Orman yol ağının orman yangınlarına etkisinin coğrafi bilgi sistemleri ile araştırılması

Yıl 2024, Cilt: 10 Sayı: 1, 72 - 77, 11.07.2024

Fatih Sivrikaya Korhan Enez Gonca Özcan

https://doi.org/10.53516/ajfr.1456067

Öz

Giriş ve hedefler Orman yangını, orman ekosistemini önemli ölçüde etkileyen doğal afetlerden bir tanesidir. Yangınlar, orman kaynaklarının sürdürülebilirliğini ve ekosistemdeki flora ve faunanın varlığını olumsuz yönde etkilemekte, insan hayatını tehdit etmekte ve orman emvalinde ekonomik kayba neden olmaktadır. Orman yangınları doğrudan veya dolaylı olarak insan faaliyetleriyle yakından ilişkilidir. Türkiye'de orman yangınlarının yaklaşık %86'sına insan faaliyetleri neden olmaktadır. Yol ağına olan mesafe yangın riskini etkileyen önemli parametrelerden bir tanesidir. Bu çalışmada orman yollarının orman yangınlarına etkisi Coğrafi Bilgi Sistemi ile araştırılmıştır. Yöntemler Çalışma, Türkiye'nin Akdeniz bölgesindeki Mersin Orman Bölge Müdürlüğü'nde yer alan Anamur Orman İşletmesi'nde gerçekleştirmiştir. 2015-2022 yılları arasında çalışma alanında meydana gelen yangınlar Orman Genel Müdürlüğünden, yol haritası ise orman yol ağı planlarından elde edilmiştir. Orman yollarına ArcGIS yazılımında 250, 500, 1000 ve 5000 metre tampon zon (buffer) atılmıştır. Yangın noktaları bu zon haritası ile çakıştırılmıştır. Orman yollarına yakınlık ile yangın noktaları arasındaki ilişki ortaya konulmuştur.
Bulgular Yola olan mesafe ile yangın noktaları arasında negatif bir ilişki bulunmuştur. Yoldan uzaklaştıkça insan faaliyetlerinden kaynaklanan yangınların sıklığında azalma görüldüğü tespit edilmiştir.
Sonuçlar En fazla yangın ve yanan alan miktarı 0-250 m tampon zonda meydana gelmiştir. Bulgular, çalışma alanında gelecekte çıkabilecek yangınların yönetimi ve tahmin edilmesi açısından büyük önem taşımaktadır.

Anahtar Kelimeler

GIS, Orman yangını, orman yolu, yangın çıkış noktaları, zon

Teşekkür

Bu makale 15-17 Kasım 2023 tarihlerinde Filipinler’de gerçekleştirilen 4th International Conference on Environment and Forest Conservation'da sunulan sözlü bildirinin genişletilmiş halidir.

Investigation of the effect of the forest road network on forest fires with geographical information systems

Öz

Background and aims: Forest fire is one of the natural disasters that significantly affects the forest ecosystem. Forest fires negatively affect the sustainability of forest resources and the existence of flora and fauna in the ecosystem, threaten human life, and cause economic losses. Forest fires are closely related, directly or indirectly, to human activities. Approximately 86% of forest fires in Türkiye are caused by human activities. Distance to the road network is one of the most important parameters affecting fire risk. This study investigated the effect of forest roads on forest fires with the Geographic Information System (GIS). Methods: The study was conducted in the Anamur Forest Enterprise in the Mersin Forest Regional Directorate in the Mediterranean region of Türkiye. The fires in the study area between 2015 and 2022 were obtained from the General Directorate of Forestry, and the road map was obtained from the forest road network map. Buffer zones of 250, 500, 1000, and 5000 meters were assigned to forest roads in ArcGIS software. Fire points are overlaid with this buffer zone map. The relationship between proximity to forest roads and fire points has been revealed. Results: A negative relationship was found between the distance to the road and fire points. It has been determined that the frequency of fires caused by human activities decreases as the distance from the road increases. Conclusion: The highest amount of fire and burned area occurred in the 0-250 m buffer zone. The results are of great importance for the management and prediction of future forest fires in the study area.

