Abatzoglou J. T., WilliamsA. P., 2016. Impact of anthropogenic climate change on wildfi across western US forests. Proceedings of the National Academy of Sciences 113(42): 11770-11775. https://doi.org/10.1073/pnas.1607171113
Amatulli G., Camia A., San-Miguel-Ayanz J., 2013. Estimating future burned areas under changing climate in the EU-Mediterranean countries. Science of the Total Environment 450: 209-222. https://doi.org/10.1016/j. scitotenv.2013.02.014
Bowman D.M., Balch J.K., Artaxo P., Bond W.J., Carlson J.M., Cochrane M.A., D’Antonio C.M., DeFries R.S., Doyle J.C., Harrison S.P., Johnston F.H., 2009. Fire in the earth system. Science 324(5926): 481-484. https://doi. org/10.1126/science.1163886
Bui D.T., Bui Q.T., Nguyen Q.P., Pradhan B., Nampak H., Trinh P.T., 2017. A hybrid artifi intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fi susceptibility modeling at a tropical area. Agricultural Meteorology Programme 233: 32-44. https://doi.org/10.1016/j.agrformet.2016.11.002
ChuviecoE.,GiglioL.,JusticeC.,2008.Globalcharacterization of fi activity: toward defi fi regimes from Earth observation data. Global Change Biology 14(7): 1488- 1502. https://doi.org/ 10.1111/j.1365-2486.2008.01585.x
Cruz M.G., Alexander M.E., Fernandes P.M., Kilinc M., Sil Â., 2020. Evaluating the 10% wind speed rule of thumb for estimating a fire forward rate of spread against an extensive independent set of observations. Environmental Modelling & Software 133: 104818. https://doi.org/10.1016/j. envsoft.2020.104818
Czech Statistical Offi 2019. Characteristics of the district Prague-East. Website. [Online 7 February 2019]. URL: https://www.czso.cz/csu/czso/home
Czech Hydrometeorological Institute, 2009. Update Comprehensivestudyofimpacts,vulnerabilities,andsources of risks related to climate change in the Czech Republic from 2015, https://www.mzp.cz/C1257458002F0DC7/ cz/studie_dopadu_zmena_klimatu/$FILE/OEOK- Aktualizovana_studie_2019-20200128.pdf
Czech Republic Hydrometeorological Institute, 2021. Historical data - meteorology and climatology in Czech Republic. http://portal.chmi.cz/historicka-data/pocasi/ denni-data/Denni-data-dle-z.-123-1998-Sb
Déqué M., Marquet P., Jones R.G., 1998. Simulation of climate change over Europe using a global variable resolution general circulation model. Climate Dynamics 14(3): 173-189. https://doi.org/10.1007/s003820050216
Eklund P.W., Kirkby S., Pollitt S., 1996. A dynamic multi- source Dijkstra's algorithm for vehicle routing. In 1996 Australian New Zealand Conference on Intelligent Information Systems, ANZIIS 96, 18-20 Nov. 1996. IEEE, Adelaide, SA, Australia, pp. 329-333. https://doi. org/10.1109/ANZIIS.1996.573976
Eskandari S., Pourghasemi H.R., Tiefenbacher J.P., 2020. Relations of land cover, topography, and climate to fi occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fi danger. Forest Ecology and Management 473: 118338. https://doi. org/10.1016/j.foreco.2020.118338
Flannigan M.D., Logan K.A., Amiro B.D., Skinner W.R., Stocks B.J., 2005. Future area burned in Canada. Climatic Change 72(1-2): 1-16. https://doi.org/10.1007/s10584-005- 5935-y
Flannigan M., Cantin A.S., De Groot W.J., Wotton M., Newbery A., Gowman L.M., 2013. Global wildland fi season severity in the 21st century. Forest Ecology and Management 294: 54-61. https://doi.org/10.1016/j. foreco.2012.10.022
Hayati E., Abdi E., Majnounian B., 2013. Application of Sensitivity analysis in forest road networks planning and assessment. Journal of Agriculture, Science and Technology 15(4): 781–792.
Hansen R., 2003. Skogsbrandsläckning. Räddningsverket, Karlstad, Sweden, 146 p. (In Swedish).
Halofsky J.E., Peterson D.L., Harvey B.J., 2020. Changing wildfi changing forests: the eff of climate change on fi regimes and vegetation in the Pacifi Northwest, USA. Fire Ecology 16(1): 4. https://doi.org/10.1186/s42408-019- 0062-8
Holuša J., Bercak R., Lukasova K., Hanuska Z., Agh P., Vanek J., ... Chromek I., 2018. Forest fi in the Czech Republic definition and classification Reports on Forestry Research, in Czech 63(2): 102-111.
