Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy

123

REMOTE SENSING OF ENVIRONMENT 26:123-147 (1988)

Prediction of Leaf Chemistry by the Use of Visible and Near Infrared Reflectance Spectroscopy

DON H. CARD, DAVID L. PETERSON, AND PAMELA A. MATSON Ecosystem Science and Technology Branch, NASA Ames Research Center, Moffett Field, California 94035

JOHN D. ABER Complex Systems Research Center, University of New Hampshire, Durham, New Hampshire 03824

The chemical content of dry, ground leaf material sampled from deciduous and conifer tree species from sites in Alaska, Wisconsin, and California was estimated using visible and shortwave infrared spectroscopy. Seven chemical components--sugar, starch, protein, cellulose, total chlorophyll, lignin, and total nitrogen--were analyzed by wet chemical methods and their concentrations regressed against l o g l / o and first and second differences of l o g l / o (where # is measured reflectance) at wavelengths selected by stepwise regression. Predictions of chemical concentrations based on cross validation suggest that this technique may be useful for extracting vegetation canopy biochemical information by remote sensing.

1. Introduction Recent research in forest ecosystems indicate that canopy chemistry, especially total nitrogen content and carbon quality (measured by lignin content), may be important in describing and modeling productivity and nutrient dynamics (Pastor et al., 1983; Van Cleve et al., 1983b). For example, Vitousek (1982) has presented evidence that total nitrogen may predict net primary productivity in both deciduous and coniferous forests, and the ratio of lignin to nitrogen in litter is known to predict the rate of litter decomposition (Meentemeyer, 1978; Melillo et al., 1982). Also, information about individual constituents such as starch may provide information about nutrient limitation in forests (Birk and Matson, 1987). Therefore, it might be expected that estimation of leaf nitrogen, lignin, and other chemicals from remote ©Elsevier Science Publishing Co., Inc., 1988 655 Avenue of the Americas, New York, NY 10010

sensing information could be helpful in describing ecosystem processes over large areas. Dry foliar mass is in large part composed of carbon-containing compounds such as lignin, cellulose, starch, and sugar, as well as nitrogen-containing compounds such as amino acids and proteins (Table 1). The organic bonds in these chemical constituents exhibit vibrational stretching modes that absorb radiation at frequencies in the middle infrared part of the spectrum with harmonics and overtones in the shortwave infrared that are characteristic of particular components. Also, chlorophyll and associated carotenoid pigments have strong absorption in the visible region due to electron energy transitions, Since absorption of radiation at these frequencies is a function of concentration of the chemical constituents present, high resolution spectroscopy in the visible and shortwave infrared can 00344257/88/$3.50

DON H. CARD ET AL.

124 TABLE 1 Chemical Composition of Shoots of Loblolly Pine as Percentages of Oven Dry Weight a CONSTITUENTS

Nitrogenous compounds Amino acids Protein Nucleic acids Carbohydrates Reducing sugars Sucrose Cellulose Hemicelluloses Pectin Lipids Lignin Organic acids Phenolics Minerals

PERCENT OF DRY WEIGHT

8.4 (7.2) (90.7) (2.1) 38.0 (5.1) (8.2) (56.0) (25.7) (5.0) 5.3 23.3 3.5 20.0 1.5

~Condensed from Chung and Barnes (1977). Nttmhers in parentheses are subfraction percentages of major fractions.

provide information on relative concentrations, as shown by research in the agricultural sciences (McClure et al., 1977; Shenk et al., 1978; Ben-Gera and Norris, 1968; Norris et al., 1976; Norris and Hart, 1965; Norris and Barnes, 1976) and recently in forest ecology (Wessman et al., 1988). It has been demonstrated for several chemicals that the accuracy and precision of estimates of concentration comparable to those of wet chemical analysis is possible through the use of statistical multiple regression analysis (Williams et al., 1983). This paper describes preliminary resuits of applying linear regression methodology to the analysis of laboratory spectra of dried lea/ samples from deciduous and coniferous species from forested sites in three states--Alaska, California, and Wisconsin. Dried, ground lea/ material was used in this study in order to reproduce as closely as possible the experimental conditions shown to be suc-

cesshtl in agricultural applications, to develop techniques and software for this type of analysis, and to establish that chemical information can be obtained from spectra of tree foliage. This lays the ground work for remote sensing studies, which must deal with the added complications of atmospheric water absorption, uncontrolled background illumination, plant canopy structure, and other complicating factors. 2. M e m o & 2.1. Data collection

Foliage for this study was collected from forests in three widely separated sites: the northern temperate hardwood forests of central Wisconsin, the taiga forests of central Alaska, and the temperate coniferous forest at Sequoia National Park in California. In Wisconsin, foliage was collected from a range of old-growth mixed hardwood forests on Blackhawk Island, a 70-ha island located in the Wisconsin River north of the Wisconsin Dells. For a complete description of the forests and nutrient cycling characteristics see Pastor et al. (1982; 1983). Major species were red, white, and black oak ( Quercus rubra L., Q. Alba L., and Q. velutina), sugar and red maple (Acer saccharum Marsh and A. rubrum Marsh), and black cherry (Prunus serotina Ehrh.). A complete list of species for all sites is given in Table 2. The Alaska samples were obtained from the Bonanza Creek Experimental Forest near Fairbanks and from a nearby site on islands in the Tanana River (Van Cleve et al., 1983a). The Alaskan coniferous species were black spruce [Picea mar/ana (Mill.) B.S.P.] and white spruce

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY TABLE 2

List o| Species by Location

Srm

SET NVMBEa

FOLtACE I~SCmPTmN

CH~MmAL ANALYSIS

SPECIES

125

NUMBEI~OF SAMPLES (SPECTRA)

Blackhawk Island

I

green

htil chemistry

red oak white oak red maple butternut hickory black cherry sugar maple

9 7 3 1 1 3

Blackhawk Island

II

green

nitrogen, lignin"

cherry box eider white oak red maple shagbark hickory sugar maple black cherry American elm red oak

1 1 6 1 1 3 1 1 5

Blacldaawk Island

III

abseised

nitrogen, lignin"

honeysuckle cherry white oak hickory witch hazel red maple black walnut sugar maple red oak American elm basswood ironwood black oak unknown

1 5 9 4 1 4 2 4 1 3 3 2 9 3

Sequoia

IV

green

full chemistry

white fir red fir

31 12

Bonanza Creek

V

green

flail chemistry

white spruce black spruce paper birch aspen balsam poplar alder willow

8 10 6 3 2 3 2

a Lignin measured on 75 of the total 95 samples.

