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Extracting Biochemical Information from Visible and Near Infrared Reflectance Spectroscopy of Fresh and Dried Leaves
J. Plant Physiol. Vol.
144. pp. 277-281 (1994)
Extracting Biochemical Information from Visible and Near Infrared Reflectance Spectroscopy of Fresh and Dried Leaves* B. LACAZE and R. JOFFRE Centre d'Ecologie Fonctionnelle et Evolutive - CNRS, BP 5051, 34033 Montpellier Cedex, France Received February 2, 1994 . Accepted April 16, 1994
Summary
Visible and near-infrared reflectance data were used to estimate nitrogen and fiber fraction concentrations in dried and fresh foliage samples from 4 Mediterranean species. Reflectance was transformed into absorbance spectra and 2 pre-processing treatments (standard normal transformation and de-trending) were applied to raw spectra and their first and second derivatives. Correlograms between nitrogen or fiber concentrations and absorbance were obtained; then calibration equations using a maximum of 3 wavelengths were established from stepwise regression. Results indicate that, using pre-processing treatments, determination coefficients were very high for dried and ground samples (0.87 to 0.99), and that they remain rather high for fresh samples (0.81 to 0.93), although using different sets of wavelengths. This suggests that nitrogen and fiber concentrations in leaves may be measured through in vivo reflectance spectroscopy.
Key words: Leaf, reflectance, near-infrared reflectance spectroscopy, nitrogen, lignin, cellulose. Introduction
Near-Infrared Reflectance Spectroscopy (NIRS) is a widely used standard technique for studying biochemical composition of food or plant material from the analysis of diffuse reflectance of dried thick ground samples (Williams, 1975; Norris et al., 1976). A number of minor absorption features have been identified, which can be used for estimating the concentration of organic compounds: protein, lignin, cellulose, starch, etc. (Curran, 1989). The instrumentation and procedures are similar to those used in absorption spectroscopy, based upon the Beer-Lambert law. If the samples both scatter and absorb radiation, it may not be possible to measure the transmitted radiation in a reproducible manner. So the reflected radiation must be relied on for an optical analysis (Birth and Hecht, 1987), and the absorbance is then defined from the reflectance R as A = log (1/R). Empirically derived equations have been used extensively to implement
* Dedicated to Prof. Dr. Hartmut K. Lichtenthaler on the occasion of his 60th anniversary. © 1994 by Gustav Fischer Verlag, Stuttgart
the relation between constituent concentrations and absorbance. In addition, the computations are usually based on the shape of the reflectance spectrum, rather than the specific value of R, through calibration equations using 1st or 2nd derivatives of log (1/R). This empirical approach is in wide use for quantitative analysis of spectra although more rigorous relationships may be elaborated from the theory of scattered radiation (Kubelka and Munk, 1931; Birth and Hecht, 1987). The possible application of spectroscopy to fresh plant material has been recently investigated (Card et al., 1988; Curran et aI., 1992). Reflectance spectra of fresh healthy leaves in the 400 - 2500 nm spectral domain (visible, near-infrared and short-wave infrared) are remarkably similar because of dominant features related to the absorption of photosynthetic pigments and water. In the visible and near-infrared domain, reflection spectra of leaves are predominantly influenced by chlorophyll content and internal structure; consequently, these features can be proposed for remotely detecting green vegetation and estimating its chlorophyll content (Buschman and Nagel, 1993). Characterizing more precisely the
278
B. LACAZE and R. JOFFRE
pigment status and net photosynthesis of leaves would require the complementary use of fluorescence measurements (Di Marco et al., 1990; Stober and Lichtenthaler, 1992). In the near-infrared and the short-wave infrared regions, main features are the four major absorption peaks of water at about 975, 1175, 1450 and 1950 nm, increasing in magnitude with wavelength. To apply NIRS technique to the study of biochemical composition of fresh plant material, one must take into account the dampening effects of the main absorption bands of water and photosynthetic pigments. A further generalization of this technique through the use of airborne imaging spectrometry has been proposed (Wessmann et al., 1988; Johnson et al., 1994), despite the fact that the biochemical signal may in this case be obscured by the effects of canopy structure on the spectral signature. Tracking minor absorption features in reflectance measurements from laboratory to field level should provide some indication of the feasibility of monitoring biochemical composition of vegetation canopies. As a first stage, this paper describes a comparative analysis of laboratory spectra of dried and fresh leaves of some Mediterranean species taken from a test area where airborne imaging spectrometry data are also available.
Materials and Methods
Data collection Leaf samples were collected in the field in June/July, 1991, during the European Multisensor Airborne Campaign including the AVIRIS measurements (Airborne Visible and Infrared Imaging Spectrometer). Leaf samples were choosen from sunlit and shaded foliage of several woody species: holm oak (Quercus ilex L.), downy oak (Quercus pubescens Willd.), box (Buxus sempervirens L.), and vine (Vitis vinifera L.). For Q. ilex and V. vinifera, both young and mature leaves were collected. Leaves were picked from trees or shrubs and placed in refrigerated plastic bags with damp towels until the time of spectroscopic measurements. Spectra of fresh leaves were acquired within 2 h of sampling. Then, all samples were dried in a ventilated oven at 60°C until constant weight and ground in a cyclone mill through a 1 mm mesh.
