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7th Workshop on Forest Fire Management - EARSeL, European ...

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82<br />

I - PRE-FIRE PLANNING AND MANAGEMENT<br />

According to the Pan-<strong>European</strong> <strong>Forest</strong>/N<strong>on</strong>-<strong>Forest</strong> Map 2000, <strong>on</strong>ly 10.7% of<br />

the igniti<strong>on</strong> points fall into the <strong>Forest</strong> category. It must be c<strong>on</strong>sidered that<br />

the <strong>Forest</strong> class excludes woodlands with trees smaller than 5 m height,<br />

burnt areas, forest roads and forest nurseries and regenerati<strong>on</strong> (with less<br />

than 30% canopy closure).<br />

Data limitati<strong>on</strong>s derived from the incorrect inserti<strong>on</strong> of coordinates in the<br />

original file and the posterior transformati<strong>on</strong> of projecti<strong>on</strong> systems must be<br />

taken into account. Nevertheless, these results provide an important<br />

insight about the fire domain c<strong>on</strong>cerning igniti<strong>on</strong>s; land covers such as<br />

artificial surfaces that represent the urban/rural interface and the agricultural<br />

areas, shouldn’t be excluded from the analysis, since fires often start<br />

in these areas and propagate to wildlands. These findings are in accordance<br />

with previous studies, which show that around 95% of forest fires in<br />

Mediterranean Europe are human-caused (e.g., Ne’eman et al., 2004).<br />

To c<strong>on</strong>firm the influence of human variables in the occurrence of fires, an<br />

additi<strong>on</strong>al preliminary analysis was carried out, based <strong>on</strong> the distributi<strong>on</strong><br />

of igniti<strong>on</strong> points in relati<strong>on</strong> to the roads network. Data of Portuguese<br />

roads were obtained from TeleAtlas (2006-2009 TeleAtlas) and analysed<br />

according to the type of roads (nati<strong>on</strong>al roads and local roads). Buffers of<br />

50, 100, 200 and 500 meters were created around the roads and the total<br />

number of igniti<strong>on</strong> points falling into each buffer was calculated.<br />

It was found that the density of igniti<strong>on</strong> points (IP/km2 /10) increases in<br />

the classes up to 100 m distance from the roads, being more evident in the<br />

local roads dataset (Fig. 2).<br />

Figure 2 - Density of igniti<strong>on</strong> points according to distance from the roads.

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