پهنه‌بندی خطر آتش‌سوزی جنگل با استفاده از روش ترکیبی نسبت فراوانی- انتروپی شانون

نوع مقاله : علمی- پژوهشی

نویسندگان

1 دکتری جنگل‌داری، باشگاه پژوهشگران جوان و نخبگان، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران

2 استادیار، گروه جنگل‌داری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران

چکیده

پژوهش پیش‌­رو به بررسی قابلیت روش­ نسبت فراوانی و ترکیبی از این روش با  انتروپی شانون در تهیه نقشه حساسیت آتش‌سوزی در استان چهارمحال و بختیاری می­‌پردازد. در ابتدا مناطق آتش­‌سوزی گذشته در این استان بر مبنای اطلاعات موجود و عملیات میدانی شناسایی شدند. از 132 آتش­‌سوزی شناسایی‌شده، به‌طور تصادفی 92 مورد (70%) برای فرآیند مد­­ل­سازی و 40 مورد (30%) باقیمانده برای اعتبارسنجی درنظر گرفته شد. بر اساس اطلاعات موجود در منطقه مورد مطالعه، 13 عامل مرتبط با خصوصیات توپوگرافی، اقلیمی و انسانی به‌عنوان عامل‌های مؤثر بر وقوع آتش­‌سوزی درنظر گرفته شد. با استفاده از مدل نسبت فراوانی و مدل ترکیبی ارتباط بین نقاط آتش­‌سوزی و عامل‌های مؤثر بررسی شد. نتایج مدل­‌ها مبنای ساخت نقشه‌‏های پتانسیل خطر آتش­سوزی قرار گرفت. نتایج ارزیابی که با استفاده از روش مشخصه عملکرد سیستم (ROC) و سطح زیر منحنی (AUC) انجام شد، نشان داد که مدل ترکیبی با قابلیت محاسبه میزان اهمیت عامل‌ها و همچنین هر یک از طبقات آنها، کارایی بهتری در تهیه نقشه پتانسیل خطر آتش‌سوزی دارد. نرخ موفقیت و پیش‌‏بینی برای مدل نسبت فراوانی و مدل ترکیبی به‌ترتیب 79/02 و 75/72 درصد و 85/16 و 82/91 درصد محاسبه شد. بر اساس نتایج، بیشتر از یک‏‌سوم مساحت استان چهارمحال و بختیاری در طبقات خطر زیاد تا خیلی زیاد آتش­‌سوزی قرار می‌گیرند که در این میان، عامل‌های کاربری اراضی، نوع خاک و فاصله از جاده دارای بیشترین اهمیت هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Wildfire hazard mapping using an ensemble method of frequency ratio with Shannon’s entropy

نویسندگان [English]

  • Abolfazl Jaafari 1
  • Davoud Mafi Gholami 2
1 Ph.D. Forestry, Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran
2 Assistant Prof., Department of Forestry, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran
چکیده [English]

This study investigates the capability of frequency ratio and an ensemble method of frequency ratio with Shannon’s entropy to produce a reliable map of wildfire susceptibility for Chaharmahal and Bakhtiari province, Iran. At first, the fire locations were identified in the study area from historical archives and field surveys. Ninety two cases (70%) out of 132 detected fire locations were randomly selected for modeling, and the remaining 40 (30 %) cases were used for the validation. Thirteen fire conditioning factors representing topography, climate, and human activities of the study area were extracted from the spatial database. Using the frequency ratio and the ensemble model, the relationship between the conditioning factors and fire locations were explored. The results were then used to produce distribution maps of wildfire hazard. The verification analysis using Receiver Operating Characteristic (ROC) curves and the Areas Under the Curve (AUC) revealed that the ensemble model with the capability of computing the weights of factors and their categories is more efficient than frequency ratio. The success and prediction rates for the frequency ratio and ensemble model were found to be 79.2 and 75.72%, and 85.16 and 82.92%, respectively. Further, the results suggest that more than one-third of the study area falls into the high and very high hazard classes, and the conditioning factors of land use, soil types, and distance from roads play major roles in fire occurrence and distribution in the study area.

