Calculation of Evapotranspiration 

Using Color-Infrared Photography

GEOLOGICAL SURVEY PROFESSIONAL PAPER 655-0

Work done in cooperation with the National Aeronautics 

and Space Administration

Calculation of Evapotranspiration 

Using Color-Infrared Photography

By JOHN EDWIN JONES

GILA RIVER PHREATOPHYTE PROJECT

GEOLOGICAL SURVEY PROFESSIONAL PAPER 655-0

Work done in cooperation with the National Aeronautics 

and Space Administration

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1977
UNITED STATES DEPARTMENT OF THE INTERIOR 

CECIL D. ANDRUS, Secretary

GEOLOGICAL SURVEY 

V. E. McKelvey, Director

First printing 1977 

Second printing 1978

Library of Congress Cataloging in Publication Data

Jones, John Edwin, 1940-

Calculation of evapotranspiration using color-infrared photography.

(Gila River phreatophyte project) (Geological Survey Professional Paper 655-O)

Bibliography: p. O44-O45.

Supt. of Docs. No.: I 19.16:655-0

1. Evapotranspiration-Measurement. 2. Evapotranspiration-Remote sensing. 3. Photography, Infrared. 4. Aerial

photography in botany. I. United States. National Aeronautics and Space Administration. II. Title. III. Series.

IV. Series: United States Geological Survey Professional Paper 655-O. 

QE75.P9 no. 655-O [QK873] 557.3'08s [551.5'72'028] 76-608355

For sale by the Superintendent of Documents, U.S. Government Printing Office

Washington, D.C. 20402

Stock Number 024-001-02985-5
CONTENTS

	Page

Symbols ________________._______________ IV

Glossary of terms _ ___ __ ___ __ _ __ __ _ ___ ___ V

Abstract _________________________.______ Ol

Introduction _____________________________ 1

Purpose and scope of the investigation ___ _ ___ ___ 1

Location and extent of the study areas ____ _ _ ___ 2

Acknowledgments ______________________ 2

Aerial photography _____________________ 2

Vegetation ____________________________ 3

Color-infrared photography as a tool for vegetation analysis _ 4

Color-infrared film ______ _____________ 4

Leaf and canopy reflectance _ _ __ __ __ _ _ ___ 5

Visual analysis_________________________ 6

Data collection ____________________________ 7

Photographic data collection__________________ 7

Hydrologic data collection___________________ 10

Vegetation data collection________________ 10

Identification and measurement of vegetation parameters __ 10

Evapotranspiration _____________________ 10

Depth to water-soil moisture ________ ______ 18

Vegetation ______________________________ 19

Signature discrimination _______ ______ 19

Time-independent signature ____ ______ 21

Time-dependent signature__________________ 24

Identification and measurement of vegetation parameters 

Continued 

Vegetation Continued

Signature discrimination Continued

Spatial computer analysis_______ O26 

Volume of canopy foliage ___ __ 28 

Foliar cover ______________________ 31

Techniques for analysis of color-infrared photography ____ 33 

Cost of photographic analysis _____________ 35

Basic photographic concepts 35 

Spectral sensitivity ___ _________ _______ 35

Film density versus exposure ______________ 35

Base plus fog density ____ __ 35 

Gamma ____________________ 35

Data standardization and sources of variability ______ 35 

Analytic optical density._______________ 36 

Film-type correction __________________ 36

Flight-altitude correction ___ __ 38 

Filter correction ___________ _______ 38

Solar angle correction__________________ 40

Standard correction____ __ 40

Statistical analysis of variables __________ 42

Summary ____________________________ 42

References cited __________________________ 44

ILLUSTRATIONS

FIGURE 1. Map of study area ______________ _________ _________ _________ _________________ O3

2. Graph showing spectral sensitivity versus wavelength for the three dye layers of color-infrared film ____ 4

3. Diagram showing color formation on color-infrared film and its relation to plant reflectance _____ 5

4. Graph showing analytic optical density versus log exposure for three dye layers of color-infrared film _ _____ 6

5. Graph showing average leaf reflectance and absorption as a function of wavelength_____________ 7

6. Photographs showing vegetation in the Gila River flood plain _______________________________- 8

7-23. Graphs showing:

7. Transmittance versus wavelength for the densitometer filters used in film analysis ___________ 9

8. General percentages of irradiance for different earth-surface characteristics __ ___ _ _ _ 11

9. Remote sensing and water-budget values of evapotranspiration versus time, Gila River Phreatophyte Project

area _______________________________________________________ 13

10. Regression relations between measured remote sensing and water-budget values of evapotranspiration,

reaches 1 and 2, 1968, Gila River Phreatophyte Project ________________________ 14

11. Monthly consumptive-use coefficients for areas of indicated percent of foliar cover of phreatophytes, and

average monthly consumptive-use factor _________ ______________________ 15

12. Monthly consumptive-use coefficient versus relative near-infrared irradiance ____________ 16

13. Consumptive use of water and modified relative near-infrared irradiance versus time for grain sorghum

during 1968 growing season __________________________-__________ 17

14. Regression relation between consumptive use of water and modified relative near-infrared irradiance for grain

sorghum during the 1968 growing season ________________________________ 17

15. Depth to water versus relative near-infrared irradiance, reach 2 of the Gila River Phreatophyte Project _ 18

16. Relative near-infrared irradiance, relative red irradiance, evaporation, and water in the capillary zone for a

dense 40-acre (16-ha) saltcedar site, 1968 _____________-___________________ 19

17. Relative near-infrared irradiance, relative red irradiance, cumulative soil moisture, and depth to water, 1968

and 1969 ________________________________________________________ 20

18. Regression relations between relative near-infrared irradiance and relative red irradiance among vegetation

types in and adjacent to the Gila River Phreatophyte Project, 1968 and 1969 ________________ 22

ill
IV CONTENTS

FIGURES 7-23. Graphs showing Continued Pase

19. Regression relations between relative near-infrared irradiance and relative red irradiance among vege tation

types in and adjacent to the Cibecue Ridge area, summer 1971 _____________________ .-- O23

20. Relative near-infrared irradiance versus relative red irradiance for five vegetation types in and adjacent to the

Gila River Phreatophyte Project, summer 1968 and 1969 _______________________________ 24

21. Relative near-infrared irradiance, relative red irradiance, and model curves, reach 2 of the Gila River

Phreatophyte Project, 1968 and 1969 ________________________________________ 25

22. Regression relation between relative near-infrared irradiance determined from film and from a timedependent model equation, reach 2 of the Gila River Phreatophyte Project, 1968-69 ________ 26

23. Relative near-infrared irradiance and relative red irradiance versus time for five vegetation types in and

adjacent to the Gila River Phreatophyte Project area, 1968 and 1969 _______________________ 27

24. Diagram showing computer printout codes used in spatial analysis of relative near-infrared, relative red, and relative

green irradiance ___________________________________________________ 28

25. Color-infrared mosiac showing spatial computer analysis by spectral signature for 3.67-acre (1.49-ha) plots in reach 2,

Gila River Phreatophyte Project, June 27, 1968 ______________________________________ 29 

26-33. Graphs showing:

26. Volume of canopy foliage versus relative near-infrared irradiance for photographic flights during 1968, reach

2, Gila River Phreatophyte Project _____________________________________________ 30

27. Relative near-infrared irradiance versus time for three saltcedar volumes of canopy foliage classes, reach 2,

Gila River Phreatophyte Project ___________________________________________ 31

28. Relative near-infrared irradiance versus time for three mesquite volumes of canopy foliage classes, reach 2,

Gila River Phreatophyte Project ___________________________________________ 32

29. Foliar cover versus relative near-infrared irradiance for 50 3.67-acre (1.49-ha) plots, subreach 2b, Gila River

Phreatophyte Project ________________________________________________ 33

30. Adjusted and unadjusted relative near-infrared irradiance, subreach 2b of the Gila River Phreatophyte Project, 1967-71_________________________________________________________ 34

31. Major versus the two minor optical densities for the three dye layers of film type 8443 ______ _____ 37

32. Filter transmittance versus wavelength for the standard filter pack used in aerial photography in the study

areas ______________________________________________________________________ 39

33. Relative near-infrared irradiance and relative red irradiance versus time of day for bare ground and dense

saltcedar ____________________________________________________________ 41

TABLES

TABLE 1. Time-independent analysis using color-infrared film for vegetation in the Gila River Phreatophyte Project and adjoining areas _____________________________________________________________ O21

2. Time-independent analysis using color-infrared film for vegetation in the Cibecue Ridge area and adjacent areas __ 23

3. Atmospheric transmittance at different flight altitudes for the peak wavelengths of dye-layer sensitivity of colorinfrared film___________________________________________________________ 38

4. Atmospheric transmittance for the peak sensitivity of dye layers of color-infrared film at summer and winter solst: ce _ 40

5. Statistical index of variables in equations used in this report _________________________________ 43

SYMBOLS

A Altitude correction factor

(A) Matrix A

(A'1 ) The inversion of matrix A

AOD Analytic optical density

a Subscript data from an evergreen species of saltcedar

(Tamarix aphylla)

at Subscript analytic transmittance 

B Integral optical density blue color 

B Relative green irradiance, in percent 

C Analytic optical density cyan dye layer 

d Calendar-year day, January 1, 6 = 1; December 31, 6=365 or

366 (leap year)

c Subscript cyan dye layer 

CI Foliar cover, in percent 

D0 Base plus fog density

D Calendar-year day for the end of dormancy, determined by the 

low relative near-infrared irradiance

D' Calendar-year day for the end of dormancy, determined by the 

high relative red irradiance

E Exposure, defined as E=(I) (t)

6 Evaporation from bare ground

E(y) Energy in ergs/cm2.

END Equivalent neutral density

ET Evapotranspiration, measurement based on water or energy 

^ budget data

ET Evapotranspiration, estimated by relative near-infrared irradiance

exp Exponential function of, as in exp (r) for e?

F Filter correction factor

f" F test (statistical test)
CONTENTS V

f Monthly consumptive-use factor, defined asf=-

G 

G 

G' 

H

H'

A/t 

A/I! 

AA2 

7

;

IOD 

J

k 

k'

L 

L'

In 

M 

m 

m

N

N'

n

OD

od

P

P'

P

q 

R 

£

R' 

r 

r 

SD

100

Integral optical density green color

Relative red irradiance, in percent

Value of the model curve for relative red irradiance

Amplitude of the sine waves, for relative near-infrared irradiance

Amplitude of the sine wave, for relative red irradiance

Difference in altitude

Camera altitude

Ground surface elevation

Illumination

Modified relative near-infrared irradiance, in percent

Integral optical density

Bridge correction factor

Monthly empirical consumptive-use coefficient

Monthly empirical consumptive-use coefficient, defined as 

being independent of soil evaporation

Duration, in days for the period of plant vigor, determined by 

the change in relative near-infrared irradiance

Duration, in days, for the period of plant vigor, determined by 

the change in relative red irradiance

Natural logarithm

Analytic optical density magenta dye layer

Subscript magenta dye layer

Subscript month

Function of time, defined as AT=|(C D) Yl

Function of time, defined as N' = \(C-D') (

Number of samples

Optical density

Subscript optical density

Mean relative near-infrared irradiance during dormancy

Mean relative red irradiance during dormancy

Percent of daylight hours in the month

Subscript bridge reading for any photographic mission

Integral optical density red color

Relative near-infrared irradiance, in percent

Value of the model curve for relative near-infrared irradiance

Coefficient of correlation

Subscript re ach

Standard deviation

s Subscript standard bridge reading (June 27, 1968, photography)

sec Secant (of)

sin Sine (of)

Sv .x Standard error of estimate; the standard deviation of residuals 

from a regression line

S(A) Spectral sensitivity, defined as S(\)=1/B(X)

T Transmittance

T' Atmospheric transmittance of radiation at a particular 

wavelength, through the total atmosphere or between th^ 

ground surface elevation and the camera altitude

f" Transpiration

t Mean monthly temperature, in degrees Fahrenheit

1 Time

t' Student t test

u Monthly evapotranspiration, in inches

v Subscript This subscript indicates that the relative irradiance data (R, G, or £) was from a nonstandard flight 

altitude or filter combination and that the data are corrected 

to standard values by the equation

W Depth to water (table) level, in feet

Y Analytic optical density yellow dye layer

y Subscript yellow dye layer

y Subscript year

Z The Fisher Z transformation

Z' The maximum and minimum values for Z 

mined by the t' distribution

Zr The Fisher Z transformation of a function

Zr~l The inverse of the Fisher Z transformation of a function

Aim Micrometers

p Value of the null hypothesis

2 Summation

7 Extinction optical thickness

<b Solar angle

/ Function (of)

[ ] Matrix notation

y Gamma

1 Subscript reach 1

2 Subscript reach 2 

2a Subscript subreach 2a 

2b Subscript subreach 2b 

8443 Film type 8443 

2443 Film type 2443

or Zp as deterGLOSSARY OF TERMS

Aerosol scattering coefficient. A coefficient of particulate scattering

in the atmosphere; particle size may approximate or exceed the

mean wavelength of light. 

Analytic optical density. The optical density of the individual film

layers of a multidye layer film. 

Base plus fog density. Film density resulting from film base optical

density and chemical fog; the optical density resulting from any

change in exposure is greater than gross fog density. 

Densitometer. A device used to measure the optical density of the

dye deposit in a photographic image. 

Equivalent neutral density. The visual density of dye layer that a

multidye layer film would have if it were converted to a neutral

gray by superimposing the minimum necessary amounts of dye in

the other dye layers. 

Evapotranspiration. Water withdrawn from soil by evaporation and

plant transpiration. 

Exposure. The product of the illumination of a unit area of sensitive

material and the duration of time through which this illuminatior 

acts, in ergs/cm2 .

Extinction optical thickness. The mean value of the sum of the 

Rayleigh scattering coefficient, aerosol scattering coefficient, anc1 

the ozone absorption coefficient.

Foliar cover. The amount of ground covered or shaded by a vertical 

projection of plant foliage; usually expressed in percent.

Gamma. Slope of the linear portion of the characteristic curve.

Ground scene. Earth surface characteristics perceived both spatially and spectrally.

Ground-truth data. Data obtained independent of a remote sensing 

system that describes the object or phenomenon sensed by the system.

Integral optical density. The total optical density of a multidye layer 

film.

Irradiance. A measure of radiant power per unit area that flow? 

across or into a surface. The specific definition of the term as used
VI CONTENTS

in this paper is denned as the energy recorded on the film. Additional clarification of this term is presented in the section "Purpose 

and Scope of the Investigation."

Light. The visible portion of the spectrum, located approximately 

between 0.40 and 0.70 micrometers.

Optical density. The negative logarithm to the base 10 of the transmittance. Optical density in a multidye layer film can be described 

in two ways, analytic optical density and integral optical density.

Ozone absorption coefficient. The coefficient of ozone absorption in 

the atmosphere.

Phreatophytes. A plant that habitually obtains its water supply 

from the zone of saturation (water table).

Radiance. The radiant flux per unit solid angle per unit of projected 

area of the source.

Rayleigh scattering coefficient. A coefficient of gaseous scattering in 

the atmosphere; the gaseous molecules (primarily nitrogen and 

oxygen) are smaller than the mean wavelength of light.

Reflectance. The ratio of the reflected to the incident radiation.

Sensitivity. The reciprocal of exposure (ergs/cm2 ) to produce an 

equivalent neutral density, usually of 1.0.

Spectral signature. The spectral signature is traditional'7 defined as 

the reflectance characteristics (curve of percent reflectance) that 

have been determined for an object. But, because of the nature of 

this study, the spectral signature will be defined in this report as 

the relationships between the relative irradiances (ft,Cr, and^) for 

a particular object. These relationsips are variable w^th time and 

changing conditions.

Step tablet. A strip of film or a glass plate whose optical density 

diminishes in incremental steps of density used to calibrate film 

density.

Transmittance. The ratio of the transmitted to the incident radiation.
GILA RIVER PHREATOPHYTE PROJECT

CALCULATION OF EVAPOTRANSPIRATION USING 

COLOR-INFRARED PHOTOGRAPHY

By JOHN EDWIN JONES

ABSTRACT

In the 5-year period 1967-71, 38 color-infrared photographic 

missions were flown over the Gila River Phreatophyte Project in 

southeastern Arizona. Data from these missions were analyzed to 

determine the possibility of identifying and measuring vegetative 

parameters and their associated hydrologic variables by spectral 

analysis of the photography. During the summer of 1971, additional 

data from six color-infrared photographic missions flown over the 

Cibecue Ridge Watershed Study in central Arizona were used to test 

the validity of some of the techniques developed in this study.

The photographic missions were flown at altitudes between 1,500 

and 60,000 feet (460 and 18,000 meters) above land surface during 

many different climatic conditions using a variety of cameras, films, 

and filter combinations.

A transmittance densitometer was used to obtain density readings 

in each of three primary colors of the positive transparencies. The 

irradiance (defined in this report as energy recorded on the film) 

sensed from the dye concentration in each of these primary colors 

was determined and related to the total energy sensed by the film, 

achieving three parameters of relative irradiance (near-infrared, 

red, and green) which were functionally related to spectral regions 

indicative of plant activity. These parameters were then corrected to 

a "standard photo flight" by adjusting the densitometric data for 

flight altitude, filter combination, film type, and a standard correction based on the spectral signature of a bridge located on the project.

Calculations of evapotranspiration based on remote sensing from 

13 photographic missions flown during 1968 were related to waterbudget measurements for a 1,700-acre (690-hectare) area cleared of 

vegetation and a 2,200-acre (890-hectare) phreatophyte-covered area 

of the Gila River flood plain.

The coefficients of correlation between the water-budget measurements and the remote sensing calculations were 0.88 for the 

cleared area and 0.86 for the phreatophyte-covered area. Evapotranspiration calculated from seven photographic missions flown 

during 1968 over a spatially homogeneous grain sorghum field gave 

a coefficient of correlation of 0.93 when related to evapotranspiration 

computed by the Blaney-Criddle equation.

Spatial variability of vegetation on the flood plain was defined by 

the discrimination between eight different plant communities including both deciduous and perennial species. A spectral evaluation 

of near-infrared versus red irradiance of these plant communities 

showed that for each community the standard error of the irradiance 

terms was less than 10 percent. Determining variations in the depth 

to ground water spectrally was not practical, although estimates of 

depths to water of less than 30 feet (9 meters) had a relatively small 

standard error of 5.4 feet (1.6 meters). Moisture content of the soil 

could not be determined spectrally either, but plants under stress 

from moisture deficiencies were detectable.

Temporal changes in vegetation were evaluated by computer meps 

showing the distribution of spectral signatures of the Gila River flood 

plain for each photographic mission flown. A mathematical model of 

the change of spectral signature with change in season for the flcod 

plain gave a coefficient of correlation of 0.98 between modeled and 

observed densitometric data. When an increase in volume of canopy 

for both mesquite and saltcedar was noted in the field, there was a 

corresponding increase of near-infrared irradiance, but the measurement errors of both irradiance and canopy volume were very 

large. The pooled standard deviations of irradiance for all cancpy 

classes of mesquite and saltcedar were 7 percent and 14 percent, 

respectively. The pooled standard deviations of the 1968 irradiarce 

data for the largest canopy classes were 13 percent for mesquite and 

12 percent for saltcedar. Foliar cover versus irradiance had a hi<rh 

coefficient of correlation, 0.85.

During this study it was determined that a color-infrared photographic mission and a computer analysis of the photographic data for 

the Gila River Phreatophyte Project area cost about a tenth of the 

amount of conventional species classification and canopymeasurement techniques.

A short discussion of the derived spectral equations and a table of 

24 statistical parameters describing the spectral and hydrologic variables is included.

INTRODUCTION

This report is the result of the study of color-infrared 

photography as a quantitative vegetative and hydrologic monitor in the Gila River Phreatophyte Project area and the Cibecue Ridge area. The study is a 

part of the comprehensive Gila River Phreatophyte 

Project, which was undertaken by the U.S. Geological 

Survey to evaluate evapotranspiration from an 

analysis of all the significant components that compose 

the hydrologic system. The work was done under the 

general supervision of R. C. Culler, project chief.

PURPOSE AND SCOPE OF THE INVESTIGATION

The purpose of this investigation was to develop and 

evaluate color-infrared photographic methods and 

techniques as a remote sensing tool for the identification and spacial measurement of vegetation cover, its 

condition, and temporal variability, and to functionally 

relate these measurements to quantitative estimates of 

hydrologic parameters, such as evapotranspiraticn,

Oi
O2 GILA RIVER PHREATOPHYTE PROJECT

depth to ground water, and soil moisture. Nearinfrared photographic sensing involves the detection of 

the spectral radiance that is reflected from the photographed surface and is particularly well adapted to hydrologic analysis. The sensitivity of two of the three 

dye layers of the film corresponds to ranges in 

wavelengths that can be used to measure plant status, 

and the photographic format lends itself to a visual 

evaluation of ground-truth data.

At this point clarification concerning the terminology used in the paper is in order. The photographic 

data used was in general standardized to a specific 

flight altitude (8,500 feet or 2600 meters) above land 

surface using a "standard filter pack." The analysis in 

the paper was, therefore, based on the effect that irradiance had on the film as a result of radiance from a 

ground scene and atmospheric "stray light" being collected and focused through the filters and lenses upon 

the film. This "at a distance" and "filtered" approach 

required the unconventional use of the word irradiance 

as a descriptor for the ground scene throughout the 

report to distinguish this data from data which are 

standardized to actual "ground-scene radiance" at surface elevation. The term "irradiance" as used in this 

report is, therefore, defined as the energy recorded on 

the film. It includes the reflected solar energy, modified 

by the atmosphere, and stray light that reaches the 

film, exposes the film, and is processed as the energy 

recorded on the film. A more general definition of the 

term is given in the section "Glossary."

The author feels that the nature of the data in this 

report is such that if presented as "radiance" the reader 

might try to compare the results in this paper directly 

with other data which are actual "radiance," and this 

would yield spurious relationships.

LOCATION AND EXTENT OF THE STUDY AREAS

The Gila River Phreatophyte Project area covers a 

6,000-acre (2,430-ha) 15-mi (24-km) span of the Gila 

River valley immediately above the San Carlos Reservoir in south-central Arizona, and the Cibecue Ridge 

Watershed Study area includes two small 60-acre (24- 

ha) watersheds in the Fort Apache Indian Reservation 

in east-central Arizona (fig. 1). For this study, the 

15-mi (24-km) span of the Gila River valley was divided into two reaches 1 and 2; reach 2 was further 

divided into two subreaches 2a and 2b (fig. 1). The 

photographic- and hydrologic-measurement sites in 

the reaches of the Gila River Phreatophyte Project area 

analyzed in this report are located by a three-number 

coordinate system. The reaches are divided by 2,000-ft 

(610 m)-square grids, which are numbered 1-28 downriver and 1-5 cross river, and the grid unit is divided 

into 25 plots, each of which has an area of 3.67 acres

(1.49 ha). Therefore, each 3.67-acre (1.49 ha) plot is 

located by a three-number coordinate system (for 

example, 14-2-25), in which the first number of the 

coordinate indicates the downriver grid, the second the 

cross-river grid, and the third the plot. The location of 

the photographic- and hydrologic-measurement sites 

in the two small watersheds in the Cibecue Ridge area 

are described only by dominant vegetation type.

ACKNOWLEDGMENTS

The interest shown in this study by many people in 

the fields of photogrammetry and the optical sciences is 

gratefully acknowledged by the writer. Of special help 

and value were the suggestions and contributions of 

Dr. P. N. Slater, Associate director of the Optical Sciences Center, University of Arizona; Mr. N. L. Fritz, 

research associate of the Photographic Research Division of Eastman Kodak Co.; and Mr. W. E. Evans, 

senior research engineer of the Electronics and Radio 

Sciences Division of Stanford Research Inst'tute. Dr. 

Ran Gerson of the Department of Geography of the 

Hebrew University in Jerusalem, Israel, reviewed the 

manuscript and offered many helpful suggestions. The 

writer also gratefully acknowledges the generous contributions to this study by the following personnel of 

the U.S. Geological Survey: R. M. Myrick furnished 

data for the Cibecue Ridge area and aided greatly in 

the computer analyses; R. M. Turner furnished the descriptions of the vegetation and made many helpful 

suggestions during the study; D. R. Dawdy aided in the 

statistical analyses; and L. B. Leopold made many 

helpful suggestions for the improvement of the paper. 

R. C. Culler conceived, initiated, and guided the study.

This study would not have been possible without the 

data from the aerial photographic missions flown by 

the U.S. Geological Survey and the National Aeronautics and Space Administration. The Survey missions 

were flown under the direction of H. E. Skibitzke; the 

National Aeronautics and Space Administration 

(Johnson Space Center) furnished the high-altitude 

photography.

AERIAL PHOTOGRAPHY

Data from 36 color-infrared photographic missions 

flown by the U.S. Geological Survey over the Gila 

River Phreatophyte Project area were analyzed for this 

study. In addition, data from two NASA (National 

Aeronautics and Space Administration) photographic 

missions flown over the area were analyzed. The U.S. 

Geological Survey missions over the project area were 

flown at altitudes of 1,500, 3,600, and 8,500 ft (460, 

1,100, and 2,600 m). Trial missions were flown at altitudes of 500 and 1,000 ft (150 and 300 m) on April 5, 

1969, and at an altitude of 11,000 ft (3,300m) on
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O3

EXPLANATION

Boundary of study area for reaches

o 

Location of study area

114 C 112 C 110C

.Peridot

50 100 MILES

SAN CARLOS 

RESERVOIR

0 50 100150 KILOMETERS INDEX

/ Gila River Phreatophyte 

/ Project and adjacent areas

.Whitethorn site

Bare ground site

Grain sorghum field

Cultivated 

grass 

field

01 234567 8 KILOMETERS 

FIGURE 1. Map of study area.

March 22, 1968; hourly missions from 9:00 a.m. to 3:00 

p.m. were flown at an altitude of 3,600 ft (1,100 m) on 

May 31, 1968. The NASA missions were flown at an 

altitude of 60,000 ft (18,000 m) on September 30,1969, 

and September 11, 1970.

From July 21 to September 28, 1971, six missions 

were flown over the Cibecue Ridge area by the U.S. 

Geological Survey. The missions were flown at altitudes of 1,500 and 4,000 ft (460 and 1,200m). An 

analysis of the data obtained during these flights is 

included in this report.

Kodak Ektachrome Infrared Aero Film 8443 l was 

used in 1967-69; after 1969, Kodak Aerochrome Infrared Film 2443 was used.

VEGETATION

In 1967 the Gila River flood plain was covered by a 

dense growth of phreatophytes mainly saltcedar 

(Tamarix pentandra). The vegetation in reach 1 was 

cleared before April 1967 as part of the comprehensive

'The use of name products in this report is for identification only and does not imply 

endorsement by the U.S. Geological Survey.

plan as outlined by Culler and others (1970, p. 3). Vegetation clearing in reach 2a was started in January 

1969 and completed in December 1969, and clearing in 

reach 2b was started in November 1970 and completed 

in March 1971.

The terraces adjacent to the flood plain support small 

open forests of mesquite (Prosopis juliflora) and plant 

communities of xerophytes, such as creosote bush 

(Larrea tridentata), whitethorn (Acacia constricta), and 

various cacti. Interspersed within these plant communities are small ephemeral plants that grow in response to winter and summer rains; the species that 

respond to winter rains are different than the species 

that respond to summer rains (Shreve, 1964, p. 127- 

142). Cultivated crops, such as tall wheat grass and 

grain sorghum, are grown adjacent to the project area. 

A small community of the evergreen species of 

saltcedar (Tamarix aphylla) is located in an adjacent 

area to reach 1 of the project area. An extensive description of the vegetation of this area was prepared by 

Turner (1973).

The main types of vegetation indigenous to the 

Cibecue Ridge area are juniper (Juniperus osteos-
04 GILA RIVER PHREATOPHYTE PROJECT

perma), pinyon pine (Pinus edulis), ponderosa pine 

(Pinus ponderosa), and native grasses. In 1967, one 

watershed was cleared of native trees and seeded with 

natural and exotic grasses.

COLOR-INFRARED PHOTOGRAPHY AS A 

TOOL FOR VEGETATION ANALYSIS

An economic method of obtaining frequent descriptions of ground-surface conditions and changes, particularly that of vegetation, is needed for watershed 

management and hydrologic analyses, especially in 

large areas. Traditionally, black and white and color 

aerial photography have been used to inventory vegetation, but recent studies show that color-infrared photography generally is superior for species identification, plant-vigor measurements, and vegetation mapping (Hunter and Bird, 1970, table 1).

COLOR-INFRARED FILM

The advantage of color-infrared film for vegetation 

analysis is due to the high reflectivity of active vegetation in the 0.70-0.90 /am (micrometer) near-infrared 

wavelength range. The tripack color infrared film has 

three dye layers, cyan, magenta, and yellow, that 

mainly are sensitive to near-infrared (0.70-0.90 /am), 

red (0.60-0.70 /am), and green (0.50-0.60 /am) 

wavelength bands, respectively. In order to eliminate

100

the sensitivities of the three dye layers in wavelengths 

of less than 0.50 /am, a yellow Wratten 12 or 15 filter is 

used (fig. 2). After film processing, the combination of 

the three dye layers exposed to their sensitivity ranges 

yields false colors near-infrared radiance generally 

appears red, red may appear green, and green may 

appear blue in the multilayered film (fig. 3). The term 

"false color photography" originates from this "incorrect to the eye" rendition.

Figure 4 shows the generalized characteristic curves 

for color-infrared photography. A knowledge of the relationship between optical density and log exposure is 

essential for any quantitative photographic analysis. 

Analytic optical density, which is shown as the scale on 

the ordinate of figure 4, is defined as the optical density 

within the individual dye layer of a multidye layer 

film.

The two parameters, optical density (OD) and exposure (E) are defined below:

OD = -log(T) (1) 

E = (I) (t) (2) 

where

T transmittance, 

7 = illumination, and 

t = time.

Any characteristic curve such as figure 4 has three 

basic parts: the shoulder, the linear portion, and the

100

^-Magenta- forming 

dye layer

0.40 0.50 0.60 0.70 0.80

WAVELENGTH, IN MICROMETERS

FIGURE 2. Spectral sensitivity versus wavelength for the three dye layers of color-infrared film.
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O5

RESULTING VISUAL COLORS ON COLOR-INFRARED 

FILM, IN MICROMETERS

Blue , Green , Red

0.40

PRINCIPLE SENSITIVITY OF 

FILM LAYER _____

0.50 0.60 0.70

DYES BLEACHED 

BY EXPOSURE

Red 

0.6-0.7

Green 

0.5-0.6

Near infrared 

0.7-0.9

Magenta

Yellow

Cyan

SOURCE OF RADIANCE, IN MICROMETERS

Blue Green

0.40 0.50

I 

0.60

Red \ 

0.70

Near infrared

0.90

Visual sensitivity range

Sensitivity range of color-infrared 

film with yellow filter

Spectral range of

low plant pigment 

reflectance

Spectral range of

high plant mesophyll 

reflectance

FIGURE 3. Color formation on color-infrared film and its relation to plant reflectance.

toe. The shoulder is the part where increasing exposure 

produces an increasing rate of density change, on the 

straight-line part the rate of change of density with log 

exposure is constant, and on the toe the rate is decreasing. It must be noted that figure 4 is for a positive film; 

the characteristic curves would be reversed for a negative film.

LEAF AND CANOPY REFLECTANCE

When radiation comes in contact with a leaf or a 

plant canopy, the energy either is reflected, absorbed, 

or transmitted (fig. 5). In the near-infrared spectral 

(0.70-0.90 //,m)-sensing range of the cyan dye layer, 

individual leaf reflectance is relatively high as great 

as 50 percent; this relatively high reflectance of nearinfrared radiation occurs because of the difference in

the refractive index when the radiance that enters the 

inner part of the leaf is refracted and reflected at the 

interface between the water film covering the 

mesophyll cell wall and the adjacent air cavity (Knipling, 1969, p. 19). In the red-color (0.60-0.70 mm) sensing range of the magenta dye layer, individual loaf 

reflectance is very low about 10 percent because the 

amount of absorption by leaf pigments (generally 

chlorophylls) is high. Leaf reflectance is these t^o 

bands is influenced by two distinct plant systems, one 

involving pigment chemistry, the other mesophyll 

anatomy. Both of these systems are indicators of the 

volume of foliage and can be remotely sensed by colorinfrared photography. 

The spectral reflectance of a typical vegetation
O6 GILA RIVER PHREATOPHYTE PROJECT

3.8

-2.0 -1.0 0.0 1.0

LOG EXPOSURE, IN METER-CANDLE-SECONDS

FIGURE 4. Analytic optical density versus log exposure for three dye layers of

color-infrared film.

canopy is considerably different from that of an individual leaf, because of angular relationships, shadows, 

and background surfaces. The visible and nearinfrared reflectances of the total canopy are of the order 

of 5 and 35 percent of incident radiation, respectively. 

The disproportionate reduction of spectral ranges for 

an individual leaf versus a canopy, which enhances the 

near-infrared radiance range, is due to greater reflection of near-infrared illuminance by multiple leaf 

layers within the canopy (Knipling, 1970, p. 157).

The difference between the spectral reflectances of 

these two radiance ranges is compensated somewhat 

by the decreased incident solar energy in the near infrared versus the red. The energy received at the earth 

surface in the near-infrared irradiance range is approximately 0.1 langley per minute, which is about 75 

percent of that received in the red region (Reifsnyder 

and Lull, 1965, fig. 12).

