Fire, a phenomenon occurs in most parts of the world and causes severe financial losses, even, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management. Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, remote sensing and the reviewed papers that predicted the fire risk in the field of remote sensing and geographic information system were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: Fuzzy analytic hierarchy process (AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices. Discussion and Conclusion: The findings indicate that RS and GIS are effective tools in the study of fire risk prediction.

1. Fearnside PM. Deforestation in Brazilian Amazonia: History, rates, and consequences. Conservation Biology 2005; 19(3): 680–688. doi: https://doi.org/10.1 111/j.1523-1739.2005.00697.x.

2. Ardakani S, Voldazoj M, Mohamadzade A, et al. Spectroscopic characterization of fire and field objectives for identification and separation in remote sensing data (PhD thesis). Tehran: Khaje-Naseerdin-Toosi University of Technology; 2010.

3. Miller DE, Hays CR. Missouri Drought Response Plan. Water Resource Report No. 44; 1995. p. 52.

4. Adab H, Kanniah D, Solaimani K. GIS-based probability assessment of fire risk in grassland and forested landscapes of Golestan Province, Iran. International Conference on Environmental and Computer Science IPCBEE; Singapore: IACSIT Press; 2011. Available from: http://www.ipcbee.com/vol19/33-IC ECS2011R30007.pdf.

5. Chuvieco E, Aguado I, Yebra M, et al. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling 2010; 221(1): 46–58.

6. Mohammadi F. Preparation of forest fire hazard map using satellite imagery and GIS in a part of Paveh forest (in Persian). Kurdistan Natural Resources Faculty 2009; p. 69.

7. Jaiswal RK, Mukherjee S, Raju KD, et al. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoformation 2002; 4(1): 1–10. doi: https://doi. org/10.1016/S0303-2434(02)00006-5.

8. Alonso-Betanzos A, Fontenla-Romero O, Guijarro-Berdiñas B, et al. An intelligent system for forest fire risk prediction and firefighting management in Galicia. Expert Systems with Applications 2003; 25(4): 545–554. doi: https://doi.org/10.1016/S0957- 4174(03)00095-2.

9. Bernabeu P, Vergara L, Bosh I, et al. A prediction/detection scheme for automatic forest fire surveillance. Digital Signal Processing 2004; 14(5): 481–507. doi: https://doi.org/10.1016/j.dsp.2004.06. 003.

10. Roy PS. Forest fire and degradation assessment using satellite remote sensing and geographic information system. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology 2003; 361–400. Available from: http://www.wamis.org/agm/pubs/agm8/Paper-18.pdf.

11. Canadian Forest Service [Internet]. Wildland fires, insects, and disturbances. Available from: http://ww w.nrcan.gc.ca/forests/fire-insects-disturbances/fire/14470.

12. Boonchut P. Decision support for hazardous material routing. Enschede: International Institute for Geo-information Science and Earth Observation (ITC) (MSc thesis); 2005. Available from: https://www.itc. nl/library/papers_2005/msc/upla/boonchut.pdf.

13. Chuvieco E, Agaudo I, Cocero D, et al. Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR analysis in forest fire danger studies. International Journal of Remote Sensing 2003; 24(8): 1621–1637. doi: https://doi.org/10.1080 /01431160210144660b.

14. Taylor SW, Alexander ME. Science, technology, and human factors in fire danger rating: The Canadian experience. International Journal of Wildland Fire 2006; 15(1): 121–135. doi: https://doi.org/10.1071/ WF05021.

15. Food and Agriculture Organization (FAO) [Internet]. International Forest Fire News. 1995. Available from: http://www.fao.org/statistics/en/.

16. SadeghiKaji H. Assessment of fire risk and probability in the natural lands of Chaharmahal-va-Bakhtiari Province (MSc thesis) (in Persian). Shahrekord: Shahrekord University; 2011; p. 86.

17. Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A. Fire risk assessment using satellite data. Advances in Space Research 2006; 37(4): 741–746. doi: 10.1016/ j.asr.2004.12.053.

