c. J. TUCKER*NASA/Goddard Space Flight Center

Greenbelt, MD 20771E. L. MAXWELL

Colorado State UniversityFort Collins, CO 80523

Sensor Design forMonitoring Vegetation CanopiesOptimization of sensor wavelengths and bandwidths has beeninvestigated based upon the analysis of in situ spectralreflectance data collected from experimental plots of bluegrama grass.

INTRODUCTION

DESCRIPTION OF RESEARCH UNDERTAKEN

T HE RESEARCH was undertaken to evaluatevarious wavelengths and bandwidths,

corresponding to simulated sensors, by in-

study because they are morphologically oneof the least complex vegetation types. Thisreduced the variability inherent in the studyand allowed for a more meaningful interpre­tation of the results.

Two data sets, one from June and one fi'om

ABSTRACT: Optimization of sensor wavelengths and bandwidths hasbeen investigated based upon the analysis of in situ spectral reflec­tance data collected from experimental plots of blue grama grass.Sensor characteristics have been simulated by integration of spec­tral data over the region from 0.350 to 1.000 J1.m. Subsequently, theintegrated reflectance values were regressed against the canopy orplot variables (total wet biomass, total dry biomass, leaf water con­tent, dry green biomass, dry brown biomass, and total chlorophyllcontent) to determine the relative significance between integratedreflectance and the canopy variables for the various wavelengthsand bandwidths simulated. Three spectral regions of strong statisti­cal significance (0.35-0.50, 0.63-0.69, and 0.74-1.00 J1.m) were iden­tified and found to be persistent both early and late in the growingseason. In addition to quantifying the significance ofvarious sensorwavelengths and bandWidths, the additive effects of adjacent spec­tral regions were also quantified. The results of this work enablequantitative judgments to be made regarding existing and hypothet­ical sensors and their effectiveness in monitoring green functioningvegetation.

tegrating narrow bandwidth (0.005 J1.m)spectral reflectance curves of blue gramagrass (Bouteloua gracilis (H.B.K.) Lag.) plots.Grass canopies were selected for detailed

* National Academy of Sciences-NationalAcademy of Engineering Research Fellow.

September, were chosen for analysis (TableI). The June data represented a less ad­vanced phenological state when most of thestanding vegetation was alive or green,whereas the September data represented amore advanced phenological state of approx­imately equal amounts of dry standing live

1399PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,

Vol. 42, No. 11, November 1976, pp. 1399-1410.

1400 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1976

and standing dead vegetation. The June dataprovided a much greater range of plot vari­ables (total wet biomass, total dry biomass,leaf water content, dry green biomass, drybrown biomass, and total chlorophyll con­tent) which will enable the results of thisanalysis to be extended to other higherbiomass ecosystems.

The data were obtained at the IBP Grass­land Biome Pawnee Site on native shortgrassprairie at the Pawnee National Grassland,about 35 miles northeast of Fort Collins,Colorado. Field measurements were madein the Ecosystem Stress Area (ESA) on con­trol, irrigated, and/or nitrogen fertilizedplots.

Prairie vegetation is dominated by variousspecies of grasses. One species, blue grama,comprises about 75 per cent of the dryweight of the gramineous vegetation at thePawnee Site (Uresk, 1971). For this reason,plots of blue grama grass were selected forexperimentation purposes.

In situ measurements of spectral reflec­tance 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 (Miller et al., 1976).

Simulation of sensors of differentwavelengths and bandwidths was ac­complished by integration of the narrowbandwidth spectral reflectance data. Sub­sequent regression analysis determined thedegree of statistical significance, in coeffi­cient of determination (r2 values) terms, be­tween the integrated reflectances and theplant canopy variable in question. The re­sults of the regression analysis identified re­gions of high sensitivity and provided aquantitative basis for sensor design. The re­sults clearly show that vegetational remotesensing can be improved in the future as­suming, as we do, that grass canopy resultsare applicable to other vegetation types.

It should be noted that this research ad­dresses only the target spectral factors affect­ing sensor selection for the 0.35 - 1.00 /Lmregion. The trade-offs between bandwidth,spatial resolution, signal/noise ratio, and at­mospheric transmission were not consideredas they apply to ground, aircraft, and satelliteremote sensing of vegetation.

CANOPY PHYSIOLOGY AND REMOTE SENSING

The physiological factors affecting or actu­ally determining reflectance from leaveshave been the subject of recent research.Knipling (1970) and Woolley (1971) are ex-

cellent references concerning leaf morphol­ogy and physiology and the resulting spec­tral reflectance, transmission, and absorption.Hoffer and Johannsen (1969) have addressedthe question of spectral signature analysisfor vegetation monitoring. Gausman et al.(1973) have investigated biological criteriafor sensor selection and found that the spec­tral wavelength intervals centered around0.680, 0.850, 1.650, and 2.200 /Lm provideoptimum discrimination of vegetation.

Colwell (1974) investigated grass canopybidirectional spectral reflectance usingcomputer modeling which was qualitativelyvalidated by field experimental measure­ments. Interested readers are directed to thiswork for an excellent treatment of grasscanopy bidirectional reflectance where dif­ferent soil backgrounds, leaf area indices,percent vegetation covers, amounts of liveand dead vegetation, solar zenith angles,look angles, and azimuth angles were consid­ered for the wavelengths of 0.55, 0.65, and0.75 /Lm (Colwell 1974).

The majority of basic research to date con­cerning plant physiology and resulting re­flectance has been done with leaf spectrameasured in the laboratory or with aircraft orspacecraft multispectral imagery ofheterogenous vegetation scenes. Little insitu canopy spectral data have been col­lected, although the US/IBP GrasslandBiome has collected approximately twothousand spectral curves of prairie scenes.

