Simulation of Lattakia Forest Fire using the Geographic Information Systems

References 1. Abatzoglou, J. T.; Balch, J. K.; Bradley, B. A.; Kolden, C. A. (2018). Human-related ignitions concurrent with high winds promote large wildfires across the USA. International journal of wildland fire , 27(6), 377-386. 2. of Iran using remote sensing and GIS techniques. Nat HazardsIran , 65: 1723-1743. 3. northern spotted owl (Strix occidentalis caurina) habitat in Central Oregon, USA. Forest Ecology and Management, 246(1), 45-56. 4. Ager, A. A.; Vaillant, N. M.; Finney, M. A. (2011). Integrating fire behavior models and kjhg analysis for wildland fire risk assessment and fuel management planning. Journal of Combustion , 2011 . 5. Aguado, I.; Chuvieco, E.; ; Nieto, H. (2007). Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. International Journal of Wildland Fire , 16(4), 390-397. 6. Airey-Lauvaux, C.; Pierce, A. D.; Skinner, C. N.; Taylor, A. H. (2022). Changes in fire behavior caused by fire exclusion and fuel build-up vary with topography in California montane forests, USA. Journal of environmental management , 304, 114255. 7. and emote sensing and GIS integration for Maramaris- New strategies for European remote sensing. Proceedings of the 24th Symposium of the European Association of Remote Sensing Laboratories, Dubrovnik, Croatia, 25-27 May 2004 (pp. 69-79). Millpress Science Publishers. 8. Alcasena, F. J.; Salis, M.; Ager, A. A.; Arca, B.; Molina, D.; Spano, D. (2015). zAssessing landscape scale wildfire exposure for highly valued resources in a Mediterranean area. Environmental management , 55(5), 1200-1216. 9. Alexander, JD.; Seavy, NE.; Ralph, CJ.; Hogoboom, B. (2006). Vegetation and topographical correlates of fire severity from two fires in the Klamath-Siskiyou region of Oregon and California . Int J Wildland Fire 15: 237 245.4. 10. Alexander, M. E., and Cruz, M. G. (2006). Evaluating a model for predicting active crown fire rate of spread using wildfire observations. Canadian Journal of Forest Research , 36(11), 3015-3028. 11. e of wildland fire simulations. International journal of wildland fire , 29(2), 160-173. 12. quantification in wildland fire spread simulation. Applied Mathematical Modelling , 90 , 527-546.13. Andrews, P. L. and Rothermel R. C. (1982). Charts for interpreting wildland fire behavior characteristics (Vol. 131). US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 14. Andrews, P. L. (1986). BEHAVE: fire behavior prediction and fuel modeling system. BURN Subsystem, Part, 1. 15. Balch, J. K.; Bradley, B. A.; Abatzoglou, J. T.; Nagy, R. C.; Fusco, E. J.; Mahood, A. L. (2017). Human-started wildfires expand the fire niche across the United States. Proceedings of the National Academy of Sciences , 114(11), 2946-2951. 16. Baldocchi, D. (2008) of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1 26. 17. Beer, T. (1991). The interaction of wind and fire. Boundary-Layer Meteorology , 54(3), 287-308. 18. Bessie, W. C. and Johnson, E. A. (1995). The relative importance of fuels and weather on fire behavior in subalpine forests. Ecology , 76 (3), 747-762. 19. Bilgili, E. R.; Durmaz, B. in immature calabrian pine plantations. Forest Ecology and Management , 234(1), S112. 20. Birch, D. S.; Morgan, P.; Kolden, C. A.; Abatzoglou, J. T.; Dillon, G. K.; Hudak, A. T.; Smith, A. M. (2015). Vegetation, topography and daily weather influenced burn severity in central Idaho and western Montana forests. Ecosphere , 6(1), 1-23. 21. management. CEEPUS Spring School, Kielce, Poland . 22. evolution of flammable ecosystems. Trends Ecol. Evol. 20(7): 387-394. 23. fire on regional evapotranspiration in the central Canadian boreal forest. Global change biology, 15(5), 1242-1254. 