Reassessing Urban Heat Island Intensity Using Vegetation-Based References in a Semi-Arid City: The Case of Diyarbakır

Abstract

The Urban Heat Island (UHI) effect has intensified due to rapid urbanization and the expansion of impervious surfaces, a phenomenon particularly critical in semi-arid cities. This study investigates the relationship between Land Surface Temperature (LST) and vegetation density (NDVI) during the 2025 summer season (May–September) in the Diyarbakır metropolitan area, Türkiye. Landsat 8–9 thermal data and Sentinel-2 imagery were processed on the Google Earth Engine platform using spatial and statistical analyses. Quantitative results reveal that the mean surface temperature in urban areas was 53.7°C, compared to 45.6°C in vegetated rural reference areas. The study identified a maximum mean UHI intensity of 8.1°C during the peak summer month of July. Furthermore, regression analysis demonstrated a robust negative correlation between NDVI and LST (R² = 0.757), indicating that densely vegetated zones (NDVI > 0.3) were up to 10–15°C cooler than the urban core. These findings underscore the pivotal function of urban green spaces in moderating UHI intensity and provide a scientific basis for thermal comfort strategies in semi-arid urban contexts.

Keywords

• Urban Heat Island
• Google Earth Engine
• Land Surface Temperature
• NDVI
• Remote Sensing

References

1. Arıkan, D., & Yıldız, F. (2023). Investigation of urban heat island and carbon monoxide change using Google Earth Engine in Konya. NÖHÜ Mühendislik Bilimleri Dergisi, 12(4), 1185-1193. https://doi.org/10.28948/ngumuh.1279129
2. Arifin, S., Hamzah, B., Mulyadi, R., & Rasyid, A. (2022). Effects of vegetation on urban heat island using Landsat 8 OLI/TIRS imagery in tropical urban climate. Civil Engineering and Architecture, 10, 395-405. https://doi.org/10.13189/cea.2022.100134
3. Ayalke, Z., & Şişman, A. (2024). Google Earth Engine kullanılarak makine öğrenmesi tabanlı iyileştirilmiş arazi örtüsü sınıflandırması: Atakum, Samsun örneği. Geomatik, 9(3), 375-390. https://doi.org/10.29128/geomatik.1472160
4. Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214. https://doi.org/10.1038/sdata.2018.214
5. Bovkır, R. (2025). İstanbul’da kentsel yeşil altyapı için çatı tarımı potansiyelinin CBS tabanlı karar analizi ile değerlendirilmesi. Geomatik, 10(1), 45-58. https://doi.org/10.29128/geomatik.1517186
6. Duymuş, H., Seyhan, S., Kavlak, M. Ö., & Çabuk, A. (2025). Kentsel ısı adası etkisinin mekânsal ve zamansal değişimlerle ilişkisi: Sistematik bir literatür analizi. JAES, 10(1), 1-14. https://doi.org/10.35229/jaes.1596060
7. Goldblatt, R., You, W., Hanson, G., & Khandelwal, A. K. (2016). Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in Google Earth Engine. Remote Sensing, 8(8), 634. https://doi.org/10.3390/rs8080634
8. Grimmond, S. (2007). Urbanization and global environmental change: Local effects of urban warming. The Geographical Journal, 173(1), 83-88. https://doi.org/10.1111/j.1475-4959.2007.232_3.x

9. 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
10. Guha, S., & Govil, H. (2022). Estimating the seasonal relationship between land surface temperature and normalized difference bareness index using Landsat data series. International Journal of Engineering and Geosciences, 7(1), 9-16. https://doi.org/10.26833/ijeg.833260
11. Gupta, R. (2025). GIS-based analysis of land surface characteristics and urban heat islands in metropolitan cities of India. International Journal of Engineering and Geosciences, 10(3), 440-455. https://doi.org/10.26833/ijeg.1638818
12. Khalil, M., & Kumar, J. S. (2025). Assessing urban heat island intensity in Damascus city using Google Earth Engine and Landsat 8 and 9: A comparative analysis. Remote Sensing in Earth Systems Sciences, 8(2), 576-592. https://doi.org/10.1007/s41976-025-00202-x
13. Koca, S., & Mentese, S. (2026). Uzaktan algılama ile kentsel ısı adasının zamansal ve mekansal değişiminin tespiti: Eskişehir örneği. Geomatik, 11(1), 161-177. https://doi.org/10.29128/geomatik.1806504
14. Kovats, R. S., & Hajat, S. (2008). Heat stress and public health: A critical review. Annual Review of Public Health, 29, 41-55. https://doi.org/10.1146/annurev.publhealth.29.020907.090843
15. Meteoroloji Genel Müdürlüğü. (2025). Resmi istatistikler: İllerimize ait istatistik verileri (1929-2024). https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=&m=DIYARBAKIR
16. Mogaraju, J. K. (2024). Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India. International Journal of Engineering and Geosciences, 9(2), 233-246. https://doi.org/10.26833/ijeg.1394111
17. Morsy, S., & Hadı, M. (2022). Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt. International Journal of Engineering and Geosciences, 7(3), 272-282. https://doi.org/10.26833/ijeg.978961
18. Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1-24. https://doi.org/10.1002/qj.49710845502
19. Ozenen Kavlak, M., Senyel Kurkcuoglu, M. A., Cabuk, A., Cabuk, S. N., & Cetin, M. (2025). A model for assessing the urban heat island effect in urban regeneration areas: Case of Mamak and the North Ankara. International Journal of Biometeorology, 69(7), 1517-1528. https://doi.org/10.1007/s00484-025-02908-5
20. Peng, S., Feng, Z., Liao, H., Huang, B., Peng, S., & Zhou, T. (2019). Spatial-temporal pattern of, and driving forces for, urban heat island in China. Ecological Indicators, 96, 127-132. https://doi.org/10.1016/j.ecolind.2018.08.059
21. Rahman, H. U., Ding, H., Wang, G., Ullah, S., & Khan, A. (2026). Impacts of spatio-temporal land use land cover changes on the development of urban heat islands in a diverse topographic region of District Nowshera, Pakistan. International Journal of Engineering and Geosciences, 11(2), 363-374. https://doi.org/10.26833/ijeg.1763338
22. Ramadan, M. S., Tariq, A., Abdo, H. G., & Al Hosani, N. (2025). GEE-based analysis of spatiotemporal patterns of land cover and LST in an arid urban environment a case study of Abu Dhabi. Frontiers in Environmental Science, 13. https://doi.org/10.3389/fenvs.2025.1634243
23. Santamouris, M. (2015). Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions. Science of The Total Environment, 512-513, 582-598. https://doi.org/10.1016/j.scitotenv.2015.01.060
24. Sidi Almouctar, M. A., et al. (2025). Temporal analysis of land surface temperature variability and urban climate dynamics: A remote sensing use case in Benguerir City, Morocco. Sustainability, 17(21), 9719. https://doi.org/10.3390/su17219719
25. Sobrino, J. A., et al. (2013). Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing. International Journal of Remote Sensing, 34(9-10), 3177-3192. https://doi.org/10.1080/01431161.2012.716548
26. Şekertekin, A., & Marangoz, A. M. (2019). Zonguldak metropolitan alanındaki arazi kullanımı arazi örtüsünün yer yüzey sıcaklığına etkisi. Geomatik, 4(2), 101-111. https://doi.org/10.29128/geomatik.497051
27. Yilmaz, S., Menteş, Y., & Jamei, E. (2025). Investigating the effect of blue–green infrastructure on thermal condition—Case study: Elazığ, Turkey. Land, 14(4), 891. https://doi.org/10.3390/land14040891