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

Forest fire risk assessment in Ibrahim river watershed-Lebanon 47
direction of the fire spread. Considering the histogram of equalization
between distribution of slope gradient and the corresponding number of
pixels, the slope gradient was divided into ten classes (ranging between
less 5° and 90°) with various fire sensitivities ranging from very low sensitivity
on the bottom of the hills to very high at the peaks. Aspect is useful
for visualizing the amount of solar illumination the vegetation receives,
which influences both the type of fuels and their moisture condition.
Aspect is divided into the eight major directions plus the non-oriented flat
areas, each one having a different sensitivity to fires.
c. Urban expansion: The urban expansion provides an important indication
of the presence of people affected by forest fire risks. It is commonly
accepted that the denser the people in a given area, the higher the forest
fire risk and vice versa. The automatic extraction of the urban expansion
was performed from the 1:20,000 land cover/use map (LNCSR-LMoA, 2002).
A buffer zone of 500 m above the urban area was created using the
Euclidian distance, and ten classes were considered.
d. Proximity to road network: Roads are usually sites inducing fires (e.g.,
cigarettes). For this reason, roads were included in this study, and extracted
from IKONOS imageries (1 m) through visual interpretation. Thus, a
buffer zone of 50 m above the road (maximum height of a talus cut created
by the construction of a road) was created.
2.2 - GIS data analysis and manipulation
Not all the considered factors have the same effect on fire, but the estimation
of their weights is not an easy task. Therefore, a regional parameterization
was done in giving weight to all fire parameters using a
weight/rate approach consisting of applying primary and secondary-level
weights. The primary-level weights are rule-based in that ratings are given
to each class of a parameter on the basis of logical referred system. The
secondary-level (factor) weights are, however, opinion-based scores, which
determine the degree of tradeoff of one parameter/against another. This
leads to the following percentages of each parameter effect on the fire risk
occurrence as follows: land cover/use (50%), slope gradient (10%), slope
aspect (15%), urban expansion (10%), and proximity to road network
(15%).
3 - Discussion and conclusion
3.1 - Production of forest fire risk map
In the produced fire risk map with eight classes (Figure 1), class 8 (high
fire risk) covers the largest area (34%), and is mostly distributed in the
centre of the studied region and on the side of the mountain hills. As one