Forest Fire Risk Mapping Using GIS Based Analytical Hierarchy Process Approach

Yıl 2024, Cilt: 10 Sayı: 1, 15 - 28

Ahmet Demir Abdullah Emin Akay

https://doi.org/10.33904/ejfe.1400233

Öz

Turkiye is located in a region sensitive to forest fires due to its climate, vegetation characteristics, and topography. Every year, forest fires, for various reasons, cause burning of thousands of hectares of forest area. Fires damage the ecosystem and have economic consequences. The Mediterranean, Aegean and Marmara coasts, where the Mediterranean climate and fire-sensitive tree species dominate, are at primary risk against forest fires. For an effective fight against forest fires, it is crucial to identify zones with fire risk based on various parameters such as forest structures (tree species, crown closure, stand development class), topographic features (slope, aspect), climate, and proximity to certain points (such as roads, settlements, agricultural areas). Fire risk data will shed light on the measures that can be taken against fire. In this study, GIS (Geographic Information Systems) based Analytical Hierarchy Process (AHP) method, one of the well know Multi-Criteria Decision Making Analysis (MCDA) methods, was used to develop the fire risk map of Mersin Forestry Regional Directorate (FRD) within the Mediterranean region of Turkiye. Then, the accuracy of the fire risk map was evaluated by taking into account the previous fires in the regional directorate. As a result, the findings showed that 13.87% of the study area was classified as very high, 25.87% as high, 24.68% as medium, 22.44% a low, and 13.14% as very low risk areas. The results also indicated that tree species are the most influential risk factor in forest fires, and followed by stand development class factor. The accuracy of the fire risk map was evaluated by using the location information of a total of 562 forest fires in Mersin FRD between 2003-2022. In order to determine the accuracy of the fire risk map, the Receiver Operating Characteristic (ROC) curve method was used in the ArcGIS environment. As a result, the Area Under Curve (AUC) value was approximately 74%, which showed that the fire risk map developed for Mersin FRD was moderately reliable. With this study, it has been demonstrated that it is possible to produce reliable fire risk maps in a short time using the GIS-based AHP method.

Anahtar Kelimeler

Forest fire, GIS, Fire risk map, AHP, ROC, Mediterranean

Etik Beyan

NA

Destekleyen Kurum

Bursa Technical University

Proje Numarası

BTU 220Y010

Teşekkür

This research was financially supported by Bursa Technical University Scientific Research Projects Unit (Project Number: BTU 220Y010).

