ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
use/land cover could be derived from high resolution remote sensing data. It permitted the compilation
of vegetation parameters from which surface roughness can be indirectly deduced.
Land use – forest parameters
The critical parameters concerning land use are mainly forest related. Here, the forest type and crown
closure were of main interest. Additionally, as described above, the separation of special tree species
(larch, dwarf mountain pine, and green alder) was requested. Although the results of forest type
classification (3 types) were very good with a mean accuracy of 92% and a Kappa value of 0.84, the
more detailed classification into 6 types, including three different categories for share of larch tress,
resulted in an accuracy of 68% (Kappa 0.59). Especially the accuracy for the percentage of larch trees
could be significantly improved by the use of SPOT 4 data(see below). The forest density has a
significant influence on the potential of avalanche release. This parameter has been classified in the
Landsat TM satellite data with an accuracy of 85% (Kappa 0.62), for the three canopy closure
categories required by the customer.
Another EC study performed in the Austrian Alps (SEMEFOR, 2000) could demonstrate, that with the
availability of SPOT 4 data the classification accuracy of forest parameters can be improved
significantly. The SPOT 4 sensor delivers images with 20m resolution in the multi-spectral bands and
10m resolution in the panchromatic band. Furthermore, the middle infra-red spectrum has been added,
compared to the former SPOT sensors. The combination of the higher resolution and the availability of
a middle infra-red band proves to be a big advantage with respect to the classification of alpine
vegetation parameters. SPOT 4 data are available on request since 1999. At the moment, it is strongly
recommended to use these data instead of Landsat TM images for a detailed investigation of forest
parameters as well as other Alpine vegetation with respect to the parameters required for avalanche
risk models.
Clearings or small openings within the forest could be detected down to a size of approx. 30m 2 in an
IRS-1D panchromatic winter image. This corresponded with the requirement previously defined by the
customer to detect forest openings with 50m 2 .
Some constraints have to be mentioned concerning two non forest land cover categories. The
requirement of the customer to separate different types of grassland with respect to their use could not
be fulfilled with the available satellite data. The only separation could be made between rich and poor
grassland, which in most cases is related to the meadows in the valleys vs. the alpine pastures above
the forest border line. Furthermore, the rock size could not be classified with these data. This might
become possible with the high resolution sensors, having in mind texture features and the amount of
shadow within a pixel.
In general it can be stated, that areas outside the forest can be classified due to their spectral variation
if they occur on large areas. Smaller areas, which are covered by a mixture of vegetation categories,
such as rhododendron, dwarf mountain pine or smaller moor areas that do not exceed 1 ha, cannot be
classified definitely because of the mixed pixel problem. Simulations have proved that a geometric
resolution of about 5 to 10m in the infrared spectral range is necessary to assess the typical small-area
distribution pattern of vegetation outside of forests. The future sensor systems will provide data in this
resolution range.
Roughness parameters which can be indirectly deduced from land use classes
Although the scientific community agrees that surface roughness is a critical parameter for snow
gliding and subsequent snow gliding avalanches, yet, there are no quantifiable measures and rules for
surface roughness available, and roughness parameters of different land cover categories have not
been quantified. This is due to the fact, that avalanche release is a very complicated process,
influenced by the interaction of numerous factors. Nevertheless, roughness parameters such as height
of small bushes and rock size have been defined by the customer as critical parameters. It was clear
from the beginning of the study, that rock size cannot be classified from the available remote sensing
data sources. Although the absolute height of bushes cannot be directly derived from satellite data,
their distribution could be estimated by means of Landsat TM image classification. But, as stated in the
conclusion to the avalanche risk study, smaller areas, which are covered by a mixture of vegetation
categories, cannot be classified definitely because of the mixed pixel problem. A geometric resolution
of about 5 to 10m in the infrared spectral range is required to assess the typical small-area distribution
pattern of alpine vegetation outside of forests. This is also true for the classification of small alpine
bush vegetation.
JR, RSDE, ALU, LMU, Seibersdorf, WSL 82