[P. glauca (Moench) Voss], and the predominant deciduous species were paper birch (Betula papyrifera Marsh) and alder ( Alnus crispa). All of the California samples came from the two coniferous

species which dominate the forests of Sequoia National Park in the southern Sierra-Nevada Mountains: red fir (Ab/es magnifica A. Murr.) and white fir [A. concolor (Gord. and Glend.) Lindl.]

126

(Peterson et al., 1986). Fresh leaf samples were collected from four canopy positions at each site (top, bottom, and two intermediate heights), and were refrigerated for shipment to the laboratory. Some of the Wisconsin samples consisted of senesced foliage that had fallen as litter. The sites in the three states will be referred to hereafter as Sequoia for the California site, Bonanza Creek for the Alaska site, and Blackhawk Island for the Wisconsin site. 2.2. Biochemical analysis

For the Blackhawk Island site, samples were oven-dried at 60 ° , ground to pass through a 40-mesh screen, and stored until analysis. Foliage from Bonanza Creek and Sequoia were lyophilized before grinding. Total nitrogen, total chlorophyll, proteins, lignin, cellulose, starch, and sugars were analyzed using standard chemical procedures. Starch was measured using methods of Matson and Waring (1984) and Haisig and Dickson (1979). Foliage samples were extracted in a methanol-chloroformwater (MCW) solution and the starchcontaining residue was dried and treated with a purified enzyme solution (Diazyme L-150 (a-l,4-glucan glucohydrolase) and Mylase 100 (a-amylase)). After a 24-h incubation, starch was measured as glucose using the glucose oxidase method (Matson and Waring, 1984). Sugars in the MCW supernatant were analyzed using the anthrone colorimetric method (Hazid and Neufeld, 1964). Chlorophyll was measured spectrophotometrically after extraction with 90% acetone buffered with CaCO. Total proteins were analyzed by hydrolyzing tissue samples in KOH for 24 h and measuring

DON H. CARD ET AL

protein content of the hydrolyzed sample using a blue dye-binding technique (BioRad Protein Assay, BioRad Chemical Division No. 85-0521 1285) and a catalase standard (Lee and Takahashi, 1966). For the Sequoia and Bonanza Creek data sets, lignin and cellulose were measured using the method of Van Soest and Wine (1968). Tissue was treated with cetyltrimethylammoninm bromide to make acid detergent fiber. Lignin was oxidized from the fiber with potassium permanganate and determined by loss of mass. Cellulose was measured in the residue by loss of weight on ashing. For the Blackhawk Island data sets, lignin was measured as the fraction insoluble in sulfuric acid following extraction in polar and nonpolar solutions (Effland, 1977; McClaugherty et al., 1985). Total nitrogen and phosphorus were measured using a micro-Kjeldahl technique after digesting samples in a block digestor using a sulfuric acid/mercuric oxide catalyst. Wet chemical analysis statistics for the Blackhawk Island, Sequoia, and Bonanza Creek data sets are shown in Table 3. 2.3. Spectroscopic measurements

The total hemispherical spectral reflectance of the dried and ground leaf sampies was measured in a Perkin-Elmer Model 330 spectrophotometer with integrating sphere attachment. The range of the instrument is 187-2500 nm, although the range was limited to 400-2446 nm for these experiments. Data were recorded as reflectance on floppy disks on a dedicated Perkin-Elmer Model 3600 microprocessor, control, and data station. These spectral files, containing about 1024 data points (subsequent analyses re-

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY TABLE 3 SET

127

Wet Chemical Analysis Statistics by Location a CHEMICAL

N

STANDARD DEVIATION

MF-.A.N

MINIMUM

MAXIMUM

Blacldaawk Island Total nitrogen Total chlorophyll Protein IAgnin Cellulose Starch Sugar

24 24 24 24 24 24 24

2.410 0.320 8.059 13.,56 17.62 0.328 4.502

0.350 0.149 1.211 3.509 3.078 0.107 1.630

1.690 0.126 5.770 9.010 13.60 0.172 1.900

2.980 0.621 10.25 20.20 25.50 0.600 8.100

II

Total nitrogen Lignin

20 20

2.431 15.74

0.301 4.429

1.830 8.460

2.900 25.48

III

Total nitrogen Lignin

51 50

1.130 17.14

0.356 6.632

0.700 3.320

1.930 30.51

0.168 0.038 0.658 0.980 3,800 1.222 1.634

0.363 0.087 3.440 6.060 5.560 1.120 4.130

1.346 0.250 6.278 9.880 31.53 6.780 11.71

0.649 0.186 1.958 1.903 3.529 1.026 2.863

0.636 0.076 3.917 6.580 10.90 0.0 4.220

3.017 0.623 13.66 14.80 21.20 4.320 18.83

Sequoia IV

Total nitrogen Total chlorophyll Protein IAgnin Cellulose Starch Sugar

43 43 43 43 43 43 43

0.79 0.158 4.504 8.300 18.87 4.288 7.159 Bonanza Creek

V

Total nitrogen Total chlorophyll Protein IAgnin Cellulose Starch Sugar

34 34 34 34 34 34 34

1.232 0.276 7.043 10.31 17.14 1.183 11.15

°Units in % dry weight. N is the number of spectra in the set.