The results ofTucker (1975) and Tucker etal. (1975) report on two of the few ground­based studies of in situ measured canopy re­flectance for which detailed information isavailable concerning the biophysical com­position of the plant canopy.

Statistical analyses have identified five re­gions of the spectrum between 0.350 and1.000 /Lm where different biological and/orphysical processes result in different rela­tionships between canopy reflectance andthe amount of vegetation present (Figure 1).The five spectral regions described byTucker (1975) where different relationshipsexist include:

(1) The 0.350 to 0.500 /Lm region(ultraviolet-blue), characterized by strongabsorption by the carotenoids andchlorophylls and strong relations betweenspectral reflectance and the plot variables.

(2) The 0.500 to 0.600 /Lm region (green),characterized by a reduced level of pigmentabsorption and hence higher reflectance (the"green" color of healthy vegetation). Thisspectral region exhibits a weaker relation­ship between spectral reflectance and the

SENSOR DESIGN FOR MONITORING VEGETATION CANOPIES 1401

:/-':.~"n/.-.""':" '. /'----------,,--- ",

'"-fotol ••ttll_.

~ CII'OfOphW Il4( L.ol_l..

.60 .m <>U ~ ~ 90 tOO

*AV[L[IilIGTH l .. ml

e = Napier's number (i.e., -2.71)f31)' = regression derived estimate

of f31 at wavelength AXi = canopy variable i (see Table

1)

or

where

f30)' = regression derived estimateof f30 at wavelength A

The area of reduced absorptance in thegreen region of the spectrum exhibits somedegree of absorptance, albeit to a lesser ex­tent than adjacent regions. The relationshipbetween reflectance and the plot variablescan be approximated in this spectral regionby either Equation 1 or Equation 2 depend­ing upon the range of plot variable Xi(Tucker, 1975).

The transition from strong pigment ab­sorption in the far red to strong reflection atthe lower end of the photographic infraredresults in weaker relations between spectralreflectance and the plot variables over thisspectral region. Note from Figure 1 that thecoefficient of determination approaches zeroat the midpoint of the interval (0.72 JLm).This effect could be caused by very similarsoil and vegetation reflectance characteris­tics in this interval, i.e., the vegetation couldhave the same per unit reflectance contribu­tion as the soil background. Thus, therewould be little effective discrimination offunctioning vegetation. Or because the pre­viously mentioned change from strong ab­sorption to enhanced reflection occurs over asmall wavelength interval (-0.040 JLm), thespectrometer may introduce a measurementerror into this region because of slightwavelength calibration errors. A combina­tion of the two hypotheses is the most logicalproposition (Tucker, 1975).

Strong relationships were found betweenspectral reflectance and the plot variables inthe photographic infrared region. The func­tion which best approximated the relation­ship between spectral reflectance and theplot variables in this region was of twoforms, depending upon the range of the plotvariables in question:

Fit. 1. Spectral coefficient of determina­tion curves for total wet biomass, totalchlorophyll, and leaf water content. otethe similarity among these three curvesresulting from the regressions betweenthe respective plot variables and spectralreflectance for 35 plots sampled in June1972.

plot variables because of the reduced levelof pigment absorption.

(3) The 0.600 to 0.700 JLm region (orange­red), characterized by strong chlorophyll ab­sorption and a strong relationship betweenspectral reflectance and the plot variables.

(4) The 0.700 to 0.740 JLm region, a transi­tion region between strong chlorophyll ab­sorption in the far red and enhanced reflec­tance in the photographic infrared whichexhibits weak relations between spectral re­flectance and the plot variables.

(5) The 0.740 to 1.000 JLm photographicinfrared region, exhibiting high or enhancedreflectance and a strong relationship be­tween spectral reflectance and the plot vari­ables. The term "enhanced reflectance" isused to denote the higher levels of photo­graphic infrared reflectance by healthy vege­tation, which result from internal leaf scat­tering mechanisms in the absence of absorp­tion. The canopy geometry also contributesto the enhancement of photographic infraredreflectance by inter-leaf scattering.

Thus the different relationships exist be­tween spectral reflectance and the sampledplot or canopy variables depending upon thedominant physiological and physical process­es in the various spectral regions. Regionsof strong absorption can be represented byfunctions of the form

(1)where

RFL). = canopy reflectance at wave­length A

A). = regression derived coeffi­cient at wavelength A *

RFL). = f3o). + f31)' Xi (3)

* Standard regression notation is used afterDraper & Smith (1966).

and

1402 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1976

where

s~ = asymptotic reflectance atwavelength A

Asymptotic spectral reflectance is reachedby adding leaf layers or biomass until a sta­ble (i.e., unchanging) reflectance is obtained(see also Gausman et aI., 1976; Tucker,1976).

METHODS AND ANALYSIS

Several thousand curves of grassland veg­etation have been collected in the fieldusing the field spectrometer laboratory. Asubset of this data base was selected for thisexperiment. It consisted of the spectralradiances and reflectances of circular 1/4 m2

plots of blue grama measured in an irrigatedarea. Thirty-five plots were measured inJune of 1972 and 40 in September of 1971.

Immediately after the reflectance meas­urements were completed, the plot wasclipped of all standing vegetation. An aliquotwas extracted for chlorophyll analysis andimmediately qUick-frozen on dry ice (Hor­witz 1970). the clipped vegetation was putinto a plastic bag, sealed, and placed in anicebox. When the clipped vegetation fi'omfour or five plots accumulated, it was trans­ported to the Pawnee Site's laboratory build­ing and stored in a refrigerator.

