AccScience Publishing / AJWEP / Volume 15 / Issue 2 / DOI: 10.3233/AJW-180022
RESEARCH ARTICLE

Zoning of Forest Fire Risk Based on Environment,  Human and Land Factors  (Case Study: Golestan Province, Iran)

Kami Kaboosi1* Osman Majidi2
Show Less
1 Department of Agricultural Science and Natural Resources, Gorgan Branch Islamic Azad University, Gorgan, Iran
2 Golestan Meteorological Administration, Gorgan, Iran
AJWEP 2018, 15(2), 99–106; https://doi.org/10.3233/AJW-180022
Submitted: 11 August 2017 | Revised: 10 March 2018 | Accepted: 10 March 2018 | Published: 11 May 2018
© 2018 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Several factors are involved in forest fire occurrence and these parameters are divided into environment,  human and land. Research showed that forest fires had significant correlation with meteorological and topographical  variables. So, one way to prevent and control forest fire is its prediction based on effective factors including  climatic ones (precipitation, air temperature and wind speed and direction), land use, elevation, slope and its  direction and human activities (distance from roads and residential and agricultural land). In this study, risk zoning  map of forest fires in Golestan province, Iran, was prepared considering all above factors. Zoning maps of these  parameters were overlaid by classification weighted method. In order to risk zoning map verification, this map  was matched with the fire events map in recent years in province. 

Results showed that safe, low, moderate, high and very high fire risk zones covered 66, 14.5, 15.3, 4.1 and  0.1 percent of province land, respectively. Also, Bandar Torkeman, Gomishan and Agh-ghalla counties had no  risky zone while other counties have risky zone with various degrees of fire. In aspect of county distribution, most  percent of very risk zone belonged to Kalaleh (550 ha) and Maraveh Tappeh (360 ha) and risk zone to Kalaleh  (36,110 ha), Galikesh (15,269 ha), Maraveh Tappeh (14,398 ha), Minoodasht (13,293 ha) and Azadshahr (2839  ha). Acceptable accuracy of the fire risk zoning map was found because a relative good compliance between the  fire risk zoning map and fire distribution map in recent years.

Keywords
Distance
elevation
precipitation
slope
temperature
Conflict of interest
The authors declare they have no competing interests.
References

Aleemahmoodi Sarab, S., Feghhi, J. and B. Jabarian Amiri (2013). Predicting the occurrence of natural fires in forests and ranges using artificial neural networks (case study: Zagros region, Izeh township). Iran J Appl Ecol, 1(2): 75-85.

Almedia, R. (1994). Forest fire risk areas and definition of the prevention priority planning actions using GIS. Ecol Mod, 3(7): 1-9.

Campbell, J.L. and D.J. Shinneman (2017) . Potential influence of wildfire in modulating climate-induced forest redistribution in a central Rocky Mountain landscape. Ecol Proc, 6: 7. DOI 10.1186/s13717-017-0073-9.

Chandra, S. (2005). Application of remote sensing and GIS technology in forest fire risk modeling and management of forest fires: A case study in the Garhwal Himalayan Region. 1239-1254. In: Van Oosterom, P., Zlatanova, S. and Fendel, M.E. (Eds) Geo-information for disaster management. Springer, Berlin.

Chuvieco, E. and Congalton, R.G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ, 29: 147-159.

Dimitrakopoulos,A.P., Bemmerzouk,A.M. and Mitsopoulos, I.D. (2011). Evaluation of the Canadian fire weather index system in an Eastern Mediterranean environment. Meteorological Application, 18(1): 83-93.

Dong, X.U., 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. J Fore Res, 16(3): 169-174.

Encinas, A.H., Encinas, L.H., White, S.H., Del Rey, A.M. and G.R. Sanchez (2007). Simulation of forest fire fronts using cellular automata. Adv Eng Softw, 38: 372-378.

Eskandari, S., Oladi Ghadikolaei, J. and H. Jalilvand (2014). Efficiency evaluation of Dong model for determination of fire risk potential in Zarrin Abad forests. Iran J Fore Poplar Res, 21(3): 439-451.

FAO (2017). FAOSTAT. http://www.fao.org/faostat/en/#home Garavand, S., Yaralli, N. and H. Sadeghi (2013). Spatial pattern and mapping fire risk occurrence at natural lands of Lorestan province. Iran J Fore Poplar Res, 21(2): 231-242.

