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Remote sensing and GIS-based inventory and analysis of the unprecedented 2021 forest fires in Türkiye’s history

Author

Listed:
  • Remzi Eker

    (İzmir Kâtip Çelebi University)

  • Tunahan Çınar

    (Düzce University)

  • İsmail Baysal

    (İzmir Kâtip Çelebi University)

  • Abdurrahim Aydın

    (Düzce University)

Abstract

In the summer of 2021, Türkiye experienced unprecedented forest fire events. Throughout that fire season, a total of 291 fire incidents, covering an area of 202,361 hectares, dominated the public agenda. This study aimed to document and analyze the 30 large fires (affecting over 100 hectares) of 2021 using remote sensing and GIS techniques. A comprehensive fire database was established, encompassing information on burned areas, fire severity, and fuel types, determined from forest-stand types and topographical properties including slope, elevation, and aspect (in eight directions). Sentinel-2 satellite images were utilized to calculate dNBR values for assessing fire severity, analyzed in the Google Earth Engine platform. Three GIS-integrated Python scripts were developed to construct the fire database. In total, 164,658 hectares were affected by these large fires, occurring solely in three regions of Türkiye: the Mediterranean, Aegean, and Eastern Anatolian. The majority of the burned area was situated in the Mediterranean region (59%), with only 3% in Eastern Anatolia. The burned areas ranged from a minimum of 150 hectares to a maximum of 58,798 hectares. Additionally, 679 hectares of residential areas and 22,601 hectares of agricultural land were impacted by the fire events. For each fire, 21 fuel types and their distribution were determined. The most prevalent fire-prone class, “Pure Turkish pine species (Pr-Çz),” accounted for 59.56% of the total affected area (99,516 hectares). Another significant fire-prone pine species, the “Pure Black pine species (Pr-Çk),” covered 7.67% (12,811 hectares) of the affected area. Fuel types were evaluated by considering both forest-stand development stages and canopy closure. Regarding forest-stand development stages, the largest area percentage burned belonged to the “Mature” class (26.48%).

Suggested Citation

  • Remzi Eker & Tunahan Çınar & İsmail Baysal & Abdurrahim Aydın, 2024. "Remote sensing and GIS-based inventory and analysis of the unprecedented 2021 forest fires in Türkiye’s history," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 10687-10707, September.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:12:d:10.1007_s11069-024-06622-0
    DOI: 10.1007/s11069-024-06622-0
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    References listed on IDEAS

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    1. Lin, Jianyi & Rinaldi, Sergio, 2009. "A derivation of the statistical characteristics of forest fires," Ecological Modelling, Elsevier, vol. 220(7), pages 898-903.
    2. Lanbo Feng & Huashun Xiao & Zhigao Yang & Gui Zhang, 2022. "A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data," Sustainability, MDPI, vol. 14(3), pages 1-16, January.
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    4. Matthias M. Boer & Víctor Resco de Dios & Ross A. Bradstock, 2020. "Unprecedented burn area of Australian mega forest fires," Nature Climate Change, Nature, vol. 10(3), pages 171-172, March.
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