Albini, F. A.: Computer-based models of wildland fire behavior: a user's manual, USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, 71 pp.,
https://www.frames.gov/catalog/8178 (last access: 26 April 2025), 1976.
Andrews, P. L.: The Rothermel surface fire spread model and associated developments: A comprehensive explanation, The Rothermel surface fire spread model and associated developments: A comprehensive explanation, Gen. Tech. Rep. RMRS-GTR-371, U.S. Department of Agriculture, Forest Service, Fort Collins, CO, Rocky Mountain Research Station, 121 pp.,
https://doi.org/10.2737/RMRS-GTR-371, 2018.
Baetens, L., Desjardins, C., and Hagolle, O.: Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure, Remote Sens.-Basel, 11, 433, https://doi.org/10.3390/rs11040433, 2019.
Beltrán-Marcos, D., Calvo, L., Fernández-Guisuraga, J. M., Fernández-García, V., and Suárez-Seoane, S.: Wildland-urban interface typologies prone to high severity fires in Spain, Sci. Total Environ., 894, 165000, https://doi.org/10.1016/j.scitotenv.2023.165000, 2023.
Benyon, R. G., Inbar, A., Sheridan, G. J., and Lane, P. N. J.: Critical climate thresholds for fire in wet, temperate forests, Forest Ecol. Manag., 537, 120911, https://doi.org/10.1016/j.foreco.2023.120911, 2023.
Blanchi, R., Leonard, J., Haynes, K., Opie, K., James, M., and Oliveira, F. D. d.: Environmental circumstances surrounding bushfire fatalities in Australia 1901–2011, Environ. Sci. Policy, 37, 192–203, https://doi.org/10.1016/j.envsci.2013.09.013, 2014.
Boegelsack, N., Withey, J., O'Sullivan, G., and McMartin, D.: A Critical Examination of the Relationship between Wildfires and Climate Change with Consideration of the Human Impact, Journal of Environmental Protection, 9, 461–467, https://doi.org/10.4236/jep.2018.95028, 2018.
Chavez Jr., P. S.: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data, Remote Sens. Environ., 24, 459–479, https://doi.org/10.1016/0034-4257(88)90019-3, 1988.
Copernicus: Home :: Corine Land Cover classes, Copernicus Land Monitoring Service [data set],
https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/, last access: 25 January 2024.
DecaMap:
https://decamap.com/, last access: 22 February 2024.
dos Reis, M., Graça, P. M. L. de A., Yanai, A. M., Ramos, C. J. P., and Fearnside, P. M.: Forest fires and deforestation in the central Amazon: Effects of landscape and climate on spatial and temporal dynamics, J. Environ. Manage., 288, 112310, https://doi.org/10.1016/j.jenvman.2021.112310, 2021.
Farguell, A., Cortés, A., Margalef, T., Miró, J. R., and Mercader, J.: Scalability of a multi-physics system for forest fire spread prediction in multi-core platforms, J. Supercomput., 75, 1163–1174, https://doi.org/10.1007/s11227-018-2330-9, 2019.
Filippi, J.-B., Bosseur, F., and Grandi, D.: ForeFire: open-source code for wildland fire spread models, in: Advances in forest fire research, Imprensa da Universidade de Coimbra, 275–282, https://doi.org/10.14195/978-989-26-0884-6_29, 2014.
Fons, W. L.: Analysis of Fire Spread in Light Forest Fuels, J. Agric. Res., 72, 92–121, 1946.
Fortune, S.: A sweepline algorithm for Voronoi diagrams, Algorithmica, 2, 153–174, https://doi.org/10.1007/BF01840357, 1987.
Frandsen, W. H.: Fire spread through porous fuels from the conservation of energy, Combust. Flame, 16, 9–16, https://doi.org/10.1016/S0010-2180(71)80005-6, 1971.
Gilmore, S., Nalband, A., and Dewan, A.: Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data, CEUR Workshop Proceedings, Brisbane, Australia, 10–12 March 2015, 1323, 100–108, 2015.
