ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
� The perspectives offered by image analysis systems working object oriented like the tested
eCognition from the Delphi2 Creative Technologies company, which seems to be the first step
to reduce the problems connected to high resolution data and in general with data fusion.
Further achievements with respect to feature extraction, shape, segmentation and object
based classification are reached with the ClassTool software currently developed at JR.
Limitations of the present as well as perspectives of the future are discussed in more detail in section
3.4. The main scientific developments were achieved within the feasibility studies, where new method
developments were necessary.
The development and implementation of the image fusion algorithm had a significant impact on the
classification and visualisation of remotely sensed images. The classification of meta classes, such as
the production of a forest/non forest mask, could be improved. For the visualisation of multi-resolution
images the fusion method solved the problem of different pixel sizes and thus suppressed the aliasing
effect in the resulting images. The methodology used for the fusion algorithm was presented to and
discussed within the scientific community during several conferences (Steinnocher, 1998, Steinnocher,
1999). For example, the University of Freiburg used this method to create a fusion image as basis not
only for visual interpretation but also for visualisation tasks (WP12). A photorealistic background for
tourist information with a ground resolution of 2m was derived out of Landsat 5 TM data and KVR-1000
data.
For the aim 'tourist information' a tourist information system was programmed by using ArvView's
programming language Avenue. In this tourist information system it is possible to choose hiking routes
by free choice and get describing information for this route. This enlarges the possibilities of a tourist
office: not only advised routes but free choice of the route with describing information like length,
altitude profile, landscape factors.
For the modelling of water run-off using the ASGI model the estimation of soil characteristics could
be significantly improved by fusion of small scale geological and soil type maps. Generally, no
convenient and wall to wall soil type information is available for alpine areas. Furthermore, it was for
the first time that land cover classification results could be implemented as one main parameter into
the ASGI water run-off model for an alpine region.
The original scientific added value of the erosion risk feasibility study is summarised in the following
points:
� The use of a land cover map derived from satellite as basic input for the erosion model.
� The development of an erosion model based on the concept of risk instead of the estimation of the
annual amount of ablated material. The concept of risk is more suitable for land planning.
� The consideration of several mass erosion phenomena typical of Alpine environment.
� The use of two different sub-models (DA and SCS-CN) to take into account the static component
of the risk, related to soil properties and slope, and the dynamic component of risk, which depends
upon the intensity of the run-off.
� The integration (FSTAB) of the above mentioned sub-models by means of an inference engine
based on fuzzy set logic. Fuzzy logic is viable method to manage in a rigorous way uncertain
situations where: 1) data accuracy is highly heterogeneous (e.g.: land cover vs. soil map) ; 2)
interpretation criteria cannot be defined rigorously because of vague definitions (e.g. the concept of
risk) and lack of detailed information.
The avalanche risk assessment mainly made profit from the classification of winter satellite images
and introduction of these results into a multi-temporal classification approach. This for the first time
enabled the separation of low growing tree species, such as dwarf mountain pine and green alder,
from the high growing tree species, which is not possible by their very similar spectral characteristics.
For the transformation of the classification results in the test sites to the corresponding CORINE land
cover maps a post-classification algorithm was developed (Steinnocher 1996). Due to the large
differences in scale this algorithm had to be extended by an iterative shrinking method, in order to
generalise the single land cover units to the minimum mapping unit of the CORINE land cover maps.
The entire post-processing has been described and published in Steinnocher et al. (2000).
JR, RSDE, ALU, LMU, Seibersdorf, WSL 72