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


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.

Share and Cite:

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.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Kawamura, K., Watanabe, N., Sakanoue, S. and Inoue, Y. (2008) Estimating Forage Biomass and Quality in a Mixed Sown Pasture Based on Partial Least Squares Regression with Waveband Selection. Grassland Science, 54, 131-145.
[2] Numata, I., Roberts, D.A., Chadwick, O.A., Schimel, J.P., Galvao, L.S. and Soares, J.V. (2008) Evaluation of Hyperspectral Data for Pasture Estimate in the Brazilian Amazon Using Field and Imaging Spectrometers. Remote Sensing of Environment, 112, 1569-1583.
[3] Pearson, R.L., Tucker, C.J. and Miller, L.D. (1976) Spectral Mapping of Shortgrass Prairie Biomass. Photogrammetric Engineering and Remote Sensing, 42, 317-323.
[4] Richardson, A.J., Everitt, J.H. and Gausman, H.W. (1983) Radiometric Estimation of Biomass and Nitrogen Content of Alicia Grass. Remote Sensing of Environment, 13, 179-184.
[5] Wylie, B.K., Harrington, J.A., Prince, S.D. and Denda, I. (1991) Satellite and Ground-Based Pasture Production Assessment in Niger: 1986-1988. International Journal of Remote Sensing, 12, 1281-1300.
[6] Todd, S.W., Hoffer, R.M. and Milchunas, D.G. (1998) Biomass Estimation on Grazed and Ungrazed Rangelands Using Spectral Indices. International Journal of Remote Sensing, 19, 427-438.
[7] Davidson, A. and Csillag, F. (2001) The Influence of Vegetation Index and Spatial Resolution on a Two-Date Remote Sensing-Derived Relation to C4 Species Coverage. Remote Sensing of Environment, 75, 138-151.
[8] Hill, M.J., Donald, G.E., Hyder, M.W. and Smith, R.C.G. (2004) Estimation of Pasture Growth Rates in South Western Australia from AVHRR NDVI and Climate Data. Remote Sensing of Environment, 93, 528-545.
[9] Edirisinghe, A., Hill, M.J., Donald, G.E., Henry, D. and Hyder, M. (2011) Quantitative Mapping of Pasture Biomass Using Satellite Imagery. International Journal of Remote Sensing, 32, 2699-2724.
[10] Norris, K.H., Barnes, R.F., Moore, J.E. and Shenk, J.S. (1976) Predicting Forage Quality by Infrared Reflectance Spectroscopy. Journal of Animal Science, 43, 889-897.
[11] Murray, I. (1989) Application of NIRS in Agriculture. Proceedings of the 2nd International Near Infrared Spectroscopy Conference, Tsukuba, 29 May-2 June 1989, 11-20.
[12] Danieli, P.P., Carlini, P., Benabucci, U. and Ronchi, B. (2004) Quality Evaluation of Regional Forage Resources by Means of Near Infrared Reflectance Spectroscopy. Italian Journal of Animal Science, 3, 363-376.
[13] Asner, G.P. (2004) Chapter 2: Biophysical Remote Sensing of Signatures of Arid and Semiarid Ecosystems. In: Ustin, S.L., Ed., Remote Sensing for Natural Resources Management and Environmental Monitoring: Manual of Remote Sensing, John Wiley & Sons, Inc., Hoboken, 53-109.
[14] Asner, G.P. and Martin, R.E. (2008) Airborne Spectranomics: Mapping Canopy and Taxonomic Diversity in Tropical Forests. Frontiers in Ecology and the Environment, 7, 269-276.
[15] Ball, D.M., Collins, M., Lacefield, G.D., Martin, N.P., Mertens, D.A., Olson, K.E., Putnam, D.H., Undersander, D.J. and Wolf, M.W. (2001) Understanding Forage Quality. American Farm Bureau Federation Publication 1-01, American Farm Bureau Federation, Park Ridge.