24. Bradstock, R. A.; Hammill, K. A.; Collins, L.; Price, O. (2010). Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia. Landscape Ecology , 25 (4), 607-619. 25. Brun, C.; ; Cencerrado, A.; Margalef, T.; computing framework for continental-scale forest fire spread prediction. Procedia Computer Science , 108 , 1712-1721. 26. Bushfire CRC. (2006). Focus on fire spread simulators. Bushfire Cooperative Research Centre, Melbourne. Bushfire CRC Update 7 (July 2006). 1 p. 27. Busse, M. D., and Riegel, G. M. (2009). Response of antelope bitterbrush to repeated prescribed burning in Central Oregon ponderosa pine forests. Forest Ecology and Management , 257(3), 904-910.28. Butler, B.; Quarles, S.; Standohar-Alfano, C.; Morrison, M.; Jimenez, D.; Sopko, P.; ... Page, W. (2020). Exploring fire response to high wind speeds: fire rate of spread, energy release and flame residence time from fires burned in pine needle beds under winds up to International journal of wildland fire , 29(1), 81-92. 29. Chuvieco, E.; Yue, C.; Heil, A.; Mouillot, F.; ; Padilla, M.; ... & Tansey, K. (2016). A new global burned area product for climate assessment of fire impacts. Global Ecology and Biogeography , 25(5), 619-629. 30. CIFFC (2003). Glossary of Forest Fire Management Terms (Winnipeg: CIFFC) . 31. Cochrane, M. A. (2003). Fire science for rainforests. Nature , 421(6926), 913-919. 32. and Sunar, F. (2020). Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International journal of disaster risk reduction , 45, 101479. 33. Coogan, S.C.; Robinne, F.N.; Jain, P.; Flannigan, M.D. (2019) wildfire a Canadian perspective. Can. J. For. Res . 49(9): 1015 1023. 34. fire dynamics in mallee-heath fuel, weather and fire behaviour prediction in south australian semi-arid shrublands . 35. Cruz, M. G. and Alexander, M. E. (2017). Modelling the rate of fire spread and uncertainty associated with the onset and propagation of crown fires in conifer forest stands. International journal of wildland fire , 26(5), 413-426. 36. Cruz, M. G.; Alexander, M. E.; Fernandes, P. M.; Kilinc, M.; the 10% wind speed rule of thumb for estimating a wildfire's forward rate of spread against an extensive independent set of observations. Environmental Modelling & Software , 133, 104818. 37. De Rigo, D.; ; Durrant, T. H.; Vivancos, T. A.; San-Miguel-Ayanz, J. (2017). Forest fire danger extremes in Europe under climate change: variability and uncertainty (Doctoral dissertation, Publications Office of the European Union). 38. Diakakis, M.; Nikolopoulos, E.I.; Mavroulis, S.; et al. (2017). Observational evidence on the effects of mega-fires on the frequency of hydrogeomorphic hazards. The case of the Peloponnese fires of 2007 in Greece. Science of the Total Environment 592 (2017) 262 276. 39. Dimitriou, A.; Mantakas, G.; Kouvelis, S. (2001) An analysis of key issues that underlie forest fires and shape subsequent fire management strategies in 12 countries in the Mediterranean basin. Final report prepared by Alcyon for WWF Mediterranean Programme Office and IUCN. 40. Dong, XU.; Li-Min, D.; Guo- Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, China. Journal Jilin of Forestry ResearchChina , 16(3): 69-174.41. D bed width on laboratory fire behaviour. International Journal of Wildland Fire, 20(2), 272-288. 42. satellite imagery and GIS a case study.ResearchGate 50: 1-7. 43. influencing fire severity under moderate burning conditions in the Klamath Mountains, northern California, USA. Ecosphere , 8(5), e01794. 44. EUFOFINET (2012) European Glossary for Wildfires and Forest Fires, 1st Edition. 45. Evelpidou, N.; Tzouxanioti, M.; Gavalas, T., Spyrou, E.; Saitis, G.; Petropoulos, A.; Karkani, A. (2022). Assessment of Fire Effects on Surface Runoff Erosion Susceptibility: The Case of the Summer 2021 Forest Fires in Greece. Land , 11(1), 21. 