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.
|Original language||English (US)|
|Number of pages||33|
|Journal||Environmental and Ecological Statistics|
|State||Published - Sep 2014|
Bibliographical noteFunding Information:
Acknowledgments Jorge Mateu and Carlos Díaz-Ávalos were partly funded in this work by the grant from the ‘Towards Excellence’ doctoral program for visiting professors, MHE2011-00258, ECD/3628/2011, from the Ministry of Education, Culture and Sport, Spain. We would like to thank the Forest Fire Prevention Service (Servei de Prevenció d’Incendis Forestals) of the Government of Catalonia (Generalitat de Catalunya) for providing wildfire data. We also thank the Environment Department of the Government of Catalonia for access to the digital map databases. We appreciate the comments of the attendees at the ‘3rd International Conference on Modelling, Monitoring and Management of Forest Fires’, on May 22– 24, 2012, at the Wessex Institute, New Forest, United Kingdom; at the Royal Statistical Society 2012 International Conference, September 3–6, 2012, in Telford, United Kingdom; and at the ‘VI International Workshop on Spatio-Temporal Modelling’, METMA VI, on September 12–14, 2012, at the Research Centre of Mathematics—CMAT of Minho University, Guimaraes, Portugal, where a preliminary version of this work was presented. We are indebted to Dr. Mark Olson by dedicating one weekend to review our manuscript. Last, but not the least we acknowledged the comments of three anonymous reviewers that, without doubt, helped us improve our work.
- Log-Gaussian Cox models
- Spatio-temporal point processes
ASJC Scopus subject areas
- Statistics and Probability
- Environmental Science(all)
- Statistics, Probability and Uncertainty