surface temperatures and induce the SUHI effect.

References

1. Sannigrahi, S., Rahmat, S., Chakraborti, S., Bhatt, S., & Jha, S. (2017). Changing dynamics of urban biophysical composition and its impact on urban heat island intensity and thermal characteristics: the case of Hyderabad City, India. Modeling Earth Systems and Environment, 3, 647-667. https://doi.org/10.1007/s40808-017-0324-x

2. Bhanage, V., Kulkarni, S., Sharma, R., Lee, H. S., & Gedam, S. (2023). Enumerating and modelling the seasonal alterations of surface Urban heat and cool Island: a case study over Indian cities. Urban Science, 7(2), 38. https://doi.org/10.3390/urbansci7020038

3. Sharma, R., Pradhan, L., Kumari, M., & Bhattacharya, P. (2021). Assessing urban heat islands and thermal comfort in Noida City using geospatial technology. Urban Climate, 35, 100751. https://doi.org/10.1016/j.uclim.2020.100751

4. Vani, M., & Prasad, P. R. C. (2020). Assessment of spatio-temporal changes in land use and land cover, urban sprawl, and land surface temperature in and around Vijayawada city, India. Environment, Development and Sustainability, 22, 3079-3095. https://doi.org/10.1007/s10668-019-00335-2

5. Jafarı-Sırızı, R., Oshnooeı-Nooshabadı, A., Khabbazı-Kenarı, Z., & Sadeghı, A. (2022). Determination of the Quality of Life using Hybrid BWM-TOPSIS Analysis: Case study of Tabriz (District 1, 2, 3 and 8), Iran. Türkiye Uzaktan Algılama Dergisi, 4(1), 7-17. https://doi.org/10.51489/tuzal.1066578

6. Khamesi-Maybodi, M. H. (2022). GIS-based assessment of land surface temperature changes over Khorramabad City (Lorestan, Iran). Türkiye Uzaktan Algılama Dergisi, 4(2), 87-95. https://doi.org/10.51489/tuzal.1116553

7. Yamak, B., Yağcı, Z., Bilgilioğlu, B. B., & Çömert, R. (2021). Investigation of the effect of urbanization on land surface temperature example of Bursa. International Journal of Engineering and Geosciences, 6(1), 1-8. https://doi.org/10.26833/ijeg.658377

8. Mushore, T., Odindi, J., & Mutanga, O. (2022). “Cool” roofs as a heat-mitigation measure in urban heat islands: A comparative analysis using Sentinel 2 and Landsat data. Remote Sensing, 14(17), 4247. https://doi.org/10.3390/rs14174247

9. García, D. H., & Díaz, J. A. (2021). Modeling of the urban heat island on local climatic zones of a city using Sentinel 3 images: Urban determining factors. Urban Climate, 37, 100840. https://doi.org/10.1016/j.uclim.2021.100840

10. Bagyaraj, M., Senapathi, V., Karthikeyan, S., Chung, S. Y., Khatibi, R., Nadiri, A. A., & Lajayer, B. A. (2023). A study of urban heat island effects using remote sensing and GIS techniques in Kancheepuram, Tamil Nadu, India. Urban Climate, 51, 101597. https://doi.org/10.1016/j.uclim.2023.101597

11. Chakraborty, T., & Lee, X. (2019). A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. International Journal of Applied Earth Observation and Geoinformation, 74, 269-280. https://doi.org/10.1016/j.jag.2018.09.015

12. Historical Weather API | Open-Meteo.com. (2023). https://open-meteo.com/en/docs/historical-weather-api

13. Historical Meteo Graphs | Jan Kühn. (2023). https://yotka.org/projects/meteo-hist

14. Neog, R., Acharjee, S., & Hazarika, J. (2019). Evaluation of spatio-temporal pattern of surface urban heat island phenomena at Jorhat, India. Arabian Journal of Geosciences, 12, 316. https://doi.org/10.1007/s12517-019-4484-z

15. He, B. J., Wang, J., Zhu, J., & Qi, J. (2022). Beating the urban heat: Situation, background, impacts and the way forward in China. Renewable and Sustainable Energy Reviews, 161, 112350. https://doi.org/10.1016/j.rser.2022.112350

16. Muthiah, K., Mathivanan, M., & Duraisekaran, E. (2022). Dynamics of urban sprawl on the peri-urban landscape and its relationship with urban heat island in Chennai Metropolitan Area, India. Arabian Journal of Geosciences, 15(23), 1694. https://doi.org/10.1007/s12517-022-10959-w

17. Nikkala, S., Peddada, J. R., & Neredimelli, R. (2022). Correlation analysis of land surface temperature on landsat-8 data of Visakhapatnam Urban Area, Andhra Pradesh, India. Earth Science Informatics, 15(3), 1963-1975. https://doi.org/10.1007/s12145-022-00850-3

18. Wan, Z., Hook, S., & Hulley, G. (2015). MOD11A1 MODIS/Terra land surface temperature/emissivity daily L3 global 1km sın grid V006. NASA EOSDIS land processes distributed active archive center. https://doi.org/10.5067/MODIS/MOD11A1.006

19. Census tables | Government of India. (2023). https://censusindia.gov.in/census.website/data/census-tables

20. Kühn, J. (2023). MeteoHist - Historical Meteo Graphs. Python. https://github.com/yotkadata/meteo_hist

21. Sarp, G., Baydoğan, E., Güzel, F., & Otlukaya, T. (2021). Evaluation of the relationship between urban area and land surface temperature determined from optical satellite data: A case of Istanbul. Advanced Remote Sensing, 1(1), 1–8

22. Ustuner, M., Sanli, F. B., Abdikan, S., Esetlili, M. T., & Kurucu, Y. (2014). Crop type classification using vegetation indices of rapideye imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 195-198. https://doi.org/10.5194/isprsarchives-XL-7-195-2014

23. Guha, S., & Govil, H. (2021). Relationship between land surface temperature and normalized difference water index on various land surfaces: A seasonal analysis. International Journal of Engineering and Geosciences, 6(3), 165-173. https://doi.org/10.26833/ijeg.821730

24. Mogaraju, J. K. (2023). Estimation of surface urban heat island (SUHI) effect over four populated cities of Andhra Pradesh state of India. Advanced Engineering Days (AED), 8, 64-67.

Downloads

Published

2024-08-20

How to Cite

Approximation of surface urban heat island (SUHI) effect over four populated cities of Andhra Pradesh state of India using ground and satellite data. (2024). Advanced Engineering Science, 4(1), 93-102. https://yakarm.com/journals/ades/article/view/147

Share