Spectroscopic measurements A Cary Model 17D Automatic Recording spectrophotometer was used to gather reflectance spectra of fresh leaves. Two spectral domains (visible and near-infrared) were successively recorded, together with reference surface spectra. A reflectance spectrum was then reconstructed for each leaf by combining data in the 2 domains, with a uniform 10nm increment between 400 and 2500nm (obtaining 210 data points). Only 20 reflectance spectra of fresh leaves have been recorded because of the time lapse (about 20 min) for recording a whole spectrum. Reflectance spectra were then transformed into absorbance, for sake of linearity between biochemical concentration and signal. Considering dried leaves, a total of 42 samples were scanned with a near-infrared reflectance spectrophotometer (NIR Systems 6500). Each sample was packed into a sample cell having a quartz-glass sample. Two reflectance measurements of monochromatic light were made from 400 to 2500 nm to produce an average spectrum with 1100 data points at 2 nm intervals over this range. The bandpass used was 10 nm and the wavelength accuracy 0.5 nm. Reflectance was also converted to absorbance, and spectra were averaged
at each 10 nm interval. This 10 nm resolution is similar to the spectral resolution of the airborne AVIRIS sensor.
Chemical analysis Nitrogen contents were determined with a Perkin Elmer elemental analyser (PE 2400 CHN) and the following fiber fractions were determined using the procedures of Van Soest (1963), 1965) adjusted for Fibertec (Van Soest and Robertson, 1985): ADL (acid detergent lignin), ADF (acid detergent fiber), and NDF (neutral detergent fiber). The NDF test (Van Soest and Wine, 1967) is considered to yield data on total fiber or cell walls, and the ADF test (Van Soest, 1963) gives the lignified portion of the plant cell wall (lignocellulose).
Statistical procedures Stepwise regression calibrations were developed and compared for nitrogen, ADL, ADF and NDF. For each calibration, six mathematical treatments corresponding to reflectance, first and second order derivative and gaps of 10 and 20 nm were compared. Forward stepwise regression is performed by selecting the wavelength that is the most highly correlated with the reference values and adding it to the equation. The second wavelength is added by calculating all partial correlations with all other wavelenths and selecting the wavelength with the highest partial correlation. The process continues until the addition of a wavelength makes no additional improvement in explaining the variation in the reference value (F value significant at 0.01). After each wavelength is added to the equation, the program re-evaluates all wavelengths in the equation before continuing (Windham et al., 1989; Shenk and Westerhaus, 1991). To avoid overfitting, the number of selected wavelengths was limited to three. Two sets of calibration have been compared: using raw spectra, and using a standard normal variate and de-trending transformation (Barnes et al., 1989). Mathematical transformation of the spectra by calculation of the standard normal variate transformation removes slope variation on an individual sample basis by the use of the following calculation: SNV(I-210)
=
(y(I-210) - y)/
E (Y(1-210)
-
y)2
n - 1
where SNV(I_210) are the individual standard normal variations for 210 wavelengths, y is the 210-wavelength 10g(1/R) values, and y is the mean of the 21C-wavelength 10g(1/R) values. De-trending accounts for the variation in baseline shift and curvilinearity with the use of a second-degree polynomial regression. Data analysis was conducted using the lSI software system (Shenk and Westerhaus, 1991).
Results
Comparison ofspectra ofdried and fresh leaves Typical spectra corresponding to the four species are shown in Fig. 1. The overall shape of the spectra is similar for the four species. Absorbance values from fresh samples are always higher than those of dried samples. Their main spectral features correspond with pigments (centred at 420 and 680nm) and main water absorption bands (centred at 1440 and 1920nm). The same features occurred with a reduced intensity in the dried sample spectra. Minor absorption features related to other biochemical constituents are
Biochemical information from Visible and NIR Spectroscopy 1.5
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2500
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r-"'
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700
Fig. 2: Correlograms for nitrogen and absorbance for dried and ground foliage: dotted line corresponds to raw data, solid line to transformed data (SNV + de-trend).
Table I: Equation calibration statistics for constituent concentration of dry, ground samples. SD = standard deviation, SEC = standard error of calibration. Math treatment indicates the transformation of spectral data: the first number is the order of the derivative function, the second is the segment length in data points (10nm) over which the derivative was taken, and the third the segment length over which the function was smoothed. mean
SD
range
math
r2
SEC
wavelength
treatment
without SNV and de-trend N 1.86 0.85 1.0- 4.1 ADL 11.54 3.44 4.4-16.6 ADF 30.02 6.81 15.6-38.3 NDF 53.05 8.48 32.9-61.2
222 222 2 2 2 00 1
0.99 0.94 0.98 0.87
0.08 0.84 0.97 3.04
1550,2380, 1980 1170, 2160 2220, 2380, 1780 1950, 1910
with SNV and de-trend N 1.86 0.85 ADL 11.54 3.44 ADF 30.02 6.81 NDF 53.05 8.48
222 122 222 2 1 1
0.99 0.98 0.98 0.97
0.08 0.48 0.97 1.47
1550, 2050, 1640 2230, 1790, 540 1980, 2380 1990,2120, 1710
1.0- 4.1 4.4-16.6 15.6-38.3 32.9-61.2
~--'
'400
700
/ - ______ 1
--1000
1300
1600
r-_/--'/
1900
Wavelength (nm)
2200
2500
Fig. I: Visible and near infrared spectra of fresh foliage (solid line), and dried and ground foliage (dotted line). a = Buxus sempervirens, b = Quercus ilex, c = Quercus pubescens, d = Vitis vinifera.
not clearly identifiable on the figure; their analysis will be discussed below.
