COMPTON J. TUCKER*

LEE D. MILLER

ROBERT L. PEARSON**

Colorado State UniversityFort Collins, CO 80523

Shortgrass PrairieSpectral MeasurementsStatistical analyses of spectral measurement data were usedto determine those spectral regions which could be used toidentify green biomass, chlorophyll concentration, and leafwater content.

INTRODUCTION

K NOWLEDGE OF THE MANNER IN WHICH SOLAR

energy interacts with grassland vegeta­tion is necessary in order to interpret remotesensing data from this ecological zone whichcomprises one-sixth of the world's land area.Grassland vegetation is dominated by vari­ous species of grasses which serve as themain state variable for solar energy flow into

craft, and spacecraft could supply the neces­sary inputs to the biosystem models for es­timating the primary productivity and hencedetermine the state variables of this ecosys­tem.

Traditional methods for estimating theproductivity of rangelands do not lend them­selves to application over large geographicalareas on a timely basis. These methods in-

ABSTRACT: Statistical analysis ofin situ spectroreflectance data fromsample plots of the shortgrass prairie indicates that green biomass,chlorophyll concentration, and leaf water content are directly inter­related to that composite property of the plot called the ''functioninggreen biomass." Correlations between the reflectance and these threemeasures of the functioning vegetation have been calculated at 91wavelength intervals of 0.005 Jlm between 0.350 and 0.800 Jlm byusing computer analysis. These spectrocorrelation results show thespectral regions of the optimum sensitivity for remotely estimatingthe green biomass, chlorophyll, and leafwater ofa prairie surface andsubstantiate the direct interrelationship betwwen these parameters.Comparisons between spectrocorrelation curves from two samplingperiods in the growing season identifying regions of the spectrumbetween 0.350 and 0.800 Jlm that are relatively unaffected by increas­ing amounts ofstanding dead vegetation and continue to show strongconsistent correlations to the amount offunctioning green biomass.

higher trophic levels of this ecosystem.Knowledge of the spatial and temporal statusof rangeland vegetation could be used as thebasis for more efficient management

* Now with Goddard Space Flight Center,Greenbelt, Md. 20771.

** Now with the Public Service Company ofColorado, Denver, CO 80201.

volve the measurement of herbage biomassby several ground sampling techniques. Theschemes with the use of biosystem models.Remotely sensed data from the ground, air­techniques most commonly used involve thehand clipping of a known area of vegetationand weighing of the resulting sample. Theusefulness of clipping as a sampling methodis limited by two characteristics. First, it is a

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,

Vol. 41, No.9, September 1975, pp.1l57-1162.

1157

U58 PHOTOGRAMMETRIC ENGI EERING & REMOTE SE SING, 1975

Both regression models were evaluated foreach ofthe 91 0.005JAm intervals. Regressionresults at each wavelength were very similaralthough the linear model statistics wereslightly more significant than the exponential

(1)

(2)

grama grass were selected for experimenta­tion purposes.

In situ measurements of spectroreflec­tance were obtained with the field spec­trometer laboratory designed and con­structed for the IBP Grassland Biome Pro­gram to test the feasibility of spectro-opticallymeasuring the aboveground plant biomassand plant cover (Pearson et aI., 1975).

Several thousand curves ofgrassland vege­tation have been collected in the field usingthe field spectrometer laboratory (Tucker etaI., in prep.). A subset of this data base wasselected for this experiment. It consisted ofthe spectroradiance and spectroereflectanceof circular 114 m2 plots of blue grama meas­ured in an irrigated area. Twenty-four plotswere measured in July and 40 in Septemberof 1971. The vegetation on each plot wasclipped immediately after the spec­troradiometrical measurements. An aliquotwas extracted for chlorophyll analysis andimmediately quick frozen in the field in dryice (Horwitz 1970). Biomass determinationswere made on the fresh clipped vegetationand on the vegetation after it had beenforced-ai r dried, separated mechanicallywith manual finishing into green and brownfractions, and weighed (Van Wyk 1972). Thevegetational characteristics of the plots werestored on punched cards for subsequent use(Table 1).

The spectral curves were converted frompunched paper tape to magnetic tape foranalysis on the University's CDC 6400 com­puter. A computer program associated thespectroreflectance data file with the respec­tive hand sampled plot parameters, sortedthe spectroreflectance data by wavelength,and computed the regressions at each of the91 0.005 JAm wavelength intervals between0.350 and 0.800 /Am, regressing the reflec­tances against each plot parameter in ques­tion.

