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Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği

Year 2022, Volume 8, Issue 1, 66 - 75, 14.01.2022
https://doi.org/10.21324/dacd.951902

Abstract

Orman yangınları önemli bir çevre sorunu olmakla beraber tüm ekosistem ve içerisindeki insan ve hayvan yaşamını olumsuz yönde etkilemektedir. Türkiye’de son 20 yılda yaşanan 46.669 orman yangınında toplamda 192.734 hektar orman alanı zarar görmüştür. Bu yangınların ortaya çıkış nedenlerinde ise ilk sırada ihmal-kaza bulunmaktadır. Bu nedenle meydana gelen orman yangınlarının sıklığını en aza indirmek ve zararları önlemek için yangın riski olan alanların belirlenerek, yangın öncesinde, sırasında ve sonrasında alınacak önlemler için hazırlıklı olunması gerekmektedir. Bu çalışmada Muğla ili Milas ilçesi için orman yangını riskini modellemede Lojistik Regresyon (LR) ve Coğrafi Bilgi Sistemleri (CBS) kullanılmıştır. Topoğrafik özellikler, meşcere verileri ve kültürel veriler dikkate alınarak, bu faktörlerin yangınların oluşumu ile ilişkisi araştırılmıştır. LR ile yangın risk tahmininin doğruluk analizleri ve farklı özelliklerdeki alanların yangın riskleri Alıcı Çalışma Karakteristiği (ROC) ve Hosmer-Lemeshow testi ile incelenmiştir. Lojistik Regresyon yöntemi ile elde edilen bulgular doğrultusunda CBS ortamında bir orman yangını risk haritası oluşturulmuştur. Burada orman yangını riski “1” çok düşük riskli ve “5” çok yüksek riskli olmak üzere beş seviyede değerlendirilmiştir. Ortaya çıkan orman yangını risk haritasında, çalışma alanında bulunan toplam orman alanlarının %16’sının yüksek ve çok yüksek risk sınıfında bulunduğu sonucuna varılmıştır.

