Spatio-temporal log-Gaussian Cox processes for modelling wildfire occurrence: the case of Catalonia, 1994-2008
Por:
Serra L., Saez M., Mateu J., Varga D., Juan P., Díaz-Ávalos C., Rue H.
Publicada:
1 sep 2014
Resumen:
Wildfires have become one of the principal environmental problems in the
Mediterranean basin. While fire plays an important role in most
terrestrial plant ecosystems, the potential hazard that it represents
for human lives and property has led to the application of fire
exclusion policies that, in the long term, have caused severe damage,
mainly due to the increase of fuel loadings in forested areas, in some
forest systems. The lack of an easy solution to forest fire management
highlights the importance of preventive tasks. The observed
spatio-temporal pattern of wildfire occurrences may be idealized as a
realization of some stochastic process. In particular, we may use a
space-time point pattern approach for the analysis and inference
process. We studied wildfires in Catalonia, a region in the north-east
of the Iberian Peninsula, and we analyzed the spatio-temporal patterns
produced by those wildfire incidences by considering the influence of
covariates on trends in the intensity of wildfire locations. A total of
3,166 wildfires from 1994-2008 have been recorded. We specified
spatio-temporal log-Gaussian Cox process models. Models were estimated
using Bayesian inference for Gaussian Markov Random Field through the
integrated nested Laplace approximation algorithm. The results of our
analysis have provided statistical evidence that areas closer to humans
have more human induced wildfires, areas farther have more naturally
occurring wildfires. We believe the methods presented in this paper may
contribute to the prevention and management of those wildfires which are
not random in space or time.
Filiaciones:
Serra L.:
CIBER of Epidemiology and Public Health (CIBERESP), University of Girona, Campus de Montilivi, 17071 Girona, Spain
Research Group on Statistics, Econometrics and Public Health (GRECS), University of Girona, Campus de Montilivi, 17071 Girona, Spain
Saez M.:
CIBER of Epidemiology and Public Health (CIBERESP), University of Girona, Campus de Montilivi, 17071 Girona, Spain
Research Group on Statistics, Econometrics and Public Health (GRECS), University of Girona, Campus de Montilivi, 17071 Girona, Spain
Mateu J.:
Department of Mathematics, University Jaume I of Castellon, Campus Riu Sec, Castellon, Spain
Varga D.:
Research Group on Statistics, Econometrics and Public Health (GRECS), University of Girona, Campus de Montilivi, 17071 Girona, Spain
Geographic Information Technologies and Environmental Research Group, University of Girona, Girona, Spain
Juan P.:
Department of Mathematics, University Jaume I of Castellon, Campus Riu Sec, Castellon, Spain
Díaz-Ávalos C.:
Univ Nacl Autonoma Mexico, IIMAS, Dept Probabil & Stat, Mexico City 04510, DF, Mexico
Rue H.:
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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