Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3791-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-3791-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 Aug 2023
Data description paper |
| 23 Aug 2023
| 23 Aug 2023
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Akli Benali
CORRESPONDING AUTHOR
Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portuga
Nuno Guiomar
MED – Mediterranean Institute for Agriculture, Environment and Development & CHANGE – Global Change and Sustainability, University of Évora-PM, Apartado 94, 7006-554 Évora, Portugal
EaRSLab – Earth Remote Sensing Laboratory, University of Évora-CLV, Rua Romão Ramalho, 59, 7000-671 Évora, Portugal
IIFA – Institute for Advanced Studies and Research, University of Évora-PV, Largo Marquês de Marialva, Apartado 94, 7002-554 Évora, Portugal
Hugo Gonçalves
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
Bernardo Mota
National Physical Laboratory (NPL), Climate Earth Observation (CEO),
Hampton Rd. Teddington, TW11 0LW, UK
Fábio Silva
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
Paulo M. Fernandes
CITAB – Centro de Investigação e de Tecnologias
Agro-Ambientais e Biológicas, Universidade de Trás-os-Montes e Alto
Douro, 5001-801 Vila Real, Portugal
Carlos Mota
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
Alexandre Penha
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
João Santos
Força Especial de Proteção Civil, 2080-221 Almeirim,
Portugal
Autoridade Nacional de Emergência e Proteção Civil,
2799-51 Carnaxide, Portugal
José M. C. Pereira
Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portuga
Ana C. L. Sá
Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portuga
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Cited articles
Albini, F. A.: Wildland Fires: Predicting the behavior of wildland
fires – among nature's most potent forces – can save lives, money, and
natural resources, Am. Sci., 72, 590–597, 1984.
Alcasena, F., Ager, A., Le Page, Y., Bessa, P., Loureiro, C., and Oliveira,
T.: Assessing wildfire exposure to communities and protected areas in
Portugal, Fire, 4, 82, https://doi.org/10.3390/fire404008, 2021.
Alexander, M. and Cruz, M. G.: Are the applications of wildland fire
behaviour models getting ahead of their evaluation again?, Environ. Model.
Softw., 41, 65–71, https://doi.org/10.1016/j.envsoft.2012.11.001, 2013.
Alexander, M. E. and Cruz, M. G.: Evaluating a model for predicting active
crown fire rate of spread using wildfire observations, Can. J. Forest Res., 36,
3015–3028, https://doi.org/10.1139/x06-174, 2006.
Alexander, M. E. and Lanoville, R. A.: Wildfires as a source of fire
behavior data: a case study from Northwest Territories, Canada. 9th Conf.
Fire and Forest Meteorology, 21–24 April, San Diego, CA, American
Meteorological Society, Boston, Mass, 86–93, 1987.
Alexander, M. E. and Thomas, D. A.: Wildland fire behavior case studies and
analyses: Other examples, methods, reporting standards, and some practical
advice, Fire Manag. Today, 63, 4–12, 2003.
Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G. R., and Randerson, J. T.: The Global Fire Atlas of individual fire size, duration, speed and direction, Earth Syst. Sci. Data, 11, 529–552, https://doi.org/10.5194/essd-11-529-2019, 2019.
Anderson, W. R., Cruz, M. G., Fernandes, P. M., McCaw, L., Vega, J. A.,
Bradstock, R. A., Fogarty, L .G., Gould, J. B., McCarthy, G. H.,
Marsden-Smedley, J. B., Matthews, S., Mattingley, G., Pearce, H. G., and van
Wilgen, B. W.: A generic, empirical-based model for predicting rate of fire
spread in shrublands, Int. J. Wildland Fire, 24, 443–460,
https://doi.org/10.1071/WF14130, 2015.
Artés, T., Oom, D., De Rigo, D., Durrant, T. H., Maianti, P.,
Libertà, G., and San-Miguel-Ayanz, J.: A global wildfire dataset for the
analysis of fire regimes and fire behaviour, Sci. Data, 6, 1–11,
https://doi.org/10.1038/s41597-019-0312-2, 2019.