Laboratory technicians then began pro­cessing the clipped vegetation. The first de­termination made was the total wet biomassweight measurement. After this measure­ment was completed, the clipped vegetationwas transferred to a paper bag and force-airdried at 50°C for 48 hours. Upon completionof the drying cycle, the total dry biomassweight measurement was made. The leafwater content was calculated as simply thedifference between the total wet biomassand total dry biomass. The leaf water contentrepresents the water present in the leaf andstem material.

The total dry biomass was then separatedmechanically with manual finishing intogreen and brown fractions, and weighed(Van Wyk 1972). The chlorophyll contentwas determined for the representative 5 galiquot after Horwitz (1970). This was thenmultiplied by the total wet biomass to yieldthe chlorophyll concentration in mg/m2 un­its.

The total wet biomass, total dry biomass,leaf water content, dry green biomass, anddry brown biomass were all expressed ing/m2 units (Table 1). Per unit area measure­ments such as the various biomass determi­nations, leaf water content, and total

chlorophyll content will be used in describ­ing the research results. Projected areas ofthese canopy variables will not be used al­though simple relationships exist betweenbiomass and leaf area indices.

INTEGRATION OF NARROW BANDWIDTH

SPECTRAL CURVES

The narrow bandwidth reflectance data(0.005 f.Lm bandpass) were numerically in­tegrated to approximate a variety ofbandwidths. Reflectance values were usedsuch that the results are implicit propertiesof vegetation.

The integration procedure was carried outin the following fashion:

RFLA = (It RFL(I)2 IlA) Y2 (5)

where

RFLA = reflectance in band from 1..(1)to ,\(n)

RFU})2 = square of reflectance at ,\(1)Ill.. = 0.005 f.Lm

This method of integration was used toapproximate the reflectance integral fromthe narrow bandwidth (0.005 f.Lm) in situspectral measurements.

Reflectance is a measure of the relative in­tensity with which electromagnetic waveswill be reflected from a surface. The squareof reflectance, therefore, is a measure of therelative amount of energy reflected from thesurface. Since the sum of narrow bandwidthreflected energies must equal the energywhich would be measured in a widebandwidth system of equal total bandwidth,Equation 5 should provide a valid estimateof reflectance for the wide bandwidth sys­tem. This procedure is consistent with thewell-known relationships for blackbodyradiation and noise intensity, both of whichshow a linear variation of power or energywith bandwidth. This procedure does as­sume a constant surface impedance and non­coherent energy across the bands being in­tegrated. The surface impedance probably isnot constant, particularly for bandwidthswhich span from strong absorbingwavelengths to highly reflecting wave­lengths, but for a first approximationthe results should be reasonably valid. Theassumption of noncoherence is certainlyvalid.

The results obtained with Equation 5 areconsistent when the integrated reflectancesare regressed against the plot variables andcompared to the results of the narrow

SENSOR DESIG FOR MONITORING VEGETATIO CANOPIES 1403

TABLE 1. STATISTICAL SUMMARY OF THE BIOPHYSICAL CHARACTERISTICS OF THE SAMPLE PLOTS.A STATISTICAL DESCRIPTION OF THE VEGETATIVE CANOPY CHARACTERISTICS FOR

(A) THE THIRTy-FIVE 114 M2 SAMPLE PLOTS OF BLUE GRAMA SAMPLED IN JUNE 1972 AND(B) THE FORTY 114 M2 SAMPLE PLOTS OF BLUE GRAMA SAMPLED IN SEPTEMBER 1971.

Standard Coefficient Standard errorSample Range Mean deviation of variation of the mean

A. June 1972Wet total biomass 52.00-1230.40 339.52 316.94 93.35 50.11

(glm2)Dry total biomass 13.04- 528.84 134.07 130.25 97.15 20.59

(glm2)Dry green biomass 12.48- 343.36 105.11 93.46 88.93 14.78

(glm2)Dry brown biomass 00.16- 185.48 28.96 40.23 138.91 6.36

(glm2)Leaf water 38.12- 701.56 205.46 187.83 91.42 29.70

(glm2)Chlorophyll 62.27-2108.06 414.41 515.56 124.41 81.52

(mglm2)

B. September 1971Wet total biomass 70.83- 491.22 261.31 134.40 51.44 21.25

(glm2)Dry total biomass 41.50- 337.84 168.55 90.81 53.88 14.36

(glm2)Dry green biomass 17.12- 185.04 89.38 50.15 56.11 7.93

(glm2)Dry brown biomass 20.40- 186.42 82.41 48.54 58.90 7.68

(glm2)Leaf water 28.03- 190.80 92.75 50.93 54.91 8.05

(glm2)Chlorophyll 53.02- 778.97 319.58 238.73 74.70 37.75

(mglm2)

bandwidth analyses. The integrated regress­ed values closely approximate the narrowbandwidth results.

The integrations were carried out in twomodes:

(1) Initially the spectral region between0.350 to 0.800 JLm was divided up into 15, 9,5, and finally 3 equal bandwidths. Thespectral reflectances corresponding to eachspectral curve were integrated according toEquation 5. Results were punched ontocards along with the six plot variables forsubsequent regression screening. Regres­sion screening denotes the use of an al­gorithm to screen various univarite combina­tions of variables (Frayer et al., 1971).