References

  • Ager A.A., Vaillant N.M., Finney M.A., (2011), Integrating fire behavior models and geospatial analysis for wildland fire risk assessment and fuel management planning, Journal of Combustion, 2011, 572452, doi: 10.1155/2011/572452.
  • Akkaş M.E., Bucak C., Boza Z., Eronat H., Bekereci A., Erkan A., Cebeci C., (2008), Büyük orman yangınlarının meteorolojik veriler ışığında incelenmesi, T.C. Çevre ve Orman Bakanlığı, Ege Ormancılık Araştırma Müdürlüğü, Teknik Bülten No 36, Urla, İzmir.
  • Bewick V., Cheek L., Ball J., (2005), Statistics Review 14: Logistic Regression, Critical Care, 9(1), 112-118.
  • Bilgili E., (2014), Orman Koruma Dersi̇ Geçici̇ Ders Notları (Yayınlanmamış), Karadeniz Teknik Üniversitesi, Trabzon, ss.13–56.
  • Brown A.A., Davis K., (1973), Forest Fire: Control And Use, New York: McGraw-Hill.
  • Butler B.W., Anderson W.R., Cathpole E.A., (2007), Influence of slope on fire spread rate, The fire environment innovations, management, and policy; conference proceedings, ss.75-83.
  • Catry F.X., Rego F.C., Moreira F., (2009), Modeling and mapping wildfire ıgnition risk in Portugal, International Journal of Wildland Fire, 18, 921–931.
  • Chuvieco E., Salas J., (1996), Mapping the spatial distribution of forest fire danger using GIS, International Journal of Geographical Information Science, 10(3), 335-340.
  • Chen D., (2018), Prediction of Forest Fire Occurrence in Daxing’an Mountains Based on Logistic Regression Model, Forest Resources Management, 1, 116-122.
  • Chuvieco E., Congalton R.G., (1989), Application of remote sensing and geographic information systems to forest fire hazard mapping, Remote Sensing Environment 29(2), 151–158.
  • Cleve C., Kelly M., Kearns F.R., Moritz M., (2008), Classification of the wildland–urban ınterface: a comparison of pixel-and object-based classifications using high-resolution aerial photography, Computers, Environment and Urban Systems, 32(4), 317-326.
  • Deng O., Su G.F., Huang Q.Y., Li Y.Q., (2013), Forest fire risk mapping based on spatial logistic model of northeastern china forest zone, Geo-Informatics in Resource Management and Sustainable Ecosystem, ss.181-192.
  • Diaz J., Martinez J., Alvarez A., Banque M., Birkmann J., Feldmeyer D., Vayreda J., (2021), Characterizing forest vulnerability and risk to climate-change hazards, Frontiers in Ecology and the Environment, 19(2), 126-133.
  • Dong X., Li-min D., Guo-fan S., Lei T., Hui W., (2005), Forest fire risk zone mapping from satellite ımages and gis for baihe forestry bureau, Jilin, China, Journal of Forestry Research, 16(3), 169–174.
  • Dupuy J.L., (1995), Slope and fuel load effects on fire behaviour: laboratory experiments in pine needles fuel beds, International Journal of Wildland Fire, 5(3), 153-164.
  • Finney M.A., (2007), A computational method for optimising fuel treatment locations, International Journal of Wildland Fire, 16(6), 702–711.
  • Gai C., Weng W., Yuan H., (2011), GIS-Based Forest Fire Risk Assessment and Mapping, Proceedings of the 2011 Fourth International Joint Conference on Computational Sciences and Optimization, 15-19 April, Kunming and Lijiang City, China, ss. 1240-1244.
  • Garcia-Martinez E., Chas M., Touza J., (2013), Forest fires ın the wildland–urban ınterface: a spatial analysis of forest fragmentation and human impacts, Applied Geography, 43, 127-137.
  • Garson D., (2008), Logistic Regression: Statnotes, North Carolina State University, 33ss.
  • Gheshlaghi A., Feizizadeh H. B., Blaschke T., (2020), GIS-based forest fire risk mapping using the analytical network process and fuzzy logic, Journal of Environmental Planning and Management, 633, 485– 495.
  • Goldarag J.F., Mohammadzadeh A., Ardakani A.S., (2016), Fire risk assessment using neural network and logistic regression, Journal of the Indian Society of Remote Sensing volume 44, 885–894.
  • Hein S., Weiskittel A.R., (2010), Cutpoint analysis for models with binary outcomes: a case study on branch mortality. European Journal of Forest Research, 129, 585–590.
  • Hosmer D.W., Lemeshow S., (2000), Applied Logistic Regression, Wiley, New York, 392ss.
  • Hosmer D.W., Hosmer T., Le Cessie S., Lemeshow S., (1997), A comparison of goodness-of-fit tests for the logistic regression model, Statistics in Medicine, 16(9), 965-980.
  • Hanley J.A., McNeil B.J., (1982), The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143(1), 29-36.
  • Hernandez-Leal P.A., Arbelo M., Gonzalez-Calvo A., (2006), Fire risk assessment using satellite data, Advances in Space Research, 37(4), 741-746.
  • 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.
  • Johnson E.A., Gutsell S.L., (1994), Fire frequency models, methods and interpretations, 25, 250–277.
  • Kalabokidis K., Konstatinidis P., Vasilkos C., (2002), GIS analysis of physical and human impact on wildfire patterns. Forest fire research and wild land fire safety2nin İçinde (Viegas D., Ed.), Millpress, Rotterdam.
  • Karabulut M., Karakoç A., Gürbüz M., Yakup K., (2013), Coğrafi Bilgi Sistemleri Kullanarak Başkonuş Dağında (Kahramanmaraş) Orman Yangını Risk Alanlarının Belirlenmesi, Uluslararası Sosyal Araştırmalar Dergisi, 6(24), 171–179.
  • Kavlak M.O., Çabuk S.N., Çetin M., (2021), Development of forest fire risk map using geographical ınformation systems and remote sensing capabilities: Ören case, Environmental Science and Pollution Research, 28, 33265–33291.
