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Title
Technical Commission VII

2. THE STUDY AREA AND DATA SOURCES
Mongolia has a relatively low forest cover, although, the
country has a total land area of 156.5 million hectares. Forest
reserve land comprises 18.3 million hectares with 12.9 million
hectares of forest-covered area. This includes 10.5 million
hectares of coniferous and hardwood forests. The forests are
mainly located in the northern and central parts of the country,
forming a transition zone between the Siberian boreal forest
and the Central Asian steppe. Climate change, global warming
and negative human activities are directly and negatively
influencing the Mongolia’s forest resource and its quality. As
the forests in Mongolia are state-owned, the Ministry for
Nature, Environment and Tourism takes the overall
responsibility for forest resource management. The main
objective of the forest management is to protect and develop
the existing forests of Mongolia so that they make maximum
contributions to soil and watershed protection, and
conservation of existing ecosystems. In the meantime, the
forests are expected to produce, on a sustainable basis,
increased volumes of industrial wood, fuel wood and minor
forest products for the needs of people (Ykhanbai 2010).
For a test site, a coniferous forest-dominated area around the
Lake Khuvsgul located in northern Mongolia has been
selected. The lake is considered as the second largest fresh
water lake in Asia after the Lake Baikal with 100 km in length,
35 km in width, and over 265 m in depth. The lake lies at
1645m above sea level where mountains on the western shore
rise up to 2961m and the mountains on the northern shore even
up to 3491m.The area surrounding the Lake Khuvsgul
represents a forest ecosystem and is characterized by such main
classes as coniferous forest, deciduous forest, grassland, light
soil, dark soil and water. The annual precipitation in the region
is about 350-400 mm and it makes the area as the most humid
region in the country.

Figure 1. Landsat ETM+ image of the test area
(R=B4, G=B3, B=B2).

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

As data sources, multispectral Landsat ETM+ data of 12 August
2007 with a spatial resolution of 28m, a panchromatic Landsat
ETM+ image of 28 August September 2007 with a spatial
resolution of 15m and ALOS PALSAR L-band HH polarization
image of 17 August 2007 with a spatial resolution of 25m, a
topographic map of 1986, scale 1:100,000 and a forest taxonomy
map of 1984, scale 1: 500,000 have been used. The selected test
site in the Landsat ETM+ image frame is shown in figure 1.
3. GEOMETRIC CORRECTION OF THE IMAGES AND
SPECKLE SUPPRESSION OF THE SAR IMAGE
Initially, the multispectral and panchromatic Landsat ETM+
images were geometrically corrected to a UTM map projection
using a topographic map of the study area, scale 1:100,000. In
general, the panchromatic image was used to improve textural
variety of the forests especially in bordering areas with other
classes. The ground control point (GCP)s have been selected on
clearly delineated crossings of rivers and other clear sites. In total
15 points were selected. For the transformation, a second order
transformation and nearest neighbour resampling approach have
been applied and the related root mean square (RMS) errors were
0.96 pixel and 0.83 pixel, respectively. In order to geometrically
correct the PALSAR image, 18 more regularly distributed GCPs
were selected comparing the locations of the selected points with
other information such as Landsat ETM+ image and the
topographic map. Then, the image was georeferenced to a UTM
map projection using the topographic map of the study area. For the
actual transformation, a second order transformation and nearest
neighbour resampling approach were applied and the related RMS
error was 1.18 pixel.
As the microwave images have a granular appearance due to
the speckle formed as a result of the coherent radiation used
for radar systems; the reduction of the speckle is a very
important step in further analysis. The analysis of the radar
images must be based on the techniques that remove the
speckle effects while considering the intrinsic texture of the
image frame (Serkan ef al. 2008, Amarsaikhan ef al. 2012). In
this study, four different speckle suppression techniques such
as local region, lee-sigma, frost and gammamap filters
(ERDAS 1999) of 3x3 and 5x5 sizes were applied to the
PALSAR image and compared in terms of delineation of forest
and other texture information. After visual inspection of each
image, it was found that the 5x5 gammamap filter created the
best image in terms of delineation of different features as well
as preserving content of texture information. In the output
image, speckle noise was reduced with very low degradation of
the textural information. After the speckle suppression, the
SAR image was added to the optical bands, thus forming
multisource images.
4. DERIVATION OF FEATURES AND STANDARD
MAXIMUM LIKELIHOOD CLASSIFICATION
Generally, it is desirable to add some orthogonal features to
any classification process to increase its decision-making. In
the present study, for this aim, texture features have been used.
To derive the texture features from the combined Landsat and
PALSAR images, contrast and dissimilarity measures (using an
11x11 window size) have been applied and the results were
compared. The bases for these measures are the co-occurrence
measures that use a grey-tone spatial dependence matrix to
calculate texture values, and the matrix shows the number of
occurrences of the relationship between a pixel and its