GDGNRW (2013). Statistical Book. General Directorate of Golestan Natural Resource and Watershed, Gorgan.

Hernandez-Leal, P.A., Arbelo, M. and A. Gonzalez-Calvo (2006). Fire risk assessment using satellite data. Adv Space Res, 37: 741-746.

Iliadis, L.S. (2005). A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environ Model and Softw, 20(5): 613-621.

Jaiswal, R.K., Mukherjee, S., Raju, K.D. and R. Saxena (2002). Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Observ Geoinform, 4: 1-10.

Kessy, J.F., Nsokko, E., Kaswamila,A. and F. Kimaro (2016). Analysis of drivers and agents of deforestation and forest degradation in Masito forests, Kigoma, Tanzania. Int J Asian Soc Sci, 6: 93-107.

Maeda, E.E., Arcoverde, G.F.B., Pellikka, P.K.E. and Y.E. Shimabukuro (2011). Fire risk assessment in the Brazilian Amazon using MODIS imagery and change vector analysis. Appl Geogr, 31: 76-84.

Martinez, J., Vega-Garcia, C. and E. Chuvieco (2009). Human-caused wildfire risk rating for prevention planning in Spain. JEnviron Manage, 90(2): 1241-1252.

Mirdeylami, T., Shataee, Sh and M.R. Kavousi (2014). Forest fire risk zone mapping in the Golestan national park using weighted linear combination (WLC) method. Iran J Fore, 5(4): 377-390.

Mohammadi, F., Shabanian, N., Pourhashemi, M. and P. Fatehi (2010). Risk zone mapping of forest fire using GIS and AHP in a part of Paveh forests. Iran J Fore Poplar Res, 18(4): 569-586.

Paz, Sh, Carmel, Y., Jahshan, F. and M. Shoshany (2011). Post-fire analysis of pre-fire mapping of fire risk: A recent case study from Mt. Carmel (Israel). Fore Ecol Manage, 262: 1184-1188.

Preisler, H.K. and A.L. Westerling (2004). Statistical model for forecasting monthly large wildfire events in western United States. J Appl Meteorology Climatology, 46(7): 1020-1030.

Soleimani, E. and H.R. Memarian (2011). Investigation of destruction trend of Northern forest of Iran (with emphasis on fire). Islamic Parliament Research Center of the Islamic Republic of Iran, Tehran.

Sowmya, S.V. and R.K. Somashekar (2010). Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. JEnviron Biol, 31(6): 969-974.

Stolle, F., Chomitz, K.M., Lambin, E.F. and T.P. Tomich (2003). Land use and vegetation fires in Jambi Province, Sumatra, Indonesia. Fore Ecol Manage, 179: 277-292.

Suleiman, M.S., Wasonga, O.V., Mbau, J.S. and Y. Ahmed Elhadi (2017). Spatial and temporal analysis of forest cover change in Falgore Game Reserve in Kano, Nigeria. Ecol Proc, 6: 11. DOI 10.1186/s13717-017-0078-4.

Vadrevu, K.P., Eaturu,A, and K.V.S. Badarinath (2010). Fire risk evaluation using multi-criteria analysis. Environ Monit Assess, 166(1-4): 223-239.

Vasilakos, C., Kalabokidis, K., Hatzopoulos, J., Kallos, G. and Y. Matsinos (2007). Integrating new methods and tools in fire danger rating. Int J WildLand Fire, 16(3): 306-316.

Wang, X., Wotton, B.M., Cantin, A.S., Parisien, M.A., Anderson, K., Moore, B. and M.D. Flannigan (2017). CFFDRS: An R package for the Canadian Forest Fire Danger Rating System. Ecol Proc, 6: 5. DOI 10.1186/ s13717-017-0070-z.

Zarekar, A., Kazemi Zamani, B., Ghorbani, S., Ashegh Moalla, M. and H. Jafari (2013) . Mapping spatial distribution of forest fire using MCDM and GIS (case study: three forest zones in Guilan province). Iran J Fore Poplar Res, 21(2): 218-230.

Zhang, Z.X., Zhang, H.Y. and D.W. Zhou (2009). Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires. J Arid Environ, 74: 386-393.

Share
Back to top
Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing