Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment

Abstract

Geographic information system analysis and artificial neural network modelling were combined to evaluate forest-fire susceptibility in the Central Portugal administrative area. Data on forest fire events, indicated by burnt areas during the years from 1990 to 2007, were identified from official records. Topographic, supporting infrastructures, vegetation cover, climatic, demographic and satellite-image data were collected, processed and integrated into a spatial database using geographic information system techniques. Eight fire-related factors were extracted from the collected data, including topographic slope and aspect, road density, viewsheds from fire watchtowers, land cover, Landsat Normalised Difference Vegetation Index, precipitation and population density. Ratings were calculated for the classes or categories of each factor using a frequency-probabilistic procedure. The thematic layers (burnt areas and fire-related factors) were analysed using an advanced artificial neural network model to calculate the relative weight of each factor in explaining the distribution of burnt areas. A forest-fire susceptibility index was calculated using the trained back-propagation artificial neural network weights and the frequency-probabilistic ratings, and then a general forest-fire susceptibility index map was constructed in geographic information system. Burnt areas were used to evaluate the forest-fire susceptibility index map, and the results showed an agreement of 78%. This forest-fire susceptibility map can be used in strategic and operational forest-fire management planning at the regional scale.