(2) A particular bandwidth was positionedat a wavelength of interest; and (a) whileholding the lower limit ofthe band constant,the upper limit was reduced by 0.010 JLmsteps. Each diminished bandwidth was in­tegrated in the same manner. (b) Then theupper limit of the band was held constantand the lower limit was increased at 0.010JLm steps until the bandwidth was equal to orgreater than 0.010 JLm. Each interval was in-

tegrated and evaluated in an identical man­ner to (1) above.

REGRESSION SCREENING

Regression screening was used to evaluatethe relationship between the various integ­rated reflectances and the plot variables interms of coefficients of determination (r2

values). This was advantageous because thecomputer program used (FSCREEN) calcu­lates the various r2 values and then outputsan ordered list of the r2's (Frayer et aI., 1971).Because of the number of bandwidth inter­vals simulated, the FSCREEN approach al­lowed for a substantial savings in computertime.

The models given in Equations 1 and 2were transformed from their original non­linear form into linear models for regressionscreening purposes. In each case, the integ­rated reflectances were regressed against thevarious plot variables.

To facilitate comparisons between theJune 1972 and the September 1971 statistics,the plot variables of total wet biomass, leafwater, and total chlorophyll content were

1404 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1976

used. Of these sampled variables, leaf waterwas used principally because it is indicativeof the amount of the alive, green, and photo­synthetically active biomass present (Tuckeret al., 1975).

EXPERIMENTAL RESULTS

EQUAL BANDWIDTH APPROACH

Coefficients of determination for the in­tegrals for five equal bandwidth intervalswithin the 0.350 and 0.800 J.Lm region werefound to closely resemble the continuousnarrow band r2 plots (Figure 2). The samesimilarity was found when the region wasdivided into 15, 9, and 3 equal bandwidths(Tucker, 1975).

Figure 2 demonstrates the statistical simi­larity between the narrow bandwidth (0.005J.Lm) regression results and the much widerbandwidth results. In fact, it is obvious thatintegration of the coefficient of determina­tion curves would have produced almostidentical results. The only exception to thisis seen for the fifth band (0.71 to 0.80 J.Lm) onFigure 2 where the area for that band islarger than the area under the curve. Thisimplies that the random error or noise in the0.71 to 0.80 J.Lm band did not appreciably de-

1.00

.80z0

.80~zi:it;;0..0

...::"~0

"

WAVELENGTH (I'm)

FIG. 2. Comparison between the continu­ous coefficient of determination curve(0.005 /-Lm bandwidth) and the coefficientsof determination resulting from dividingthe 0.350 to 0.800 /-Lm region into five equalbandwidth intervals for the September1971 data. The continuous curve resultedfrom the series of regressions betweenspectral reflectance and total wet biomass atninety-one 0.005 /-Lm intervals. The histo­grams resulted from the five regressions be­tween integrated reflectance and total wetbiomass. ote how the five equalbandwidth intervals closely approximatethe area under the continuous coefficient ofdetermination curve.

grade the strong infrared relationship be­tween spectral reflectance and total wetbiomass. When the interval was made wider(0.65 to 0.80 J.Lm) and began to include moreof the strong chlorophyll absorption region,as in the case of the three band simulation, amarked reduction in significance occurred.

DECREASI G BANDWIDTH APPROACH

The following bands were selected forevaluation by this method:

(1) 0.37 to 0.55 J.Lm (September dataonly),(2) 0.50 to 0.68 J.Lm (June and Septemberdata), and(3) 0.60 to 0.78 J.Lm (June and Septemberdata).

The spectral estimation of total wetbiomass was investigated in the 0.37 to 0.55J.Lm band. The area of greatest significancewas in the ultraviolet region as noted inTable 2. The strong ultraviolet-blue sensitiv­ity which existed between the various plotvariables and reflectance in this region hasnot previously been reported. The possibil­ity that this region shows a strong sensitivityas the growing season wanes is of particularinterest for the estimation of grass canopybiomass.

The 0.50 to 0.68 J.Lm band includes theorange-red chlorophyll absorption region. Ofparticular interest here were the differencesbetween the June and September data sets.Although the total bandwidth of 0.50 to 0.68J.Lm was the most significant for both datasets (Table 3), the June r 2 value of 0.88 wasfar greater than the September r 2 value of0.56. The lowest June r 2 value, 0.65, washigher than the highest September value.

The spectral interval of 0.60 to 0.78 /-Lm

was considered in tripartite fashion:

(I) 0.60 to 0.72 J.Lm: absorption +noise.

(II) 0.72 to 0.78 J.Lm: noise + enhancedreflectance.

(III) 0.60 to 0.78 J.Lm: absorption +noise + enhanced reflectance.

This was done to evaluate the effect(s) ofpigment absorption, noise, and enhancedspectral reflectance and the combinations ofthese three effects taken together or two at atime.

One would expect a slight correlation de­grading of situation (I) and (II) of the abovedue to the noise in the 0.70 to 0.74 J.Lm regionand a serious correlation degrading of situa-

SENSOR DESIG FOR MONITORI G VEGETATIO CA OPIES 1405

TABLE 2. INTEGRATED SIMULATION RESULTS FOR THE 0.37 TO 0.55 !Lm INTERVAL REGRESSED AGAINSTTOTAL WET BIOMASS. NOTE THE GREATER SIGNIFICANCE IN THE ULTRAVIOLET REGION.

(A) 0.37 TO 0.55 !Lm; (B) 0.37 TO 0.55 fLm. (EVERY OTHER LINE DELETED FOR BREVITY).