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 fi danger from 1979 to 2013. Nature Communications 6(1): 1-11. https://doi. org/10.1038/ncomms8537
Karali A., Hatzaki M., Giannakopoulos C., Roussos A., Xanthopoulos G., Tenentes V., 2014. Sensitivity and evaluation of current fi risk and future projections due to climate change: the case study of Greece. Natural Hazards and Earth System Sciences 14(1): 143. http://doi. org/10.5194/nhess-14-143-2014
Kane J.M., Prat-Guitart N., 2018. Fuel moisture. In: Manzello S. (eds) Encyclopedia of Wildfi and Wildland-Urban Interface (WUI) Fires. Springer, Chame, Switzerland. https://doi.org/10.1007/978-3-319-51727-8_115-1
Konca-Kędzierska K., Pianko-Kluczyńska K., 2018. The infl of relative humidity on fi in forests of Central Poland. Forest Research Papers 79(3): 269-279. https://doi. org/10.2478/frp-2018-0027
Koutsias N., Xanthopoulos G., Founda D., Xystrakis F., Nioti F., Pleniou M., Mallinis G. and Arianoutsou, M., 2013. On the relationships between forest fi and weather conditions in Greece from long-term national observations (1894–2010). International Journal of Wildland Fire 22(4): 493-507. https://doi.org/10.1071/WF12003
Lohmander P., 2020. Adaptive mobile firefighting resources: stochastic dynamic optimization of international cooperation, International Journal of Robotics and Automation 6(4): 150-155. https://doi.org/10.15406/ iratj.2020.06.00213
Lohmander P., 2021a. Forest fi expansion under global warming conditions: multivariate estimation, function properties and predictions for 29 countries. Central Asian Journal of Environmental Science and Technology Innovation 1(5). https://doi.org/ 10.22034/ CAJESTI.2020.05.03
Lohmander P., 2021b. Optimization of forestry, infrastructure and fi management. Caspian Journal of Environmental Sciences. 19: 287-316. https://doi.org/10.22124/ CJES.2021.4746
Majlingová A., 2012. Opening-up of forests for fi extinguishing purposes. Croatian Journal of Forest Engineering 33(1):159-168.
Marlon J.R., Bartlein P.J., Carcaillet C., Gavin D.G., Harrison S.P., Higuera P.E., Joos F., Power M.J., Prentice I.C., 2008. Climate and human infl on global biomass burning over the past two millennia. Nature Geoscience 2(4): 307-307. https://doi.org/10.1038/ngeo313
Mohammadi Z., Lohmander P., Kašpar J., Berčák R., Holuša J., Marušák R., 2021a. The eff of climate factors on the size of forest wildfi (Case study: Prague-East district, Czech Republic). Journal of Forestry Research. https://doi. org/10.1007/s11676-021-01413-w
Mohammadi Z., Lohmander P., Kašpar J., Marušák R., 2021b. The eff of road networks on the forest wildfi ‘size. In EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12000. https://doi.org/10.5194/egusphere- egu21-12000
Moriondo M., Good P., Durao R., Bindi M., Giannakopoulos C., Corte-Real J., 2006. Potential impact of climate change on fire risk in the Mediterranean area. Climate Research 31(1): 85-95. https://doi.org/10.3354/cr031085
Mouillot F., Rambal S., Joff R., 2002. Simulating climate change impacts on fi frequency and vegetation dynamics in a Mediterranean‐type ecosystem. Global Change Biology 8(5): 423-437. https://doi.org/10.1046/j.1365- 2486.2002.00494.x
Monjarás-Vega N.A., Briones-Herrera C.I., Vega-Nieva D.J., Calleros-Flores E., Corral-Rivas J.J., López-Serrano P.M., Pompa-García M., Rodríguez-Trejo D.A., Carrillo-Parra A., González-Cabán A., Alvarado-Celestino E., 2020. Predicting forest fi kernel density at multiple scales with geographically weighted regression in Mexico. Science of the Total Environment 718: 137313. https://doi. org/10.1016/j.scitotenv.2020.137313
Mozny M., Trnka M., Brázdil R., 2021. Climate change driven changes of vegetation fi in the Czech Republic. Theoretical and Applied Climatology 143(1), 691-699. https://doi.org/10.1007/s00704-020-03443-6
Nhongo E.J.S., Fontana D.C., Guasselli L.A., Bremm C., 2019. Probabilistic modelling of fi occurrence based on logistic regression, Niassa Reserve, Mozambique. Geomatics, Natural Hazards and Risk 10(1): 1772-1792. https://doi.org/10.1080/19475705.2019.1615559.
OpenStreetMap Statistics, 2021. OpenStreetMap Foundation. Retrieved 11 May 2020. Web: https://www.openstreetmap. org/#map=6/49.817/15.478
Pinto G.A.S.J., Rousseu F., Niklasson M., Drobyshev I., 2020. Eff of human-related and biotic landscape features on the occurrence and size of modern forest fi in Sweden. Agricultural and Forest Meteorology 291: 108084. https:// doi.org/10.1016/j.agrformet.2020.108084.