46. Ex, S. A.; Ziegler, J. P.; Tinkham, W. T.; Hoffman, C. M. (2019). Long-term impacts of fuel treatment placement with respect to forest cover type on potential fire behavior across a mountainous landscape. Forests , 10(5), 438. 47. g vegetation, topography and surface moisture availability in a Eurasian boreal forest landscape. Forests , 9(3), 130. 48. FAO. (2000). Global Forest Resources Assessment 2000 main report. FAO Forestry. 49. FAO. (2001). International Handbook on Forest Fire Protection, Food & Agriculture Org. 50. Finney, M. A. (2006). An overview of FlamMap fire modeling capabilities. In In: -How to Measure Success: Conference Proceedings. 28- OR. Proceedings RMRS-P-41. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 213-220 (Vol. 41). 51. Fontaine, J. B.; Westcott, V. C.; Enright, N. J.; Lade, J. C.; Miller, B. P. (2012). Fire behaviour in south-western Australian shrublands: evaluating the influence of fuel age and fire weather. International Journal of Wildland Fire , 21(4), 385-395. 52. Garbolino, E.; Sanseverino-Godfrin, V.; Hinojos-Mendoza, G. (2017): Reprint of: Describing and predicting of the vegetation development of Corsica due to expected climate change and its impact on forest fire risk evolution. Safety Science 97: 81-87. in press. 53. annual burned are Journal of Geophysical Research: Biogeosciences, 118(1), 317-328 54. study of forest fires in Portugal. Mediterranean Identities-Environment, Society, Culture; Fuerst-Bielis, B., Ed , 305-335.55. Gugan Vignesh, S. (2015). Simulation of spread of forest fire using Cellular Automata Network. 56. and Deniz, B. (2020). Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey. , 21(1), 15-24. 57. importance. In Proceedings of the 14th International Combustion Symposium ( (pp. 25-27). 58. ; Flannigan, M.; Bergeron, Y.; McRae, D. (2001). Role of vegetation and weather on fire behavior in the Canadian mixedwood boreal forest using two fire behavior prediction systems. Canadian journal of forest research , 31(3), 430-441. 59. Hoffman, C. M.; Canfield, J.; Linn, R. R.; Mell, W.; Sieg, C. H.; Pimont, F.; Ziegler, J. (2016). Evaluating crown fire rate of spread predictions from physics-based models. Fire Technology , 52 (1), 221-237. 60. Holden, Z. A.; Morgan, P.; Hudak, A. T. (2010). Burn severity of areas reburned by wildfires in the Gila National Forest, New Mexico, USA. Fire Ecology , 6(3), 77-85. 61. ICAP. (2020). International Carbon Action Partnership (ICAP) ETS Prices. (Last access 9 May 2021). Retrieved from https://icapcarbonaction.com/en/ets-prices. 62. Ice, G. G.; Neary, D. G.; Adams, P. W. (2004). Effects of wildfire on soils and watershed processes. Journal of Forestry , 102(6), 16-20. 63. IPCC. (2006) IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme (Eds. HS Eggleston, L Buendia, K Miwa, T Ngara, K Tanabe). IGES, Japan. 64. wildfire and simulation of its spread (case study: Siahkal forest in northern Iran). Journal of Agricultural Science and Technology , 16(5), 1109-1121. 65. Jahdi, R.; Salis, M.; Darvishsefat, A. A.; Alcasena, F.; Mostafavi, M. A.; Etemad, V., ... & Spano, D. (2016). Evaluating fire modelling systems in recent wildfires of the Golestan National Park, Iran. Forestry , 89(2), 136-149. 66. Jolly, W. M.; Cochrane, M. A.; Freeborn, P. H.; Holden, Z. A., Brown, T. J.; Williamson, G. J.; Bowman, D. M. (2015). Climate-induced variations in global wildfire danger from 1979 to 2013. Nature communications , 6(1), 1-11. 67. Jones, K. W.; Cannon, J. B.; Saavedra, F. A.; Kampf, S. K.; Addington, R. N.; Cheng, A. S.; ... & Wolk, B. (2017). Return on investment from fuel treatments to reduce severe wildfire and erosion in a watershed investment program in Colorado. Journal of Environmental Management , 198, 66-77. 