کلیدواژه‌ها [English]

  • Chaharmahal and Bakhtiari
  • GIS
  • modeling
  • Zagros forests
- Adab, H., Kanniah, K.D. and Solaimani, K., 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3): 1723-1743.
- Adab, H., Kanniah, K.D., Solaimani, K. and Sallehuddin, R., 2015. Modelling static fire hazard in a semi-arid region using frequency analysis. International Journal of Wildland Fire, 24(6): 763-777.
- Beygi Heidarlou, H., Banj Shafiei, A. and Erfanian, M., 2014. Forest fire risk mapping using analytical hierarchy process technique and frequency ratio method (case study: Sardasht forests, NW Iran). Iranian Journal of Forest and Poplar Research, 22(4): 559-573 (In Persian).
- Chen, F., Du, Y., Niu, S. and Zhao, J., 2015. Modeling forest lightning fire occurrence in the Daxinganling mountains of northeastern China with MAXENT. Forests, 6(5): 1422-1438.
- Chuvieco, E. and Congalton, R.G., 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2): 147-159.
- Chuvieco, E. and Salas, J., 1996. Mapping the spatial distribution of forest fire danger using GIS. International Journal of Geographical Information Science, 10(3): 333-345.
- Eskandari, S. and Chuvieco, E., 2015. Fire danger assessment in Iran based on geospatial information. International Journal of Applied Earth Observation and Geoinformation, 42: 57-64.
- Eskandari, S., Oladi Ghadikolaei, J., Jalilvand, H. and Saradjian, M.R., 2013. Forest fire risk modeling and prediction in district three of Neka-Zalemroud forest using geographic information system. Iranian Journal of Forest and Poplar Research, 21(2): 203-217 (In Persian).  
- Faramarzi, H., Hosseini, S.M. and Gholamalifard, M., 2014. Fire hazard zoning in Golestan National Park using logistic regression and GIS. Geography and Environmental Hazards, 3(10): 73-90 (In Persian).  
- Jaafari, A., Gholami, D.M. and Zenner, E.K., 2017. A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran. Ecological Informatics, 39: 32-44.
- Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J. and Sattarian, A., 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11(4): 909-926.
- Jaafari, A., Najafi, A., Rezaeian, J., Sattarian, A. and Ghajar, I., 2015. Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy, 47: 198-208.
- Mohammadi, F., Shabanian, N., Pourhashemi, M. and Fatehi, P., 2010. Risk zone mapping of forest fire using GIS and AHP in a part of Paveh forests. Iranian Journal of Forest and Poplar Research, 18(4): 569-586. 
- Mohammadi Sarvaleh, F., Pir bavaghar, M. and Shabanian, N., 2013. Application of artificial neural network for forest fire risk mapping based on physiographic, human and climate factors in Sarvabad, Kurdistan province. Iranian Journal of Forest and Range Protection Research, 11(2): 97-107 (In Persian).  
- Naghibi, S.A., Pourghasemi, H.R., Pourtaghi, Z.S. and Rezaei, A., 2015. Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics, 8(1): 171-186.
- Oliveira, S., Pereira, J. M., San-Miguel-Ayanz, J. and Lourenço, L., 2014. Exploring the spatial patterns of fire density in southern Europe using geographically weighted regression. Applied Geography, 51: 143-157.
- Pausas, J.G. and Keeley, J.E., 2009. A burning story: the role of fire in the history of life. Bioscience, 59: 593-601.
- Pourtaghi, Z.S., Pourghasemi, H.R., Aretano, R. and Semeraro, T., 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators, 64: 72-84.
- Pourtaghi, Z.S., Pourghasemi, H.R. and Rossi, M., 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environmental Earth Sciences, 73(4): 1515-1533.
- Semeraro, T., Mastroleo, G., Aretano, R., Facchinetti, G., Zurlini, G. and Petrosillo, I., 2016. GIS fuzzy expert system for the assessment of ecosystems vulnerability to fire in managing Mediterranean natural protected areas. Journal of Environmental Management, 168: 94-103.
- Shannon, C.E., 1948. A mathematical theory of communication. Bulletin System Technology Journal, 27: 379-423.
- Silva, G.L., Soares, P., Marques, S., Dias, M.I., Oliveira, M.M. and Borges, J.G., 2015. A bayesian modelling of wildfires in Portugal: 723-733. In: Bourguignon, J.P., Jeltsch, R., Pinto, A.A. and Viana, M. (Eds.). Dynamics, Games and Science. Springer, Switzerland, 772p.
- Syphard, A.D., Radeloff, V.C., Keuler, N.S., Taylor, R.S., Hawbaker, T.J., Stewart, S.I. and Clayton, M.K., 2008. Predicting spatial patterns of fire on a southern California landscape. International Journal of Wildland Fire, 17(5): 602-613.