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

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

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