14
presence of some few indicator species or
have even been relatively homogenously
reformed by cattle grazing which led to
mutual misclassifications. A fusion of the
24 associations to only 16 classes let the
classification accurasy rise from 40 % to
80 %.
6 CONCLUSION
Within the present study once more it turned
out that object classes of geoscientific
maps can be automatically reclassified using
remote sensing data with very different
success. Some of the reasons for these
stronly diverging results are the following:
- poor geometrical quality of boundaries
of object classes;
- generosity in field mapping;
cartographic generalization;
- problematic spatial delimitation of
object classes and their topographic
neighbourhood (length and shape of
borderlines) ;
- different radiometric quality within one
image;
- similarity of object classes in spectral
characteristics (feature space);
- changes between date of field mapping
and date of remote sensing data
acquisition;
selection of training areas;
- selection of classification algorithm;
- different suitability of remote sensing
as a measuring method for various object
classes;
- different empirical contents of
predominantly theory-derived categories;
different validity of indirect
conclusions from sensor-exposed surface
classes to subterraneous object classes;
allochthonous geo-ecosystems.
Based on our experiences we would like to
emphasize that the combined interpretation
of remote sensing data and thematic maps by
means of digital image processing is an
indispensable help in geoscientific and
geoecological studies. It is mainly two
applications which are of special interest.
One is the possiblity to relativize the
information derived from various sources in
a way, that as a final product more
plausible, more consistent and hence, more
reliable maps exist. This causes the
necessity to disclose the theoretical
reflections on the problems of thematic and
spatial delimitation of object classes and
thus to stimulate the examination and
development of geoscientific and ecological
theories.
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Symposiur
Digital ]
for fore:
Kuo-mu Ch
National Taiwi
ABSTRACT: The
types classif
the MSS data ■
vised/unsuper
component tra:
to standard c
seven best bai
and classifiei
types classif:
time and man-]
combination wl
cation accurai
images and mi:
1 INTRODUCTIOt
Resource surve
an airborne mt
the satellite
scanner, which
resolution, na
scanning time,
mapping than t
vegetation typ
The goal of
length band co
cation in fore
that when work
accuracy in cl
all wavelength
a marked incre
frequently des
utilizing only
fication proce
of wavenlength
in the classif:
2 MATERIALS AN]
2.1 Data utili:
The airborne mi
study were col]
with 11 channel
mental Forest c
Taiwan on Woven
tape (CCT) cont
was used in dat
In order to p
cover types cla
niques were use
protray spectra
surface feature
are principal c
spatial filteri
REFERENCES
2.2 Processing
Beutel, P. & W. Schubö 1983. SPSS 9 Stati-
The image proce
