The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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2. APPROACHES 
2.1 Study area and ground truth data collection 
There are three provinces in western part of Mongolia. These are 
Bayanulgii, KJhovd, Uvs provinces. 
where L is the canopy background adjustment that addresses 
nonlinear, differential NIR and Red radian transfer through a 
canopy, and Cl, C2 are the coefficients of the aerosol resistance 
term, which uses the blue band to correct the aerosol influences 
of the red band. The coefficients adopted in the EVI algorithm 
are, L=l, Cl=6, C2=7.5, and G (gain factor)=2.5 (Kaurivi, Jorry 
Z.U., 2003,). 
Figure 1 Land cover classification for the study area using EVI 
data from MODIS , June 2006 
Collection of good ground truth data is a key issue for reliable 
land cover mapping. Ground truth data were collected mainly 
from field trip to the study area and existing thematic maps from 
Joint Russian-Mongolian complex biological expedition Map 
Ecosystems of Mongolia, Moscow 2005, Soil Mapping of 
Mongolia. Ground observation data from Khovd Agricultural 
University was used for the project work in western provinces of 
Mongolia. The ground truth collection was held from June 2007 to 
September 2007. Totally 57 sample areas chosen for ground truth 
collection. Land cover class code is taken from Land Cover 
Working group, Asian Association of Remote Sensing 2001). 
The land cover classification map was done using Enhanced 
Vegetation Index (EVI) data from MODIS , June 2006 (Figure 
1). 
2.2 Methodology and data 
Different land cover/use maps were developed for the west part 
Mongolia; land cover, vegetation change, land surface 
temperature, snow coverage, forestry, crop and socio 
economic information maps using multi spectral data from PAL 
NDVI NOAA 8km, SPOT VEGETATION (www.free.vgt.vito.be), 
MODIS and LANDSAT (http://glcf.umiacs.umd.edu) images. 
For the land cover classification map we used MODIS 250 m 
Enhanced Vegetation Index (EVI) data. The Land-use GIS data, 
Landsat TM/ETM, were employed as reference data. Enhanced 
vegetation index (EVI) was developed to optimize the vegetation 
signal with improved sensitivity for high biomass regions and 
improved monitoring through de-coupling of the canopy 
background signal and reduction in atmospheric influences. The 
EVI is represented by the following equation 1 : 
Land Surface Temperature: 
The MODIS Land Surface Temperature (LST) products are 
created through spatial and temporal transformations, to daily, 
eight-day and monthly global gridded products. For this research 
work we downloaded MODIS Land Surface Temperature Daily 
daytime 1 km grid data from the MOD 11 production 
(http://edcdaac.usgs.gov/includes/edg_bridge.php). After 
downloading MODIS LST data we made mosaic and reprojection 
and converted it to C degree units. LST data were applied in the 
study area in the time period of 2001 to 2006 from April to 
September . As evident in figure 2 LST of study area is 
increasing. 
EVI = G 
NIR-Red 
NIR + C, Re d - C 2 Blue + L 
(1) 
Figure 2. Change of surface temperature using LST data between 
the years 2001-2006