Doğal Afetler ve Çevre Dergisi
Araştırma Makalesi
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Türkiye Muğla İlindeki Çam Balı Arılıkları için Doğal Afet Riskinin Değerlendirilmesi

Yıl 2022, Cilt 8, Sayı 2, 250 - 263, 30.07.2022

Fatih SARI

https://doi.org/10.21324/dacd.1009499

Öz

Muğla ili dünya toplam çam balı üretiminin %90'ına sahip olduğu için verimliliğin sağlanması, arılık lokasyonları için önlemlerin belirlenmesi ve risklerin önceden tahmin edilmesini gerektirmektedir. Orman yangını ve sel gibi doğal afetlerin önceden tahmin edilmesi, verimliliğin sürdürülmesinde ve ekonomik kayıpların tahmin edilmesinde hayati öneme sahiptir. Muğla ili, yoğun orman örtüsü nedeniyle yüksek bir orman yangını potansiyeline sahiptir ve her yıl yaklaşık 200 orman yangını meydana gelmektedir. Çam balı için ormanlarda yüksek miktarda (yaklaşık 15.000) arılık yeri bulunduğundan, orman yangınları arılıkları tehdit eden ana faktörlerden biridir. Öte yandan, ilde tüm koloniyi yok ederek arı kovanı yerlerini tehdit eden yüksek sel oluşum oranı (yılda 20 adet) bulunmaktadır. Bu çalışmada, Orman Yangını Risk İndeksi (FFRI) ve Taşkın Tehlike Risk İndeksi (FHRI) uygulanarak, arılık lokasyonları için sigorta sürecine rehberlik edecek Arılık Lokasyonları Risk İndeksi (ALRI) gerçekleştirilmiştir. Sonuç olarak, çalışma alanının 1533.40 ha (%11.82)'si arılık yerleri için aşırı riskli bölgeler olarak belirlenmiştir. Sonuçlar 1454 orman yangın yeri ve Eşen, Dalaman, Sarıçay, Akçay, Kamiişdere ve Namnam nehirlerinin sel tehlikesine yüksek derecede duyarlı olduğu belirtilen 20 taşkın tehlikesi yeri ile doğrulanmıştır.

Anahtar Kelimeler

Orman Yangınları, Sel Felaketi, Analitik Hiyerarşi Yöntemi, Uygunluk Analizi, Arıcılık