VISUAL ANALYSIS

Visual color-infrared photographic interpretation is 

useful for the identification of the shape and color of 

objects and is of value for studies of a reconnaissance 

nature. The interpretation of subtle temporal changes, 

however, requires a rigorous classification of color. In 

such a classification, the amount of spectral irradiance 

absorbed by individual dye layers of the film may be 

measured and then related to the total irradiance, 

sensed by the film, which gives a set of parameters of 

relative irradiance that may be functionally related to 

plant activity. Figure 6 shows two color-infrared aerial 

photographs taken over the Gila River flood plain at 

times of general vegetation dormancy (fig. 6A) and of 

vigorous plant activity (fig. 6B). Photograph A is visually interpreted as showing leafless saltcedar a nd mesquite and some spring grass on the south-facing ter-
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O7

100

90

_IUJ

80

EC uj

il-- 70

s" 60

_i }2 _i z

> ID si 50

(0 0- i ii

40

_i oc 30

u. O

20

10

Leaf absorption

NOTE: The transmitted radiance is equal to 

100 percent minus the sum of reflectance 

plus absorption

Leaf reflectance

0.40 0.50 0.60 0.70 0.80

WAVELENGTH, IN MICROMETERS 

FIGURE 5. Average leaf reflectance and absorption as a function of wavelength.

0.90

races. Photograph B is visually interpreted as showing 

increased foliation and vigor of the saltcedar and mesquite, particularly by the saltcedar adjacent to the 

river, and dormant spring grass; this is a major vegetation change that occurred in less than a month March 

22, 1968, to April 19, 1968. Healthy growing vegetation is shown as a red tone on figure 6B , which is principally due to high values of near-infrared irradiance. 

Differences in soil types and the amount of soil moisture probably account for the braided patterns that are 

shown in figure 6.

DATA COLLECTION

The evaluation of color-infrared photography as a 

means of evaluating hydrologic variables required the 

collection and tabulation of photographic data, which, 

in turn, was related to hydrologic data.

PHOTOGRAPHIC DATA COLLECTION

Optical density data were obtained from the positive 

transparencies using a Macbeth Model No. TD 402 

transmittance densitometer with a 3-millimeter aperature using Wratten filters numbers 106, 92, 93, and 94. 

These filters transmit, respectively, the entire light 

(0.40-0.70 /mi) spectrum (corrected to the visual response), the red (0.60-0.70 /mi), green (0.50-0.60 /mi), 

and blue (0.40-0.50 /mi) light (fig. 7). The resulting 

data from each filter is expressed in terms of the optical 

density of the multilayered film in each respective 

wavelength range.

The instrumentation and calibration limitations and 

the nature of the study prohibited photogrammetric 

data from being analyzed as a precise spectral radiometric pheonomenon. The need for quantitative data 

for computer analysis did, however, require an em-
O8 GILA RIVER PHREATOPHYTE PROJECT

t- ^:*f Y+*yy.* 

' , --.-7^^

 <. . .^ »v>*^

FIGURE 6. Vegetation in the Gila River flood plain. A, General dormancy;!?, Vigorous plant growth.
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY

0.1-

1.0 

I 

CO

z 

<

DC*- 10.0

100.0

3.0 i

2.0 

1.0

0.0

Filter

92

(red)

0.40 0.50 0.60 0.70 

Data from Kodak (1970).

WAVELENGTH, IN MICROMETERS 

FIGURE 7. Transmittance versus wavelength for the densitometer filters used in film analysis.

piric^al approach, which could be used to monitor the 

ground-scene changes and would allow the necessary 

corrections. It was decided to view each dye layer of the 

film as a radiometer, which was sensitive to a particular wavelength range, and then to relate the irradiance 

seeeennsed in each dye layer to the sum of the irradiance sensed by the three layers (fig. 3). This procedure allowed the film to be utilized as a relative 

radiometer.

Using such an approach required converting the integral optical density, R, G, and B of the film to the 

analytic optical density of each dye layer of the film. 

This technique is explained in the section "Analytic 

Optical Density"; these analytic optical densities C, M, 

and Y, respectively, were then converted to analytic 

trasmittance (Cat , Mat , Yat ) for each dye layer.

T=10 -(OD) (3)

Where

R = integral optical density red color,

G = integral optical density green color,

B integral optical density blue color,

C = analytic optical density cyan dye layer,

M = analytic optical density magenta dye layer,

and 

Y = analytic optical density yellow dye layer.

The analytic transmittances for each dye layer were 

then divided by the sum of the dye layer transmittances, thus achieving a set of parameters of relative 

irradiance which could be used to monitor the vegetative and hydrologic variables. The three equations for 

converting to relative irradiance are shown below:

R = relative near-infrared irradiance, in percent=

[CJ (Cat +Mat +Yat )]x 100, (4) 

G = relative red irradiance, in percent=

[MJ (Cat +Mat +Yat)]x 100, (5)
O10

(6)

B relative green irradiance, in percent=

[YJ (Cat +Mat +Yat )]x 100.

Thus, by definition

£ +G +B = 100 percent. (7) 

Two characteristics of these trichromatic parameters 

(Wright, 1969, p. 83) are of particular importance to 

the researcher. $ and & are generally inversely proportional and linearly related, because of the three 

following effects.

1. The spurious correlation due to the "closed system" (Chayes, 1971) interrelationship between the variables as shown in equation 7. For example, an increase in $ dictates an equal decrease in the sum & +B. 

This effect is discussed in the section "Statistical 

Analysis of Variables."

2. The high reflectance in the near infrared and high 

absorption in the red by active vegetation as shown in 

figure 5.

3. The radiance from vegetation affecting the yellow 

dye layer (0.50-0.60 /w-m) is not significantly related to 

plant vigor.

Ideally, photographic step tablets and spectral sensitivity curves should be generated for each roll of film 

to calibrate precisely the energy received by the film 

and to compensate for processing and aging effects. 

This was not done because of the limitations of the 

study, which have already been discussed. The actual 

area sampled by the 3-millimeter aperature on the 

8,500-foot (2,600-m) photography is 0.50 acre (0.20 ha) 

per plot. This was a 14 percent area sample and was 

considered representative owing to the large sampling 

populations.

Densitometric data which were not on the linear 

parts of the characteristic curves were rejected. The 

difficulty of handling data from the toe or shoulder of a 

characteristic curve is obvious from figure 4.

HYDROLOGIC DATA COLLECTION

Hydrologic data sampling on the Gila River Phreatophyte Project was done using the plot system 

already described. The hydrologic variables were monitored by a network of ground-water observation wells, 

soil-moisture access tubes, rain gages, and river and 

tributary gaging stations. The sampling techniques 

and methods of evaluation have been discussed by 

Hanson, Kipple, and Culler (1972, p. 315).

VEGETATION DATA COLLECTION

The plant type and spatial distribution of vegetation 

on the project area was described by a combination of 

photographic reconnaissance and field checking.

Black and white aerial photographs with a scale of 

1:7,200 were viewed through a dissecting binocular

GILA RIVER PHREATOPHYTE PROJECT

microscope, which had been fitted with an appropriate 

reticle, and were used to measure plant-crovn coverage (Culler and others, 1972; Turner, 1971). Tentative 

boundaries defining areas of homogeneous vegetation 

were first drawn on the photographs. A field check was 

then made to confirm these boundaries and determine 

the dominant species within each area. Fir ally, the 

photographs were examined under magnification. The 

squares on the reticle, when viewed through the microscope, were considered to be plots projected onto the 

photographs. Plant coverage within each plo* was estimated according to a size-class system. Averaged 

data, taken from an examination of each plot, provided 

numerical values of percentage of crown coverage. 

Comparison of the photogrammetric method with one 

of the standard field measures of crown coverage was 

made (Turner, 1973), and agreement between the 

methods was close.

IDENTIFICATION AND MEASUREMENT OF 

VEGETATION PARAMETERS

Conventional hydrologic instrumentation is usually 

limited to point samples, but photographic remote 

sensing offers a method of obtaining a record of both 

the spatial and temporal variability of many vegetative parameters.

Definitive radiometric identification of gross earthsurface characteristics, such as bare ground, water, 

and vegetation type by photographic remote sensing, is 

prerequisite to the quantitative monitoring of many 

hydrologic variables. Figure 8 illustrates the relative 

irradiance characteristics C&, &, and B) of eight 

ground-scene conditions viewed with color-infrared 

photography. Automatic computer identification of 

earth-surface characteristics based on the spectral irradiance appears possible and should be a productive 

research field, particularly with the advent of sat^llite 

imagery.

EVAPOTRANSPIRATION

The feasibility of estimating evapotranspiration by 

photographic remote sensing has been studied by the 

personnel of the Gila River Phreatophyte Project for 

several years. A primary objective of this project is the 

study and measurement of evapotranspiration.

Evapotranspiration is water withdrawn from a land 

area by evaporation from water surfaces, from moist 

soil, and by plant transpiration (Langbein and Iseri, 

1960, p. 9). The vaporization process has three basic 

requirements: (1) heat for the phase change from liquid 

to vapor; (2) unsaturated air to remove the vapor; and 

(3) liquid water available at an air-water interface. 

Any of these factors can control the rate of vaporization, and the quantity of water vaporized can be meas-
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY Oil

100n

60-

40-

20-

EXPLANATION 

EARTH-SURFACE CHARACTERISTIC

Watar in reservoir

Water more than 10 feet (3 metars) deep; 

small amount of suspended sediment

Water in river

Water less than 2 feet (0.6 meters) deep; 

large amount of suspended sediment

Deciduous forest in summer 

(Saltcedar)

Deciduous forest in autumn

(Saltcedar)

o . -o

Conifer forest in summer

(Pine)

Conifer forest in autumn 

(Pine)

Bare ground 

(Desert)

o . . . - .-o 

Asphalt paved road

Relative green 

irradiance (0.50-0.60);

sensed by 

yellow dye layer

Relative red 

irradiance (0.60-0.70);

sensed by 

magenta dye layer

Relative near-infrared 

irradiance (0.70-0.90);

sensed by 

cyan dye layer

WAVELENGTH RANGE, IN MICROMETERS, AS A PERCENT OF THE 

SUM OF IRRADIANCES SENSED BY THE FILM (0.50-0.90 MICROMETERS)

FIGURE 8. General percentages of irradiance for different eart h-surface characteristics. Photographed using color-infrared film

2443 and filters numbered W12, CC20B, and CC30M.

ured by a rigorous budget of any of the three factors. 

The definition of the spatial variability of these factors 

is one of the major problems in estimating evapotranspiration. Heat and unsaturated air can be measured 

by thermal and other meteorological instruments, although conventional instrumentation limits these observations to point samples. The availability of liquid 

water is dependent on the type of surface and, for most 

types of land area, the condition of the surface. As an 

example, the area of air-water interface for a lake can 

be defined simply as the surface of the lake. For bare 

soils or for vegetated areas, however, the air-water 

interface may be in the pores of unsaturated soils or 

within the mesophyll tissue of leaves. Thus, for a remote sensing technique to be practical, these gross 

surface types must be observed and determined. The 

feasibility of discriminating earth-surface conditions 

by the relative radiometer technique is demonstrated 

in figure 8 and has been discussed in the section 

"Identification and Measurement of Vegetation Parameters."

Remote sensing offers a method of obtaining a record 

of spatial variability of these surface conditions within 

the detection capabilities of the sensing equipment.

This method of observation can be applied to the 

estimation of evapotranspiration by detecting and 

monitoring surface types or conditions which are fur rations of, or are functionally related to, the factors 

controlling evapotranspiration. Spatial mapping of 

spectral signatures will be discussed in the section 

"Spatial Computer Analysis."

Temporal variability also presents a problem in estimating evapotranspiration. The seasonal change in 

deciduous vegetation, ranging from bare limbs in 

winter to complete foliation in summer, is an examp^. 

Disease and moisture stress can also affect transpiration. Shallow soil moisture resulting from rains affects 

both evaporation from bare soils and the distribution of 

annual vegetation. In addition to these natural temporal changes, the activities of man can dramatically 

alter surface conditions in a relatively short tirre. 

Thus, spatial variability is not uniform in time. Repetitive remote sensing is an efficient means of monitoring 

and analyzing this variability and is discussed at 

length in the section "Time-Dependent Signature."

The usual method of estimating evapotranspiration 

is to use one of a number of empirical equations, which 

express the relations between measured evapotranspi-
O12 GILA RIVER PHREATOPHYTE PROJECT

ration and climatic conditions as described by Veihmeyer (1964, table 11-2).

Remote sensing in the form of color-infrared photography has been applied to the widely used equation 

developed by Blaney and Criddle (1962). Studies by 

Cruff and Thompson (1967, p. 22) indicate that this 

equation is the most reliable for estimating potential 

evapotranspiration in arid and subhumid climates 

yielding variability ranging from -44 to +22 percent 

of adjusted evaporation data from Weather Bureau 

Class A pans. The equation is

u=kf, (8) 

where

u = calculated monthly evapotranspiration, in 

inches, derived from the Blaney-Criddle 

equation;

k = empirical consumptive-use coefficient, which 

can be defined for a vegetative area by remote sensing (the coefficient is dependent on 

the species, composition, and quantity of 

vegetation as well as the background signature which may be soil or water), and

f = consumptive-use factor, defined as ^ ,

summed for the number of months of analysis, in which 

p = monthly percentage of annual daytime hours

and 

t = mean monthly temperature, in degrees

Fahrenheit. 

In metric units,

u kp (

45.7^+813 

100 ) = monthly consumptive

use, in millimeters and

t = mean monthly temperature, in degrees centigrade.

The k value is assumed to represent a gross measure of 

the air-water interface. It therefore represents the spatially variable factor defined by the surface conditions, 

including such botanical parameters as vegetation 

type and aerial extent, plant density, and physiologic 

conditions. For the purposes of this study, k was defined in two ways. The vegetation description of the 

project area was used to define a k which was used in 

the water-budget computations of evapotranspiration, 

and a second k was defined by remote sensing which 

was dependent on relative near-infrared irradiance. 

Values ofp are tabulated in Blaney and Criddle (1962), 

and t is generally available in published U.S. National 

Weather Service (issued annually) summaries. It is assumed that the factor/" describes the thermal and vapor 

conveyance requirements of evapotranspiration.

The assumption that the relative near-infrared irradiance can be used as a measure of the BlaneyCriddle k for determining evapotranspiration was 

tested on two large reaches of the Gil a River 

Phreatophyte Project area using the photographic data 

for 1968. The two test sites were reach 1, which is a 

1,732-acre (701-ha) area cleared of phreatophytes but 

partially covered by grasses, and reach 2, a 2,268-acre 

(918-ha) area covered by phreatophytes. Th« evapotranspiration was computed as a residual in the water 

budget. Median monthly values of evapotranspiration 

were calculated for reach 1 after clearing and for reach 

2 prior to clearing (Hanson and others, 1972, figure 5). 

These median monthly values were then corrpared to 

calculated evapotranspiration based on relative nearinfrared irradiance of 13 separate photographic flights 

of both reach 1 and 2 of the project area during 19682 . 

The mean deviation between the evapotranspiration 

computed from the water budget and the calculation 

derived from the photography was 32 percent. Figure 9 

shows the evapotranspiration values derived by both 

methods for both reach 1 and 2; also shown is the percent deviation of the spectral calculation from the 

evapotranspiration computed from the water budget.

The equation used to calculate evapotranspiration 

by relative near-infrared irradiance for both reaches 1 

and 2 is in a form comparable to the Blaney-C riddle 

equation (8), u=kf. The general remote sensing form is 

shown below:

ET=[f(fi)W(f)], (9)

where

ET - evapotranspiration, calculated by relative

near-infrared irradiance,

= 0.37+8.25 [V(£/100)2/rcj2 -45 (this term is considered to be equivalent to k for this test 

site),

n = number of samples, 

$ = relative near-infrared irradiance, and

The k term was developed by optimization procedures 

using the water-budget data and the remote sensing 

data. For convenience, equation 9 has been expanded 

and is shown below:

2Only data from 13 of the 15 flights flown during 1968 were used because of the large 

departure from the expected signature exhibited on the May 3, 1968, and August 28, 1968, 

photography. The May 3, 1968, photography was affected by a frost occurnrg on April 20, 

which defoliated all vegetation with 8 ft (2.4ml of the ground. The August 28, 1968, 

photography demonstrates the 0.89 inches (23 mm) of ram which occurred that day on 

reach 2. This water on the ground appreciably decreased the near -infrared albedo, therefore decreasing the R and proportionally increasing the G and tl. Because of these two 

events, the May 3 and August 28, 1968, photographic missions appear as anomalies on 

figures 13, 16, 17, 21, 23, 27, 28, and 30 in this report, and the data from these missions 

were not used in the computations presented in figures 9, 10, 21, and 22.
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O13

7r- EXPLANATION -,

6

5

CO

0 4

O

o

OC 

LU 

0. »

CO

LU

X 

O 2

2

2~ 1

O 

t- 

< 0

OC

0.

o

CM

- ^

t^

Calculated evapotranspiration:

Reach 2, 2,268 acres (918 hectares).

,_

*;

t _ i

Calculated using relative near- infrared ;£> ,

_ irradiance. Calculatesd values are from +

13 photographic m ssions flown in1 968.

Number, +20, is percent of deviation

from measured evapotranspiration

D

Median monthly evapotransp ration.

Computed using water -budget method 

~ n

0 *> «

CM

 1

i i i | i I i i i I ill

2 4 

OCIO *3

Q_ ^

^

^ 2

1

0

-

-

i i i i i i i i i i i i i 1--! i 

|

1

* 

s

s \

\ \

S

s

N 

S

^ '

^ 1^

II' * '

in

_ ^ m

n «. +

? ± ra

\

v

V .; Jan. Feb. Mar.

^

Apr.

1

^

|

1 '

-

s \

\

1 '

;

;

-J s

5

;

;

;

|

§

|

\

s 

s

s

s

s

s 

s

s 

s

\

s 

s

s

\

N

s s

s 

s 

s 

s

s

CD

^

i iii

S

S

V

^

S

|

^

^

|

1

i i i

before clearing phreatophytes

Mean absolute dev ation, 25 percent _

00

*"

s

s

n

1

\

i 7 i i i

O)

i

I

) i i i i

00ini

1

-

' 

T ii i i i i i ii Iii

^ ^ Reach 1, 1,732 acres (710 hectares).

£ ^ ^ 0 CM after clearing phreatophytes

^

o ^ 

n $

|^V

I

1

fi !

N.

N

|

i s*1sK1

s 

s

\ 

s

s 

\

s

s

 N 

S 

S

s

\ \

s,

s

\

;

s 

s 

s 

s 

s

$

S ~ 

S

s 

s

s 

s

\

s 

s

s

s

s, \

s 

s 

s 

s 

s 

s

s

-l ^ i i i i i i i i 

May 1 June

^ + Mean absolute deviation, 39 percent

s

1 1V

V

$ o o

V

s

- ' 1 1 1 1iJuly i

n ~

i i

 o

!V

s1

Aug.

CM

h 1

s

s

'l 1 1

Sept.

-| 0) ~

1 i i O

r^

i

^

1

CM

+

N -

|

- 

 1 1 1 1 1 1 1 1 III

Oct. Nov. Dec.

- 150

CO

<

o

o

- 100 £

LU

a.

CO 

OC

LU 

t11)

- 50 I

i

2 

-0 \

t-

<(

OC

- 100 £

CO

2

OClo

a.

- 50 ^

LU

- 0

FIGURE 9. Remote sensing and water-budget values of evapotranspiration versus time, Gila River Phreatophyte Project area.

ET = {0.37+8.25[V(£/ioO)%]2 '45 }[(/l(103/12)].aO)

Several items will be noted by scrutiny of figure 9.

1. The evapotranspiration calculated spectrally is 

consistently high for reach 1 and consistently low for 

reach 2. To be an effective tool over large areas of landscape, the remote sensing estimate of evapotranspiration should be independent of any knowledge of ground 

condition, and for this reason the same spectral equation (equation 10) was used for both the phreatophytecovered reach 2 and reach 1, which was cleared.

2. The nature of the different covers in the two 

reaches shown in figure 9 must also be considered to 

gain an understanding of the departure noted in item 

1. Reach 1 was cleared of phreatophytes prior to the 

1968 photographic flights, and the bare areas produced 

by the clearing have been reoccupied to varying degrees by perennial and ephemeral herbaceous plants 

(Culler and others, 1972). The primary source of water 

for this replacement vegetation is from shallow soil 

moisture. The water availability for these plants is 

considerably less than for the phreatophytes on reach 

2, which predominately obtain their water from the 

water table. The relative near-infrared irradiance of

these replacement grasses is probably disproportionately higher than phreatophytes for a given amount of 

water use because they are short, yet dense, and refect 

greatly in the near infrared. Therefore, evapotranspiration calculated by relative near-infrared irradiance 

on reach 1 is assumed too high. This would also account 

for the underestimation of evapotranspiration on reach 

2 because of the same equation being used simultaneously for both sets of data.

3. Figure 9 indicates that the relation between k and 

relative near-infrared irradiance is closer for dense 

vegetation than for sparse cover. The evapotransp;ration calculated by irradiance has a closer correlatior on 

the phreatophyte-covered reach 2 (25 percent versur 39 

percent). This assumption is also shown in later 

examples.

4. An error source, which should be considered in 

analyzing figure 9, is the inherent deviation of the 

evapotranspiration derived from the water budget. 

These median evapotranspiration values have a deviation of as much as 40 percent (Hanson and others, 

1972, p. 326). This fact may also explain a portion of 

the deviation between the water-budget evapotranspiration and the remote sensing approach.
a.

(D rt9-

"2.

I. 

5

Q 

£>*

MEASURED EVAPOTRANSPIRATION,

IN INCHES PER 30 DAYS 

O -» ro u

(D

O 

>

r 

O

c

r

> _.

H

m

O

m

<

> o

O ro

H

13

>

Z

V)

2

I"

6

z

S -1 -7

<b ^ £.

3 o_ _ _

3 3J o, -

cr <

g. 8

O 

o>

O

Z

z 

^1

-or 

or

MEASURED EVAPOTRANSPIRATION, 

IN MILLIMETERS PER 30 DAYS

MEASURED EVAPOTRANSPIRATION, IN INCHES PER 30 DAYS

_. ro to ^ 01 oi

| | | | | |

MEASURED EVAPOTRANSPIRATION, IN MILLIMETERS PER 30 DAYS

<Q Q.. Q O

0) K ' B 

 D 3 O £

O < £. "D

S 0) Q) ^'

3 c" ""

3 S » O

_» 0) 3 3

S S 2 "

§?:.

O 3

3 ?

O "D

3 5

m 

X-D 

> z. 

>

H 

O z.

3 =t

ffi 0)

i 3
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O15

the coefficient of correlation for this equation is 0.1 

and

ET2 = 0.88( ET) + 108.2; (12)

the coefficient of correlation for the equation is 0.86; 

where

ET± evapotranspiration, reach 1, calculated as a

residual by the water-budget method and

ET2 = evapotranspiration, reach 2, calculated as a

residual by the water-budget method. 

Figure 10 is a refinement of the data presented in 

figure 9, and although figure 10 should be viewed with 

caution (for the reasons presented in the discussion of 

fig. 9), the correlation within each reach is good, and 

more research of this type would appear justified.