Two regression models were used to ap­proximate the functional relationship be­tween canopy reflectance and the plotparameters sampled. Equation 1 represents alinear model while Equation 2 is a nonlinearmodel similar to the Beer-Lambert law andis applicable for regions of spectroabsor~tion.

Reflectance, = A. + B,,(Plot Parameter)

Reflectance = A e B,,(Plot Parameter)•

slow, tedious, and time-consuming opera­tion. Second, clipping is a destructive sam­pling procedure which precludes samplingthe plot on a repetitive basis.

Several nondestructive sampling methodsinclude the ocular estimation procedure(Pechanec and Pickford 1937), the pointquadrant method (Warren-Wilson 1963), thecapacitance meter (Van Dyne et aI., 1968),the f3-attenuation technique (Mitchell 1972),and recently reported spectral methods(Pearson and Miller 1972). The spectralmethods of vegetation analysis not onlymeasure herbage biomass on a nondestruc­tive basis but also can be adapted to aircraftand satellite devices to map the spatial dis­tribution over an area in an efficient andeconomical fashion. The periodic remotesensing measurements ofthis rangeland veg­etation parameter can serve as primary inputto management models which truly repres­ents the real, distributed grassland ecosys­tem. It is difficult to imagine that the neces­sary spatial information can be measured bythe other means noted earlier on a timelybasis. This paper reviews the ground basedin situ field spectrometry in the 0.350 to 0.800/Am region of the spectrum which has beenused to develop this approach. The applica­tion of the method with ground, aircraft, orsatellite-based remote sensors for the spec­tral measurement of the amount and status ofthe functioning green biomass in a prairieecosystem will be reviewed in a subsequentpaper.

MATERIALS, METHODS, AND EQUIPMENT

The experimental results reported hereinwere obtained at the IBP Grassland BiomePawnee Site on native shortgrass prairie atthe Pawnee National Grassland about 35miles northeast of Fort Collins, Colorado.Average annual precipitation of the area isabout 31 cm with approximately 80 percent ofthe precipitation falling during the growingseason from May 1 to September 30. Annualwind velocity averages approximately 10kilometers per hour and the mean low andhigh temperatures during the growing seasonare 8° and 26°C with an average frost-freeperiod of 135 days. Field measurements weremade in the Ecosystem Stress Area (ESA) oncontrol, irrigated, and/or nitrogen fertilizedplots.

Prairie vegetation is dominated by variousspecies of grasses. One species, blue grama(Bouteloua gracilis (H.B.K.) Lag.), comprisesabout 75 per cent of the dry weight of thegramineous vegetation at the Pawnee Site(Uresk 1971). For this reason, plots of blue

SHORTGRASS PRAIRIE SPECTRAL MEASUREMENTS 1159

TABLE 1. A STATISTICAL DESCRJPTlON OF THE VEGETATIVE CHARACTERISTICS OF THE 40 % m2

SAMPLE PLOTS OF BLUE GRAMA SAMPLED IN EARLY SEPTEMBER, 1971 AND USED INCONSTRUCTING FIG. 1 THROUGH 7.

Coef. of SE of theSample Range Mean SD Variation Mean

Wet total biomass 70.83- 261.31 134.40 51.44 21.25(g/m2 ) 491.22

Dry total biomass 41.50- 168.55 90.81 53.88 14.36(g/m2 ) 337.84

Dry green biomass 17.12- 89.38 50.15 56.11 7.93(g/m 2 ) 185.04

Dry brown biomass 20.40- 82.41 48.54 58.90 7.68(g/m2 ) 186.42

Leaf water 28.03- 92.75 50.93 54.91 8.05(g/m2 ) 190.80

Chlorophyll 53.02- 319.58 238.73 74.70 37.75(mg/m2 ) 737.00

-.80

.80

O~-----------+-----l

.. . .

A = .775 "mN = 40

r = .849

RFL = 16.569 + O.053(X)

.......

I.OO,~---------------,

14.00 20 40 60 80 100 120 140 160 180 2IXl

ORY GREEN 810MASS (91m2)

FIG. 1. Simple linearregression ofthe reflec­tance at 0.775 f1.m (0.005 f1.m spectral band­width) against the dry green biomass clip­ped from 40 % m2 in situ sample plots ofblue grama.

.40

-.40

-.60

-.20

.20

.60

-I.~ !lO .4!l .!lO .!l!l .60 .6!l .70 .7!l .80

WAVELENGTH (pm)

FIG. 2. Simple linear spectrocorrelationcurve for dry green biomass constructed byplotting the correlation coefficients (R val­ues) between reflectance and dry greenbiomass for each of the 91 wavelength inter­vals.

