Estimating Carex quality with laboratory-based hyperspectral measurements

The quality of certain plants is considered to be a key factor affecting the food habitat or migration of some herbivorous species, and, thus, to estimate the spatial and temporal variation of plant quality is crucial for understanding the grazing and migrating behaviours of these herbivores. This study aimed to explore the possibilities of estimating plant protein and phosphorus contents, with the laboratory-based hyperspectral measurements of fresh Carex leaves, which are the main food source of many wintering bird species in Poyang Lake, China. Fifty-four Carex leaf samples were collected, and their hyperspectral reflectance (at 350–2500 nm) and crude protein and phosphorus contents were measured in the laboratory. The successive projections algorithm (SPA) was applied for spectral dimension reduction, and a multiple linear regression model was calibrated to estimate the crude protein and phosphorus contents from the wavelengths selected with the SPA. The model validation results showed that the root mean square errors (RMSEs) of estimation were 2.51% for crude protein and 0.06% for phosphorus. Compared with a multiple linear model with randomly selected inputs and full-spectrum partial least-square regression (PLSR), the multiple linear regression model combined with the SPA method exhibited a significant advantage in terms of accuracy in estimating the crude protein and phosphorus contents of Carex leaves.

[1]  P. Filho.  Rapid determination of sucrose in chocolate mass using near infrared spectroscopy , 2009 .

[2]  D. Massart,et al.  The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. , 1999, Journal of Pharmaceutical and Biomedical Analysis.

[3]  Andrew K. Skidmore,et al.  Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Quansheng Chen,et al.  Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. , 2008, Journal of pharmaceutical and biomedical analysis.

[5]  Simon D. Jones,et al.  Remote sensing of nitrogen and water stress in wheat , 2007 .

[6]  B. Turner,et al.  Estimating foliage nitrogen concentration from HYMAP data using continuum, removal analysis , 2004 .

[7]  D. Gianelle,et al.  Nadir and off‐nadir hyperspectral field data: strengths and limitations in estimating grassland biophysical characteristics , 2007 .

[8]  S. Ollinger,et al.  A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems , 2008 .

[9]  Dong Wang,et al.  Successive projections algorithm combined with uninformative variable elimination for spectral variable selection , 2008 .

[10]  Andrew K. Skidmore,et al.  Can nutrient status of four woody plant species be predicted using field spectrometry , 2007 .

[11]  L. Kumar,et al.  Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data , 2007 .

[12]  Philippe Lagacherie,et al.  Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements , 2008 .

[13]  R. Phillips,et al.  Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie , 2007 .

[14]  J. Ferwerda.  Charting the quality of forage : measuring and mapping the variation of chemical components in foliage with hyperspectral remote sensing , 2005 .

[15]  Les M. Lavkulich,et al.  Colorimetric determination of phosphorus in citrate‐bicarbonate‐dithionite extracts of soils , 1995 .

[16]  A. K. Skidmore,et al.  Nitrogen prediction in grasses: effect of bandwidth and plant material state on absorption feature selection , 2010 .

[17]  A. Ramoelo,et al.  Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations , 2011 .

[18]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[19]  T. A. Hanley.  A nutritional view of understanding and complexity in the problem of diet selection by deer (Cervidae) , 1997 .

[20]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[21]  Clement Atzberger,et al.  Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Clement Atzberger,et al.  LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements , 2008 .

[23]  Jiaguo Qi,et al.  Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis , 2005 .

[24]  G. V. D. Heijden,et al.  Imaging spectroscopy for on-farm measurement of grassland yield and quality , 2006 .

[25]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[26]  R. B. Bradstreet.  CHAPTER II – The Kjeldahl Digestion , 1965 .

[27]  Onisimo Mutanga,et al.  Forage quality of savannas - Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery , 2010 .

[28]  Maria Fernanda Pimentel,et al.  Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry , 2001 .

[29]  Patrick J. Starks,et al.  Estimation of nitrogen concentration and in vitro dry matter digestibility of herbage of warm-season grass pastures from canopy hyperspectral reflectance measurements , 2008 .

[30]  Andrew K. Skidmore,et al.  Dry season mapping of savanna forage quality, using the hyperspectral Carnegie Airborne Observatory sensor , 2011 .

[31]  F. Meer,et al.  Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN) , 2007 .

[32]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[33]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[34]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[35]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[36]  C. Petisco,et al.  Quantitative Analysis of Chlorophyll and Protein in Alfalfa Leaves Using Fiber‐Optic Near‐Infrared Spectroscopy , 2009 .

[37]  H. Eckersten,et al.  Prediction of grain protein in spring malting barley grown in northern Europe , 2007 .

[38]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[39]  S. McNaughton,et al.  Spatial variation in forage nutrient concentrations and the distribution of Serengeti grazing ungulates , 1992, Landscape Ecology.

[40]  Shiv O. Prasher,et al.  Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn , 2003 .

[41]  Jie Song,et al.  Flood frequency in China's Poyang Lake region: trends and teleconnections , 2006 .

[42]  J. Dungan,et al.  Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies , 2001 .

[43]  A. Skidmore,et al.  Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa , 2004 .

[44]  Xu De,et al.  Analysis on hydrologic characteristics of Poyang lake , 2001 .

[45]  Desire L. Massart,et al.  Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.) , 2003 .

[46]  Rasmus Nyholm Jørgensen,et al.  Modelling nitrogen and phosphorus content at early growth stages in spring barley using hyperspectral line scanning , 2004 .

[47]  J. Stahlb,et al.  Patch choice of avian herbivores along a migration trajectory – From Temperate to Arctic , 2007 .

[48]  J. Duckworth.  Spectroscopic Quantitative Analysis , 1998 .

[49]  Raymond F. Kokaly,et al.  Investigating a Physical Basis for Spectroscopic Estimates of Leaf Nitrogen Concentration , 2001 .

[50]  A. Skidmore,et al.  Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features , 2004 .

[51]  R. B. Bradstreet.  The Kjeldahl Method for Organic Nitrogen , 1965 .