duced this number to fewer wavelengths due to subsampling), were transferred to a VAX 11/780 computer for reformatting and statistical analysis. The ground and dried leaf samples were placed in a small sample holder for measurement in an upright position. The sample holder consisted of a small aluminum cup with a Spectrosil IR silica window ( 5 / 8 in. diameter) on one side and a screw-on cap on the other. About 2

cm of sample were pressed into the cup, an amount which did not permit any light penetration to the back sudace. The sample was scanned once. 2.4. Statistical methods Rather than show plots of individual spectra, we show the average of 103 absorption spectra of ground, dried leaf material in Fig. 1 to demonstrate the

128

DON H. CARD ET AL.

3°f

3.3

3.0 2.7

.-. 2.4 tr ~2.1 ¢3 Q _l 1.8 Z <~ ',' 1.5

1.2 .9 .6

.3 0 ~

400

800

1200 1600 W A V E L E N G T H , nm

2000

2400

FIGURE 1. Average of 99 spectra (logl/p) with + 1 standard deviation limits for Blackhawk Island data set (deciduous mixed species), plotted against wavelength (AX = 10 nm), (Each symbol • represents 99 spectra)

subtlety of absorption features to be correlated with chemical concentrations. The structure evident in the correlograms in Figs. 2 and 3 for nitrogen and lignin make plausible the hypothesis that chemical concentrations might be predicted from spectra using a linear regression model. The correlograms are plots of the correlation between chemical concentration and log l / p vs. wavelength. (Each ordinate in the correlograms represents 103 spectra.) In the agricultural sciences, in which much of the pioneer work in shortwave infrared (SWIR) spectroscopy was done

[usually called near-infrared (NIR) in the agricultural literature], the workhorse for statistical analysis has been stepwise multiple linear regression (Efroymson, 1960), although ratioing of derivatives at various standard wavelengths has also been extensively used (Williams et al., 1983). Stepwise regression is a procedure for automatically selecting, in a stepwise manner based on partial correlations of a dependent variable with the independent variables, a set of independent variables that are close to optimal in the sense of maximizing the squared multiple correlation coefficient (R z) of the dependent

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY

129

1.0 .8

,,-I,

8

0 -.2

-.4 400

800

1200 1600 WAVELENGTH, nm

2000

2400

FIGURE 2. Correlation plot (correlogram) for total nitrogen for leaf material from mixed deciduous and conifer species pooled data set (Bonanza Creek, Sequoia, and Blackhawk Island). The ordinate is correlation between chemical concentration and log(I/p), and the abscissa is wavelength. The number of sample spectra was 103 at each wavelength (Ah = 10 nm).

1.0 .8 .6

oo

.2 0 -.2

-.4 400

800

1200 1600 WAVELENGTH, nm

2000

2400

FIGURE 3. Correlogram for lignin for leaf material from mixed deciduous and conifer species, pooled data set. Number of sample spectra was 103 at each wavelength (Ak = 10 nm).

130

DON H. CARD ET AL.

variable with the set of selected independent variables. In the present context, the dependent variable is chemical concentration, and the independent variables are reflectances (or transformations of reflectances) measured at a set of equally spaced wavelengths. Although the stepwise procedure has never had a strong theoretical basis, and several recent statistical papers have expressed concern over the potentially large Type-I error (selecting independent variables that are merely noise, and inflating the R 2) (Flack and Chang, 1987; Rencher and Pun, 1980), there is evidence in our work that stepwise regression works quite well as long as the number of sample spectra is sufficiently large (say, n > 100). As evidence for this, Figs. 2 and 3 show correlograms for nitrogen and lignin in which the con-

centrations of these chemicals correlated against log l / p are shown in boldface, and correlograms for nine random permutations of the same set of concentrations are shown on the same plots. Clearly the random permutation (scrambling) of the concentrations have destroyed the correlation between the concentration and the independent variables. This is confirmed in Table 4, which shows the results of running stepwise regression on the same data set (103 spectra and 205 wavelengths) for 18 random permutations of nitrogen concentration (similar results, not shown, were obtained for lignin). The highest R 2 (0.43) after 10 steps (10 wavelengths in the equation) for the scrambled data never approached the R e for the unscrambled data for one step (0.71). Although this does not prove that the R 2

Results of Running Stepwise Regression Program oil 18 Random Permutations of the Set of 103 Nitrogen Concentrations for the Pooled Data Set"

TABLE 4

COMPUTER

/{2

Rvn No.

(AVl'ER 10 STEPS)

1

(unscrambled)

O.93

2

(random permutation)

0,29 0.25 0.43 0.24 0.30 0.23 0.37 0.34 0.29 0.30 0.35 0.37 0,33 0.28 0.32 0.34 O.22 0,22

3

4 5

6 7

8 9 10 11 12 13 14 15 16 17 18 19

"103 spectra, 205 wavelengths, l o g ( I / p ) data. For the unscrambled data, the R2 (coefficient of determination) after the first step was 0.71.

