Zoning forest fire risk in the Manesht and Qalarang Protected Area using a network analysis model and geographic information system

Document Type : علمی - پژوهشی


1 Department of Remote Sensing and Geographical Information Systems, Faculty of Geography and Urban Planning, Tabriz University, Iran

2 Islamic Azad University, Science and Research Branch of Tehran, Tehran, Iran


Introduction: Forests are natural resources most of which are destroyed each year by fire. One way to deal with forest fires is to identify the hot spots in forest fires in the region.This phenomenon destroys hectares of trees, shrubs and plants annually, with an annual average of six to fourteen million hectares of the world's forests estimated to be damaged by fires. This reveals the need for research in this area in order to preserve this invaluable resource. Manesht and Qalarang Protected Area is located in the northern Province of Ilam, covering Chardaval and Ivan. The research aims to assess the level of fire hazard in this protected area using network analysis and geographic information systems.Materials and Methods: This will apply the most important factors in the process of forest fires according to experts and researchers in these areas, including 9 factors, such as the density of vegetation, rainfall, temperature, slope, aspect, distance from the road, distance from the village height and distance from the river. In the process of modelling for the evaluation of forest fire risk, a sensitivity model network analysis was conducted over three stages and, in this way, the structure of the model was formed in the SuperDecisions software. The matrix of pairwise comparisons was performed using all of 1 to 9 and, then, the super matrix was prepared. Finally,  the  criteria weighting was determined.Results and Discussion: The results from this study showed that weighting of criteria for density of vegetation, rainfall, temperature, slope, aspect, distance from the road, distance from the village, elevation and distance from the river to the values were 0.294, 0.226, 0.134, 0.121,0.075,0.051, 0.041, 0.29 and 0.025, respectively; hence, density of vegetation, rainfall, temperature and slope had the greatest weight. Finally, by combining the layers according to the weights obtained in ARC GIS software, a zoning map was prepared. The results showed that the top 50% of regions with dense vegetation, southerly directions and slopes greater than 20 percent are prone to fire hazard. The southerly and easterly directions were determined as receiving the maximum amount of sunlight. Approximately 30 square kilometers (10 percent) of the total area of the 62 square-kilometer area were in the very high risk class (20%) in terms of the probability of fire. So it is essential that measures be taken to prevent the occurrence of fire in these areas.Conclusion: In this research, the zoning map was classified in the five classes of very small, low, medium, high and very high. The results showed that, according to maps from the running model, slopes greater than 20 percent, southerly and easterly directions and areas where vegetation density is over 50 percent are among the areas with high and very high risk of likelihood of fires occurring. It is therefore essential that in these areas the necessary measures be taken to prevent the possibility of fire.


  1. منابع
  2. Abedi Gheshlaghi, H., Valizadeh Kamran, Khm. and Hasheminasab H., 2016. Assessing and Zoning forest fire risk by using an analysis network process. International Conference on Natural Hazards and Enviromental Crises, Tabriz, Iran. 11-1. (In Persian with English abstract).
  3. Adab, H., Kanniah, D. and Solaimani K., .2011 GISbased Probability Assessment of Fire Risk inGrassland and Forested Landscapes of Golestan Province, Iran. International Conference on Environmental and Computer Science, IPCBEE.19, 175-170. (In Persian with English abstract).
  4. Adab, H., Kanniah, KD. 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.(In Persian with English abstract).
  5. Allgower, B., Carlson JD. and Wagtendonk JWV., 2003. Introduction to fire danger rating and remote sensing—will remote sensing enhance wildland fire danger rating? In E. Chuvieco (Eds.). Wild land fire danger estimation and mapping. The role of remote sensing data.Newjersey: World Scientific.
  6. Balzter, H., Gerardm, F., George, Ch., Rowland, C., Jupp, T., McCallum, I., Shivdenko, A., Nilsson, S., Sukhinin, A., Ounchin, A. and Schmullius, Ch., 2005. Impact of the Arctic Oscillation pattern on interannual forest fire variability in Central Siberia. Geophysica Research Letters; 32.
  7. Chuvieco, E. and Congalton, R., 1989. Application of Remote Sensing and Geographic Information Systems to Forest Fire Hazard Mapping. Remote Sensing of Environment. 29, 147-159.
  8. Chuvieco, E., Cocero, D., Riano, D., Martin, P., Martinez-Vega, J., Riva j. and Perez, F., 2004. Combining NDVI and surface tem perature for the estimation of live, fuel moisture content in forest fire danger rating. Remote Sensing of Environment. 92, 322–331.
  9. Dong, XU., Li-min, D., Guo-fan, Sh,. Lei, T. and Hui, W., 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.
  10. Faramarzi, H., Hoseini, SM. and Gholamali fard, M., 2014. zoning fire hazard in Golestan National Park using logistic regression. Geography and environmental hazards. 10, 73-90. (In Persian with English abstract).
  11. Franklin, J., McCullough, P. and Gray, C., 2000. Terrain variables used for predictive mapping of vegetation communities in Southern California. In ‘Terrain Analysis: Principles and Applications. 331–353.
  12. http://forestfire.nau.edu
  13. Jaiswal, RK., Mukherjee, S., Raju, KD. and 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.
  14. Keeney, RL. and Raiffa H., 1976. Decisions with multiple objectives: Preferences and value tradeoffs. New York: John Wiley.
  15. Lami, IM. and Abastante F., 2014. Decision making for urban solid waste treatment in the context of territorial conflict: Can the Analytic Network Process help?. Land Use Policy. 1, 11-20.
  16. Lowell, KE. and Astroth, JH., 1989. Vegetative Succession and Controlled Fire in a Glades Ecosystem – A Geographical Information Systems Approach. International Journal of Geographical Information Systems. 3(1), 69-81.
  17. Mahdavi, A., 2012. Forests and rangelands Wild fire risk zoning using GIS and AHP techniques. Caspian Journal of Environmental Sciences. 10 (1), 43-52.(In Persian with English abstract).
  18. Rundel, PW. and King JA., 2001. Ecosystem processes and dynamics in the urban/ wildland interface of Southern California. Journal of Mediterranean Ecology. 2, 209–219.
  19. Saaty TL.,1980 . The analytic hierarchy process. McGraw-Hill, New York.
  20. Saranya, KRL., Sudhakar Reddy, C., Prasada Rao, PVV. and Jha, CS., 2014. Decadal time-scale monitoring of forest fires in Similipal Biosphere Reserve, India using remote sensing and GIS. Environmental Monitoring and Assessment.186, 3283–3296.
  21. Silvia Merino-de-Miguela Huescab, M. and Gonzalez-Alonsob, F., 2010. Modis reflectance and active fire data for burn mapping and assessment at regional level. Ecological Modelling. 221, 67-74.
  22. Thakur, AK. and Singh, D., 2014. Forest Fire Risk Zonation Using Geospatial Techniques and Analytic Hierarchy Process in Dehradun District, Uttarakhand, India. Universal Journal of Environmental Research and Technology.4(2), 82-89.
  23. Tuzkaya, G., Tuzkaya, UR. and Lsun, BG., 2008. An Analytic Network Process Approach for Locating Undesirable Facilities: An Example from Istanbul, Turkey. Journal of Environmental Management. 88, 970-983.
  24. Vasilakos, C., Kalabokidis, K., Hatzopoulos, J. and Matsinos, I., 2009. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Natural Hazards. 50(1), 125–143.
  25. Whelan Robert, J., 1995. The ecology of fire. Cambridge University Press, New York: NY;P. 346.
  26. Xu, D., Dai, LM., Shao, GF., Tang, L. and Wang, H., 2005. Forest fire risk zone mapping from satellite images and GIS for Baihe forestry Bureau, Jilin China. Journal of Forestry Research.15 (3), 169-174.
  27. Zebardast, E., 2010. Application of Analytic Network Process (ANP) in urban and regional planning. Beautiful arts- archtecture and urbanism. 41, 79-90. (In Persian with English abstract).
  28. Zhang, QF. and Chen, WJ., 2007. Fire cycle of the Canada’s boreal region and its potential response to global change. Journal of Forestry Research. 18(1), 55-61.
  29. Zhang, ZX., Zhang, HY. and Zhou, DW., 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.