Türkiye’deki 2000 – 2021 Yılları Arasındaki Bitki Örtüsü Yangınlarının Mekânsal Analizi

Year 2023, Volume: 4 Issue: 1, 33 - 46, 28.03.2023

Özer Akyürek

https://doi.org/10.48123/rsgis.1179051

Cited By: 1

Abstract

Doğal veya insani sebeplerden meydana gelen yangınlar, yeryüzünde yaşayan tüm canlıları etkileyen en önemli doğal afetlerden bir tanesidir. Yeryüzünde her yıl ortalama olarak 3-5 milyon km2lik ormanlık alan yanmaktadır. Meydana gelen yangınlar, canlıları etkileyen küresel ısınma, atmosferde bulunan hava kirletici parametrelerin yoğunluğunda artış vb. çeşitli faktörler meydana getirmektedirler. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri gelişen günümüz teknolojisinde meydana gelen doğal afetlerin izlenmesinde ve yönetiminde sıklıkla başvurulan araçlardandır. Konumsal verileri yönetmedeki başarısı, konumsal analiz yapabilme yeteneği, sonuçları sunabilme ve görselleştirme kapasitesi nedeniyle sıklıkla tercih edilmektedir. Bu çalışmada 2000 – 2021 yılları arasında Türkiye’de ormanlık ve otlak alanlarda meydana gelen bitki örtüsü yangınları mekânsal istatistik yöntemlerle incelenerek analizi yapılmış ve haritalandırılması sağlanmıştır. Analizler sonucunda 21 yıllık süreç içerisinde ormanlık alan yangınlarından en çok etkilenen bölgelerin Ege, Akdeniz ve Güneydoğu Anadolu bölgeleri, otlak alan yangınlarından ise İç ve Güneydoğu Anadolu bölgesi olduğu tespit edilmiştir. Ormanlık alan yangınlarından en çok etkilenen ilin Antalya, otlak alan yangınlarından en çok etkilenen ilin ise Şanlıurfa olduğu belirlenmiştir.

Keywords

Bitki örtüsü yangını, Mekânsal istatistik, FIRMS, Moran’s I, Getis Ord Gi*, Anselin yerel moran’s I

Spatial Analysis of Wildfires in Türkiye between 2000 – 2021

Abstract

Fires caused by natural or human causes are one of the most important natural disasters that affect all living things on earth. Fires cause an average of 3-5 million km2 of area to burn annually. Fires that occur, global warming affecting living things, increase in the density of air pollutant parameters in the atmosphere, etc. is caused by various factors. Remote Sensing and Geographic Information Systems are frequently used in the monitoring and management of natural disasters that occur in today's developing technology. It is preferred because of its success in managing spatial data, ability to perform spatial analysis, and capacity to present and visualize results. In this study, vegetation fires that occurred in forest and grassland areas in Türkiye between 2000 and 2021 were analyzed and mapped by using spatial statistical methods. As a result of the analysis, it has been determined that the regions most affected by forest fires in the 21-year period are the Aegean, Mediterranean and Southeastern Anatolia regions and Central and Southeastern Anatolia regions by grassland fires. It has been determined that the province most affected by forest fires is Antalya, and the province most affected by grassland fires is Şanlıurfa.

Keywords

Wildfires, Spatial statistics, FIRMS, Moran’s I, Getis Ord Gi*, Anselin local moran’s I