Rank

(A)13579

111315

Orderedr2's

y = aeb:r

0.690.680.660.650.650.640.610.55

Bandwidth(fLm)

0.370-0.4200.370-0.4300.370-0.4700.370-0.4600.370-0.4500.370-0.5000.370-0.5200.370-0.540

Rank

(B)13579

111315

Orderedr2's

y = aebr

0.520.510.480.460.440.410.350.27

Bandwidth(fLm)

0.370-0.5500.390-0.5500.410-0.5500.430-0.5500.450-0.5500.470-0.5500.490-0.5500.510-0.550

TABLE 3. ORDERED COEFFICIENTS OF DETERMINATION VALUES RESULTING FROM THE REGRESSIONSBETWEEN INTEGRATED REFLECTANCE INTERVALS AND LEAF WATER CONTENT FOR THE 0.50 TO 0.68 fLm

REGION. (A) REPRESENTS THE RESULTS FROM THE THIRTy-FIVE PLOTS SAMPLED IN JUNE 1972 AND(B) REPRESENTS THE FORTY PLOTS SAMPLED IN SEPTEMBER 1971. NOTE THE SIMILAR ORDERINGS

BETWEEN THE COEFFICIENTS OF DETERMINATION AND ASSOCIATED BANDWIDTHS FOR THESE Two SAMPLEPERIODS. (EVERY OTHER LINE DELETED FOR BREVITY).

Integral IntegralOrdered bandwidth Ordered bandwidth

Rank r 2 's (!Lm) Rank r 2 's (!Lm)

(A) y = a + b/x (B) y = aebr

1 0.88 0.500-0.680 1 0.56 0.500-0.6803 0.86 0.500-0.660 3 0.51 0.500-0.6605 0.85 0.500-0.650 5 0.46 0.500-0.6407 0.84 0.500-0.620 7 0.43 0.500-0.6309 0.81 0.500-0.600 9 0.39 0.500-0.550

11 0.76 0.500-0.580 11 0.37 0.500-0.62013 0.70 0.500-0.560 13 0.35 0.500-0.59015 0.65 0.500-0.540 15 0.33 0.500-0.570

ro ~ 00 00 100 lro I~ 100 IOO~

LEAF WATER (g/m2)

ro ~ 00 00 100 lro I~ 100 IOO~

LEAF WATER (g/m2)

3.0

2.8

"' 2.6uz

2.4>'!u"' 2.2...Ju.

"' 2.0'""' 1.8>ii 1.6...J

"'a:14

1.2

1.00

oo

oo 0

0-

•o 0

oo 0

00. . ..

,'= 0.68

o0,

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"' 4.2uz

38>'!u"' 34...J.."' 30a:

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FIG. 3. Relative integrated reflectance plotted against leaf water content for the spectralintervals of (a) 0.37 to 0.50 fLm and (b) 0.60 to 0.70 !Lm (ultraviolet-blue and orange-redabsorption regions respectively). These data from the September sampling period indicatethe inverse relationship between integrated reflectance and the leaf water content for theseregions of strong pigment absorption. Relative reflectance is used because of the unitchanges which resulted from integration by Equation 5.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1976

PRIMARY EFFECTS

Three primary effects were evident be­tween 0.350 and 1.000 /Lm and were persis­tent for both data sets:

(1) Absorption by plant pigments whichoccurs in the 0.350 to 0.500 /Lm and 0.600 to

LEAF WATER (g/m2)

FIG. 4. Relative integrated reflectanceplotted against leaf water content for the~ectral interval of 0.75 to 0.78 /-Lm (photo­graphic infrared enhanced reflectance).These data from the September samplingperiod show the direct relationship be­tween integrated reflectance and the leafwater content.

tion (III) resulting from the confounding in­teraction of pigment absorptance, noise, andenhanced spectral reflectance.

As the upper limit of o. 78 /Lm remains con­stant and the lower limit is increased from0.60 /Lm toward 0.75 /Lm in 0.01 /Lm incre­ments, a marked improvement in r 2 valuesoccurred for both the June and Septemberdata sets (Table 4).

0.700 JLm regions (Figure 3). The orange-redand ultraviolet-blue regions of the spectrumshowed similar relationships between integ­rated ref1ectance and the plot variables.These results identified the greatest sen­sitivity to be in the 0.37 to 0.46 and 0.67 to0.69 /Lm wavelengths, respectively .

There was a decrease in r 2 values in theorange-red region as the upper limit of integ­ration decreased from 0.68 to 0.54 /Lm (Table3). This is undoubtedly due to weaker ab­sorption by the chlorophylls as the upperlimit of integration moves away from 0.68/Lm. This also indicates that the -0.67 to 0.69/Lm region is where maximum chlorophyllabsorption occurs in the orange-red.

(2) The photographic infrared region (0.74to 1.00 JLm) showed the enhancement ofspectral reflectance by the functioning vege­tation present and the strong sensitivitywhich existed between the functioningbiomass and the resulting spectral ref1ec­tance (Figure 4). Note the decrease in r2 val­ues as the lower limit of integration is de­creased (Table 4). This is undoubtedly dueto the inclusion of random effects (noise) inthe 0.70 to 0.74 /Lm region and the absorp­tion in the 0.60 to 0.68 /Lm region.

(3) The 0.70 to 0.74 /Lm portion of thespectrum demonstrated the random effectswhich occur in this transition region (Figure5). The regression results from this spectralregion indicated nonsignificance when in­tegrated reflectances were regressed againstthe plot variables.

