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3.Data Processing 
The image processing techniques of these remotely 
sensed data were implemented through the use of the 
Earth Resources Data Analysis System (ERDAS). In 
the geometric correction aspect, 22 * 21 and 26 widely 
scattered ground control points were selected 
respectively from the airborne MSS, SPOT HRV and 
Landsat-TM images to compute the equations that 
transformed the images into the Transverse Mercator 
Coordinate System of the base map. The pixels of these 
remotely sensed data were resampled to a 10m X 10m » 
20m x 20m and 30m X 30m respectively by the nearest 
neighbor algorithm, The root-mean-squared error 
(RMSE) of the linear transformation model of these 
three images were 0.831 ~ 0.711, and 0.489. 
4.Independent Variables 
On the sample plots selecting, forest maps of the study 
area were overlaid on the three remotely sensed images 
respectively by the assistance of GIS data. The total 78 
sample plots were drawn systematically from each 
image. The digital data utilized in this study were 
collected from these sample plots. The independent 
variables of the equation in this study were the digital 
mumber of individual band, vegetation index, tree ages 
and crown closures. The digital number of individual 
band were collected from each sample plots. Six 
vegetation indices were computed from the equation as 
follows IND] =NIR-R, IND2-(NIR-R)/(NIR*R), 
IND3=(IND, +053, IND4=NIR-G, IND5=(NIR- 
G)/(NIR+G), and IND6= (IND5+0.5) ^, where, NIR is 
near-infrared band, R is red band , and G is grcen band. 
Tree ages were adopted from tree plantation's records. 
Forest crown closures were measured with a crown 
density scale under a stereoscope from the 
corresponding plots on the aerial photographs. 
5.Regression equtation 
Regression equations were derived from multiple 
regression analysis by using the digital number of 
spectral individual bands and vegctation indices. The 
individual band and vegetation index were the 
independent variables, and the forest crown closures and 
trce volumes were the dependent variables. The stepwise 
regression approach in the Statistical Analysis System 
(SAS) package was used to select the equations. Among 
the selected equations, one optimum equation was 
selected by signifjcant F-value and higher coefficient of 
determination (R)value. 
RESULTS AND DISCUSSIONS 
1.Individual Band 
As mentioned precviously, the Statistical Analysis 
System (SAS) was used to perform the multiple 
regression analysis. In these regression equations, the 
individual band of remotely sensed data were used as the 
independent variable, and the forest crown closure 
which was measured in the corresponding plot on the 
acrial photographs was the dependent variable. Stepwise 
regression approach was uscd to selection the equation. 
Only the one with a significant F-value and higher R2 
value was adopted. Table 2 was the result of regression 
analysis of crown closure for the three indiviaual bands 
of the airborne multispectral scanning data. Selected 
individual bands were MSSs(green) ^ MSSg(near- 
infrared) and MSSo(near-infrared). Follow the same 
125 
procedure, the results of regression analysis of stand 
volume for three individual bands of airborne 
multispectral data was obtained. The three selected 
individual bands of stand volume equation were 
MSS5(green) » MSSgo(red) and MSSg (near-infrored). 
The SPOT data and Landsat-TM data were treated in 
the same way. All of the three SPOT XS bands were 
selected in the crown closure and stand volume 
estimation equation. In the Landsat data aspect, the 
TM, * TM, and, TMs were selected in the crown closure 
equation; the TM; * TM; and TM, were selected in the 
stand volume estimation equation. The results were 
shown in Table 3. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996