[1]. Adab H., Kanniah K., & Solaimani K. (2011). GIS-based probability assessment of fire risk in grassland and forested landscapes of Golestan province, Iran. Environmental Science, Geography, 19, 170-175.
[2]. Adab, H., Kanniah, K.D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(2), 1723-1743. doi: 10.1007/s11069-012-0450-8.
[3]. Artes, T., Cencerrado, A., Cortes, A., & Margalef, T. (2013). Relieving the effects of uncertainty in forest fire spread prediction by hybrid MPI-OpenMP parallel strategies.
International Conference on Computational science, Procedia Computer Science, 18, 2278-2287.
https://doi.org/10.1016/j.procs.2013.05.399.
[4]. Babu, K.N., Gour, R., Ayushi, K., Ayyappan, N., & Parthasarathy, N. (2023). Environmental drivers and spatial prediction of forest fires in the Western Ghats biodiversity hotspot, India: An ensemble machine learning approach.
Forest Ecology and Management, 504, 121057. doi: 10.1016/j.foreco.2023.121057.
[5]. Bagherabadi, R., Shikhkanloo Milan, F., & Zarei Mohammadabad, M. (2022). Evaluation of fire risk in the Zagros forests (Case study: Dalahu County). Management of Natural Ecosystems, 2(2), 60-72. Doi: 10.22034/emj.2022.254859 [in Farsi].
[6]. Bahrami-Pichaghchi, H., Norooz-Valashedi, R., & Gholami Sefidkouhi, M. A. (2024). Investigating the trend of wildfires and its relationship with climate variables using satellite data (case study: Mazandaran province). Journal of Natural Environmental Hazards, 13(39), 127-140. Doi:10.22111/jneh.2023.45573.1967 [in Farsi].
[7]. Bazyar, M., Oladi Ghadikolaii, J., Pourghasemi, H.R., & Serajyan maralan M.R. (2018). Zoning and Investigation of Factors Affecting Forest Fire Using Evidential Belief Function Algorithm and Support Vector Machine in Boyer Ahmad City. Iranian Journal of Forests and Rangelands Protection Research, 17(2), 197 -222. doi: 10.22092/ijfrpr.2020.128649.1406 [in Farsi]
[8]. Benbakkar, H.A., Souidi, Z., Bardadi, A., & Bento-Gonçalves, A. (2024). Forest Fires Risk in A semi -Arid Region: A Case Study in Western Alger. International Journal of Professional, Business. Review, 9(5), 1-20.
[9]. Chamandeh, J., Alvaninejad, S., & Gholami, P. (2017). A survey of composition and diversity of herbaceous species after a fire in Persian Oak forests of Southern Zagros. Journal of Wood & Forest Science and Technolog, 24(3), 1-15. doi: 10.22069/jwfst.2017.11742.1620 [in Farsi]
[10]. Charizanos, G., & Demirhan, H. (2023). Bayesian prediction of wildfire event probability using normalized difference vegetation index data from an Australian forest. Ecological Informatics, 73, 101899. doi: 10.1016/j.ecoinf.2022.101899.
[11]. Ellison, D., Morris, C.E., Locatelli, B., Sheil, D., Cohen, J., Murdiyarso, D., Gutierrez, V., Van Noordwijk, M., Creed, I.F., Pokorny, J., & Gaveau, D. (2017). Trees, forests and water: Cool insights for a hot world. Global Environmental Change, 43, 51-61. doi: 10.1016/j.gloenvcha.2017.01.002.
[12]. Emami, H., & Shahriari, H. (2020). Quantification of environmental and human factors in the occurrence of forest fire with RS and GIS methods. Arsbaran protected areas. Research Quarterly Geographical Data (SEPEHR), 28(112), 35- 53. doi: 10.22131/sepehr.2020.38606 [in Farsi].
[13]. Enoh, M.A., Okeke, U.C., & Narinua, N. Y. (2021). Identification and modelling of forest fire severity and risk zones in the Cross – Niger transition forest with remotely sensed satellite data.
The Egyptian Journal of Remote Sensing and Space Science, 24(3), 879-887. doi: 10.1016/j.ejrs.2021.09.002.
