A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain
Lara Vilar A C , Douglas. G. Woolford B , David L. Martell B and M. Pilar Martín AA Centre for Human and Social Sciences, Spanish Council for Scientific Research, Albasanz 26-28, E-28037 Madrid, Spain.
B Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON, M5S 3B3, Canada.
C Corresponding author. Email: lara.vilar@cchs.csic.es
International Journal of Wildland Fire 19(3) 325-337 https://doi.org/10.1071/WF09030
Submitted: 27 March 2009 Accepted: 10 November 2009 Published: 13 May 2010
Abstract
This paper describes the development and validation of a spatio-temporal model for human-caused wildfire occurrence prediction at a regional scale. The study area is the 8028-km2 region of Madrid, located in central Spain, where more than 90% of wildfires are caused by humans. We construct a logistic generalised additive model to estimate daily fire ignition risk at a 1-km2 grid spatial resolution. Spatially referenced socioeconomic and weather variables appear as covariates in the model. Spatial and temporal effects are also included. The variables in the model were selected using an iterative approach, which we describe. We use the model to predict the expected number of fires in our study area during the 2002–05 period, by aggregating the estimated probabilities over space–time scales of interest. The estimated partial effects of the presence of railways, roads, and wildland–urban interface in forest areas were highly significant, as were the observed daily maximum temperature and precipitation.
Additional keywords: fire risk, generalised additive models, geographic information systems, logistic, non-parametric spline smoothing, socioeconomic variables, wildland fire, wildland–urban interface.
Acknowledgements
This research received partial support from Firemap project CGL2004-06049-C04-01/CLI, funded by the Spanish Ministry of Education, through FPI scholarship BES-2005-7712. Additional funding from the National Institute for Complex Data Structures and Geomatics for Informed Decisions (GEOIDE SII Project 51) is also gratefully acknowledged. We thank Inmaculada Aguado, Mariano García, Héctor Nieto, Marta Yebra and Felipe Verdú from the Department of Geography of the University of Alcalá (Spain) for their advice and the data they supplied. We would like also thank Robert Kruus from the Fire Management Systems Laboratory in the Faculty of Forestry at the University of Toronto (Canada) for his assistance in database preparation. Historic fire data has been provided by the Fire Department of the region of Madrid and the Spanish Ministry of Environment, while other data was provided by the Madrid Regional Environmental Office.
Amatulli G, Rodrigues MJ, Trombetti M , Lovreglio R (2006) Assessing long-term fire risk at local scale by means of decision tree technique. Journal of Geophysical Research 111, G04S05.
| Crossref | GoogleScholarGoogle Scholar |
Brillinger DR, Preisler HK , Benoit JW (2006) Probabilistic risk assessment for wildfires. Environmetrics 17, 623–633.
| Crossref | GoogleScholarGoogle Scholar |
Cardille JA, Ventura SJ , Turner MG (2001) Environmental and social factors influencing wildfires in the upper midwest, United States. Ecological Applications 11(1), 111–127.
| Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Vilar L , Martínez J (2009) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling 221, 46–58.
| Crossref | GoogleScholarGoogle Scholar |
Golub GH, Heath M , Wahba G (1979) Generalized cross validation as a method for choosing a good ridge parameter. Technometrics 21(2), 215–223.
| Crossref | GoogleScholarGoogle Scholar |
Kalabokidis KD, Koutsias N, Konstantinidis P , Vasilakos C (2007) Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece. Area 39(3), 392–402.
| Crossref | GoogleScholarGoogle Scholar |
Lin C (1999) Modelling probability of ignition in Taiwan Red Pine Forests. Taiwan Journal Forest Science 14(3), 339–344.
Maingi JK , Henry MC (2007) Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire 16, 23–33.
| Crossref | GoogleScholarGoogle Scholar |
Martell DL , Belivacqua E (1989) Modelling seasonal variation in daily people-caused forest fire occurrence. Canadian Journal of Forest Research 19(12), 1555–1563.
| Crossref | GoogleScholarGoogle Scholar |
Martínez J, Vega-García C , Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management 90, 1241–1252.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Pew KL , Larsen CPS (2001) GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada. Forest Ecology and Management 140, 1–18.
| Crossref | GoogleScholarGoogle Scholar |
Syphard AD, Radeloff VC, Keeley JE, Hawbaker TJ, Clayton MK, Stewart SI , Hammer RB (2007) Human influence on California fire regimes. Ecological Applications 17(5), 1388–1402.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Vilar del Hoyo L, Martín Isabel MP , Martínez Vega FJ (2008) Empleo de técnicas de regresión logística para la obtención de modelos de riesgo humano de incendio forestal a escala regional. Boletín de la AGE 47, 5–29.
Wood SN , Augustin NH (2002) GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecological Modelling 157, 157–177.
| Crossref | GoogleScholarGoogle Scholar |
A Reignited fire: the re-occurrence of a wildfire that was previously classified as having been under control.