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY

values are not inflated, it does indicate that the fear of achieving high R 9 when there is no association between concentration and reflectance is unfounded, at least for these data. In addition to this evidence, the use of cross validation explained below indicates that stepwise regression selects equations that have faidy good predictive power. The program that we developed for stepwise multiple regression is called STEPWISE, and is based on subroutine RLSTP from the IMSL Library (1980) Edition 8 and implemented on a VAX 11/780 minicomputer. The final regression equation, after running the stepwise program, can be written y = b o + b l X l + bgX 2 + . . . + b k X k ,

(1) where X i is the measured reflectance p at wavelength )~i, or a function of reflectance (e.g., l o g l / p ) , Y is the concentration of the chemical component, and b i are the coefficients giving "best" fit by the stepwise procedure. The number of wavelengths k is selected by minimizing the standard error of prediction SEP, defined in Eq. (5) below. Each chemical component is treated independently of the others, and so each yields an independent set of regression coefficients. Goodness-of-fit is measured by R 2, the squared multiple correlation coefficient, or coefficient of determination, which can be interpreted as the fraction of the total sum of squares explained by the regression. Also, the scatter of the dependent variable Y about the regression plane is described by the

131

standard error for calibration: SEC = (residual sum-of-squares) /(n-k+l),

(2)

where n is the sample size (number of spectra) and k is defined after Eq. (1) above. In addition to the raw reflectance p, several transformations of reflectance have been proposed for prediction of chemical concentration, with varying degrees of success (Shenk et al., 1978); log l / p (a measure of absorbance), the first, second, and third derivatives of log l / p , and various normalized differences such as (P2 - P l ) / ( P 2 + Pl). In this study we examined several of the above-mentioned transformations, but report only the most successful; p, log l / p , first and second differences ( P i + l - Pi) and ( p i + 2 - 2 p i + l + p i ) . These two differences are approximations to the first and second derivatives, respectively (as long as the increment between wavelengths is constant). In this study, the number of sample spectra was limited, compared to the 200 spectra or more recommended by people experienced in agricultural applications (Marten et al., 1985). Therefore, overfitring of the chemical data was a real possib i l i t y - t h a t is, too many wavelengths might be selected by the stepwise program before reaching an acceptable R 2. Figure 4 shows, for several transformations of reflectance, the increase in R 2 as terms are added to the regression equation. The result of using more wavelengths than necessary is to accommodate peculiarities of the data set used for calibration, thereby increasing prediction

132

DON H. CARD ET AL.

1.0

I

I

I

I

.8

.6

.4 0 Log 1/R U N S M O O T H E D [ ] Log 1/R S M O O T H E D

O R UNSMOOTHED

.2 I 0

-

r~ S E C O N D D E R I V A T I V E

Log 1/R

A SECOND DERIVATIVE

R

I I I I 2 4 6 8 N U M B E R OF STEPS ( W A V E L E N G T H )

I 10

FIGURE 4. Multiple squared correlation coefficient (R 2) vs. the number of terms in the regression equation for the stepwise regression of total nitrogen concentration against various transformations of reflectance. Number of spectra = 24, Blackhawk Island data set.

bias. In order to estimate the optimal n u m b e r of wavelengths to minimize overfitting, cross validation was u s e d - - t h a t is, a portion of the data was set aside, which we called the prediction set, to test the equation developed on the remainder, called the calibration set. Various measures of prediction performance may be defined; we discuss four: 1) correlation between actual and predicted concentrations; 2) standard error of prediction; 3) average bias; and 4) percent variation explained. If y is the actual chemical concentration from laboratory analysis, and Y the concentration predict-

ed by the regression equation, then since they are both random variables, we have for the mean square prediction error of Y as an estimator of y: MSPE(Y) =

E(y- y)2

= var( Y ) + var( U )

+ [ e t y t - e(Y)] 2 - 2coy(Y, u),

E[,] E(y)- E(Y) where

(3)

is expectation. The term is called the bias and is estimated for a sample of size n (number

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY

of spectra) as n

bias = ~., ( g, - Yi) / n = ~t - "Y. 1

(4)

The standard error for prediction is S E P = [MSPE(Y)] 1/2

(5)

and is estimated by n

S E P = Z(Y~-y,)2/(n

- 1),

(6)

1

where Yi is the predicted chemical concentration for sample i and gi is the actual concentration for sample i. The correlation between actual and predicted values of y can be found by rearranging Eq. (3), inserting SEP from (5) and using the definition of correlation in terms of the covariance:

133

error in predicting set g concentration using its own mean as the predictor. A thorough discussion of the problem of prediction and the reason for shrinkage of R 2 between the calibration and prediction sets is given by Nicholson (1960) for multiple linear regression. He notes that the usual interpretation of R 2 as the percentage of variance explained by the regression is no longer true for the prediction R 2, and therefore Eqs. (6) and (8) above will not in general give the same results. This should be kept in mind in interpreting the results in Tables 5 and 6; e.g., in certain cases E can increase when R~ decreases. A case can be made that E is more important as a prediction criterion than R~, and should be taken as the definition of prediction correlation (Nicholson, 1960). In this paper, we use both criteria. 3. Results

Re

= [var(g) + var(Y ) + bias 2(Y ) - Sr, P 2] /

{2[var(v)var(Y)]'/2}.

(7/

The percent variance explained by the regression is n

E = 1 - E (Y~2) _ yi(1)) 1

/.

