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Analysis of Urbanization Impact on Land Surface Temperature Variability by Using Landsat Imagery

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Abstract

Urbanization is one of the most important human-dominated activities that has impacted the Earth’s ecosystem, biodiversity, and regional climate. With the advent of COVID-19 in 2020, the imposition of complete lockdown in India significantly altered urban activities in India in terms of industrial activities, economic development, import–export and means of transport. Environmental experts anticipate substantial alterations in various environmental conditions due to the lockdown measures. With this view, this paper investigates the influence of different levels of urban activities on land surface temperature (LST) in major Indian cities: Bangalore, Delhi, Kolkata, and Mumbai. LST variations are analyzed across pre-COVID and COVID lockdown periods to understand the impact of urban dynamics on surface temperature. A total of 54 images from Landsat 8 sensor data, spanning 2018–2020, are utilized to capture changes in LST amidst varying levels of urban activities. Results indicate higher LST in 2018 and 2019, followed by a reduction in 2020 attributed to COVID-19 related restrictions. The observed decrease in LST during the lockdown underlines the influence of urban activities on surface temperature dynamics. This study underscores the importance of considering urban dynamics in environmental assessments and emphasizes the need for sustainable urban planning and extreme climate mitigation strategies.

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Data Availability

The data used in this study are publicly available from the USGS website.

Notes

  1. https://www.earthexplorer.usgs.gov.

  2. https://www.esri.com.

References

  1. Dash P, Göttsche F-M, Olesen F-S, Fischer H. Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. Int J Remote Sens. 2002;23(13):2563–94. https://doi.org/10.1080/01431160110115041.

    Article  Google Scholar 

  2. Bechtel B. A new global climatology of annual land surface temperature. Remote Sens. 2015;7(3):2850–70. https://doi.org/10.3390/rs70302850.

    Article  Google Scholar 

  3. Trigo IF, Monteiro IT, Olesen F, Kabsch E. An assessment of remotely sensed land surface temperature. J Geophys Res Atmos. 2008. https://doi.org/10.1029/2008JD010035.

    Article  Google Scholar 

  4. Li Z-L, Duan S-B. Land surface temperature. In: Comprehensive remote sensing. Elsevier; 2018. p. 264–83. https://doi.org/10.1016/B978-0-12-409548-9.10375-6

  5. Zhou S, Cheng J, Shi J. A physical-based framework for estimating the hourly all-weather land surface temperature by synchronizing geostationary satellite observations and land surface model simulations. IEEE Trans Geosci Remote Sens. 2022;60:1–22. https://doi.org/10.1109/TGRS.2022.3222563.

    Article  Google Scholar 

  6. ESA: Land Surface Temperature: The European Space Agency (2021) https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview/geophysical-measurements/land-surface-temperature. Accessed 15 Nov 2023

  7. Dar I, Qadir J, Shukla A. Estimation of LST from multi-sensor thermal remote sensing data and evaluating the influence of sensor characteristics. Ann GIS. 2019;25(3):263–81. https://doi.org/10.1080/19475683.2019.1623318.

    Article  Google Scholar 

  8. Parastatidis D, Mitraka Z, Chrysoulakis N, Abrams M. Online global land surface temperature estimation from Landsat. Remote Sens. 2017;9(12):1208. https://doi.org/10.3390/rs9121208.

    Article  Google Scholar 

  9. Reuter D, Richardson C, Pellerano F, Irons J, Allen R, Anderson M, Jhabvala M, Lunsford A, Montanaro M, Smith R, Tesfaye Z, Thome K. The Thermal Infrared Sensor (TIRS) on Landsat 8: design overview and pre-launch characterization. Remote Sens. 2015;7(1):1135–53. https://doi.org/10.3390/rs70101135.

    Article  Google Scholar 

  10. Wang L, Lu Y, Yao Y. Comparison of three algorithms for the retrieval of land surface temperature from Landsat 8 images. Sensors. 2019;19(22):5049. https://doi.org/10.3390/s19225049.

    Article  Google Scholar 

  11. Qin ZH, Zhang MH, Karnieli A, Berliner P. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data. Acta Geogr Sin. 2001;56(4):456–66.

