Spectroscopic determination of health levels of coast live oak (Quercus agrifolia) leaves

Abstract

Three sets of coast live oak (Quercus agrifolia) leaf samples were collected on three dates: 20 April 2002, 23 July 2002 and 11 September 2001, respectively, for Sudden Oak Death (SOD) monitoring. A total of 330 reflectance spectra (covering 350-2500 nm) were measured in the laboratory with a spectrometer FieldSpec®Pro FR. In this study, the spectroscopic determination of two health levels of the coast live oak leaves was conducted with three sets of spectra. We used two classification algorithms, penalized discriminant analysis (PDA) and cross correlogram spectral matching (CCSM), to discriminate between healthy and infected leaves. PDA is a penalized version of Fisher's linear discriminant analysis (LDA) and can considerably improve upon LDA when it is used for the classification of hyperspectral data. CCSM is practised by calculating the cross correlation at different match positions between a test spectrum and a reference spectrum and is also suitable for processing hyperspectral data. Experimental results indicate that the PDA algorithm has produced approximately 7% higher classification accuracy than that produced by CCSM, although both are very low. When considering the subtle spectral differentiation between the two health levels, the PDA method demonstrates its promise as a classification algorithm. Among the 10 spectral ranges, some higher accuracies are produced by both PDA and CCSM algorithms from those spectral range wavelengths shorter than 1400 nm. Based on our experimental results and previous work, existing remote sensing techniques, including airborne or satellite remote sensing and multispectral or hyperspectral remote sensing, may be insufficient for monitoring and mapping disease-induced moisture stress in trees that have recently been infected. However, this does not preclude the analysis of trees at very advanced stages of disease, and the practicality of finding trees within weeks of dying is considerable.