high ridge in its surrounding. This U-shaped envelope makes 
solar radiation hard to totally reach all sites in Tong-Feng 
watershed. Hence, most of the sites have relatively low 
evaporation and keep a high humidity for the entire watershed. 
In contrast, Kuan-Dau watershed has not only gentle sloping 
valley but also low ridge in its surrounding. This incomplete 
V-shaped envelope makes the west side of valley receive 
enough amount solar radiation, and thereby has a stronger 
evaporation. Hence, Kuan-Dau watershed was relatively drier 
and. hotter than Tong-Feng watershed. To sum up, the 
topographic attributes of the Tong-Feng watershed are quite 
different from those of the Kuan-Dau watershed. Furthermore, 
table 2 summarizes the statistics of environmental variables for 
CGT samples in three sites (Tong-Feng, Yo-Shan and 
Kuan-Dau). The table shows that species with broad 
elevation ranges and environmental tolerance. Besides, 
hill-shade, by its definition, captures the effects of differential 
solar radiation due to a variation in slope angle, aspect and 
position, and shading from adjacent hills. According SPOT 
summer images of the study area (07/10/2004), which sun 
elevation of 71 degrees and sun azimuth of 91 degrees will be 
used. | The output shaded raster considers both local 
illumination angles and shadows. The output raster contains 
values ranging from 0 to 255, with 0 representing the shadow 
areas, and 255 the brightest. Then we got high mean value 
with CGTs sites since CGTs prefer to grow at gentler slopes 
and near-ridge positions. Therefore, we may make an indirect 
inference that CGTs always occur on the sites facing solar 
illumination. 
We assigned sampling design-1 (SD-1) as base model to 
compare other sampling designs and overlaid environmental 
factors including five topographic factors and vegetation index 
derived from SPOT-5 satellite images. Owing to very large 
amount of calculation, we need to reduce dimension to improve 
calculating efficiency. Each method can calculate relative 
importance of six predictor variables with three predictive 
models for predicting the potential habitat of CGTs, as a 
reference for screening effective variable. The results showed 
that three predictor variables (elevation, slope and terrain 
position) are the relative important variable. Hence, we used 
    
   
   
   
   
   
   
   
    
    
   
   
   
   
    
      
   
   
    
   
    
  
   
   
    
   
   
   
   
   
   
   
    
    
  
three predictor variables to build models. 
The test results of kappa values for the four modeling methods 
for each of three scale designs are shown in table 3. As base 
model in SD-1, accuracy assessment results indicated that 
kappa values with MAXENT (0.70) was the best among them, 
followed by DOMAIN (0.62) and GLM (0.59), and ANN (0.58) 
was the last as these models were developed only from 
Tong-Feng sample set and tested by another independent 
Tong-Feng sample set. As shown in figure 4, predictions of 
MAXENT and DOMAIN models generated high potential 
areas of CGTs and considerably reduced the area of field 
survey to less than 6% (1,028 ha) of the entire study area 
(17,136 ha), and thus they were better suited for predicting the 
tree's potential habitat (also see table 4). 
Next discuss how the extrapolation ability of those models (see 
table 3). According to the base model, we extended 
prediction from one area to predict another and assessed the 
robustness of underlying relationships. As SD-2 and SD-3, 
the kappa values of these models originally from 0.58-0.70 
declined sharply to about 0.3, eventually near zero, with 
increasing spatial distance from 0.5 km to 5.0 km as the four 
models were tested by independent samples from Tong-feng, 
Yo-Shan, and Kuan-Dau sites, respectively. 
Consequently, “Tong-Feng base models” built based on four 
algorithms failed to pass validation by Yo-Shan and Kuan-Dau 
test samples despite passing validation by Tong-Feng test 
samples. The outcome clearly indicated that the models 
merely based on topographic variables are most easily 
measured in the field and are considerably used because of 
their good correlation with observed species patterns in small 
spatial scale. Such variables usually replace a combination of 
different resources and direct gradients (e.g. climate, rainfall, 
etc) in a simple way (Guisan ef al., 1999). However, the 
model performed poorly on spatial extrapolation from 
Tong-Feng to Kuan-Dau because the topographic attributes of 
the two watersheds are quite different from each other. Then, 
the models developed from topographic variables can only be 
applied within a limited geographical extent without significant 
error. 
  
Statistics 
Kuan-Dau watershed Tong-Feng watershed 
  
Mean valley-wide (km) 
Mean elevation of valley (m) 
Mean elevation of west ridge (m) 
The difference of valley to west ridge (m) 
Mean elevation of east ridge (m) 
The difference of valley to east ridge (m) 
33 4.9 
989 882 
1614 1841 
625 959 
1910 1774 
921 892 
  
Table 1. Microterrains of the two watersheds 
Kuan-Dau watershed 
Yo-Shan Mountain Tong-Feng watershed 
  
  
Statistics Mean Max Min Mean Max Min Mean Max Min 
Elevation (m) 1277 1640 681 1804 1884 1681 1787 2096 1157 
Slope (°) 26 53 11 20 33 4 20 46 1 
Aspect (°) — 359 2 € 355 60 — 359 2 
Terrain Position 6 8 1 6 8 5 6 8 2 
Vegetation Index 27 48 21 21 23 20 24 47 20 
Hill-shade 210 254 124 167 232 124 186 253 a 
  
Table 2. The statistics of environmental variables for CGTs in the two watersheds