Predictions of biochemical content ofdried leaves Figure 2 shows the correlograms for nitrogen content of dried leaves and absorbance data (raw and pre-processed data). The effect of applying pre-processing treatment is clearly visible, as it leads to higher correlations, especially in the near-infrared domain. Calibrations were developed that allow nitrogen and fiber fractions to be determined in ground and dried samples (Table 1). Determination coefficients (r) ranged from 0.87 to 0.99. The use of scatter correction and de-trending gave similar or better results in all cases. For ADL and NDF, the im-
provement of standard error of calibration (SEC) was about 50 %. The best calibration equations were obtained through the use of derivative treatments. Except in one case (540 nm for ADL equation), the selected wavelengths belong to the near-infrared domain (from 1550 to 2380 nm in the case of transformed data).
Biochemical analysis offresh leaves Figure 3 shows the correlograms for nitrogen content of fresh leaves and absorbance. Correlations are lower than in the previous case, but still remain acceptable at selected wavelengths. Developed in a similar way, calibration equations obtained showed that the absorbance spectra of fresh material were closely related to biochemical composition (Table 2). The accuracy of calibrations was slightly lower than in the case of dried and ground leaves, but remained satisfactory. Determination coefficients ranged from 0.81 to 0.93. First derivative treatments led to the best equation in most cases, second derivative never improved the results. Nitrogen was best estimated using the raw spectra. The selected wavelengths were not the same as in the previous case, and were more equally distributed in the near-infrared region.
280
B.
LACAZE
and R. JOFFRE Table 3: Ratio between SD and SEC for dry, ground samples.
c
N ADL ADF NDF
.S? o
Q) L.L.-
o u -0.5 -1. 0
~b::---::~' ----::-I:-::---:-L:'-----lL---L..---L---l.ld 400 700 1000 1300 1600 1900 2200 2500 Wavelength (nm)
Fig. 3: Correlograms for nitrogen and absorbance for fresh foliage: dotted line corresponds to raw data, solid line to transformed data (SNV + de-trend). Table 2: Equation calibration statistics for constituent concentrations of fresh samples. mean
SD
range
math
r2
SEC
wavelength
treatment
without SNV and de-trend 0.87 N 1.0- 4.1 2.06 ADL 10.93 4.4-16.4 3.45 ADF 15.6-38.3 28.52 6.76 NDF 51.04 8.69 32.9-61.1
00 1 111 122 00 1
0.93 0.90 0.89 0.88
0.24 1.1 2.22 3.07
990, 950,2450 1210, 720, 1680 960, 1050, 1090 1860,2200
with SNV and de·trend 0.87 N 2.06 ADL 10.93 3.45 6.76 ADF 28.52 NDF 51.04 8.69
00 1 1 1 1 111 122
0.81 0.91 0.88 0.90
0.38 1.03 2.29 2.80
1820, 1210, 1210, 1840,
1.0- 4.1 4.4-16.4 15.6-38.3 32.9-61.1
2230 880,2430 880,2430 1380, 980
Discussion and Conclusion
The wavelengths selected from the best mathematical fit are not always located at the known absorption peaks of the studied biochemical compounds. This may be explained as a result of strong intercorrelations between several constituents. For ecological applications, an estimate of combined concentration of some constituents (e.g. cellulose and lignin) may still be useful. Three main sources of variations of reflectance spectra can be attributed to 1) non-specific scatter of radiation at the surface of the particles, 2) variable spectral path length through the sample, and 3) chemical composition of the sample (Barnes et al., 1989). Scatter and spectral path length are largely dependent on the physical nature of the samples. Derivatization resolves anomalies in the spectra that are caused by variations in particle size, shape, bulk and density, which means that difference spectra are limited to those caused by chemical composition (see Windham et al., 1989). The improvement due to the use of derivative is much more pronounced for dried and ground samples than for fresh samples (Tables 1 and 2). In the same way, the use of pre-processing treatments leads to spectra free of multi-collinearity between wavelengths and improves the calibration statistics (excepted for nitrogen of fresh samples). The comparison of the standard deviation (SD) of the reference data and the standard error of calibration (SEC) gives the effectiveness of NIR methods to predict the composition of any material. As emphasized by Williams (1993), if the SEC approaches the SD, it means that the NIR calibration is
without SNV and de-trend
with SNV and de-trend
10.6 4.1 7.0
10.6 7.2 7.0 5.8
2.8
Table 4: Ratio between SD and SEC for fresh samples. without SNV and de-trend
N ADL ADF NDF
3.6 3.1 3.1
2.8
with SNV and de-trend 2.3
3.8 3.0 3.1
not really predicting composition very efficiently; a ratio SD/SEC > 3 seems a reasonable limit to use NIRS calibration. For the ~ied and ground samples, this ratio is always > 2.8 and the improvement due to pre-processing is import~nt (Table 3). Nitrogen is very well predicted by NIRS (rat~o > 10), and the values of the ratio for lignin (ADL) and lIgnocellulose (ADF) remains very good. For fresh leaves, values of this ratio are lower (Table 4), but can be evaluated ~ «reasonable», according to the guidelines of Williams (op. at.) ..The effect of pre-processing is very different than in the prevIOus case and the only significative improvement concerns ADL. For the two populations, NDF is the less well predi~ed constituent. Among all the analyzed constituents, NDF is the most complex, as it represents lignins plus celluloses plus hemicelluloses. For different species, the NDF res~lts fro~ several different constituents in variable proportIOns, registered by chemical analysis as a single entity, but probably related to different spectra. Thus, calibration of ND~ wi~l be m~re a~curate using more wavelengths or using multivanate calibratIOn procedures using all spectral information (Martens and Naes, 1989). Our results indicate that reflectance spectra contain information related to the nitrogen and fiber concentrations and that this information is present in fresh as well as dried and ground samples. This suggests that in vivo reflectance spec~rom~try and high spectral resolution (10 nm) airborne ~magmg spectr?metry could be used for estimating biochemiCal concentration of leaves or biochemical content of canopies, ~nd deri~ing spatialize~ information about key biogeochemical cychng processes m terrestrial ecosystems. Establishing predi~ive calibration equations is a crucial step before a generahzed use of the proposed technique. The use of stepwise regression with a small number of samples presents the possibility of overfitting and the selection of regression term~ by chance. ~nalysis of a higher number of samples, increasmg sample Size and range of biochemical concentrations remains necessary to determine the precise relationship between reflectance spectra and biochemical content of fresh foliage and the biological basis of predicting equations. Acknowledgements
This research was partially supported by the projects DEMON and MOST of the Program Environment of the DG XII of the
Biochemical information from Visible and NIR Spectroscopy Commission of the European Communities. The authors gratefully acknowledge F. Baret (INRA Avignon) for the use of the Cary spectrophotometer.