Kaynakça

  • Abou-Shaara H.F., Al-Ghamdi A.A., Mohamed A.A., (2013), A suitability map for keeping honey bees under harsh environmental conditions using geographical information system, World Applied Science Journal, 22, 1099–1105.
  • Adab H., Kanniah K.D., Solaimani K., (2013), Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques, Natural Hazards, 65(3), 1723–1743.
  • Ajin R.S., Loghin A.M., Jacob M.K., Vinod P.G., Krishnamurthy R.R., (2016), The risk assessment of potential forest fire in Idukki Wildlife Sanctuary using RS and GIS techniques, International Journal of Advanced Earth Science and Engineering, 5, 308-18.
  • Alexakis D.D., Grillakis M.G., Koutroulis A.G., Agapiou A., Themistocleous K., Tsanis I.K., Michaelides S., Pashiardis S., Demetriou C., Aristeidou K., Retalis A., Tymvios F., Hadjimitsis D.G., (2014), GIS and remote sensing techniques for the assessment of land use changes impact on flood hydrology: the case study of Yialias Basin in Cyprus, Natural Hazards and Earth System Sciences, 14, 413–426.
  • Amraoui M., Pereira M.G., DaCamara C.C., Calado T.J., (2015), Atmospheric conditions associated with extreme fire activity in the Western Mediterranean region, Science of The Total Environment, 524, 32–39.
  • Arentze T.A., Timmermans HJP., (2000), ALBATROSS: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of Technology, The Netherlands.
  • Bonora L., Conese C., Marchi E, Testi E., Montorselli N.B., (2013), Wildfire occurrence: Integrated model for risk analysis and operative suppression aspects management, American Journal of Plant Sciences, 4, 705–710.
  • Bapalu G.V., Sinha R., (2005), GIS in flood hazard mapping: a case study of Kosi River Basin, India, http://www.gisdevelopment.net/application/natural_hazards/floods/floods001pf.htm [Accessed 17 January 2014].
  • Cheng S.P., Wang R.Y., (2004), Analyzing hazard potential of typhoon damage by applying grey analytic hierarchy process, Natural Hazards, 33(1), 77-103.
  • Chen Y., Yua J., Khan S., (2010), Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation, Environmental Modelling & Software, 25, 1582-1591.
  • Chuvieco E., Aguado I., Yebra M., Nieto H., Salas J., Martín M.P., Zamora R., (2010), Development of a framework for fire risk assessment using remote sensing and geographic information system technologies, Ecological Modelling, 221(1), 46–58.
  • Damián G.C., (2016), GIS-based optimal localisation of beekeeping in rural Kenya, Master degree thesis, Master in Geographical Information Sciences Department of Physical Geography and Ecosystems Science, Lund University.
  • Elkhrachy İ., (2015), Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA), The Egyptian Journal of Remote Sensing and Space Sciences, 18, 261–278.
  • Estoque R.C., Murayama Y., (2010), Suitability Analysis for Beekeeping Sites in La Union, Philippines, Using GIS and Multi-Criteria Evaluation Techniques, Research Journal of Applied Sciences, 5(3), 242-253.
  • Estoque R.C., Murayama Y., (2011), Suitability Analysis for Beekeeping Sites Integrating GIS & MCE Techniques, In Spatial Analysis and Modeling in Geographical Transformation Process (Murayama Y., Thapa R.B., Ed.), Springer, Netherlands, ss.215-233.
  • Eugenio F.C., Dos Santos A.R., Fiedler N.C., Ribeiro G.A., Da Silva A.G., Dos Santos Á.B., Paneto G.G., Schettino V.R., (2016), Applying GIS to develop a model for forest fire risk: a case study in Espírito Santo, Brazil, Journal of Environmental Management, 173, 65–71.
  • Fernandez P., Roque N., Anjos O., (2016), Spatial multicriteria decision analysis to potential beekeeping assessment. Case study: Montesinho Natural Park (Portugal), Proceedings 19th AGILE International Conference on Geographic Information Science—Geospatial Data in a Changing World, Helsinki, Finland.
  • Gessler P.E., Moore I.D., McKenzie N.J., Ryan P.J., (1995), Soil-landscape modelling and spatial prediction of soil attributes, International Journal of GIS, 9(4), 421-432.
  • 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, Human and Ecological Risk Assessment: An International Journal, 23(2), 388-406.
  • Haq M., Akhtar M., Muhammad S., Paras S., Rahmatullah J., (2012), Techniques of remote sensing and GIS for flood monitoring and damage assessment: a case study of Sindh province, Pakistan, The Egyptian Journal of Remote Sensing and Space Science, 15, 135–141.
  • Hernandez-Leal P.A., Arbelo M., Gonzalez-Calvo A., (2006), Fire risk assessment using satellite data, Advances in Space Research, 37(4), 741–746.
  • Huyen D.T.T., Tuan V.A., (2008), Applying GIS and Multi Criteria Evaluation in Forest Fire Risk Zoning in Son La Province, Vietnam, International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS) 2008, 4-6 December, Hanoi, Vietnam.
  • Iwan S., Mahmud A.R., Mansor S., Mohamed Shariff A.R., Nuruddin A.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: An International Journal, 13(5), 379-386.
  • Jahan M., Bahraminasab K.L., Edwards A., (2012), Target-based normalization technique for materials selection, Materials & Design, 35, 647-654.
  • 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.
  • Jose K.S., (2012), Geospatial characterization and conservation potential for Agasthyamala Biosphere Reserve, Western ghats, India, PhD thesis, School of Environmental Sciences, Mahatma Gandhi University.
  • Kushla J.D., Ripple W.J., (1997), The role of terrain in a fire mosaic of a temperate coniferous forest, Forest Ecology and Management, 95, 97–107.
  • Liu J.F., Li J., Liu J., Cao R.Y., (2008), Integrated GIS/AHP-based flood risk assessment: a case study of Huaihe River Basin in China, Journal of Natural Disasters, 17(6), 110–114.
  • Løken, E., (2007), Use of multicriteria decision analysis methods for energy planning problems, Renewable and Sustainable Energy Reviews, 11(7), 1584-1595.
  • Maris N., Mansor S., Shafri H., (2008), Apicultural Site Zonation Using GIS and Multi-Criteria Decision Analysis, Pertanika Journal of Tropical Agricultural Science, 31(2), 147 – 162.
