{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T22:26:08Z","timestamp":1782253568840,"version":"3.54.5"},"reference-count":116,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Administration of Science, Technology and Industry for National Defence, PRC","award":["KJSP2020020303"],"award-info":[{"award-number":["KJSP2020020303"]}]},{"name":"State Administration of Science, Technology and Industry for National Defence, PRC","award":["42077259"],"award-info":[{"award-number":["42077259"]}]},{"name":"State Administration of Science, Technology and Industry for National Defence, PRC","award":["2021YFB3901205"],"award-info":[{"award-number":["2021YFB3901205"]}]},{"name":"National Natural Science Foundation of China","award":["KJSP2020020303"],"award-info":[{"award-number":["KJSP2020020303"]}]},{"name":"National Natural Science Foundation of China","award":["42077259"],"award-info":[{"award-number":["42077259"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFB3901205"],"award-info":[{"award-number":["2021YFB3901205"]}]},{"name":"National Key Research and Development Program of China","award":["KJSP2020020303"],"award-info":[{"award-number":["KJSP2020020303"]}]},{"name":"National Key Research and Development Program of China","award":["42077259"],"award-info":[{"award-number":["42077259"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFB3901205"],"award-info":[{"award-number":["2021YFB3901205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Fire"],"abstract":"<jats:p>Wildfire is a sudden and highly destructive natural disaster that poses significant challenges in terms of response and rescue efforts. Influenced by factors such as climate, combustible materials, and ignition sources, wildfires have been increasingly occurring worldwide on an annual basis. In recent years, researchers have shown growing interest in studying wildfires, leading to a substantial body of related research. These studies encompass various topics, including wildfire prediction and forecasting, the analysis of spatial and temporal patterns, the assessment of ecological impacts, the simulation of wildfire behavior, the identification of influencing factors, the development of risk assessment models, techniques for managing combustible materials, decision-making technologies for firefighting, and fire-retardant methods. Understanding the factors that affect wildfire spread behavior, employing simulation methods, and conducting risk assessments are vital for effective wildfire prevention, disaster mitigation, and emergency response. Consequently, it is imperative to comprehensively review and explore further research in this field. This article primarily focuses on elucidating and discussing wildfire spread behavior as a key aspect. It summarizes the driving factors of wildfire spread behavior and introduces a wildfire spread behavior simulation software and its main applications based on these factors. Furthermore, it presents the research progress in wildfire risk assessment based on wildfire spread behavior factors and simulation, and provides an overview of various methods used for wildfire risk assessment. Finally, the article proposes several prospects for future research on wildfire spread: strengthening the dynamic monitoring of wildfires and utilizing comprehensive data from multiple sources, further exploring the differential effects of key factors on wildfire spread, investigating differences in driving factors, improving wildfire models in China, developing applicable software, and conducting accurate and scientific assessments of wildfire risks to protect ecological resources.<\/jats:p>","DOI":"10.3390\/fire6060228","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T02:02:15Z","timestamp":1686103335000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going?"],"prefix":"10.3390","volume":"6","author":[{"given":"Jingjing","family":"Sun","sequence":"first","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8439-4339","authenticated-orcid":false,"given":"Wenwen","family":"Qi","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1122-1795","authenticated-orcid":false,"given":"Yuandong","family":"Huang","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3956-4925","authenticated-orcid":false,"given":"Chong","family":"Xu","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"},{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7253-7814","authenticated-orcid":false,"given":"Wentao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"ref_1","first-page":"212","article-title":"Role of machine learning algorithms in forest fire management: A literature review","volume":"5","author":"Arif","year":"2021","journal-title":"J. Rob. Autom."