  • Koutsias N., Riva J., Cabello F., Renault N., (2004), Mapping wildfire occurrence at regional scale, Remote Sensing of Environment, 92(3), 363-369.
  • Legendre P., Legendre L., (1998), Numerical Ecology, Elsevier, 1006ss.
  • Massada A.B., Radeloff V.C., Stewart S.I., Hawbaker, T.J., (2009), Wildfire risk in the wildland-urban ınterface: a simulation study in Northwestern Wisconsin, Forest Ecology and Management, 258, 1990-1999.
  • McArthur A.G., (1976), Fire Danger Rating Systems, FAO Consultation on Fires in the Mediterranean Region, Rome.
  • Milanovic S., Markovic N., Pamucar D., Gigovic L., Kostic D., (2020), Forest fire probability mapping in Eastern Serbia: Logistic regression versus random forest method, Forests, 12(1), 5, doi: doi.org/10.3390/f12010005.
  • Modugno S., Balzter H., Cole B., Borrell P., (2016), Mapping regional patterns of large forest fires in wildland–urban ınterface areas in Europe, Journal of Environmental Management, 172, 112-126.
  • Neyişçi T., Ayaşlıgil Y., Ayaşlıgil T., Sönmezışık S., (1999), Yangına Dirençli Orman Kurma İlkeleri, TMMOB Orman Müh. Odası Yayın No:21, Ankara, 140ss.
  • Novo A., Farinas-Alvarez N., Martinez-Sanchez J., Gonzalez-Jorge H., Fernandez-Alonso J.M., Lorenzo H., (2020), Mapping forest fire risk—a case study in Galicia (Spain), Remote Sensing, 12(22), doi: 10.3390/rs12223705.
  • Nuissl H., Siedentop S., (2021), Urbanisation and land use change, Sustainable land management in a European Context’in İçinde (Weith, T., Barkmann, T., Gaasch, N., Rogga, S., Strauß, C., Zscheischler, J., Eds.), Springer, Cham, ss.75-99.
  • Pais S., Aquilue N., Campos J., Sil A., Marcos B., Martinez-Freiria F., Dominguez J., Brotons L., Honrado J.P., Regos A., (2020), Mountain farmland protection and fire-smart management jointly reduce fire hazard and enhance biodiversity and carbon sequestration, Ecosystem Services, 44, 101143, doi: 10.1016/j.ecoser.2020.101143.
  • Pan J., Wang W., Li J., (2016), Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China, National Hazards, 81, 1879–1899.
  • Parajuli R.R., (2020), Citizen disaster science education for effective disaster risk reduction in developing countries, Geoenvironmental Disasters, 7(1), 12, doi: 10.1186/s40677-020-00150-2.
  • Patz J.A., Frumkin H., Holloway T., (2014), Climate Change: Challenges and Opportunities for Global Health, Jama, 312(15), 1565-1580.
  • Pham B.T., Prakash I., (2018), Machine Learning Methods of Kernel Logistic Regression and Classification and Regression Trees for Landslide Susceptibility Assessment at Part of Himalayan Area, India, Indian Journal of Science and Technology, 11(12), 1-10, doi: 10.17485/ijst/2018/v11i12/99745.
  • Pyne S.J., Andrews P.L., Laven R.D., (1996), Introduction To Wildland Fire, Wiley, Toronto, 808ss.
  • Rogan J., Miller J., (2006), Integrating GIS and remotely sensed data for mapping forest disturbance and change, Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches, ss.133-172.
  • Sarı F., (2020), 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(15), 118644, doi: 10.1016/j.foreco.2020.118644.
  • Setiawan I., Mahmud A., Masnsor S., Shariff A., Nuruddin A., (2004), GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia, Disaster Prevention and Management, 13(5), 379–386.
  • Stoltzfus J. C., (2011), Logistic regression: a brief primer, Academic Emergency Medicine, 18(10), 1099-1104.
  • URL-1, (2021), Ormancılık istatistikleri 2017-2020, Orman Genel Müdürlüğü, https://www.ogm.gov.tr/tr/ormanlarimiz/resmi-istatistikler, [Erişim 7 Mayıs 2021].
  • URL-2, (2021), Büyükşehir’den Yangın Mağdurlarına Destek, Muğla Büyükşehir Belediyesi Resmî Web Sayfası, https://www.mugla.bel.tr/haber/buyuksehirden-yangin-magdurlarina-destek, [Erişim 2 Eylül 2021].
  • URL-3, (2019), Orman Yangınlarıyla Mücadelede Yenilikçi Yaklaşımlar Grubu Çalışma Belgesi, Tarım Orman Şûrası, http://www.tarimormansurasi.gov.tr/Sayfa/Detay/1416, [Erişim 7 Mayıs 2021].
  • URL-4, (2021), Sayısal yükseklik modeli (DEM), Alaska Uydu Tesisi (ASF), https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/, [Erişim 5 Mayıs 2021].
  • Van Wagner C.E., (1988), Effect of slope on fires spreading downhill, Canadian Journal of Forest Research, 18(6), doi: doi.org/10.1139/x88-125.
  • Viegas D.X., (2004), A mathematical model for forest fires blowup, Combustion Science and Technology, 177(1), 27-51.
  • Xiangwei C., Long S., Qiangxue W., Shujing W., Dao G., Haiqing H., (2011), Response characteristics and prospect of forest fire disasters in the context of climate change in China, College of Forestry, Northeast Forestry University.
  • Xu D., Dai L., Shao G., Tang L., Wang H., (2005), Forest fire risk zone mapping from satellite images and GIS for baihe forestry Bureau, Jilin, China, Journal of Forestry Research, 16, 169–174.
  • Zglobicki W., Gawrysiak L., Baran-Zglobicka B., (2016), Long-term forest cover changes, within an agricultural region, in relation to environmental variables, Lubelskie province, Eastern Poland, Environment and Earth Sciences, 75, 1373, doi: 10.1007/s12665-016-6195-z.
  • Zhang Y., Xin J., Mu L., Jiao Z., Liu H., Liu D., (2019), A deep learning based forest fire detection approach using UAV and YOLOv3, 2019 1st International Conference on Artificial Intelligence, ss. 1-5, doi: 10.1109/ICIAI.2019.8850815.
  • Zhang Z.X., Zhang H.Y., Zhou D.W., (2010), Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires, Journal of Arid Environments, 74(3), 386-393.

