Conifer Species Recognition: Effects of Data Transformation

In situ hyperspectral data obtained with a high spectral resolution radiometer were analysed for identification of six conifer species. Hyperspectral data were measured in the summer and late fall seasons at 15-20 cm above portions of tree canopies from both the sunlit and shaded sides. An artificial neural network algorithm was applied for identification purposes. Six types of transformation were applied to the hyperspectral reflectance data ( R ), preprocessed with a simple smoothing, followed by band aggregation. These include log( R ), first derivative of R, first derivative of log( R ), normalized R, first derivative of normalized R, and log(normalized R ). First derivative of log( R ) and first derivative of normalized R resulted in best species recognition accuracies with greater than 90% average accuracies, more than 20% greater than the average accuracy obtained from the pre-processed hyperspectral data. The effect of hyperspectral data taken from the shade sides of tree canopies can be minimized by applying normalization or by taking the derivatives after applying a logarithm to the pre-processed data. We found that a big difference in solar angle did not cause a noticeable difference in accuracies of species recognition.