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/s41558-020-0716-1","article-title":"Unprecedented burn area of Australian mega forest fires","volume":"10","author":"Boer","year":"2020","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.foreco.2005.02.010","article-title":"The challenge of quantitative risk analysis for wildland fire","volume":"211","author":"Finney","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S83","DOI":"10.1175\/BAMS-D-20-0165.1","article-title":"Attribution of the extreme drought-related risk of wildfires in spring 2019 over southwest China","volume":"102","author":"Du","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Feng, H. (2021, January 16\u201317). Exploration and thinking on related mechanisms of forest fire prevention. Proceedings of the 2021 International Conference on Social Science: Public Administration, Law and International Relations (SSPALIR 2021), Moscow, Russia.","DOI":"10.2991\/assehr.k.210916.003"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huang, Q., Razi, A., Afghah, F., and Fule, P. (September, January 31). Wildfire spread modeling with aerial image processing. Proceedings of the 2020 IEEE 21st International Symposium on \u201cA World of Wireless, Mobile and Multimedia Networks\u201d, Cork, Ireland.","DOI":"10.1109\/WoWMoM49955.2020.00063"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8779","DOI":"10.1038\/s41598-021-88131-9","article-title":"Spatial and temporal pattern of wildfires in California from 2000 to 2019","volume":"11","author":"Li","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1038\/s41598-019-56967-x","article-title":"Near real-time wildfire progression monitoring with Sentinel-1 SAR time series and deep learning","volume":"10","author":"Ban","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10661-010-1731-x","article-title":"Advancing effects analysis for integrated, large-scale wildfire risk assessment","volume":"179","author":"Thompson","year":"2011","journal-title":"Environ. Monit. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1007\/s12665-015-5184-y","article-title":"Modelling the impacts of wildfires on runoff at the river basin ecological scale in a changing Mediterranean environment","volume":"75","author":"Pereira","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_11","unstructured":"Liu, N., Zhong, S., and Zhu, W. (2022). Knowledge map analysis of forest fire research at home and abroad. For. Sci. Technol., 1\u201313."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102266","DOI":"10.1016\/j.apgeog.2020.102266","article-title":"A survey on systematic approaches in managing forest fires","volume":"121","author":"Dhall","year":"2020","journal-title":"Appl. Geogr."},{"key":"ref_13","first-page":"46","article-title":"A review of wildland fire spread modelling","volume":"30","author":"Zhao","year":"2017","journal-title":"World For. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1080\/00102209408935341","article-title":"The properties of elliptical wildfire growth for time dependent fuel and meteorological conditions","volume":"95","author":"Richards","year":"1993","journal-title":"Combust. Sci. Technol."},{"key":"ref_15","unstructured":"Du, J., and Tian, X. (2012). Forest fire spread model and its application overview. For. Fire. Prev., 31\u201334."},{"key":"ref_16","first-page":"93","article-title":"Analysis of fire spread in light forest fuels","volume":"72","author":"Fons","year":"1946","journal-title":"J. Agric. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1111\/j.1442-9993.1980.tb01243.x","article-title":"McArthur\u2019s fire-danger meters expressed as equations","volume":"5","author":"Noble","year":"1980","journal-title":"Aust. J. Ecol."},{"key":"ref_18","unstructured":"Rothermel, R.C. (1972). A mathematical model for predicting fire spread in wildland fuels, Intermountain Forest & Range Experiment Station."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"450","DOI":"10.5558\/tfc65450-6","article-title":"The Canadian Forest fire danger rating system: An overview","volume":"65","author":"Stocks","year":"1989","journal-title":"For. Chron."},{"key":"ref_20","first-page":"42","article-title":"The mesurement method of the wildfire initial spread rate","volume":"1","author":"Wang","year":"1983","journal-title":"Mt. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"161782","DOI":"10.1016\/j.scitotenv.2023.161782","article-title":"Seasonal differences in the spatial patterns of wildfire drivers and susceptibility in the southwest mountains of China","volume":"869","author":"Wang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_22","first-page":"769","article-title":"Forest fire risk assessment for China under different climate scenarios","volume":"27","author":"Tian","year":"2016","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s42408-019-0062-8","article-title":"Changing wildfire, changing forests: The effects of climate change on fire regimes and vegetation in the Pacific northwest, USA","volume":"16","author":"Halofsky","year":"2020","journal-title":"Fire Ecol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1071\/WF08078","article-title":"Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel","volume":"19","author":"Casady","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.geoderma.2012.01.032","article-title":"Characterization of wildfire effects on soil organic matter using analytical pyrolysis","volume":"191","author":"Faria","year":"2012","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.geomorph.2007.05.011","article-title":"Post-wildfire erosion response in two geologic terrains in the western USA","volume":"95","author":"Moody","year":"2008","journal-title":"Geomorphology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.earscirev.2011.01.001","article-title":"Post-wildfire soil erosion in the Mediterranean: Review and future research directions","volume":"105","author":"Shakesby","year":"2011","journal-title":"Earth-Sci. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2021JF006374","DOI":"10.1029\/2021JF006374","article-title":"Hydrogeomorphic recovery and temporal changes in rainfall thresholds for debris flows following wildfire","volume":"126","author":"Hoch","year":"2021","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"eabm0320","DOI":"10.1126\/sciadv.abm0320","article-title":"Climate change increases risk of extreme rainfall following wildfire in the western United States","volume":"8","author":"Touma","year":"2022","journal-title":"Sci. Adv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108219","DOI":"10.1016\/j.knosys.2022.108219","article-title":"Fast forest fire smoke detection using MVMNet","volume":"241","author":"Hu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9223","DOI":"10.5194\/acp-17-9223-2017","article-title":"Wildfire air pollution hazard during the 21st century","volume":"17","author":"Knorr","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.foreco.2013.05.045","article-title":"Wildland fire emissions, carbon, and climate: Emission factors","volume":"317","author":"Urbanski","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106874","DOI":"10.1016\/j.compag.2022.106874","article-title":"A high-precision forest fire smoke detection approach based on ARGNet","volume":"196","author":"Zhan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17312","DOI":"10.1038\/s41598-020-74338-9","article-title":"Effects of canopy midstory management and fuel moisture on wildfire behavior","volume":"10","author":"Banerjee","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.1073\/pnas.1718850115","article-title":"Rapid growth of the US wildland-urban interface raises wildfire risk","volume":"115","author":"Radeloff","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Radke, D., Hessler, A., and Ellsworth, D. (2019, January 10\u201316). FireCast: Leveraging deep learning to predict wildfire spread. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China.","DOI":"10.24963\/ijcai.2019\/636"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1365-2486.2008.01784.x","article-title":"Analysing Forest recovery after wildfire disturbance in boreal Siberia using remotely sensed vegetation indices","volume":"15","author":"Gerard","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"119250","DOI":"10.1016\/j.foreco.2021.119250","article-title":"Survival of prescribed burning treatments to wildfire in Portugal","volume":"493","author":"Davim","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1071\/WF20096","article-title":"Effects of fuel spatial distribution on wildland fire behaviour","volume":"30","author":"Atchley","year":"2021","journal-title":"Int. J. Wildland Fire"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"095003","DOI":"10.1088\/1748-9326\/aa7e6e","article-title":"Potential climate change impacts on fire intensity and key wildfire suppression thresholds in Canada","volume":"12","author":"Wotton","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1002\/fee.2359","article-title":"Wildfires and global change","volume":"19","author":"Pausas","year":"2021","journal-title":"Front. Ecol. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1007\/s13753-019-00233-1","article-title":"Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China","volume":"10","author":"Zhang","year":"2019","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1071\/WF15121","article-title":"What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests","volume":"25","author":"Guo","year":"2016","journal-title":"Int. J. Wildland Fire"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.foreco.2013.08.007","article-title":"Climate change, fire management, and ecological services in the southwestern US","volume":"327","author":"Hurteau","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1126\/science.1128834","article-title":"Warming and earlier spring increase western US forest wildfire activity","volume":"313","author":"Westerling","year":"2006","journal-title":"Science"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1080\/19475705.2021.1920477","article-title":"Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan","volume":"12","author":"Tariq","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_47","unstructured":"Ruan, J., and Li, J. (2021). Research progress on two-way fire-wind coupling simulation in forest fire spread. Mod. C., 37\u201342."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1071\/WF07049","article-title":"Fire intensity, fire severity and burn severity: A brief review and suggested usage","volume":"18","author":"Keeley","year":"2009","journal-title":"Int. J. Wildland Fire"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wu, Z., Li, M., Wang, B., Tian, Y., Quan, Y., and Liu, J. (2022). Analysis of factors related to forest fires in different forest ecosystems in China. Forests, 13.","DOI":"10.3390\/f13071021"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.2298\/TSCI151006121Z","article-title":"Thermal conditions for stopping pyrolysis of forest combustible material and applications to firefighting","volume":"21","author":"Zhdanova","year":"2017","journal-title":"Therm. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.scitotenv.2015.02.063","article-title":"Defining fire environment zones in the boreal forests of northeastern China","volume":"518","author":"Wu","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_52","first-page":"55","article-title":"Characteristics and potential fire behavior of combustibles in the canopy of Pinus tabuliformis forest in Badaling Forest Farm of Beijing","volume":"44","author":"Chen","year":"2022","journal-title":"J. Beijing For. Univ."},{"key":"ref_53","first-page":"347","article-title":"Fuel models in the national fire-danger rating system","volume":"73","author":"Deeming","year":"1975","journal-title":"J. For."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s10694-015-0500-3","article-title":"Evaluating crown fire rate of spread predictions from physics-based models","volume":"52","author":"Hoffman","year":"2016","journal-title":"Fire Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"108653","DOI":"10.1016\/j.ecolind.2022.108653","article-title":"Simulation of forest fire spread based on artificial intelligence","volume":"136","author":"Wu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s00267-012-9961-z","article-title":"A review of the main driving factors of forest fire ignition over Europe","volume":"51","author":"Ganteaume","year":"2013","journal-title":"Environ. Manag."},{"key":"ref_57","first-page":"151","article-title":"Comparative study on surface litter load and fireintensity of Pinus armandii and Pinus yunnanensis in northeastern Yunnan Province","volume":"39","author":"Xu","year":"2019","journal-title":"J. Southwest For. Coll."},{"key":"ref_58","first-page":"111","article-title":"Characteristics of litter and corresponding fire risk of different forest types in Saihanba Forestry Center","volume":"41","author":"Ding","year":"2021","journal-title":"J. Southwest For. Coll."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1929137","DOI":"10.1155\/2021\/1929137","article-title":"Fire prediction based on catboost algorithm","volume":"2021","author":"Zhou","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s12517-013-1259-9","article-title":"Comparative analysis in GIS-based landslide hazard zonation\u2014A case study in Bodi-Bodimettu Ghat section, Theni District, Tamil Nadu, India","volume":"8","author":"Kannan","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"19029","DOI":"10.1038\/s41598-022-23697-6","article-title":"Predictive model of spatial scale of forest fire driving factors: A case study of Yunnan Province, China","volume":"12","author":"Li","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1071\/WF09075","article-title":"The effects of slope and fuel bed width on laboratory fire behaviour","volume":"20","author":"Dupuy","year":"2011","journal-title":"Int. J. Wildland Fire"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.foreco.2011.10.031","article-title":"Factors influencing national scale wildfire susceptibility in Canada","volume":"265","author":"Gralewicz","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s00704-018-2628-9","article-title":"A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data","volume":"137","author":"Tehrany","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1007\/s11069-016-2533-4","article-title":"Analysis of recent spatial\u2013temporal evolution of human driving factors of wildfires in Spain","volume":"84","author":"Rodrigues","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.5194\/nhess-11-3343-2011","article-title":"The history and characteristics of the 1980\u20132005 Portuguese rural fire database","volume":"11","author":"Pereira","year":"2011","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_67","unstructured":"Wang, M., Xu, Y., and Zhao, M. (2021). Spatio-temporal distribution pattern and cause analysis of forest fires in my country in recent 10 years. Bull. Agric. Sci. Technol., 201\u2013204."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.scitotenv.2017.06.219","article-title":"Understanding fire drivers and relative impacts in different Chinese forest ecosystems","volume":"605","author":"Guo","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.forpol.2017.04.011","article-title":"Impact of human factors on wildfire occurrence in Mississippi, United States","volume":"81","author":"Grala","year":"2017","journal-title":"For. Policy Econ."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wu, Z., Li, M., Wang, B., and Zhang, X. (2022). Forest fire spread monitoring and vegetation dynamics detection based on multi-source remote sensing images. Remote Sens., 14.","DOI":"10.3390\/rs14184431"},{"key":"ref_71","unstructured":"Tymstra, C., Bryce, R., Wotton, B., Taylor, S., and Armitage, O. (2010). Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Information Report NOR-X-417, Canadian Forest Service Publications."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1071\/WF12167","article-title":"Current status and future needs of the BehavePlus fire modeling system","volume":"23","author":"Andrews","year":"2013","journal-title":"Int. J. Wildland Fire"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Finney, M.A. (1998). FARSITE, Fire Area Simulator\u2014Model Development and Evaluation.","DOI":"10.2737\/RMRS-RP-4"},{"key":"ref_74","first-page":"171","article-title":"Assessing Forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey","volume":"18","author":"Yavuz","year":"2018","journal-title":"Kast. Univ. J. For. Fac."