Forest Fire Risk Modeling Using Logistic Regression and Geographic Information Systems: A Case Study in Muğla - Milas

Year 2022, Volume 8, Issue 1, 66 - 75, 14.01.2022
https://doi.org/10.21324/dacd.951902

Abstract

Forest fires are an important environmental problem, they negatively affect the entire ecosystem and human and animal life in it. In Turkey 192.734 hectares of forest area has been damaged in 46.669 forest fires in the last 20 years. Negligence-accident is the primary cause of these fires. For this reason, in order to minimize the frequency of forest fires and prevent damages, areas with fire risk should be determined and it is necessary to be prepared for the precautions to be taken before, during and after the fire. In this study, Logistic Regression (LR) and Geographic Information Systems (GIS) were used to model the forest fire risk for the Milas province in Muğla. Considering the topographic features, stand data and cultural data, the relationship of these factors with the occurrence of fires was investigated. Accuracy analyzes of fire risk estimation with LR and fire risks of areas with different properties were examined by Receiver Operating Characteristic (ROC) and Hosmer-Lemeshow test. In line with the findings obtained by the LR method, a forest fire risk map was created in the GIS environment. Here, forest fire risk is evaluated at five levels, with “1” very low risk and “5” very high risk. In the resulting forest fire risk map, it was concluded that 16% of the total forest areas in the study area are in high and very high risk classes.