References BARNES, R. J., M. S. DHANOA, and S. J. LISTER: Standard normal variate transformation and detrending of NIR spectra. App!. Spectrosc. 43, 772-777 (1989). BIRTIf, G. S. and H. G. HECHT: The physics of near-infrared reflectance. In: WILLIAMS, P. and K. NORRIS (eds.): Near Infrared Technology in the Agriculture and Food industries, 1-14, American Assoc. of cereal chemists, Inc., St. Paul, Minnesota, USA (1987). BUSCHMANN, C. and E. NAGEL: In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. Int. J. Remote Sensing 14, 711-722 (1993). CARD, D. H., D. L. PETERSON, P. A. MATSON, and J. D. ABER: Prediction of leaf chemistry by the use of visible and near-infrared reflectance spectroscopy. Remote Sensing Environ. 26, 123 - 147 (1988). CURRAN, P. J., J. L. DUNGAN, B. A. MAcLER, S. E. PLUMMER, and D. L. PETERSON: Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration. Remote Sensing Environ. 39, 153-166 (1992). CURRAN, P. J.: Remote sensing of foliar chemistry. Remote Sensing Environ. 30, 271-278 (1989). DI MARco, G., F. MANES, D. TRicou, and E. VITALE: Fluorescence parameters measured concurrently with net photosynthesis to investigate chloroplastic CO2 concentration in leaves of Quercus ilex L. J. Plant Physiol. 136, 538-543 (1990). JOHNSON, L. F., C. A. HLAVKA, and D. L. PETERSON: Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect, Remote Sensing Environ. 47, 216230 (1994).
281
KUBELKA, P. and F. MUNK: Ein Beitrag zur Optik der Farbanstriche. Z. Tech. Phys. 12, 593-601 (1931). MARTENS, H. and T. NAES: Multivariate calibration. John Wiley & Sons, Chichester (1989). NORRIS, K. H., R. F. BARNES, J. E. MOORE, and J. S. SHENK: Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43, 889-897 (1976). SHENK, J. S. and M. O. WESTERHAUS: lSI NIRS-2. Software for nearinfrared instruments Infrasoft international (1991). STOBER, F. and H. K. LICHTENTIfALER: Changes of the laser-induced blue, green and red fluorescence signatures during greening of etiolated leaves of wheat. J. Plant Physiol. 140, 673-680 (1992). VAN SOEST, P. J. and R. H. WINE: Use of detergents in the analysis of fibrous feed. N. Determination of plant cell-wall constituents. J. Assoc. Off. Anal. Chern. 50, 50-55 (1967). VAN SOEST, P. J. and J. B. ROBERTSON: Analysis of forages and fibrous foods: a laboratory manual for animal science. Cornell Univ. Pub. (1985). VAN SOEST, P. J.: Use of detergent in the analysis of fibrous feeds. II A rapid method for the determination of fiber and lignin. J. Assoc. Off. Anal. Chern. 46, 829-835 (1963). VAN SOEST, P. J.: Use of detergent in the analysis of fibrous feeds. III Study of effects of heating and drying on yield of fiber and lignin in forages. J. Assoc. Off. Anal. Chern. 48, 785 -790 (1965). WESSMAN, C. A., J. D. ABER, D. L. PETERSON, and J. M. MELULO: Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature 335, 154-156 (1988). WILLIAMS, P. c.: Applications of near-infrared reflectance spectroscopy to analysis of cereal grains and oilseeds. Cereal Chern. 52, 561-576 (1975). - What does the raw material have to say? NIR news, 4, 13 (1993). WINDHAM, W. R., D. R. MERTENS, and F. E. BARTON: Protocol for NIRS calibration: Sample selection and equation development and validation. In: MARTEN, G. c., J. S. SHENK, and F. E. BARTON (eds.): Near infrared reflectance spectroscopy: Analysis of forage quality, 96-103. Agricultural Handbook 643. USDA-ARS, Washington (1989).