References

• Affan, M., Syukri, M., Wahtuna, L., & Sofyan, H. (2016). Spatial Statistic Analysis of Earthquakes in Aceh Province Year 1921-2014: Cluster Seismicity. Aceh International Journal of Science and Technology, 5(2), 54-62.
• Aftergood, O. S. R., & Flannigan, M. D. (2022). Identifying and analyzing spatial and temporal patterns of lightning-ignited wildfires in Western Canada from 1981 to 2018. Canadian Journal of Forest Research, 52, 1399-1411.
• Aksoy, N., Tuğ, G. N., & Eminağaoğlu, Ö. (2014). Türkiye’nin Vejetasyon Yapısı. In Ü. Akkemik (Eds.), Türkiye'nin Doğal-Egzotik Ağaç ve Çalıları-I (pp. 54-68), Ankara: Orman Genel Müdürlüğü Yayınları.
• Al-Ahmadi, K., Al-Amri, A., & See, L. (2013). A Spatial Statistical Analysis of the occurence of earthquakes along the Red Sea floor spreading: Clusters of Seismicity. Arabian Journal of Geosciences, 7(7), 2893-2904.
• Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geographical Analysis, 27(2), 93-115.
• Arca, D., Hacısalihoğlu, M., & Kutoğlu, H. (2020). Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Natural Hazards, 104(1), 73-89.
• Arslantürk, N. (2007). Yangının vejetasyon üzerine etkisi. Selçuk Üniversitesi Fen Fakültesi Fen Dergisi, 29(2), 141-153.
• Bae, G., Jung, Y., & Yoo, H. (2015). An analysis on the characteristics of spatial clustering distribution in the urban fire of Gyeongsangnam-do, Korea. In ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, 2015. Proceedings. (pp. 4597-4603). Asian Association on Remote Sensing.
• Barreal, J., & Loureiro, M. L. (2015). Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain). Forest Systems, 24(2), e022. doi: 10.5424/fs/2015242-05713.
• Bone, C., Wulder, M. A., White, J. C., Robertson, C., & Nelson, T. A. (2013). A GIS-based risk rating of forest insect outbreaks using aerial overview surveys and the local Moran’s I statistic. Applied Geography, 40, 161-170.
• Busico, G., Giuditta, E., Kazakis, N., & Colombani, N. (2019). A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role. Sustainability, 11(24), 7166. doi: 10.3390/su11247166.
• Chen, C. Y., & Yang, Q. H. (2018, March). Hotspot analysis of the spatial and temporal distribution of fires. In 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018). Proceedings. (pp. 15-21).
• Cheruiyot, K. (2022). Detecting spatial economic clusters using kernel density and global and local Moran’s I analysis in Ekurhuleni metropolitan municipality, South Africa. Regional Science Policy and Practice, 14(2), 307-327.
• Çetin, M., Isik Pekkan, Ö., Ozenen Kavlak, M., Atmaca, I., Nasery, S., Derakhshandeh, M., & Cabuk, S. N. (2022). GIS-based forest fire risk determination for Milas district, Turkey. Natural Hazards. doi: 10.1007/s11069-022-05601-7.
• Çolak, E., & Sunar, F. (2020a). Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, İzmir. International Journal of Disaster Risk Reduction, 45, 101479. doi: 10.1016/j.ijdrr.2020.101479.
• Çolak, E., & Sunar, F. (2020b). Spatial pattern analysis of post-fire damages in the Menderes District of Turkey. Frontiers of Earth Science, 14(2), 446-461.
• Coşkuner, K. A. (2022). Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. IForest, 15(2), 85-94.
• Duran, C. (2014). Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi, 1(1A), 38-49.
• Erdoğan, S. (2010). Epidemiyolojide CBS Uygulamaları: Konumsal Kümeleme Yöntemlerinin Karşılaştırılması-Menenjit Örneği. Harita Teknolojileri Elektrik Dergisi, 2(2), 23-31.
• Feng, Y., Chen, X., Gao, F., & Liu, Y. (2018). Impacts of changing scale on Getis-Ord Gi* hotspots of CPUE: a case study of the neon flying squid (Ommastrephes bartramii) in the northwest Pacific Ocean. Acta Oceanologica Sinica, 37(5), 67-76.
• Fornacca, D., Ren, G., & Xiao, W. (2017). Performance of Three MODIS fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of Northwest Yunnan, China, characterized by frequent small fires. Remote Sensing, 9(11), 1131. doi: 10.3390/rs9111131.
• Gajovic, V., & Todorovic, B. (2013). Spatial and temporal analysis of fires in Serbia for period 2000-2013. Journal of the Geographical Institute Jovan Cvijic, SASA, 63(3), 297-312.
• Gayır, B., & Arslan, O. (2018). Orman Yangınlarının CBS Tabanlı Konumsal İstatistik Analizi: 2011 -2015 Yılları Arasında Muğla Orman Bölge Sınırları İçerisinde Çıkan Yangınlar. Anadolu Orman Araştırmaları Dergisi, 4(1), 44-60.
• Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217, 72-85.
• Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178(2016), 31-41.
• Gökkaya, K. (2022). Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey. Turkish Journal of Bioscience and Collections, 6(2), 37-44.
