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

Abstract

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 selection 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 discriminating tree species. Overall, this study shows the potential of SPLS-DA to perform simultaneous 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.

Potential use of hyperspectral data to monitor sugarcane nitrogen status

“…Among the limited research on the application of sPLS in the study of the spectral response of vegetation was a study performed by Peerbhay, Mutanga, and Ismail (2014), who compared the traditional partial least squares (PLS) method with sPLS in the selection of variables for discriminant analysis of pine varieties in South Africa. The efficiency of the discriminant analysis increased from 71.88% using PLS to 80.21% using sPLS, as observed by Abdel-Rahman et al (2014), who compared the two methodologies for variable selection and model generation to predict the oleraceous yield, also in South Africa.…”

Detecting canopy damage caused by Uromycladium acaciae on South African Black Wattle forest compartments using moderate resolution satellite imagery

“…framework is adopted in this study owing to the recent success in forest type applications globally (dos Santos et al, 2017;Peerbhay et al, 2013;Peerbhay et al, 2014;Peerbhay et al, 2016) and to the best of our knowledge the method has not being used for forest defoliation mapping using remotely sensed data in South Africa.…”

The identification and remote detection of alien invasive plants in commercial forests: An Overview

“…Subsequently, the main advantage associated with hyperspectral data is that they provide detailed spectral profiles that can distinguish native from non-native plants and that the most significant spectral regions in detecting the species of interest can be analysed (Huang and Asner, 2009). Many studies have used hyperspectral remote sensing to detect alien plants (Ustin et al, 2002, Williams and Hunt, 2002, Asner et al, 2006, Asner et al, 2008a, Asner et al, 2008b, Peerbhay et al, 2014a, Peerbhay et al, 2015. Williams and Hunt (2002) for example, successfully mapped European Euphorbia esula (leafy spurge) utilising AVIRIS hyperspectral data.…”