A geospatial analysis of land surface temperature and surface urban heat island in Tomsk City: a study based on Landsat 8 satellite imagery | Geosphere Research. 2025. № 3. DOI: 10.17223/25421379/36/11

A geospatial analysis of land surface temperature and surface urban heat island in Tomsk City: a study based on Landsat 8 satellite imagery

An urban heat island defines a specific microclimate, where urban areas experience higher temperatures relative to surrounding rural or suburban areas. This phenomenon is linked to reduced wind speeds, alterations in wind direction, reduced urban ventilation and the accumulation of air pollutants within cities, resulting in negative health outcomes to urban residents. In recent years, advancements in thermal remote sensing technologies and the implementation of open data initiatives have prompted numerous investigations into the surface urban heat island. This study aims to investigate the geospatial distribution of land surface temperature and the surface urban heat island over Tomsk City, during the summer season of 2023. To achieve this, the research utilizes remote sensing imagery obtained from the Thermal Infrared Sensor 1 (TIRS1) and the Operational Land Imager (OLI), both instruments onboard the Landsat 8 Satellite. By applying geospatial analysis and modern satellite remote sensing techniques, including the surface urban heat island index method and zonal statistics, the study aims to; quantify the intensity and spatial extent of surface urban heat island; identify urban hotspots of land surface temperature anomalies and their spatial distribution over land cover and land use classes. The findings indicate a significant variation in land surface temperature and the surface urban heat island across the research area, with higher land surface temperature and more pronounced surface urban heat island effect predominantly observed in densely built-up areas. Specifically, 83 % of the urban hot spots exhibiting high land surfaces temperature anomalies were observed in densely built-up areas that is urban areas and 16% on bare soil surfaces. Notably, no surface urban heat island was observed in 54 % of the study area, wherein the corresponding major land use and land cover class was vegetation. The mean surface urban heat island intensity, herein the difference between the averages of land surface temperatures between urban and suburban areas ranged between 2°C to 3 °C. The findings of the study provide valuable insights into the dynamics of land surface temperature and the surface urban heat island effect in Tomsk City. The observed relationships between land surface temperature and surface urban heat island with specific land use and land covers emphasises that vegetation and water surfaces are crucial for reducing urban temperatures and creating favourable microclimate conditions which may consequently increase thermal comfort, and decrease energy consumption. The author declares no conflicts of interests.

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Keywords

land surface temperature, surface urban heat island, urban heat island, Landsat 8, urban climate, land use and land cover

Authors

NameOrganizationE-mail
Marimira ClideTomsk State Universitymarimira.clyde@gmail.com
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References