[14]. Eskandari, S., Pourghasemi, H.R., |& Tiefenbacher, J.P. (2020). Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger. Forest Ecology and Management, 473, 118338. doi: 10.1016/j.foreco.2020.118338.
[15]. Farfán, M., Dominguez, C., Espinoza, A., Jaramillo, A., Alcántara, C., Maldonado, V., Tovar, I., & Flamenco, A. (2021). Forest fire probability under ENSO conditions in a semi-arid region: a case study in Guanajuato. Environmental Monitoring and Assessment, 193 (10), 684. doi: 10.1007/s10661-021-09494-0.
[16]. Gerdzheva, A.A. (2014). A comparative analysis of different wildfire risk assessment models (a case study for Smolyan district, Bulgaria. European Journal of Geography, 5 (3), 22 -36.
[17]. Huang, L., Zhou, M., Lv, J., & Chen, K. (2020). Trends in global research in forest carbon sequestration: A bibliometric analysis.
Journal of Cleaner Production, 252, 119908. doi: doi.org/10.1016/j.jclepro.2019.119908.
[18]. Jafari, M., Karamshahi, A., Heydari, M., Mirzaei, J., & Jafarian, N. (2024). Evaluation of oak decline in relation to the diversity of woody species, soil properties and physiographic factors in southern Zagros forests. Journal of Arid Biome, 14(1), 91-106. doi: 10.29252/aridbiom.2024.21417.2006.
[19]. Jahdi, R.; Beiranvandi, V., & Amini, H. (2024). Wildfire Risk Assessment in Zagros Forests using Geographic Information System and Best-Worst Method (BWM) (Case Study: Dore Chegeni County, Lorestan Province). Journal of Geography and Environmental Studies, 13 (49): 68-85. https://www.magiran.com/p2740687 [in Farsi].
[20]. Javanmiri Pour., M., Valipour, J., & Hasanzadeh, A. (2022). Study of structural factors and effective motives in causing forest and pasture fires in semi-arid ecosystems of the Zagros Mountain. Journal of Arid Biome, 11(2), 15-27. doi: 10.29252/aridbiom.2022.18321.1888.
[21]. Kermani, F., Rayegani., B., Nezami, B., Goshtasb, H., & Khosravi, H. (2016). Assessing the vegetation trends in arid and semi-arid regions (Case study: Touran Protected Area). Desert Ecosystem Engineering Journal, 6(17), 1-14. doi: 10.22052/6.17.1 [in Farsi].
[22]. Kolanek, A., Szymanowski, M., & Raczyk, A. (2021). Human activity affects forest fires: the impact of anthropogenic factors on the density of forest fires in Poland. Forests, 12(6), 728. doi: 10.3390/f12060728.
[23]. Lal, R. (2003). Soil erosion and the global carbon budge. Environment International, 29(4), 437-450. doi: 10.1016/S0160-4120(02)00192-7.
[24]. Merino-de-Miguela, S., Huescab, M., & González-Alonsob, F. (2010). Modis reflectance and active fire data for burn mapping and assessment at regional level. Ecological Modelling, 221(1),67–74. doi: 10.1016/j.ecolmodel.2009.09.015.
[25]. Michael, Y., Helman, D., Glickman, O., Gabay, D., Brenner, S., & Lensky, I.M. (2020). Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series. Science of the Total Environment, 746, 142844. doi: 10.1016/j.scitotenv.2020.142844.
[26]. Moghim, S., & Mehrabi, M. (2024). Wildfire assessment using machine learning algorithms in different region. Fire Ecology, 20, 104. doi: 10.1186/s42408-024-00335-2.
[27]. Mohammadi, F., Bavaghar, M.P., & Shabanian, N. (2014). Forest fire risk zone modeling using logistic regression and GIS: an Iranian case study. Small-scale Forestry, 13, 117-125. doi: 10.1007/s11842-013-9244-4.
[28]. Morovati, M., & Karami, P. (2024). Modeling the seasonal wildfire cycle and its possible effects on the distribution of focal species in Kermanshah Province, western Iran. PLoS ONE, 19(10), e0312552. doi: 10.1371/journal.pone.0312552.
