The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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position variables, and spectral absorption features were 
extracted from the in situ hyperspectral measurements and 
analyzed for classifying the 11 species. 
Wavelength (nm) 
Figure 2. Figure 2(a) shows a plot of unnormalized curves of 
five oak species versus band wavelength for two observations 
of each of the five oak species (Blue, Laur, Live, Sand and 
Turk). Figure 2(b) shows the same curves of Figure 2(a) after 
normalization. Figure 2(c) shows a plot of normalized curves 
versus band wavelength for all the 11 species (Elm through 
Turk). 
3.3 ANOVA 
To select a subset of spectral variables from the total 46 spectral 
variables for running ANN and LAD for species recognition, an 
one-way ANOVA analysis was performed. This was done 
based on greater spectral separability between any two species 
(paired-species) of the 11 species, using the SPSS statistical 
package (www.spss.com, 2007). For any paired-species from 
the 11 species, all spectral measurements for the paired-species 
were used to conduct the ANOVA analysis across the 46 
spectral variables (Table 2). Then based on the degree of 
spectral separability of each spectral variable between the 
paired-species, a statistical frequency was calculated at 
probability levels p<0.01 and p<0.05 for each spectral variable. 
For this analysis, a maximum frequency at either p<0.01 or 
p<0.05 is 55 (because of n c 2 =55)- 
3.4 Species Recognition Schemes 
Two supervised classification schemes were employed for the 
broadleaf tree classification: non-linear artificial neural network 
(ANN) and linear discriminant analysis (LDA). In this analysis, 
a feed-forward ANN algorithm was used for classifying the 11 
species. An LDA classifier was also used to classify the 11 tree 
species with inputs of the same subset of spectral variables as 
for ANN to compare with the classified results by ANN. The 
procedure DISCRIM in the SAS system (SAS Institute, 1991) 
was used. 
Two sets of samples were allocated - training and test samples, 
from a total of 394 samples collected from 11 tree species. The 
training samples were used for training ANN and LDA while 
test samples are used to evaluate the tree species recognition 
accuracies, generated with ANN and LDA. About 2/3 of the 
samples were used for training and about 1/3 of the samples 
were used as test samples. This procedure was repeated three 
times (runs) (see Table 1) to obtain three different sets of test 
samples (but training sets with a part overlaid between any two 
sets). Finally, an overall accuracy (OAA) and Kappa index are 
calculated from a confusion matrix produced with the test 
samples using ANN and LDA. 
4. RESULTS AND ANALYSIS 
4.1 ANOVA 
After the in situ hyperspectral data were preprocessed, 
including smoothing and normalization, according to the 
definitions for spectral variables listed in Table 2, the 46 
spectral variables were extracted. An one-way ANOVA 
analysis was first performed for all the extracted spectral 
variables from which a subset of spectral variables was selected. 
If the frequency threshold was set to greater than the half of 
maximum possible frequency of 55, a total of 30 spectral 
variables were selected. 
Among the 30 selected spectral variables, all 10 Vis are 
included, which imply that those Vis make a substantial 
contribution to separating most of the 11 tree species. The 30 
selected spectral variables can be further classified into two 
groups. The first group of spectral variables mainly describes 
the variation of foliage water content among the difference 
species and its spectral variables consist of WI, DSWI, 
Ratio 1200, E-ID, NDWI, DEP-975, AREA-975, H-WP, PRI, 
DEP-1200, AREA-1200, Ratio975, and WID-1200. The