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