  • Miguel S., Pukkala T. Yeşil A., (2014), Integrating pine honeydew honey production into forest management optimization, European Journal of Forest Research, 133(3), 423-432.
  • Mitchell J.W., (2013), Power line failures and catastrophic wildfires under extreme weather conditions, Engineering Failure Analysis, 35, 726–735.
  • Moore I.D., Grayson R.B., Ladson A.R., (1991), Terrain based catchment partitioning and runoff prediction using vector elevation data, Water Resources Research, 27, 1177–1191.
  • Mulliner E., Malys N., Maliene V., (2016), Comparative analysis of MCDM methods for the assessment of sustainable housing affordability, Omega, 59, 146–156.
  • Oldroyd P.B., Nanork P., (2009), Conservation of Asian honey-bees, Apidologie Bee Conservation, 40, 296-312.
  • Pourghasemi H.R., (2016), GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models, Scandinavian Journal of Forest Research, 31(1), 80–98. Pradhan B., Hagemann U., Shafapour Tehrany M., Prechtel N., (2014), An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image, Computers & Geosciences, 63, 34–43.
  • Puri K., Areendran G., Raj K., Mazumdar S., Joshi P.K., (2011), Forest fire risk assessment in parts of Northeast India using geospatial tools, Journal of Forestry Research, 22, 641, doi: 10.1007/s11676-011-0206-4.
  • Rothermel R.C., (1983), How to predict the spread and intensity of forest and range fires, USDA Forest Service Intermountain Forest and Range Experiment Station General Technical Report INT-143.Ogden, UT, USA, 161ss.
  • Saaty T.L., (1977), A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology, 15, 234–281.
  • Saaty T.L, (1980), The Analytical Hierarchy Process, New York: McGraw-Hill.
  • Saaty T.L., (1994), Fundamentals of Decision Making and Priority Theory with The Analytical Hierarchy Process, RWS Publ. Pittsburg, ss.69-84.
  • Saaty T.L., (2001), Decision Making with Dependence and Feedback: The Analytic Network Process, 2nd edition, PRWS Publications, Pittsburgh PA.
  • Saaty T.L, Vargas, L.G., (1991), Prediction, Projection and Forecasting, Kluwer Academic Publishers, Dordrecht, 25ss.
  • Sarı F., (2020), Assessment of Land Use Change Effects On Future Beekeeping Suitability Via Ca-Markov Prediction Model, Journal of Apicultural Sciences, 64(2), 263-276.
  • Sarı F., Ceylan D.A., Özcan M.M., Özcan M., (2020), A comparison of Multi Criteria Decision Analysis Techniques for Determining Beekeeping Suitability, Apidologie, 51(4), 481-498.
  • 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(2), 118644, doi: 10.1016/j.foreco.2020.118644.
  • Seyed H.M., Alireza S.A., (2017), A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems, Materials & Design, 121, 237-253.
  • Simonovic S.P., Nirupama N., (2005), A spatial multi-objective decision-making under uncertainty for water resources management, Journal of Hydroinformatics, 7(2), 117–133.
  • Sinha R., Bapalu G.V., Singh L.K., Rath B., (2008), Flood risk analysis in the Kosi river basin, north Bihar using multi-parametric approach of analytical hierarchy process (AHP), Journal of the Indian Society of Remote Sensing, 36(4), 335-349.
  • 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.
  • Sun L., Wang Q.X., Wei S.J., Hu H.Q., Guan D., Chen X.W., (2014), Response characteristics and prospect of forest fire disasters in the context of climate change in China, Journal of Catastrophology, 29(1), 12–17. In Chinese.
  • Suryabhagavan K.V., Alemu M., Balakrishnan M., (2016), GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia, Tropical Ecology, 57(1), 33-43.
  • Tkach R.J., Simonovic S.P., (1997), A new approach to multi-criteria decision making in water resources, Journal of Geographic Information and Decision Analysis, 1(1), 25–44.
  • Thinh N.X., Vogel R., (2006), GIS-based multiple criteria analysis for land-use suitability assessment in the content of flood risk management, InterCarto-InterGIS 12, August 28-30, Berlin, Germany.
  • URL-1, (2019), Turkish Statistical Institute Web Page, http://tuik.gov.tr/UstMenu.do?metod=kategorist, [Accessed 17 January 2019].
  • URL-2, (2019), Turkish State Meteorological Service Web Page, https://www.mgm.gov.tr/ [Accessed 17 January 2019].
  • Vadrevu K.P., Eaturu A., Badarinath K.V., (2010), Fire risk evaluation using multicriteria analysis--a case study, Environ Monitoring and Assessment, 166(1-4), 223-239.
  • Wang R.Y., Huang H.Y., Cheng S.P., (2002), The study of fuzzy analytic hierarchy process and grey system theory for analyzing the damage potential of inundation effect, Taiwan Water Conserv, 50, 1-23.
  • Wang Y., Li Z., Tang Z. Guangming Z., (2011), A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China, Water Resources Management, 25, 3465, doi: 10.1007/s11269-011-9866-2.
  • Wang P, Zhu Z, Wang Y., (2016), A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design, Information Sciences, 345, 27–45.
  • You W., Lin L., Wu L., Ji Z., You J., Zhu J., Fan Y., He D., (2017), Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability, Ecological Indicators, 77, 176–184.
  • Zhang J, Okada N, Tatano H., Hayakawa S., (2002), Risk assessment and zoning of flood damage caused by heavy rainfall in Yamaguchi prefecture, Japan, In Flood Defence (Wu B.S., Ed.), Science Press, New York Ltd., ss.162-169.
  • Zhou C.H., Wan Q., Huang S.F., Chen D.Q., (2000), A GIS-based approach to flood risk Zonation, Acta Geographica Sinica, 55(1), 15–24.
  • Zoccali P., Malacrin A., Campolo O., Laudani F., Algeri G.M., Giunti G., Palmeri V., (2017), A novel GIS-based approach to assess beekeeping suitability of Mediterranean lands, Saudi Journal of Biological Sciences, 24(5), 1045–1050.
  • Zou Q., Zhou J., Zhou C., Song L., Guo J., (2013), Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP, Stochastic Environmental Research and Risk Assessment, 27, 525-546.

Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey

Yıl 2022, Cilt 8, Sayı 2, 250 - 263, 30.07.2022

Fatih SARI

https://doi.org/10.21324/dacd.1009499

Öz

Since Muğla province has 90% of the world's total pine honey production, ensuring efficiency and economic income requires the determination of measures for apiary locations and estimation of risks. However, ensuring development and productivity requires identifying natural disasters susceptibility such as forest fires and floods to maintain productivity. Muğla province has a high forest fire potential due to its dense forest cover and approximately 200 forest fires occur each year. Forest fires are one of the main factors that threaten apiaries, as there are a lot of apiary places (approximately 15,000) in forests for pine honey. On the other hand, due to the mountainous topography and high precipitation rate of Muğla, the province has a high rate of flood formation (20 per year), which threatens the hive sites by destroying the entire colony. In this study, Apiary Locations Risk Index (ALRI) was carried out to guide the insurance process for apiary locations by applying the Forest Fire Risk Index (FFRI) and the Flood Hazard Risk Index (FHRI). Determination of forest fire risk zones and flood hazard maps requires environmental, forestry, topographic, economic and meteorological parameters to be handled within a decision support platform. For this purpose, Analytical Hierarchy Process (AHP) technique supported by Geographic Information System (GIS) was used in the creation of sensitivity maps. As a result, 1533.40 ha (11.82%) of the study area was determined as extremely risky areas for apiary areas. The results were confirmed with 1454 forest fire sites and 20 flood hazard sites where the Eşen, Dalaman, Çine, Sarıçay, Akçay, Kamiişdere and Namnam rivers were stated to be highly susceptible to flood hazard.