E

,

(8)

1

where the superscript I refers to the calibration set and 2 refers to the prediction set. E in Eq. (8) compares the error in predicting chemical concentration for set 2 (prediction set) using set 1 for developing calibration equations, with the

3.1. Nitrogen and lignin analysis for deciduous foliage (Blackhawk Island)

Although stepwise regression was run on the Blackhawk Island data set I for all seven chemicals (see Fig. 4 for the results for nitrogen), the number of spectra (n = 24) was not considered sufficient for assessment of prediction. However, combining sets I and II, which included all green-collected deciduous foliage, allowed prediction of set II nitrogen and lignin after calibrating on set I. Figure 5 shows the resulting standard error of prediction (SEP) for nitrogen as a function of the number of wavelengths in the regression equation. The minimum value of SEP for six steps indicates that six wavelengths yield the best regression equation for prediction. For six wavelengths the percent

1:34

DON H. CARD ET AL TABLE 5

Prediction Results for Nitrogen and Ligalin, Blackhawk Island Site"

MATHEMATICAL TREATMENT

SEP (%)

/~t':

RI'

BIAS (%)

E (%)

WAVELENCTH (rim)

Total Nitrogen log 1/R

0.233

0.94

0.95

0.024

89.1

670 460 2360 2110 2180 2050 1660 2080 1701)

First difference

0.265

0.93

0.93

0.006

86.0

620 850 6,50 1670 510 680

Second difference

0.362

0.87

0.88

0.141

73.8

600 1360 16,50 1380

42.40

400 1910 700 610 2320 2140 23,50 1860 1740 1840

Lignin log 1/R

4.880

0.93

0.67

0.592

First difference

5.419

0.56

0.56

- 1.243

25.9

Second difference

5.804

0.15

0.43

- 1.057

15.0

For nitrogen, number of spectra for calibration = 62, number of spectra for prediction = 30. For ligalin, number of spectra for calibration = 46, number of spectra for prediction = 23. The optimal set of wavelengths (minimal SEP) is shown for each transformation. (Wavelengths are not shown for the last two lines, since regression results were poor for these cases.)

135

P R E D I C T I O N O F LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY

of variation explained was approximately 40% and the correlation between actual nitrogen concentration and predicted concentration (Re) was 0.64, showing quite good prediction even for a moderate sample size. Results for lignin were poor for this set of spectra. Larger sample sizes were made available for Wisconsin deciduous foliage by combining sets I, II, and III, which yielded 92 spectra for nitrogen and 69 for lignin. This set of spectra was subdivided into calibration and prediction sets in the ratio 2:1. The best predictions for nitroTABLE 6

gen were obtained from the log 1/p and first difference of log 1/p transformations (R e = 0.95 and 0.93, respectively) with slightly inferior performance (R e = 0.88) for the second difference, even though fewer wavelengths were needed for the second difference (4 vs. 9). These results are shown in Table 5. The first two wavelengths selected, 670 and 460 nm (log 1/p data), correspond clearly to absorption peaks of chlorophyll, the component which, along with protein, accounts for most of the leaf nitrogen (see Table 1). For the first difference transformation,

Prediction Results [or Pooled Data Sets (Blackhawk Island, Sequoia, Bonanza Creek) a FIRST DIFFERENCE OF log 1/p

log 1 / p E

SEP

SECOND DIFFERENCE OF log 1/p

CrrEMICAL

SEP

CoMPo~

(%)

a~

(%)

#A

X

(%)

R~

(%)

E

#~

X

SEP

(%)

R~

(%)

E

Total nitrogen

0.33

0.90

80

2

580 480

0.42

0.83

68

3

0.44

0.85

64

5

Total chlorophyll

0.07

0.88

76

2

680 2110

0.09

0.82

59

4

0.10

0.77

54

5

Protein

1.28

0.77

50

6

1.52

0.64

30

4

1.53

0.68

29

8

Lignin

2.27

0.70

39

2

3.33

0.57

0

7

--

2.52

0.64

25

2

Cellulose Starch

3.84 1.34

0.42 0.74

6 53

8 7

4.06 1.72

0.02 0.65

-6 23

2 7

---

3.84 1.24

0.47 0.78

5 60

5 2

Sugar

2.38

0.71

50

2

580 1930 600 610 1990 1950 550 670 -580 590 600 610 630 680 720 400

790 1500 640 610 430 1420 2240 590 1730 1500 750

3.01

0.51

21

7

--

2.68

0.63

37

4

690 600 570 690 700

690 600 1260 460 570 1420 410 670 -420 690

490 820 750 2200

~Number of spectra for calibration = 52 (odd numbered samples). Ntunber for prediction = 51 (even numbered samples). Wavelength increment 10 nm. SEP = standard error of prediction. R e = correlation between predicted and actual chemical concentration. E = percent variation explained by the regression. ~ = number of wavelengths at minimum SEP. ~ = wavelengths selected (not shown if E < 25%, first six wavelengths shown i[ #A > 6).

136

DON H. CARD ET AL.

.50

I

.40

O

.30

n" LU

c~ n.20 z < I-O'3

.10

0 ~

0

I

2 4 6 8 N U M B E R OF STEPS ( W A V E L E N G T H S )

10

FIGURE 5. Standard errors of calibration (SEC: D) and prediction (SEP: O) for stepwise regression analysis of nitrogen concentration vs. log l / o for deciduous ground leaf material (Blackhawk Island data set) plotted vs. number of terms in the regression equation (number of spectra for calibration = 24, for prediction = 20; AX = 16 nm).

most of the wavelengths were selected from the visible part of the spectrum, and were probably associated with chlorophyll and possibly other related pigments. Scatterplots for the log 1/p and first and second difference regressions for nitrogen using the optimal number of wavelengths for prediction are shown in Figures 6, 7, and 8, with calibration and prediction spectra identified by different symbols. The plots all indicate a good distribution for the nitrogen values and a low bias in the regression, as shown by symmetry of

points about the 45 ° line through the origin. The prediction correlations for lignin were much lower than those for nitrogen; the best was Re = 0.67 for 50 samples predicting 25 and the scatterplot of predicted vs. actual concentration is shown in Fig. 9. 3.2. Analysis of seven chemical eonstituents (pooled data set) In order to work with a data set large enough to assess predictions for the full

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY

3.0

I

0 []

I

I

137 I

PREDICTION SET CALIBRATION SET

2.4

E)

1.8 uJ

[]

p. []

uJ

[] (3

~a . 1.2

.6

E!