[29]. Najafi, A., Irannezhad, M. H., Sotoudeh, A., Mokhtari, M.H., & Kiani, B. (2016). Modeling and Risk Mapping of Forest Fires using Remote Sensing and GIS (Case Study: Baghe-Shadi Protected Area, Yazd Province). Iranian Journal of Applied Ecology, 4 (14), 13-26. doi: 10.18869/acadpub.ijae.4.14.13.
[30]. Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., & Ciais, P. (2011). A large and persistent carbon sink in the world’s forests. Science, 333 (6045), 988–993. doi: 10.1126/science.1201609.
[31]. Rodriguez-Jimenez, F., Lorenzo, H., Acuna-Alonso, C., & Alvarez, X. (2023). PLS-PM analysis of forest fires using remote sensing tools. The case of Xurés in the Transboundary Biosphere Reserve. Ecological Informatics, 75, 102010. doi: 10.1016/j.ecoinf.2023.102010.
[32]. Saha, S., Bera, B., Shit, P.K., Bhattacharjee, S., & Sengupta, N. (2023). Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources. Remote Sensing Applications: Society and Environment, 29, 100917. doi: 10.1016/j.rsase.2022.100917.
[33]. Saranya, K.R.L., Sudhakar Reddy, C., Prasada Rao, P.V.V., & Jha, C.S. (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. doi: 10.1007/s12040-016-0685-y.
[34]. Scott, J. H., Thompson, M. P., & Calkin, D. E. (2013). A Wildfire Risk Assessment Framework for Land and Resource Management. USDA Forest Service. Rocky Mountain Research Station. doi: 10.2737/rmrs-gtr-315.
[35]. Serpa, D., Ferreira, R.V., Machado, A.I., Cerqueira, M.A., & Keizer. J.J. (2020). Mid-term post-fire losses of nitrogen and phosphorus by overland flow in two contrasting eucalypt stands in north-central Portugal.
Science of The Total Environment, 705, 135843. doi: 10.1016/j.scitotenv.2019.135843.
[36]. Soualah, L., Bouzekri, A., & Chenchouni, H. (2024). Hoping the best, expecting the worst: Forecasting Forest fire risk in Algeria using fuzzy logic and GIS. Trees, Forests and People, 17, 100614. doi: 10.1016/j.tfp.2024.100614.
[37]. Sowmya, S.V., & Somashekar, R.K. (2010). Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology, 31(6), 969-974.
[38]. Szpakowski, D.M., & Jensen, J.L.R. (2019). A Review of the Applications of Remote Sensing in Fire Ecology. Remote Sensing, 11, 2638. doi: 10.3390/rs11222638.
[39]. Veysi, R., Fattahi, B., & Khosrow Beigi, S. (2022). Predicting and preparing a risk map of rangeland fires using random forest algorithms and support vector machine (Case study: Arak rangelands). Journal of Rangeland, 16(1), 413-426. doi:20.1001.1.20080891.1401.16.2.13.7 [in Farsi].
[40]. Watson, J.E., Evans, T., Venter, O., Williams, B., Tulloch, A., Stewart, C., Thompson, I., Ray, J.C., Murray, K., Salazar, A., & McAlpine, C. (2018). The exceptional value of intact forest ecosystems. Nature Ecology & Evolution, 2(4), 599-610. doi: 10.1038/s41559-018-0490-x.
[41]. Xie, Y., & Peng, M. (2019). Forest fire forecasting using ensemble learning approaches. Neural Computing and Applications, 31, 4541–4550. doi: 10.1007/s00521-018-3515-0.
[42]. Xu, H., & Schoenberg, F.P. (2011). Point process modeling of wildfire hazard in Los Angeles County, California. Annals of Applied Statistics, 5(2), 684-704. doi: 10.1214/10-AOAS401
[43]. Yin, S., Wang, X., Guo, M., Santoso, H., & Guan, H. (2020). The abnormal change of air quality and air pollutants induced by the forest fire in Sumatra and Borneo in 2015.
Atmospheric Research, 243, 105027. doi: 10.1016/j.atmosres.2020.105027.
[44]. Zhao L., Ge, Y., Guo, S., Li, X., & Chen, S. (2024). Forest fire susceptibility mapping based on precipitation-constrained cumulative dryness status information in Southeast China: A novel machine learning modeling approach. Forest Ecology and Management, 558, 121771. doi: 10.1016/j.foreco.2024.121771..