ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
Low growing tree species such as dwarf mountain pine and green alder show areas with greater
roughness, which indicates lower gliding rates. The distribution of these tree species could be
classified with very high accuracy by a multi-seasonal approach, introducing a winter satellite image
into the investigation.
Further roughness parameters are related to the litter within forest, which is depending on the forest
type respectively tree species. Leaves of broad-leaved trees as well as needles of larch trees build a
smooth surface on which snow gliding is significantly higher that on rougher surfaces in spruce stands.
This parameter can be directly deduced from the classification of forest type respectively tree species.
High precision digital terrain model
Further parameters influencing avalanche release are related to topographic parameters such as
slope, aspect, and elevation. These parameters were derived in sufficient accuracy from a digital
elevation model with 25m resolution. The surface roughness depending on the topography would also
be of great interest for the customer. E.g. cow steps on pastures was of interest to the customer. This
parameter could not be assessed with the current satellite image resolution. Furthermore, the shape or
homogeneity of slopes is influencing the risk of avalanche release. Generally, this can be easily
calculated from the DEM with commercially available software. However, the currently available DEM
resolution (25m) is too rough to detect morphological surface characteristics, that might affect
avalanche release, such as shape of slope (convex/concave), length of slope, and slope homogeneity
in non forested areas or areas with critical forest canopy density, in sufficient detail in the sub-meter
range. Currently, only laser scanner data can deliver DEMs with the required resolution.
The time resolution is another crucial factor for information extraction. A yearly update in general
seems to be possible with the currently available satellite systems. The bottleneck might be the
availability of images from winter with the specific snow situation required. On the other hand, the
distribution of dwarf mountain pine and greenalder shows no extensive variations. Therefore, an
update of this information within a 5 years period seems to be sufficient and practicable.
With this, general statements on the feasibility and the constraints of satellite remote sensing based on
the high resolution sensors available for this project (not taking into account the very high resolution
sensors of the new generation) are given.
3.3.9.4 Erosion Risk
The feasibility study on erosion risk was aimed to assess the contribution of remote sensing to the
evaluation of erosion risk in alpine environment. Land cover parameters were therefore selected to
satisfy both the general requirements of ALPMON and the specific needs of the erosion risk
assessment, like the estimation of pasture density, which was classified into three density classes: 10-
30%, 30-60%, > 60%.
The identification of forest border was performed through a slicing of an IRS-1C pan image. This
method, in spite its simplicity, turned out to be accurate (97%) and efficient in outlining sparse stands
and small gaps inside the forest, which contributed to improve the detail of the erosion risk
assessment. The classification was performed using a Landsat 5 TM image dated 01/06/1996
topographically corrected using the Minnaert algorithm. The criteria of selection of the image date
were: to minimise the shadowed areas; to maximise the spectral discrimination between broad-leaved
and coniferous forest types; to avoid the period of peak water stress on pastures (July-August)
because the discrimination of the pasture density levels could become very difficult during drought.
The forest stands of the Cordevole test site, as several other Italian alpine forest stands, are highly
natural therefore the tree age distribution is highly heterogeneous and spatially mixed making very
difficult a spectral discrimination of the forest age on the basis of the TM image. The signature analysis
over ground truth forest samples confirmed a very high spectral variability. A preliminary maximumlikelihood
classification resulted into low accuracy.
Unsupervised classification, based on the Isodata algorithm with a considerably high number (20) of
spectral classes and the support of ground truth samples, demonstrated to be a simple and efficient
approach to account both for the high spectral variability of forest stands and for the discrimination of
the pasture density levels which were suitable for the erosion risk assessment, apart from the case of
talus slopes with very low vegetation which in many cases were classified as rocks producing, in the
erosion model, an apparent decrease in the erosion risk.
JR, RSDE, ALU, LMU, Seibersdorf, WSL 83