SEC = 0.202 SEP = 0 . 2 3 3 R e = 0.94 Rp = 0.95

(3

0 0

I .6

I 1.2

I

1.8 ACTUAL, %

I 2.4

3.0

FIGURE 6. Total nitrogen predicted concentrations (log l J 0 data, nine-step regression) vs. concentration according to wet chemical analysis. Sixty-two calibration samples, 30 prediction samples, Blackhawk Island data, deciduous mixed species.

set of seven chemicals, the samples from the Blackhawk Island, Bonanza Creek, and Sequoia sites were pooled. This permitted evaluation of the robustness of the technique over different forest sites, tree species, and leaf morphologies. However, in doing so, two complicating factors were i n t r o d u c e d - - t h e method of drying (oven drying for the Blackhawk Island samples, freeze drying for the others) and two different wet chemical analytical techniques for lignin. Of the 103 pooled samples, 52 (odd-numbered samples) were used for calibration to predict the other 51 (even-numbered samples). Pooling of

the data over the three major sites resulted in wavelengths spaced every 10 nm, since wavelengths in the separate sets did not exactly match. Results of the regression analysis and cross validation procedure are shown in Table 6 and scatterplots of predicted concentration versus actual concentration for each of the seven chemicals are shown in Fig. 10. 1. Total nitrogen: Total nitrogen was predicted for log 1 / p with a correlation coefficient of R e = 0.90, but there was a higher standard error of prediction (SEP = 0.33) than there was for the Blackhawk-Island-only

1:38

DON H. CARD ET AL.

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set. However, the calibration equation used only two wavelengths, at 580 and 480 nm, probably corresponding to chlorophyll (and possibly other pigments). The two difference transformations produced lower correlations, but similar wavelengths. . Total chlorophyll: The first wavelength selected for total chlorophyll for all mathematical transformations was close to a known strong absorption feature at 680 nm. The remain-

ing wavelengths might be explained as either other chlorophyll features (430 nm; first difference), as features associated with other pigments, or as protein features associated with chlorophyll. . Protein: Protein prediction was inconclusive--the best prediction (log l / p data) explained only 50% of the variation in concentration, although an Rio of 0.77 might be considered fairly high. Only the wavelengths for log l / p are shown

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY

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in Table 5, three of which were in the visible region, probably corresponding to leaf pigments, and the other three were centered around 1950 nm. . Lignin: While the prediction correlation coefficient for lignin for the pooled set ( R e = 0.70) is nearly the same as that for the BlackhawkIsland-only set (sets I, II, and III) ( R e = 0.67), the SEP is lower (2.27) in the pooled set. This may be somewhat misleading because most of the high lignin values occurred in

the Blackhawk Island data, while most of the lower values (determined by a different analysis method) were from the Bonanza Creek and Sequoia sites. . Cellulose: Results for cellulose were negative for all transformations for the pooled data set. The scatterplot in Fig. 10 for cellulose shows an obvious lack of correlation between actual and predicted concentrations. . Starch and sugar: The chemical structures of cellulose, sugar, and starch are very similar, and in the

140

DON H. CARD ET AL.

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analysis for the latter two, only visible wavelengths were selected, due possibly to the relationship of these compounds to nitrogen. The values of R e a n d E are quite difficult to use in assessing prediction quality, since there seems to be no adequate theory available for estimating significance tests when calibration equations are developed from stepwise regression; however, SEP can be evaluated informally by tabulating SEP s percentage of mean concentration and comparing the

result with coefficients o{ variation (CV) for each chemical. This gives the improvement in the CV using the regression equations for prediction over the CV expected in the absence of spectral data for prediction (lower CV means lower percentage error). Table 7 shows the CVs for prediction vs. the standard CVs for the pooled Blackhawk Island data sets. In most cases (excluding starch and cellulose first differences in the pooled set), the prediction CVs are lower than the standard CVs. This indicates predictive

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PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY TABLE 7 Coefficients of Variation (CV) Using SEP vs. SD (Standard Deviation of Chemical Concentration) for Pooled and Blackhawk Island Data Sets" CV (SEP/MEnS) DATA SET Pooled

log 1/p

FIRST DIFF.

SECOND DIFF.

CV (SD/MEaN)

nitrogen

25.0 (22.8)

31.9 (28.1)

33.4 (40.2)

58.0

total chlorophyll

30.2 (51.7)

38.8 (64.7)

43.1 (60.3)

64.4

protein

20.8 (20.9)

24.7 (23.9)

24.8 (23.4)

33.0

lignin

21.9 (27.8)

32.1 (29.3)

24.3 (32.7)

30.6

21.9

CHEMICAL

cellulose

Blackhawk Island

21.2

22.4

21.2

(21.2)

(25.2)

(22.6)

starch

59.0 (66.1)

75.8 (65.6)

54.6 (96.9)

88.4

sugar

30.7 (38.2)

38.8 (39.5)

34.6 (39.9)

43.9

nitrogen

13.7

15.6

21.4

43.6

lignin

29.2

32.5

34.8

38.8

"Means are over total data set, calibration set+prediction set. Values in parentheses are for SWIR-only analysis.

success, although the question of how good the prediction is for each chemical is still open. 3.3. Analysis of Shortwave Infrared (SWIR) spectral region only As shown in Sec. 3.2, stepwise regression analysis using the entire spectrum (400-2400 nm) tends to select wavelengths from the visible region, a spectral region of strong absorption by organic material and high variability between sample spectra (see Fig. 1). As most of the stretching modes (harmonics and

overtones) of the organic constituents occur in the SWIR region, we were interested in the limiting analysis to just this region and limited our spectral data to 1200-2400 nm and reran the regressions for the pooled data set. Results are shown in Tables 7 and 8. A comparison of Table 8 with Table 6 shows that, for nitrogen and protein, prediction correlations were slightly higher in the SWIR-only log 1/p analysis (R e = 0.92 vs. 0.90 for nitrogen; R e = 0.81 vs. 0.77 for protein), although more wavelengths were necessary to achieve comparable SEPs.

