with limited site data. 
The target tree species of this study is Elaeocarpus japonicas 
(Japanese Elaeocarpus tree, JET), a kind of evergreen tree 
species. It widely spread in whole Taiwan from low upland to 
mountainous areas with elevation 2200 m above sea level. 
JET is also founded in Japan and China. JET is a kind of 
dominant tree species in the Huisun forest station. It is 
usually founded on the ridge with thinner soil layer, direct 
sunlight and water stress. JET is a kind of pioneer tree species 
in second succession, and therefore it plays a necessary role in 
ecosystem. 
We aimed at applying 3S (GIS, GPS and RS) technology to 
derive elevation, slope, aspect and terrain position from DEM 
and vegetation index (derived from the two-date SPOT-5 
images), and using these five environmental layers to build 
predictive models. In this study, we adopted five methods 
(DA, DT, MAXENT, ML and DOMAIN) and three sampling 
designs (SD) to build “Tong-Feng (SD1)” model, “Kuan-Dau 
(SD2)” model and “two watersheds (SD3)”, eventually we 
totally built 15 models. The models’ reliability and 
performance were evaluated, and used as the criteria of model 
comparison. 
2. STUDY AREA 
We chose the study area with rectangular shape, which covers 
the Huisun Forest Station and has the total area of 17,136 ha. 
The Huisun Forest Station is in central Taiwan, situated within 
242°-24’5" N latitude and 12131217” E longitude. The 
station is the property of National Chung-Hsing University, and 
study area ranges in elevation from 454 m to 3,419 m, and its 
climate is temperate and humid. Hence, the study area has 
nourished many different plant species and is a representative 
forest in central Taiwan. It comprises five watersheds, 
including two larger watersheds, Kuan-Dau at west and 
Tong-Feng at east. All of the JET samples were collected 
from the two watersheds by using a GPS (Figure 1.). 
3. METHODS AND METERIAL 
3.1 Data Collection 
The collected data contained DEM with 5 m x 5 m resolution, 
orthophoto maps with 1: 10,000 scale and two-date SPOT-5 
images taken in 2004/07/10 and 2005/11/11. The JET 
samples were acquired by field survey with Trimble PRO XR 
series GPS system. Furthermore, an expandable antenna rod 
with 5m in length and a laser ranging were adopted with GPS 
for enhancing the capacity of the system. All of the JET point 
data were field-collected from Tong-Feng and Kuan-Dau 
watersheds (© SPOT Image Copyright 2004 and 2005, CSRSR 
NCU). 
5 
3.2 Data Processing 
Slope and aspect data layers were generated from 5 x 5 m 
DEM by using ERDAS Imagine software module. The ridges 
and valleys in the study area were used together with DEM to 
derive terrain position layer. The main ridges and valleys over 
the study area were directly interpreted from the contour lines 
shown on the orthophoto base maps; these lines were then 
digitized to establish the data layer of main ridges and valleys 
by using ARC/INFO software for later use. The data layer of 
  
     
main ridges and valleys in a vector format was converted into a 
new data layer in a raster format by ERDAS Imagine software, 
and then combined with DEM to generate terrain position layer 
(Skidmore, 1990). The equation is expressed as follows. 
Vegetation indices were derived from the two-date SPOT-5 
images, one in autumn, the other in summer, by using Spatial 
Modeler of ERDAS Imagine. JET samples obtained by a GPS 
were corrected by using post-processed differential correction 
and converted into ArcView shapefile format for later use. 
Pj - PV / (PV 4 PR) 
Where PV = the Euclidean distance between a certain pixel P 
and the nearest valley pixel; 
PR - the Euclidean distance between a certain pixel P and the 
nearest ridge pixel; 
When Pj = 0.0, it is referred to valley; P; = 1.0, it is referred to 
ridge. The P; from 0.0 to 1.0 is partitioned into eight equal 
intervals. 
The change in water content and pigment composition in plant 
owing to the season or stress can be detected by using 
multi-date imagery. These two phenomena could result in 
changing plant's spectral reflectance of different bands in 
multi-band image (Jensen, 2005). The concept of the 
vegetation index adopted in this study is explained in Hoffer 
(1978). The following equation is used to derive the 
vegetation index data layer. 
NIB umm = MER ver 
NIR — MIR 
summer summer 
Vegetation Index — 
Where NIR summer/autumn iS the reflectance of near infrared band 
during summer and autumn, and the reflectance of middle 
infrared is denoted as MIR summer/autuma- The output value is 
scaled in 8-bits data type. 
3.3 Overlaying the Environmental Layers 
The layers of elevation, slope, aspect, terrain position, 
vegetation index, and JET sample data were overlaid by 
ERDAS Imagine software. We used the function “AOI (area 
of interest)” in ERDAS imagine software to clip the concurrent 
environment factor value of JET locations. These clipped-out 
data were used as independent variable for building predictive 
model. 
3.4 Target and Background Samples 
Target sample is the GPS-located JET point sample and the 
concurrent environment factor value. The ratio of background 
to target we adopted was followed the criteria Sperduto and 
Congalton (1996) proposed that the ratio should be more than 3. 
The sampling strategy is randomly selected following Pereira 
and Itami (1991) suggested avoiding spatial autocorrelation. 
3.5 Sampling Designs and Model Building 
We designed three sampling designs (SD) for the comparison 
of model reliability, “Tong-Feng (SD1)”, “Kuan-Dau (SD2)" 
and “merged samples of two watersheds (SD3)”. SDI had 
104 individual JET samples, and SD2 had 80. SD3 had all of 
the 184 JET samples. For each of these three SDs, the dataset 
was split into two subsets, 2/3 and 1/3 of all, used for 
  
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