In addition to the 0.70 to 0.74 /Lm region ofnonsignificance, the green region (0.50 to0.60 /Lm) was found to not be significant forthe September data set (Figure 5). As the

....... , .... ..-....

r2~ 0.73

..,

20 40 60 80 100 120 140 160 leo 200

1406

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4.7

w 4.4UZ

4.1<1...Uw 3.8--'"-w 3.'a:

w 3.2?...<1 2.'--'wa: 2.6

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TABLE 4. ORDERED COEFFICIENTS OF DETERMINATION VALUES RESULTING FROM THE REGRESSIONS

BETWEEN INTEGRATED REFLECTANCE INTERVALS AND LEAF WATER CONTENT FOR THE 0.60 TO 0.78 /-LmREGION. (A) REPRESENTS THE RESULTS FROM THE THIRTy-FIVE PLOTS SAMPLED IN JUNE 1972 AND

(B) REPRESENTS THE FORTY PLOTS SAMPLED IN SEPTEMBER 1971. NOTE THE SIMILAR ORDERINGS

BETWEEN THE COEFFICIENTS OF DETERMINATION AND ASSOCIATED BANDWIDTHS FOR THESE Two SAMPLE

PERIODS. (EVERY OTHER LINE DELETED FOR BREVITY).

Integral IntegralOrdered bandwidth Ordered bandwidth

Rank r2's (/Lm) Rank r2's (/-Lm)

(A) y = a + bx (B) y = a + bx1 0.82 0.750-0.780 1 0.73 0.750-0.7803 0.78 0.730-0.780 3 0.66 0.730-0.7805 0.70 0.710-0.780 5 0.55 0.710-0.7807 0.58 0.690-0.780 7 0.39 0.690-0.7809 0.46 0.670-0.780 9 0.23 0.670-0.780

11 0.35 0.650-0.780 11 0.12 0.650-0.78013 0.26 0.630-0.780 13 0.05 0.630-0.78015 0.19 0.610-0.780 15 0.01 0.610-0.780

SENSOR DESIGN FOR MONITORING VEGETATION CANOPIES 1407

40

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> 28;::

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w0:

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25

200 20 40 60 80 100 120 140 IW 180 200

LEAF WATER (g/m')

2.00 20 40 60 80 100 120 140 160 100 200

LEAF WATER (g/m')

FIG. 5. Relative integrated reflectance plotted against leaf water content for the spectralintervals of (a) 0.50 to 0.60 J-Lm and (b) 0.70 to 0.74 J-Lm transition region. Note the lack ofanyapparent relationship between integrated reflectance and leaf water content for thiswavelength interval for the September sampling period.

growing season wanes, the significance be­tween integrated reflectance in the 0.50 to0.60 ILm region decreases.

COMBINATION OF TWO PRIMARY EFFECTS

The inclusion of the 0.50 to 0.60 ILm and0.70 to 0.74ILm regions (green and transitionregions, respectively) of reduced signifi­cance with wavelengths showing strongpigment absorption (the 0.350 to 0.500 ILmand 0.600 to 0.700 ILm regions) demonstratedconclusively (Figure 6) the degrading effectof these regions. Furthermore, the integralsevaluated in Table 4 show the reduction in r

values which occur as proportionally more ofthe 0.70 to 0.74 ILm region is included withthe 0.74 to 0.78 ILm spectral interval of en­hanced reflectance. This is also evidentwhen comparing Figures 4 and 7.

COMBINATION OF ALL THREE PRIMARY EFFECTS

Sensor bandwidths were also evaluatedwhere the integrated reflectances encom­passed areas of strong pigment absorption,lessened spectral sensitivity, and enhancednear infrared reflectance (0.60 - 0.78ILm )(Table 4) (Figure 8). Results of these evalua­tions demonstrated conclusively that any

. . .

r Z =0.48

...............

:.:..

8.0

7.5

w 7.0uz 6.5"....uw 6.0...Ju..w 5.50:

w 50>~.. 4.5...Jw0: 4.0

3.5

3.00 ~ 40 60 00 100 I~ 140 160 100~

LEAF WATER (g/m')

FIG. 7. Relative integrated reflectanceplotted against leaf water content for thespectral interval of 0.70 to 0.78 J-Lm. Notethe degrading effect indicated by this plotwhen one compares this figure to Figure 4.The added variability is introduced by theinclusion of the 0.70 to 0.74 J-Lm region intothis interval. These data are from the Sep­tember sampling period.

,

r'. 0.43

.....

.. :.

... .:.­•

7.0

6.6

w 6.2uz" 5.8....uw 5.4...Ju..w 5.00:

w 4.6~...... 4.2...Jw0: 3.8

3.4

3.00 ~ ~ 60 00 100 I~ I~ 160 100~

LEAF WATER (g/m')

FIG. 6. Relative integrated reflectanceplotted against leaf water content for thespectral interval of 0.60 to 0.74 J-Lm. otethe degrading effect upon the relationshipbetween integrated reflectance and leafwater content when the 0.70 to 0.74 J-Lm re­gion of reduced significance is included withthe 0.60 to 0.70 J-Lm region of strong absorp­tion (see Figure 3). These data are from theSeptember sampling period.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1976

INFLUENCE OF TIME OF SAMPLING DURING THE

GROWING SEASON

The phenological influence can be infer­red from comparisons between early in the

sensor bandwidth where absorption and en­hanced reflectance both occur was ex­tremely insensitive for spectrally estimatinggrass canopy vegetational status. Sensorssuch as this should most certainly be a­voided.

growing season data (June) and late in thegrowing season data (September). The Junedata showed a higher degree of significance,and sensor wavelength and bandwidthcharacteristics were not crucial. The Sep­tember data, however, indicated that as thegrowing season progresses and the transferor conversion of live to dead standing vege­tation occurs, a lower level of significanceexisted. Furthermore, sensor wavelength andbandwidth criteria were extremely impor­tant.

