TITLE:
Predicting Levels of Crude Protein, Digestibility, Lignin and Cellulose in Temperate Pastures Using Hyperspectral Image Data
AUTHORS:
Susanne Thulin, Michael J. Hill, Alex Held, Simon Jones, Peter Woodgate
KEYWORDS:
Pasture Quality; Crude Protein; Digestibility; Lignin; Cellulose; Hyperspectral Remote Sensing; Partial-Least Squares Regression
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.5 No.7,
March
26,
2014
ABSTRACT: Hyperspectral sensors provide the potential for direct
estimation of pasture feed quality attributes. However, remote sensing
retrieval of digestibility and fibre (lignin and cellulose) content of vegetation
has proven to be challenging since tissue optical properties may not be
propagated to the canopy level in mixed cover types. In this study, partial
least squares regression on spectra from HyMap and Hyperion imagery were used
to construct predictive models for estimation of crude protein, digestibility,
lignin and cellulose concentration in temperate pastures. HyMap and Hyperion
imagery and field spectra were collected over four pasture sites in southern
Victoria, Australia. Co-incident field samples were analyzed with wet chemistry
methods for crude protein, lignin and cellulose concentration, and
digestibility was calculated from fiber determinations. Spectral data were
subset based on sites and time of year of collection. Reflectance spectra were
extracted from the hyperspectral imagery and collated for analysis. Six
different transformations including derivatives and continuum removal were
applied to the spectra to enhance absorption features sensitive to the quality
attributes. The transformed reflectance spectra were then subjected to partial
least squares regression, with full cross-validation “leave-one-out” technique, against the quality attributes to assess effects of the spectral
transformations and post-atmospheric smoothing
techniques to construct predictive models. Model performance between spectrometers,
subsets and attributes were assessed using a coefficient of variation (CV),
—the interquantile (IQ) range of the attribute values divided by the root mean
square error of prediction (RMSEP) from the models. The predictive models with
the highest CVs were obtained for digestibility for all spectra types, with
HyMap the highest. However, models with slightly lower CVs were obtained for
crude protein, lignin and cellulose. The spectral regions for diagnostic
wavelengths fell within the chlorophyll well, red edge, and 2000-2300
nm ligno-cellulose-protein regions, with some wavelengths selected between the
1600 and 1800 nm region sensitive to nitrogen, protein, lignin and cellulose.
The digestibility models with the highest CV’s had confidence intervals corresponding
to ±5% digestibility, which constitutes
approximately 30% of the measured range. The cellulose and lignin models with
the highest CV’s also had similar confidence intervals but the slopes of the
prediction lines were substantially less than 1:1 indicating reduced
sensitivity. The predictive relationships established here could be applied to
categorizing pasture quality into range classes and to determine whether
pastures are above or below for example threshold values for livestock
productivity benchmarks.