Anahtar Kelimeler

Forest Fire, Flood Hazard, Analytical Hierarchy Process, Site Suitability, Apiary

Kaynakça

  • Abou-Shaara H.F., Al-Ghamdi A.A., Mohamed A.A., (2013), A suitability map for keeping honey bees under harsh environmental conditions using geographical information system, World Applied Science Journal, 22, 1099–1105.
  • Adab H., Kanniah K.D., Solaimani K., (2013), Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques, Natural Hazards, 65(3), 1723–1743.
  • Ajin R.S., Loghin A.M., Jacob M.K., Vinod P.G., Krishnamurthy R.R., (2016), The risk assessment of potential forest fire in Idukki Wildlife Sanctuary using RS and GIS techniques, International Journal of Advanced Earth Science and Engineering, 5, 308-18.
  • Alexakis D.D., Grillakis M.G., Koutroulis A.G., Agapiou A., Themistocleous K., Tsanis I.K., Michaelides S., Pashiardis S., Demetriou C., Aristeidou K., Retalis A., Tymvios F., Hadjimitsis D.G., (2014), GIS and remote sensing techniques for the assessment of land use changes impact on flood hydrology: the case study of Yialias Basin in Cyprus, Natural Hazards and Earth System Sciences, 14, 413–426.
  • Amraoui M., Pereira M.G., DaCamara C.C., Calado T.J., (2015), Atmospheric conditions associated with extreme fire activity in the Western Mediterranean region, Science of The Total Environment, 524, 32–39.
  • Arentze T.A., Timmermans HJP., (2000), ALBATROSS: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of Technology, The Netherlands.
  • Bonora L., Conese C., Marchi E, Testi E., Montorselli N.B., (2013), Wildfire occurrence: Integrated model for risk analysis and operative suppression aspects management, American Journal of Plant Sciences, 4, 705–710.
  • Bapalu G.V., Sinha R., (2005), GIS in flood hazard mapping: a case study of Kosi River Basin, India, http://www.gisdevelopment.net/application/natural_hazards/floods/floods001pf.htm [Accessed 17 January 2014].
  • Cheng S.P., Wang R.Y., (2004), Analyzing hazard potential of typhoon damage by applying grey analytic hierarchy process, Natural Hazards, 33(1), 77-103.
  • Chen Y., Yua J., Khan S., (2010), Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation, Environmental Modelling & Software, 25, 1582-1591.
  • Chuvieco E., Aguado I., Yebra M., Nieto H., Salas J., Martín M.P., Zamora R., (2010), Development of a framework for fire risk assessment using remote sensing and geographic information system technologies, Ecological Modelling, 221(1), 46–58.
  • Damián G.C., (2016), GIS-based optimal localisation of beekeeping in rural Kenya, Master degree thesis, Master in Geographical Information Sciences Department of Physical Geography and Ecosystems Science, Lund University.
  • Elkhrachy İ., (2015), Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA), The Egyptian Journal of Remote Sensing and Space Sciences, 18, 261–278.
  • Estoque R.C., Murayama Y., (2010), Suitability Analysis for Beekeeping Sites in La Union, Philippines, Using GIS and Multi-Criteria Evaluation Techniques, Research Journal of Applied Sciences, 5(3), 242-253.
  • Estoque R.C., Murayama Y., (2011), Suitability Analysis for Beekeeping Sites Integrating GIS & MCE Techniques, In Spatial Analysis and Modeling in Geographical Transformation Process (Murayama Y., Thapa R.B., Ed.), Springer, Netherlands, ss.215-233.
  • Eugenio F.C., Dos Santos A.R., Fiedler N.C., Ribeiro G.A., Da Silva A.G., Dos Santos Á.B., Paneto G.G., Schettino V.R., (2016), Applying GIS to develop a model for forest fire risk: a case study in Espírito Santo, Brazil, Journal of Environmental Management, 173, 65–71.
  • Fernandez P., Roque N., Anjos O., (2016), Spatial multicriteria decision analysis to potential beekeeping assessment. Case study: Montesinho Natural Park (Portugal), Proceedings 19th AGILE International Conference on Geographic Information Science—Geospatial Data in a Changing World, Helsinki, Finland.
  • Gessler P.E., Moore I.D., McKenzie N.J., Ryan P.J., (1995), Soil-landscape modelling and spatial prediction of soil attributes, International Journal of GIS, 9(4), 421-432.