    Google Scholar 

  12. Du C, Ren H, Qin Q, Meng J, Zhao S. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sens. 2015;7(1):647–65. https://doi.org/10.3390/rs70100647.

    Article  Google Scholar 

  13. Jimenez-Munoz JC, Cristobal J, Sobrino JA, Soria G, Ninyerola M, Pons X, Pons X. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Trans Geosci Remote Sens. 2009;47(1):339–49. https://doi.org/10.1109/TGRS.2008.2007125.

    Article  Google Scholar 

  14. Jin M, Li J, Wang C, Shang R. A practical split-window algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China. Remote Sens. 2015;7(4):4371–90. https://doi.org/10.3390/rs70404371.

    Article  Google Scholar 

  15. Zhao J, Tang B-H, Sima O. A nonlinear split-window algorithm for retrieving land surface temperatures from Fengyun-4B thermal infrared data. IEEE Trans Geosci Remote Sens. 2024;62:1–12. https://doi.org/10.1109/TGRS.2023.3348526.

    Article  Google Scholar 

  16. Arora S, Bhaukhandi KD, Mishra PK. Coronavirus lockdown helped the environment to bounce back. 2020. https://doi.org/10.1016/j.scitotenv.2020.140573.

  17. Levin K, Boehm S, Carter R, 6 Big findings from the IPCC. Report on Climate Impacts, Adaptation and Vulnerability. Washington: World Resources Institute; 2022. p. 1–12.

  18. El-Zeiny AM, Effat HA. Environmental monitoring of spatiotemporal change in land use/land cover and its impact on land surface temperature in el-fayoum governorate, egypt. Remote Sens Appl Soc Environ. 2017;8:266–77. https://doi.org/10.1016/j.rsase.2017.10.003.

    Article  Google Scholar 

  19. Li Z-L, Wu H, Duan S-B, Zhao W, Ren H, Liu X, Leng P, Tang R, Ye X, Zhu J, et al. Satellite remote sensing of global land surface temperature: definition, methods, products, and applications. Rev Geophys. 2023. https://doi.org/10.1029/2022RG000777.

    Article  Google Scholar 

  20. Zhang X, Chen W, Chen Z, Yang F, Meng C, Gou P, Zhang F, Feng J, Li G, Wang Z. Construction of cloud-free modis-like land surface temperatures coupled with a regional weather research and forecasting (wrf) model. Atmos Environ. 2022;283: 119190. https://doi.org/10.1016/j.atmosenv.2022.119190.

    Article  Google Scholar 

  21. Candy B, Saunders R, Ghent D, Bulgin C. The impact of satellite-derived land surface temperatures on numerical weather prediction analyses and forecasts. J Geophys Res Atmos. 2017;122(18):9783–802. https://doi.org/10.1002/2016JD026417.

    Article  Google Scholar 

  22. Zink M, Mai J, Cuntz M, Samaniego L. Conditioning a hydrologic model using patterns of remotely sensed land surface temperature. Water Resour Res. 2018;54(4):2976–98. https://doi.org/10.1002/2017WR021346.

    Article  Google Scholar 

  23. Silvestro F, Gabellani S, Delogu F, Rudari R, Boni G. Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the continuum model. Hydrol Earth Syst Sci. 2013;17(1):39–62. https://doi.org/10.5194/hess-17-39-2013.

    Article  Google Scholar 

  24. Sruthi S, Aslam MM. Agricultural drought analysis using the ndvi and land surface temperature data; a case study of Raichur district. Aquat Procedia. 2015;4:1258–64. https://doi.org/10.1016/j.aqpro.2015.02.164.

    Article  Google Scholar 

  25. Son NT, Chen C, Chen C, Chang L, Minh VQ. Monitoring agricultural drought in the lower Mekong basin using Modis ndvi and land surface temperature data. Int J Appl Earth Obs Geoinf. 2012;18:417–27. https://doi.org/10.1016/j.jag.2012.03.014.

