Does simultaneous variable selection and dimension reduction improve the classification ofPinusforest species?

Abstract

Tree species information is important for forest inventory management and supports decisions related to the composition and distribution of forest resources. However, traditional methods of obtaining such information involve time consuming and cost intensive ground-based methods. Hyperspectral data offer an alternative source for obtaining information related to forest inventory. Utilizing Airborne Imaging Spectrometer for Applications Eagle hyperspectral data (393 to 994 nm), this study compares the utility of two partial least squares (PLS)-based methods for the classification of three commercial Pinus tree species. Results indicate that the sparse partial least squares discriminant analysis (SPLS-DA) method performed variable selec- tion and dimension reduction successfully to produce an overall accuracy of 80.21%. In comparison, the PLS-DA method and variable importance in the projection (VIP) selected bands produced an overall accuracy of 71.88%. The most effective bands selected by PLS-DA and VIP coincided within the visible region of the spectrum (393 to 700 nm). However, SPLS-DA selected fewer wavebands within the blue (415 to 483 nm), green (515 to 565 nm), and red regions (674 to 694 nm) to confirm the importance of the visible in dis- criminating tree species. Overall, this study shows the potential of SPLS-DA to perform simul- taneous variable selection and dimension reduction of hyperspectral remotely sensed data resulting in improved classification accuracies. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10