30.

28.4

26.8

l 2!l.2.. 23.6\Jz...

22.0....<>..oJ 2011"-..0:

18.8

17.2

1!l.6

model at each wavelength. This implies thatfor the range of plot parameters sampled inthis experiment, the functional relationshipbetween canopy reflectance and the plotparameters can be approximated by thelinear model. Greater vegetation density,such as a mid-grass or tall-grass prairie,would require a non-linear model. The re­gression statistics referred to in the balance ofthis report resulted from the use of the linearmodel.

The results of the regressions and the datapoint scatter at each 0.005 {tm wavelengthinterval are displayed in a tabular fashion andas a microfilm plot (Figure 1). At the conclu­sion of computing each of the 91 individualspectral relations, the simple, linear correla­tion values are plotted on microfilm as a spec­trocorrelation curve (Figure 2). This curveshows at a glance the sensitivity of the spec­troreflectance to the hand sampled plotparameter in terms of correlation values.Spectrocorrelation curves have been deter­mined for dry green biomass, dry brownbiomass, chlorophyll, and leaf water.

RESULTS AND DISCUSSION

Strong correlation between spectroreflec­tance and green biomass occurs in two pig­ment absorption bands of the visible spec­trum and in the photoinfrared region of thespectrum (Figure 2). Spectrocorrelation re­sults for green biomass are based upon thechlorophyll content, the internal structure ofthe leaves, and the geometrical arrangementof the plant canopy. The relationship be­tween green biomass and chlorophyll was ex­pected because chlorophyll is largely re­sponsible for the "green" color of greenbiomass. The physiological basis for a highnegative spectrocorrelation in the visible re­gion (Figure 2) is due to the absorption of

1160 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1975

1.00~---------------'

-I.~ .40 All .!lO .llll .60 .60 .70 .75 .80

WAVELENGTH (pm)

FIG. 3. Simple linear spectrocorrelationcurves for the dry green biomass,chlorophyll content, and leaf water for thesame 40 in situ plots of 114 m2 ofblue grama.Note the similarity in the three curves.

iability associated with each type of determi­nation. Chlorophyll determinations are themost variable due to the sampling error, dilu­tions, blendings, and extractions necessaryfor this laboratory measurement. Drybiomass measurements use machine sortingon a gravity basis with hand finishing to sepa­rate the dried vegetation into green andbrown fractions. This process is very time­consuming and is prone to technician error.Leaf water measurements only require thatthe dry weight of the grass clipped from theplot be subtracted from the wet weight meas­ured for that grass in the field. Leaf waterdetermination is thus the least prone tomeasurement error and shows the highestspectrocorrelation of the three measures offunctioning green biomass.

The coefficients of variation for the plotparameters (Table 1) show statistically thevariability introduced by the various deter­minations. Leaf water has the smalllest coef­ficient of variation, the coefficient of varia­tion for dry green biomass is slightly larger,

r- Gr••n Slomo..- - - L.af Wattr_._. - Chloroph,l t

.40

.20

.60

.80

solar irradiance by plant pigments, predom­inantly the chlorophylls in the plant canopy(Knipling 1970). This effect is amplified bythe geometric layering characteristics of theplant canopy (Woolley 1971). The exact rela­tionship in the field situation between greenbiomass, chlorophyll, and leaf water, how­ever, was not fully understood prior to thisexperiment.

Functioning green vegetation maintainsproportional amounts of green biomass,chlorophyll, and leaf water in an interrelatedfashion. Approximately 70 per cent ofthe wetweight of green vegetation is water. Leafwater contents at which metabolic activitiesare not impaired help maintain thechlorophyll concentration by apparentlypreventing its enzymatic and photo-oxidativebreakdown. During active growth and undernonlimiting leaf water potentials, photosyn­thetically absorbed energy is passed alongthe pigment system without modifying thechlorophyll molecules. When leaf water de­creases, however, the pigment system is nolonger capable of transporting all of the cap­tured electrons and photo-oxidation ofchlorophyll probably occurs at a rate depend­ing on the leaf water potential (Cleon Ross,personal communication).

