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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
underestimations. The comparison of total area of 
agricultural field and grassland is shown in Figure 4 (f). 
Total area has much better agreement than individual 
comparison. This might be because of the difficulty in 
discriminating the single crop agricultural field from natural 
grassland mentioned above. To overcome this insufficiency 
of performance, it might be given some indication of the 
utilization of additional information such as other temporal 
metrics, fine resolution satellite data, and digital elevation 
model. The tuning of the threshold values will be 
implemented by means of TM and ETM+ data. These higher 
resolution image are geometrically overlaid to the MODIS, 
and finer distribution of land use is interpreted manually 
and semi-automatically to get the appropriate threshold 
values. 
4. CONCLUSIONS 
The land cover classification over Yellow River basin by 
means of simple decision tree classification method using 
MODIS data is shown in this paper. This method is designed 
to control the classification result by tuning of threshold 
values, and to be applied to the multi-temporal metrics that 
represent the phenological characteristics of the vegetation. 
The classification result shows basically good agreements 
by comparison with the existing land cover map, though a 
certain degrees of overestimation or underestimation are 
recognized. The tuning of the threshold values would be 
implemented by the aid of fine resolution satellite data and 
existing maps. The additional data to get around the 
difficulty in discrimination of single cropped agricultural 
field from natural vegetation should be investigated. 
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Friedl, M. A., et al., 1997. Decision tree classification of land 
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Friedl, M. A., et al., 2002. Global land cover mapping from 
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