The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
runs of test samples are around 88% and Kappa values of 0. 87 
and 0.84, respectively. The species recognition accuracies 
produced by ANN are acceptable when considering the 
spectral similarity among most of the 11 species (Figure 2). 
With exactly the same inputs of 30 selected spectral variables 
as for ANN for both 11 species and 5 oak species recognition, 
it is apparent that all accuracy indices produced by LDA are 
very close to those by ANN, including both OAA and Kappa 
values. The recognition accuracies generated by ANN and 
LDA are not statistically different (Z<0.24), indicating, for this 
particular case, that a non-linear recognition method does not 
outperform a linear method. It might be due to most selected 
spectral variables following a normal distribution by their 
corresponding spectral samples. 
Algorithm 
Overall accuracy (%) 
Kappa value 
11 species 
5 oak species 
11 species 
5 oak species 
ANN 
87.82 
87.49 
0.8656 
0.8428 
LDA 
86.80 
86.31 
0.8546 
0.8280 
Note: The overall accuracies produced by ANN and LDA are not significantly different 
at 0.95 confidence level for identifying either 11 species or 5 oak species 
Table 3. Summary of species identification accuracy using 
ANN and LDA algorithms with 30 selected spectral variables. 
5. DISCUSSION 
In this analysis, among the 30 selected spectral variables 
evaluated by ANOVA, most of the spectral variables are 
directly related to leaf chemistry, especially water status and 
chlorophyll content in leaves. For example, some selected 
spectral variables relate to water absorption bands: WI, DEP- 
975, AREA-975, and Ratio975 directly relate to the 970 nm 
water absorption band; Ratio 1200, NDWI, E-ID, DEP-1200, 
AREA-1200, and WID-1200 are directly correlated with the 
1200 water absorption band; and DSWI relates to the 1750 
water absorption band. Other spectral variables relate to 
chlorophyll content: C-1D, A-ID, B-1D, R550, A-WP, LCI, B- 
WP, NDVI, SR, C-WP, and R680 that may directly describe 
the variation of leaf chlorophyll content. In general, the full 
range (350 nm to 2500 nm) of spectral wavelength covered by 
the ASD spectrometer is useful for differentiating species that 
differ in their foliage content, water status, pigment content 
and other biochemicals, including visible, near infrared (NIR) 
and middle infrared (MIR) regions (Nagendra, 2001). 
However, due to the heavy water absorption bands near 1.4 pm 
and 1.9 pm, which always happen in in situ spectral 
measurement, visible and NIR bands are generally more useful 
than MIR bands. In this case, most of the spectral variables 
with high frequency of spectral separabilty between any 
paired-species have been constructed from some of the visible 
and NIR bands. 
Although the recognition accuracy (around 88%) derived from 
this study is acceptable for in situ species differentiation much 
work is needed before applying this method to remote-sensing 
image data, including high spatial resolution data, e.g., 
IKONOS (Carleer and Wolff, 2004) and QuickBird (Wang et 
al., 2004) or hyperspectral data, e.g., AVIRIS (Xiao et a., 2004) 
and HyMap (Buddenbaum et al., 2005). In this study, 
atmospheric effects on the in situ hyperspectral measurements 
was minimal except for the two major water absorption bands 
in the MIR region. However, for remote-sensing image data, 
atmospheric effects have to be corrected or compressed before 
conducting a species recognition analysis (Nagendra, 2001; 
Clark et al., 2005). Atmospheric correction will enhance the 
spectral separability between species with multi/hyperspectral 
remote-sensing data. Even so, the preliminary results with the 
in situ hyperspectral data imply that current remote-sensing 
techniques are still difficult but possible to identify similar 
species to such 11 broadleaf species with an acceptable 
accuracy. 
6. CONCLUSIONS 
The ANOVA analysis results indicate that the extracted 30 
spectral variables are effective for differentiating the 11 
species. The 30 selected spectral variables include spectral 
variables related to water absorption bands: WI, DEP-975, 
AREA-975, and Ratio975 (directly related to the 970 nm water 
absorption band); Ratio 1200, NDWI, E-ID, DEP-1200, 
AREA-1200, and WID-1200 (directly correlated to the 1200 
water absorption band); and DSWI (related to the 1750 water 
absorption band). The remaining spectral variables relate to 
chlorophyll content: C-1D, A-ID, B-1D, R550, A-WP, LCI, B- 
WP, NDVI, SR, C-WP, and R680 (may directly describe the 
variation of leaf chlorophyll content). Both classification 
algorithms (ANN and LDA) produced acceptable accuracies 
(OAA from 86.3 % to 87.8%, Kappa from 0.83 to 0.87). In 
this study, ANN and LDA for classifying the 11 broadleaf 
species have a similar performance and the difference of 
species recognition accuracies between the two classification 
algorithms is not statistically significant at 0.95 confidence 
level. The preliminary results of identifying the 11 species 
with the in situ hyperspectral data imply that current remote 
sensing techniques, including high spatial and spectral sensors’ 
data, are still difficult but possible to identify similar species to 
such 11 broadleaf species with an acceptable accuracy. 
ACKNOWLEDGEMENTS 
This work was supported by University of South Florida (USF) 
under the New Researcher Grant (Grant #: 18300). USF 
graduate students, Mr. John Kunzer and Mr. John Merrill, are 
greatly appreciated for their helping collect spectral 
measurements in the field. Thanks also to USGS George R. 
Kish for his valuable comments on this paper at an early stage. 
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