1. Ager, A.A., N.M. Vaillant, M.A. Finney and H.K. Preisler. 2012. Analyzing wildfire exposure and source-sink relationships on a fire prone forest landscape. Forest Ecology and Management, 267: 271-283. [
DOI:10.1016/j.foreco.2011.11.021]
2. Alaska Satellite Facility (ASF). 2021. https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/
3. Amiri, T., A. Banj Shafiei, M. Erfanian, O. Hosseinzadeh and H. Beygi Heidarlou. 2017. Determining of effective criteria in locating firefighting station in forest. Forest Research and Development, 2(4): 379-393 (In Persian).
4. Andela, N. and G.R. Van Der Werf. 2014. Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nature Climate Change, 4(9): 791-795. [
DOI:10.1038/nclimate2313]
5. Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior [Grass, shrub, timber, and slash, photographic examples, danger ratings]. USDA Forest Service general technical report INT-Intermountain Forest and Range Experiment Station (USA).
6. Baeza, M.J., M. De Luıs, J. Raventós and A. Escarré. 2002. Factors influencing fire behaviour in shrublands of different stand ages and the implications for using prescribed burning to reduce wildfire risk. Journal of Environmental Management, 65(2): 199-208. [
DOI:10.1006/jema.2002.0545]
7. Beyers, J.L., J.K. Brown, M.D. Busse, L.F. DeBano, W.J. Elliot P.F. Folliott, G.R. Jacoby, J.D. Knoepp, J.D. Landsberg, D.G. Neary, J.R. Reardon, J.N. Rinne, P.R. Robichaud, K.C. Ryan, A.R. Tiedemann and M.J. Zwolinski. 2005. Wild land fire in ecosystems effects of fire on soil and water. United States Department of Agriculture Forest Service Rocky Mountain Research Station General Technical, 250 p.
8. Beygi Heidarlou, H., A. Banj Shafiei and M. Erfanian. 2015. Evaluating the Fuzzy Weighted Linear Combination Method in Forest Fire Risk Mapping (Case study: Sardasht Forests, West Azerbaijan Province, IRAN). Journal of Wood and Forest Science and Technology, 22(3): 29-52 (In Persian).
9. Beygi Heidarlou, H., A. Banj Shafiei and M. Erfanian. 2015. 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).
10. Bihamta, M.R. and M.A. Zare Chahouki. 2010. Principles of statistics for the natural resources science. 1st edn., University of Tehran Press, Tehran, Iran. 300 pp.
11. Bilgili, E. and B. Saglam. 2003. Fire behavior in maquis fuels in Turkey. Forest Ecology and Management, 184(1-3): 201-207. [
DOI:10.1016/S0378-1127(03)00208-1]
12. Biranvand, A., S. Babaei Kafaki and H. Kiadaliri. 2011. Investigation the Ecological Factors Affecting Fire Spread in Forest Ecosystems (Case Study: Kakareza-Lorestan). Journal of Renewable Natural Resources Research, 2(2): 1-13 (In Persian).
13. Bond-Lamberty, B., C. Wang, S.T. Gower and J. Norman. 2002. Leaf area dynamics of a boreal black spruce fire chronosequence. Tree physiology, 22(14): 993-1001. [
DOI:10.1093/treephys/22.14.993]
14. Brown, J.K., R.D. Oberheu and C.M. Johnston. 1982. Handbook for inventorying surface fuels and biomass in the interior West. General technical report (No. PB-83-118125). Forest Service, Ogden, UT (USA). Intermountain Forest and Range Experiment Station, 52 pp. [
DOI:10.2737/INT-GTR-129]
15. De Luis, M., M.J. Baeza, J. Raventós and J.C. González-Hidalgo. 2004. Fuel characteristics and fire behaviour in mature Mediterranean gorse shrublands. International Journal of Wildland Fire, 13(1): 79-87. [
DOI:10.1071/WF03005]
16. Dimitrakopoulos, A.P. 2002. Mediterranean fuel models and potential fire behaviour in Greece. International Journal of Wildland Fire, 11(2): 127-130. [
DOI:10.1071/WF02018]
17. Encinas, A.H., L.H. Encinas, S.H. White, A.M. del Rey and G.R. Sánchez. 2007. Simulation of forest fire fronts using cellular automata. Advances in Engineering Software, 38(6): 372-378. [
DOI:10.1016/j.advengsoft.2006.09.002]
18. Fernandes, P.M. 2009. Examining fuel treatment longevity through experimental and simulated surface fire behaviour: a maritime pine case study. Canadian Journal of Forest Research, 39(12): 2529-2535. [
DOI:10.1139/X09-145]
19. Finney, M.A. 2006. An overview of FlamMap modeling capabilities. In Proc. of Conf. on fuels management - How to measure success, Andrews P.L., and Butler B.W. (eds.). 213-220 pp. USDA Forest Service, RMRS-P41.