144. pp. 277-281 (1994)
Extracting Biochemical Information from Visible and Near Infrared Reflectance Spectroscopy of Fresh and Dried Leaves* B. LACAZE and R. JOFFRE Centre d'Ecologie Fonctionnelle et Evolutive - CNRS, BP 5051, 34033 Montpellier Cedex, France Received February 2, 1994 . Accepted April 16, 1994
Summary
Visible and near-infrared reflectance data were used to estimate nitrogen and fiber fraction concentrations in dried and fresh foliage samples from 4 Mediterranean species. Reflectance was transformed into absorbance spectra and 2 pre-processing treatments (standard normal transformation and de-trending) were applied to raw spectra and their first and second derivatives. Correlograms between nitrogen or fiber concentrations and absorbance were obtained; then calibration equations using a maximum of 3 wavelengths were established from stepwise regression. Results indicate that, using pre-processing treatments, determination coefficients were very high for dried and ground samples (0.87 to 0.99), and that they remain rather high for fresh samples (0.81 to 0.93), although using different sets of wavelengths. This suggests that nitrogen and fiber concentrations in leaves may be measured through in vivo reflectance spectroscopy.
Key words: Leaf, reflectance, near-infrared reflectance spectroscopy, nitrogen, lignin, cellulose. Introduction
Near-Infrared Reflectance Spectroscopy (NIRS) is a widely used standard technique for studying biochemical composition of food or plant material from the analysis of diffuse reflectance of dried thick ground samples (Williams, 1975; Norris et al., 1976). A number of minor absorption features have been identified, which can be used for estimating the concentration of organic compounds: protein, lignin, cellulose, starch, etc. (Curran, 1989). The instrumentation and procedures are similar to those used in absorption spectroscopy, based upon the Beer-Lambert law. If the samples both scatter and absorb radiation, it may not be possible to measure the transmitted radiation in a reproducible manner. So the reflected radiation must be relied on for an optical analysis (Birth and Hecht, 1987), and the absorbance is then defined from the reflectance R as A = log (1/R). Empirically derived equations have been used extensively to implement
* Dedicated to Prof. Dr. Hartmut K. Lichtenthaler on the occasion of his 60th anniversary. © 1994 by Gustav Fischer Verlag, Stuttgart
the relation between constituent concentrations and absorbance. In addition, the computations are usually based on the shape of the reflectance spectrum, rather than the specific value of R, through calibration equations using 1st or 2nd derivatives of log (1/R). This empirical approach is in wide use for quantitative analysis of spectra although more rigorous relationships may be elaborated from the theory of scattered radiation (Kubelka and Munk, 1931; Birth and Hecht, 1987). The possible application of spectroscopy to fresh plant material has been recently investigated (Card et al., 1988; Curran et aI., 1992). Reflectance spectra of fresh healthy leaves in the 400 - 2500 nm spectral domain (visible, near-infrared and short-wave infrared) are remarkably similar because of dominant features related to the absorption of photosynthetic pigments and water. In the visible and near-infrared domain, reflection spectra of leaves are predominantly influenced by chlorophyll content and internal structure; consequently, these features can be proposed for remotely detecting green vegetation and estimating its chlorophyll content (Buschman and Nagel, 1993). Characterizing more precisely the
278
B. LACAZE and R. JOFFRE
pigment status and net photosynthesis of leaves would require the complementary use of fluorescence measurements (Di Marco et al., 1990; Stober and Lichtenthaler, 1992). In the near-infrared and the short-wave infrared regions, main features are the four major absorption peaks of water at about 975, 1175, 1450 and 1950 nm, increasing in magnitude with wavelength. To apply NIRS technique to the study of biochemical composition of fresh plant material, one must take into account the dampening effects of the main absorption bands of water and photosynthetic pigments. A further generalization of this technique through the use of airborne imaging spectrometry has been proposed (Wessmann et al., 1988; Johnson et al., 1994), despite the fact that the biochemical signal may in this case be obscured by the effects of canopy structure on the spectral signature. Tracking minor absorption features in reflectance measurements from laboratory to field level should provide some indication of the feasibility of monitoring biochemical composition of vegetation canopies. As a first stage, this paper describes a comparative analysis of laboratory spectra of dried and fresh leaves of some Mediterranean species taken from a test area where airborne imaging spectrometry data are also available.
Materials and Methods
Data collection Leaf samples were collected in the field in June/July, 1991, during the European Multisensor Airborne Campaign including the AVIRIS measurements (Airborne Visible and Infrared Imaging Spectrometer). Leaf samples were choosen from sunlit and shaded foliage of several woody species: holm oak (Quercus ilex L.), downy oak (Quercus pubescens Willd.), box (Buxus sempervirens L.), and vine (Vitis vinifera L.). For Q. ilex and V. vinifera, both young and mature leaves were collected. Leaves were picked from trees or shrubs and placed in refrigerated plastic bags with damp towels until the time of spectroscopic measurements. Spectra of fresh leaves were acquired within 2 h of sampling. Then, all samples were dried in a ventilated oven at 60°C until constant weight and ground in a cyclone mill through a 1 mm mesh.