References

  • Ager A.A., Vaillant N.M., Finney M.A., (2011), Integrating fire behavior models and geospatial analysis for wildland fire risk assessment and fuel management planning, Journal of Combustion, 2011, 572452, doi: 10.1155/2011/572452.
  • Akkaş M.E., Bucak C., Boza Z., Eronat H., Bekereci A., Erkan A., Cebeci C., (2008), Büyük orman yangınlarının meteorolojik veriler ışığında incelenmesi, T.C. Çevre ve Orman Bakanlığı, Ege Ormancılık Araştırma Müdürlüğü, Teknik Bülten No 36, Urla, İzmir.
  • Bewick V., Cheek L., Ball J., (2005), Statistics Review 14: Logistic Regression, Critical Care, 9(1), 112-118.
  • Bilgili E., (2014), Orman Koruma Dersi̇ Geçici̇ Ders Notları (Yayınlanmamış), Karadeniz Teknik Üniversitesi, Trabzon, ss.13–56.
  • Brown A.A., Davis K., (1973), Forest Fire: Control And Use, New York: McGraw-Hill.
  • Butler B.W., Anderson W.R., Cathpole E.A., (2007), Influence of slope on fire spread rate, The fire environment innovations, management, and policy; conference proceedings, ss.75-83.
  • Catry F.X., Rego F.C., Moreira F., (2009), Modeling and mapping wildfire ıgnition risk in Portugal, International Journal of Wildland Fire, 18, 921–931.
  • Chuvieco E., Salas J., (1996), Mapping the spatial distribution of forest fire danger using GIS, International Journal of Geographical Information Science, 10(3), 335-340.
  • Chen D., (2018), Prediction of Forest Fire Occurrence in Daxing’an Mountains Based on Logistic Regression Model, Forest Resources Management, 1, 116-122.
  • Chuvieco E., Congalton R.G., (1989), Application of remote sensing and geographic information systems to forest fire hazard mapping, Remote Sensing Environment 29(2), 151–158.
  • Cleve C., Kelly M., Kearns F.R., Moritz M., (2008), Classification of the wildland–urban ınterface: a comparison of pixel-and object-based classifications using high-resolution aerial photography, Computers, Environment and Urban Systems, 32(4), 317-326.
  • Deng O., Su G.F., Huang Q.Y., Li Y.Q., (2013), Forest fire risk mapping based on spatial logistic model of northeastern china forest zone, Geo-Informatics in Resource Management and Sustainable Ecosystem, ss.181-192.
  • Diaz J., Martinez J., Alvarez A., Banque M., Birkmann J., Feldmeyer D., Vayreda J., (2021), Characterizing forest vulnerability and risk to climate-change hazards, Frontiers in Ecology and the Environment, 19(2), 126-133.
  • Dong X., Li-min D., Guo-fan S., Lei T., Hui W., (2005), Forest fire risk zone mapping from satellite ımages and gis for baihe forestry bureau, Jilin, China, Journal of Forestry Research, 16(3), 169–174.
  • Dupuy J.L., (1995), Slope and fuel load effects on fire behaviour: laboratory experiments in pine needles fuel beds, International Journal of Wildland Fire, 5(3), 153-164.
  • Finney M.A., (2007), A computational method for optimising fuel treatment locations, International Journal of Wildland Fire, 16(6), 702–711.
  • Gai C., Weng W., Yuan H., (2011), GIS-Based Forest Fire Risk Assessment and Mapping, Proceedings of the 2011 Fourth International Joint Conference on Computational Sciences and Optimization, 15-19 April, Kunming and Lijiang City, China, ss. 1240-1244.
  • Garcia-Martinez E., Chas M., Touza J., (2013), Forest fires ın the wildland–urban ınterface: a spatial analysis of forest fragmentation and human impacts, Applied Geography, 43, 127-137.
  • Garson D., (2008), Logistic Regression: Statnotes, North Carolina State University, 33ss.
  • Gheshlaghi A., Feizizadeh H. B., Blaschke T., (2020), GIS-based forest fire risk mapping using the analytical network process and fuzzy logic, Journal of Environmental Planning and Management, 633, 485– 495.