  • 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, Human and Ecological Risk Assessment: An International Journal, 23(2), 388-406.
  • Haq M., Akhtar M., Muhammad S., Paras S., Rahmatullah J., (2012), Techniques of remote sensing and GIS for flood monitoring and damage assessment: a case study of Sindh province, Pakistan, The Egyptian Journal of Remote Sensing and Space Science, 15, 135–141.
  • Hernandez-Leal P.A., Arbelo M., Gonzalez-Calvo A., (2006), Fire risk assessment using satellite data, Advances in Space Research, 37(4), 741–746.
  • Huyen D.T.T., Tuan V.A., (2008), Applying GIS and Multi Criteria Evaluation in Forest Fire Risk Zoning in Son La Province, Vietnam, International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS) 2008, 4-6 December, Hanoi, Vietnam.
  • Iwan S., Mahmud A.R., Mansor S., Mohamed Shariff A.R., Nuruddin A.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: An International Journal, 13(5), 379-386.
  • Jahan M., Bahraminasab K.L., Edwards A., (2012), Target-based normalization technique for materials selection, Materials & Design, 35, 647-654.
  • 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.
  • Jose K.S., (2012), Geospatial characterization and conservation potential for Agasthyamala Biosphere Reserve, Western ghats, India, PhD thesis, School of Environmental Sciences, Mahatma Gandhi University.
  • Kushla J.D., Ripple W.J., (1997), The role of terrain in a fire mosaic of a temperate coniferous forest, Forest Ecology and Management, 95, 97–107.
  • Liu J.F., Li J., Liu J., Cao R.Y., (2008), Integrated GIS/AHP-based flood risk assessment: a case study of Huaihe River Basin in China, Journal of Natural Disasters, 17(6), 110–114.
  • Løken, E., (2007), Use of multicriteria decision analysis methods for energy planning problems, Renewable and Sustainable Energy Reviews, 11(7), 1584-1595.
  • Maris N., Mansor S., Shafri H., (2008), Apicultural Site Zonation Using GIS and Multi-Criteria Decision Analysis, Pertanika Journal of Tropical Agricultural Science, 31(2), 147 – 162.
  • Miguel S., Pukkala T. Yeşil A., (2014), Integrating pine honeydew honey production into forest management optimization, European Journal of Forest Research, 133(3), 423-432.
  • Mitchell J.W., (2013), Power line failures and catastrophic wildfires under extreme weather conditions, Engineering Failure Analysis, 35, 726–735.
  • Moore I.D., Grayson R.B., Ladson A.R., (1991), Terrain based catchment partitioning and runoff prediction using vector elevation data, Water Resources Research, 27, 1177–1191.
  • Mulliner E., Malys N., Maliene V., (2016), Comparative analysis of MCDM methods for the assessment of sustainable housing affordability, Omega, 59, 146–156.
  • Oldroyd P.B., Nanork P., (2009), Conservation of Asian honey-bees, Apidologie Bee Conservation, 40, 296-312.
  • Pourghasemi H.R., (2016), GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models, Scandinavian Journal of Forest Research, 31(1), 80–98. Pradhan B., Hagemann U., Shafapour Tehrany M., Prechtel N., (2014), An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image, Computers & Geosciences, 63, 34–43.
  • Puri K., Areendran G., Raj K., Mazumdar S., Joshi P.K., (2011), Forest fire risk assessment in parts of Northeast India using geospatial tools, Journal of Forestry Research, 22, 641, doi: 10.1007/s11676-011-0206-4.
  • Rothermel R.C., (1983), How to predict the spread and intensity of forest and range fires, USDA Forest Service Intermountain Forest and Range Experiment Station General Technical Report INT-143.Ogden, UT, USA, 161ss.
  • Saaty T.L., (1977), A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology, 15, 234–281.
  • Saaty T.L, (1980), The Analytical Hierarchy Process, New York: McGraw-Hill.
  • Saaty T.L., (1994), Fundamentals of Decision Making and Priority Theory with The Analytical Hierarchy Process, RWS Publ. Pittsburg, ss.69-84.
  • Saaty T.L., (2001), Decision Making with Dependence and Feedback: The Analytic Network Process, 2nd edition, PRWS Publications, Pittsburgh PA.