Simple, linear spectrocorre1ation curvesfor leaf water, green biomass, andchlorophyll were calculated by using the ap­proach outlined earlier. Spectrocorrelationsfor these plot parameters are slightly differ­ent in magnitude but are identical in charac­ter (Figure 3). This situation indicates thatthese plot parameters are highly interrelatedand are measurements of the same biologicalphenomenon-the amount of the functioninggreen biomass on the plot. Correlations be­tween the plot parameters have been calcu­lated and support the spectrocorrelationsimilarities (Table 2). The absence of nearperfect correlation between the hand sam­pled values of dry green biomass,chlorophyll, and leaf water is due to the var-

TABLE 2. CORRELATION MATRIX OF THE HAND SAMPLED CHARACTERISTICS OF THE 40 SAMPLE

PLOTS DESCRIBED IN TABLE 1. NOTE THE HIGH INTERCORRELATIONS BETWEEN DRY GREEN

BIOMASS, CHLOROPHYLL, AND LEAF WATER.

Total Total Dry Drywet dry green brown Leaf Chloro-

biomass biomass biomass biomass water phyll

Total wet biomass 1.00 0.97 0.98 0.84 0.91 0.89Total dry biomass 1.00 0.95 0.92 0.78 0.88Dry green biomass 1.00 0.78 0.89 0.88Dry brown biomass 1.00 0.56 0.70Leaf water 1.00 0.85Chlorophyll 1.00

SHORTGRASS PRAIRIE SPECTRAL MEASUREMENTS 1161

1..00.-------------------,

.40 J

:+ -II+-!I_--1-.20 L'JI-40 /

-.60 !_.80P-0..-»-~=-", ----- )1

-1.00~ .40 .4/1 .00 .:10 .60 .6!l .70 .7!l .80

WAVELENGTH (~m)

FIG. 4. Simple linear spectrocorrelationcurves for dry green biomass taken at twodifferent times in the growing season. Notethe consistency in the high negative correla­tions inthe two chlorophyll absorptionbands in the blue-violet and red-orange andthe high positive correlations in the photoin­frared spectral region.

ence of so much dry brown biomass in theplant canopy. More significant spectrocorre­lations exist earlier in the growing seasonwhen the ratio of dry green to dry brownbiomass is much higher than later in thegrowing season. The September results rep­resent sampling at a less advantageous timein the growing season although certain spec­tral intervals continue to be sensitive to the

(I

n·24--- Jul

N-40.80 - Sop

.60

and the chlorophyll coefficient ofvariation of75 per cent is 40 per cent larger than eitherthe dry green biomass or leaf water value.

The analysis of the 40 plots sampled inearly September and outlined above hasbeen compared with that of24 plots sampled8 weeks earlier in July that have approxi­mately equal total dry biomass mean values,standard deviations, coefficients ofvariation,and standard errors of the mean (Table 3).The data set analyzed from the early part ofSeptember (Table 1) represents the morecomplex vegetational situation containingsignificant amounts of both dry and greenbiomass fractions. A comparison ofplot statis­tics for the two dates illustrates that a compos­itional change occurred in the 8 weeks al­though the mean amount of total dry biomasswas relatively constant (Table 3). The ex­perimental plots contained a dry green to drybrown biomass ratio ofapproximately 2.5: 1 inJuly during the active growing season, butthat by September this ratio had become ap­proxi mately 1: 1. Spectrocorrelation curvesfor dry green biomass values from the twosampling dates also reflect the change incomposition in the vegetation canopy (Fig­ure 4). The two spectral regions of strongpigment absorption have high spectrocorre­lations with dry green biomass on both datesas does the p)1otoinfrared spectral region.The remainder of the September spectrocor­relation curve is degraded due to the pres-

TABU3. A STATISTICAl. COMPARISON OFTHE VEGETATIVE CHARACTERISTICS OF Pl.OTS OF Bl.UEGRAMA SAMPl.ED 8 WEEKS APART IN JULY AND SEPTEMBER AND USED IN CONSTRUCTING FIG. 5.

NOTE THE CONSISTENCY OF THE TOTAL DRY BIOMASS BETWEEN THE Two SAMPLINGPERIODS BUT THAT DRY GREEN BIOMASS HAS BEEN TRANSFORMED To DRY BROWN BIOMASS.

Vegetative characteristics July September

Total dry biomass (glm2 )

Number of sample plotsRangeMeanStandard deviationCoefJicient of variationStandard error of the mean

Dry brown biomass (glm 2 )

Number of sample plotsRangeMeanStandard deviationCoefficient of variationStandard error of the mean

Dry green biomass (glm 2 )

Number of sample plotsRangeMeanStandard deviationCoefficient of variationStandard error of the mean

2469.32 to 352.40

171.3682.3648.0616.81

2417.84 to 91.48

48.5321.8144.95

4.45

2438.72 to 260.92

122.8371.4458.1614.58

4041.50 to 337.84

168.5590.8153.8814.36

4020.40 to 186.42

82.4148.5458.90

7.68

4017.12 to 185.04

89.3850.1556.11

7.93

1162 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSI G, 1975

functioning green biomass regardless of thepresence of standing dead vegetation.