Benali, A., Guiomar, N., Gonçalves, H., Mota, B., Silva, F., Fernandes,
P. M., Mota, C., Penha, A., Santos, J., Pereira, J. M. C., and Sá, A. C. L:
The Portuguese Large Wildfire Spread Database (PT-FireSprd), Zenodo [data set],
https://doi.org/10.5281/zenodo.7495506, 2022.
Briones-Herrera, C. I., Vega-Nieva, D. J., Monjarás-Vega, N. A.,
Briseño-Reyes, J., López-Serrano, P. M., Corral-Rivas, J. J.,
Alvarado-Celestino, E., Arellano-Pérez, S., Álvarez-González,
J. G., Ruiz-González, A. D., Jolly, W. M., and Parks, S. A.: Near
real-time automated early mapping of the perimeter of large forest fires
from the aggregation of VIIRS and MODIS active fires in Mexico, Remote
Sens., 12, 2061, https://doi.org/10.3390/rs12122061, 2020.
Butler, B. W. and Reynolds, T. D.: Wildfire case study: Butte City,
southeastern Utah, 1 July 1994, USDA For. Serv., Intermt. Res. Stn., Ogden,
UT. Gen. Tech. Rep. INT-GTR-351, https://doi.org/10.2737/INT-GTR-351, 1997.
Catchpole, W. R., Catchpole, E. A., Butler, B. W., Rothermel, R. C., Morris,
G. A., and Latham, D. J.: Rate of spread of free-burning fires in woody
fuels in a wind tunnel, Combust. Sci. Technol., 131, 1–37,
https://doi.org/10.1080/00102209808935753, 1998.
Chen, Y., Hantson, S., Andela, N., Coffield, S. R., Graff, C. A., Morton, D.
C., Ott, L.E., Foufoula-Georgiou, E., Smyth, P., Goulden, M. L., and
Randerson, J. T.: California wildfire spread derived using VIIRS satellite
observations and an object-based tracking system, Sci. Data, 9, 1–15,
https://doi.org/10.1038/s41597-022-01343-0, 2022.
Cheney, N. P.: Fire behaviour during the Pickering Brook wildfire, January
2005 (Perth Hills Fires 71-80), Conserv. Sci. West. Aust., 7, 451–468,
2010.
Cheney, N. P., Gould, J. S., McCaw, W. L., and Anderson, W. R.: Predicting
fire behaviour in dry eucalypt forest in southern Australia, Forest Ecol.
Manag., 280, 120–131, https://doi.org/10.1016/j.foreco.2012.06.012, 2012.
Coen, J. L. and Riggan, P. J.: Simulation and thermal imaging of the 2006
Esperanza Wildfire in southern California: application of a coupled
weather–wildland fire model, Int. J. Wildland Fire, 23, 755–770,
https://doi.org/10.1071/WF12194, 2014.
Collins, B. M., Miller. J. D., Thode, A. E., Kelly, M., van Wagtendonk, J.
W., and Stephens, S. L.: Interactions among wildland fires in a long-
established Sierra Nevada natural fire area, Ecosystems 12, 114–128,
https://doi.org/10.1007/s10021-008-9211-7, 2019.
Countryman, C. M.: The fire environment concept, USDA Forest Service,
Pacific Southwest Range and Experiment Station, Berkeley, California, USA,
1972.
Crowley, M. A., Cardille, J. A., White, J. C., and Wulder, M. A.: Generating
intra-year metrics of wildfire progression using multiple open-access
satellite data streams, Remote Sens. Environ., 232, 111295,
https://doi.org/10.1016/j.rse.2019.111295, 2019.
Cruz, M. G.: Monte Carlo-based ensemble method for prediction of grassland
fire spread, Int. J. Wildland Fire, 19, 521–530, https://doi.org/10.1071/WF08195,
2010.
Cruz, M. G. and Alexander, M. E.: Uncertainty associated with model
predictions of surface and crown fire rates of spread, Environ. Modell.
Softw., 47, 16–28, https://doi.org/10.1016/j.envsoft.2013.04.004, 2013.
Cruz, M. G. and Alexander, M. E.: The 10 % wind speed rule of thumb for
estimating a wildfire's forward rate of spread in forests and shrublands,
Ann. Forest Sci., 76, 1–11, https://doi.org/10.1007/s13595-019-0829-8, 2019.
