boundary are temporarily excluded from the decision making
process. In such a way, the image can be classified several
times using different threshold boundaries and the results can
be merged (Amarsaikhan ef al. 2010).
The result of the classification using the refined method is
shown in figure 2d. For the accuracy assessment of the
classification result, the overall performance has been used,
taking the same number of sample points as in the previous
classifications. The confusion matrix produced for the refined
classification method showed overall accuracy of 90.78%. As
could be seen from figure 2d, the result of the classification
using the refined classification is better than the results of the
standard method. A general diagram of the refined
classification method is shown is figure 3.
RS Image
(SAR}
La
RS image
{ Optical)
>»
Image fusion
Derivation of features
vim and spatial thresholds
b
Threshold determination
(contextual knowledge)
rarer
Maximum Likelihood
Classification
Y Threshold applied
Ancillary classification
results
Y Threshold agai
Merging the ancillary
classification results
|
Land Caver Map
Figure 3. A general diagram of the refined classification.
To compare the final result with the existing information, a GIS
layer was created using a forest map of 1984 and for its
digitizing ArcGIS system was used. It is the only forest map
available in the region and a digitized map is shown in figure
4. The initial aim of the study was to compare the forest
changes occurred between these two periods However, the
existing forest map was not reliable, because ground truth
information and contextual knowledge indicated that it is not
accurate at all. This is a common problem in many of the
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
developing countries, where the old maps need to be updated
through processing of satellite images. In the current study, as
the overall classification accuracy of the classified multisource
images is more than 90%, the result can be directly used to
update the existing forest layer and used for planning and
management.
Coniferous Cedar
Pine Birch
ob 0459 18 27 36
Fire affected forest mS nal
Figure 4. A digitized forest map of the test region.
6. CONCLUSIONS
The aim of this research was to conduct a forest resources
study in northern Mongolia using advanced spatial
technologies. As data sources, panchromatic and multispectral
Landsat 7 images, ALOS PALSAR L-band HH polarization data,
a topographic map, and a forest taxonomy map were used. To
produce a reliable land cover map from the multisensor
images, a novel refined maximum likelihood classification
based on the spectral and spatial thresholds defined from the
contextual knowledge, was constructed. The contextual
knowledge was defined on the basis of the spectral variations
of the land surface features on the fused images as well as the
texture information delineated on the dissimilarity image. For
determination of the spectral thresholds, the pixels falling
within 1.0 standard deviation were used. The result of the
constructed method was better than the results of the
traditional method and it could be used to update a forest layer
within a GIS. Overall, the study demonstrated that advanced
spatial technologies based on optical and microwave RS are
reliable tools for forest planning and management.
REFERENCES
Amarsaikhan, D., Ganzorig, M., Batbayar, G., Narangerel, D.
and Tumentsetseg, Sh., 2004. An integrated approach of
optical and SAR images for forest change study. Asian Journal
of Geoinformatics, 4(3), pp. 27-33.
Amarsaikhan, D., Bolorchuluun, Ch., Narangerel, Z. and
Gantuya, R., 2009. Integration of RS and GIS for forest
