Non-parametric comparative analysis of the spatiotemporal pattern of human-caused and natural wildfires in Galicia

  1. Marey-Pérez, M. F. 3
  2. Fuentes-Santos, Isabel 2
  3. Saavera-Nieves, Paula 1
  4. González-Manteiga, Wenceslao 1
  1. 1 Department of Statistics, Mathematical Analysis and Optimisation, University of Santiago de Compostela, Spain.
  2. 2 Marine Research Institute, Spanish National Research Council, Spain.
  3. 3 PROePLA Research Group, Department of Crop Production and Engineering Projects, Universidade de Santiago de Compostela, Spain.
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Revista:
International Journal of Wildland Fire

ISSN: 1049-8001,  1448-5516

Ano de publicación: 2023

Volume: 32

Número: 2

Páxinas: 178-194

Tipo: Artigo

DOI: 10.1071/WF22030 GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: International Journal of Wildland Fire

Obxectivos de Desenvolvemento Sustentable

Resumo

. Wildfire is a major environmental threat worldwide and climate change isexpected to increase its severity. Galicia has suffered high wildfire incidence during thelast decades, most wildfires being from arson, in contrast with the low rate of natural wildfires.Aim. This work aims to characterise the spatiotemporal dynamics of human-caused and naturalfires in Galicia. Methods. We apply first- and second-order non-parametric inference tospatiotemporal wildfire point patterns. Key results. The distribution of natural wildfiresremained stable over years, with high incidence in summer and in the eastern area of Galicia.Arson wildfires had aggregated patterns, with strong interaction between outbreaks and fires,and their distribution varied both over and within years, with high incidence shifting between thesouthern and western areas, and high hazard in early spring and late summer. Negligence wildfirepatterns showed short-distance aggregation, but large-distance aggregation between outbreaksand fires; their spatial distribution also varied between and within yearsConclusions. Differentmodels and covariates are required to predict the hazard from each wildfire type. Natural firesare linked to meteorological and environmental factors, whereas socioeconomic covariates arecrucial in human-caused wildfires. Implications. These results are the basis for the futuredevelopment of predictive spatiotemporal point process models for human-caused wildfires