Almeida C.R. de, Teodoro A.C., Gonsalves A. Study of the urban heat island (Uhi) using remote sensing data/techniques: A systematic review // Environments - MDPI. 2021. V. 8, No. 10. doi: 10.3390/environments8100105.
Badugu A. et al. Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques // Geod Geodyn. 2023. V. 14. No. 3. pp. 275-291. doi: 10.1016/j.geog.2022.10.004.
Chapman S. et al. The impact of urbanization and climate change on urban temperatures: a systematic review // Landsc Ecol. 2017. V. 32, No. 10. С. doi: 1921-1935. doi: 10.1007/s10980-017-0561-4.
Cleland S.E. et al. Urban heat island impacts on heat-related cardiovascular morbidity: A time series analysis of older adults in US metropolitan areas // Environ Int. 2023. V. 178. pp. 108005. doi: 10.1016/j.envint.2023.108005.
Costanzini S. et al. Identification of SUHI in Urban Areas by Remote Sensing Data and Mitigation Hypothesis through Solar Reflective Materials // Atmosphere (Basel). 2022. V. 13. 1. doi: 10.1175/JAMC-D-17-.
Crum S.M., Jenerette G.D. Microclimate Variation among Urban Land Covers: The Importance of Vertical and Horizontal Structure in Air and Land Surface Temperature Relationships // Journal of Applied Meteorology and Climatology. 2017. V. 56, No. 9. pp. 25312543. doi: 10.1175/JAMC-D-17-.
Diem P.K. et al. Remote sensing for urban heat island research: Progress, current issues, and perspectives // Remote Sens Appl. 2024. V. 33. doi: 10.1016/j.rsase.2023.101081.
Dudorova N.V., Belan B.D. Ocenka faktorov, opredeljajushhih formirovanie gorodskogo ostrova tepla v Tomske [Estimation of factors determining formation of the urban heat island in Tomsk] // Optika atmosfery i okeana [Atmospheric and ocean optics]. 2016. V. 29, No. 5. pp. 426-436. In Russian. doi: 10.15372/AOO20160510.
Garcia D.H., Riza M., Diaz J. A. Land Surface Temperature Relationship with the Land Use/Land Cover Indices Leading to Thermal Field Variation in the Turkish Republic of Northern Cyprus // Earth Systems and Environment. 2023. V. 7, No. 2. pp. 561-580. doi: 10.1007/s41748-023-00341-5.
Gazimov T.F., Kuzhevskaya I. V. Ocenka letnegopoverhnostnogo gorodskogo ostrova tepla goroda Novosibirskpo dannym Landsat 8 [Assessment of the summer surface urban heat island of the city of Novosibirsk according to Landsat 8 data] // Geograficheskiy vestnik [Geographical Bulletin]. 2021. V. 4, No. 59. pp. 84-98. In Russian. doi: 10.17072/2079-7877-2021-4-84-98.
Gore R.W. et al. Analiz zemlepol'zovaniya s ispol'zovaniem nekontroliruemoy klassifikacii [LULC-Analysis of land-use with the help of unsupervised classification] // Izvestiya SFedU. Engineering sciences. 2020. No. 3. pp. 184-192. In Russian. doi: 10.18522/2311-3103-2020-3-184-192.
Hidalgo-Garcia D., Arco-Diaz J. Modeling the Surface Urban Heat Island (SUHI) to study of its relationship with variations in the thermal field and with the indices of land use in the metropolitan area of Granada (Spain) // Sustain Cities Soc. 2022. V. 87. doi: 10.1016/j.scs.2022.104166.
Hsu A. et al. Disproportionate exposure to urban heat island intensity across major US cities // Nature Communications 2021 12:1. 2021. V. 12, No. 1. pp. 1-11. doi: 10.1038/s41467-021-22799-5.
Karyati N.E. et al. Application of Landsat-8 OLI/TIRS to assess the Urban Heat Island (UHI) // IOP Conference Series: Earth and Environmental Science.: Institute of Physics, 2022. doi: 10.1088/1755-1315/1109/1/012069.
Khorrami B., Gunduz O. Spatio-temporal interactions of surface urban heat island and its spectral indicators: a case study from Istanbul metropolitan area, Turkey // Environ Monit Assess. 2020. V. 192, No. 6. doi: 10.1007/S10661-020-08322-1.
Korniyenko S., Dikareva E. Optical Remote Sensing for Urban Heat Islands Identification; 2022; Construction of Unique Buildings and Structures // Construction of Unique Buildings and Structures. 2022. V. 104. No. 10404. doi: 10.4123/CUBS.104.4.
Le M.T., Bakaeva N.A. Technique for Generating Preliminary Satellite Data to Evaluate SUHI Using Cloud Computing: A Case Study in Moscow, Russia // Remote Sens (Basel). 2023. V. 15, No. 13. doi: 10.3390/rs15133294.