[16] Schroeder, J.W. (1994) Interpreting Forage Analysis. North Dakota State University.
[17] Sullivan, J.T. (1973) Drying and Storing Herbage as Hay. In: Butler, G.W. and Bailey, R.W., Eds., Chemistry and Biochemistry of Herbage, Vol. 3, Academic Press, London and New York, 1-28.
[18] Van Soest, P.J. (1985) Composition, Fiber Quality and Nutritive Value of Forages. Chapter 44. In: Heath, M.E., Barnes, E.M. and Metcalfe, D.S., Eds., Forages-The Science of Grassland Agriculture, Iowa State University Press, Ames, 412-444.
[19] Curran, P.J., Dungan, J.L. and Peterson, D.L. (2001) Estimating the Foliar Biochemical Concentration of Leaves with Reflectance Spectrometry: Testing the Kokaly and Clark Methodologies. Remote Sensing of Environment, 76, 349-359.
[20] Kokaly, R.F. and Clark, R.N. (1999) Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression. Remote Sensing of Environment, 67, 267-287.
[21] Filella, I. and Peñuelas, J. (1994) The Red Edge Position and Shape as Indicators of Plant Chlorophyll Content, Biomass and Hydric Status. International Journal of Remote Sensing, 15, 1459-1470.
[22] Jago, R.A., Cutler, M.E. and Curran, P.J. (1999) Estimation of Canopy Chlorophyll Concentration from Field and Airborne Spectra. Remote Sensing of Environment, 68, 217-224.
[23] Garcia-Ciudad, A., Ruano, A., Becerro, F., Zabalgogeazcoa, I., Vazquez de Aldana, B.R. and Garcia-Criado, B. (1999) Assessment of the Potential of NIR Spectroscopy for the Estimation of Nitrogen Content in Grasses from Semiarid Grasslands. Animal Feed Science and Technology, 77, 91-98.
[24] Lamb, D.W., Steyn-Ross, M., Schaares, P., Hanna, M.M., Silvester, W. and Steyn-Ross, A. (2002) Estimating Leaf Nitrogen Concentration in Ryegrass (Lolium spp.) Pasture Using the Chlorophyll Red-Edge: Theoretical Modelling and Experimental Observations. International Journal of Remote Sensing, 23, 3619-3648.
[25] Dash, J. and Curran, P.J. (2004) The MERIS Terrestrial Chlorophyll Index. International Journal of Remote Sensing, 25, 5403-5413.
[26] Dash, J. and Curran, P.J. (2007) Evaluation of the MERIS Terrestrial Chlorophyll Index (MTCI). Advances in Space Research, 39, 100-104.
[27] Pinzon, J.E., Ustin, S.L., Castaneda, C.M. and Smith, M.O. (1998) Investigation of Leaf Biochemistry by Hierarchical Foreground/Background Analysis. IEEE Transactions on Geoscience and Remote Sensing, 36, 1913-1927.
[28] Soukupova, J., Rock, B.N. and Albrechtova, J. (2002) Spectral Characteristics of Lignin and Soluble Phenolics in the Near Infrared—A Comparative Study. International Journal of Remote Sensing, 23, 3039-3055.
[29] Serrano, L., Peñuelas, J. and Ustin, S.L. (2002) Remote Sensing of Nitrogen and Lignin in Mediterranean Vegetation from AVIRIS Data: Decomposing Biochemical from Structural Signals. Remote Sensing of Environment, 81, 355-364.
[30] Asner, G.P., Wessman, C.A., Bateson, C.A. and Privette, J.L. (2000) Impact of Tissue, Canopy and Landscape Factors on the Hyperspectral Reflectance Variability of Arid Ecosystems. Remote Sensing of Environment, 74, 69-84.
[31] Lewis, M., Jooste, V. and de Gasparis, A.A. (2001) Discrimination of Arid Vegetation with Airborne Multispectral Scanner Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, 1471-1479.
