Improving Species Spectral Discrimination Using Derivatives Spectra For Mapping of Tropical Forest From Airborne Hyperspectral ImageryAs shown in Figure 2, although the original reflectance spectra have almost identical values between 680nm and 730nm (red-edge) region, they differ in magnitude and to some extent shapes in the 1st derivative spectra over the same wavelength range. Several interesting spectral features are apparent in the derivative spectra that were obscure in the original spectra. For example is the double-peak feature that is observed on the canopy derivative reflectance, which according to the study by Zarco-Tejada et al.(2003) is a function of the steady-state natural fluorescence emission bands centered at 690nm and 730nm. Horler et al. (1983), on the other hand attributed the first peak at around 700nm to chlorophyll content in the plant leaves and the second at around 725nm to cellular scattering in the leaf. Based on these features that were observed in the derivative data set of our study, we were able to discriminate between the dipterocarp and non dipterocarp genera. This could be seen from the spectral derivative plots in Figure 2(bottom), where the dipterocarps has a higher 2nd peak (centred at 730nm) as compared to the non dipterocarps which generally has an equal or higher 1st peak (centred at 690nm).  Figure 2: Double peak feature as apparent in the spectrally enhanced red edge region of the 1st derivative spectra (bottom) as compared to the reflectance spectra Spectral Separability The dipterocarps are slightly more separable as compared to the non dipterocarps. This could be seen from the results which show distinct separation of 8 species (Meranti Sengkawang Merah, Meranti Paang, Meranti Tembaga, Meranti Rambai Daun, Kapur, Balau Laut, Balau Kumus and Merawan Siput Jantan) as compared to 6 species (Sesendok, Inggir Burong, Karas, Pulai, Kelat and 2 classes of the Jelutong’s) of non dipterocarps from the derivative image data set. This result also shows that the Shorea’s (Meranti’s and Balau) are spectrally distinct from the other species groups. 65.8% of the spectral derivatives data set shows full separation (2.0) based on the JM distance measure with an overall improvement of 95.3% as compared to the reflectance data set. Based on the reflectance spectra, the Keladan (D.oblongfolia) groups (KLD1 and KLD2) have similar response to the other 14 species, however a 50% improvement in separability could be seen for KLD1 using the spectral derivatives data. The derivatives spectra seems to improve the separability of the non dipterocarp groups from only 2 species (Sesendok and Inggir Burong) which are distinctly separable in the reflectance data set to 6 species (Sesendok, Inggir Burong, Karas, Pulai, Kelat and Jelutong) when using the derivatives data set. Classification Accuracy An error matrix was calculated by comparing the crown ROI’s from the field data to the tree crown polygons from the respective data sets established by the Maximum Likelihood algorithm. The overall accuracy of the classification from the reflectance data set was 67.58%, with 7247 out of 10723 pixels correctly classified, with a kappa value of 0.64. Improvement in accuracy is seen in the spectral derivative data set with an overall accuracy of 70.15%, with a kappa value of 0.67 (Figure 3). Only 2501 pixels were misclassified which shows an improvement of 13.5% over the reflectance data set. Highest misclassifications were among Inggir Burong (IBU2), Meranti Paang (MPA) and Jelutong (JEL3) on both data sets however there are improvements in accuracy in the derivative data set with the exception of the Jelutong (JEL3) class.  Figure 3: Classified image of 1st derivative data set using the maximum likelihood classifier CONCLUSION This study has shown that the use of derivative analysis can improve the separability of canopy spectra of the tree species, even if there are spectrally similar, which is common to tropical forest environment. It has shown that the subtle differences within the red edge spectral region could be enhanced based on both amplitude and shape differences using the 1st derivative spectra hence giving the characteristic signature of the respective tree species. From this study it is concluded that hyperspectral data obtained from the airborne imaging spectrometer (AISA) could to a certain extent discriminate between tree species of the tropical forest and the use of derivative spectroscopy as an image enhancement technique could improve species discrimination and classification accuracy. This has provided a potential for application of the hyperspectral imaging system for mapping of the tropical forest at an operational level. ACKNOWLEDGEMENT The authors thank Datuk Amar Haji Abdul Aziz Dato Haji Husain, the State Secretary of Sarawak for the support and encouragement, financial support from the Sarawak State Government (UPM vote 62188), Dr. Noor Azlin Yahya of FRIM for providing the research facilities and field support. Special thanks to Jukka Okkonen, Specim Finland for the technical advice and Aeroscan Precision (M) Sdn.Bhd for the airborne data collection. REFERENCES
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