    Article  Google Scholar 

  26. Silva JM, Damásio CV, Sousa AM, Bugalho L, Pessanha L, Quaresma P. Agriculture pest and disease risk maps considering msg satellite data and land surface temperature. Int J Appl Earth Obs Geoinf. 2015;38:40–50. https://doi.org/10.1016/j.jag.2014.12.016.

    Article  Google Scholar 

  27. Chen S-S, Chen X-Z, Chen W-Q, Su Y-X, Li D. A simple retrieval method of land surface temperature from amsr-e passive microwave data—a case study over southern china during the strong snow disaster of 2008. Int J Appl Earth Obs Geoinf. 2011;13(1):140–51. https://doi.org/10.1016/j.jag.2010.09.007.

    Article  Google Scholar 

  28. Ahmed B, Kamruzzaman M, Zhu X, Rahman MS, Choi K. Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sens. 2013;5(11):5969–98. https://doi.org/10.3390/rs5115969.

    Article  Google Scholar 

  29. Wikipedia: COVID-19 lockdown in India (2020). https://en.wikipedia.org/wiki/COVID-19_lockdown_in_India. Accessed 18 Feb 2024.

  30. Nigam R, Pandya K, Luis AJ, Sengupta R, Kotha M. Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India. Sci Rep. 2021;11(1):4285. https://doi.org/10.1038/s41598-021-83393-9.

    Article  Google Scholar 

  31. Buo I, Sagris V, Jaagus J. Gap-filling satellite land surface temperature over heatwave periods with machine learning. IEEE Geosci Remote Sens Lett. 2022;19:1–5. https://doi.org/10.1109/LGRS.2021.3068069.

    Article  Google Scholar 

  32. Qi Y, Zhong L, Ma Y, Fu Y, Wang X, Li P. Estimation of land surface temperature over the Tibetan plateau based on Sentinel-3 SLSTR data. IEEE J Sel Top Appl Earth Obs Remote Sens. 2023;16:4180–94. https://doi.org/10.1109/JSTARS.2023.3268326.

    Article  Google Scholar 

  33. Fu P, Weng Q. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with landsat imagery. Remote Sens Environ. 2016;175:205–14. https://doi.org/10.1016/j.rse.2015.12.040.

    Article  Google Scholar 

  34. Sekertekin A, Bonafoni S. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens. 2020;12(2):294. https://doi.org/10.3390/rs12020294.

    Article  Google Scholar 

  35. Nagihara S, Kiefer WS, Taylor PT, Williams DR, Nakamura Y. Examination of the long-term subsurface warming observed at the Apollo 15 and 17 sites utilizing the newly restored heat flow experiment data from 1975 to 1977. JGeophys Res Planets. 2018;123(5):1125–39. https://doi.org/10.1029/2018JE005579.

    Article  Google Scholar 

  36. Abbasi B, Qin Z, Du W, Li S, Fan J, Zhao S. Effects of cloud on land surface temperature (lst) change in thermal infrared remote sensing images: a case study of Landsat 8 Data. In: International geoscience and remote sensing symposium (IGARSS). 2020. p. 5430–5433. https://doi.org/10.1109/IGARSS39084.2020.9324415.

  37. Guha S, Govil H. COVID-19 lockdown effect on land surface temperature and normalized difference vegetation index. Geomat Nat Haz Risk. 2021;12(1):1082–100. https://doi.org/10.1080/19475705.2021.1914197.

    Article  Google Scholar 

  38. Mukherjee F, Singh D. Assessing land use-land cover change and its impact on land surface temperature using Landsat data: a comparison of two urban areas in india. Earth Syst Environ. 2020;4(2):385–407. https://doi.org/10.1007/s41748-020-00155-9.

    Article  Google Scholar 

  39. Barsi J, Schott J, Hook S, Raqueno N, Markham B, Radocinski R. Landsat-8 Thermal Infrared Sensor (TIRS) vicarious radiometric calibration. Remote Sens. 2014;6(11):11607–26. https://doi.org/10.3390/rs61111607.

    Article  Google Scholar 

  40. Emami H, Safari A, Mojaradi B. Fusion methods for land surface emissivity and temperature retrieval of the Landsat data continuity mission data. IEEE Trans Geosci Remote Sens. 2016;54(7):3842–55. https://doi.org/10.1109/TGRS.2016.2529422.