Referencias bibliográficas

  • (2015), European Journal of Forest Research, 134, pp. 1087, 10.1007/s10342-015-0911-1
  • (2016), European Journal of Forest Research, 135, pp. 451, 10.1007/s10342-016-0945-z
  • Baddeley A, Rubak E, Turner R (2015) ‘Spatial point patterns: methodology and applications with R.’ (Chapman and Hall/CRC)
  • (2000), Statistica Neerlandica, 54, pp. 329, 10.1111/1467-9574.00144
  • (2013), Forest Systems, 22, pp. 497, 10.5424/fs/2013223-04165
  • (1977), Journal of the Royal Statistical Society, Series B (Methodological), 39, pp. 193
  • Birot Y, Rigolot E, Goldammer JG (2009) Conclusions. In ‘Living with wildfires: what science can tell us. A contribution of the science‐policy dialogue’. (Ed. Y Birot) pp. 79–82. (European Forest Institute: Joensuu, Finland) Available at 
  • (2015), Journal of Environmental Management, 154, pp. 151, 10.1016/j.jenvman.2015.02.009
  • (2016), International Journal of Wildland Fire, 25, pp. 669, 10.1071/WF15125
  • (2019), International Journal of Wildland Fire, 28, pp. 237, 10.1071/WF17037
  • (2019), Nature Geoscience, 12, pp. 113, 10.1038/s41561-018-0294-2
  • Bowman AW, Azzalini A (1997) ‘Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations’. Oxford Statistical Science Series 18. (Oxford University Press)
  • Bowman AW, Azzalini A (2014) ‘R package sm: non-parametric smoothing methods (version 2.2-5.5).’ (University of Glasgow, UK and Università di Padova: Italia)
  • (1963), Fire Control Notes, 24, pp. 45
  • (2019), Oeno One, 53, pp. 573, 10.20870/oeno-one.2019.53.3.2379
  • (2018), PeerJ, 6, 10.7717/peerj.5657
  • (2016), Forest Ecology and Management, 372, pp. 1, 10.1016/j.foreco.2016.04.002
  • Camia A, Durrunt HT, San-Miguel-Ayanz J (2013) Harmonized classification scheme of fire causes in the EU adopted for the European Fire Database of EFFIS. EUR 25923. Luxembourg (Luxembourg): Publications Office of the European Union. JRC80682.
  • (2021), Forest Policy and Economics, 122, 10.1016/j.forpol.2020.102330
  • (2010), Test, 19, pp. 375, 10.1007/s11749-009-0168-4
  • (2015), Applied Geography, 56, pp. 164, 10.1016/j.apgeog.2014.11.025
  • (2014), Natural Hazards and Earth System Sciences Discussions, 2, pp. 2891, 10.5194/nhessd-2-2891-2014
  • (2016), Forests, 7, 10.3390/f7090185
  • (2017), International Journal of Wildland Fire, 26, pp. 983, 10.1071/WF17026
  • Crosby JS (1954) Probability of fire occurrence can be predicted. USDA Forest Service, Central States Forest Experimental Station.
  • (2019), Forests, 10, 10.3390/f10030229
  • (2018), Statistics in Medicine, 37, pp. 1191, 10.1002/sim.7577
  • (2020), Natural Hazards and Earth System Sciences, 20, pp. 363, 10.5194/nhess-20-363-2020
  • (2016), Forest Ecology and Management, 381, pp. 360, 10.1016/j.foreco.2016.09.013
  • (2021), Land, 10, 10.3390/land10101004
  • (1985), Journal of the Royal Statistical Society. Series C (Applied Statistics), 34, pp. 138, 10.2307/2347366
  • Diggle PJ (2013) ‘Statistical analysis of spatial and spatio-temporal point patterns’, 3rd edn. (CRC Press: Boca Raton, Florida)
  • (1985), Journal of Environmental Management, 20, pp. 87
  • Duong T (2018) ks: Kernel smoothing. R package version 1.11.1. Available at
  • (2020), Annals of Forest Science, 77, 10.1007/s13595-020-00933-5
  • (2021), Air, Soil and Water Research, 14, 10.1177/11786221211028185
  • (2019), Science of the Total Environment, 654, pp. 43, 10.1016/j.scitotenv.2018.11.049
  • (2013), Journal of Environmental Management, 128, pp. 30, 10.1016/j.jenvman.2013.04.020
  • (2016), Scandinavian Journal of Statistics, 43, pp. 416, 10.1111/sjos.12183
  • (2017), Spatial Statistics, 22, pp. 240, 10.1016/j.spasta.2017.02.007
  • (2018), Environmetrics, 29, 10.1002/env.2482
  • (2021), Spatial Statistics, 42, 10.1016/j.spasta.2020.100431
  • (2009), Statistica Neerlandica, 63, pp. 43, 10.1111/j.1467-9574.2008.00407.x
  • (2015), Risk analysis, 35, pp. 1197, 10.1111/risa.12339
  • (2022), Geophysical Research Letters, 49, 10.1029/2021GL096814
  • (2019), Journal of Environmental Management, 241, pp. 575, 10.1016/j.jenvman.2018.09.107
  • (2004), Natural Resource Modeling, 17, pp. 359, 10.1111/j.1939-7445.2004.tb00141.x
  • (2016), International Journal of Wildland Fire, 25, pp. 811, 10.1071/WF15012
  • (2019), Revista Galega de Economía, 28, pp. 1, 10.15304/rge.28.3.6168
  • (2022), Journal of Cleaner Production, 357, 10.1016/j.jclepro.2022.131876
  • (2021), Sustainability, 13, 10.3390/su13158472
  • (2010), iForest - Biogeosciences and Forestry, 3, pp. 8, 10.3832/ifor0521-003