Hanson, Kipple, and Culler (1972) described an 

analysis in which the measured evapotranspiration for 

each month of record on each reach of the Gila River 

Phreatophyte Project is related to foliar cover for the 

respective reach by solving the following transformation of equation 8:

'k v rm v =

U'rmy 

I my

(13)

where subscripts r, m, and y represent the reach, 

month, and year, respectively. The months of available 

evapotranspiration data were used to compute the 

monthly k coefficients for various values of foliar cover 

seasonally as shown on figure 11.

The foregoing discussion has been directed toward 

an analysis of evapotranspiration based on the variability of near-infrared irradiance. The image of wet

bare soil on color-infrared film is dark green; thus, the 

dominant irradiance is in the green and red (fig. 3). The 

interpretation of this condition will require multiband 

analysis, which is not possible with the available photography. Most logically the comparison should be between near-infrared irradiance and that component of 

evapotranspiration directly related to plant volume 

that is, transpiration. Because evapotranspiration includes both transpiration from plants and evaporation 

from the soil, transpiration can be calculated by subtracting evaporation from total evapotranspiration. A 

comparison has been made, although direct soilevaporation data are not presently available for the 

Gila River site. Data for this area can be estimated by 

using published values from evapotranspirometers operated near Yuma, Ariz. (McDonald and Hughes, 1968, 

table 6).

In order to define the relationship between transmration CP) and k, it was necessary to subtract the evaporation (E) calculated for the Yuma site from the 

evapotranspiration computed from the water budget on 

the Gila River:

?=ET-E. (14)

A consumptive-use coefficient (k'} independent of 

evaporation may then be defined by modification of 

equation 8:

,, T k = -. (15)

Figure 12 shows the relationship between k' and 

relative near-infrared irradiance for the Gila River

1.8

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

From Hanson, Kipple, and Culler 

(1972, fig. 7).

I 200

^ 15 °§1

H«

 100

UJ Q_

 50

 0

 DC

111 UJ

FIGURE 11. Monthly consumptive-use coefficients for areas of indicated percent of foliar cover 

of phreatophytes, and average monthly consumptive-use factor.
O16 GILA RIVER PHREATOPHYTE PROJECT

EXPLANATION

2.0

g1.5 

O

r 1.0

> 0.5

_i

x 

h

0 20 40 60 80 100

RELATIVE NEAR-INFRARED IRRADIANCE, IN PERCENT

Relationship between k' and data for uncleared subreach (2b)

Relation between k' and pooled data 

for cleared (2a) and uncleared (2b) 

subreaches

Data from energy-budget station, representing especially dense vegetation, is included in both regression analyses

o 

Subreach 2a (cleared of phreatophytes)

A 

Subreach 2 b (uncleared of phreatophytes)

 

Energy budget station

R 

Relative near-infrared irradiance

k' 

Monthly consumptive-use coefficient

r 

Coefficient of correlation

" y-x 

Standard error of estimates

2 

Subscript, reach 2

2a 

Subscript, subreach 2a

FIGURE 12. Monthly consumptive-use coefficient versus relative near-infrared irradiance.

Phreatophyte Project. The equations shown on figure 

12 are shown below:

k'2a = 0.046(£)-1.35 (16)

(the coefficient of correlation is 0.80, and the standard 

error is 0.33) and

'o = 0.041(£)-1.14 (17)

(the coefficient of correlation is 0.77, and the standard

error is 0.32)

where

k'za = monthly empirical consumptive-use coefficient (independent of soil evaporation) for 

subreach 2a;

k'2 = monthly empirical consumptive-use coefficient (independent of soil evaporation) of 

all of reach 2; and 

fi = relative near-infrared irradiance.

The data (k' versus $) for subreaches 2b (cleared of 

phreatophytes with a partial cover of annual vegetation) and 2a (phreatophytes undisturbed) and one data 

point computed from an energy-budget station located 

on the project are plotted on figure 12. Equation 16 is 

based on data from reach 2a and the energy-budget 

station. Equation 17 is based on data from subreaches 

2a and 2b and the energy-budget data; this was developed to test the effect on the regression of the 

sparsely vegetated subreach 2a. The standard error is 

less for equation 17 than for equation 16 because of the 

greater number of data points. However, the coefficient 

of correlation is higher for equation 16, indicating that

the relation between k' and irradiance is closer for 

dense vegetation than for sparse cover.

It must be emphasized that the statistical significance of equations 16 and 17 are not great because of 

the small number of data points. This illustration is 

not meant to show the precise relationship between k' 

and &, but rather to show the development of an approach to evaluating evapotranspiration.

A different approach to correlating spectral signature to evapotranspiration was made using £ BlaneyCriddle consumptive-use curve modified from Erie, 

French, and Harris (1965, fig. 10). The consumptiveuse factors (f) were evaluated for the project area. The 

tests were performed using a grain sorghum crop adjacent to the Gila River Phreatophyte Project during its 

growing season. The regression equation describing 

the relationship between consumptive use an d & is

u = 0.0075 (#)-0.29 (18)

(the coefficient of correlation is 0.84, and the standard 

error is 0.09).

A refinement using a modified j$ parameter was 

made of the sorghum crop photography (figs. 13, 14). 

The spectral signatures was modified by the equation

(19)

where

/ = modified relative near-infrared irradiance, 

q = subscript, bridge reading for any photographic 

mission,
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O17

0.5

Irradiance prior 

to plant emergence

1968 Consumptive-use data modified 

from Erie, French, and Harris 

(1965, fig. 10)

£t 

UJ

10oCO

cc

LU

K8 UJ

 6

 2

FIGURE 13. Consumptive use of water and modified relative near-infrared irradiance versus time for grain sorghum during

1968 growing season.

0.5

Q

It 0.4

UJ

0.3

UJ 

COD 0.2

UJ

>

KQ_

| 0-1

o 

o

EXPLANATION 

Modified relative r nfrared

Consu

Coeff

r- 12 > 

Q

tr. 

hioS

CO

tc

UJ

- 8 uj

- 6

 4

- 2

- 0

0 20 40 60 80 100 120 

MODIFIED RELATIVE NEAR - IN FR ARED IRRADIANCE IN PERCENT

FIGURE 14. Regression relation between consumptive use of water and modfied 

relative near-infrared irradiance for grain sorghum during the 1969 growing 

season.

a = subscript, data from a sampled plot of an evergreen species of saltcedar (Tamarix aphylla) 

located on the project area.

/ was then related to the consumptive use; the regression equation is

w =0.0049 (/)-0.14 (20)

(the coefficient of correlation is 0.93, and the standard 

error is 0.06).

The parameter T presents the amount of radiance of 

the grain sorghum crop as a function of two presumed 

constant radiating surfaces, and although this assumption is not exactly true, particularly with regard to the 

saltcedar, this empirical ratio does show promise. The 

increase in the coefficient of correlation and the decrease of the standard error of estimate between equations 18 and 20 seem to verify this. Equation 19 represents a method of calibrating for the change of chrcma
O18 GILA RIVER PHREATOPHYTE PROJECT

(and to a lesser extent the hue) as sensed by the film. 

Ideally this should be done by using a series of large 

panels with precise reflectance characteristics, which 

could be photographed and used to calibrate the spectral data.

DEPTH TO WATER-SOIL MOISTURE

Vegetation is dependent upon water for growth. This 

water may be obtained from the water table, from soil 

moisture, or from both of these sources. Photographic 

remote sensing is a means of approximating a canopy 

measurement, but this technique does not allow one to 

determine the source of the available water.

One of the principal objectives of this study was to 

determine if a photographic remote sensing technique 

could be developed for measuring depth to ground water. This was found impractical. Figure 15 shows the 

depth to water versus relative near-infrared irradiance 

for the 15-acre (6-ha) vegetation areas surrounding 14

observation wells located in reach 2 of the Gila River 

Phreatophyte Project. The regression equation for this 

relationship is

W=-0.64 (£)+45.4 (21) 

where

W=depth to water (table level, in feet). 

The coefficient of correlation is 0.67. The coefficient of 

correlation associated with equation 21 is not high, but 

an inverse relationship between depth to vater and 

irradiance is apparent for depths of less than 30 ft (9 m). 

The relationship between soil-moisture depletion, 

which results in plant stress, and the resulting change 

in spectral signature is implied by figure 16, which 

shows the £ and & signature curves for 1968 for the 

dense saltcedar growth located in grid 24-2 (see fig. 1). 

Below the $ curve is a plot of a quantity of wrter in the 

capillary zone (defined for this analysis as the quantity 

of water, in inches, from the water table to 3 ft (0.9 m)

30.0

25.0

20.0

15.0

10.0

5.0

 8

 4

10 20 30 40 50 60 70 80 

RELATIVE NEAR-INFRARED IRRADIANCE, IN PERCENT

EXPLANATION

W 

Depth to water, in feet

R 

Relative near-infrared irradience

Coefficient of correlation

90 100 

Data for June 27, 1968

Standard error of estimate

Observation well

Relative neer-infrared irradiance was measured 

in a 15-acre area eround each well

FIGURE 15. Depth to water versus relative near-infrared irradiance, reach 2 of the Gila River Phreatophyte

Project.
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O19

100

90

80

70

I2

UJ

O 60

DC 

UJ 

Q.

40

30

20

10

2

<

°- 0.5

>

Q 0.4

CO 

UJ 1

O

2

~ 0.3

< ft 13.0

^

0-. Z 12.0

<

O 2

- ui" 11.0

DC 2

UJ o

S N 10.0 

§

EXPLANATION

R 

Relative near-infrared irradiance

r 13

- 12

- 11

DC 

lOff

to

DC

9 ^

ID 

I

13_j

i

2

h- 8

- 6

- 5

2

350 O 

N

300 5- £

o=J

2!

£-

L- 250 h

Jan. T Feb. I Mar. I Apr. I May I June I July I Aug. I Sept. I Oct. I Nov. 1 Dec^I

1968

FIGURE 16. Relative near-infrared irradiance, relative red irradiance, evaporation, and water in the capillary zone for a dense 40-a?re

(16-ha) saltcedar site, 1968.

above the water table). The effect of soil-moisture depletion is very evident on both the August 13, 1968, 

and August 28, 1968, flights. Another interesting feature of figure 16 is that the May 3,1968, flight was not 

significantly affected by the April 20, 1968, frost. The 

dense saltcedar at this site has a mean height of 13 ft 

(4 m), whereas the frost only affected vegetation less 

than 8 ft (2.4 m). Also shown in figure 16 is the pan 

evaporation (Class A, Weather Bureau), in inches per 

day, for part of the growing season during 1968. The 

various effects (temperature, radiation, and so forth) 

which contribute to pan evaporation are probably also 

contributing factors for the anomalous August signature. The confusion as to the relative effects of different 

hydrologic parameters on spectral signature emphasizes the need for more research, particularly research of a quantitative nature.

Figure 17 shows the respective $ and & signatures 

for two 15-acre (6-ha) sites of different types of vegetation during 1968 and 1969. The lower diagram shows a 

mesquite site which has an approximate 50 percent 

foliar cover, the corresponding accumulated soil moisture above the water table, and the water-table elevation. The upper diagram depicts some data for a dense 

(nearly 100 percent foliar cover) saltcedar area.

The respective signature for the two sites show that 

during the summer months the $ is greater than the & 

at the saltcedar site, while the $ is generally less than 

the G at the mesquite site. A correlation between foil 

moisture or water table is not immediately apparent 

from the $ and & signatures.

VEGETATION

The two spectral parameters of leaf reflectance, relative near-infrared irradiance (&) and relative red irradiance (d?), can be used as a tool for the timeindependent discrimination of plant species and a^ a 

means of monitoring the change in vegetation status 

with time.

