EVALUATING FOREST FIRE SUSCEPTIBILITY LEVELS IN ŞAHINKAYA CANYON (NORTHERN TÜRKİYE) USING AHP
DOI:
https://doi.org/10.2298/IJGI250926004BKeywords:
forest fire susceptibility, AHP, Şahinkaya CanyonAbstract
The aim of this study is to classify the forests in and around the Şahinkaya Canyon (Central Black Sea Region in Northern Türkiye) according to their levels of fire susceptibility, to identify priority areas in fire management, and to provide recommendations for the prevention and effective control of forest fires. To determine fire susceptibility levels, the Analytic Hierarchy Process (AHP), a multicriteria decision-making method, was employed, and the results were visualized in a Geographic Information Systems (GIS) environment. The parameters used, along with their impacts and weight values, were determined based on expert opinions, field surveys, and relevant literature. In this study, the effects of topography, forest resources and characteristics, and human activities on fire susceptibility were analyzed using nine parameters. The results indicate that fire susceptibility varies with forest structure and composition, topography, climatic factors, and anthropogenic influences. Of the study area, 34% falls within the high and very high fire susceptibility classes, while 38% is classified as low and very low. The remaining 28% represents moderate fire susceptibility, corresponding to transitional zones between low and high susceptibility. According to the fire inventory, 11 of the 17 recorded fires occurred in areas with high and very high fire susceptibility. This distribution indicates a spatial correspondence between observed fire occurrences and the susceptibility classes derived from the analysis. This study reveals the spatial distribution of forest fire susceptibility in and around the ecologically sensitive Şahinkaya Canyon, providing a data-based framework for the use of the findings in conservation and planning efforts.
Article metrics
References
Agee, J. K., & Skinner, C. N. (2005). Basic principles of forest fuel reduction treatments. Forest Ecology and Management, 211(1–2), 83–96. https://doi.org/10.1016/j.foreco.2005.01.034
Ahmad, H., Wu, Z., Huang, H., Muhammad, S., Hayat, M., Abbas, K., Yang, X., & Shu, Z. (2025). A comparative evaluation of forest fire hazard vulnerability through geographic information system-based techniques. Frontiers in Forests and Global Change, 8, Article 1635041. https://doi.org/10.3389/ffgc.2025.1635041
Akay, A. E., & Şahin, H. (2019). Forest Fire Risk Mapping by using GIS Techniques and AHP Method: A Case Study in Bodrum (Turkey). European Journal of Forest Engineering, 5(1), 25–35. https://doi.org/10.33904/ejfe.579075
Baltacı, U. (2021). Türkiye’de Orman Yangını Riskinin Coğrafi Bilgi Sistemleri Tabanlı Olarak Çok Kriterli Analizi ve Haritalandırılması [Geographic Information System Based Multi-Criteria Analysis and Mapping of Forest Fire Risk in Turkey; Doctoral dissertation, Gazi University, Fen Bilimleri Enstitüsü]. Gazi University Research Information System. https://avesis.gazi.edu.tr/yonetilen-tez/1126225f-8eb2-47f8-b816-d7fbe1a19bf3/turkiyede-orman-yangini-riskinin-cografi-bilgi-sistemleri-tabanli-olarak-cok-kriterli-analizi-ve-haritalandirilmasi
Baltacı, U., & Yıldırım, F. (2021). Muğla Orman Bölge Müdürlüğü’nde orman yangını riskinin çok kriterli analizi ve haritalandırılması [Multi-criteria analysis and mapping of forest fire risk in Muğla Regional Directorate of Forestry]. Ormancılık Araştırma Dergisi, 8(1), 1–11. https://dergipark.org.tr/tr/pub/ogmoad/article/708385
Baykal, T. M. (2023). GIS‐based spatiotemporal analysis of forest fires in Turkey from 2010 to 2020. Transactions in GIS, 27(5), 1289–1317. https://doi.org/10.1111/tgis.13066
Boydak, M. (2004). Silvicultural characteristics and natural regeneration of Pinus brutia Ten.— a review. Plant Ecology, 171, 153–163. https://doi.org/10.1023/B:VEGE.0000029373.54545.d2
Chowdhury, E. H., & Hassan, Q. K. (2015). Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data. Remote Sensing, 7(3), 2431–2448. https://doi.org/10.3390/rs70302431
Coşkun, M., & Toprak, F. (2023). Coğrafi bilgi sistemleri (CBS) tabanlı orman yangını risk analizi: Bartın İli örneği [Geographical Information Systems (GIS) based forest fire risk analysis: Case of Bartın]. Geomatik, 8(3), 250–263. https://doi.org/10.29128/geomatik.1192219
Çanakçıoğlu, H. (1993). Orman koruma [Forest protection]. İstanbul Üniversitesi Orman Fakültesi Yayınları.
