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EXTRACTION OF FOLIAR BIOCHEMISTRY FROM HYPERSPECTRAL DATA USING 
WAVELET DECOMPOSITION. 
G. A. Blackburn 3 ’ *, J. G. Ferwerda b 
a Department of Geography, Lancaster University, Lancaster, LAI 4YB, UK - alan.blackbum@lancaster.ac.uk 
b Department of Zoology, Oxford University, Oxford, 0X1 3PS, UK -jelle@bio-vision.nl 
KEY WORDS: Leaf, Biochemicals, Water content, Reflectance, Hyperspectral, Wavelet transform. 
ABSTRACT: 
This study explores the potential of wavelet decomposition of leaf reflectance spectra for quantifying foliar biochemicals and water. 
A leaf-scale radiative transfer model was used to generate a very large spectral data set with which to develop and rigorously test the 
technique. The size of the data set enabled a thorough statistical analysis of the performance a range of alternative methods for 
constructing predictive models including the selection of specific wavelet functions, continuous or discrete transforms, reflectance or 
derivative input spectra and number of wavelet coefficients used as predictors. The results demonstrated that wavelet decomposition 
techniques can generate accurate predictions of protein, lignin/cellulose and water content, despite wide variations in of all of the 
biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on 
untransformed spectra and enabled the greatest improvements in performance for protein followed by lignin/cellulose then water 
content. Hence, the study highlights the capabilities of wavelet decomposition for extracting information concerning leaf 
components that have narrow, weak absorption features, which are otherwise difficult to characterise in untransformed reflectance 
spectra. 
1. INTRODUCTION 
The spatial and temporal dynamics of foliar biochemicals and 
water content are intimately linked to the interactions between 
plants and the environment. Quantifying such dynamics can 
provide important evidence concerning the functioning of 
ecosystems and the physiological status of crops. It is with 
variable success that hyperspectral remote sensing has been 
applied to quantifying foliar biochemistry and water content 
and there is considerable scope for developing more accurate 
and robust analytical techniques. One particular approach, 
developed by the authors, that has proven successful in 
quantifying chlorophyll concentrations is based on the wavelet 
decomposition of reflectance spectra (Blackburn and Ferwerda, 
2008). 
Wavelet decomposition is used to separate complex signals into 
their basic component signals. Comprehensive descriptions of 
wavelet analysis are provided by Graps (1995) and Strang and 
Nguyen (1996). In the context of leaf reflectance spectra, the 
component signals result from the various biochemical and 
biophysical properties that control the optical properties of 
leaves. Some of these properties such as leaf structure, affect 
broad regions of the reflectance spectrum, while other 
properties such as particular biochemicals can affect specific 
narrow wavebands. Hence a technique which is able to 
discriminate between effects at different spectral scales and 
identify wavelength positions that contain the greatest 
information would be valuable in a quantitative remote sensing 
context. Wavelet analysis is able to decompose signals 
according to both frequency and time domains, meaning that 
the wavelet coefficients that result contain information 
concerning the scale and position of component signals. This 
capability has proven useful in extracting information 
concerning chlorophyll concentrations from leaf reflectance 
spectra despite large variations in the range of other controlling 
factors. The aim of the present study is to determine the 
suitability of wavelet-based methods for quantifying other foliar 
biochemicals and water. 
2. METHODS 
The experimental strategy adopted was to generate a data set 
that incorporated leaves with a wide range in biochemical and 
water content and encompassed a large variability in all of the 
other factors that influence leaf reflectance spectra. Our 
previous experience in collecting leaf reflectance spectra and 
measuring leaf biophysical and biochemical properties 
indicated that the time and resources required to physically 
collect the large data set needed for this project would be 
prohibitive. Therefore, leaf radiative transfer (RT) modelling 
was used to generate spectral data sets based on a very wide 
range and combinations of leaf properties that influence 
reflectance. 
The LIBERTY model adapts RT theory for determining the 
optical properties of powders for describing light interaction 
within cells which are roughly spherical and separated by air 
spaces (Dawson et al., 1998). LIBERTY simulates conifer 
needle reflectance over the wavelengths 400 to 2500nm using 
the input parameters: chlorophyll content, water content, 
lignin/cellulose content, protein content, average cell diameter, 
intercellular air space, leaf thickness, baseline absorption and 
albino leaf absorption. The parameter described in this paper as 
protein content is termed nitrogen content in the original model 
description but this is actually based on the absorption spectrum 
of protein and does not incorporate absorption of other 
significant nitrogen containing components such as chlorophyll. 
LIBERTY was used to simulate 1100 spectra and for each 
model run the value of each input parameter was selected 
Corresponding author.