ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
95% (Kappa 0.91) for the forest border.
In more detail for some most relevant parameters: shrubs reached a mean class accuracy of 89%, for
grassland it amounted to 92%. Furthermore, the mean deviation for the share of coniferous trees was
only 7%, whereas the share of larch trees varied with 12% and tended to be underestimated.
Additionally, visual interpretation of the classification results showed quite a realistic image, thus
confirming the quality of the output.
Implementation of a High Spatial Resolution Winter Image
The separation of typical Alpine low growing tree species such as dwarf mountain pine and green alder
(in general not higher than 2-3m) from high growing forest stands could not be reached with the
classification of Landsat TM data from the vegetation period, because their spectral characteristics are
very similar. For improving these results, a high spatial resolution IRS-1D panchromatic winter scene
was analysed by way of example for a small sub-set of the Dachstein test site.
The data acquisition time was from a period, where snow was covering the low growing tree species
but has already fallen from the crowns and branches of the high growing trees. Through multi-seasonal
classification it was possible to separate high growing from low growing trees with high accuracy. The
classification accuracy of the high growing forest border was very high with a mean of 98% and a
Kappa value of 0.95. With this approach also the dwarf mountain pine and green alder itself can be
separated from each other.
The data was also investigated with regard to their potential for detecting small gaps (approx. 50m 2 )
within the forest, which has been defined as one important parameter for the assessment of avalanche
risk. Due to the better spatial resolution of the winter-IRS-pan image there was also an advantage in
detecting gaps/forest openings down to a size of 25-30 m². In classifications of the summer Landsat
TM and SPOT data only a detection down to approximately 240 m² can be reached.
In conclusion, the big advantages of the forest border derived from the IRS-1D panchromatic winter
image are:
� the separation of dwarf mountain pine and green alder from high growing forest stands,
� the detection of small gaps within the forest due to the high ground resolution of 6m.
2.3.3.5 Results
The main outcome of the feasibility study on avalanche risk assessment are maps of the single
parameters as defined in the requirement study. These parameters can be implemented in a region
based avalanche risk model which requires information on land cover, especially vegetation and forest,
and on topography. As no avalanche risk model was available within the course of this study, the
implementation, unfortunately, could not be tested. Although many parametric requirements, and to
some extent also their interdependencies, could be ascertained, the information yet is too
heterogeneous, and in most cases too much related to a specific investigation area, to establish a
comprehensive avalanche risk model.
The classification results were plotted as maps with a scale of 1:50 000, corresponding to the Austrian
Topographic Map, sheet 127 Schladming. They were overlaid with contour lines in order to enable a
good orientation within the map. Areas affected by shadow in the satellite data were masked out and
not classified. They covered 2% of the entire test site. Four maps were generated for land cover
(including forest type and non forest classes), forest canopy closure, natural forest age, and more
detailed forest types as required for the avalanche risk analysis. Additionally, for the area which was
covered by the IRS-1D winter image, an even more detailed forest type classification is available,
revealing dwarf mountain pine and green.
To show an example for the possible combination of diverse risk parameters, in Figure 7 the forest
canopy closure is shown only on slopes between 25° and 50°, as these are the slopes with potential for
frequent avalanche release. As stated before, no rules for the combination of risk parameters were
available, thus no risk analysis could be performed within the duration of this project. For assessing the
potential avalanche risk zones, an hierarchical approach could be suggested, e.g. by first selecting only
those areas with critical slope steepness, then within these areas the forests with critical canopy
closure, and so on.
JR, RSDE, ALU, LMU, Seibersdorf, WSL 41