Demir, A. (2023). CBS tabanlı AHP yöntemiyle orman yangını risk haritasının geliştirilmesi [Development of forest fire risk map with GIS based AHP method; Master’s thesis, Bursa Teknik Üniversitesi]. Bursa Technical University Institutional Repository. https://hdl.handle.net/20.500.12885/3330
Dilekçi, S., Marangoz, A. M., & Ateşoğlu, A. (2021). Zonguldak ve Ereğli Orman İşletme Müdürlükleri Orman Yangını Risk Alanlarının Belirlenmesi [Detection of Forest Fire Risk Areas of Zonguldak and Ereğli Regional Forest Directorate]. Geomatik, 6(1), 44–53. https://doi.org/10.29128/geomatik.660623
Dogan Ciftci, N., Şahin, A. D., Yousefpour, R., & Christen A. (2024). Effects of climate trends and variability on tree health responses in the Black Sea and Mediterranean forests of Türkiye. Theoretical and Applied Climatology, 155, 3969–3991. https://doi.org/10.1007/s00704-024-04853-6
Doğanay, H., & Doğanay, S. (2011). Türkiye’de Orman Yangınları ve Alınması Gereken Önlemler [Forest Fires and Measures to be taken in Turkey]. Doğu Coğrafya Dergisi, 9(11), 31–48. https://dergipark.org.tr/tr/pub/ataunidcd/article/30960
Ebel, B. A. (2012). Impacts of Wildfire and Slope Aspect on Soil Temperature in a Mountainous Environment. Vadose Zone Journal, 11(3), Article vzj2012.0017. https://doi.org/10.2136/vzj2012.0017
Ekberzade, B., Görüm, T., Karabacak, F., Akay, S. S., & Şen, Ö. L. (2025). Up in flames: the human factor behind a megafire in Mediterranean Türkiye. npj Natural Hazards, 2, Article 65. https://www.nature.com/articles/s44304-025-00120-4
Erdin, C., & Çağlar, M. (2021). GIS-based forest fire risk assessment using the AHP and fuzzy AHP methods. Fresenius Environmental Bulletin, 30(6B), 7217–7227. https://avesis.yildiz.edu.tr/yayin/1813ae8f-6af7-45d4-a5e9-d385103c816b/gis-based-forest-fire-risk-assessment-using-the-ahp-and-fuzzy-ahp-methods
Erdönmez, C., Atmiş, E., Yurdakul Erol, S., Tutmaz, V., & Kurdoğlu, O. (2023). Orman yangınları ile ilgili yasal ve yönetsel düzenlemelerin değerlendirilmesi [Evaluation of Legal and Administrative Regulations Related to Forest Fires]. In A. Kavgacı & M. A. Başaran (Eds.), Orman Yangınları (pp. 74–100). Türkiye Ormancılar Derneği. https://www.researchgate.net/publication/369596317_ORMAN_YANGINLARI_ILE_ILGILI_YASAL_VE_YONETSEL_DUZENLEMELERIN_DEGERLENDIRILMESI
Eron, Z. (1987). Ecological factors restricting the regeneration of Pinus brutia in Turkey. Ecologia Mediterranea, 13(4), 57–67. https://doi.org/10.3406/ecmed.1987.1189
Ersoy, İ., Ünsal, E., & Gürsoy, Ö. (2025). A Multi-Criteria Forest Fire Danger Assessment System on GIS Using Literature-Based Model and Analytical Hierarchy Process Model for Mediterranean Coast of Manavgat, Türkiye. Sustainability, 17(5), Article 1971. https://doi.org/10.3390/su17051971
Esen, F., & Avcı, V. (2018). Forest fire susceptibility analysis of Kahramanmaraş Province. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 8(16), 335–356. https://doi.org/10.29029/busbed.437858
FAO. (2020). Global Forest Resources Assessment 2020: Main report. https://doi.org/10.4060/ca9825en