Li X., Chakraborty T.C., Wang G.Comparing land surface temperature and mean radiant temperature for urban heat mapping in Philadelphia // Urban Clim. 2023. V. 51. doi: 10.1016/j.uclim.2023.101615.
Liou Y.A., Tran D.P., Nguyen K.A. Spatio-temporal patterns and driving forces of surface urban heat island in Taiwan // Urban Clim. 2024. V. 53. pp. 101806. doi: 10.1109/TGRS.2023.3285912.
Liu S. et al. Land Use and Land Cover Mapping in China Using Multimodal Fine-Grained Dual Network // IEEE Transactions on Geoscience and Remote Sensing. 2023. V. 61. pp. 1-19. doi: 10.1109/TGRS.2023.3285912.
Moharram M., Sundaram D. Land Use and Land Cover Classification with Hyperspectral Data: A comprehensive review of methods, challenges and future directions // Neurocomputing. 2023. V. 536.
Official Portal of «The city of Tomsk»http://en.admin.tomsk.ru/ [Electronic resource]. URL: http://en.admin.tomsk.ru/ (Date of accessed: 01.06.2024).
Peacock R. Accuracy assessment of supervised and unsupervised classification using Landsat imagery of little rock, Arkansas a thesis presented to the department of humanities and social sciences in candidacy for the degree of Master of Science, 2014.
Rakhmanova L. et al. Perspectives of climate change: A comparison of scientific understanding and local interpretations by different Western Siberian communities // Ambio. 2021. V. 50. No. 11. pp. 2072-2089. doi: 10.1007/s13280.
Santamouris M. et al. On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings - A review // Energy Build. 2015. V. 98. pp. 119-124.
Siswanto S. et al. Spatio-temporal characteristics of urban heat Island of Jakarta metropolitan // Remote Sens Appl. 2023. V. 32. doi: 10.1016/j.rsase.2023.101062.
Svarovsky A.I., Starchenko A.V. Application of a Weather Research and Forecasting model to study the urban heat island in Tomsk // Journal of Physics: Conference Series.: IOP Publishing Ltd, 2021b.
Svarovsky A.I., Starchenko A.V. Primenenie modeli weather research and forecasting dlya issledovaniya yavleniya «ostrov tepla» dlya usloviy goroda Tomsk // XVIII mezhdunarodnaja konferenciya studentov, aspirantov i molodyh uchenyh «perspektivy razvitiya fundamental"nyh nauk>) : Nacional’niy issledovatel'skiy Tomskiy politehnicheskiy universitet . 2021a. V. 3. pp. 76-78. In Russian.
Tepanosyan G. et al. Studying spatial-temporal changes and relationship of land cover and surface Urban Heat Island derived through remote sensing in Yerevan, Armenia // Build Environ. 2021.
Thammaboribal P. Investigating Land Surface Temperature Variation and Land Use Land Cover Changes in Pathumthani, Thailand (1997-2023) using Landsat Satellite Imagery: A Comprehensive Analysis of LST and Urban Hot Spots (UHS) // International Journal of Geoinformatics. 2024. V. 20. No. 2. pp. 27-41.
Thanush Kodi K., Babykalpana Y. Supervised/ Unsupervised Classification of LULC using remotely Sensed Data for Coimbatore city, India, 2010. 975-8887 p.
U.S. Geological Survey. Landsat 8 metadata [Электронный ресурс]. 2023. URL: https://earthexplorer.usgs.gov (Date of accessed: 14.03.2024).
Young N. E. et al. A survival guide to Landsat pre-processing // Ecology. 2017. V. 98. No. 4. pp. 920-932.
Zahir I. L. M. Application of Geo-informatics Technology to Access the Surface Temperature Using LANDSAT 8 OLI/TIRS Satellite Data: A Case Study in Ampara District in Sri Lanka. 2020.
Zhang J., Tu L., Shi B. Spatiotemporal Patterns of the Application of Surface Urban Heat Island Intensity Calculation Methods // Atmosphere (Basel). 2023. V. 14. No. 10.
 A geospatial analysis of land surface temperature and surface urban heat island in Tomsk City: a study based on Landsat 8 satellite imagery | Geosphere Research. 2025. № 3. DOI: 10.17223/25421379/36/11

A geospatial analysis of land surface temperature and surface urban heat island in Tomsk City: a study based on Landsat 8 satellite imagery | Geosphere Research. 2025. № 3. DOI: 10.17223/25421379/36/11

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