Cruz, M. G., Gould, J. S., Alexander, M. E., Sullivan, A. L., McCaw, W. L.,
and Matthews, S.: Empirical-based models for predicting head-fire rate of
spread in Australian fuel types, Aust. Forestry, 78, 118–158,
https://doi.org/10.1080/00049158.2015.1055063, 2015.
Cruz, M. G., Alexander, M. E., Sullivan, A. L., Gould, J. S., and Kilinc,
M.: Assessing improvements in models used to operationally predict wildland
fire rate of spread, Environ. Modell. Softw., 105, 54–63,
https://doi.org/10.1016/j.envsoft.2018.03.027, 2018.
Cruz, M. G., Alexander, M. E., and Kilinc, M.: Wildfire rates of spread in
grasslands under critical burning conditions, Fire, 5, 55,
https://doi.org/10.3390/fire5020055, 2022.
Cruz, M. G., Cheney, N. P., Gould, J. S., McCaw, W. L., Kilinc, M., and
Sullivan, A. L.: An empirical-based model for predicting the forward spread
rate of wildfires in eucalypt forests, Int. J. Wildland Fire, 31, 81–95,
https://doi.org/10.1071/WF21068, 2021.
Dale, M. R. T. and Fortin, M. J.: From graphs to spatial graphs, Annu. Rev.
Ecol. Evol. Systs., 41, 21–38, https://doi.org/10.1146/annurev-ecolsys-102209-144718, 2010.
Duff, T. J., Chong, D. M., and Tolhurst, K. G.: Quantifying spatio-temporal
differences between fire shapes: Estimating fire travel paths for the
improvement of dynamic spread models, Environ. Modell. Softw, 46, 33–43,
https://doi.org/10.1016/j.envsoft.2013.02.005, 2013.
Fernandes, P. M., Botelho, H. S., Rego, F. C., and Loureiro, C.: Empirical
modelling of surface fire behaviour in maritime pine stands, Int. J.
Wildland Fire, 18, 698–710, https://doi.org/10.1071/WF08023, 2009.
Fernandes, P. M., Barros, A. M., Pinto, A., and Santos, J. A.:
Characteristics and controls of extremely large wildfires in the western
Mediterranean Basin, J. Geophys. Res.-Biogeo., 121, 2141–2157,
https://doi.org/10.1002/2016JG003389, 2016.
Fernandes, P. M., Sil, A., Ascoli, D., Cruz, M. G., Alexander, M. E., Rossa,
C. G., Baeza, J., Burrows, N., Davies, G. M., Fidelis, A., Gould, J. S.,
Govender, N., Kilinc, M., and McCaw, L.: Drivers of wildland fire behaviour
variation across the Earth, in: Advances in Forest Fire
Research, Chapter 7 – Short contributions, edited by: Viegas, D. X., ADAI/CEIF, University of Coimbra, 1267–1270,
https://doi.org/10.14195/978-989-26-16-506_154, 2018.
Fernandes, P. M., Sil, A., Ascoli, D., Cruz, M. G., Rossa, C. G., and
Alexander, M. E.: Characterizing fire behavior across the globe, in: Proceedings of the Fire
Continuum-Preparing for the future of wildland fire, edited by: Hood,
S. M., Drury, S., Steelman, T., and Steffens, R., 21–24 May 2018,
Missoula, MT, Proceedings RMRS-P-78, Fort Collins, CO, US Department of
Agriculture, Forest Service, Rocky Mountain Research Station, 258–263,
2020.
Finney, M. A., McAllister, S. S., Forthofer, J. M., and Grumstrup, T. P.:
Wildland Fire Behaviour: Dynamics, Principles and Processes, CSIRO Pub., ISBN 978148309108,
2021.
Forestry Canada Fire Danger Group: Development and structure of the Canadian
Forest Fire Behavior Prediction System. For. Can., Ottawa, Ont. Inf. Rep.
ST-X-3, ISBN 0662198123, 1992.
Frantz, D., Stellmes, M., Röder, A., and Hill, J.: Fire spread from
MODIS burned area data: Obtaining fire dynamics information for every single
fire, Int. J. Wildland Fire, 25, 1228–1237, https://doi.org/10.1071/WF16003, 2016.
Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J.: An enhanced
contextual fire de- tection algorithm for MODIS, Remote Sens. Environ., 87,
273–282, https://doi.org/10.1016/S0034-4257(03)00184-6, 2003.
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active
fire detection algorithm and fire productsm Remote Sens. Environ., 178,
31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Gollner, M., Trouve, A., Altintas, I., Block, J., de Callafon, R., Clements,
C., Cortes, A., Ellicott, E., Filippi, J. B., Finney, M., Ide, K., Jenkins,
M. A., Jimenez, D., Lautenberger, C., Mandel, J., Rochoux, M., and Simeoni,
A.: Towards data-driven operational wildfire spread modeling, in: Report of the NSF-Funded Wildfire Workshop, College Park, MD, USA, University of Maryland, 2015.
Guerreiro, J., Fonseca, C., Salgueiro, A., Fernandes, P., Iglésias, E.
L., Neufville, R., Mateus, P., Castellnou, M., Silva, J. S., Moura, J. M.,
Rego, F. C., and Caldeira, D.: Análise e apuramento dos factos relativos
aos incêndios que ocorreram em Pedrogão Grande, Castanheira de
Pêra, Ansião, Alvaiázere, Figueiró dos Vinhos, Arganil,
Góis, Penela, Pampilhosa da Serra, Oleiros e Sertã, entre 17 e 24 de
junho de 2017, Comissão Técnica Independente, Assembleia da
República, Lisboa,
https://www.parlamento.pt/Documents/2017/Outubro/RelatórioCTI_VF.pdf (last access: December 2022), 2017.
Guerreiro, J., Fonseca, C., Salgueiro, A., Fernandes, P., Iglésias, E.
L., Neufville, R., Mateus, P., Castellnou, M., Silva, J. S., Moura, J. M.,
Rego, F. C., and Caldeira, D.: Avaliação dos Incêndios ocorridos
entre 14 e 16 de outubro de 2017 em Portugal Continental, Comissão
Técnica Independente, Assembleia da República, Lisboa,
https://www.parlamento.pt/Documents/2018/Marco/RelatorioCTI190318N.pdf (last access: December 2022),
2018.
Humber, M., Zubkova, M., and Giglio, L.: A remote sensing-based approach to
estimating the fire spread rate parameter for individual burn patch
extraction, Int. J. Remote Sens., 43, 649–673,
https://doi.org/10.1080/01431161.2022.2027544, 2022.
Khanmohammadi, S., Arashpour, M., Golafshani, E. M., Cruz, M. G.,
Rajabifard, A., and Bai, Y.: Prediction of wildfire rate of spread in
grasslands using machine learning methods, Environ. Modell. Softw., 156,
105507, https://doi.org/10.1016/j.envsoft.2022.105507, 2022.
Kilinc, M., Anderson, W., and Price, B.: The Applicability of Bushfire
Behaviour Models in Australia, Victorian Government, Department of
Sustainability and Environment, DSE Schedule 5: Fire Severity Rating
Project, Melbourne, VIC, Technical Report 1, 2012.
McCaw, W. L., Gould, J. S., Cheney, N. P., Ellis, P. F. M., and Anderson, W.
R.: Changes in behaviour of fire in dry eucalypt forest as fuel increases
with age, Forest Ecol. Manag., 271, 170–181, https://doi.org/10.1016/j.foreco.2012.02.003,
2012.
Niro, F., Goryl, P., Dransfeld, S., Boccia, V., Gascon, F., Adams, J.,
Themann, B., Scifoni, S. and Doxani, G.: European Space Agency (ESA)
Calibration/Validation Strategy for Optical Land-Imaging Satellites and
Pathway towards Interoperability, Remote Sens., 13, 3003,
https://doi.org/10.3390/rs13153003, 2021.
Oom, D., Silva, P. C., Bistinas, I., and Pereira, J. M. C.: Highlighting
biome-specific sensitivity of fire size distributions to time-gap parameter
using a new algorithm for fire event individuation, Remote Sens., 8, 663,
https://doi.org/10.3390/rs8080663, 2016.
Palaiologou, P., Kalabokidis, K., Ager, A. A., and Day, M. A.: Development
of Comprehensive Fuel Management Strategies for Reducing Wildfire Risk in
Greece, Forests, 11, 789, https://doi.org/10.3390/f11080789, 2020.