Anahtar Kelimeler

GIS, fire incidence, fire management, number of fire, buffer

Kaynakça

• Abid, F., 2021. A survey of machine learning algorithms based forest fires prediction and detection systems. Fire technology, 57(2), 559-590.
• 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.
• Akbulak, C., Tatlı, H., Aygün, G., Sağlam, B., 2018. Forest fire risk analysis via integration of GIS, RS and AHP: the case of Çanakkale, Turkey. Journal of Human Sciences ,15(4), 2127–2143. https://www.j-humansciences.com/ojs/index.php/IJHS/article/view/5491.
• Assaker, A., Darwish, T., Faour, G., Noun, M. 2012 Use of Remote Sensing and GIS to Assess the Anthropogenic Impact on Forest Fires in Nahr Ibrahim Watershed, Lebanon. Lebanese Science Journal, 13(1), 15-28.
• 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). Natural Hazards 104, 811–831.
• Bilgili, E. Goldammer, J.G., 2000. Fire in the Mediterranean Basin: Towards an interdisciplinary science programme. In proc. XXI IUFRO World Congress 2000, Forests and Society: The role of research, Vol.1, P.45-54.
• Cardille, J.A., Ventura, S.J., Turner, M.G., 2001. Environmental and social factors influencing wildfires in the upper midwest, United States. Ecological. Applications. 11(1), 111–127.
• Catry, F. X., Damasceno, P., Silva, J. S., Galante, M., Moreira, F., 2007. Spatial distribution patterns of wildfire ignitions in Portugal. Modelação espacial do risco de ignição em Portugal Continental, 8.
• Colak, E., Sunar, F., 2020. Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International Journal of Disaster Risk Reduction 45.
• Çoban, H.O., Erdin, C., 2020. Forest fire risk assessment using GIS and AHP integration in Bucak forest enterprise, Turkey. Applied Ecology and Environmental Research. 18(1), 1567–1583.
• Dimitrakopulos, A.M., Bemmerzouk, A.M., Mitsopoulos, I.D., 2011. Evaluation of the Canadian fire weather index system in eastern Mediterranean environment. Meteorological Applications. 18, 83–93.
• Duran, C., 2014. Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi, 1(1A), 38-49.
• Erten, E., Kurgun, V., Musaoğlu, N. 2005 Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Kullanarak Orman Yangını Bilgi Sisteminin Kurulması. TMMOB Harita ve Kadastro Mühendisleri Odası 10. Türkiye Harita Bilimsel ve Teknik Kurultayı 28 Mart - 1 Nisan 2005, Ankara.
• Eskandari, S., Ghadikolaei, J., Jalilvand, H., Saradjian, M.R., 2013. Detection of fire high-risk areas in northern forests of Iran using dong model. World Applied Sciences Journal. 27(6), 770–773.
• 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. In: IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. Fuzhou, China.
• Ghobadi, G.J., Gholizadeh, B., Dashliburun, O.M., 2012. Forest fire risk zone mapping from geographic information system in Northern forests of Iran (case study, Golestan Province). International Journal of Agriculture and Crop Sciences. 4, 818–824.
• Güngöroğlu, C., 2017. Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: the case of Turkey/Çakırlar. Hum. Ecol. Risk. Assess. 23(2), 388–406.
• Hoang, T.V., Chou, T.Y., Fang, Y.M., Nguyen, N.T., Nguyen, Q.H., Canh, P.X., Toan, D.N. B., Nguyen, X.L., Meadows, M.E., 2020. Mapping forest fire risk and development of early warning system for NW Vietnam using AHP and MCA/GIS methods. Applied Sciences. 10, 4348.
• Jaiswal, R.K., Mukherjee, S., Raju, D.K., 2002. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation. 4(1), 1–10.
• Jin, X. Y., Jin, H. J., Iwahana, G., Marchenko, S. S., Luo, D. L., Li, X. Y., & Liang, S. H. (2021). Impacts of climate-induced permafrost degradation on vegetation: A review. Advances in Climate Change Research. 12(1), 29-47.
• Joaquim, G. S., Bahaaeddin, A. E., Josep, R. C. 2007. Remote Sensing Analysis to Detect Fire Risk Locations, GeoCongres-2007, Quebec, Canada.
• Karabulut, M., Karakoç, A., Gürbüz, M., Kızılelma, Y. 2013. “Coğrafi Bilgi Sistemleri Kullanarak Başkonuş Dağında (Kahramanmaraş) Orman Yangını Risk Alanlarının Belirlenmesi. Uluslararası Sosyal Araştırmalar Dergisi, 6(24), 171-179.