Hackett, C., Moral, R. D. A., and Markham, C.: Simulating Disease in Periods of Low Mobility Using a Hybrid Diffusion and Compartmental Model Built on Geographic Data, in: 2021 32nd Irish Signals and Systems Conference (ISSC), Athlone, Ireland, 10–11 June 2021, IEEE, https://doi.org/10.1109/ISSC52156.2021.9467871, 2021.
Haghani, M., Kuligowski, E., Rajabifard, A., and Kolden, C. A.: The state of wildfire and bushfire science: Temporal trends, research divisions and knowledge gaps, Safety Sci., 153, 105797, https://doi.org/10.1016/j.ssci.2022.105797, 2022.
Halofsky, J. E., Peterson, D. L., and Harvey, B. J.: Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA, Fire Ecol., 16, 4, https://doi.org/10.1186/s42408-019-0062-8, 2020.
Helene, P., Britez, C., and Carvalho, M.: Fire impacts on concrete structures. A brief review, Revista ALCONPAT, 10, 1–21, https://doi.org/10.21041/ra.v10i1.421, 2019.
Janssen, T. A. J., Jones, M. W., Finney, D., van der Werf, G. R., van Wees, D., Xu, W., and Veraverbeke, S.: Extratropical forests increasingly at risk due to lightning fires, Nat. Geosci., 16, 1136–1144, https://doi.org/10.1038/s41561-023-01322-z, 2023.
Jiao, Q., Fan, M., Tao, J., Wang, W., Liu, D., and Wang, P.: Forest Fire Patterns and Lightning-Caused Forest Fire Detection in Heilongjiang Province of China Using Satellite Data, Fire, 6, 166, https://doi.org/10.3390/fire6040166, 2023.
Jones, M. W., Santín, C., van der Werf, G. R., and Doerr, S. H.: Global fire emissions buffered by the production of pyrogenic carbon, Nat. Geosci., 12, 742–747, https://doi.org/10.1038/s41561-019-0403-x, 2019.
Kala, C. P.: Environmental and socioeconomic impacts of forest fires: A call for multilateral cooperation and management interventions, Natural Hazards Research, 3, 286–294, https://doi.org/10.1016/j.nhres.2023.04.003, 2023.
Kaur, I., Mentrelli, A., Bosseur, F., Filippi, J.-B., and Pagnini, G.: Turbulence and fire-spotting effects into wild-land fire simulators, Commun. Nonlinear Sci., 39, 300–320, https://doi.org/10.1016/j.cnsns.2016.03.003, 2016.
Kebede, T. A., Hailu, B. T., and Suryabhagavan, K. V.: Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia, Environmental Challenges, 8, 100568, https://doi.org/10.1016/j.envc.2022.100568, 2022.
Keeley, J. E. and Syphard, A. D.: Large California wildfires: 2020 fires in historical context, Fire Ecol., 17, 22, https://doi.org/10.1186/s42408-021-00110-7, 2021.
Małecki, K.: Graph Cellular Automata with Relation-Based Neighbourhoods of Cells for Complex Systems Modelling: A Case of Traffic Simulation, Symmetry-Basel, 9, 322, https://doi.org/10.3390/sym9120322, 2017.
McArthur, A. G.: Weather and grassland fire behaviour, 1923–1978 & Australia, Forestry and Timber Bureau, Canberra,
https://catalogue.nla.gov.au/catalog/752731 (last access: 26 April 2025), 1966.
McElwain, L. and Sweeney, J.: Climate change in Ireland- recent trends in temperature and precipitation, Irish Geography, 36, 97–111, https://doi.org/10.1080/00750770309555815, 2003.
Meier, S., Elliott, R. J. R., and Strobl, E.: The regional economic impact of wildfires: Evidence from Southern Europe, J. Environ. Econ. Manag., 118, 102787, https://doi.org/10.1016/j.jeem.2023.102787, 2023.
Noble, I. R., Gill, A. M., and Bary, G. A. V.: McArthur's fire-danger meters expressed as equations, Aust. J. Ecol., 5, 201–203, https://doi.org/10.1111/j.1442-9993.1980.tb01243.x, 1980.
Pais, C., Carrasco, J., Martell, D. L., Weintraub, A., and Woodruff, D. L.: Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning, Frontiers in Forests and Global Change, 4, 692706, https://doi.org/10.3389/ffgc.2021.692706, 2021.