  • Saaty T.L, Vargas, L.G., (1991), Prediction, Projection and Forecasting, Kluwer Academic Publishers, Dordrecht, 25ss.
  • Sarı F., (2020), Assessment of Land Use Change Effects On Future Beekeeping Suitability Via Ca-Markov Prediction Model, Journal of Apicultural Sciences, 64(2), 263-276.
  • Sarı F., Ceylan D.A., Özcan M.M., Özcan M., (2020), A comparison of Multi Criteria Decision Analysis Techniques for Determining Beekeeping Suitability, Apidologie, 51(4), 481-498.
  • 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(2), 118644, doi: 10.1016/j.foreco.2020.118644.
  • Seyed H.M., Alireza S.A., (2017), A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems, Materials & Design, 121, 237-253.
  • Simonovic S.P., Nirupama N., (2005), A spatial multi-objective decision-making under uncertainty for water resources management, Journal of Hydroinformatics, 7(2), 117–133.
  • Sinha R., Bapalu G.V., Singh L.K., Rath B., (2008), Flood risk analysis in the Kosi river basin, north Bihar using multi-parametric approach of analytical hierarchy process (AHP), Journal of the Indian Society of Remote Sensing, 36(4), 335-349.
  • 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.
  • Sun L., Wang Q.X., Wei S.J., Hu H.Q., Guan D., Chen X.W., (2014), Response characteristics and prospect of forest fire disasters in the context of climate change in China, Journal of Catastrophology, 29(1), 12–17. In Chinese.
  • Suryabhagavan K.V., Alemu M., Balakrishnan M., (2016), GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia, Tropical Ecology, 57(1), 33-43.
  • Tkach R.J., Simonovic S.P., (1997), A new approach to multi-criteria decision making in water resources, Journal of Geographic Information and Decision Analysis, 1(1), 25–44.
  • Thinh N.X., Vogel R., (2006), GIS-based multiple criteria analysis for land-use suitability assessment in the content of flood risk management, InterCarto-InterGIS 12, August 28-30, Berlin, Germany.
  • URL-1, (2019), Turkish Statistical Institute Web Page, http://tuik.gov.tr/UstMenu.do?metod=kategorist, [Accessed 17 January 2019].
  • URL-2, (2019), Turkish State Meteorological Service Web Page, https://www.mgm.gov.tr/ [Accessed 17 January 2019].
  • Vadrevu K.P., Eaturu A., Badarinath K.V., (2010), Fire risk evaluation using multicriteria analysis--a case study, Environ Monitoring and Assessment, 166(1-4), 223-239.
  • Wang R.Y., Huang H.Y., Cheng S.P., (2002), The study of fuzzy analytic hierarchy process and grey system theory for analyzing the damage potential of inundation effect, Taiwan Water Conserv, 50, 1-23.
  • Wang Y., Li Z., Tang Z. Guangming Z., (2011), A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China, Water Resources Management, 25, 3465, doi: 10.1007/s11269-011-9866-2.
  • Wang P, Zhu Z, Wang Y., (2016), A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design, Information Sciences, 345, 27–45.
  • You W., Lin L., Wu L., Ji Z., You J., Zhu J., Fan Y., He D., (2017), Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability, Ecological Indicators, 77, 176–184.
  • Zhang J, Okada N, Tatano H., Hayakawa S., (2002), Risk assessment and zoning of flood damage caused by heavy rainfall in Yamaguchi prefecture, Japan, In Flood Defence (Wu B.S., Ed.), Science Press, New York Ltd., ss.162-169.
  • Zhou C.H., Wan Q., Huang S.F., Chen D.Q., (2000), A GIS-based approach to flood risk Zonation, Acta Geographica Sinica, 55(1), 15–24.
  • Zoccali P., Malacrin A., Campolo O., Laudani F., Algeri G.M., Giunti G., Palmeri V., (2017), A novel GIS-based approach to assess beekeeping suitability of Mediterranean lands, Saudi Journal of Biological Sciences, 24(5), 1045–1050.
  • Zou Q., Zhou J., Zhou C., Song L., Guo J., (2013), Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP, Stochastic Environmental Research and Risk Assessment, 27, 525-546.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Yayınlanma Tarihi Temmuz 2022
Bölüm Araştırma Makalesi
Yazarlar