  • Goldarag J.F., Mohammadzadeh A., Ardakani A.S., (2016), Fire risk assessment using neural network and logistic regression, Journal of the Indian Society of Remote Sensing volume 44, 885–894.
  • Hein S., Weiskittel A.R., (2010), Cutpoint analysis for models with binary outcomes: a case study on branch mortality. European Journal of Forest Research, 129, 585–590.
  • Hosmer D.W., Lemeshow S., (2000), Applied Logistic Regression, Wiley, New York, 392ss.
  • Hosmer D.W., Hosmer T., Le Cessie S., Lemeshow S., (1997), A comparison of goodness-of-fit tests for the logistic regression model, Statistics in Medicine, 16(9), 965-980.
  • Hanley J.A., McNeil B.J., (1982), The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143(1), 29-36.
  • Hernandez-Leal P.A., Arbelo M., Gonzalez-Calvo A., (2006), Fire risk assessment using satellite data, Advances in Space Research, 37(4), 741-746.
  • 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.
  • Johnson E.A., Gutsell S.L., (1994), Fire frequency models, methods and interpretations, 25, 250–277.
  • Kalabokidis K., Konstatinidis P., Vasilkos C., (2002), GIS analysis of physical and human impact on wildfire patterns. Forest fire research and wild land fire safety2nin İçinde (Viegas D., Ed.), Millpress, Rotterdam.
  • Karabulut M., Karakoç A., Gürbüz M., Yakup K., (2013), Coğrafi Bilgi Sistemleri Kullanarak Başkonuş Dağında (Kahramanmaraş) Orman Yangını Risk Alanlarının Belirlenmesi, Uluslararası Sosyal Araştırmalar Dergisi, 6(24), 171–179.
  • Kavlak M.O., Çabuk S.N., Çetin M., (2021), Development of forest fire risk map using geographical ınformation systems and remote sensing capabilities: Ören case, Environmental Science and Pollution Research, 28, 33265–33291.
  • Koutsias N., Riva J., Cabello F., Renault N., (2004), Mapping wildfire occurrence at regional scale, Remote Sensing of Environment, 92(3), 363-369.
  • Legendre P., Legendre L., (1998), Numerical Ecology, Elsevier, 1006ss.
  • Massada A.B., Radeloff V.C., Stewart S.I., Hawbaker, T.J., (2009), Wildfire risk in the wildland-urban ınterface: a simulation study in Northwestern Wisconsin, Forest Ecology and Management, 258, 1990-1999.
  • McArthur A.G., (1976), Fire Danger Rating Systems, FAO Consultation on Fires in the Mediterranean Region, Rome.
  • Milanovic S., Markovic N., Pamucar D., Gigovic L., Kostic D., (2020), Forest fire probability mapping in Eastern Serbia: Logistic regression versus random forest method, Forests, 12(1), 5, doi: doi.org/10.3390/f12010005.
  • Modugno S., Balzter H., Cole B., Borrell P., (2016), Mapping regional patterns of large forest fires in wildland–urban ınterface areas in Europe, Journal of Environmental Management, 172, 112-126.
  • Neyişçi T., Ayaşlıgil Y., Ayaşlıgil T., Sönmezışık S., (1999), Yangına Dirençli Orman Kurma İlkeleri, TMMOB Orman Müh. Odası Yayın No:21, Ankara, 140ss.
  • Novo A., Farinas-Alvarez N., Martinez-Sanchez J., Gonzalez-Jorge H., Fernandez-Alonso J.M., Lorenzo H., (2020), Mapping forest fire risk—a case study in Galicia (Spain), Remote Sensing, 12(22), doi: 10.3390/rs12223705.
  • Nuissl H., Siedentop S., (2021), Urbanisation and land use change, Sustainable land management in a European Context’in İçinde (Weith, T., Barkmann, T., Gaasch, N., Rogga, S., Strauß, C., Zscheischler, J., Eds.), Springer, Cham, ss.75-99.
  • Pais S., Aquilue N., Campos J., Sil A., Marcos B., Martinez-Freiria F., Dominguez J., Brotons L., Honrado J.P., Regos A., (2020), Mountain farmland protection and fire-smart management jointly reduce fire hazard and enhance biodiversity and carbon sequestration, Ecosystem Services, 44, 101143, doi: 10.1016/j.ecoser.2020.101143.
