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Journals / Turkish Journal of Geosciences / 2026 / Volume: 1 Issue: 1

Determining The Forest Fire Risk with Sentinel 2 Images

Rutkay Atun(SİVAS CUMHURİYET ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0001-9959-2058
Kaan Kalkan(UZAY TEKNOLOJİLERİ ARAŞTIRMA ENSTİTÜSÜ)0000-0002-2732-5425
Önder Gürsoy(SİVAS CUMHURİYET ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-1531-135X
Pages
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Publication Date
2020-06-15
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results of the study, the size of the vegetation area that was destroyed due to fire determined, and the probability of forest fire exposure of these areas established.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Forest fires are one of the most important disasters since past.
📊
Key Findings & Data:In this study, a forest fire that started at 07.23.2018 in Athens, in Greece and continued until July 26 was discussed.
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Conclusion & Impact:According to the results of the study, the size of the vegetation area that was destroyed due to fire determined, and the probability of forest fire exposure of these areas established.
🔬Design:Research Article
📍Scope:2018 Verileri
⚙️Variables:forest fire · fire severity indices · fire risk

Abstract

Forest fires are one of the most important disasters since past. The necessary preventions should be taken promptly to prevent these disasters. Remote sensing, which is a very effective and practical tool, is one of these tools that provide a timely receipt of measures with the development of technology. In this study, a forest fire that started at 07.23.2018 in Athens, in Greece and continued until July 26 was discussed. Mati region where the most loss of life was examined as the study area. Sentinel 2 images were used in order to detect forest fire risk class. Normalized Burn Ratio (NBR), Differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR) spectral indices and Normalized Difference Vegetation İndex (NDVI) were used in order to determine the forest area damaged by fire and to establish fire risk classes. According to the results of the study, the size of the vegetation area that was destroyed due to fire determined, and the probability of forest fire exposure of these areas established.

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Cite This Article

Atun, R., Kalkan, K., Gürsoy, Ö. (2020). Determining The Forest Fire Risk with Sentinel 2 Images. Turkish Journal of Geosciences, Volume: 1 Issue: 1. https://ajindex.com/articles/1556428

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References (15)

  1. Barsi, J.A., Lee, K., Kvaran, G., Markham, B.L., Pedelty, J.A. (2014). The Spectral Response of the Landsat-8 Operational Land Imager. Remote Sensing, 6. 10232-10251.
  2. Boer, M.M., Macfarlane, C., Norris, J., Sadler, R.J., Wallece, J., Grierson, P.F. (2008). Mapping Burned Areas And Burn Severity Patterns İn Sw Australian Eucalypt Forest Using Remotely-Sensed Changes İn Leaf Area İndex. Remote Sensing of Environment, 112, 4358-4369.
  3. Canbaz, O., Gürsoy, Ö., Gökçe, A. (2018). Detecting Clay Minerals in Hydrothermal Alteration Areas with Integration of ASTER Image and Spectral Data in Kösedag-Zara (Sivas), Turkey. Journal Geological Society of India, 91(April), 483-488.
  4. Clevers, J.G.P.W., Kooistra, L., Van Den Brande, M.M.M. (2017). Sentinel-2 Data For Retrieving Lai and Leaf and Canopy Chlorophyll Content of a Potato Crop. Remote Sensing, 405(9), 1-15.
  5. Cömert, R., Matcı, D.K., Emir, H., Avdan, U. (2017). Nesne Tabanlı Sınıflandırma ile Yanmış Orman Alanlarının Tespiti. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17, 27-34.
  6. Cömert, R., Matcı, D.K., Avdan, U. (2019). Object Based Burned Area Mapping With Random Forest Algorithm. International Journal of Engineering and Geosciences, 4(2), 78-87.
  7. Delgado, R.D., Lloret, F., Pons, X. (2003). Influence of Fire Severity on Plant Regeneration By Means Of Remote Sensing İmagery. International Journal of Remote Sensing, 24(8), 1751-1763.
  8. Francos, M., Ubeda, X., Tort, J., Panereda, J.M., Cerda, A. (2016). The Role Of Forest Fire Severity On Vegetation Recovery After 18 Years. Implications For Forest Management of Quercus suber L. İn Iberian Peninsula. Global and Planetary Change, 145, 11-16. Holben, B.N. (1986). Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data. International Journal of Remote Sensing, 7(11), 1417-1434.
  9. Kalkan, K., Maktav, D. (2018). A Cloud Removal Algorithm to Generate Cloud and Cloud Shadow Free Images Using Information Cloning. Journal of the Indian Society of Remote Sensing, 46(8), 1255-1264.
  10. Key, C.H., Benson, N.C. (2006). Landscape assessment: ground measure of severity, the composite burn index; and remote sensing of severity, the Normalized Burn Ratio. In: Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., Gangi, L.J. (Gtr.), FIREMON: Fire effects monitoring and inventory system (pp. 1-55), Washington DC.
  11. Kerr, J.T., Ostrosky, M. (2003). From Space To Species: Ecological Applications For Remote Sensing. TRENDS in Ecology and Evolution, 18(6), 299-305.
  12. Matin, M.A., Chitale, V.S., Murthy, M.S.R., Uddin, K., Bajracharya, B., Pradhan, S. (2017). Understanding Forest Fire Patterns And Risk İn Nepal Using Remote Sensing, Geographic İnformation System And Historical Fire Data. International Journal of Wildland Fire, 26, 276-286.
  13. Navarro, G., Caballero, I., Silva, G., Parra, P.C., Vazquez, A., Calderia, R. (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSIimagery. International Journal of Applied Earth Observation and Geoinformation, 58, 97-106.
  14. Tucker, C.J. (2017). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  15. Yuan, C., Liu, Z., Zhang, Y. (2017). Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles. The Journal of Intelligent and Robotic Systems, 88, 635-654.