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

Classification of site and stand characteristics based on remote sensing data 61
It was necessary to test if our results were fire related, or just being a sample
of the whole raster layer. The test of composition of vegetation types
in the whole LANDSAT image and the composition in the fire buffer showed
that there is clearly trend to herbs and grass cover.
With that test it seems likely that the extrapolation of surface cover derived
from crown cover reflectance similarities has some verification.
3 - Results
To identify fire prone forest communities a decision tree was build were certain
characteristics of forest communities were listed and compared to the
trends in our analysis. The trends were the following:
Forest stands consisting from mostly coniferous trees with a dense structure
from 900 till 1700 meter on a south-facing exposition and grass or dry
herb under-storey were more likely to be burned in Tirol. With the result of
the frequency analysis it was possible to identify four forest communities
which are more likely to be threatened by fire incidents because they occur
in the area of the eco-region 1.2 and fulfil the above mentioned characterizations.
In Tirol this forest communities are Larici - Piceetum, Picealuzulo
nemerosae, Pinus sylvestris-erico pinetum, Picea - calamagrosti var.
Picetum.
Percent off Pixel with in fire buffer
Elevation montane 65%
crwon closure dense 66%
Veg-type herbs/gras 79%
slope 0-20° 62%
aspect southfacing 42%
coniferous 70-100% 42%
4 - Conclusion & outlook
The above listed method and the results can be used to identify and map
forest stands with a higher fire risk. But risk identified with this method
does not account the human aspect neither does it analyse the hazard scenario.
The human aspect is part of another research approach in the AFFRI
project. Hazard has to be analyzed with a broader approach in mind
because risk and hazard have to be analysed individually (Allgoewer,
Bachmann, 2001). For hazard rating accurate modelling of fire behaviour is
a main factor. The next task will be the development of a Austrian fuel
model, which allows to classify forest communities not from a static but
dynamic perspective. This fuel models will be linked to other disturbance
regimes as well. To approach fire and fuel models we will sample fuels not