Spectroscopic measurements A Cary Model 17D Automatic Recording spectrophotometer was used to gather reflectance spectra of fresh leaves. Two spectral domains (visible and near-infrared) were successively recorded, together with reference surface spectra. A reflectance spectrum was then reconstructed for each leaf by combining data in the 2 domains, with a uniform 10nm increment between 400 and 2500nm (obtaining 210 data points). Only 20 reflectance spectra of fresh leaves have been recorded because of the time lapse (about 20 min) for recording a whole spectrum. Reflectance spectra were then transformed into absorbance, for sake of linearity between biochemical concentration and signal. Considering dried leaves, a total of 42 samples were scanned with a near-infrared reflectance spectrophotometer (NIR Systems 6500). Each sample was packed into a sample cell having a quartz-glass sample. Two reflectance measurements of monochromatic light were made from 400 to 2500 nm to produce an average spectrum with 1100 data points at 2 nm intervals over this range. The bandpass used was 10 nm and the wavelength accuracy 0.5 nm. Reflectance was also converted to absorbance, and spectra were averaged
at each 10 nm interval. This 10 nm resolution is similar to the spectral resolution of the airborne AVIRIS sensor.
Chemical analysis Nitrogen contents were determined with a Perkin Elmer elemental analyser (PE 2400 CHN) and the following fiber fractions were determined using the procedures of Van Soest (1963), 1965) adjusted for Fibertec (Van Soest and Robertson, 1985): ADL (acid detergent lignin), ADF (acid detergent fiber), and NDF (neutral detergent fiber). The NDF test (Van Soest and Wine, 1967) is considered to yield data on total fiber or cell walls, and the ADF test (Van Soest, 1963) gives the lignified portion of the plant cell wall (lignocellulose).
Statistical procedures Stepwise regression calibrations were developed and compared for nitrogen, ADL, ADF and NDF. For each calibration, six mathematical treatments corresponding to reflectance, first and second order derivative and gaps of 10 and 20 nm were compared. Forward stepwise regression is performed by selecting the wavelength that is the most highly correlated with the reference values and adding it to the equation. The second wavelength is added by calculating all partial correlations with all other wavelenths and selecting the wavelength with the highest partial correlation. The process continues until the addition of a wavelength makes no additional improvement in explaining the variation in the reference value (F value significant at 0.01). After each wavelength is added to the equation, the program re-evaluates all wavelengths in the equation before continuing (Windham et al., 1989; Shenk and Westerhaus, 1991). To avoid overfitting, the number of selected wavelengths was limited to three. Two sets of calibration have been compared: using raw spectra, and using a standard normal variate and de-trending transformation (Barnes et al., 1989). Mathematical transformation of the spectra by calculation of the standard normal variate transformation removes slope variation on an individual sample basis by the use of the following calculation: SNV(I-210)
=
(y(I-210) - y)/
E (Y(1-210)
-
y)2
n - 1
where SNV(I_210) are the individual standard normal variations for 210 wavelengths, y is the 210-wavelength 10g(1/R) values, and y is the mean of the 21C-wavelength 10g(1/R) values. De-trending accounts for the variation in baseline shift and curvilinearity with the use of a second-degree polynomial regression. Data analysis was conducted using the lSI software system (Shenk and Westerhaus, 1991).
Results
Comparison ofspectra ofdried and fresh leaves Typical spectra corresponding to the four species are shown in Fig. 1. The overall shape of the spectra is similar for the four species. Absorbance values from fresh samples are always higher than those of dried samples. Their main spectral features correspond with pigments (centred at 420 and 680nm) and main water absorption bands (centred at 1440 and 1920nm). The same features occurred with a reduced intensity in the dried sample spectra. Minor absorption features related to other biochemical constituents are
Biochemical information from Visible and NIR Spectroscopy 1.5
279
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1.0
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-- 0.5 0::
--------""",.-------
"
400 CJ'I
o
700
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400
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/-_/
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,-
2500
1.5 C
1.0
0::
400 CJ'I
0
/------I'"'
---
"
1000
700
1300
1.5
1600
---
1900
,---"
2200
r
2500
d
--.J
1000
1300
1600
1900
2200
2500
Wavelength (nm)
r-"'
2200
700
Fig. 2: Correlograms for nitrogen and absorbance for dried and ground foliage: dotted line corresponds to raw data, solid line to transformed data (SNV + de-trend).
Table I: Equation calibration statistics for constituent concentration of dry, ground samples. SD = standard deviation, SEC = standard error of calibration. Math treatment indicates the transformation of spectral data: the first number is the order of the derivative function, the second is the segment length in data points (10nm) over which the derivative was taken, and the third the segment length over which the function was smoothed. mean
SD
range
math
r2
SEC
wavelength
treatment
without SNV and de-trend N 1.86 0.85 1.0- 4.1 ADL 11.54 3.44 4.4-16.6 ADF 30.02 6.81 15.6-38.3 NDF 53.05 8.48 32.9-61.2
222 222 2 2 2 00 1
0.99 0.94 0.98 0.87
0.08 0.84 0.97 3.04
1550,2380, 1980 1170, 2160 2220, 2380, 1780 1950, 1910
with SNV and de-trend N 1.86 0.85 ADL 11.54 3.44 ADF 30.02 6.81 NDF 53.05 8.48
222 122 222 2 1 1
0.99 0.98 0.98 0.97
0.08 0.48 0.97 1.47
1550, 2050, 1640 2230, 1790, 540 1980, 2380 1990,2120, 1710
1.0- 4.1 4.4-16.6 15.6-38.3 32.9-61.2
~--'
'400
700
/ - ______ 1
--1000
1300
1600
r-_/--'/
1900
Wavelength (nm)
2200
2500
Fig. I: Visible and near infrared spectra of fresh foliage (solid line), and dried and ground foliage (dotted line). a = Buxus sempervirens, b = Quercus ilex, c = Quercus pubescens, d = Vitis vinifera.
not clearly identifiable on the figure; their analysis will be discussed below.
