ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
3.3.7 Classification
Classification in the test sites had to fulfil the requirements of the local as well as the European
customers. This was taken into consideration with the selection of special classes for each application.
However, classes which had been defined as common classes within ALPMON turned out to be not
existing in some test sites and therefore could not be classified. This is true for example for broadleaved
forest, which in some test sites only occurs in form of greenalder shrubs. In some test sites age
classes could not be separated, as the natural type of forest has mixed age, whereas e.g. in the
Dachstein area the managed forest is generally dominated by a common age category.
Due to the different customer needs, the main focus of the classification effort was quite different in the
test sites. Whereas the needs of the local customers of the Tarvisio test site concentrate on value
added satellite image products and classification was performed mainly with respect to the Alpine
Convention and CORINE requirements, in other test sites the outcome of satellite image classification
was essential for the local customers. Here, main focus was put on the categories required by the local
customer, such as tree species composition in the Engadine National Park, or different non-forest
vegetation density in the Cordevole test site.
According to the heterogeneous test site characteristics and the varying reference data sets also
different classification methods were chosen. A Maximum Likelihood classification was performed for
the Tarvisio, Mangfall, Dachstein, and Engadine test sites. Thresholds were applied in nearly all test
sites, partly for classification of the forest border, partly for the entire classification. Furthermore, in the
Cordevole and the Engadine test site diverse unsupervised classification algorithms (ISODATA and
Narendra-Goldberg clustering) were used. Some classifications were performed in hierarchical
manner. A forest non-forest separation preceded the separate classification of forest parameters and
non-forest categories respectively in the Tarvisio, Dachstein, and Cordevole test sites. For the Mangfall
test site a completely hierarchical approach was applied. The classifications were based on detailed
signature analyses.
Additional information, such as a DEM, GIS data layers, or digital map layers, was integrated in the
classification process in some test sites. On one hand classes, which could hardly be classified with
sufficient accuracy from the satellite data, where substituted by the auxiliary information sources. For
example in the Dachstein test site the water layer of the digital topographic map was used instead of
classification of water bodies, as these would partly get mixed up with weakly illuminated coniferous
forest. Furthermore, streams could not be classified due to their representation in mixed pixels
together with bank vegetation. On the other hand the auxiliary data supported classification itself by
establishing rules for selected classes (e.g. sealed surfaces do not occur on steep slopes).
Partly, additional features derived from the high resolution satellite data sets were integrated in the
classification process. Texture features were used for the delineation of sealed surfaces in the
Dachstein and Cordevole test sites. The forest non-forest separation in the Dachstein test site was
based on a fused Landsat TM/Spot pan image.
Some main difficulties crystallised in most classifications:
� Forest age in many test sites could not be classified due to the natural appearance of the forest
with a mixture of all tree age categories. Only the Dachstein test site is dominated by managed
forest with a common age class within one stand.
� Forest type in some test sites is mainly mixed, with only a few pure coniferous or broad-leaved
stands.
� Tree species composition is an important information for two applications (national park
management and avalanche risk). However, some tree species cannot be separated sufficiently
(e.g. percentage of larch trees) or at all (spruce, pine, and stone pine). Dwarf mountain pine and
green alder could not be separated sufficiently with the satellite data acquired during the vegetation
period. This is on one hand due to spectral similarity with other coniferous respectively broadleaved
trees and, on the other hand, due to mixture with other tree species in one stand. However,
in the Dachstein and Mangfall test sites it was possible to classify dwarf mountain pine by means
of winter or spring images, where the low growing trees are covered with snow whereas the high
growing trees are snow free.
� Wet lands were well recognised in the Mangfall test site by using the thermal infrared band of
Landsat TM (TM6). However, this approach is not applicable e.g. to the Dachstein test site, as wet
land areas here are relatively small and beyond the resolution of TM band 6 with 120m.
JR, RSDE, ALU, LMU, Seibersdorf, WSL 79