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Predicting Levels of Crude Protein, Digestibility, Lignin and Cellulose in Temperate Pastures Using Hyperspectral Image Data

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American Journal of Plant Sciences 2014年第7期5卷 997-1019页

作者： 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 di

关键词： Pasture Quality Crude Protein Digestibility Lignin Cellulose Hyperspectral Remote Sensing Partial-Least Squares Regression

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