Predictions of biochemical content ofdried leaves Figure 2 shows the correlograms for nitrogen content of dried leaves and absorbance data (raw and pre-processed data). The effect of applying pre-processing treatment is clearly visible, as it leads to higher correlations, especially in the near-infrared domain. Calibrations were developed that allow nitrogen and fiber fractions to be determined in ground and dried samples (Table 1). Determination coefficients (r) ranged from 0.87 to 0.99. The use of scatter correction and de-trending gave similar or better results in all cases. For ADL and NDF, the im-
provement of standard error of calibration (SEC) was about 50 %. The best calibration equations were obtained through the use of derivative treatments. Except in one case (540 nm for ADL equation), the selected wavelengths belong to the near-infrared domain (from 1550 to 2380 nm in the case of transformed data).
Biochemical analysis offresh leaves Figure 3 shows the correlograms for nitrogen content of fresh leaves and absorbance. Correlations are lower than in the previous case, but still remain acceptable at selected wavelengths. Developed in a similar way, calibration equations obtained showed that the absorbance spectra of fresh material were closely related to biochemical composition (Table 2). The accuracy of calibrations was slightly lower than in the case of dried and ground leaves, but remained satisfactory. Determination coefficients ranged from 0.81 to 0.93. First derivative treatments led to the best equation in most cases, second derivative never improved the results. Nitrogen was best estimated using the raw spectra. The selected wavelengths were not the same as in the previous case, and were more equally distributed in the near-infrared region.
280
B.
LACAZE
and R. JOFFRE Table 3: Ratio between SD and SEC for dry, ground samples.
c
N ADL ADF NDF
.S? o
Q) L.L.-
o u -0.5 -1. 0
~b::---::~' ----::-I:-::---:-L:'-----lL---L..---L---l.ld 400 700 1000 1300 1600 1900 2200 2500 Wavelength (nm)
Fig. 3: Correlograms for nitrogen and absorbance for fresh foliage: dotted line corresponds to raw data, solid line to transformed data (SNV + de-trend). Table 2: Equation calibration statistics for constituent concentrations of fresh samples. mean
SD
range
math
r2
SEC
wavelength
treatment
without SNV and de-trend 0.87 N 1.0- 4.1 2.06 ADL 10.93 4.4-16.4 3.45 ADF 15.6-38.3 28.52 6.76 NDF 51.04 8.69 32.9-61.1
00 1 111 122 00 1
0.93 0.90 0.89 0.88
0.24 1.1 2.22 3.07
990, 950,2450 1210, 720, 1680 960, 1050, 1090 1860,2200
with SNV and de·trend 0.87 N 2.06 ADL 10.93 3.45 6.76 ADF 28.52 NDF 51.04 8.69
00 1 1 1 1 111 122
0.81 0.91 0.88 0.90
0.38 1.03 2.29 2.80
1820, 1210, 1210, 1840,
1.0- 4.1 4.4-16.4 15.6-38.3 32.9-61.1
2230 880,2430 880,2430 1380, 980
Discussion and Conclusion
The wavelengths selected from the best mathematical fit are not always located at the known absorption peaks of the studied biochemical compounds. This may be explained as a result of strong intercorrelations between several constituents. For ecological applications, an estimate of combined concentration of some constituents (e.g. cellulose and lignin) may still be useful. Three main sources of variations of reflectance spectra can be attributed to 1) non-specific scatter of radiation at the surface of the particles, 2) variable spectral path length through the sample, and 3) chemical composition of the sample (Barnes et al., 1989). Scatter and spectral path length are largely dependent on the physical nature of the samples. Derivatization resolves anomalies in the spectra that are caused by variations in particle size, shape, bulk and density, which means that difference spectra are limited to those caused by chemical composition (see Windham et al., 1989). The improvement due to the use of derivative is much more pronounced for dried and ground samples than for fresh samples (Tables 1 and 2). In the same way, the use of pre-processing treatments leads to spectra free of multi-collinearity between wavelengths and improves the calibration statistics (excepted for nitrogen of fresh samples). The comparison of the standard deviation (SD) of the reference data and the standard error of calibration (SEC) gives the effectiveness of NIR methods to predict the composition of any material. As emphasized by Williams (1993), if the SEC approaches the SD, it means that the NIR calibration is
without SNV and de-trend
with SNV and de-trend
10.6 4.1 7.0
10.6 7.2 7.0 5.8
2.8
Table 4: Ratio between SD and SEC for fresh samples. without SNV and de-trend
N ADL ADF NDF
3.6 3.1 3.1
2.8
with SNV and de-trend 2.3
3.8 3.0 3.1
not really predicting composition very efficiently; a ratio SD/SEC > 3 seems a reasonable limit to use NIRS calibration. For the ~ied and ground samples, this ratio is always > 2.8 and the improvement due to pre-processing is import~nt (Table 3). Nitrogen is very well predicted by NIRS (rat~o > 10), and the values of the ratio for lignin (ADL) and lIgnocellulose (ADF) remains very good. For fresh leaves, values of this ratio are lower (Table 4), but can be evaluated ~ «reasonable», according to the guidelines of Williams (op. at.) ..The effect of pre-processing is very different than in the prevIOus case and the only significative improvement concerns ADL. For the two populations, NDF is the less well predi~ed constituent. Among all the analyzed constituents, NDF is the most complex, as it represents lignins plus celluloses plus hemicelluloses. For different species, the NDF res~lts fro~ several different constituents in variable proportIOns, registered by chemical analysis as a single entity, but probably related to different spectra. Thus, calibration of ND~ wi~l be m~re a~curate using more wavelengths or using multivanate calibratIOn procedures using all spectral information (Martens and Naes, 1989). Our results indicate that reflectance spectra contain information related to the nitrogen and fiber concentrations and that this information is present in fresh as well as dried and ground samples. This suggests that in vivo reflectance spec~rom~try and high spectral resolution (10 nm) airborne ~magmg spectr?metry could be used for estimating biochemiCal concentration of leaves or biochemical content of canopies, ~nd deri~ing spatialize~ information about key biogeochemical cychng processes m terrestrial ecosystems. Establishing predi~ive calibration equations is a crucial step before a generahzed use of the proposed technique. The use of stepwise regression with a small number of samples presents the possibility of overfitting and the selection of regression term~ by chance. ~nalysis of a higher number of samples, increasmg sample Size and range of biochemical concentrations remains necessary to determine the precise relationship between reflectance spectra and biochemical content of fresh foliage and the biological basis of predicting equations. Acknowledgements
This research was partially supported by the projects DEMON and MOST of the Program Environment of the DG XII of the
Biochemical information from Visible and NIR Spectroscopy Commission of the European Communities. The authors gratefully acknowledge F. Baret (INRA Avignon) for the use of the Cary spectrophotometer.
