Assessment of Burned Area and Fire Severity Using Sentinel-2 Imagery: Case Studies from Türkiye’s Summer 2025 Wildfires

Çiğdem Şerifoğlu Yılmaz

doi:10.48123/rsgis.1751391

EN TR

Assessment of Burned Area and Fire Severity Using Sentinel-2 Imagery: Case Studies from Türkiye’s Summer 2025 Wildfires

Abstract

This study examines five major wildfires in Türkiye during the summer of 2025 (Aliaga-Foca, Seferihisar-Menderes, Akhisar, Tarakli-Geyve-Golpazari, and Antakya) using Sentinel-2 imagery. Burned areas and fire severity were assessed with Normalized Burn Ratio (NBR), differenced NBR (dNBR), and Normalized Difference Vegetation Index (NDVI). Burn severity mapping was complemented by analyses of pre-fire vegetation stress, wind dynamics, and topographic factors influencing fire spread. Results were validated against European Forest Fire Information System (EFFIS) data. Findings confirm that Sentinel-2 dNBR captured both the extent and severity of fire damage, showing consistency with EFFIS estimates. The most accurate results were achieved for Aliağa-Foça, Akhisar and Taraklı-Geyve-Gölpazarı. Seferihisar-Menderes (96.13 km²) and Tarakli-Geyve-Golpazari (61.31 km²) experienced the largest and most severe fires, marked by heterogeneous burn patterns. Notably, the strongest pre-fire drought signal did not align with the largest fire, underscoring the key role of real-time weather, fuel availability, and topography in shaping fire size and spread. For visual interpretation, False Colour Composite (FCC) imagery outperformed True Colour Composite (TCC) and NDVI. Overall, results highlight the effectiveness of Sentinel-2 and dNBR-based methods for post-fire monitoring and their importance for fire management under growing climate-related fire risks.

Keywords

Wildfires , Burned area mapping , Burn severity , Sentinel-2 , Remote sensing

Sentinel-2 Görüntüleri Kullanılarak Yanmış Alan ve Yangın Şiddetinin Değerlendirilmesi: Türkiye 2025 Yazı Orman Yangınlarından Örnekler

Öz

Bu çalışma, 2025 yazında Türkiye’de meydana gelen beş büyük orman yangınını (Aliağa-Foça, Seferihisar-Menderes, Akhisar, Taraklı-Geyve-Gölpazarı ve Antakya) Sentinel-2 görüntüleri kullanarak incelemektedir. Yanmış alanlar ve yangın şiddeti, Normalize Yanıklık Oranı (NBR), fark alınmış NBR (dNBR) ve Normalize Bitki Örtüsü İndeksi (NDVI) üzerinden değerlendirilmiştir. Yangın şiddeti haritalaması, yangın yayılımını etkileyen yangın öncesi bitki örtüsü stresi, rüzgâr dinamikleri ve topoğrafik faktörlerin analizi ile desteklenmiştir. Sonuçlar, Avrupa Orman Yangını Bilgi Sistemi (EFFIS) verileri ile doğrulanmıştır. Bulgular, Sentinel-2 dNBR yönteminin yangın hasarının boyutunu ve şiddetini ortaya koyduğunu ve EFFIS tahminleri ile uyum gösterdiğini kanıtlamaktadır. En doğru sonuçlar Aliağa-Foça, Akhisar ve Taraklı-Geyve-Gölpazarı için elde edilmiştir. Seferihisar-Menderes (96.13 km²) ve Taraklı-Geyve-Gölpazarı (61.31 km²) en geniş ve en şiddetli alanları kapsayarak heterojen yanıklık desenleri sergilemiştir. İlginç bir şekilde, en güçlü kuraklık sinyali en büyük yangınla örtüşmemiştir; bu durum yangın boyutu ve yayılımında anlık meteoroloji, yakıt mevcudiyeti ve topoğrafyanın kritik rolünü vurgulamaktadır. Görsel yorumlamada Sahte Renk Kombinasyon görüntülerinin, Gerçek Renk Kombinasyon ve NDVI’dan daha etkili olduğu belirlenmiştir. Genel olarak sonuçlar, Sentinel-2 ve dNBR tabanlı yöntemlerin farklı iklim ve ekolojik bölgelerde yangın sonrası izleme için uygunluğunu ve iklim kaynaklı artan yangın riskleri karşısında yönetim açısından önemini ortaya koymaktadır.