Fernandes, P. M. (2009). Combining forest structure data and fuel modelling to classify fire hazard in Portugal. Annals of Forest Science, 66(4), Article 415. https://link.springer.com/article/10.1051/forest/2009013
Fyllas, N. M., Dimitrakopoulos, P. G., & Troumbis, A. Y. (2008). Regeneration dynamics of a mixed Mediterranean pine forest in the absence of fire. Forest Ecology and Management, 256(8), 1552–1559. https://doi.org/10.1016/j.foreco.2008.06.046
Gai, C., Weng, W., & Yuan, H. (2011, April 15–19). GIS-Based Forest Fire Risk Assessment and Mapping. 2011 Fourth International Joint Conference on Computational Sciences and Optimization, Kunming and Lijiang City, China. https://doi.org/10.1109/CSO.2011.140
General Directorate of Forestry (GDF). (2023). Official statistics 2022 [Data set]. https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler
General Directorate of Forestry (GDF). (2024a). Official forestry statistics 2023. Retrieved from https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler
General Directorate of Forestry (GDF). (2024b). ArcGIS-based forest stand (meşcere) and inventory data sets [Unpublished raw data]. General Directorate of Forestry.
General Directorate of Mapping (GDM). (2024). Digital Elevation Model (DEM) data of Türkiye [Data set].
Google Earth Pro. (2024). Google Earth Pro (Version 7.3) [Computer software]. Google LLC. https://www.google.com/earth/versions/
Hacısalihoğlu, M. (2018). Çok Kriterli Karar Analizi İle Orman Yangını Risk Haritalarının Oluşturulması: Karabük Örneği [Forest Fire Risk Mapping Using Multi Criteria Decision Analysis Method: The Case of Karabük; Master’s thesis, Zonguldak Bülent Ecevit Üniversitesi, Fen Bilimleri Enstitüsü]. Scribd. https://www.scribd.com/document/789181931/Cok-Kri-terli-Karar-Anali-zi-i-le-Orman-Yangini-Ri-sk
Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert Systems with Applications, 38(11), 14336–14345. https://doi.org/10.1016/j.eswa.2011.04.143
Işık Pekkan, Ö., Derakhshandeh, M., Atmaca, İ., Tunca, Y. S., Özenen Kavlak, M., & Çabuk, S. N. (2022). Coğrafi Bilgi Sistemleri ve Uzaktan Algılama Teknikleri Kullanılarak Orman Yangını Risk Haritasının Geliştirilmesi: Muğla, Milas Örneği [Development of Forest Fire Risk Map Using Geographical Information Systems and Remote Sensing Technics: Muğla, Milas Case]. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 26(2), 190–199. https://doi.org/10.19113/sdufenbed.901677
Ju, W., Xing, Z., Wu, J., & Kang, Q. (2023). Evaluation of forest fire risk based on multicriteria decision analysis techniques for Changzhou, China. International Journal of Disaster Risk Reduction, 98, Article 104082. https://doi.org/10.1016/j.ijdrr.2023.104082
Karabulut, M., Karakoç, A., Gürbüz, M., & Kızılelma, Y. (2013). Coğrafi Bilgi Sistemleri Kullanarak Başkonuş Dağında (KAHRAMANMARAŞ) Orman Yangını Risk Alanlarının Belirlenmesi [Determination of Forest Fire Risk Areas Using Geographical Information systems in Başkonuş Mountain (KAHRAMANMARAŞ)]. Uluslararası Sosyal Araştırmalar Dergisi, 6(24), 171–179. https://www.sosyalarastirmalar.com/articles/coraf-blg-sstemler-kullanarak-bakonu-dainda-kahramanmara-orman-yangini-rsk-alanlarinin-belrlenmes.pdf