Fatih SARI> (Sorumlu Yazar)
KONYA TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ, HARİTA MÜHENDİSLİĞİ BÖLÜMÜ
0000-0001-8674-9028
Türkiye

Yayımlanma Tarihi 30 Temmuz 2022
Yayınlandığı Sayı Yıl 2022, Cilt 8, Sayı 2

Kaynak Göster

Bibtex @araştırma makalesi { dacd1009499, journal = {Doğal Afetler ve Çevre Dergisi}, eissn = {2528-9640}, address = {}, publisher = {Artvin Çoruh Üniversitesi}, year = {2022}, volume = {8}, number = {2}, pages = {250 - 263}, doi = {10.21324/dacd.1009499}, title = {Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey}, key = {cite}, author = {Sarı, Fatih} }
APA Sarı, F. (2022). Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey . Doğal Afetler ve Çevre Dergisi , 8 (2) , 250-263 . DOI: 10.21324/dacd.1009499
MLA Sarı, F. "Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey" . Doğal Afetler ve Çevre Dergisi 8 (2022 ): 250-263 <https://dacd.artvin.edu.tr/tr/pub/issue/71418/1009499>
Chicago Sarı, F. "Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey". Doğal Afetler ve Çevre Dergisi 8 (2022 ): 250-263
RIS TY - JOUR T1 - Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey AU - Fatih Sarı Y1 - 2022 PY - 2022 N1 - doi: 10.21324/dacd.1009499 DO - 10.21324/dacd.1009499 T2 - Doğal Afetler ve Çevre Dergisi JF - Journal JO - JOR SP - 250 EP - 263 VL - 8 IS - 2 SN - -2528-9640 M3 - doi: 10.21324/dacd.1009499 UR - https://doi.org/10.21324/dacd.1009499 Y2 - 2022 ER -
EndNote %0 Doğal Afetler ve Çevre Dergisi Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey %A Fatih Sarı %T Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey %D 2022 %J Doğal Afetler ve Çevre Dergisi %P -2528-9640 %V 8 %N 2 %R doi: 10.21324/dacd.1009499 %U 10.21324/dacd.1009499
ISNAD Sarı, Fatih . "Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey". Doğal Afetler ve Çevre Dergisi 8 / 2 (Temmuz 2022): 250-263 . https://doi.org/10.21324/dacd.1009499
AMA Sarı F. Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey. Doğ Afet Çev Derg. 2022; 8(2): 250-263.
Vancouver Sarı F. Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey. Doğal Afetler ve Çevre Dergisi. 2022; 8(2): 250-263.
IEEE F. Sarı , "Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey", Doğal Afetler ve Çevre Dergisi, c. 8, sayı. 2, ss. 250-263, Tem. 2022, doi:10.21324/dacd.1009499
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