20. Finney, M.A., I.C. Grenfell, C.W. McHugh, R.C. Seli, D. Trethewey, R.D. Stratton and S. Brittain. 2011. A method for ensemble wildland fire simulation. Environmental Modeling & Assessment, 16(2): 153-167. [
DOI:10.1007/s10666-010-9241-3]
21. Finney, M.A., S. Britten and R. Seli. 2003. FlamMap2 Beta Version 3.0.1. Fire Sciences Lab and Systems for Environmental Management, Missoula, Montana.
22. Fitzgerald, S.A., C.A. Berger and D.M. Leavell. 2019. Fire FAQs: What is Forest Fuel, and what are Fuel Treatments? Oregon State University Extension Service.
23. Foody, G.M. 2020. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment, 239: 111630. [
DOI:10.1016/j.rse.2019.111630]
24. Gambiza, J., B.M. Campbell, S.R. Moe and P.G. Frost. 2005. Fire behaviour in a semi-arid Baikiaea plurijuga savanna woodland on Kalahari sands in western Zimbabwe. South African Journal of Science, 101(5): 239-244.
25. Gillett, N.P., A.J. Weaver, F.W. Zwiers and M.D. Flannigan. 2004. Detecting the effect of climate change on Canadian forest fires. Geophysical Research Letters, 31(18): L18211. [
DOI:10.1029/2004GL020876]
26. Glasa, J. and L. Halada. 2008. On elliptical model for forest fire spread modeling and simulation. Mathematics and Computers in Simulation, 78(1): 76-88. [
DOI:10.1016/j.matcom.2007.06.001]
27. Global Forest Watch (GFW) https://www.globalforestwatch.org/dashboards/country/IR
28. Gomes, L., H.S. Miranda, D.V. Silvério and M.M. Bustamante. 2020. Effects and behaviour of experimental fires in grasslands, savannas, and forests of the Brazilian Cerrado. Forest Ecology and Management, 458: 117804. [
DOI:10.1016/j.foreco.2019.117804]
29. Gould, J.S., W.L. McCaw, N.P. Cheney, P.F. Ellis, I.K. Knight and A.L. Sullivan (Eds.). 2008. Project Vesta: fire in dry eucalypt forest: fuel structure, fuel dynamics and fire behaviour. Csiro Publishing. Perth, WA, 218 p. [
DOI:10.1071/9780643101296]
30. Gray, K.L. and E. Reinhardt. 2003. Analysis of algorithms for predicting canopy fuel. In In: Second international wildland fire ecology and fire management congress and fifth symposium on fire and forest meteorology; 2003 November 16-20; Orlando, FL. Boston, MA: American Meteorological Society. P5. 8. 11 p.
31. Hély, C., S. Alleaume, R.J. Swap, H.H. Shugart and C.O. Justice. 2003. SAFARI-2000 characterization of fuels, fire behavior, combustion completeness, and emissions from experimental burns in infertile grass savannas in western Zambia. Journal of Arid Environments, 54(2): 381-394. [
DOI:10.1006/jare.2002.1097]
32. ICONA. 1993. Manual de Operaciones Contra Incendios Forestales, Instituto Nacional Para la Concervacion de la Naturaleza (Spain), Madrid, 283 pp.
33. Jahdi, R., A.A. Darvishsefat and V. Etemad. 2016. Assessing the impact of fuel moisture conditions on fire spread and behavior in Golestan National Park. Journal of Forest and Wood Products, 68(4): 799-813 (In Persian).
34. Jahdi, R., A.A. Darvishsefat and V. Etemad. 2015. Local scale fuel type mapping and surface fire behavior prediction using FARSITE (case study: Toshi Forest-Siahkal). Journal of Forest and Wood Products, 22(2): 207-225 (In Persian).
35. Jahdi, R., M. Salis, A.A. Darvishsefat, M.A. Mostafavi, F.J. Alcasena Urdíroz, V. Etemad, O.M. Lozano and D. Spano. 2015. Calibration of FARSITE simulator in northern Iranian forests. Natural Hazards and Earth System Sciences, 2015, núm. 15: 443-459. [
DOI:10.5194/nhess-15-443-2015]
36. Jahdi, R., M. Salis, M. Arabi and B. Arca. 2019. Fire modelling to assess spatial patterns of wildfire exposure in Ardabil, NW Iran. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42: 577-581. [
DOI:10.5194/isprs-archives-XLII-4-W18-577-2019]
37. Jaiswal, R.K., S. Mukherjee, K.D. Raju and R. Saxena. 2002. Forest fire risk zone mapping from satellite imagery and GIS. International journal of applied earth observation and geoinformation, 4(1): 1-10. [
DOI:10.1016/S0303-2434(02)00006-5]
38. Keane, R.E., R. Burgan and J. van Wagtendonk. 2001. Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire, 10(4): 301-319. [
DOI:10.1071/WF01028]
39. Kim, D.W., W. Chung and B. Lee. 2016. Exploring tree crown spacing and slope interaction effects on fire behavior with a physics-based fire model. Forest Science and Technology, 12(4): 167-175. [
DOI:10.1080/21580103.2016.1144541]
40. Larson, M.G. 2008. Analysis of variance. Circulation, 117(1): 115-121. [
DOI:10.1161/CIRCULATIONAHA.107.654335]
41. Liu, W., S. Wang, Y. Zhou, L. Wang and S. Zhang. 2010. Analysis of forest potential fire environment based on GIS and RS. In 2010 18th International Conference on Geoinformatics (1-6 pp). Ieee. [
DOI:10.1109/GEOINFORMATICS.2010.5567966]
42. Morton, D.C., R.S. DeFries, Y.F. Shimabukuro, L.O. Anderson, E. Arai, F. del Bon Espirito-Santo, R. Freitas and J. Morisette. 2006. Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon. Proceedings of the National Academy of Sciences, 103(39): 14637-14641. [
DOI:10.1073/pnas.0606377103]
43. Nolan, R.H., O.F. Price, S.A. Samson, M.E. Jenkins, S. Rahmani and M.M. Boer. 2022. Framework for assessing live fine fuel loads and biomass consumption during fire. Forest Ecology and Management, 504: 119830. [
DOI:10.1016/j.foreco.2021.119830]
44. Ntaimo, L. and B.P. Zeigler. 2005. Integrating Fire Suppression into a DEVS Cellular Forest Fire Spread Model. Proc. of the 2005 Spring Simulation Multi Conference, San Diego, CA, USA, April 3-7, 48-54 pp.
45. Okafor, V.N., M.C. Obiadi and J.N. Obiefuna. 2020. Correlations of major flame characteristics of some fire tolerant trees in South-East Nigeria by coefficient of determination (R2). Journal of Scientific Research and Reports, 26(4): 81-98. [
DOI:10.9734/jsrr/2020/v26i430250]
46. Ozenen Kavlak, M., S.N. Cabuk and M. Cetin. 2021. Development of forest fire risk map using geographical information systems and remote sensing capabilities: Ören case. Environmental Science and Pollution Research, 28(25): 33265-33291. [
DOI:10.1007/s11356-021-13080-9]
47. Peek, J.M. 1970. Relation of canopy area and volume to production of three woody species. Ecology, 51(6): 1098-1101. [
DOI:10.2307/1933640]
48. Pierce, J.L., G.A. Meyer and A.T. Jull. 2004. Fire-induced erosion and millennial-scale climate change in northern ponderosa pine forests. Nature, 432(7013): 87-90. [
DOI:10.1038/nature03058]
49. Podur, J., D.L. Martell and K. Knight. 2002. Statistical quality control analysis of forest fire activity in Canada. Canadian Journal of Forest Research, 32(2): 195-205. [
DOI:10.1139/x01-183]
50. Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station Research Paper, INT-115, Ogden, UT, 49 pp.