68. Kanga, S.; Sharma, L. approach for forest fire risk assessment and management. International Journal of Advancement in Remote Sensing, GIS and Geography , 2(1), 30-44.69. Kanga, S., and Singh, S. K. (2017). Forest Fire Simulation Modeling using Remote Sensing & GIS. International Journal of Advanced Research in Computer Science , 8(5). 70. -Chavez, L. L. (2000). Controls on patterns of biomass burning in Alaskan boreal forests. In Fire, climate change, and carbon cycling in the boreal forest (pp. 173-196). Springer, New York, NY. 71. Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International journal of wildland fire , 18(1), 116-126. 72. Keeley, J. E.; Pausas, J. G.; Rundel, P. W.; Bond, W. J.; Bradstock, R. A. (2011). Fire as an evolutionary pressure shaping plant traits. Trends in plant science , 16(8), 406-411 73. Kucuk, O.; Bilgili, E.; Fernandes, P. M. (2015). Fuel modelling and potential fire behavior in Turkey. , 139(11-12), 553-560. 74. conditions conducive to the rapid spread of the deadly wildfire in eastern Attica, Greece. Bulletin of the American Meteorological Society , 100(11), 2137-2145. 75. Lentile, L. B.; Holden, Z. A.; Smith, A. M.; Falkowski, M. J.; Hudak, A. T.;Morgan, P.; ... & Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire , 15(3), 319-345. 76. Lentile, L. B.; Morgan, P.; Hudak, A. T.; Bobbitt, M. J.; Lewis, S. A.; Smith, A. M.; Robichaud, P. R. (2007). Post-fire burn severity and vegetation response following eight large wildfires across the western United States. Fire Ecology , 3(1), 91-108. 77. influences of topography and wind on wildland fire behaviour. International Journal of Wildland Fire ,16(2), 183-195. 78. Linn, R. R.; Winterkamp, J. L.; Weise, D. R.; Edminster, C. (2010). A numerical study of slope and fuel structure effects on coupled wildfire behaviour. International Journal of Wildland Fire , 19(2), 179-201. 79. Linn, R. R.; Goodrick, S. L.; Brambilla, S.; Brown, M. J.; Middleton, R. S.; O'Brien, J. J.; Hiers, J. K. (2020). QUIC-fire: A fast-running simulation tool for prescribed fire planning. Environmental Modelling & Software , 125, 104616. 80. ethod for measuring the relative particle fire hazard properties of forest species. Thermochimica Acta , 437(1-2), 150-157. 81. Lopes, A. M. G.; Ribeiro, L. M.; Viegas, D. X.; Raposo, J. R. (2019). Simulation of forest fire spread using a two-way coupling algorithm and its application to a real wildfire. Journal of Wind Engineering and Industrial Aerodynamics , 193, 103967. 82. Lydersen, J. M.; Collins, B. M.; Coppoletta, M.; Jaffe, M. R.; Northrop, H.; Stephens, S. L. (2019). Fuel dynamics and reburn severity following high-severity fire in a Sierra Nevada, USA, mixed-conifer forest. Fire Ecology , 15(1), 1-14. 83. Mahfoud, I. (2020). The Impact of Syrian Crisis on the Forestry Areas in North Latakia Governorate . Syrian Journal of Agricultural Research . 7(4): 467-477.84. Mar Forest fuel management for wildfire prevention in Spain: a quantitative SWOT analysis. International journal of wildland fire , 23(3), 373-384 85. Marshall, G.; Thompson, D. K.; Anderson, K.; Simpson, B.; Linn, R.; Schroeder, D. (2020). The Impact of Fuel Treatments on Wildfire Behavior in North America Boreal Fuels: A Simulation Study Using FIRETEC. Fire , 3(2), 18. 86. Mendes- lame characteristics, temperature time curves, and rate of spread in fires propagating in a bed of Pinus pinaster needles. International Journal of Wildland Fire , 12 (1), 67-84. 87. fire spread model for north Patagonia based on fire occurrence maps. Ecological Modelling , 300 , 73- 80 . 