Evaluating and mapping the fire risk in the forests and rangelands of Sirachal using fuzzy analytic hierarchy process and GIS

Document Type : Scientific article

Authors

1 Assistant Prof., Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

2 Senior Research Expert, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

Abstract

The current research was performed to evaluate and to map the fire risk occurrence in the forests and rangelands of Sirachal in Alborz province using fuzzy analytic hierarchy process. The used criteria included four main criteria (physiographic, biologic, climatic and human-made) and their sub-criteria. The maps of all these factors were provided using digital elevation model, satellite images, ground sampling and available data. Also, the map of past fires during past decade was prepared by existence data and GPS sampling. Then, the weight of the effective criteria in fire occurrence was calculated using fuzzy analytic hierarchy process. The fire risk potential map was prepared using the weighted combination of the effective criteria maps. Finally, the fire risk potential map was validated using the past fires and its accuracy was evaluated in identifying the fire high-risk areas. The results showed that among the main criteria, human-made criterion had the most impact (weight) on the fire occurrence risk in the area. Also, the sub-criteria slope and distance from river, type and density of vegetation and distance from the road had the highest importance (weight) in the fire occurrence risk based on fuzzy analytic hierarchy process. According to the results of the fire risk potential map, 58.55 percent of the study area has the high-risk potential for fire.

