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International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

A multivariate analysis of biophysical factors and forest fires in Spain, 1991–2005

Felipe Verdú A C , Javier Salas A and Cristina Vega-García B
+ Author Affiliations
- Author Affiliations

A Department of Geography, University of Alcalá, Colegios, 2, E-28801 Alcalá de Henares, Spain.

B Department of Agriculture and Forest Engineering, University of Lleida, Avenida Alcalde Rovira Roure 198, E-25198 Lleida, Spain.

C Corresponding author. Email: felipe.verdu@edu.uah.es

International Journal of Wildland Fire 21(5) 498-509 https://doi.org/10.1071/WF11100
Submitted: 21 July 2011  Accepted: 24 January 2012   Published: 1 June 2012

Abstract

The main goal of this study was to explain the relationship between forest fires and different climatic, topographic and vegetation factors, establishing explanatory models from multivariate analysis. The study area comprised peninsular Spain. Two dependent variables were considered: probability of burning and fire size class, from a forest-fire map derived from visual analysis of satellite images from 1991 to 2005 (3337 fires greater than 25 ha). Logistic regression, discriminant analysis and regression trees were used to analyse the probability of burning. The models showed a significant relationship with land cover and slope, where the classification achieved an agreement of ~66%, and this was very similar for the three statistical methods used. Discriminant analysis and regression trees were used to model fire size class. These models appeared more related to ecozones and climatic variables (winter precipitation and mean summer temperature). In this case, the best classification results were obtained in the category of very large fires (>5000 ha), with an agreement above 80%. Regression trees achieved better results for fire size class models.

Additional keywords: discriminant analysis, fire size, logistic regression, probability of burning, regression trees.


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