  • MAGRAMA (2016) Los incendios forestales en Espana. Decenio 2006–2015. (Coord. AL López-Santalla, M López-García). Ministerio de Agricultura, Pesca y Alimentación. Secretaría General Técnica. [In Spanish]
  • MAGRAMA (2019) Anuario de Estadística Forestal 2017. Ministerio de Agricultura Pesca y Alimentación. Secretaría General Técnica. [In Spanish]
  • (2012), Small-scale Forestry, 11, pp. 453, 10.1007/s11842-012-9195-1
  • (2014), Bosque (Valdivia), 35, pp. 207, 10.4067/S0717-92002014000200008
  • (2021), Sustainability, 13, 10.3390/su13094761
  • (2019), Nature Geoscience, 12, pp. 108, 10.1038/s41561-018-0266-6
  • (2009), Journal of Environmental Management, 90, pp. 1241, 10.1016/j.jenvman.2008.07.005
  • (2021), Waste and Biomass Valorization, 12, pp. 3091, 10.1007/s12649-020-01227-7
  • Moreno JM, Morales-Molino C, Torres I, Arianoutsou M (2021). Fire in Mediterranean pine forests: past, present and future. In ‘Pines and their mixed forest ecosystems in the Mediterranean Basin’. (Eds G Ne'eman, Y Osem) pp. 421–456. (Springer)
  • (2014), Environmental Science & Policy, 37, pp. 11, 10.1016/j.envsci.2013.08.005
  • (2021), Fire, 4, 10.3390/fire4010010
  • Otero-Varela A, Paül V (2022) The never-ending story of the metropolitan area of Vigo (Galicia, Spain). In ‘International symposium: New metropolitan perspectives’. (Eds F Calabrò, L Della Spina, MJ Piñeira Mantiñán) pp. 946–956. (Springer)
  • (2021), Fire, 4, 10.3390/fire4020018
  • (2016), Science of the Total Environment, 559, pp. 151, 10.1016/j.scitotenv.2016.03.129
  • (2018), Science of the Total Environment, 624, pp. 424, 10.1016/j.scitotenv.2017.12.013
  • (2021), Frontiers in Ecology and the Environment, 19, pp. 387, 10.1002/fee.2359
  • (2017), Agricultural and Forest Meteorology, 239, pp. 249, 10.1016/j.agrformet.2017.03.016
  • (2014), Atmospheric Research, 135–136, pp. 380, 10.1016/j.atmosres.2012.07.011
  • (2022), Science of the Total Environment, 810, 10.1016/j.scitotenv.2021.152002
  • Prestemon JP, Hawbaker TJ, Bowden M, Carpenter J, Brooks MT, Abt KL, Sutphen R, Scranton S (2013) Wildfire ignitions: a review of the science and recommendations for empirical modeling. General Technical Reports SRS-GTR-171, 1–20. (USDA Forest Service, Southern Research Station: Asheville, NC)
  • R Core Team (2018) ‘R: A Language and Environment for Statistical Computing.’ (R Foundation for Statistical Computing: Vienna, Austria)
  • (2017), Journal of Environmental Management, 187, pp. 154, 10.1016/j.jenvman.2016.11.044
  • (2018), Environmental Modelling & Software, 110, pp. 107, 10.1016/j.envsoft.2018.03.008
  • (1977), Journal of the Royal Statistical Society: Series B (Methodological), 39, pp. 172, 10.1111/j.2517-6161.1977.tb01615.x
  • Ripley BD (1981) ‘Spatial statistics.’ (Wiley: New York)
  • (2016), Natural Hazards, 84, pp. 2049, 10.1007/s11069-016-2533-4
  • (2018), Journal of Environmental Management, 225, pp. 177, 10.1016/j.jenvman.2018.07.098
  • (2019), Science of the Total Environment, 689, pp. 634, 10.1016/j.scitotenv.2019.06.467
  • (2020), Science of The Total Environment, 721, 10.1016/j.scitotenv.2019.135841
  • (2017), International Journal of Wildland Fire, 26, pp. 498, 10.1071/WF16181
  • (2013), Forest Ecology and Management, 294, pp. 11, 10.1016/j.foreco.2012.10.050
  • (2013), Environmental Modelling & Software, 40, pp. 235, 10.1016/j.envsoft.2012.09.014
  • (2019), Regional Environmental Change, 19, pp. 515, 10.1007/s10113-018-1415-6
  • (2016), International Journal of Disaster Risk Reduction, 18, pp. 138, 10.1016/j.ijdrr.2016.06.005
  • (2017), Natural Hazards, 85, pp. 1489, 10.1007/s11069-016-2637-x
  • (2019), Annals of Forest Science, 76, 10.1007/s13595-019-0874-3
  • (2020), Methodology and Computing in Applied Probability, 22, pp. 995, 10.1007/s11009-019-09749-x
  • (2014), Natural Hazards and Earth System Sciences, 14, pp. 1477, 10.5194/nhess-14-1477-2014
  • (2010), International Journal of Wildland Fire, 19, pp. 325, 10.1071/WF09030
  • (2016), Forest Ecology and Management, 378, pp. 68, 10.1016/j.foreco.2016.07.020
  • (2011), European Journal of Forest Research, 130, pp. 983, 10.1007/s10342-011-0488-2
  • (2015), Modern Applied Science, 9, pp. 117, 10.5539/mas.v9n3p117
  • (2017), International Journal of Wildland Fire, 26, pp. 789, 10.1071/WF17021
  • XERA (2018) La cadena forestal – madera en Galicia 2017. 2018, Axencia Galega da Industria Forestal. (Coord. J Picos) (Consellera de Economa e Industria, Universidade de Vigo)
  • (2016), Geomatics, Natural Hazards and Risk, 7, pp. 1800, 10.1080/19475705.2016.1155501