Keywords

References

- Adab, H., K.D. Kanniah & K. Solaimani, 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65: 1723-1743.
- Akbarzadeh, M. 1994. Provision of vegetation cover map of Sirachal area using floristic and physiognomic method. Research Institute of Forests and Rangelands Press, Tehran, 71 p. (In Persian)
- Almedia, R. 1994. Forest fire risk areas and definition of the prevention priority planning actions using GIS. Proceedings of the Fifth European Conference and Exhibition on Geographic Information Systems, EGIS 94, Utrecht, Netherlands. pp. 1700-1706.
- Alonso-Betanzos, A., O. Fontenla-Romero, B. Guijarro-Berdinas, E., Hernandez- Pereira, E. Canda, J. Jimenez, J. Luis Legido, S. Muniz, C. Paz-Andrade & M.I. Paz-Andrade, 2002. A neural network approach for forest fire risk estimation. Proceedings of the 15th European Conference on Artificial Intelligence, Lyon, France. pp. 643-647.
- Atesoglu, A. 2014. Forest hazard identifying, mapping using satellite imagery-geographic information system and analytic hierarchy process: Bartin-Turkey. Journal of Environmental Protection and Ecology, 15: 715-725.
- Benker, S.Ch., R.P. Langford & T.L. Pavlis, 2011. Positional accuracy of the Google Earth terrain model derived from stratigraphic unconformities in the Big Bend region, Texas, USA. Geocarto International, 26(4): 291-303.
- Beygi Heidarlou, H., A. Banj Shafiei & M. Erfanian, 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)
- Chang, D.Y. 1996. Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95: 649-655.
- Chuvieco, E. & R.G. Congalton, 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29: 147-159.
- Chuvieco, E., I. Aguado, S. Jurdao, M.L. Pettinari, M. Yebra, J. Salas, S. Hantson, J. de la Riva, P. Ibarra, M. Rodrigues, M. Echeverria, D. Azqueta, M.V. Roman, A. Bastarrika, S. Martinez, C. Recondo, E. Zapico & F.J. Martinez-Vega, 2014. Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire, 23(5): 606-619.
- Congalton, R.G. & K. Green, 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (Second Edition). CRC Press, Boca Raton, 184 p.
- Dong, X.U., D. Li-min, Sh. Guo-fan, T. Lei & W. Hui, 2005. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research, 16(3): 169-174.
- Eskandari, S. & E. Chuvieco, 2015. Fire danger assessment in Iran based on geospatial information. International Journal of Applied Earth Observation and Geoinformation, 42: 57-64.
- Eskandari, S. & J.R. Miesel, 2017. Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran. Geomatics, Natural Hazards and Risk, 8: 1-17.
- Eskandari, S. 2017. A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arabian Journal of Geosciences, 10 (8): 1-13.
- Esmaeili Sharif, M., H. Jalilvand, M. Amoozad, A.A. Jafari & S.M. Moslemi SayedMahale, 2018. The effect of ecological factors on fire in Hyrcanian forests (Case study: forest areas of Neka, Mazandaran, Iran). Journal of Forest Research and Development, 4 (1): 113-129.
- FAO, 2007. Fire Management-Global Assessment 2006, A Thematic Study Prepared in the Framework of the Global Forest Resources Assessment 2005. FAO Press, Rome.
- Farah, A. & D. Algarni, 2014. Positional accuracy assessment of GoogleEarth in Riyadh. Artificial Satellites, 49(2): 101-106.
- Ghodsipour, S.M. 2011. Analytical Hierarchy Process (Ninth Edition). Amirkabir University Press, Tehran, 220 p. (In Persian)
- Goudarzi, M.A. & R.J. Landry, 2017. Assessing horizontal positional accuracy of GoogleEarth imagery in the city of Monteral, Canada. Geodesy and Cartography, 43(2): 56-65.
- Guo F., L. Zhang, S. Jin, M. Tigabu, Zh. Su & W. Wang, 2016. Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests. Forests, 7: 250, DOI: 10.3390/f7110250
- Huang, B., C. Xie & R. Tay, 2010. Support vector machines for urban growth modeling. GeoInformatica, 14: 83-99.
- Jaafari A. & D. Mafi Gholami, 2017. Wildfire hazard mapping using an ensemble method of frequency ratio with Shannon’s entropy. Iranian Journal of Forest and Poplar Research, 25(2): 232-243. (In Persian)
- Jaiswal, R.K., S. Mukherjee, D.K. Raju & R. Saxena, 2002. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4: 1-10.
- Jazireyi, M.H. 2005. Forest Protection. Press Institute of Tehran University, Tehran, 231 p. (In Persian)
- Jurdao, S., E. Chuvieco & J.M. Arevalillo, 2012.Modelling fire ignition probability from satellite estimates of live fuel moisture content. Fire Ecology, 8(1): 77-97.
- Kazemi, S.M. 2005. Fire and forest ecosystems. Livestock, Agriculture and Industry, 70: 46-56. (In Persian)
- Leuenberger, M., M. Kanevski & C.D. Vega Orozco, 2013. Forest Fires in a Random Forest. Geophysical Research Abstracts, EGU General Assembly, Vienna, Austria. pp. 32-38.
- Lozano, F.J., S. Suárez-Seoane, M. Kelly & E.Luis, 2008. A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region. Remote Sensing of Environment, 112: 708-719.
- Maleki, D. 2010. Urban development modeling using cellular automata method. M.Sc. thesis, Khajeh Nasir University of Technology, Tehran, 122 p. (In Persian)
- Martinez, J., C. Vega-Garcia & E. Chuvieco, 2009. Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management, 90: 1241-1252.
- Mehregan, M.R. 2018. Operational research: linear programming and its applications. Academic Books Publishing, Tehran, 524 p.
- Mustafa, A., A. Rienow, I. Saadi, M. Cools & J. Teller, 2018. Comparing support vector machines with logistic regression for calibrating cellular automata land use change models. European Journal of Remote Sensing, 51(1): 391-401.
- Podur, J., D.L. Martell & K. Knight, 2002. Statistical quality control analysis of forest fire activity in Canada. Canadian Journal of Forest Research, 32: 195-205.
- Potere, D. 2008. Horizontal Positional Accuracy of Google Earth’s High- Resolution Imagery Archive. Sensors, 8: 7973-7981.
- Pulighe, G., V. Baiocchi & F. Lupia, 2015. Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy. International Journal of Digital Earth, 9(4): 342-362.
- Rollins, M.G., R.E. Keane & R.A. Parsons, 2004. Mapping fuels and fire regimes using remote sensing, ecosystem simulation and gradient modeling. Ecological Applications,14(1): 75-95.
- Roman, M.V., D. Azqueta, & M. Rodrigues, 2013. Methodological approach to assess the socio-economic vulnerability to wildfires in Spain. Forest Ecology and Management, 294: 158-165.
- Saaty, T.L. 1980. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York, USA, 287 p.
- Santiago, I.T.F. & N. Kheladze, 2011. GIS wildland fire hazard modeling in Georgia. MATRA project report, Caucasus Environmental NGO Network, 98 p.
- Satir, O., S. Berberoglu & C. Donmez, 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk, 7(5): 1645-1658.
- Smith, M.J., M.F. Goodchild & P.A. Longley, 2007. Geospatial analysis- a comprehensive guide to principles, techniques and software tools. Troubador Publishing Ltd, Leicester, 516 p.
- Song, Ch., M.P. Kwan, W. Song & J. Zhu, 2017. A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence. Sustainability, 9: 819, DOI: 10.3390/su9050819
- Sowmya, S.V. & R.K. Somashekar, 2010. Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology, 31(6): 969-974.
- Stolle, F., K.M. Chomitz, E.F. Lambin & T.P. Tomich, 2003. Human ecological intervention and the role of forest fires in human ecology. Forest Ecology and Management, 179: 277-292.
- Stolzenburg, W. 2001. Fire in the rain forest. Nature Conservancy, 31: 22-27.
- Vadrevu, K.P., A. Eaturu & K.V.S. Badarinath, 2010. Fire risk evaluation using multicriteria analysis, a case study. Environmental Monitoring and Assessment, 166(1): 223-239.
- Vakalis, D., H. Sarimveis, C.T. Kiranoudis, A. Alexandridis & G.V. Bafas, 2004. A GIS based operational system for wildland fire crisis management, I. Mathematical modelling and simulation. Applied Mathematical Modelling, 28(4): 389-410.
- Valdez, M.C., K.T. Chang, Ch.F. Chen, Sh.H. Chiang & J.L. Santos, 2017. Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems. Geomatics, Natural Hazards and Risk, DOI: 10.1080/19475705.2016.1278404.
- Vasconcelo, M.J., S. Silva, M. Tome, M. Alvim & J.M.C. Perelra, 2001. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric Engineering & Remote Sensing, 67(1): 73-81.
- Vasilakos, C., K. Kalabokidis, J. Hatzopoulos & I. Matsinos, 2009. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Natural Hazards, 50(1): 125-143.
- Vazquez, A. & J.M. Moreno, 2001. Spatial distribution of forest fires in Sierra de Gredos (central Spain). Forest Ecology and Management, 147(1): 55-65.
- Zarekar, A., B. Kazemi Zamani, S. Ghorbani, M. Ashegh Moalla & H. Jafari, 2013. Mapping spatial distribution of forest fire using MCDM and GIS (Case study: three forest zones in Guilan province). Iranian Journal of Forest and Poplar Research, 21 (2): 218-230. (In Persian)
- Zobeiri, M. 2008. Forest Biometry. Tehran University Press, Tehran, 411 p. (In Persian)
- Zumbrunnen, T., G.B. Pezzattic, P. Menéndezd, H. Bugmann, M. Bürgia & M. Conederac, 2011. Weather and human impacts on forest fires: 100 years of fire history in two climatic regions of Switzerland. Forest Ecology and Management, 261: 2188-2199.
Forest Research and Development
Volume 6, Issue 2
June 2020
Pages 219-245

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