Author(s)

Susanne Thulin, Michael J. Hill, Alex Held, Simon Jones, Peter Woodgate

AusCover, CSIRO Marine and Atmospheric Research, Canberra, Australia.
Cooperative Research Centre for Spatial Information, Carlton, Australia.
Department of Earth System Science and Policy, University of North Dakota, Grand Forks, USA.
Department of Geospatial Sciences, RMIT Univer-sity, Melbourne, Australia.
Department of Geospatial Sciences, RMIT University, Melbourne, Australia; Brockmann-Geomatics Sweden AB, Kista, Sweden.

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.

Pasture Quality; Crude Protein; Digestibility; Lignin; Cellulose; Hyperspectral Remote Sensing; Partial-Least Squares Regression

Thulin, S. , Hill, M. , Held, A. , Jones, S. and Woodgate, P. (2014) Predicting Levels of Crude Protein, Digestibility, Lignin and Cellulose in Temperate Pastures Using Hyperspectral Image Data. American Journal of Plant Sciences, 5, 997-1019. doi: 10.4236/ajps.2014.57113.