• İban, M. C., & Sekertekin, A. (2022). Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics, 69, 101647. doi: 10.1016/j.ecoinf.2022.101647.
• İban, M. C., & Şahin, E. (2022). Monitoring burn severity and air pollutants in wildfire events using remote sensing data: the case of Mersin wildfires in summer 2021. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12(2), 487-497.
• Karabacak, K., Türkşen, Ö., & Bayar, R. (2019, June). Spatial statistics analysis of forest fires in Antalya province. In 1st Istanbul International Geography Congress, 2019. Proceedings. (pp. 615-630). Istanbul: Istanbul University Press.
• Ma, C., Pu, R., Downs, J., & Jin, H. (2022). Characterizing Spatial Patterns of Amazon Rainforest Wildfires and Driving Factors by Using Remote Sensing and GIS Geospatial Technologies. Geosciences, 12(6), 237. doi: 10.3390/geosciences12060237.
• Novo, A., Fariñas-álvarez, N., Martínez-Sánchez, J., González-Jorge, H., Fernández-Alonso, J. M., & Lorenzo, H. (2020). Mapping forest fire risk—a case study in Galicia (Spain). Remote Sensing, 12(22), 3705. doi: 10.3390/rs12223705.
• OGM. (2021, Kasım 2). 2020 Türkiye Orman Varlığı. Retrieved from https://www.ogm.gov.tr/tr/ormanlarimiz-sitesi/TurkiyeOrmanVarligi/Yayinlar/2020%20T%C3%BCrkiye%20Orman%20Varl%C4%B1%C4%9F%C4%B1.pdf
• Ohyama, T., Hanyu, K., Tani, M., & Nakae, M. (2022). Investigating crime harm index in the low and downward crime contexts: A spatio-temporal analysis of the Japanese Crime Harm Index. Cities, 130, 103922. doi: 10.1016/j.cities.2022.103922.
• Oom, D., & Pereira, J. M. C. (2013). Exploratory spatial data analysis of global MODIS active fire data. International Journal of Applied Earth Observation and Geoinformation, 21(1), 326-340.
• Rossi, F., & Becker, G. (2019). Creating forest management units with Hot Spot Analysis (Getis-Ord Gi*) over a forest affected by mixed-severity fires. Australian Forestry, 82(4), 166-175.
• Sarı, 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. doi: 10.1016/j.foreco.2020.118644.
• Sarı, F. (2022). Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models. Journal of Forestry Research. doi: 10.1007/s11676-022-01502-4.
• 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. doi: 10.1016/j.ecoinf.2021.101537.
• Tariq, S., ul‐Haq, Z., Mariam, A., Mehmood, U., & Ahmed, W. (2022). Assessment of air quality during worst wildfires in Mugla and Antalya regions of Turkey. Natural Hazards, 115, 1235-1254.
• Tavşanoğlu, Ç. (2017). Yangın Coğrafyası: Vejetasyon Yangınlarının ve Ekolojik Sonuçlarının Alansal Dağılımı. Kebikeç (İnsan Bilimleri Için Kaynak Araştırmaları Dergisi), 43, 289-300.
• Tavşanoğlu, Ç. (2021). Akdeniz Bölgesindeki Büyük Orman Yangınlarının Sebepleri ve Yangın Sonrası Yapılması Gerekenler. Teknik Rapor, Hacettepe Üniversitesi, Ankara.
• Trucchia, A., Meschi, G., Fiorucci, P., Gollini, A., & Negro, D. (2022). Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level. Fire, 5(1), 30. doi: 10.3390/fire5010030.
• Tüfekçioğlu, İ., Ergan, G., Kaynaş, B., Aktepe, N., & Tavşanoğlu, Ç. (2022). Akdeniz iklim bölgesindeki alt yükselti orman ve çalılıklarında yangın sonrası hızlı ekolojik değerlendirme ile restorasyon önerilerinin geliştirilmesi: Datça-Bozburun Özel Çevre Koruma Bölgesi örneği. Türkiye Ormancılık Dergisi, 23(3), 163-177.
• Visner, M., Shirowzhan, S., & Pettit, C. (2021). Spatial analysis, interactive visualisation and GIS-based dashboard for monitoring spatio-temporal changes of hotspots of bushfires over 100 years in New South Wales, Australia. Buildings, 11(2), 37. doi: 10.3390/buildings11020037.
• Yakar, M. (2011). Nüfus Dağılımının Mekansal Analizi: Afyonkarahisar ili Örneği. Uluslararası Sosyal Araştırmalar Dergisi, 4(19), 389-406.
• Yuan, Y., Cave, M., & Zhang, C. (2018). Using Local Moran’s I to identify contamination hotspots of rare earth elements in urban soils of London. Applied Geochemistry, 88, 167-178.
• Zahran, E. S. M. M., Shams, S., & Said, S. N., Matullah B. M. (2020). Validation of forest fire hotspot analysis in GIS using forest fire contributory factors. Systematic Reviews in Pharmacy, 11(12), 249-255.
• Zhang, L., Tao, Z., & Wang, G. (2022). Assessment and determination of earthquake casualty gathering area based on building damage state and spatial characteristics analysis. International Journal of Disaster Risk Reduction, 67, 102688. doi: 10.1016/j.ijdrr.2021.102688.
• Zúñiga-Vásquez, J. M., Cisneros-González, D., Pompa-García, M., Rodríguez-Trejo, D. A., & Pérez-Verdín, G. (2017). Modelación espacial de incendios forestales en México: Una integración de dos bases de datos. Bosque, 38(3), 563-574.