The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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growth on the shady side of Qilian Mountain may significantly 
affect the local water cycle and climate. 
Third observation made in Figure 2 is the rate of change in the 
NDVI values with elevation. This rate varies more gently at 
lower elevations from 2000 m to 3400 m and it changes more 
quickly when elevation is higher than 3400 m, implying that the 
vegetation growth is more sensitive in high altitude area. On the 
average, for example, it takes about 300 m (roughly from 2600 
m to 2900 m) for the NDVI value to change from 0.3 to 0.4 at 
the lower altitude zone and only about 200 m at the higher 
altitude zone. 
The NDVI values corresponding to the same elevation were 
averaged in order to clearly show the relationship between the 
vegetation growth and elevation. A total of 221142 pairs of 
NDVI and elevation were obtained based on the 28 MODIS 
NDVI images of the 16-day composites of June, July, August 
and September in seven years from 2000 to 2006. The 
relationship between the averaged NDVI and elevation for the 
study area is clearly shown in Figure 3: the averaged NDVI 
increases with elevation and reaches its maximum value of 
about 0.56 at 3400 m and then decreases as the elevation 
increases beyond 3400 m, an clear indication that the vegetation 
growth is at its best at the elevation of 3400 m. 
The effect of aspect on the vegetation growth is more clearly 
demonstrated in Figure 4 where the change of the NDVI values 
with aspect between the elevations of 3200 m and 3600 m was 
plotted. It is seen in Figure 4 that the NDVI value is larger than 
0.55 or the vegetation growth is best in the aspect range of 
NW340 0 to NE70° and the NDVI value is less than 0.54 or the 
vegetation is worse between E90° to W270°. As we discussed 
above, this shows that the aspect of the mountain slopes 
significantly affects the vegetation growth in the study area. In 
general, the vegetation coverage on the sunny side in the 
semi-arid Qilian mountain area is less developed than that on 
the shady side because of more evapotranspiration in the sunny 
side than in the shady side due to the differences in their solar 
radiation and higher land surface temperature. 
5. CONCLUSIONS 
The spatial distribution of vegetation in the Qilian Mountain 
area was quantified with remote sensing data. The MODIS 
NDVI values for June, July, August and September are the best 
indicators for the vegetation growth during a year in this area 
and thus were used in this study. Based on the results obtained 
by analyzing the NDVI data for seven years from 2000 to 2006, 
the following important conclusions can be drawn. 
1) Elevation is the dominating factor determining the vertical 
distribution of vegetation in the Qilian Mountain area: the 
vegetation growth is at its best between the elevations of 3200 
m and 3600 m with the NDVI values larger than 0.50 and a 
peak value of larger than 0.56 around 3400 m. 
2) The horizontal distribution of vegetation within the elevation 
range of 3200 m and 3600 m is significantly impacted by the 
aspect of hillslopes: the best vegetation growth is found in the 
shady slope between NW340 0 to NE70° with the largest NDVI 
value (>0.56) due to relatively less evapotranspiration. 
3) Better vegetation growth occurs over a larger elevation range 
on the shady than sunny side because of less ET in the former 
than in the latter. 
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