[32] Ben-Dor, E., Chabrillat, S., Demattê, J.A.M., Taylor, G.R., Hill, J., Whiting, M.L. and Sommer, S. (2009) Using Imaging Spectroscopy to Study Soil Properties. Remote Sensing of Environment, 113, S38-S55.
[33] Asner, G.P. and Lobell, D.B. (2000) A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation. Remote Sensing of Environment, 74, 99-112.
[34] Schut, A.G.T., Lokhorst, C., Hendriks, M.M.W.B., Kornet, J.G. and Kasper, G. (2005) Potential of Imaging Spectroscopy as Tool for Pasture Management. Grass and Forage Science, 60, 34-45.
[35] Schut, A.G.T., Thompson, A.N., Gherardi, S. and Metternicht, G. (2006) Seasonal Changes in Pasture Quality in Mediterrenean Regions in Australia. 13th Australian Agronomy Conference, Perth, 10-14 September 2006.
[36] Thulin, S.M., Hill, M.J., Held, A.H., Jones, S. and Woodgate, P. (2012) Hyperspectral Determination of Feed Quality Constituents in Temperate Pastures: Effect of Processing Methods on Predictive Relationships from Partial Least Squares Regression. International Journal of Applied Earth Observation and Geoinformation, 19, 322-334.
[37] Mutanga, O. and Skidmore, A.K. (2004) Hyperspectral Band Depth Analysis for a Better Estimation of Grass Biomass (Cenchrus ciliaris) Measured under Controlled Laboratory Conditions. International Journal of Applied Earth Observation and Geoinformation, 5, 87-96.
[38] Mutanga, O., Skidmore, A.K., Kumar, L. and Ferwerda, J. (2005) Estimating Tropical Pasture Quality at Canopy Level Using Band Depth Analysis with Continuum Removal in the Visible Domain. International Journal of Remote Sensing, 26, 1093-1108.
[39] Cocks, T.D., Jenssen, R., Steward, A., Wilson, I. and Shields, T. (1998) The HyMapTM Airborne Hyperspectral Sensor: The System, Calibration and Performance. In The 1st EARSeL Workshop on Imaging Spectroscopy, University of Zurich, Zurich.
[40] Pearlman, J.S., Barry, P.S., Segal, C.S., Shepanski, J., Beiso, D. and Carman, S.L. (2003) Hyperion, a Space-Based Imaging Spectrometer. IEEE Transactions on Geoscience and Remote Sensing, 41, 1160-1173.
[41] Barry, P. (2001) EO-1/Hyperion Science Data User’s Guide. TRW Space, Defense & Information Systems, Redondo Beach, CA.
[42] Apan, A. and Held, A. (2002) In-House Workshop on Hyperion Data Processing: Echoing the Sugarcane Project Experience. CSIRO Land and Water, Black Mountain Laboratories, Canberra.
[43] Datt, B. and Jupp, D.L.B. (2004) Hyperion Data Processing Workshop: Hands-On Processing Instructions. CSIRO Office of Space Science & Applications Earth Observation Centre, Canberra.
[44] Datt, B., McVicar, T.R., Van Niel, T.G., Jupp, D.L.B. and Pearlman, J.S. (2003) Preprocessing EO-1 Hyperion Hyperspectral Data to Support the Application of Agricultural Indexes. IEEE Transactions on Geoscience and Remote Sensing, 41, 1246-1259.
[45] Green, A.A., Berman, M., Switzer, P. and Craig, M.D. (1988) A Transformation for Ordering Multispectral Data in terms of Image Quality with Implications for Noise Removal. IEEE Transactions on Geoscience and Remote Sensing, 26, 65-74.
[46] Jupp, D.L.B., Datt, B., Lovell, J., Campbell, S. and King, E.A. (2002) Discussions around Hyperion Data: Background Notes for the Hyperion Data Users Workshop. CSIRO EOC, Canberra.
[47] Research Systems Inc. (2003) ENVI Online Help. Retrieved Help Manual for ENVI, RSI, Boulder, CO.