  • Pan J., Wang W., Li J., (2016), Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China, National Hazards, 81, 1879–1899.
  • Parajuli R.R., (2020), Citizen disaster science education for effective disaster risk reduction in developing countries, Geoenvironmental Disasters, 7(1), 12, doi: 10.1186/s40677-020-00150-2.
  • Patz J.A., Frumkin H., Holloway T., (2014), Climate Change: Challenges and Opportunities for Global Health, Jama, 312(15), 1565-1580.
  • Pham B.T., Prakash I., (2018), Machine Learning Methods of Kernel Logistic Regression and Classification and Regression Trees for Landslide Susceptibility Assessment at Part of Himalayan Area, India, Indian Journal of Science and Technology, 11(12), 1-10, doi: 10.17485/ijst/2018/v11i12/99745.
  • Pyne S.J., Andrews P.L., Laven R.D., (1996), Introduction To Wildland Fire, Wiley, Toronto, 808ss.
  • Rogan J., Miller J., (2006), Integrating GIS and remotely sensed data for mapping forest disturbance and change, Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches, ss.133-172.
  • Sarı F., (2020), 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(15), 118644, doi: 10.1016/j.foreco.2020.118644.
  • Setiawan I., Mahmud A., Masnsor S., Shariff A., Nuruddin A., (2004), GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia, Disaster Prevention and Management, 13(5), 379–386.
  • Stoltzfus J. C., (2011), Logistic regression: a brief primer, Academic Emergency Medicine, 18(10), 1099-1104.
  • URL-1, (2021), Ormancılık istatistikleri 2017-2020, Orman Genel Müdürlüğü, https://www.ogm.gov.tr/tr/ormanlarimiz/resmi-istatistikler, [Erişim 7 Mayıs 2021].
  • URL-2, (2021), Büyükşehir’den Yangın Mağdurlarına Destek, Muğla Büyükşehir Belediyesi Resmî Web Sayfası, https://www.mugla.bel.tr/haber/buyuksehirden-yangin-magdurlarina-destek, [Erişim 2 Eylül 2021].
  • URL-3, (2019), Orman Yangınlarıyla Mücadelede Yenilikçi Yaklaşımlar Grubu Çalışma Belgesi, Tarım Orman Şûrası, http://www.tarimormansurasi.gov.tr/Sayfa/Detay/1416, [Erişim 7 Mayıs 2021].
  • URL-4, (2021), Sayısal yükseklik modeli (DEM), Alaska Uydu Tesisi (ASF), https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/, [Erişim 5 Mayıs 2021].
  • Van Wagner C.E., (1988), Effect of slope on fires spreading downhill, Canadian Journal of Forest Research, 18(6), doi: doi.org/10.1139/x88-125.
  • Viegas D.X., (2004), A mathematical model for forest fires blowup, Combustion Science and Technology, 177(1), 27-51.
  • Xiangwei C., Long S., Qiangxue W., Shujing W., Dao G., Haiqing H., (2011), Response characteristics and prospect of forest fire disasters in the context of climate change in China, College of Forestry, Northeast Forestry University.
  • Xu D., Dai L., Shao G., Tang L., Wang H., (2005), Forest fire risk zone mapping from satellite images and GIS for baihe forestry Bureau, Jilin, China, Journal of Forestry Research, 16, 169–174.
  • Zglobicki W., Gawrysiak L., Baran-Zglobicka B., (2016), Long-term forest cover changes, within an agricultural region, in relation to environmental variables, Lubelskie province, Eastern Poland, Environment and Earth Sciences, 75, 1373, doi: 10.1007/s12665-016-6195-z.
  • Zhang Y., Xin J., Mu L., Jiao Z., Liu H., Liu D., (2019), A deep learning based forest fire detection approach using UAV and YOLOv3, 2019 1st International Conference on Artificial Intelligence, ss. 1-5, doi: 10.1109/ICIAI.2019.8850815.
  • Zhang Z.X., Zhang H.Y., Zhou D.W., (2010), Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires, Journal of Arid Environments, 74(3), 386-393.