Khan, M. A., Gupta, A., Sharma, P., & Roy, A. (2024). Investigation of wildfire risk and its mapping using GIS-integrated AHP method: a case study over Hoshangabad Forest Division in Central India. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-05225-w
Krueger, E. S., Ochsner, T. E., Carlson, J. D., Engle, D. M., Twidwell, D., & Fuhlendorf, S. D. (2016). Concurrent and antecedent soil moisture relate positively or negatively to probability of large wildfires depending on season. International Journal of Wildland Fire, 25(6), 657–668. https://connectsci.au/wf/article-abstract/25/6/657/21126/Concurrent-and-antecedent-soil-moisture-relate
Kuzucuoğlu, C., Çiner, A., & Kazancı, N. (2019). The Geomorphological Regions of Turkey. In C. Kuzucuoğlu, A. Çiner, & N. Kazancı (Eds.), Landscapes and Landforms of Turkey (pp. 41–178). Springer. https://doi.org/10.1007/978-3-030-03515-0_4
Küçükosmanoğlu, A. (1986). Türkiye Ormanlarında Çıkan Yangınların Sınıflandırılması İle Büyük Yangınların Çıkma Ve Gelişme Nedenleri [Classification of the forest fires and the causes of occurance and spread of big forest fires in Turkey]. Journal of the Faculty of Forestry Istanbul University, 36(1), 131–154. https://dergipark.org.tr/en/pub/jffiu/article/197718
Küçükosmanoğlu, A. (1993). Ahşap malzemenin yanma özellikleri ve binalarda yangın güvenliği [Combustion properties of wood materials and fire safety in buildings]. Journal of the Faculty of Forestry Istanbul University, 43(3–4), 145–152. https://dergipark.org.tr/tr/pub/jffiu/article/197606
Ministry of Agriculture and Forestry, General Directorate of Forestry (GDF). (2024). Distribution of Forest Areas [Unpublished raw data].
Ocak, F., Cesur, F., Ismail, A., & Keklik, S. (2024). Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Kullanılarak Yangın Hasarının ve Yangına Duyarlı Alanların Belirlenmesi: Aydıncık (Mersin) İlçesi Örneği, Türkiye [Determination of Fire Damage and Fire Susceptible Areas Using Remote Sensing and Geographic Information Systems: A Case Study Aydıncık (Mersin) District, Türkiye]. Doğal Afetler ve Çevre Dergisi, 10(2), 344–364. https://doi.org/10.21324/dacd.1419616
Özenen Kavlak, M., Kurtipek, A., & Çabuk, S. N. (2020). Coğrafi Bilgi Sistemleri İle Orman Yangını Risk Haritası Oluşturulması: Ören Örneği [The Development of Forest Fire Risk Maps Using Geographical Information Systems Capabilities for Ören Forest Management Unit-Kütahya]. Resilience, 4(1), 33–54. https://doi.org/10.32569/resilience.597887
Özşahin, E. (2014). CBS ve AHS Kullanılarak Orman Yangını Duyarlılık Analizi: Antakya Orman İşletme Müdürlüğü Örneği [Forest Fire Susceptibility Analysis Using GIS and AHP: The Case of Antakya Forestry Operation Directorate]. Route Educational and Social Science Journal, 3, 50–71. https://doi.org/10.17121/ressjournal.106
Potter, B. E. (2012). Atmospheric interactions with wildland fire behaviour – I. Basic surface interactions, vertical profiles and synoptic structures. International Journal of Wildland Fire, 21(7), 779–801. https://doi.org/10.1071/WF11128
Republic of Türkiye, Ministry of Agriculture and Forestry. (2023). Forest Fire Fighting Action Plan. General Directorate of Forestry.