• Kernan, J.T., Hessl, A.E., 2010. Spatially heterogeneous estimates of fire frequency in ponderosa pine forests of Washington, USA. Fire Ecology. 6, 117-135.
• Kolanek, A., Szymanowski, M., Raczyk, A. 2021. Human activity affects forest fires: the impact of anthropogenic factors on the density of forest fires in Poland. Forests, 12(6), 728.
• Levine, J.S. 2000. Global Biomass Burning: A Case Study of the Gaseous and Particulate Emissions Released to the Atmosphere During the 1997 Fires in Kalimantan and Sumatra, Indonesia. In Biomass Burning and Its Inter-Relationships with the Climate System; Innes, J.L., Beniston, M., Verstraete, M.M., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2000; pp. 15–32.
• Li, J. Q., Li, Z. Y., Yi, H.R. 2010. Interaction relation between forest and global climate change. Journal of Northwest Forestry University (in Chinese). 25(4), 23-28.
• Maingi, J. K., Henry, M. C. 2007. Factors influencing wildlife occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire, 16, 23-33.
• Maktite, A., Faleh, A., 2017. Cartographie des zones à risque d’incendies de forêts à l’aide Du SIG et la télédétection dans l’arrière-pays du port Tanger Med. European Scientific Journal, 13(32), 205-224.
• Minsley, B. J., Pastick, N. J., Wylie, B. K., Brown, D. R., Andy Kass, M. 2016. Evidence for nonuniform permafrost degradation after fire in boreal landscapes. Journal of Geophysical Research: Earth Surface. 121(2), 320-335.
• Morrison, P.H. 2007. Roads and Wildfires. Pacific Biodiversity Institute, Winthrop, Washington. 40 p.
• Narayanaraj, G., Wimberly, M. C. 2012. Influences of forest roads on the spatial patterns of human-and lightning-caused wildfire ignitions. Applied Geography, 32(2), 878-888.
• OGM, 2020. Forestry Statistics. Publications of General Directorate of Forestry. htt ps://web.ogm.gov.tr/ekutuphane/Sayfalar/Istatistikler.aspx.
• Özşahin, E. 2014. Forest fire susceptibility analysis using GIS and AHP: the case of Antakya Forestry Operation Directorate. Route Educational and Social Science Journal, 1(3), 50-71.
• Price, O. F., Bradstock, R. A. 2010. The effect of fuel age on the spread of fire in sclerophyll forest in the Sydney region of Australia. International Journal of Wildland Fire. 19(1), 35-45.
• 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, 118644.
• Satir, O., Berberoglu, S., Donmez, C., 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics Natural Hazards Risk 7(5), 1645–1658.
• Sivrikaya, F., Bozali, N., Okumuş, A., Çankaya, E. Ç. 2015. Forest road network effect on forest fire: a case study of Turkey. 38th Annual Meeting of COFE Symposium, Engineering Solutions for Non-Industrial Private Forest Operations, 282-290, 19-22 June, Lexington, Kentucky, USA.
• 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, 101537.
• Sivrikaya, F., Günlü, A., Küçük, Ö., Ürker, O. 2024. Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices. Ecological Informatics, 79, 102461.
• Stolle, F., K.M. Chomitz, E.F. Lambin and T.P. Tomich, 2003. Human ecological intervention and the role of forest fires in human ecology. Forest Ecology and Management, 179, 277-292.
• Syphard, A. D., Radeloff, V. C., Keeley, J. E., Hawbaker, T. J., Clayton, M. K., Stewart, S. I. 2007. Human influence on California fire regimes. Ecological Applications, 17, 1388e1402.
• USDA, 2000. Forest roads: a synthesis of scientific information (Vol. 509). US Department of Agriculture, Forest Service, Pacific Northwest Research Station.
• Vilar, L., Woolford, D.G., Martell, D.L., Martin, M.P., 2010. A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain. International Journal of Wildland Fire 19(3), 325–337.
• Yang, J., He, H. S., Sturtevant, B. R., Miranda, B. R., Gustafson, E. J. 2008. Comparing effects of fire modeling methods on simulated fire patterns and succession: a case study in the Missouri Ozarks. Canadian Journal of Forest Research, 38, 1290e1302.