Assessing the Impact of Forest Fires on LULC Changes and LST: Case Study of Kavaklidere, Mugla

Year 2025, Volume: 11 Issue: 2, 192 - 200, 25.12.2025

Eda Aşci , Burak Arıcak , Hakan Şevik

https://doi.org/10.33904/ejfe.1786461

https://izlik.org/JA77EY92ZW

Abstract

The Mediterranean region of the Türkiye is among the areas most susceptible to forest fires. Although the reasons for fire outbreaks vary over time, these events consistently result in the loss of forest resources and significant ecological damage. Forest fires reduce or eliminate many forms of vegetation from the land surface. The Kavaklıdere-Muğla-Yatağan-Yılanlı fire, which occurred between 2 and 8 August 2021, affected a large area. Therefore, the study aims to investigate the land use and land cover (LULC) changes in 2020, 2021, and 2024 within Kavaklıdere district of Muğla. In this study, six LULC classes, Agriculture (A), Bare and Other (BO), Forest (F), Urban (U), Water (W) and Burnt Area (BA) were identified using Sentinel-2 satellite imagery and random forest classification technique on the Google Earth Engine (GEE) platform. In addition, land surface temperature (LST) data were obtained using the split-window algorithm applied to Landsat-8 data. The findings indicated that LULC changes are clearly in fire-affected areas, with LST values are notably higher in burned areas than in other classes. The results demonstrate the utility of remote sensing techniques for monitoring post-fire changes in land cover and surface temperature.

Keywords

Remote sensing , Forest fire , GIS , Classification , Land surface temperature

Ethical Statement

All data used in this research were obtained from publicly available satellite imagery and secondary sources, which comply with ethical standards. The authors confirm that there are no ethical concerns regarding the conduct of this study.

References

• Alkayiş, M.H., Karslioğlu, A., Onur, M.İ. 2021. Determination of forest fires risk potential map of Menteşe region of Muğla with geographic information systems. Geomatik, 7(1): 10–16. (In Turkish)
• Ambrosia, V., Schwandner, F.M., Hadjimitsis, D., Gitas, I., Gutman, G. 2024. The Mediterranean Regional Information Network (MedRIN): An EO partnership for land cover /land use change science. IEEE International Geoscience and Remote Sensing Symposium. 07-12 July, Athens, Greece. 2885–2888.
• Breiman, L. 2001. Random forests. Machine Learning. 45: 5–32.
• Campbell, J.B., Wynne., R.H. 2011. Introduction to remote sensing. Fifth Edition. In A Division of Guilford. The Guilford Press, New York. 717 p.
• Cavdaroglu, C.G. 2021. Google Earth Engine Based Approach for Finding Fire Locations and Burned Areas in Muğla, Turkey. American Journal of Remote Sensing. 9(2): 72.
• Cohen, J.B., Lecoeur, E., Ng, D. H.L. 2017. Decadal-scale relationship between measurements of aerosols, land-use change, and fire over Southeast Asia. Atmospheric Chemistry and Physics, 17(1): 721–743.
• Cochran, W.G. 1977. Sampling techniques (3rd ed.). John Wiley and Sons. New York.
• Çamalan, G., Çamalan, İ. 2023. Türkiye’de Büyük Orman Yangınları ve Uydu-Model Verileri Kullanımı. V. Meteorolojik Uzaktan Algılama Sempozyumu (Uzalmet). 14-17 Kasım, Alanya. https://www.mgm.gov.tr/FILES/genel/makale/ormanyangin.pdf
• Eker, R., Çınar, T., Baysal, İ., Aydın, A. 2024. Remote sensing and GIS-based inventory and analysis of the unprecedented 2021 forest fires in Türkiye’s history. Natural Hazards. 120(12): 10687–10707.
• European Space Agency. 2024. Sentinel-2. Retrieved from www.esa.int website: https://www.esa.int/ Applications/Observing_the_Earth/Copernicus/Sentinel-2
• Foody, G.M. 2002. Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment. 80(1): 185–201.
• Foody, G.M. 2008. Harshness in image classification accuracy assessment. International Journal of Remote Sensing. 29(11): 3137–3158.
• Guan, H., Yu, J., Li, J., Luo, L. 2012. Random forests-based feature selection for land-use classification using Lidar data and orthoimagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7. https://doi. org/10.5194/isprsarchives-xxxix-b7-203-2012
• Habte, D.G., Belliethathan, S., Ayenew, T. 2021. Evaluation of the Status of Land Use/Land Cover Change Using Remote Sensing and GIS in Jewha Watershed, Northeastern Ethiopia. SN Applied Sciences. 3, 501.
• Heriza, D., Wu, C. Da, Syariz, M.A., Lin, C.H. 2023. Analysis of spatial–temporal variability of pm2.5 concentrations using optical satellite images and Geographic Information System. Remote Sensing. 15(8): 2009.
• Jiménez-Muñoz, J. C., Sobrino, J. A., Skoković, S., Mattar, C., Cristóbal, J. 2014. Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters. 11(10).
• Logan, T.M., Zaitchik, B., Guikema, S., Nisbet, A. 2020. Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature. Remote Sensing of Environment. 247: 111861.
• Lyapustin, A. 2015. Multi-Angle Implementation of Atmospheric Correction (MAIAC) - LAADS DAAC. Retrieved July 2, 2025, from Nasa.gov website: https://ladsweb.modaps.eosdis.nasa. gov/missions-and-measurements/science-domain/maiac/
• Muğla Provincial Directorate of Culture and Tourism. 2025. Geography (Location-Climate-Transportation). Retrieved from https://mugla. ktb.gov.tr/TR-270692/cografya-konum-iklim-ulasim.html
• NASA MODIS. 2025. MODIS Web. Retrieved from modis.gsfc.nasa.gov website: https://modis.gsfc. nasa.gov/data/dataprod/mod04.php
• Pal, M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing. 26(1): 217–222.
• Remer, L.A., Kaufman, Y.J., Tanré, D., Mattoo, S., Chu, D.A., Martins, J.V., Holben, B.N. 2005. The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences. 62(4): 947–973.
• Research Department Directorate, 2025. 2024 Climate assessment. Climate and Agricultural Meteorology Department Directorate, General Directorate of Meteorology, Ministry of Environment, Urbanization and Climate Change of the Republic of Turkey. 49 p. https://mgm.gov.tr/FILES/iklim/yillikiklim/2024-iklim-raporu.pdf
• San-Miguel-Ayanz, J., Durrant, T., Boca, R., Maianti, P., Liberta`, G., Jacome Felix Oom, D., Branco, A., De Rigo, D., Suarez-Moreno, M., Ferrari, D., Roglia, E., Scionti, N., Broglia, M., Onida, M., Tistan, A. and Loffler, P. 2022. Forest fires in Europe, Middle East and North Africa 2022, Publications Office of the European Union, Luxembourg, 2023, DOI:10.2760/348120, JRC135226.
• Sertel, E., Topgul, S.N. 2025. Comparative analysis of deep learning approaches for forest stand type classification: insights from the new VHRTreeSpecies benchmark dataset. International Journal of Digital Earth. 18(1): 2522394.
• Sismanis, M., Gitas, I.Z., Georgopoulos, N., Stavrakoudis, D., Gkounti, E., Antoniadis, K. 2024. A Spectral–Spatial Approach for the classification of tree cover density in Mediterranean Biomes using Sentinel-2 imagery. Forests. 15(11): 2025
• Sismanis, M., Gitas, I.Z., Stavrakoudis, D., Georgopoulos, N., Antoniadis, K., Gkounti, E. 2024. A novel spectral–spatial methodology for hierarchical fuel type mapping in Mediterranean ecosystems using Sentinel-2 timeseries and auxiliary thematic data. Fire. 7(11): 407.
• Szantoi, Z., Brink, A., Lupi, A. 2021. An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region. Earth System Science Data. 13(8): 3767–3789.
• Tariq, S., ul‐Haq, Z., Mariam, A., Mehmood, U., Ahmed, W. 2023. Assessment of air quality during worst wildfires in Mugla and Antalya regions of Turkey. Natural Hazards. 115(2):1235-1254.
• Tuygun, G.T., İşsever, G., Elbi̇r, T. 2023. Satellite monitoring of burned forest areas and atmospheric aerosols originated from major forest fires in Turkey in 2021. The Dokuz Eylul University Faculty of Engineering Journal of Science and Engineering, 25(74): 351–369. https://doi.org/10.21205/ deufmd.2023257408
• USGS. 2023. Landsat 8 imagery. U.S. Geological Survey. Retrieved from https://www.usgs.gov/ landsat-missions/landsat-8
• Uysal, M.M., Uysal, M. 2025. Monitoring meteorological impacts for forest fires with remote sensing and data science techniques. Afyon Kocatepe University Journal of Science and Engineering. 25(1): 137–143.
• Yang, R., Wang, L., Tian, Q., Xu, N., Yang, Y. 2021. Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data. Remote Sensing. 13(21): 4426.
• Yilmaz, O.S., Acar, U., Sanli, F.B., Gulgen, F., Ates, A.M. 2023. Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Science Informatics. 16(1): 221–240.
• Youn, Y., Kim, S., Kim, S. H., Lee, Y. 2024. Spatial Gap-Filling of Himawari-8 hourly AOD products using machine learning with model-based AOD and meteorological data: A focus on the Korean Peninsula. Remote Sensing. 16(23): 4400.
• Zhang, T., Su, J., Xu, Z., Luo, Y., Li, J. 2021. Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Applied Sciences. 11: 543.