Rossa, C. G. (2017). The effect of fuel moisture content on the spread rate of forest fires in the absence of wind or slope. International Journal of Wildland Fire, 26(1), 24–31. https://doi.org/10.1071/WF16049
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Saaty, T. L., Vargas, L. G., & Dellmann, K. (2003). The allocation of intangible resources: the analytic hierarchy process and linear programming. Socio-Economic Planning Sciences, 37(3), 169–184. https://doi.org/10.1016/S0038-0121(02)00039-3
Samsun Governorship. (n.d.). Şahinkaya Kanyonu [Şahinkaya Canyon]. Retrieved February 20, 2026 from https://www.samsun.gov.tr/sahinkaya-kanyonu
Sari, F. (2021). Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, 480, Article 118644. https://doi.org/10.1016/j.foreco.2020.118644
Scbuk. (n.d.). Analytic Hierarchy Process, AHP. Retrieved December 12, 2024 from https://www.scbuk.com/ahp.html
Sinha, A., Nikhil, S., Ajin, R. S., Danumah, J. H., Saha, S., Costache, R., Rajaneesh, A., Sajinkumar, K. S., Amrutha, K., Johny, A., Marzook, F., Mammen, P. C., Abdelrahman, K., Fnais, M. S., & Abioui, M. (2023). Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models. Fire, 6(2), Article 44. https://doi.org/10.3390/fire6020044
Sivrikaya, F., & Küçük, Ö. (2022). Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 68, Article 101537. https://doi.org/10.1016/j.ecoinf.2021.101537
Stephens, S. L., Burrows, N., Buyantuyev, A., Gray, R. W., Keane, R. E., Kubian, R., Liu, S., Seijo, F., Shu, L., Tolhurst, K. G., & van Wagtendonk, J. W. (2014). Temperate and boreal forest mega-fires: Characteristics and challenges. Frontiers in Ecology and the Environment, 12(2), 115–122. https://doi.org/10.1890/120332
Tezcan, B., & Eren, T. (2024). Orman Yangınına Sebep Olan Kriterlerin Bulanık Ortamda Değerlendirilmesi [Evaluation of criteria that cause forest fires in fuzzy environment]. Politeknik Dergisi, 27(2), 545–558. https://doi.org/10.2339/politeknik.1138806
Turco, M., Rosa-Cánovas, J. J., Bedia, J., Jerez, S., Montávez, J. P., Llasat, M. C., & Provenzale, A. (2018). Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nature Communications, 9, Article 3821. https://doi.org/10.1038/s41467-018-06358-z
Turkish State Meteorological Service (TSMS). (2024). Meteorological observation and climate data [Data set].
Turkish State Meteorological Service (TSMS). (2025). Climate data of Vezirköprü Meteorological Station [Data set].
Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
Westerling, A. L., & Bryant, B. P. (2008). Climate change and wildfire in California. Climatic Change, 87(1 supplement), 231–249. https://doi.org/10.1007/s10584-007-9363-z
Westerling, A. L., Hidalgo, H. G., Cayan, D. R., & Swetnam, T. W. (2006). Warming and Earlier Spring increase Western U.S. Forest Wildfire Activity. Science, 313(5789), 940–943. https://doi.org/10.1126/science.1128834
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Journal of the Geographical Institute “Jovan Cvijić” SASA
This work is licensed under a Creative Commons Attribution 4.0 International License.
