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.
Highlights
Using remote sensing to retrieve key components of the feed budget for livestock grazing enterprises is an important tool for efficient management of livestock, as well as in renewable resource application, e.g. [1] [2]
The partial least squares regression (PLSR) predictive models for pasture quality attributes derived from field spectra convolved to HyMap and Hyperion band pass functions were similar in terms of R, root mean square error of prediction (RMSEP) and coefficient of variation (CV) to the models from the original ASD spectra, except for lignin where the results for the band pass models produced lower CVs (Table 3)
Results from comparison of smoothing approaches on Hyperion spectra, showed that models with highest CVs were obtained for digestibility and lignin when MNF or MNF-EFF smoothing was applied, which is reflected in the results presented in Table 3, i.e. five out of six pasture attributes
Summary
Using remote sensing to retrieve key components of the feed budget for livestock grazing enterprises is an important tool for efficient management of livestock, as well as in renewable resource application, e.g. [1] [2]. [3]-[7] and methods have recently been derived for the description of key components of the feed budget, the growth rate of the pasture [8] and the quantity of available feed on offer [9]. The other important component required to complete the “total” feed budget is a measure of feed quality in pastures. The rising prospects in the five years for routine availability of high quality satellite based hyperspectral sensors provide the potential for use of imaging spectroscopy techniques to provide more direct measurement of feed quality, and for linking in-situ data to remote-sensing derived information, to help characterize and even quantify the heterogeneity of pasture feed quality attributes across the landscape. Hyperspectral sensing provides a larger number of wavelengths that are physically linked to electron transitions or vibrations and overtones of absorptions of molecules of different biochemical constituents and provide high levels of fidelity in chemical detection and quantification, e.g. [1] [13] and together with lidar data and emerging analysis techniques will become increasingly better at mapping the fingerprints of different types of vegetation species [14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