SIGNATURE DISCRIMINATION

The spectral signature is defined in this report as the 

relationships between the relative irradiances C&, &, 

and 6) for a particular object. These relationships are 

variable with time and changing conditions. The more 

traditional definition is given in the "Glossary." P; scrimination of vegetation by spectral signature can be 

achieved using the two spectral bands, $ and &, which 

are functionally dependent on vegetation status.
CUMULATIVE DEPTH TO WATE R,

SOIL MOISTURE, D J,N««^I'K,«

IM IMrHF«? BELOW LAND

IN INCHES SURFACE

Ol Ol Ol Ol

0) Ji 10 O

~i i r

0> Ol A W

i i r

A AND G. IN PERCENT

-»WCO*«O101 svlCO(O 

OO OOOOOOO O

1II1IIIr~

CUMULATIVE

SOIL MOISTUR

IN INCHES

<D

O) ~

» «_

>

o 

§

2.

- Z

O ~

CD _

IS3 IS3 KJ IS3 KJ -

00 0) ^ IS3 O 0

i i i i 

-^ CJ ^ W

o o

O O DEPTH TO WATER, 

CUMULATIVE IN METERS, 

SOIL MOISTURE, BELOW LAND 

IN MILLIMETERS SURFACE

I I I I I 

] I 1 

o 1 o 

O O o 

CUMULATIVE 

SOIL MOISTUR

IN MILLIMETER
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O21

TIME-INDEPENDENT SIGNATURE

A series of time-independent linear relations for P. 

versus (j was found for all of reach 2 of the Gila River 

Phreatophyte Project and five vegetation types located 

in the adjacent to reach 2 for all photographic missions 

flown in 1968 and 1969. The regression equations, coefficients of correlation, and standard errors of estimate for each vegetation type are shown in table 1. The 

relationships between ft and (j are graphically illustrated in figure 18.

The same type of analysis was performed for the vegetation in and adjacent to the Cibecue Ridge area and 

is presented in table 2 and figure 19. Several conclusions can be postulated from the linear relations shown 

on figures 18 and 19.

1. The sparse upland vegetation creosote bush and 

whitethorn differs from the other vegetation shown 

in figure 18. The slope of the regression equations and 

the coefficients of correlation (table 1) distinguish this 

type of ground cover. The nonuniformity of spectral 

signature between the upland vegetation and the other 

vegetation types shown in figure 18 mainly is because 

the creosote bush and whitethorn have a foliar cover of 

10-25 percent and a low moisture availability. The 

predominant signature that the film records is actually 

a combination of bare ground, cacti, litter, and shortlived annuals that form the understory.

2. The dense saltcedar, cultivated grass, and all 

saltcedar and mesquite in reach 2 show a general trend 

of diminishing ft spectral signatures in that order. The 

trend from dense saltcedar to mesquite is the expected 

tendency; the more open mesquite community has a

lower $ signature than the saltcedar community. The 

regression equation for the vegetation in reach 2, 

which is a combination of mesquite and saltcedar, is 

between the regression lines of the two tree types. The 

cultivated grass has a higher ft than any veget&tion 

type shown except that for dense saltcedar; the grass 

has a foliar cover of about 50 percent and is subject to 

little or no stress owing to irrigation. The d^nse 

saltcedar community is adjacent to the river, where the 

average depth to water is 10 feet (3.0 m) below the land 

surface, and has the highest £ signature because of the 

high water availability.

3. The slope of the regression lines for the denser 

vegetation types is roughly parallel, indicating that 

the rate of ft versus (j change for vegetation with high 

moisture availability is fairly constant. Low moisture 

availability, as shown by the creosote bush and 

whitethorn signature, tends to decrease the slope.

4. The vegetation types of the Cibecue Ridge area 

(fig. 19) show the same trends as the vegetation in and 

adjacent to the Gila River Phreatophyte Project, with 

an increase of ft signature from bare ground to 

nonhomogeneous grass sites to the juniper-pinyon 

stands and, finally, to the denser ponderosa pine.

5. The very low coefficient of correlation of the grass 

sites in figure 19 is due to the nonhomogeneity of sampling; the six grass sites used have a foliar cover of 

between 5 and 20 percent. An added signature error is 

encountered because of the large percentage of mulch, 

which in many cases obscures the grass. This is also 

true to a lesser extent of the bare-ground signature.

6. The ponderosa pine and juniper-pinyon regresTABLE 1. Time independent analysis using color-infrared film for vegetation in the Gila River Phreatophyte Project and adjoining areas

[Data used in analysis are from nights made in 1968-69, except as indicated; for a more complete statistical analysis see table 5]

Vegetation

Number

Area of Equation 

(in acres) flights number

Regression 

equation

Coefficient

of 

correlation

Stendard 

error

of 

estimate

Reach 2 of the Gila River Phreatophyte Project area

25

25

22

23

24

£ = -0.91 «3) + 73.9

#-- .89(6) + 71.0

-0.92

- .89

- .90

7.9

7.6

7.9

Adjacent area

Creosote bush (Larrea tridentata)

Whitethorn (Acacia constricta I - .

_____ 66

22

24 25

27

8

K

R

ft

--0.811(5) + 63.9

= -1.01 (6) + 82.3

= -1.02 (6) + 96.2

-0.86

 .51

- .91

- .99

6.8

7.7 

9.4

2.4

'Owing to clearing of reach 2 during 1969, the sample area changes. The sample areas and their corresponding flights are given below: 

2Only 1968 data used.

Dates

of 

flights

Mar. 22, 1968-Mar. 6, 1969 _ Mar. 6-June 17, 1969 __. 

June 17-Nov. 20, 1969 __.

Number

of 

flights

16

4

5

Acreage of 

saltcedar 

sampled

1,380

1,020

800

Acreage of 

mesquite 

sampled

960

380

190
O22 GILA RIVER PHREATOPHYTE PROJECT

100

90

80

UJ

O

z

< 60

Q

CC 

CC

S 50

CC

CC 

u_

? 40

i CC

UJ

30

CC 20

10

\

\

EXPLANATION

Dense saltcedar

Cultivated grass

All saltcedar in reach 2

Average radiance in reach 2

All mesquite in reach 2

 <-H I-I-H-I

Whitethorn 

Creosote bush

10 20 30 40 50 60 70

RELATIVE RED IRRADIANCE, IN PERCENT

80 90 100 

Data from Table 1

FIGURE 18. Regression relations between relative near-infrared irradiance and relative red irradiance 

among vegetation types in and adjacent to the Gila River Phreatophyte Project, 1968 and 1969.

sion equations (table 2) indicate that the spectral signature for ponderosa pine is more nearly constant and 

has less variation than juniper-piny on.

The spectral signature of the total ground cover may 

well be used as a remotely sensed index of hydrologic 

activity. The signature is an integrated index of the 

actual plant morphology and pigment chemistry, 

which is influenced by many direct and indirect hydrologic parameters such as radiation, temperature, 

ground-water level, and available soil moisture.

During the period of vigorous plant activity on the 

Gila River Phreatophyte Project (May through August 

for most vegetation, April through September for dense 

saltcedar), the time-independent signature of the vegetation types were related (fig. 20). This illustration 

demonstrates that discrimination of the primary vegetation types (dense saltcedar, saltcedar, and mesquite) 

on the Gila River Phreatophyte Project is possible with

these parameters during this seasonal period. The 

sparse upland vegetation cannot be distinguished because of the reasons given in conclusion 1.

The signature equations expressed for saltcedar and 

mesquite in table 1 do not show a large difference in 

the $ versus & signature between the two plrnt varieties. However, these signature equations represent 

very large areas where foliar cover (the amount of 

ground covered or shaded by the vegetation foliage) 

varies from 0 to 100 percent and the underlying cover 

and soil type vary considerably. These variabilities 

tend to mask the actual ft versus & signature of the 

specific plants. The plant signature must be isolated 

from the contribution of underlying vegetation and 

ground irradiance. Horizontal photography at a distance of approximately 50 feet (15 m) from a densely 

foliated saltcedar and a similar mesquite was used to 

determine the ft versus & signature for the two types of
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY

TABLE 2. Time-independent analysis using color-infrared film for vegetation in the Cibecue Ridge area

[Data used in analysis are from flights made in 1971; for a more complete statistical analysis see table 5]

O23

Vegetation

Juniper-pinyon pine (Juniperus osfeos; 

Grass sites (Blue grama, Boueteloui 

weeping love grass Eragrostis cw

perma, Pinus edidis} ____ 

i gracilis; 

"vula} _ _

Number 

of 

flights

6 

6

6 

6

Number of 

sample points 

per flight

5 

10

30

15

Equation 

number

29 

30

31 

32

Regression 

equation

ft = -0.85(6) + 88.3 

# = - .64(6) + 70.1 

& = - .42(6) + 55.4

Coefficient 

of 

correlation

-0.86 

- .94 

- .68

- .92

Standard 

error of 

est:mate

2.9 

3.2 

5.5

2.2

100

90

I- 80

in

o

DC 

111

t 70

m 

O

z

< 60

O

<

DC 

DC

Q 50

40

DC

30

20

10

EXPLANATION

Ponderosa pine

Juniper-pinyon

Grass sites

Bare-ground sites

10 20 30 40 50 60 70 80 90 100

RELATIVE RED IRRADIANCE, IN PERCENT Data from Table 2

FIGURE 19. Regression relations between relative near-infrared irradiance and relative red irradiance 

among vegetation types in and adjacent to the Cibecue Ridge area, summer 1971.

vegetation. The regression equation for saltcedar is

£ = -2.30(6) +109.0, (33)

and the coefficient of correlation is 0.99. The regression equation for mesquite is

£ = -0.92 (6)+90.9, (34) 

and the coefficient of correlation is 0.94. Discrimination of vegetation type is possible with ground-level 

photography, because the rate of change of$ versus &

with ground-level photography for saltcedar is approximately twice that of mesquite. The given slope of the 

ft versus dr equations for the two vegetation type*" illustrated cannot be directly equated to aerial photography, because of the increased contrast ratio which is 

used to compensate for haze in aerial photography. 

This effect will be discussed in the section "Gamma." 

The signature equations (table 1) for these two types of 

vegetation are integrated readings of the total ir-
O24 GILA RIVER PHREATOPHYTE PROJECT

100

90

80

70

60

DC 

DC

Q 50

DC

Z 40

z

LU 30

DC 20

10

EXPLANATION

Dense saltcedar

D 

Saltcedar

Mesquite

Whitethorn

Creosote bush

Vegetation zone

10 20 30 40 50 60 70 80 90 100

RELATIVE RED IRRADIANCE, IN PERCENT

FIGURE 20. Relative near-infrared irradiance versus relative red irradiance for five vegetation types in 

and adjacent to the Gila River Phreatophyte Project, summer 1968 and 1969.

radiance from the photographed surface. For precise 

discrimination of plant type by aerial photogaphy, the 

relative contributions of each ground parameter 

(short-lived ephemeral plants, soil, water, and others) 

should be evaluated.

TIME-DEPENDENT SIGNATURE

The spectral signature change with time is used to 

monitor a dynamic phenomenon, and this precludes its 

use as a predictive tool; however, a model curve can be 

used to determine the departure of the spectral signature from the expected value, thus enabling the researcher to see trends and anomalies which should be 

investigated. The variability of the $ and <j spectral 

signature change with time is shown in figure 21; ft, G, 

and the modeling curves are given for all of reach 2 

during 1968 and 1969.

