The Thermal Footprint of Urbanisation: Surface Urban Heat Island and Land-Use Dynamics in Sakarya Province
Authors
Department of Environmental Engineering, Sakarya University, Sakarya, Türkiye (Türkiye)
Department of Environmental Engineering, Sakarya University, Sakarya, Türkiye (Türkiye)
Department of Environmental Engineering, Sakarya University, Sakarya, Türkiye (Türkiye)
Department of Environmental Engineering, Sakarya University, Sakarya, Türkiye (Türkiye)
Article Information
DOI: 10.51583/IJLTEMAS.2026.150900120
Subject Category: Surface Urban Heat Island
Volume/Issue: 15/9 | Page No: 2529-2540
Publication Timeline
Submitted: 2026-09-28
Accepted: 2026-10-03
Published: 2026-10-19
Abstract
This study investigates the spatial and temporal variation in Surface Urban Heat Island (SUHI) intensity and its relationship with land use across the districts of Adapazarı, Serdivan, Erenler, and Arifiye in Sakarya Province, Türkiye. The analysis draws on Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS Collection 2 Level-2 imagery acquired under comparable day-and-time conditions during June, July, and August of 2000, 2006, 2012, 2018, and 2024. Using geographic information systems (GIS), median composites were generated for each study period in the Google Earth Engine environment. Land Surface Temperature (LST), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Built-up Index (NDBI) were then calculated at a spatial resolution of 30 m. SUHI intensity was determined as the difference between the mean LST values of urban and rural areas. The findings show that built-up areas expanded over the study period, while agricultural and forested areas became increasingly fragmented. Mean LST increased from 31.77 °C in 2000 to 38.15 °C in 2024, whereas urban LST rose from 34.79 °C to 45.38 °C. Over the same period, rural LST increased from 29.84 °C to 36.11 °C, and SUHI intensity rose from 4.95 °C to 9.27 °C. The negative correlation between mean LST and NDVI (r=-0.42) and the positive correlation between mean LST and NDBI (r=0.48) highlight the contrasting effects of vegetation cover and built-up surfaces on the thermal environment. These findings underscore the need to conserve land cover, strengthen green infrastructure, and promote climate-sensitive urban development that accounts for the thermal impacts of the built environment in rapidly urbanizing, medium-sized cities. Future research using datasets with higher temporal resolution may yield more precise results. Nevertheless, this study provides a useful case study for examining SUHI dynamics and developing climate-resilient planning approaches in medium-sized cities with similar characteristics.
Keywords
Surface urban heat island, land surface temperature, Landsat, NDVI, NDBI, green spaces.
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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References
1. Arsiso, B. K. (2025). Urban land cover transformations and thermal dynamics through integrated LULC, UHI, and ecological vulnerability assessment using remote sensing indices in the City of Addis Ababa, Ethiopia. Sustainable Cities and Society, 107017. 10.1016/j.scs.2025.107017 [Google Scholar] [Crossref]
2. Aydın, B. H. K., & Özüduru, B. H. (2025). Urban heat island effect of large-scale residential areas: A spatiotemporal analysis of Etimesgut district in Ankara. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 14(1), 435–463. https://doi.org/10.17798/bitlisfen.1596470 [Google Scholar] [Crossref]
3. Feng, L., Hussain, S., Pricope, N., Arshad, S., Tariq, A., Feng, L., & Mubeen, M. (2024). Seasonal Dynamics in Land Surface Temperature in Response to Land Use Land Cover Changes Using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 17, 17983-17997. 10.1109/JSTARS.2024.3466191 [Google Scholar] [Crossref]
4. Guha, S., Govil, H. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability 23, 1944–1963 (2021). https://link.springer.com/article/10.1007/s10668-020-00657-6 [Google Scholar] [Crossref]
5. Gunawardena, K. R., Wells, M. J., & Kershaw, T. (2017). Utilising green and bluespace to mitigate urban heat island intensity. Science of the total environment, 584, 1040-1055. [Google Scholar] [Crossref]