References BARNES, R. J., M. S. DHANOA, and S. J. LISTER: Standard normal variate transformation and detrending of NIR spectra. App!. Spectrosc. 43, 772-777 (1989). BIRTIf, G. S. and H. G. HECHT: The physics of near-infrared reflectance. In: WILLIAMS, P. and K. NORRIS (eds.): Near Infrared Technology in the Agriculture and Food industries, 1-14, American Assoc. of cereal chemists, Inc., St. Paul, Minnesota, USA (1987). BUSCHMANN, C. and E. NAGEL: In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. Int. J. Remote Sensing 14, 711-722 (1993). CARD, D. H., D. L. PETERSON, P. A. MATSON, and J. D. ABER: Prediction of leaf chemistry by the use of visible and near-infrared reflectance spectroscopy. Remote Sensing Environ. 26, 123 - 147 (1988). CURRAN, P. J., J. L. DUNGAN, B. A. MAcLER, S. E. PLUMMER, and D. L. PETERSON: Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration. Remote Sensing Environ. 39, 153-166 (1992). CURRAN, P. J.: Remote sensing of foliar chemistry. Remote Sensing Environ. 30, 271-278 (1989). DI MARco, G., F. MANES, D. TRicou, and E. VITALE: Fluorescence parameters measured concurrently with net photosynthesis to investigate chloroplastic CO2 concentration in leaves of Quercus ilex L. J. Plant Physiol. 136, 538-543 (1990). JOHNSON, L. F., C. A. HLAVKA, and D. L. PETERSON: Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect, Remote Sensing Environ. 47, 216230 (1994).
281
KUBELKA, P. and F. MUNK: Ein Beitrag zur Optik der Farbanstriche. Z. Tech. Phys. 12, 593-601 (1931). MARTENS, H. and T. NAES: Multivariate calibration. John Wiley & Sons, Chichester (1989). NORRIS, K. H., R. F. BARNES, J. E. MOORE, and J. S. SHENK: Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43, 889-897 (1976). SHENK, J. S. and M. O. WESTERHAUS: lSI NIRS-2. Software for nearinfrared instruments Infrasoft international (1991). STOBER, F. and H. K. LICHTENTIfALER: Changes of the laser-induced blue, green and red fluorescence signatures during greening of etiolated leaves of wheat. J. Plant Physiol. 140, 673-680 (1992). VAN SOEST, P. J. and R. H. WINE: Use of detergents in the analysis of fibrous feed. N. Determination of plant cell-wall constituents. J. Assoc. Off. Anal. Chern. 50, 50-55 (1967). VAN SOEST, P. J. and J. B. ROBERTSON: Analysis of forages and fibrous foods: a laboratory manual for animal science. Cornell Univ. Pub. (1985). VAN SOEST, P. J.: Use of detergent in the analysis of fibrous feeds. II A rapid method for the determination of fiber and lignin. J. Assoc. Off. Anal. Chern. 46, 829-835 (1963). VAN SOEST, P. J.: Use of detergent in the analysis of fibrous feeds. III Study of effects of heating and drying on yield of fiber and lignin in forages. J. Assoc. Off. Anal. Chern. 48, 785 -790 (1965). WESSMAN, C. A., J. D. ABER, D. L. PETERSON, and J. M. MELULO: Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature 335, 154-156 (1988). WILLIAMS, P. c.: Applications of near-infrared reflectance spectroscopy to analysis of cereal grains and oilseeds. Cereal Chern. 52, 561-576 (1975). - What does the raw material have to say? NIR news, 4, 13 (1993). WINDHAM, W. R., D. R. MERTENS, and F. E. BARTON: Protocol for NIRS calibration: Sample selection and equation development and validation. In: MARTEN, G. c., J. S. SHENK, and F. E. BARTON (eds.): Near infrared reflectance spectroscopy: Analysis of forage quality, 96-103. Agricultural Handbook 643. USDA-ARS, Washington (1989).