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"105526","DOI":"10.1016\/j.envsoft.2022.105526","article-title":"Visualization and modeling of forest fire propagation in Patagonia","volume":"158","author":"Denham","year":"2022","journal-title":"Environ. Modell. Softw."},{"key":"ref_76","unstructured":"Pais, C., Carrasco, J., Martell, D.L., Weintraub, A., and Woodruff, D.L. (2019). Cell2fire: A cell-based forest fire growth model. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Filippi, J.-B., Bosseur, F., and Grandi, D. (2014). ForeFire: Open-Source Code for Wildland Fire Spread Models, Imprensa da Universidade de Coimbra.","DOI":"10.14195\/978-989-26-0884-6_29"},{"key":"ref_78","first-page":"451","article-title":"Modelling the spread of grass fires","volume":"23","author":"Anderson","year":"1982","journal-title":"Anziam J."},{"key":"ref_79","unstructured":"Mitsopoulos, I., Mallinis, G., Karali, A., Giannakopoulos, C., and Arianoutsou, M. (2014, January 27\u201328). Mapping fire behaviour in a Mediterranean landscape under different future climate change scenarios. Proceedings of the International Conference AdaptToClimate, Nicosia, Cyprus."},{"key":"ref_80","first-page":"337","article-title":"Burning issues with Prometheus, the Canada\u2019s wildfire growth simulator","volume":"16","author":"Barber","year":"2009","journal-title":"Can. Appl. Math Q."},{"key":"ref_81","first-page":"13","article-title":"FARSITE\u2014A program for fire growth simulation. Fire Manage","volume":"59","author":"Finney","year":"1999","journal-title":"Fire Manag. Notes"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"103167","DOI":"10.1016\/j.firesaf.2020.103167","article-title":"Combined estimation of fire perimeters and fuel adjustment factors in FARSITE for forecasting wildland fire propagation","volume":"116","author":"Zhou","year":"2020","journal-title":"Fire Saf. J."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1016\/j.procs.2016.05.328","article-title":"Wildfire spread prediction and assimilation for FARSITE using ensemble Kalman filtering","volume":"80","author":"Srivas","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Ghodrat, M., Shakeriaski, F., Fanaee, S.A., and Simeoni, A. (2023). Software-based simulations of wildfire spread and wind-fire interaction. Fire, 6.","DOI":"10.3390\/fire6010012"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Mallinis, G., Mitsopoulos, I., Beltran, E., and Goldammer, J.G. (2016). Assessing wildfire risk in cultural heritage properties using high spatial and temporal resolution satellite imagery and spatially explicit fire simulations: The case of Holy Mount Athos, Greece. Forests, 7.","DOI":"10.3390\/f7020046"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0168-1699(02)00120-5","article-title":"Information systems in support of wildland fire management decision making in Canada","volume":"37","author":"Lee","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_87","unstructured":"Hagelin, H., and Cluzel, M. (2016). Student Thesis Series INES, Department of Physical Geography and Ecosystem Science, Lund University."},{"key":"ref_88","first-page":"3144","article-title":"Evaluating fire behavior simulators in southwestern China forest area","volume":"28","author":"Zhao","year":"2017","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1155\/2010\/823018","article-title":"Forest fire risk assessment: An illustrative example from Ontario, Canada","volume":"2010","author":"Braun","year":"2010","journal-title":"J. Probab. Stat."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.11834\/jrs.20219427","article-title":"Forest fire spread simulation based on VIIRS active fire data and FARSITE model","volume":"26","author":"Xu","year":"2022","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"6176","DOI":"10.5846\/stxb201109111333","article-title":"Spatial distribution characteristics of potential fire behavior in Fenglin Nature Reserve based on FARSITE model","volume":"32","author":"Wu","year":"2012","journal-title":"Acta Phytoecol. Sin."},{"key":"ref_92","first-page":"897","article-title":"Wildfire assessment using FARSITE fire modeling: A case study in the chihuahua desert of mexico","volume":"80","author":"Brakeall","year":"2013","journal-title":"Procedia Comput. Sci."},{"key":"ref_93","unstructured":"Finney, M.A. (2006). An overview of FlamMap fire modeling capabilities, Fuels Management\u2014How to Measure Success: Conference Proceedings."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1139\/x02-068","article-title":"Fire growth using minimum travel time methods","volume":"32","author":"Finney","year":"2002","journal-title":"Can. J. For. Res."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"117927","DOI":"10.1016\/j.foreco.2020.117927","article-title":"Climate change effects on wildfire hazards in the wildland-urban-interface\u2013Blue pine forests of Bhutan","volume":"461","author":"Keeton","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_96","first-page":"139","article-title":"An integrated approach for mapping fire suppression difficulty in three different ecosystems of eastern Europe","volume":"62","author":"Mitsopoulos","year":"2017","journal-title":"J. Spat. Sci."