6. Güller, C., & Toy, S. (2024). The Impacts of Urban Morphology on Urban Heat Islands in Housing Areas: The Case of Erzurum, Turkey. Sustainability 2024, 16(2), 791; [Google Scholar] [Crossref]
7. https://doi.org/10.3390/su16020791 [Google Scholar] [Crossref]
8. Gümrükçüoğlu, M., 2015 Urbanization Effect on Urban Heat Island, A Case Study: Sakarya International journal of energy and enviıronment Volume 9, 83-89. [Google Scholar] [Crossref]
9. Gümüş, S. & Gümüş, M. (2025). Assessment of the impact of impervious surface increase on urban heat island and vegetation by remote sensing and statistical analysis: the case of Türkiye/Niğde city center (2013-2024). Turkish Journal of Remote Sensing 7(1):69-90. 10.51489/1663695 [Google Scholar] [Crossref]
10. Karaçam, M., Kabadayı, E.M., Akar, F.İ. et al. (2026). Spatial and temporal patterns of daytime surface urban heat Islands in Istanbul, Türkiye: insights from a decade of google earth engine analysis. Med. Geosc. Rev. 8, 55–70. https://doi.org/10.1007/s42990-026-00219-4 [Google Scholar] [Crossref]
11. Mekonnen, G. T., Berlie, A. B., Legesse, S. A., & Anteneh, M. (2025). Identifying the prominent cause of urban heat Island of Gondar City, Ethiopia: using GIS, Mann-Kendall test, Oke's algorithm and regression analysis. Theoretical and Applied Climatology, 156(6), 316. [Google Scholar] [Crossref]
12. Ng, Y.L., Lim, M.H., Huang, Y.F. et al. Thermal intensification and urban land cover change in Southeast Asian capitals: over three decades of satellite observations and trend analysis. npj Urban Sustain (2025). https://doi.org/ 10.1038/s42949-025-00315-8 [Google Scholar] [Crossref]
13. Rao, P., Tassinari, P., Torreggiani, D., (2023). Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data. Heliyon, 10.1016/j.heliyon.2023.e18423 [Google Scholar] [Crossref]
14. Rose, R. & Kucharik, C., (2026). Diverging temperature and apparent temperature-based urban heat island of a midsize city. Urban Climate, 10.1016/j.uclim.2026.103054. [Google Scholar] [Crossref]
15. Sharmin, T., Chappell, A., & Lannon, S. (2025). Spatio-temporal analysis of LST, NDVI and SUHI in a coastal temperate city using local climate zone. Energy and Built Environment. 6 (6), 1142-115. [Google Scholar] [Crossref]
16. Sinha, S., & Banerjee, A. (2025). A systematic review of land use and land cover changes and their influence on urban heat island dynamics. Discover Geoscience, 3(1), 253. [Google Scholar] [Crossref]
17. Sun, T., Sun, R., & Chen, L. (2020). The trend inconsistency between land surface temperature and near surface air temperature in assessing urban heat island effects. Remote sensing, 12(8), 1271. 10.3390/rs12081271 [Google Scholar] [Crossref]
18. Şahin, M. T., Hadimli, H., Çakır, Ç., Yasak, Ü., & Genişyürek, F. (2025). The role of urban landscape on land surface temperature: The case of Muratpaşa, Antalya. Land, 14(4), 663. [Google Scholar] [Crossref]
19. https://doi.org/10.3390/land14040663 [Google Scholar] [Crossref]
20. TÜİK. (2024). Address-based population registration system results, 2024. Turkish Statistical Institute. https://www.tuik.gov.tr [Google Scholar] [Crossref]
21. Ünsal, Ö. (2025). Land use change in Türkiye and determination of priority areas for urban heat island studies. Coğrafi Bilimler Dergisi, 23(1), 1–21. [Google Scholar] [Crossref]
22. Weiting, X., Qianlei, W., Junheng, Q., Jingbo, L , Sijie, Z., Bing. Q. (2025). Spatiotemporal dynamics of land surface temperature and its drivers within the local climate zone framework. Sustainable Cities and Society. 10.1016/j.scs.2025.106859 [Google Scholar] [Crossref]
23. 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(10), 1999–2014. https://doi.org/10.1080/713860788 [Google Scholar] [Crossref]
24. Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote sensing of environment, 86(3), 370-384. 10.1016/S0034-4257(03)00079-8 [Google Scholar] [Crossref]
25. Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., ... & Sobrino, J. A. (2018). Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing, 11(1), 48. https://doi.org/10.3390/rs11010048 [Google Scholar] [Crossref]
26. Zhou, D., Zhao, S., Liu, S., Zhang, L., & Zhu, C. (2021). Surface urban heat island in China's 32 major cities: Spatial patterns and drivers. Remote Sensing of Environment, 10.1016/j.rse.2014.05.017 [Google Scholar] [Crossref]
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