Palheiro, P. M., Fernandes, P. M., and Cruz, M. G.: A fire behaviour-based fire
danger classification for maritime pine stands: comparison of two
approaches, Forest Ecol. Manag., 234, p. S54, https://doi.org/10.1016/j.foreco.2006.08.075,
2006.
Parisien, M. A., Parks, S. A., Miller, C., Krawchuk, M. A., Heathcott, M., and
Moritz, M. A.: Contributions of ignitions, fuels, and weather to the burn
probability of a boreal landscape, Ecosystems, 14, 1141–1155,
https://doi.org/10.1007/s10021-011-9474-2, 2011.
Parks, S. A.: Mapping day-of-burning with coarse-resolution satellite
fire-detection data, Int. J. Wildland Fire, 23, 215–223,
https://doi.org/10.1071/WF13138, 2014.
Pereira, J. M., Oom, D., Silva, P. C., and Benali, A.: Wild, tamed, and
domesticated: Three fire macroregimes for global pyrogeography in the
Anthropocene, Ecol. Appl., 32, e2588, https://doi.org/10.1002/eap.2588, 2022.
Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980–2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343–3358, https://doi.org/10.5194/nhess-11-3343-2011, 2011.
Pinto, M. M., DaCamara, C. C., Trigo, I. F., Trigo, R. M., and Turkman, K. F.: Fire danger rating over Mediterranean Europe based on fire radiative power derived from Meteosat, Nat. Hazards Earth Syst. Sci., 18, 515–529, https://doi.org/10.5194/nhess-18-515-2018, 2018.
Rodríguez y Silva, F. and Molina-Martínez, J. R.: Modeling
Mediterranean forest fuels by integrating field data and mapping tools, Eur.
J. For. Res., 131, 571–582, https://doi.org/10.1007/s10342-011-0532-2, 2012.
Rothermel, R. C.: A mathematical model for predicting fire spread in wildland
fuels, Res. Pap. INT-115. Ogden, UT, U.S. Department of Agriculture,
Intermountain Forest and Range Experiment Station, 1972.
Sá, A. C.,
Benali, A., Fernandes, P. M., Pinto, R. M., Trigo, R. M., Salis, M., Russo,
A., Jerez, S., Soares, P. M. M., Schroeder, W., and Pereira, J. M. C.:
Evaluating fire growth simulations using satellite active fire data, Remote
Sens. Environ., 190, 302–317, https://doi.org/10.1016/j.rse.2016.12.023, 2017.
Salis, M., Del Giudice, L., Arca, B., Ager, A. A., Alcasena-Urdiroz, F.,
Lozano, O., Bacciu, V., Spano, D., and Duce, P.: Modeling the effects of
different fuel treatment mosaics on wildfire spread and behavior in a
Mediterranean agro-pastoral area, J. Environ. Manage., 212, 490–505,
https://doi.org/10.1016/j.jenvman.2018.02.020, 2018.
Santoni, P.-A., Filippi, J.-B., Balbi, J.-H., and Bosseur, F.: Wildland fire
behaviour case studies and fuel models for landscape-scale fire modeling, J.
Combust., 2011, 613424, https://doi.org/10.1155/2011/613424, 2011.
Schag, G. M., Stow, D. A., Riggan, P. J., Tissell, R. G., and Coen, J. L.:
Examining landscape-scale fuel and terrain controls of wildfire spread rates
using repetitive airborne thermal infrared (ATIR) imagery, Fire, 4, 6,
https://doi.org/10.3390/fire4010006, 2021.
Schroeder, W., Oliva, P., Giglio, L., and Csiszar, I. A.: The New VIIRS 375 m active fire detection data product: Algorithm description and initial
assessment, Remote Sens. Environ., 143, 85–96,
https://doi.org/10.1016/j.rse.2013.12.008, 2014.
Scott, J. H. and Reinhardt, E. D.: Assessing Crown Fire Potential by
Linking Models of Surface and Crown Fire Behavior, US Department of
Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins,
CO, Research Paper RMRS-RP-29, https://doi.org/10.2737/RMRS-RP-29, 2001.