144

D O N H. C A R D E T AL.

T A B L E 8 Prediction Results Only ( 1 2 0 0 - 2 4 0 0 n m ) '~

for Pooled Data Sets (Blacldaawk Island, Boimnza Creek, Sequoia) for S W l R Region FIRST DIFFERENCE OF log 1 / p

log I/# CHEMICAL COMPONENT

SEP (0/%)

ap

E (%)

:~

)k

SEP (%)

Rp

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0.30

0.92

83

10

2210 2110 2180 2310 2120 2030 1460 1730

0.37

0.87

Total chlorophyll

0.12

0.62

34

2

0.15

Protein

1.29

0.81

50

9

5

2220 2110 1730 1460 1740 2170 --

Lignin

2.89

0.52

0

Cellulose

3.84

0.42

6

8

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1.50

0.68

41

8

Sugar

2.95

0.58

24

5

SECOND DIFFERENCE OF log 1 / p

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"Number of spectra for calibration = 52 (odd numbered samples). Nmnber for prediction = 51 (even n u m b e r e d samples). Wavelength increment 10 nm. S E P = standard error of prediction. R v = correlation b e t w e e n predicted and actual chemical concentration. E = percent variation explained by the regression. ~ , = n u m b e r of wavelengths selected at minimum SEP. ~t = wavelengths selected (not shown if E < 25%, first six wavelengths shown if ; ~ > 6).

As might be expected, prediction of total chlorophyll was inferior in the SWlR-only analysis, since the strong absorption peaks in the visible region were not available. There was no significant cellulose prediction using the SWIR-only data, as in the full-range analysis. Lignin and sugar prediction deteriorated in the SWlR-only analysis, whereas starch prediction was slightly degraded, but used no visible wavelengths in the SWIR-only analysis and only SWIR wavelengths in the fldlrange analysis, implying that either spec-

tral range can be used to predict starch. Table 7 compares coefficients of variation for each chemical under the two spectral ranges. 4. Discussion Associating particular chemical bonds with the wavelengths selected by stepwise regression, as we did rather casually in the previous section, should perhaps not be taken too literally in our present state of knowledge, and should be treated

PREDICTION OF LEAF CHEMISTRY BY REFLECTANCE SPECTROSCOPY

as suggestions only, since, as noted by other researchers (McClure et al., 1977; Shenk et al., 1978), the wavelengths selected by stepwise regression depend on the samples chosen for calibration, the mathematical transformations selected, and other factors, with the set of chosen wavelengths becoming more consistent between data sets as sample size increases. This is not surprising since samples contain several constituents each with a number of absorption peaks, causing peak broadening and shifting, and so selected wavelengths will not always occur precisely at known stretching frequencies. In addition, interference between different constituents at the same or similar wavelengths can occur. These factors make interpretation of results complicated in a scientific sense; nevertheless, the results show potential for predicting chemical concentrations over a wide range of conditions. The most important consideration is that calibration samples should include a wide range of variation, so that any future sample using the calibration equations is included in the range of applicability (Norris and Barnes, 1976).

5. Summary Visible and near-infrared spectroscopy predicted concentrations of seven chemical constituents of dried leaf material with varied degrees of success. Stepwise regression showed highly significant coefficients of determination (R 2) for all cases; however, insufficient sample sizes did not allow prediction for all chemicals to be assessed in data sets that were either homogeneously deciduous or coniferous. Formation of a large data set by combining data from sites in three states

145

(California, Maska, and Wisconsin) and from both broad-leaved and coniferous leaf material allowed predictive power to be evaluated by cross validation. Prediction for the combined data set explained at least 50% of the variation in chemical concentration in all constituents except cellulose and lignin. Although excellent predictions were obtained in particular cases, better results can be expected when wet chemical analyses are available for larger data sets and techniques are implemented for reducing instrument error.

The laborious process of collecting fresh leaves from the canopies of forests in three different states and then processing each batch through many analytical steps involved many people who deserve recognition. Our apologies to any we may have overlooked. The authors thank C. Berger, C. Volkmann, 1. Mazzurco, D. Wang, P. Yiu, and S. Damazonio for the biochemical analyses. Some samples were analyzed by the University of Wisconsin Soils and Plant Analysis Laboratory and by the Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA. Many people were involved in the collection of foliage: our appreciation to S. McNulty, S. Kaat, R. Harrington, J. Fownes and C. Wessman in Wisconsin; L. Viereck and J. Foote in Alaska; T. Stohlgren and D. Parsons in Sequoia National Park; and J. Brass, K. Weinstock, C. Hlavka, and N. Swanberg at Ames Research Center. Our thanks to M. Spanner for organizing and carrying out the sampling efforts in Wisconsin and Alaska, and to W. Westman for the same in Sequoia. Finally, we thank A. Covington at Antes Research Center for use of the spectrophotometer. This research was

146

supported by the NASA Earth Science and Applications Division Terrestrial Ecosystems Program. References Ben-Gera, I., and Norris, K. H. (1968), Determination of moisture content in soybeans by direct spectrophotometry, Isr. J. Agric. Res. 18(3): 125-132.

DON H. CARDET AL. Lee, L. P., and Takahashi, T. (1966), An improved colorimetric determination of amino acids with the use of ninhydrin, Anal. Biochem. 14:71-77. Marten, G. C., Shenk, J. S., and Barton, F. E., II (Eds.) (1985), Near Infrared Reflectance Spectroscopy (NIRS): Analysis for Forage Quality, United States Department of Agriculture Handbook No. 643, Washington, DC.