[48] Center for the Study of Earth from Space (CSES) (1999) Atmosphere Removal Program (ATREM). Version 3.1, User’s Guide. CIRES, University of Colorado, CSES Center for the Study of Earth from Space, Boulder.
[49] Mason, P. (2000) HYCORR User’s Manual. CSIRO, Sydney.
[50] Boardman, J.W. (1998) Post-ATREM Polishing of AVIRIS Apparent Reflectance Data Using EFFORT: A Lesson in Accuracy versus Precision. In: Summaries of the 7th JPL Airborne Earth Science Workshop, 12-16 January 1998, Pasadena, CA. JPL Publication 97-21. Pasadena, CA, 1, 1-53.
[51] Tsai, F. and Philpot, W. (1998) Derivative Analysis of Hyperspectral Data. Remote Sensing of Environment, 66, 41-51.
[52] Huang, Z., Turner, B.J., Dury, S.J., Wallis, I.R. and Foley, W.J. (2004) Estimating Foliage Nitrogen Concentration from HYMAP Data Using Continuum Removal Analysis. Remote Sensing of Environment, 93, 18-29.
[53] Jupp, D.L.B. (2001) Discussion around Hyperion Data.
[54] Schlerf, M., Atzberger, C., Udelhoven, T., Jarmer, T., Mader, S., Werner, W. and Hill, J. (2003) Spectrometric Estimation of Leaf Pigments in Norway Spruce Needles Using Band-Depth Analysis, Partial Least-Squares Regression and Inversion of a Conifer Leaf Model. 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 13-16 May, 559-568.
[55] Martens, H. and Martens, M. (2001) Multivariate Analysis of Quality: An Introduction. John Wiley & Sons, Chichester.
[56] Naes, T., Isaksson, T., Fearn, T. and Davies, T. (2002) A User-Friendly Guide to Multivariate Calibration and Classification. 1st Edition, NIR Publications, Chichester.
[57] Ollinger, S.V., Smith, M.L., Martin, M.E., Hallett, R.A., Goodale, C.L. and Aber, J.D. (2002) Regional Variation in Foliar Chemistry and N Cycling among Forests of Diverse History and Composition. Ecology, 83, 339-355.
[58] Park, R.S., Agnew, R.E., Gordon, F.J. and Steen, R.W.J. (1998) The Use of Near Infrared Reflectance Spectroscopy (NIRS) on Undried Samples of Grass Silage to Predict Chemical Composition and Digestibility Parameters. Animal Feed Science and Technology, 72, 155-167.
[59] Curran, P.J. (1989) Remote Sensing of Foliar Chemistry. Remote Sensing of Environment, 30, 271-278.
[60] Gordon, F.J., Cooper, K.M., Park, R.S. and Steen, R.W.J. (1998) The Prediction of Intake Potential and Organic Matter Digestibility of Grass Silages by Near Infrared Spectroscopy Analysis of Undried Samples. Animal Feed Science and Technology, 70, 339-351.
[61] Nagler, P.L., Daughtry, C.S.T. and Goward, S.N. (2000) Plant Litter and Soil Reflectance. Remote Sensing of Environment, 71, 207-215.
[62] Park, R.S., Agnew, R.E., Gordon, F.J. and Barnes, R.J. (1999) The Development and Transfer of Undried Grass Silage Calibrations between Near Infrared Reflectance Spectroscopy Instruments. Animal Feed Science and Technology, 78, 325-340.
[63] Hill, R. (1999) PROGRAZE®: Profitable, Sustainable Grazing. Department of Natural Resources and Environment, Ballarat.
[64] Saul, G.R. (2006) Chapter 9: Livestock Production from Pastures. In: Nie, Z. & Saul, G.R., Eds., Greener Pastures for South West Victoria, 2nd Edition, Victorian Department of Primary Industries, Hamilton, 80-89.
[65] Horizon Agriculture Pty. Ltd. (2005) Linking Science to Productive Solutions—Mean DM Digestibility.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.