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

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I - PRE-FIRE PLANNING AND MANAGEMENT
an overview about the general situation, like when and where typically wild
land fires occur in Austria. Additionally a comprehensive database was created,
where information, provided by fire fighters and forest managers
about fires in their region, is collected. Estimations about the exact coordinates
of the fire out break and the uncertainties linked to that are established.
These coordinates build the foundation for the fire risk mapping
approach. They are mapped with their information attached in an attribute
table and can be accessed via the fire ID. In a first step fire “hot spots” in
Austrian forest ecosystem are localized.
To describe similarities and frequencies in wild land fire incidents forest
parameter collected by the national forest inventory campaign (ÖWI, 2002,
Gabler & Schadauer, 2002) are used. The inventory data on forest parameters
exist as a point grid with stored attributes per sampling point. For the
region Tirol the inventory parameters where extrapolated (Mattiuzzi, 2008).
Using a LANDSAT image the spectral
reflectance from the inventory
points where used as ground truth
for a reflectance similarity classification.
With this method parameters like
forest type, crown cover, forest composition
and stage where derived.
Forest ground vegetation communities
were also derived; here the thesis
is that similar crown reflectance
has similar ground vegetation communities.
In Austria forest communities
are classified according to the
Picture 1 - Geo climate units, the region
Tirol, forest fires and their error buffer.
eco-regions in Killian, et al., 1993).
These units define what kind of forest
communities can be found under
a certain range of altitude and
aspect and other abiotic conditions. This eco-regions are an important
information for the stratification of forest communities.
For fire behaviour modelling or risk mapping slope and aspect play an
important part (Han et al., 2003). Slope and aspect was derived from a DEM
of the Alpine arc. All data sets were re-projected to WGS 84 UTM zone 32N.
In order to indentify the most forest fire prone forest communities we wanted
to analyze the forest and topographic characterisation near to the forest
fires occurred and identify the highest frequencies in order to make a
design for the fuel sampling. The uncertainties in the localisation of the
forest fires lead to the approach of buffering the most likely site of a forest
fire. Depending on the uncertainty in the localisation of the coordinates
of each forest fire record the buffer was classified large (> 2 km) or small
(< 100m).
In the case study for the region Tirol we had to face the problem that sev-