Land cover distribution is one of the factors that influence fire behaviour and its consequences in the landscape. The relation between land cover type and fire was investigated at a broad scale, in order to analyse land cover differences in fire proneness. The selection ratio for nine different land cover categories was calculated for the fire perimeters mapped in Southern Europe between 2000 and 2008. The results obtained were then compared per country and region. The fire proneness of topographic classes and its potential association with land cover types were also assessed. At a broad scale, shrublands and grasslands were the most preferred by fire, whereas artificial surfaces and agricultural areas were less fire prone. Forests showed intermediate values of selection ratio. Principal components and cluster analysis identified three regions with significant differences among them: the Mediterranean area, the Balkans and Turkey–Cyprus. Slopes >25% and with a north aspect were also less susceptible to burning. The identification of common land cover and topographic characteristics allows for the application of common management strategies in Southern Europe, coupled with particular measures adjusted to the conditions that are country- and region-specific.
Devastating fires affected Greece in the summer 2007, with the loss of more than 60 human lives, the destruction of more than 100 villages and hundreds of square kilometres of forest burned. This Letter presents a map of the extent burned and the approximate day of burning in Greece mapped by the MODIS burned area product for 22 June to 30 August 2007 and the burned areas mapped independently by the European Forest Fires Information Service (EFFIS). The characteristics of the two datasets, and an evaluation of the areas burned comparing the MODIS and EFFIS data for the same temporal interval are described.
The physical geography of the Mediterranean renders it an ideal landscape for burning. But for thousands of years its fire regimes have been set directly and indirectly by humans. Because of the region's significance in Antiquity, it has been studied for a long time and has become for good or ill a paradigm for thinking about fire. In this regard the Mediterranean has been both a place to export ideas and a place to receive them. Today's thinking about the Mediterranean and fire is thus as complex as its intricate landscapes. But the fundamental reality remains, as first voiced by Theophrastus: fire is tame or feral as humans contain or unleash it, which they do not only by the torch but by close tending of the landscape.
Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a nonsupervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.
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