Details

Primary Language Turkish
Subjects Geosciences, Multidisciplinary
Published Date Ocak 2022
Journal Section Research Articles
Authors

İlker ATMACA> (Primary Author)
YOZGAT BOZOK ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ, ŞEHİR VE BÖLGE PLANLAMA BÖLÜMÜ
0000-0001-9950-2833
Türkiye


Masoud DERAKHSHANDEH>
İSTANBUL GELİŞİM ÜNİVERSİTESİ
0000-0002-7924-8396
Türkiye


Özge IŞIK PEKKAN>
Eskişehir Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü
0000-0003-4634-4864
Türkiye


Mehtap ÖZENEN-KAVLAK>
ESKİŞEHİR TEKNİK ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ
0000-0002-5369-4494
Türkiye


Yavuz Selim TUNCA>
ESKİŞEHİR TEKNİK ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ
0000-0003-3164-926X
Türkiye


Saye Nihan ÇABUK>
ESKİŞEHİR TEKNİK ÜNİVERSİTESİ, YER VE UZAY BİLİMLERİ ENSTİTÜSÜ
0000-0003-4859-2271
Türkiye

Publication Date January 14, 2022
Published in Issue Year 2022, Volume 8, Issue 1

Cite

Bibtex @research article { dacd951902, journal = {Doğal Afetler ve Çevre Dergisi}, eissn = {2528-9640}, address = {}, publisher = {Artvin Çoruh University}, year = {2022}, volume = {8}, number = {1}, pages = {66 - 75}, doi = {10.21324/dacd.951902}, title = {Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği}, key = {cite}, author = {Atmaca, İlker and Derakhshandeh, Masoud and Işık Pekkan, Özge and Özenen-kavlak, Mehtap and Tunca, Yavuz Selim and Çabuk, Saye Nihan} }
APA Atmaca, İ. , Derakhshandeh, M. , Işık Pekkan, Ö. , Özenen-kavlak, M. , Tunca, Y. S. & Çabuk, S. N. (2022). Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği . Doğal Afetler ve Çevre Dergisi , 8 (1) , 66-75 . DOI: 10.21324/dacd.951902
MLA Atmaca, İ. , Derakhshandeh, M. , Işık Pekkan, Ö. , Özenen-kavlak, M. , Tunca, Y. S. , Çabuk, S. N. "Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği" . Doğal Afetler ve Çevre Dergisi 8 (2022 ): 66-75 <http://dacd.artvin.edu.tr/en/pub/issue/68003/951902>
Chicago Atmaca, İ. , Derakhshandeh, M. , Işık Pekkan, Ö. , Özenen-kavlak, M. , Tunca, Y. S. , Çabuk, S. N. "Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği". Doğal Afetler ve Çevre Dergisi 8 (2022 ): 66-75
RIS TY - JOUR T1 - Forest Fire Risk Modeling Using Logistic Regression and Geographic Information Systems: A Case Study in Muğla - Milas AU - İlkerAtmaca, MasoudDerakhshandeh, ÖzgeIşık Pekkan, MehtapÖzenen-kavlak, Yavuz SelimTunca, Saye NihanÇabuk Y1 - 2022 PY - 2022 N1 - doi: 10.21324/dacd.951902 DO - 10.21324/dacd.951902 T2 - Doğal Afetler ve Çevre Dergisi JF - Journal JO - JOR SP - 66 EP - 75 VL - 8 IS - 1 SN - -2528-9640 M3 - doi: 10.21324/dacd.951902 UR - https://doi.org/10.21324/dacd.951902 Y2 - 2021 ER -
EndNote %0 Journal of Natural Hazards and Environment Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği %A İlker Atmaca , Masoud Derakhshandeh , Özge Işık Pekkan , Mehtap Özenen-kavlak , Yavuz Selim Tunca , Saye Nihan Çabuk %T Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği %D 2022 %J Doğal Afetler ve Çevre Dergisi %P -2528-9640 %V 8 %N 1 %R doi: 10.21324/dacd.951902 %U 10.21324/dacd.951902
ISNAD Atmaca, İlker , Derakhshandeh, Masoud , Işık Pekkan, Özge , Özenen-kavlak, Mehtap , Tunca, Yavuz Selim , Çabuk, Saye Nihan . "Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği". Doğal Afetler ve Çevre Dergisi 8 / 1 (January 2022): 66-75 . https://doi.org/10.21324/dacd.951902
AMA Atmaca İ. , Derakhshandeh M. , Işık Pekkan Ö. , Özenen-kavlak M. , Tunca Y. S. , Çabuk S. N. Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği. J Nat Haz Environ. 2022; 8(1): 66-75.
Vancouver Atmaca İ. , Derakhshandeh M. , Işık Pekkan Ö. , Özenen-kavlak M. , Tunca Y. S. , Çabuk S. N. Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği. Doğal Afetler ve Çevre Dergisi. 2022; 8(1): 66-75.
IEEE İ. Atmaca , M. Derakhshandeh , Ö. Işık Pekkan , M. Özenen-kavlak , Y. S. Tunca and S. N. Çabuk , "Lojistik Regresyon ve Coğrafi Bilgi Sistemleri Kullanılarak Orman Yangını Risk Modellemesi: Muğla-Milas Örneği", Doğal Afetler ve Çevre Dergisi, vol. 8, no. 1, pp. 66-75, Jan. 2022, doi:10.21324/dacd.951902