ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
The concept of object-wise data management is common in GIS systems like ArcView or the newest
ArcInfo 8.0 release. Clicking on the object you will open the data base with the attributes assigned to this
object. Looking for areas with specific properties, you will succeed starting a data base search following
the rules of .sql language of relational data base systems.
The result of object oriented image analysis is a data base with information on each object distinguished
during the segmentation and classification process. This allows to identify objects with statistics changed
between the observations by data base .sql queries and more, to quantify the changes. An image analysis
system like eCognition, which additionally is able to use existing administrative or thematic boundaries
from other investigations, has major advantages for specific purposes, like they occur for example in the
forest administration, which is managing the forest on stand level, for biotope monitoring, a.s.o.
Post-processing
There is still a need for advanced post-classification techniques that can at least partially compete with
human interpretation skills. While the spectral classification and the recognition of land cover types can
be highly automated, the interpretation of spatial context and the derivation of land use patterns is still
a complex matter. Post-classification as applied in ALPMON is a first step towards rule based
generalisation but is limited to simple spatial parameters. Recent developments, such as object based
classification schemes or graph based representation, offer new answers to these questions, which
become even more evident when the very high spatial resolution data will be used in the near future.
Concerning landscape monitoring not only satellite image classification could be an automated way.
Another possibility to get cheaper results in landscape monitoring is the automatic recognition of
objects. This would have to be tested in future.
Table 23 finally illustrates by way of example the improvements in algorithms and software which are
expected to increase the accuracy and performance of the erosion risk assessment. Part of these
parameters do also apply to other alpine applications.
Table 23: Expected methodological improvements useful to erosion risk assessment
Methodological
Improvements
Elimination of the overcorrections
in topographic
normalisation
Unsupervised classification
algorithm taking into account
the Mahalanobis distance
instead of the Euclidean
distance (like ISODATA).
Database containing the
phenological calendars of the
most common species in the
study area, better if updated to
the actual meteorological trend.
Benefits
� Improvement of the land cover accuracy. Reduction of
manpower in revision of the classification.
� Increase in discrimination of vegetation classes.
� Helps in selecting the best acquisition dates for the satellite
images.
� Useful in interpretation of spectral signatures.
Rock-falls risk sub-model. � Accounts for the mass movements over slopes deepest than
45°. The sub-model should be integrated to the existing FSTAB.
Model tuning support system � Should help the geologist not very expert in fuzzy set logic in the
definition of the inference rules and the tuning of the
membership functions.
3.4.3 Needs from Users Point of View
The inventory of actual vegetation coverage is considered as the starting point of a monitoring program to
supervise local or regional changes due to global change effects. Applying the methods developed under
ALPMON it will be possible to draw up an inventory of forest habitats, tree species, tree ages, stocking
JR, RSDE, ALU, LMU, Seibersdorf, WSL 89