The September data represented a morecomplex interaction between incident spec­tral irradiance and the grass canopy becauseof the presence of more dead vegetation.The results of the more complex canopystate represented by the September datawere therefore the most useful to developoptimum sensor wavelength and bandwidthcriteria for remote sensing sensors.

EVALUATION OF LA DSAT CHANNELS

The seven LANDSAT sensors wereevaluated by simulating the bandwidths ofthe various sensors. The utility of the sevenLANDSAT Return Beam Vidicon (RBV) andMulti Spectral Scanner (MSS) channelsranged from excellent to poor in terms of op­timal channel bandwidths for monitoringgrass canopy vegetational status (Table 5).MSS bands 5 (0.60 to 0.70 JLm) and 7 (0.80 tol.l0JLm) and RBV band 2 (0.58 to 0.68 JLm)are well situated to sense blue grama canopyradiances, which in turn were highly relatedbiologically to the canopy vegetationalstatus.

RBV band 1 encompasses the 0.50 to 0.56

r2 • 0.01

.. ...... .... ..... .. . .. ...20 40 60 80 100 120 140 160 180 200

LEAF WATER (g/m2 )

FIG. 8. Relative integrated reflectanceplotted against leaf water content for thespectral interval of 0.60 to 0.78 /Lm. otethe confounding effect which results whena region of absorption, a region of reducedsignificance, and a region of enhanced re­flectance are combined into the same spec­tral interval. Refer to figures 3, 4, and 5b forthe three primary effects included in thisfigure. These data are from the Septembersampling period.

1408

9.0

8.6

w 8.2uz

7.8<l>-Uw 7.4...Ju.w 7.0a:

w 6.6>;:::

6.2<l...Jw

5.8a:

5.4

5q)

TABLE 5. RBV AND MSS COEFFICIENTS OF DETERMINATION RESULTING BETWEEN THE REGRESSIONS OFINTEGRATED REFLECTANCE AND THREE PLOT VARIABLES FOR Two SAMPLING PERIODS. THE JUNE DATA

INCLUDED THIRTy-FIVE PLOTS AND WAS MEASURED FOR THE 0.50 TO 1.00 /Lm REGION; THE SEPTEMBERDATA INCLUDED FORTY PLOTS MEASURED FOR THE 0.35 TO 0.80 /Lm REGION.

Highest r 2 values

June (n = 35) September (n = 40)

Total wet Total wetBandwidth biomass Leaf water Chlorophyll biomass Leaf water Chlorophyll

Channel (/Lm) (g/m2) (g/m2 ) (mg/m2 ) (g/m2) (g/m2 ) (mg/m2)

RBV-l 0.475-0.575 0.72 0.76 0.77 0.32 0.42 0.25RBV-2 0.580-0.680 0.88 0.91 0.91 0.38 0.62 0.32RBV-3 0.690-0.800 0.68 0.68 0.68 0.49 0.44 0.41RBV-4 0.500-0.600 0.77 0.81 0.82 0.26 0.37 0.21MSS-5 0.600-0.700 0.88 0.91 0.91 0.39 0.65 0.33MSS-6 0.700-0.800 0.72 0.72 0.71 0.54 0.51 0.45MSS-7 0.800-1.100* 0.70 0.71 0.75

• The September data was not collected over this region. The June data was, but suffers from a progressively lower signal to noise ratiobeyond -0.90 /LlTI and thus is seriously degraded beyond -0.95/LlTI.

SENSOR DESIGN FOR MONITORING VEGETATION CANOPIES 1409

ILm region of lessened significance and doesnot include enough of the blue region to beeffective as the growing season wanes. RBVband 3 includes the 0.69 to 0.70 ILm region ofabsorption by chlorophyll, the 0.7C to 0.74ILm region of noise, and the 0.74 to 0.80 ILmregion of enhanced reflectance. Thus RBVband 3 is degraded by some pigment absorp­tion at its lower wavelengths and by thenoise present in the 0.70 to 0.74 ILm region.

MSS band 6 is redundant to band 7 andincludes the noisy 0.70 to 0.74 ILm region.Bands 5 and 7 are well situated for vegeta­tional remote sensing of the blue gramacanopy studied. Band 4, however, is placedover the 0.50 to 0.60 ILm region of lessenedstatistical significance and what physiologi­cal information is sensed by this band is bet­ter sensed by band 5.

It should be noted that MSS 6 has beenshown to be of greater utility than MSS 7 formonitoring rangelands by Rouse et al.(1974). This has been corroborated byJohnston (1976) for LANDSAT imagery ofthe Pawnee National Grassland, Colorado.In any event, MSS 6 does include the 0.70 to0.74 ILm transition region of noise and theinformation that is highly correlated to vege­tational density or biomass for this sensor isreceived in the 0.74 to 0.80 ILm region.

We thus feel that MSS 6 and MSS 7 arehighly redundant in that the same vegeta­tional information is equally as well sensedover the 0.74 - 1.30 ILm. However, sensordetector sensitivities and atmospheric ef­fects make direct comparisons between MSS6 and MSS 7 difficult to draw.

SUMMARY A 0 CONCLUSIO s

(1) Simulation-integrations for varioussensor wavelength and bandwidths for twosample periods, one early and one late in thegrowing season, indicated that greater spec­tral sensitivity existed earlier in the growingseason between reflectance and the grasscanopy variables.

(2) The spectral regions of 0.37 - 0.50,0.63 - 0.69, and 0.75 - 0.80 ILm were foundto be statistically significant, in a regressionsense, both early and late in the growingseason.

(3) Inclusion of noisy bands (green andtransition region) degrade sensor results andshould be avoided to optimize vegetationalremote sensing.