Price C., Rind D., 1994. The impact of a 2× CO2 climate on lightning-caused fi Journal of Climate 7(10): 1484-1494. https://doi.org/10.1175/1520-0442(1994)007<1484:TIOA CC>2.0.CO;2
Reid A. M., Fuhlendorf S. D., Weir J. R., 2010. Weather variables affecting Oklahoma wildfires Rangeland Ecology & Management 63(5): 599-603. https://doi.org/10.2111/ REM-D-09-00132.1
Roulet N., Moore T.I.M., Bubier J., Lafl P., 1992. Northern fens: methane fl and climatic change. Tellus 44(2): 100-105. https://doi.org/10.1034/j.1600-0889.1992.t01-1-00002.x.
San-Miguel-Ayanz J., Schulte E., Schmuck G., Camia A., Strobl P., Liberta G., ... &Amatulli G., 2012. Comprehensive monitoring of wildfi in Europe: the European Forest Fire Information System (EFFIS). In Approaches to managing disaster - Assessing hazards, emergencies and disaster impacts. IntechOpen. https://doi.org/10.5772/28441
Santín C., Doerr S.H., MerinoA., Bryant R., Loader N.J., 2016. Forest fl chemical transformations in a boreal forest fi and their correlations with temperature and heating duration. Geoderma 264: 71-80. https://doi.org/10.1016/j. geoderma.2015.09.021
Schelhaas M.J., Hengeveld G., Moriondo M., Reinds G.J., Kundzewicz Z.W., Ter Maat H., Bindi, M., 2010. Assessing risk and adaptation options to fi and windstorms in European forestry. Mitigation and Adaptation Strategies for Global Change 15(7): 681-701. https://doi.org/10.1007/ s11027-010-9243-0
Tian X., Cui W., Shu L., Zong X., 2019. Eff of climate change on burn probability of forests in Daxing’anling. Forests 10(8): 611. https://doi.org/10.3390/f10080611
Úhúl., 2021. Forest roads ‘map in Czech Republic, http:// www.uhul.cz/
Viedma O., Quesada J., Torres I., De Santis A., Moreno J.M., 2015. Fire severity in a large fi in a Pinus pinaster forest is highly predictable from burning conditions, stand structure, and topography. Ecosystems 18(2): 237-250. https://doi. org/10.1007/s10021-014-9824-y
Wastl C., Schunk C., Lüpke M., Cocca G., Conedera M., Valese E., Menzel A., 2013. Large-scale weather types, forest fi danger, and fi occurrence in the Alps. Agricultural and Forest Meteorology 168: 15-25. https://doi.org/10.1016/j. agrformet.2012.08.011
Westerling A.L., Hidalgo H.G., Cayan DR., Swetnam TW., 2006. Warming and earlier spring increase western US forest wildfi activity. Science 313(5789): 940-943. https:// doi.org/10.1126/science.1128834
WesterlingA.L., Bryant B.P., Preisler H.K., Holmes T.P., Hidalgo H.G., Das T., Shrestha S.R., 2011. Climate change and growth scenarios for California fi Climatic Change 109(1): 445- 463. https://doi.org/10.1007/s10584-011-0329-9
Wu M., Knorr W., Thonicke K., Schurgers G., Camia A., Arneth A., 2015. Sensitivity of burned area in Europe to climate change, atmospheric CO2 levels, and demography: A comparison of two fi models. Journal of Geophysical Research: Biogeosciences 120(11): 2256-2272. https://doi.org/10.1002/2015JG003036
Wu Z., He H.S., Fang L., Liang Y., Parsons R.A., 2018. Wind speed and relative humidity infl spatial patterns of burn severity in boreal forests of northeastern China. Annals of Forest Science 75(3): 66. https://doi.org/10.1007/ s13595-018-0749-z
Zhang G., Wang M., Liu K., 2019. Forest fi susceptibility modeling using a convolutional neural network for Yunnan province of China. International Journal of Disaster Risk Science 10(3): 386-403. https://doi.org/10.1007/s13753-019-00233-1
Zhang F., Dong Y., Xu S., Yang X., Lin H., 2020. An approach for improving firefighting ability of forest road network. Scandinavian Journal of Forest Research 35(8): 547-561. https://doi.org/10.1080/02827581.2020.1829029
Živanović S., Gocić M., Ivanović R., Martić-Bursać N., 2015. The eff of air temperature on forest fi risk in the municipality of Negotin. Bulletin of the Serbian Geographical Society 95(4): 67-76. https://doi.org/10.2298/ GSGD1504067Z
Živanović S., Ivanović R., Nikolić M., Đokić M., Tošić I., 2020. Infl of air temperature and precipitation on the risk of forest fi in Serbia. Meteorology and Atmospheric Physics 132: 869–883. https://doi.org/10.1007/s00703-020-00725-6