51. Roussopoulos, P.J. and R.M. Loomis. 1979. Weights and Dimensionsal Properties of Shrubs and Small Trees of the Great Lakes Conifer Forest (Vol. 178). Department of Agriculture, Forest Service, North Central Forest Experiment Station.
52. Sağlam, B., O. Küçük, E. Bilgili, B.D. Durmaz and I. Baysal. 2008. Estimating fuel biomass of some shrub species (Maquis) in Turkey. Turkish Journal of Agriculture and Forestry, 32(4): 349-356.
53. Sah, J.P., M.S. Ross, S. Koptur and J.R. Snyder. 2004. Estimating aboveground biomass of broadleaved woody plants in the understory of Florida Keys pine forests. Forest Ecology and Management, 203(1-3): 319-329. [
DOI:10.1016/j.foreco.2004.07.059]
54. Salis, M. 2007. Fire Behavior simulation in Mediterranean Maquis using FARSITE (Fire Area Simulator), PhD Doctoral Thesis, Universita' Degli Studi Di Sassari, 166 pp.
55. Salis, M., B. Arca, L. Del Giudice, P. Palaiologou, F. Alcasena-Urdiroz, A. Ager, M. Fiori, G. Pellizzaro, C. Scarpa, M. Schirru, A. Ventura and P. Duce. 2021. Application of simulation modeling for wildfire exposure and transmission assessment in Sardinia, Italy. International Journal of Disaster Risk Reduction, 58: 102189. [
DOI:10.1016/j.ijdrr.2021.102189]
56. Scott, J.H. and R.E. Burgan. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station; 72 pp. [
DOI:10.2737/RMRS-GTR-153]
57. Soto, B., R. Basanta and F. Diaz-Fierros. 1997. Effects of burning on nutrient balance in a area of gorse (Ulex europaeus L.) scrub. Science of the Total Environment, 204(3): 271-281. [
DOI:10.1016/S0048-9697(97)00185-X]
58. Specht, R.L. 1969. A comparison of the sclerophyllous vegetation characteristic of Mediterranean type climates in France, California, and Southern Australia. I. Structure, morphology, and succession. Australian Journal of Botany, 17(2): 277-292. [
DOI:10.1071/BT9690277]
59. Van Laar, A. and A. Akca. 2007. Forest mensuration, Springer, The Netherlands. [
DOI:10.1007/978-1-4020-5991-9]
60. Wan, T.A.N.G., H.U. Jun, H. Zhang, W.U. Pan and H.E. Hua. 2015. Kappa coefficient: a popular measure rater agreement. Shanghai Archives of Psychiatry, 27(1): 62.
61. Wagner III, W.E. 2019. Using IBM® SPSS® statistics for research methods and social science statistics. 208 pp., Sage Publications, California, USA.
62. Williams, R.A. and J.R. McClenahen. 1984. Biomass prediction equations for seedlings, sprouts, and saplings of ten central hardwood species. Forest Science, 30(2): 523-527.
63. Xofis, P., G. Tsiourlis and P. Konstantinidis. 2020. A Fire Danger Index for the early detection of areas vulnerable to wildfires in the Eastern Mediterranean region. Euro-Mediterranean Journal for Environmental Integration, 5(2): 1-13. [
DOI:10.1007/s41207-020-00173-z]
64. Yassemi, S., S. Dragićević and M. Schmidt. 2008. Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour. ecological modelling, 210(1-2): 71-84. [
DOI:10.1016/j.ecolmodel.2007.07.020]
65. Yavuz, M., B. Saglam, O. Kucuk and A. Tufekcioglu. 2018. Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu Üniversitesi Orman Fakültesi Dergisi, 18(2): 171-188. [
DOI:10.17475/kastorman.459698]
66. Zigner, K., L. Carvalho, S. Peterson, F. Fujioka, G.J. Duine, C. Jones, D. Roberts and M. Moritz. 2020. Evaluating the ability of FARSITE to simulate wildfires influenced by extreme, downslope winds in Santa Barbara, California. Fire, 3(3): 29. [
DOI:10.3390/fire3030029]