(4) Inclusion of a bandwidth whereorange-red absorption and enhanced reflec­tance both occur result in a near total degrad­ing of any spectral sensitivity and should beavoided.

(5) Sensor location and bandwidth criteriacan be accurately approximated by integra­tion of spectral coefficient of determinationcurves.

(6) LA DSAT MSS bands 5 and 7 andRBV band 2 are well situated for biologicalremote sensing.

(7) MSS band 6 is redundant to band 7(biologically speaking) and is degraded bynoise from the 0.70 to 0.74ILm region. How­ever, actual MSS 7 performance may be de­graded by sensor characteristics and/or at­mospheric effects.

(8) RBV band 3 includes the 0.69 to 0.70ILm region of absorption, the 0.70 to 0.74ILmtransition region of noise, and the 0.74 to0.80 ILm region of enhanced spectral reflec­tance and should be moved to a longerwavelength interval (say 0.75 to 0.85ILm) forbetter results.

(9) Remote sensing missions concernedwith vegetation monitoring could increasethe potential information content by includ­ing these criteria in system design.

ACK OWLEDGMENTS

We would like to thank Marvin Bauer,Harold Gausman, and Jerry Richardson fortheir assistance in reviewing and comment­ing upon the manuscript.

This paper reports on work supported inpart by National Science Foundation GrantsGB-31862X2, GB-41233X, and BMS73­02027 A02 to the Grassland Biome, U. S. In­ternational Biological Program, for "Analysisof Structure, Function, and Utilization ofGrassland Ecosystems."

REFERENCES

Colwell, J. E. 1974. Grass canopy bidirectionalspectral reflectance. In Proceedings of the 9thInternational Symposium on Remote Sensingof Environment. ERIM, Univ. Michigan, AnnArbor, pp. 1061-1085.

Draper, N. R., and H. Smith. 1966. Applied Re­gression Analysis. John Wiley and Sons, NewYork. 417 pp.

Frayer, W. E., R. W. Wilson, and G. M. Furnival,1971, FSCREEN: A computer program forscreening all combinations of independentvariables in univariate multiple linea?' re­gressions, Dept. Forestry and Wood Science,Colorado State Univ., Fort Collins. 23 pp.

Gausman, H. W., W. A. Allen, R. Cardenas, andA. J. Richardson, 1973, "Reflectance dis­crimination of cotton and corn at four growthstages," Agron.]. 65:194-198.

Gausman, H. W., R. R. Rodriquez, and A. J.Richardson, 1976, "Infinite reflectance ofdead compared to live vegetation," Agron. ].(in press).

Hoffer, R. M., and C. J. Johannsen, 1969, "Ecolog-

1410 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1976

ical potentials and spectral analysis," In P. L.Johnson (ed.) Remote sensing and ecology.Univ. Georgia Press, Athens. pp. 1-16.

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

Johnston, G. R., 1976, Remote estimation of her­baceous biomass, M.S. thesis, Colorado StateUniv., Fort Collins. 120 pp.

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

Miller, L. D., R. L. Pearson, and C. J. Tucker,1976, "Design of a mobile field spectrometerlaboratory," Photogram. Eng. and RemoteSensing 42(4):569-572.

Rouse, J. W., R. H. Haas, J. A. Schell, D. W. Deer­ing, and J. C. Harlan, 1974, "Monitoring thevernal advancement and retrogradation(green-wave effect) of natural vegetation,"NASA Type III Final Report, GSFC. 347 pp.

Tucker, C. J., 1975, Spectral estimation of grass

canopy vegetational status, Ph.D. disserta­tion, Colorado State Univ., Fort Collins. 106pp.

Tucker, C. J., L. D. Miller, and R. L. Pearson,1975, "Shortgrass prairie spectral measure­ments," Photogram. Eng. Remote Sensing41(9):1157-1162.

Tucker, C. J. 1976. Asymptotic nature of grasscanopy spectral reflectance. Applied Optics(in press).

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

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

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

New Sustaining Member

Col-East, Inc.

COL-EAST, INC., had its beginning in 1946and has literally grown up with the

aerial mapping and photogrammetric indus­try. It became involved with the preparationof topo mapping via photogrammetric meansin 1956 and so has completed twenty years ofthis particular type of work.

Col-East, Inc., has its main office at NorthAdams, Massachusetts with a small operat­ing unit located in Norwood, Massachusetts.It is a wholly contained operation, operatingthree aircraft, utilizing a modern Zeiss cam­era, and a complete ground control crewusing EDM's and other precision equip­ment. Col-East does in-house semi-

analytical bridging and much of its compila­tion is turned out on a Kern PG-2/AT-12 plot­ter. The final drafting is usually presented tothe client on mylar with ink.

Col-East's clientele includes some of thelarger engineering firms on the East Coast,and by maintaining good quality it has ob­tained repeat contracts from these com­panies.

Col-East reportedly is in a position to pro­vide assessors' plans, complete through allphases, or provide base map and photo pack­ages for the use of others such as surveyors,engineers, or town engineering departmentsin constructing property maps.

ASP Needs Old MagazinesBecause of an unexpected demand for journals and student requests, the supply of some

back issues of PHOTOGRAMMETRIC ENGINEERING has been depleted. Consequently, until fur­ther notice, National Headquarters will pay to the Regions-or to individual members­$1.00 for each usable copy of the following issues sent to Headquarters, 105 N. VirginiaAve., Falls Church, Va. 22046:

YearFebruary. 1975-Vol. XLIMarch 1975-Vol. XLI

NumbersNo.2No.3