Kaynakça

• Adab, H. 2017. Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data. Natural Hazards, 87(3):1807–1825.
• Akay, A.E., Erdoğan, A. 2017. GIS-based multi-criteria decision analysis for forest fire risk mapping. In 4th International Geoadvances Workshop-Geoadvances 2017: ISPRS Workshop on Multi-Dimensional & Multi-Scale Spatial Data Modeling. October 14-15. Safranbolu, Turkiye.
• Ateşoğlu, A. 2014. Forest hazard identifying, mapping using satellite imagery-geographic information system and analytic hierarchy process: Bartin-Turkey. J. Environ. Prot. Ecol. 15(2):715–725.
• Baysal, İ. 2014. Integration of forest fires into forest management planning. PhD. Thesis, Karadeniz Technical University, Trabzon. 128 p.
• Bentekhici, N., Bellal, S., Zegrar, A. 2020. Contribution of remote sensing and GIS to mapping the firerisk of Mediterranean forest case of the forest massif of Tlemcen (North-West Algeria). Nat. Hazards, 104:811–831.
• Bilgili, E. 2003. Stand development and fire behavior. Forest Ecology and Management, 179(1-3), 333-339.
• Bilgili, E., Küçük, Ö. 2002. Determination of forest fires with remote sensing technique. GAP IV. Engineering Congress 06-08 June, Şanlıurfa, Turkiye.
• Bilici, E. 2009. A Study on the Integration of Firebreaks and Fireline with Forest Roads Networks and It's Planning and Construction (A Case Study of Gallipoly National Park), Istanbul University. Faculty of Forestry Journal Series: A 59(2):86-102.
• Bonora, L., Conese, C., Marchi, E. 2013. Wildfire occurrence: integrated model for risk analysis and operative suppression aspects management. Am. J. Plant Sci. 4:705–710.
• Carmel, Y., Paz, S., Jahashan, F., Shoshany, M. 2009. Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management, 257(1):370-377.
• Çanakçıoğlu, H. 1993. Forest Conservation. Istanbul University, Faculty of Forestry Press. Istanbul, Turkiye.
• Erten, E., Kurgun, V., Musaoğlu, N. 2005. Forest fire risk zone mapping by using satellite imagery and GIS. TMMO Chamber of Surveying and Cadastre Engineers. 10th Turkish Mapping Scientific and Technical Congress. 28 March - 1 April. Ankara, Turkiye.
• Ertuğrul, M. (2005). The Situation of Forest Fires in the World and in Turkey. ZKÜ Bartın Faculty of Forestry Journal. 7(7):43-50.
• Eskandari, S. 2017. A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arab. J. Geosci. 10, 190.
• Gao, X., Fei, X., Xie, H. 2011. Forest fire risk zone evaluation based on high spatial resolution RS image in Liangyungang Huaguo Mountain Scenic Spot. International Conference on Spatial Data Mining and Geographical Knowledge Service, pp. 593-596. China: Fuzhou.
• Gazzard, R. 2012. Risk management control measure: Toolkit for practitioners and advisors. UK Vegetation Fire Risk Management, 24.
• GDF, 2022. General Directorate of Forestry, Forestry Statistics 2022, https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler. Last visit: 29-10-2023.
• Gheshlaghi, H.A., Feizizadeh, B., Blaschke, B. 2020. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of Environmental Planning and Management, 63(3):481-499.
• Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Aryal, J. 2019. Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire, 2(3):1–27.
• Jaiswal, R. K., Mukherjee, S., Raju, K. D., Saxena, R. 2002. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4(1):1-10.
• Küçük Ö., Bilgili, E., Durmaz, B.D., Sağlam, B., Baysal, İ. 2009. The Effect Factors on Transition from Surface Fire to Crown Fire. Kastamonu Univ., Journal of Forestry Faculty, 9(2):80-85.
• Lin, J., Rinaldi, S. 2009. A derivation of the statistical characteristics of forest fires. Ecological Modelling, 220(7):898-903.
• Özden, Ü.H. 2008. Choosing primary school with analytic hierarchy process, Marmara University, İ.İ.B.F. Journal, 24(1):299-320.
• Pant, S., Kumar, A., Ram, M., Klochkov, Y., Sharma, H.K. 2022. Consistency Indices in Analytic Hierarchy Process: A Review. Mathematics, 10(8):1206.
• Saaty, T.L. 1977. A scaling method for priorities in hierarchical structures. J Math Psychol. 15:234-281. Saaty, T.L. 1980. The Analytic hierarchy process. ISBN 0-07-054371-2, USA.
• Sağlam, B., Bilgili, E., Durmaz, B.D., Kadıoğulları, A. İ., Küçük, Ö. 2008. Spatio-temporal analysis of forest fire risk and danger using LANDSAT imagery. Sensors, 8(6):3970-3987.
• Sari, F. 2021. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: a comparative analysis of VIKOR and TOPSIS. For. Ecol. Manag. 480(2021): 118644.
• Satir, O., Berberoğlu, S., Dönmez, C. 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics Nat. Hazards Risk, 7(5):1645-1658.
• Silva, I.D.B., Valle, M.E., Barros, L.C., Meyer, J.F.C.A. 2020. A wildfire warning system applied to the state of Acre in the Brazilian Amazon. Appl. Soft Comput. J. 89:106075.
• Sivrikaya, F., Akay, A.E., Oğuz, H., Yenilmez, N. 2011. Mapping Forest Fire Danger Zones Using GIS: A Case Study from Kahramanmaraş. 6th International Symposium on Ecology and Environmental Problems. 17-20 November, Antalya.
• 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(2022):101537.
• Suryabhagavan, K.V., Misrak, A., Balakrishnan, M. 2016. GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia. Trop. Ecol. 57(1):33–43.
• Şakar, D. 2010. Determining the optimum route providing the fastest transportation to the fire areas by using GIS based decision support system. MSc Thesis. KSU, Faculty of Forestry, Kahramanmaraş. Turkiye. 81 p.
• Wilkie., V.A. 2003. Sustainable Forest Management and The Ecosystem Approach: Two Concepts, One Goal. FAO, Rome: Forestry Department.
• Yeşilnacar, E.K. 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. PhD. Dissertation, Department of Geomatics, The University of Melbourne. 709 p.