18. Chuvieco E, Sandow Ch, Günther KP, et al. Global burned area mapping from European satellites: The ESA FIRE-CCI project. Journal of Photogrammetry and Remote Sensing 2012; XXXIX-B8: 13–16. doi: https://doi.org/10.5194/isprsarchives-XXXI X-B8-13-2012.

19. Chuvieco E, Aguado I, Jurdao S, et al. Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire 2012; 23(5): 606–619. doi: 10.1071/WF12052.

20. HajiMohammadi H, Bazajeed M, Qalahiri F, et al. The structure of the atmosphere, in the event of a fire in northern Iran (in Persian). Journal of Golestan University (Geospatial Space Magazine Quarterly) 2015; 25(7): 187–206. Available from: http://gps.gu. ac.ir/article_54249.html.

21. Mosavari A, Adhami. Fire hazard zonation using GIS, AHP case study—Caspian forests of northern Iran — Golestan Province (In Persian) 2012; p. 11.

22. Murta A, Bozer R. Estimation of the burned area in forest fires using computational intelligence techniques. Procedia Computer Science 2012; 12: 282–285. doi: https://doi.org/10.1016/j.procs.2012.09.070

23. Makia M, Ishiahra M, Tamura M. Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment 2004; 90(4): 441–450. doi: https://doi.org/1 0.1016/j.rse.2004.02.002.

24. Nepstad DC. 2007. The Amazon’s vicious cycles: Drought and fire in the greenhouse - ecological and climatic tipping points of the world’s largest tropical rainforest, and practical preventive measures. A report to the World Wide Fund for Nature (WWF). Available from: https://digital.library.unt.edu/ark:/67 531/metadc226671/m2/1/high_res_d/WWFBinaryitem7658.pdf.

25. Xu D, Dai L, Shao G, et al. Forest fire risk zone mapping from 2005 satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research 2005; 16(3): 169–174. Available from: https://doi.org/10.1007/BF02856809.

26. Beygi H, Shafiei AB, Erfanian M. Evaluating the fuzzy weighted linear combination method in forest fire risk mapping (Case study: Sardasht Forests, West Azerbaijan Province, Iran). Journal of Science and Technology of Wood and Forest 2015; 22(3): 29–51.

27. Nasiri M. Investigation on wood resistance of different tree species to fire at caspian forests of Iran. Iranian Journal of Forest & Poplar Research 2012; 20(3): 505–513.

28. Chandra S. 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. In: Van Oosterom P, Zlatanova S, Fendel EM (editors). Geo-information for Disaster Management. Berlin, Heidelberg: Springer; 2005. Available from: https://doi.org/10.1007/3-540-27468-5_86.

29. Sharma D, Hoa V, Cuong PV, et al. Forest fire risk zonation for Jammu District forest division using remote sensing and GIS. Hanoi, Vietnam: 7th FIG Regional Conference, Spatial Data Serving People: Land Governance and the Environment—Building the Capacity. October 1-12, 2009.

30. Mohammadi F, Shabani N, Pourhashemi M, et al. Forest fire hazard mapping using AHP and GIS (In Persian). Iranian Forest and Poplar Researches Journal 2010; 18(4): 586–569.

31. Jahdi R, Darvishsefat A, Etemad V. Predicting forest fire spread using fire behavior model (Case study: Malekroud Forest-Siahkal). Iranian Journal of Forest and Poplar Research 2013; 5(4): 419–430.

32. Darvishi L, Ghods-Khah M, Gholami V. A regional model for forest fire hazard zonation in forests of Dorud city (Case study: Babahar region). Iranian Journal of Forest and Range Protection Research 2013; 11(1): 10–20. doi: http://dx.doi.org/10.22092/ ijfrpr.2013.106396.

33. Behzadfar M, Vahid H. Fire risk zonation in North Khorasan Province, Iran. The first international conference on wildfire in natural resources lands; September 2011. Available from: https://www.researchg ate.net/publication/236134271.

34. Aghajani H, Fallah A, Fazlollah Emadian S. Modelling and analyzing the surface fire behavior in Hyrcanian forest of Iran, Journal of Forest Science 2014; 60(9): 353–362. doi:10.17221/97/2013-JFS.