Anahtar Kelimeler

Orman yangınları , Yanmış alanların tespiti , Yangın şiddeti , Sentinel-2 , Uzaktan algılama

References

Achour, H., Toujani, A., Trabelsi, H., & Jaouadi, W. (2022). Evaluation and comparison of Sentinel-2 MSI, Landsat 8 OLI, and EFFIS data for forest fires mapping. Illustrations from the summer 2017 fires in Tunisia. Geocarto International, 37, 7021–7040. https://doi.org/10.1080/10106049.2021.1980118.
Altun, N. (2006). Urla–Seferihisar (İzmir) bölgesinin jeolojisi ve toprak özellikleri [Uzmanlık tezi, T.C. Çevre ve Orman Bakanlığı, Ege Ormancılık Araştırma Müdürlüğü]. https://www.ogm.gov.tr/egearastirma/yayinlarimiz/muhtelif-yayinlar
Atak, B. K., & Tonyaloğlu, E. E. (2020). Evaluating spectral indices for estimating burned areas in the case of Izmir / Turkey. Eurasian Journal of Forest Science, 8, 49–59. https://doi.org/10.31195/ejejfs.657253
Atalay, İ., & Mortan, K. (2011). Türkiye bölgesel coğrafyası. İnkılap Kitabevi.
Bitek, D., Sanli, F. B., & Erenoglu, R. C. (2025). Spatial and statistical analysis of burned areas with Landsat-8/9 and Sentinel-2 satellites: 2023 Çanakkale forest fires. Environmental Monitoring and Assessment, 197, Article 60. https://doi.org/10.1007/s10661-024-13474-5.
Boer, M. M., Macfarlane, C., Norris, J., Sadler, R. J., Wallace, J., & Grierson, P. F. (2008). Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index. Remote Sensing of Environment, 112(12), 4358–4369. https://doi.org/10.1016/j.rse.2008.08.005.
Çelik, M. Ç., & Karabulut, M. (2014). Antakya–Kahramanmaraş Grabeninde Kızılçam (Pinus brutia Ten.) orman alanları ile yağış arasındaki ilişkilerin MODIS verileri (2000–2010) kullanılarak incelenmesi. Coğrafi Bilimler Dergisi, 12(1), 49–68. https://doi.org/10.1501/Cogbil_0000000152.
Chu, T., & Guo, X. (2014). Remote sensing techniques in monitoring post-fire effects and patterns of forest recovery in boreal forest regions: A review. Remote Sensing, 6, 470–520. https://doi.org/10.3390/rs6010470
Chuvieco, E., Riaño, D., Danson, F. M., & Martin, P. (2006). Use of a radiative transfer model to simulate the postfire spectral response to burn severity. Journal of Geophysical Research: Biogeosciences, 111, Article G04S09. https://doi.org/10.1029/2005JG000143
European Forest Fire Information System. (2025). Welcome to EFFIS. Retrieved July 7, 2025, from https://effis.jrc.ec.europa.eu/

Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2), Article RG2004. https://doi.org/10.1029/2005RG000183
Filipponi, F. (2019). Exploitation of Sentinel-2 time series to map burned areas at the national level: A case study on the 2017 Italy wildfires. Remote Sensing, 11, Article 622. https://doi.org/10.3390/rs11060622
Gülci, S., Yüksel, K., Gümüş, S., & Wing, M. (2021). Mapping wildfires using Sentinel-2 MSI and Landsat 8 imagery: Spatial data generation for forestry. European Journal of Forest Engineering, 7, 57–66. https://doi.org/10.33904/ejfe.1031090
Henner, D. N., & Kirchengast, G. (2025). Forest fire risk under climate change in Austria and comparable European regions. Trees, Forests and People, 20, Article 100889. https://doi.org/10.1016/j.tfp.2025.100889
Ibrahim, S., Kose, M., Adamu, B., & Jega, I. M. (2025). Remote sensing for assessing the impact of forest fire severity on ecological and socio-economic activities in Kozan District, Turkey. Journal of Environmental Studies and Sciences, 15, 342–354. https://doi.org/10.1007/s13412-024-00951-z
İkiel, C. (2018). Sakarya’nın fiziki, beşeri ve iktisadi coğrafya özellikleri. Sakarya Üniversitesi.
Jin, S., & Sader, S. A. (2005). Comparison of time series Tasseled Cap Wetness and the Normalized Difference Moisture Index in detecting forest disturbances. Remote Sensing of Environment, 94(3), 364–372.
Kendall, M. G. (1948). Rank correlation methods. Griffin, Oxford, England.
Key, C. H., & Benson, N. C. (2006). Landscape assessment: Sampling and analysis methods. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD, Ogden.
Kobayashi, S., & Sanga-Ngoie, K. (2008). The integrated radiometric correction of optical remote sensing imageries. International Journal of Remote Sensing, 29, 5957–5985.
Kurnaz, B., Bayik, C., & Abdikan, S. (2020). Forest fire area detection by using Landsat-8 and Sentinel-2 satellite images: A case study in Mugla, Turkey. Research Square. https://doi.org/10.21203/rs.3.rs-26787/v1
Lentile, L. B., Holden, Z. A., Smith, A. M., Falkowski, M. J., Hudak, A. T., Morgan, P., Lewis, S. A., Gessler, P. E., & Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15(3), 319–345.
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245–259.
Meteoroloji Genel Müdürlüğü. (2025). 2025 Yılı Haziran Ayı Ortalama Sıcaklıklarının 1991–2020 Normallerine Göre Mukayesesi. Retrieved July 25, 2025, from https://www.mgm.gov.tr/
Misseyanni, A., Christopoulou, A., Kougkoulos, I., Vassilakis, E., & Arianoutsou, M. (2025). The impact of forest fires on ecosystem services: The case of Greece. Forests, 16(3), Article 533. https://doi.org/10.3390/f16030533
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., ... & Thépaut, J.-N. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349–4383.
Öncü, G., & Çorumluoğlu, Ö. (2023). Determination of spatial distribution of damage intensity of Tınazlı-İzmir forest fire using remote sensing indexing techniques. International Journal of Environment and Geoinformatics, 10, 151–158.
Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
Robichaud, P. R., Lewis, S. A., Laes, D. Y., Hudak, A. T., Kokaly, R. F., & Zamudio, J. A. (2007). Postfire soil burn severity mapping with hyperspectral image unmixing. Remote Sensing of Environment, 108(4), 467–480. https://doi.org/10.1016/j.rse.2006.11.027
Şerifoğlu Yılmaz, C. (2024). Assessing air pollutant emissions in the aftermath of the 2021 forest fires in Marmaris and Manavgat, Türkiye: Insights from satellite-based monitoring. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 329–336. https://doi.org/10.5194/isprs-archives-XLVIII-4-W9-2024-329-2024
Soydan, O. (2022). Detection of burnt areas by remote sensing techniques: Antalya Manavgat forest fire. Turkish Journal of Agriculture-Food Science and Technology, 10, 3029–3035. https://doi.org/10.24925/turjaf.v10isp2.3029-3035.5764
Tonbul, H. (2024). Google Earth Engine ile Türkiye’de yanmış alanların MODIS ve FireCCI51 küresel yanmış alan uydu gözlem verileriyle karşılaştırmalı değerlendirilmesi. Türk Uzaktan Algılama ve CBS Dergisi, 5(1), 69–82. https://doi.org/10.48123/rsgis.1410382
Turco, M., Herrera, S., Tourigny, E., Chuvieco, E., & Provenzale, A. (2019). A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe. International Journal of Applied Earth Observation and Geoinformation, 82, Article 101887. https://doi.org/10.1016/j.jag.2019.05.020
U.S. Geological Survey. (2025). SRTM mission. Retrieved September 5, 2025, from https://www.usgs.gov/centers/eros/ science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm
Vittucci, C., Cordari, F., Guerriero, L., & Di Sanzo, P. (2025). Design and evaluation of a cloud-oriented procedure based on SAR and multispectral data to detect burnt areas. Earth Science Informatics, 18(3), Article 322. https://doi.org/10.1007/s12145-025-01829-6
Wang, L., Qu, J. J., Hao, X., & Hunt, E. R., Jr. (2011). Estimating dry matter content from spectral reflectance for green leaves of different species. International Journal of Remote Sensing, 32, 7097–7109.

Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

Çiğdem Şerifoğlu Yılmaz ^*
0000-0002-9738-5124
Türkiye

Publication Date

September 27, 2025

Submission Date

July 26, 2025

Acceptance Date

September 12, 2025

Published in Issue

Year 2025 Volume: 6 Number: 2

DOI

https://doi.org/10.48123/rsgis.1751391

IZ

https://izlik.org/JA86DF76MH

APA

Şerifoğlu Yılmaz, Ç. (2025). Assessment of Burned Area and Fire Severity Using Sentinel-2 Imagery: Case Studies from Türkiye’s Summer 2025 Wildfires. Türk Uzaktan Algılama Ve CBS Dergisi, 6(2), 300-314. https://doi.org/10.48123/rsgis.1751391

This journal is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Assessment of Burned Area and Fire Severity Using Sentinel-2 Imagery: Case Studies from Türkiye’s Summer 2025 Wildfires

Abstract

Keywords

Sentinel-2 Görüntüleri Kullanılarak Yanmış Alan ve Yangın Şiddetinin Değerlendirilmesi: Türkiye 2025 Yazı Orman Yangınlarından Örnekler

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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