Improving Species Spectral Discrimination Using Derivatives Spectra For Mapping of Tropical Forest From Airborne Hyperspectral ImageryMATERIALS AND METHODS Study Site This study was conducted at an old growth plot (as early as 1927) in the Forest Research Institute of Malaysia (FRIM) Kepong, Selangor. The reserve is situated 11km north-west of Kuala Lumpur with the latitude 3013' to 3015'N and longitude of 101036' and 101039' E. The study was confined to a mixed species plot, which comprise 16 tree species from both Dipterocarps (Meranti Sengkawang Merah, Meranti Paang, Meranti Tembaga, Meranti Rambai Daun, Kapur, Keladan, Balau Laut, Balau Kumus and Merawan Siput Jantan) and Non-Dipterocarps (Jelutong, Kelat, Pulai, Karas, Sesendok, Melembu and Inggir Burong) taxa. Hyperspectral Imagery The airborne campaign over FRIM was conducted on May 2005 using a compact push-broom airborne imaging spectrometer (AISA), covering the 430nm to 900nm. The sensor is integrated with a GPS/IMU system which measures the position, velocity, timing altitude and linear accelerations of the sensor head during flight. The system also consists of an internal fiber optic downwelling irradiance sensor (FODIS), which provide information of radiation during the measurement as it comes from the source, the sun, and use that information to correct the data imaged from the ground to at sensor reflectance. As the objective of the study was to map individual tree species, the AISA sensor was operated at spatial mode with a 1m ground pixel and 20 wavelength channels spectrally configured (Affendi et al. 2005) for tropical forest mapping. First Derivative Reflectance Derivative spectroscopy is applied by numerically computing the derivatives of the original spectrum with respect to the wavelength and using them to detect the absorption band positions of the spectrum. In this study, the 1st derivative transformation is calculated using IDL’s DERIV function (David Gorodetzky) a plug-in to the ENVI 4.0 software. It performs a numerical differentiation using 3-point Lagrangian interpolation where the wavelength of the respective spectral bands (AISA) are used as input for defining the spacing between the bands. Separability and Accuracy Measures A comparison was made to the Reflectance data set and its 1st Derivative Transformation in order to evaluate the effectiveness of using this technique in spectrally discriminating between the tree species. A measure of separation of the various tree species based on their spectral response (pixel scale) was obtained with the Jefferies-Matuista distance measure and the classification accuracy of the data sets were assessed based on an error or confusion matrix and the kappa coefficient which compares the crown region of interest (ROI) from the field measured data to the tree crown polygons from the respective data sets established by the Maximum Likelihood algorithm. RESULTS AND DISCUSSION Visual assessment of the image shows that the 1st derivative data set could better discriminate among the different tree species classes in the image based on the distinct tonal variations present as compared to the reflectance data set. Figure 1 compares a false color composite image from the original reflectance data set (a) and the spectrally enhanced 1st derivative data set (b) using band 13(R), 11(G) and 9(B) in the mixed forest plot.  Figure 1a: False composite image [R(Band 13) G(Band 11) B(Band 9)] of the AISA reflectance data set over the old growth forest plot.  Figure 1b: False composite image [R(Band 13) G(Band 11) B(Band 9)] of the transformed 1st derivative data set over the old growth forest plot. |