Birk, E. M., and Matson, P. A. (1987), Site fertility affects seasonal carbon reserves in lobloUy pine, Tree Physiol. 2:17-27.

Matson, P. A., and Waring, R. H. (1984), Effects of nutrient and light limitation on mountain hemlock: Susceptibility to laminated root rot. Ecology 65:1517-1524.

Chung, H. H., and Barnes, R. L. (1977), Photosynthate allocation in Pinus taeda. Substrate requirements for synthesis of shoot biomass, Can. J. Forest Res. 7:106-111.

McClangherty, C. A., Pastor, J., Aber, J. D., and MeliUo, J. M. (1985), Forest litter decomposition in relation to soil nitrogen dynamics and litter quality, Ecology 66:266-275.

Effland, M. J. (1977), Modified procedure to determine acid insoluble lignin in wood and pulp, Tech. Assoc. Pulp Paper Ind. 10:143-144. Efroymson, M. A. (1960), in Mathematical Methods for Digital Computers, (A. Ralston and H. S. Will, Eds.), Wiley, New York, pp. 191-203. Flack, V. F., and Chang, P. C. (1987), Frequency of selecting noise variables in subset regression analysis: a simulation study, Am. Stat. 41(1):84-86. Haissig, B. E., and Dickson, R. E. (1979). Starch measurement in plant tissue using enzymatic hydrolysis, Physiol. Plants 47:151-157. Hazid, W. Z., and Neufeld, E. F. (1964), Quantitative determination of starch in plant tissue, in Methods in Carbohydrate Chemistry, 1V (R. L. Whistler, R. J. Smith, and J. N. BeMiller, Eds.), Academic, New York.

The IMSL Library Contents Document (1980), International Mathematical and Statistical Libraries, Edition 8, Houston, Tx.

McClure, W. F., Norris, K. H., and Weeks, W. W. (1977). Rapid spectrophotometric analysis of the chemical composition of tobacco, Beitr. Tabakforsch. 9(1): 13-18. Meentemeyer, V. (1978), Macroclimate and lignin control of decomposition rates, Ecology 59:465-472. Melillo, J. M., Aber, J. D., and Muratore, J. F. (1982), Nitrogen and lignin control of hardwood leaf litter decomposition dynamics, Ecology 63:621-626. Nicholson, G. E. (1960), Prediction in future samples, in Contributions' to Probability and Statistics (I. Olkin et al., Eds.), Stanford University Press, Palo Alto, CA. Norris, K. H., and Barnes, R. F. (1976), Infrared reflectance analysis of nutritive value of feedstuffs, in Proceedings' of First International Symposium on Feed Composition, Animal Nutrient Requirements, and Computerization of Diets, Utah State University, Logan, UT. Norris, K. H., and Hart, J. R. (1965), Direct spectrophotometric determination of moisture content of grain and seeds, in Proceedings of the 1963 International Symposium

PREDICTION OF LEAFCHEMISTRYBYREFLECTANCESPECTROSCOPY on Humidity and Moisture, Reinhold, New

147

age Hanoesting Conference Proceedings,

Published by Amer. Soc. Agricultural EnYork, vol. 4, pp. 19-25. gineers, Ames, Iwoa. Norris, K. H., Barnes, R. F., Moore, J. E., and Shenk, J. S. (1976), Predicting forage Qual- Van Cleve, K. L., Dyrness, C. T., Viereck, L. ity by infrared reflectance spectroscopy, A., Fox, J., Chapin, F. S., III, and Oechel, 1. Animal Sci. 43(4):889-897. W. (1983a), Taiga ecosystems in interior Alaska, Bioscience 33:39-44. Pastor, J., Aber, J. D., McClangherty, C. A., and Melillo, J. M. (1982), Geology, softs Van Cleve, K. L., Oliver, L., Schlenter, R., and vegetation of Blackhawk Island, Viereck, L. A., and Dymess, C. T. (1983b), Wisconsin, Am. Midl. Nat. 108:266-277. Productivity and nutrient cycling in Taiga forest ecosystems, Can. 1. Forest Res. Pastor, J., Aber, J. D., McClaugherty, C. H., 13:747-766. and Malillo, J. M. (1983), Above ground production and N and P cycling along Van Soest, P. J., and Wine, R. H. (1968), a nitrogen mineralization gradient on Determination of lignin and cellulose in Blackhawk Island, Wisconsin, Ecology acid-detergent fiber with permanganate, 65:256-268. I. A.O.A.C. 51:780-785. Peterson, D. L., Westman, W. E., Stephen- Vitousek, P. M. (1982), Nutrient cycling son, N. L., Ambrosia, V., Brass, J. A., and and nutrient use efficiency, Am. Nat. Spanner, M. A. (1986), Analysis of forest 119:553-472. structure using Thematic Mapper simulator Wessman, C. A., Aber, J. D., Peterson, D. L., data, IEEE Trans. Geosci. Remote Sens. and Melillo, J. M. (1988), Foliar analysis GE-24(1): 113-121. using near infrared reflectance specRencher, A. C., and Pun, F. C. (1980), Inflatroscopy, Can. 1. Forest Res. 18:6-11. tion of R in best subset regression, Techno- Williams, P. C., Norris, K. H., Gehrke, C. W., metrics 22:49-53. and Bernstein, K. (1983), Comparison of near-infrared methods for measuring proShenk, J. K., Westerhaus, M. O., and Hoover, tein and moisture in wheat, Cereal Foods M. R. (1978), Infrared reflectance analysis Wor/d 28(2): 149-152. of forages, in International Grain and ForReceived 9 September1987; revised81une 1988.