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Scott, J.H. (2005). Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel\u2019s Surface Fire Spread Model.","DOI":"10.2737\/RMRS-GTR-153"},{"key":"ref_98","unstructured":"Almeida, R.M., and Macau, E.E. (2011). Journal of Physics: Conference Series, IOP Publishing."},{"key":"ref_99","unstructured":"Sullivan, A., and Knight, I. (2004, January 14\u201316). A hybrid cellular automata\/semi-physical model of fire growth. Proceedings of the Engineering of Complex Computer Systems, Florence, Italy."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.cjph.2020.04.001","article-title":"Simulating forest fire spread and fire-fighting using cellular automata","volume":"65","author":"Mutthulakshmi","year":"2020","journal-title":"Chin. J. Phys."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s11069-017-3127-5","article-title":"Forest fire spread simulation algorithm based on cellular automata","volume":"91","author":"Rui","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, D., Ma, H., Lin, R., and Zhang, F. (2022). Modeling forest fire spread using machine learning-based cellular automata in a GIS environment. Forest, 13.","DOI":"10.3390\/f13121974"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Gao, X., Fei, X., and Xie, H. (July, January 29). Forest fire risk zone evaluation based on high spatial resolution RS image in Liangyungang Huaguo Mountain Scenic Spot. Proceedings of the 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, Fuzhou, China.","DOI":"10.1109\/ICSDM.2011.5969116"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.asr.2004.12.053","article-title":"Fire risk assessment using satellite data","volume":"37","author":"Arbelo","year":"2006","journal-title":"Adv. Space Res."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s00477-008-0233-7","article-title":"GIS-based risk assessment of grassland fire disaster in western Jilin Province, China. Stochastic Environ","volume":"23","author":"Zhijun","year":"2009","journal-title":"Res. Risk Assess"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.knosys.2009.07.002","article-title":"Information diffusion-based spatio-temporal risk analysis of grassland fire disaster in northern China","volume":"23","author":"Liu","year":"2010","journal-title":"Knowl.-Based Syst."},{"key":"ref_107","first-page":"38","article-title":"Risk analysis of forest firesand protection of forest resources in China based on information diffusion theory","volume":"46","author":"Zhang","year":"2018","journal-title":"Environ. Prot."},{"key":"ref_108","first-page":"1","article-title":"Forest fire risk zone mapping from satellite imagery and GIS","volume":"4","author":"Jaiswal","year":"2002","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1071\/WF12052","article-title":"Integrating geospatial information into fire risk assessment","volume":"23","author":"Chuvieco","year":"2012","journal-title":"Int. J. Wildland Fire"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Santopaolo, A., Saif, S.S., Pietrabissa, A., and Giuseppi, A. (2021, January 22\u201325). Forest fire risk prediction from satellite data with convolutional neural networks. Proceedings of the 2021 29th Mediterranean Conference on Control and Automation (MED), Puglia, Italy.","DOI":"10.1109\/MED51440.2021.9480226"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Naderpour, M., Rizeei, H.M., and Ramezani, F. (2021). Forest fire risk prediction: A spatial deep neural network-based framework. Remote Sens., 13.","DOI":"10.3390\/rs13132513"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/BF02856809","article-title":"Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China","volume":"16","author":"Dong","year":"2005","journal-title":"J. For. Res."},{"key":"ref_113","first-page":"43","article-title":"Forest fire risk assessment and prevention and control suggestions in Heilongjiang Province","volume":"47","author":"Zheng","year":"2022","journal-title":"For. Sci. Technol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.foreco.2008.09.039","article-title":"Assessing fire risk using Monte Carlo simulations of fire spread","volume":"257","author":"Carmel","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_115","first-page":"83","article-title":"Quantitative assessment for forest fire risk based on fire simulation: Taking the Subtropical Forest Experimental Center of Chinese Academy of Forestry as an example","volume":"44","author":"Zong","year":"2022","journal-title":"J. Beijing For. Univ."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1016\/j.foreco.2009.07.051","article-title":"Wildfire risk in the wildland\u2013urban interface: A simulation study in northwestern Wisconsin","volume":"258","author":"Massada","year":"2009","journal-title":"For. Ecol. Manag."}],"container-title":["Fire"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2571-6255\/6\/6\/228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:49:39Z","timestamp":1760125779000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2571-6255\/6\/6\/228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":116,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["fire6060228"],"URL":"https:\/\/doi.org\/10.3390\/fire6060228","relation":{},"ISSN":["2571-6255"],"issn-type":[{"value":"2571-6255","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]}}}