35. Huyền DTT, Tuân VA. 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; 2008. Available from: http://wgrass.media.osaka-cu.ac.jp/gisid-eas10/papers/8918d883b5c5b166ca47d6733c18.pdf.

36. Zarekar A, Kazemi-Zamani B, Ghorbani S, et al. 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 2013; 21(2): 218–230. doi: http://dx.doi. org/10.22092/ijfpr.2013.3854.

37. Farooq M, Malik T, Rabbani G. Forest fire risk zonation using remote sensing and GIS technology in Kansrao forest range of Rajaji National Park, Uttarakhand, India. International Journal of Advanced Remote Sensing and GIS 2013; 2: 86–95. Available from: https://www.researchgate.net/publication/278159215.

38. Yin H, Kong F, Li X. RS and GIS-based forest fire zone mapping in Dahinggan Mountains. Chinese Geographical Science 2004; 14(3): 251–257. doi: 10.1007/s11769-003-0055-y.

39. Tran AT, Dinh ND, Danh T, et al. Forest fire risk mapping by using satellite imagery and GIS for Quang Ninh Province, Vietnam. 2008; Available from: https://www.researchgate.net/publication/260871777.

40. Saxena A, Chandra S, Srivastava P. Geospatial modeling for forest fire risk zonation in Himalayas and Siwaliks, India. Remote Sensing and GIS Applications to Forest Fire Management, Fire Effects Assessment 2005; 133–137.

41. Kartoolinezhad D. Wildfires risk assessment of North-East Hyrcanyan Forests of Iran by using Keetch-Byram and Mc-Arthur Indices 2016; 14(1): 48–57. doi: 10.22092/ijfrpr.2016.107641.

42. Prasad Vadrevu K, Badarinath KVS, Anuradha E. Spatial patterns in vegetation fires in the Indian region. Environmental Monitoring and Assessment 2008; 147(1-3): 1–13. doi: 10.1007/s10661-007-0092-6.

43. Sowmya SV, Somashekar RK. Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology 2010; 31(6): 969–974.

44. Mohammadinejad M, Tavakoli M. Survey of fire status in oak and wild pistachio (Pistacia atlanticaDesf) forest zone of Lorestan Province (in Persian). The first National Conference on Oak and wild pistachio (Pistacia atlanticaDesf) in Zagros 1998; 76–77.

45. Burgan RE, Klaver RW, Klaver JM. Fuel models and fire potential from satellite and surface observations. International Journal of Wildland Fire 1998; 8: 159–170. doi: https://doi.org/10.1071/WF9980159

46. Chuvieco E, Salas J. Mapping the spatial distribution of forest fire danger using GIS. International Journal of Geographic Information Systems 1996; 10(3): 333–345. Available from: https://doi.org/10.1080/02693799608902082.

47. Chen W, Sakai K, Moriya L, et al. Estimation of vegetation in semi-arid sandy land based on multivariate statistical modeling using remote sensing data. Environmental Modeling & Assessment 2013; 18(5): 547–558. doi: 10.1007/s10666-013-9359-1.

48. Lozano FJ, Suárez-Seoane S, Kelly M, et al. A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case studying a mountainous Mediterranean region. Remote Sensing of Environment 2008; 112(3): 708–719. doi: https://doi.org/10.1016/j.rse.2 007.06.006.

49. Stolle F, Chomitz KM, Lambin EF, et al. Land use and vegetation fires in Jambi Province, Sumatra, Indonesia. Forest Ecology and Management 2003; 179 (1-3): 277–292. doi: https://doi.org/10.1016/S0 378-1127(02)00547-9.

50. Vazquez A, Moreno JM. Spatial distribution of forest fires in Sierra de Credos (Central Spain). Forest Ecology and Management 2001; 147(1): 223–239. Available from: https://doi.org/10.1016/S0378-1127 (00)00436-9.

51. Juan de la Riva, Pérez-Cabello F, Lana-Renault N, et al. Mapping wildfire occurrence at regional scale. Remote Sensing of Environment 2004; 92(3): 363–369. doi: https://doi.org/10.1016/j.rse.2004.06.022.