88. Moreira, F.; Ascoli, D.; Safford, H.; Adams, M. A.; Moreno, J. M.; Pereira, J. M.; ... & Fernandes, P. M. (2020). Wildfire management in Mediterranean-type regions: paradigm change needed. Environmental Research Letters , 15(1), 011001. 89. assumption of shrubland fire management: how important is fuel age?. Frontiers in Ecology and the Environment , 2 (2), 67-72. 90. Nisanci, R. (2010). GIS based fire analysis and production of fire -risk maps: The Trabzon experience. Scientific Research and Essays , 5(9), 970-977. 91. Oliveira, S.; Moreira, F.; Boca, R.; San-Miguel-Ayanz, J.; Pereira, J. M. (2014). Assessment of fire selectivity in relation to land cover and topography: a comparison between Southern European countries. International Journal of Wildland Fire , 23(5), 620-630. 92. Palaiologou, P.; Kalabokidis, K.; Ager, A. A.; Day, M. A. (2020). Development of Comprehensive Fuel Management Strategies for Reducing Wildfire Risk in Greece. Forests , 11(8), 789. 93. Palmero-Iniesta, M.; ; Molina- ; Espelta, J. M. (2017). Fire behavior in Pinus halepensis thickets: Effects of thinning and woody debris decomposition in two rainfall scenarios. Forest Ecology and Management , 404, 230-240 94. Parks, S. A.; Parisien, M. A.; Miller, C., Dobrowski, S. Z. (2014). Fire activity and severity in the western US vary along proxy gradients representing fuel amount and fuel moisture. PLoS One , 9(6), e99699. 95. Pausas, J. G. and Vallejo, V. R. (1999): The role of fire in European Mediterranean ecosystems. In: Chuvieco, E. (ed.) Remote Sensing of large wildfires . Springer, Berlin, pp. 3-16. 96. Pausas, J. G. and Keeley, J. E. (2014). Evolutionary ecology of resprouting and seeding in fire-prone ecosystems. New Phytol . 204: 55-65. 97. Pausas, J. G. (2015). Bark thickness and fire regime. Functional Ecology , 29(3), 315-327.98. Penman, T. D.; Ababei, D. A.; Cawson, J. G.; Cirulis, B. A.; Duff, T. J.; Swedosh, W., Hilton, J. E. (2020). Effect of weather forecast errors on fire growth model projections. International journal of wildland fire , 29(11), 983-994. 99. Phillips, R. J.; Waldrop, T. A.; Simon, D. M. (2006). Assessment of the FARSITE model for predicting fire behavior in the southern Appalachian Mountains. In Proceedings of the 13th biennial Southern Silvicultural Research Conference. Gen. Tech. Rep. SRS-92. Asheville, NC: US Department of Agriculture, Forest Service, Southern Research Station: 521-525. 100. Pimont, F.; Ruffault, J.; Martin-StPaul, N. K.; Dupuy, J. L. (2019). Why is the effect of live fuel moisture content on fire rate of spread underestimated in field experiments in shrublands?. International journal of wildland fire , 28(2), 127-137. 101. Plucinski, M. P.; Anderson, W. R.; Bradstock, R. A.; Gill, A. M. (2010). The initiation of fire spread in shrubland fuels recreated in the laboratory. International Journal of Wildland Fire , 19(4), 512-520 102. Prentice, I. C.; Kelley, D. I.; Foster, P. N.; Friedlingstein, P.; Harrison, S. P.; Bartlein, P. J. (2011). Modeling fire and the terrestrial carbon balance. Global Biogeochemical Cycles , 25(3). 103. Qiao, C.; Wu, L.; Chen, T.; Huang, Q.; Li, Z. (2018). Study on forest fire spreading model based on remote sensing and GIS. In IOP Conference Series: Earth and Environmental Science (Vol. 199, No. 2, p. 022017). IOP Publishing. 104. Rahimi I.; Azeez S.N.; Ahmed I.H. (2020) Mapping Forest-Fire Potentiality Using Remote Sensing and GIS, Case Study: Kurdistan Region-Iraq. In: Al-Quraishi A., Negm A. (eds) Environmental Remote Sensing and GIS in Iraq. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-030-21344-2_20. 105. Ra ez, J.; Mendes, M.; Monedero, S. (2015). Enhanced forest fire risk assessment through the use of fire simulation models. In ISCRAM . 