Sharples, J. J., McRae, R. H., and Wilkes, S. R.: Wind–terrain effects on
the propagation of wildfires in rugged terrain: fire channelling, Int. J.
Wildland Fire, 21, 282–296, https://doi.org/10.1071/WF10055, 2012.
Sifakis, N. I., Iossifidis, C., Kontoes, C., and Keramitsoglou, I.: Wildfire
detection and tracking over Greece using MSG-SEVIRI satellite data, Remote
Sens., 3, 524–538, https://doi.org/10.3390/rs3030524, 2011.
Stocks, B. J., Alexander, M. E., Wotton, B. M., Stefner, C. N., Flannigan,
M. D., Taylor, S. W., Lavoie, N., Mason, J. A., Hartley, G. R., Maffey, M.
E., Dalrymple, G. N., Blake, T. W., and Cruz, M. G., and Lanoville, R. A.: Crown
fire behaviour in a northern jack pine black spruce forest, Can. J. For.
Res., 34, 1548–1560, https://doi.org/10.1139/x04-054, 2004.
Storey, M. A., Price, O. F., Sharples, J. J., and Bradstock, R. A.: Drivers
of long-distance spotting during wildfires in south-eastern Australia, Int.
J. Wildland Fire, 29, 459–472, https://doi.org/10.1071/WF19124, 2020.
Storey, M. A., Bedward, M., Price, O. F., Bradstock, R. A., and Sharples, J.
J.: Derivation of a Bayesian fire spread model using large-scale wildfire
observations, Environ. Model. Softw., 144, 105127,
https://doi.org/10.1016/j.envsoft.2021.105127, 2021.
Stow, D. A., Riggan, P. J., Storey, E. A., and Coulter, L. L.: Measuring fire
spread rates from repeat pass airborne thermal infrared imagery, Remote
Sens. Lett., 5, 803–812, https://doi.org/10.1080/2150704X.2014.967882, 2014.
Vaillant, N. M., Ewell, C. M., and Fites-Kaufman, J. A.: Capturing crown fire
behavior on wildland fires - the Fire Behavior Assessment Team in action,
Fire Manag. Today, 73, 41–45, 2014.
Valero, M. M., Rios, O., Pastor, E., and Planas, E.: Automated location of
active fire perimeters in aerial infrared imaging using unsupervised edge
detectors, Int. J. Wildland Fire, 27, 241–256, https://doi.org/10.1071/WF17093, 2018.
Veraverbeke, S., Sedano, F., Hook, S. J., Randerson, J. T., Jin, Y., and
Rogers, B. M.: Mapping the daily progression of large wildland fires using
MODIS active fire data, Int. J. Wildland Fire, 23, 655–667,
https://doi.org/10.1071/WF13015, 2014.
Viegas, D. X., Almeida, M. F., Ribeiro, L. M., Raposo, J., Viegas, M. T.,
Oliveira, R., Alves, D., Pinto, C., Rodrigues, A., Ribeiro, C., Lopes, S.,
Jorge, H., and Viegas, C. X.: Análise dos Incêndios Florestais
Ocorridos a 15 de outubro de 2017, Centro de Estudos sobre Incêndios
Florestais (CEIF/ADAI/LAETA), 2019.
Wade, D. D. and Ward, D. E.: An analysis of the Air Force Bomb Range Fire,
Res. Pap. SE–105, Asheville, NC, USDA Forest Service, Southeastern Forest
Experiment Station, 1973.
Wolfe, R. E., Roy, D. P., and Vermote, E.: MODIS land data storage,
gridding, and compositing methodology: level 2 grid, IEEE T. Geosci.
Remote, 36, 1324–1338, https://doi.org/10.1109/36.701082, 1998.
Wooster, M. J., Roberts, G., Freeborn, P. H., Xu, W., Govaerts, Y., Beeby, R., He, J., Lattanzio, A., Fisher, D., and Mullen, R.: LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis, Atmos. Chem. Phys., 15, 13217–13239, https://doi.org/10.5194/acp-15-13217-2015, 2015.
Wotton, B. M.: Interpreting and using outputs from the Canadian Forest Fire
Danger Rating System in research applications, Environ. Ecol. Stat., 16,
107–131, https://doi.org/10.1007/s10651-007-0084-2, 2009.
Short summary
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and...
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