    Article  Google Scholar 

  41. Wang Y, Zhou J, Li M, Zhang X. Validation of landsat-8 tirs land surface temperature retrieved from multiple algorithms in an extremely arid region. In: 2016 IEEE international geoscience and remote sensing symposium (IGARSS). 2016. p. 6934–7. https://doi.org/10.1109/IGARSS.2016.7730809.

  42. Li S, Jiang G-M. Land surface temperature retrieval from Landsat-8 data with the generalized split-window algorithm. IEEE Access. 2018;6:18149–62. https://doi.org/10.1109/ACCESS.2018.2818741.

    Article  Google Scholar 

  43. Verichev K, Mikhaylyukova P, Salimova A, Salazar C, Carpio M. Accuracy assessment of the urban land surface temperature calculation based on landsat-8/oli data (case study: Coyhaique, Chile). In: IGARSS 2019—2019 IEEE international geoscience and remote sensing symposium. 2019. p. 7463–6. https://doi.org/10.1109/IGARSS.2019.8898233.

  44. Bangalore, India Metro Area Population 1950–2024. https://www.macrotrends.net/cities/21176/bangalore/population. Accessed 18 Dec 2023.

  45. Latitude and Longitude Finder (2024). https://www.latlong.net/. Accessed 18 Feb 2024.

  46. Average weather in March in Bangalore (Karnataka), India (2023). https://weather-and-climate.com/bangalore-March-averages-fahrenheit. Accessed 18 Feb 2024.

  47. Delhi, India Metro Area Population 1950–2024. https://www.macrotrends.net/cities/21228/delhi/population. Accessed 18 Feb 2024.

  48. Wikipedia: Climate of Delhi (2022). https://en.wikipedia.org/wiki/Climate_of_Delhi. Accessed 18 Feb 2024.

  49. Calcutta, India Metro Area Population 1950–2024. https://www.macrotrends.net/cities/21211/calcutta/population. Accessed 18 Feb 2024.

  50. Wikipedia: Climate of Kolkata (2022). https://en.wikipedia.org/wiki/Climate_of_Kolkata. Accessed 18 Feb 2024.

  51. Mumbai, India Metro Area Population 1950–2024. https://www.macrotrends.net/cities/21206/mumbai/population. Accessed 18 Feb 2024.

  52. Wikipedia: Climate of Mumbai (2022). https://en.wikipedia.org/wiki/Climate_of_Mumbai. Accessed 18 Feb 2024.

  53. Meng F, Xiao Q. Landsat 8 image-based analysis of the urban heat island characteristics in Jinan, China. In: IGARSS 2018—2018 IEEE international geoscience and remote sensing symposium. IEEE; 2018. p. 2976–9. https://doi.org/10.1109/IGARSS.2018.8517305.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Cape Town, Private Bag X3, Rondebosch, 7700, South Africa

    Vikash Kumar Mishra

  2. Department of Information Technology, Indian Institute of Information Technology, Allahabad, Prayagraj, UP, 211015, India

    Kamlesh Kumar Verma & Triloki Pant

  3. Department of Computer Applications, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan, 303007, India

    Govind Murari Upadhyay, Pangambam Sendash Singh & Pramod Kumar Soni

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Contributions

Vikash Kumar Mishra: conceptualization, data collection and analysis. Kamlesh Kumar Verma: experimentation, original draft preparation. Triloki Pant: supervision of the whole study. Govind Murari Upadhyay: data and result interpretation. Pangambam Sendash Singh: critical revision of the manuscript. Pramod Kumar Soni: problem formulation, administrative support. All authors read and approved the final version of the manuscript.

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Correspondence to Pramod Kumar Soni.

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Mishra, V.K., Verma, K.K., Pant, T. et al. Analysis of Urbanization Impact on Land Surface Temperature Variability by Using Landsat Imagery. SN COMPUT. SCI. 5, 863 (2024). https://doi.org/10.1007/s42979-024-03226-0

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