106. - patterns, wildfire risk and wildfire occurrence at continental Spain. Physics and Chemistry of the Earth, Parts A/B/C , 35(9-12), 553-560. 107. Ritter, S. M.; Hoffman, C. M.; Battaglia, M. A.; ; Mell, W. E. ecosystems. Ecosphere , 11(7), e03177. 108. Sader, S. A., and Jin, S. (2006). Feasibility and accuracy of MODIS 250m imagery for forest disturbance monitoring. In ASPRS Annual Conference: Prospecting for Geospatial Information Ingegration (pp. 1-5). 109. Saidi, A., and Missoumi, A. (2003).The use of the GIS into the forest fire prediction-the simulation model, InV: 2nd Forest engineering conference, Vaxjo, Sweden. 110. Santoni, P. A., and Balbi, J. H. (1998). Modelling of two-dimensional flame spread across a sloping fuel bed. Fire Safety Journal , 31(3), 201-225.111. Scott, J. H. (2007). Nomographs for estimating surface fire behavior characteristics . US Department of Agriculture, Forest Service, Rocky Mountain Research Station. 112. Scott, J.H. (2012).Introduction to wildfire behavior modeling. National Interagency Fuel, Fire, & Vegetation Technology Transfer. 113. Shiraishi, T.; Hirata, R.; Hirano, T. (2021). New Inventories of Global Carbon Dioxide Emissions through Biomass Burning in 2001 2020. Remote Sensing , 13(10), 1914. 114. Sibanda, C. (2011). Modelling forest fire behaviour and carbon emission in the Ludikhola Watershed, Gorkha District, Nepal (Master's thesis, University of Twente). 115. through video images and multiple-point thermal measurements. Experimental Thermal and Fluid Science , 41, 99-111. 116. Skowronski, N. S.; Gallagher, M. R.; Warner, T. A. (2020). Decomposing the interactions between fire severity and canopy fuel structure using multi-temporal, active, and passive remote sensing approaches. Fire , 3(1), 7. 117. Stocks, B. J., and Kauffman, J. B. (1997). Biomass consumption and behavior of wildland fires in boreal, temperate, and tropical ecosystems: Parameters necessary to interpret historic fire regimes and future fire scenarios. In Sediment records of biomass burning and global change (pp. 169-188). Springer, Berlin, Heidelberg. 118. -based multi- criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia . Tropical Ecology , 57(1): 33-43. 119. Szpakowski, D. M., and Jensen, J. L. (2019). A review of the applications of remote sensing in fire ecology. Remote Sensing , 11(22), 2638. 120. Tolunay, D. (2011). Total carbon stocks and carbon accumulation in living tree biomass in forest ecosystems of Turkey. Turkish Journal of Agriculture and Forestry , 35(3), 265- 279. 121. Trucchia, A.; ; Baghino, F.; Fiorucci, P.; Ferraris, L.; Negro, D.; ... & Severino, M. (2020). PROPAGATOR: an operational cellular-automata based wildfire simulator. Fire , 3(3), 26. 122. Tubbesing, C. L.; Fry, D. L.; Roller, G. B.; Collins, B. M.; Fedorova, V. A.; Stephens, S. L.; & Battles, J. J. (2019). Strategically placed landscape fuel treatments decrease fire severity and promote recovery in the northern Sierra Nevada. Forest ecology and management , 436, 45-55. 123. UNDP. 2008). INC-SY_V&A_Forest-Ar, United Nation Development Programme (UNDP) / GCEA. 124. ; Ikiel, C.; Dutucu, A. A.; osion Susceptibility Iranian Journal of Science and Technology, Transactions A: Science , 45(2), 557-570.125. forest microclimate vary with biophysical context. Ecosphere , 12(5), e03467. 126. WWF. (2019) prevention of rural fires. 127. WWF. (2020). fires, forests and the future: a crisis raging out of control. 128. Yavuz, M.; lam, B.; ; behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty , 18(2), 171-188. 129. Zigner, K.; Carvalho, L.; Peterson, S.; Fujioka, F.; Duine, G. J.; Jones, C.; ... & Moritz, M. (2020). Evaluating the ability of FARSITE to simulate wildfires influenced by extreme, downslope winds in Santa Barbara, California. Fire , 3(3), 29.