Although the spectrocorrelation results aresomewhat different in magnitude and charac­ter for dry green biomass, chlorophyll, andleaf water, it is apparent from the similaritiesbetween the curves and the intercorrelationbetween the sampled plot parameters thatthe same biological characteristic is beingestimated-the amount of functioning greenbiomass present in the plant canopy. Thespectrocorrelation approach identifies thoseintervals of the region tested, in this case0.350 to 0.800 Jim, which are sensitive to theplot parameters and indicates as well the rel­ative sensitivity of these intervals as com­pared to other spectral intervals. These re­sults then can be applied to grassland remotesensing inventories in order to gather spatialdata of the productivity of these areas and thechanges in it with various management prac­tices, such as type conversions, reseedings,fertilization treatments, and grazing systems.

SUMMARY

Green biomass, chlorophyll, and leafwaterare measurements of the amount of function­ing green biomass and are highly interrelatedin blue grama (B. gracilis) plots of theshortgrass prairie. Spectrocorrelation curvesfor dry green biomass, chlorophyll, and leafwater are very similar. Differences are due tothe sampling errors associated with each typeof determination. Regions of high negativespectrocorrelation for these sample parame­ters occur in the two chlorophyll absorptionbands of the visible spectrum. These remainrelatively constant for functioning greenvegetation and are unaffected by increasingamounts of standing dead vegetation on theplot. The near infrared region ofthe spectrumshows a high positive spectrocorrelation tothese three sample parameters and is alsounaffected by increasing amounts ofstandingdead vegetation.

ACKNOWLEDGMENT

This research was supported by NationalScience Foundation Grants GB-7824,GB-13096, GB-31862X, GB-31862X2, andGB-41233X to the Grassland Biome, U. S.International Biological Program, for

"Analysis ofStructure, Function, and Utiliza­tion of Grassland Ecosystems."

REFERENCES

Horwitz, W. (Ed.), 1970, OffiCial methods ofanalysis, 11th Ed. Association of AnalyticalChemists, Washington D.C. pp. 53-55.

Knipling, E. B., 1970, "Physical and physiologicalbasis for the reflectance of visible and near­infrared radiation from vegetation," RemoteSensing Environment 1(3): 155-159.

Mitchell, J. E., 1972, "An analysis of the beta­attenuation technique for estimating standingcrop of prairie range," ]. Range Manage.25:300-304.

Pearson, R. L., L. D. Miller, and C. J. Tucker,1975, "Design of a mobile field spectrometerlaboratory," US/IBP Grassland Biome Pre­print No. 140, Colorado State Univ., Fort Col­lins. 16 pp.

Pearson, R. L., and L. D. Miller, 1972, "Remotemapping of standing crop biomass for estima­tion of the productivity of the shortgrassprairie, Pawnee National Grasslands, Col­orado," p, 1357-1381. In: Proceedings of the8th International Symposium on RemoteSensing of the Environment, Univ. Michigan,Ann Arbor.

Pechanec, J. F., and G. D. Pickford, 1937, "Aweight estimation method for the determina­tion of range or pasture production,"]. Amer.Soc. Agron. 29:894-904.

Tucker, C. J., L. D. Miller, and R. L. Pearson, (inprep.), "Grassland spectrometer experimentaldata: 1971 and 1972," US/IBP GrasslandBiome Tech. Rep., Colorado State Univ., FortCollins. Approx. 100 pp.

Uresk, D. M., 1971, "Dynamics of blue gramawithin a shortgrass ecosystem," Ph.D. disser­tation, Colorado State Univ., Fort Collins. 52pp.

Van Dyne, G. M., F. M. Glass, and P. A. Opstrup,1968, Development and use of capacitancemeters to measure standing crop of herbace­ous vegetation, Oak Ridge (Tennessee) Na­tional Laboratory, ONRL-TM 2247. 47 pp.

Van Wyk, J. J. P., 1972, "A preliminary report onnew separation techniques for live-deadaboveground grass herbage and roots from drysoil cores," US/IBP Grassland Biome Tech.Rep. No. 144, Colorado State Univ., Fort Col­lins. 16 pp.

Warren Wilson, J., 1963, "Estimation of foliagedenseness and foliage angle by inclined pointquadrats, Aust.]' Bot. 11:95-105.

Woolley, J. T., 1971, "Reflectance and transmit­tance of light by leaves," Plant Physiol.47:656-662.