Park, H., Nam, K., and Lim, H.: Is critical infrastructure safe from wildfires? A case study of wildland-industrial and -urban interface areas in South Korea, Int. J. Disast. Risk Re., 95, 103849, https://doi.org/10.1016/j.ijdrr.2023.103849, 2023.
Penney, G., Habibi, D., and Cattani, M.: Firefighter tenability and its influence on wildfire suppression, Fire Safety J., 106, 38–51, https://doi.org/10.1016/j.firesaf.2019.03.012, 2019.
Piñol, J., Beven, K., and Viegas, D. X.: Modelling the effect of fire-exclusion and prescribed fire on wildfire size in Mediterranean ecosystems, Ecol. Model., 183, 397–409, https://doi.org/10.1016/j.ecolmodel.2004.09.001, 2005.
Prat-Guitart, N., Nugent, C., Mullen, E., Mitchell, F. J. G., Hawthorne, D., Belcher, C. M., and Yearsley, J. M.: Peat Fires in Ireland, in: Coal and Peat Fires: A Global Perspective, Elsevier, 451–482, https://doi.org/10.1016/B978-0-12-849885-9.00020-2, 2019.
Pringle, M. J., Schmidt, M., and Tindall, D. R.: Multi-decade, multi-sensor time-series modelling – based on geostatistical concepts – to predict broad groups of crops, Remote Sens. Environ., 216, 183–200, https://doi.org/10.1016/j.rse.2018.06.046, 2018.
QGIS:
https://qgis.org/en/site/, last access: 25 January 2024.
Rothermel, R. C.: A mathematical model for predicting fire spread in wildland fuels, Res. Pap. INT-115, U.S. Department of Agriculture, Intermountain Forest and Range Experiment Station, Ogden, UT, 40 pp., 1972.
San Martin, D. and Torres, C.: 2D Simplified Wildfire Spreading Model in Python: From NumPy to CuPy, CLEI Electronic Journal, 26, https://doi.org/10.19153/cleiej.26.1.5, 2023.
Sibanda, S. and Ahmed, F.: Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub-catchment, Zimbabwe, Modeling Earth Systems and Environment, 7, 57–70, https://doi.org/10.1007/s40808-020-00963-y, 2021.
Sun, C., Bian, Y., Zhou, T., and Pan, J.: Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region, Sensors, 19, 2401, https://doi.org/10.3390/s19102401, 2019.
Trucchia, A., Egorova, V., Butenko, A., Kaur, I., and Pagnini, G.: RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models – implementation in WRF-SFIRE and response analysis with LSFire
+, Geosci. Model Dev., 12, 69–87, https://doi.org/10.5194/gmd-12-69-2019, 2019.
Weber, R. O.: Modelling fire spread through fuel beds, Prog. Energ. Combust., 17, 67–82, https://doi.org/10.1016/0360-1285(91)90003-6, 1991.
Wilson, R.: Reformulation of forest fire spread equations in SI units, Research Note INT-292, U.S. Department of Agriculture, Forest Service, Intermountain Range and Forest Experiment Station Ogden, UT, 5 pp., https://doi.org/10.2737/INT-RN-292, 1980.
Windy:
https://windy.app/, last access: 25 January 2024.
Xue, C., Krysztofiak, G., Ren, Y., Cai, M., Mercier, P., Fur, F. Le, Robin, C., Grosselin, B., Daële, V., McGillen, M. R., Mu, Y., Catoire, V., and Mellouki, A.: A study on wildfire impacts on greenhouse gas emissions and regional air quality in South of Orléans, France, J. Environ. Sci., 135, 521–533, https://doi.org/10.1016/j.jes.2022.08.032, 2024.
Zehra, F., Javed, M., Khan, D., and Pasha, M.: Comparative Analysis of C
and Python in Terms of Memory and Time, Preprints-Basel, https://doi.org/10.20944/preprints202012.0516.v1, 2020.
Zhang, S., Liu, J., Gao, H., Chen, X., Li, X., and Hua, J.: Study on Forest Fire spread Model of Multi-dimensional Cellular Automata based on Rothermel Speed Formula, CERNE, 27, e-102932, https://doi.org/10.1590/01047760202127012932, 2021.
