Generating Annual Fire Risk Maps Using Bayesian Hierarchical Models
Vegetation fires are an important environmental and socioeconomic problem, and large budgets are spent in fire prevention and fire fighting. Detailed knowledge of spatiotemporal patterns of fire occurrence is required for effective and efficient fire management, and annual fire risk maps can be an important tool to support strategic decisions relating to location-allocation of equipment and human resources. Here, we define risk of fire in the narrow sense as the probability of its occurrence, without addressing the loss component. We propose and evaluate two alternative approaches to the development of annual fire risk maps, using an atlas of annual burned area maps of Portugal (1975–2009), derived from the classification of satellite imagery, and a set of environmental maps representing vegetation, climatic, and topographic covariates. We look at current approaches for producing annual fire risk maps, and suggest improvements by incorporating the strong spatial and temporal dependence that exists in the data. This is accomplished using two different modeling strategies. The first strategy consists of modeling interarrival times between fires using a discrete version of the Weibull model. The second strategy consists of modeling annual fire occurrences using a first-order nonhomogeneous Markov model. These two distinct strategies accommodate different possibilities to introduce time-dependent covariates and make complementary probabilistic statements.
KeywordsFire frequency data Discrete Weibull model Non-homogeneous Markov models Spatio-temporal models
AMS Subject Classification62M30 62P12
Unable to display preview. Download preview PDF.
- European Commission. 2012. Forest fires in Europe, Middle East, and North Africa 2011. EUR 25483 EN, 109. Luxembourg: Publications Office of the European Union.Google Scholar
- Flannigan, M. D., and B. M. Wotton. 2001. Climate, weather, and area burned. In Forest fires—Behavior and ecological effects, ed. E. A. Johnson and K. Miyanishi (pp. 351–373). San Diego, CA: Academic Press.Google Scholar
- Mather, A., and J. M. C. Pereira. 2006. Transição florestal e fogo em Portugal. In Incêndios florestais em Portugal: caracterização, impactes e prevenção, (ed. J. S. Pereira, J. M. C. Pereira, F. Rego, J. M. N. Silva, and T. P. Silva, 257–282. Lisboa, Portugal: ISAPress.Google Scholar
- O’Donnell, A. J., M. M. Boer, W. L. McCaw, and P. F. Grierson. 2010. Vegetation and landscape connectivity control wildfire intervals in unmanaged semiarid shrublands and woodlands in Australia. J. Biogeogr., 28, 37–48.Google Scholar
- Oliveira, S. L. J., M. A. Amaral Turkman, and J. M. C. Pereira. 2012a. An analysis of fire frequency in tropical savannas of northern Australia, using a satellite-based fire atlas Int. J. Wildland Fire, http://dx.doi.org/10.1071/WF12021.
- Pereira, J. M. C., and T. N. Santos. 2003. Fire risk and burned area mapping in Portugal. Direcção-Geral das Florestas, Lisboa, Portugal.Google Scholar
- Pereira, J. M. C., J. M. B. Carreiras, J. M. N. Silva, and M. J. P. Vasconcelos. 2006. Alguns conceitos básicos sobre os fogos rurais em Portugal. In Incêndios florestais em Portugal: caracterização, impactes e prevenção, ed. J. S. Pereira, J. M. C. Pereira, F. Rego, J. M. N. Silva, and T. P. Silva, 133–162. Lisboa, Portugal: ISAPress.Google Scholar
- Pereira, P., and K. F. Turkman. 2012. Preliminary analysis of the forest fires in Portugal using point processes. Research report, CEAUL 10/12. http://www.ceaul.fc.ul.pt/notas.html?ano=2012.
- Pereira, P., M. A. Amaral Turkman, and K. F. Turkman. 2012. Complementary report of the article “Annual Fire Risk maps: Some statistical issues.” Research report, CEAUL 11/12. http://www.ceaul.fc.ul.pt/notas.html?ano=2012.
- Plummer, M., N. Best, K. Cowles, and K. Vines. 2006. CODA: Convergence diagnosis and output analysis for MCMC. R News, 6, 7–11.Google Scholar
- Pyne, S. J., P. L. Andrews, and R. D. Laven. 1996. Introduction to wildland fire, 2nd ed. New York, NY: John Wiley & Sons.Google Scholar
- San-Miguel-Ayanz, J., J. D. Carlson, M. Alexander, K. Tolhurst, G. Morgan, R. Sneeuwjagt, and M. Dudley. 2003. Current methods to assess fire danger potential. In Wildland fire danger estimation and mapping—The role of remote sensing data, ed. E. Chuvieco, 21–61. Singapore: World Scientific.CrossRefGoogle Scholar
- Santos, C., A. Leite, E. Santos, J. Pinho. 2005. A estratégia sectorial florestal num sistema de planeamento regional (A strategy for the forest sector in a regional planning system). 5th National Forestry Congress, Theme 6—Forest Policy. Viseu, May 16–19. Portuguese Forest Sciences Society.Google Scholar
- Vasconcelos, M. J. P., S. Silva, M. Tomé, M. Alvim, and J. M. C. Pereira. 2001. Spatial prediction of fire ignition probabilities. Photogrammetric Eng. Remote Sensing, 67(1), 73–82.Google Scholar