The general model curve used in this study has four 

distinct segments. First, is a linear segment for the

mean spectral signature during the period of dormancy 

of the deciduous vegetation, from late fall to early 

spring (4-1' on the/?' curve). Two nonlinear segments, 

1-2 and 3-4, on the R' curve (fig. 21) represent the 

transition from winter dormancy to high summer plant 

activity and from high summer plant activity to winter 

dormancy, respectively; these segments are described 

by equation 35. The last is a linear segment (2-3 of the 

R' curve) derived by assuming linearity between the 

two peaks of the bimodal curve from equation 35. 

These peaks occur on May 19 and August 11. This 

segment is assumed to represent the average summer 

maximum plant activity value.

The generalized form for the equations describing 

the model curves are given below:

R' =[H(sin N+l/5 sin 3N) +P] x 100, (35)

where

R' = value of the model curve for relative near-
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O25

1968 1969

EXPLANATION

Relative red irradiance

Model curve for 

relative red irradiance

Relative near-infrared irradiance

1

Model curve for relative near-infrared irradiance; number 

indicates atimeofchange in 

the spectral signature of 

vegetation

FIGURE 21. Relative near-infrared irradiance, relative red irradiance, and model curves, reach 2 of the Gila River Phreatophyte

Project, 1968 and 1969.

infrared irradiance, in percent, 

H - the amplitude of the sine wave for relative

near-infrared irradiance, 

P = the mean relative near-infrared irradiance

during dormancy, and 

N a function of time, expressed as

where

d = the calendar-year day on which the photographic mission was flown [January 1,0 = 1; 

December 31, 0=365 or 366 (leap year)], 

D = calendar-year day for the end of dormancy determined by the low relative near-infrared 

irradiance, and

L duration, in days, for the period of plant vigor, 

determined by the change in relative nearinfrared irradiance, 

and 

G' = {/T[sin(180°+/n +

1/5 sin(1800 +37V')]+P'}xlOO, (36) 

where

G' = value of the model curve for relative red irradiance, in percent, 

H' = the amplitude of the sine wave, for relative

red irradiance, 

P' = the mean relative red irradiance during

dormancy, and 

N' = a function of time, expressed as

where

D' = calendar-year day for the end of dormancy 

determined by the high relative red irradiance and

L' = duration, in days, for the period of plant 

vigor, determined by the change in relative 

red irradiance.

The actual model equations used for figure 21 are given 

below:

#'=[0.47(sin7V + l/5 sin 37V)+0.03]x 100, (37)

where

214

' = {0.48[sin(180°+7V') + l/5 sin

+ 3An]+0.76}xlOO, (38)

where
O26 GILA RIVER PHREATOPHYTE PROJECT

245

The accuracy of fit of the model equations to spectral 

data was checked by relating the data from the 233 

data points presumed to be representative of the 

change of signature with time to the predicted values 

from iheR' model equation 37 represented in figure 21. 

The standard error of estimate (Sv, x ) from the line of 

equality of 0.04 is shown in figure 22.

The time-dependent plant signature (£ and G) of 

various types of vegetation are shown on figure 23, and 

curves similar to those of figure 21 could be developed.

Several observations can be made with the aid of the 

model curve.

1. The time-dependent signature of large areas can 

be used to recognize and map vegetation types by using 

the sensing bands of satellite imagery in a similar 

manner to that proposed here for aerial photography. 

The 9- or 18-day cycle of LANDSAT is ideal for 

monitoring these changes.

2. Probably the most useful application of the model 

curve for aerial photography is to determine a schedule 

of flights to define the periods of growth at a particular 

study site. The minimum number of flights during a 

year to define the growth periods reliably for the Gila 

River Phreatophyte Project, as indicated by figure 21, 

is five: one taken during the period of dormancy 

(November through February), one during the spring 

transition period (April 1), two taken while the plants 

are vigorously active (June 1, July 30), and one during 

the fall transition period (September 30). For a long 

term investigation where only one flight per year is 

feasible, it would appear that sometime in June is the 

best choice for this particular area.

3. The transition period, from dormancy to vigorous 

activity as sensed by the G sensitivity range, was not 

the same in 1968 as it was in 1969. The transition 

period started approximately 1 month later and ended 

1 month earlier in 1968 than in 1969. The reflectance 

in the visual spectral range, of which G (red color) is 

functionally related, which responds to photosynthetic 

activity, is well documented (Knipling, 1970, p. 158; 

Gates, 1970, p. 226). This time lag in activity between 

1968 and 1969 is probably a function of many interrelated contributing variables such as radiation, temperature, rainfall, and others.

4. These modeling techniques were not successful on 

the Cibecue Ridge area, primarily because the predominant vegetation is perennial and the small signa3Only data from 23 of the 25 photographic missions flown during 1968 and 1969 were used 

in this computation because of the reasons given in footnote 2.

cc O

< DC

CC U-

"- I50

40

R= 1.00 R + 0.00 

r = 0.98 

x = 0.04

0 10 20 30 40 50

RELATIVE NEAR-INFRARED IRRADIANCE, IN PERCENT 

FROM TIME-DEPENDENT MODEL EQUATION

EXPLANATION

Relative near-infrared irradiance sensed by the film

R'

Relative near-infrared irradiance predicted 

by the model equation

r 

Coefficient of correlation

Sy.X 

Standard error of estimate

1968

A

1969

FIGURE 22. Regression relation between relative near-infrared irradiance determined from film and from a time-dependent model 

equation, reach 2 of the Gila River Phreatophyte Project, 1968 and 

1969.

ture change with time is largely over-shadowed by the 

inherent error in the photography.

SPATIAL COMPUTER ANALYSIS

The .6 versus G signatures can also be used as a 

means of spatial evaluation. Figure 24 shows the trichromatic coordinates subdivided into 10 percent subgroups. The first digit indicates the percent of .6, and 

the second digit the percent of £; for example, the 

number 72 means 70 percent relative near-infrared irradiance and 20 percent relative red irradiance. The 

remaining parts out of a hundred, in this case 10 percent, signify the relative green irradiance. Figure 25 

shows a computer printout of the ft and & trichromatic
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O27

l_ 90

u 80

O 70

UJ 60

Q.

2 50

" 4°

Z 20

5 10

NOTE: Irregularity of 

spectral signature 

mainly is due to 

irrigation

90

CULTIVATED GRASS STUDY SITE

Z 80

UJ

O 70

60

- 40 

'S 30 

§20 

< 10

1 I I I

WHITETHORN STUDY SITE

CREOSOTE BUSH STUDY SITE

i I i I II II 

ALL MESQUITE IN REACH 2

S ' O ' N

1969

ALLSALTCEDAR IN REACH 2

EXPLANATION 

G = Relative red irradiance 

& = Relative near-infrared irradiance

FIGURE 23. Relative near-infrared irradiance and relative red irradiance versus time for five vegetation 

types in and adjacent to the Gila River Phreatophyte Project area, 1968 and 1969.
O28 GILA RIVER PHREATOPHYTE PROJECT

100 PERCENT

100

90

tZ

U 80

OC

O 60

50

4°

30

< 20

10

EXPLANATION

ox

09

08

07

06

05

04

03

02

01

00

N18

17

16

15

14

13

12

11

10

G 

&

\HB

28\

27

26

25

24

23

22

21

20

= Relative near- infrared irradiance, in percent 

' = Relative red irradiance, in percent 

= Relative green irradiance, in percent

i] Multiply first digit by 1 0 to obtain percent R, 30 

percent; ^multiply second digit by 10 to obtain^ 

percent G, 60 percent; percent B = 100 - R - G

37\

36

35

34

33

32

31

30

46\

45

44

43

42

41

40

55\

54

53

52

51

50

64\

63

62

61

60

73\

72 82\

71 81 91\

70 80 90 [Q\

'0 10 20 30 40 50 60 70 80 90 100 

RELATIVE NEAR-INFRARED IRRADIANCE, IN PERCENT /

100 PERCENT 100 PERCENT

Ba n

FIGURE 24. Computer printout codes used in spatial analysis of relative 

near-infrared, relative red, and relative green irradiance.

coordinates for each 3.67-acre (1.49-ha) plot in the 

heavily vegetated reach 2, sampled photographically 

on June 27, 1968. Areas of near-equal signature can be 

contoured on the printout and compared with ground 

truth. Above the printout is a reduction of a composite 

print mosiac copied from the positive transparencies 

analyzed. The connecting lines link nine pairs of trichromatic coordinates (P, and G) with their corresponding position on the photograph.

Five photographs were combined for the photo mosaic 

(fig. 25). An important feature of the photomosaic is the 

inconsistent spectral signatures of the five sequential 

photographs. The mosaic demonstrates the necessity of 

quantitative adjustment of the signature data by some 

technique if a numerical approach to photographic 

evaluation is required.

Computer printouts of the type shown on figure 25 

were run for all reaches in each flight. This aided 

greatly in the spatial and temporal evaluation of the 

vegetation. The potential for computer interpretation 

of large areas by this method appears to merit considerable research.

VOLUME OF CANOPY FOLIAGE

Volume of canopy foliage is a difficult and expensive 

hydrologic parameter to measure by conventional 

means; it is conventionally defined in cubic feet per 

square foot of area. The technique used to determine 

this parameter on the Gila River Phreatophyte Project 

has already been outlined in the section "Vegetation 

Data Collection."

Thomas, Wiegand, and Myers (1967, p. 553) found 

that an exponential increase in reflectance was 

achieved by stacking individual leaves on one another. 

This enhancement of reflectance would imply that 

canopy volume could be estimated by photographic remote sensing, but the condition of stacked leaves might 

result in quite different reflectance relations than 

those of a natural plant canopy.

Figure 26 shows the relationship between nine 

canopy volume classes of saltcedar and three canopy 

classes of mesquite versus relative near-infrared irradiance from four photographic missions.

The photographic missions chosen represent the 

spring period of new leaf production (April 5), the stable summer period of dense foliage (May 31 and June 

27), and the fall transition period leading to dormancy 

(October 10). These periods are demonstrated on figure 

21 and discussed in the section "Time-Dependent Signature."

The data from the saltcedar classes (B) indicate that 

the relationship between canopy volume and $ is not 

linearly related. An important point, which must be 

considered while viewing figure 26, is that the vegetation description of canopy classes was done in 1965, 

while the photography used in the figure was taken in

FIGURE 25. Color-infrared mosiac showing spatial computer 

analysis by spectral signature for 3.67-acre (1.49-ha) plots in reach 

2, Gila River Phreatophyte Project, June 27, 1968.
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O29

IL* «»E» pnOEnrorwrn MOJCCT

I4L INFWWOr 

6-27-aa ELE»»TICTJ» OF PLIB4T IS BSffn

1 KILOMETER
O30 GILA RIVER PHREATOPHYTE PROJECT

1 1 1 1 1 1 1

EXPLANATION

 4- 5-68 Partial foliation 

_._._._ 5_3i-68 Full -foliation

 6-28-68 Full foliation

Average Range 10-10-68 Unfoliated 

number Range of of 1 Standard deviation

of canopy, height, t i

sampled in percent in feet Pooled standard devjation of irra_ 

plots of area (meters) Hi»nr» f«r th0 th roo * .,-,**

173

45

51

50-75

25-50

0-25

7 + 

(2.1)

7 + 

(2.1)

7 +

(2.1)

canopy classes;4-5-68 data is

7 percent

at

>- UJ

< m u.

O^uj 3.0

LL°a: 

oz<

LU 3 2.0 5 uj a

30"

J -S T-0

>QQLL

T i , . ,

\ I /

\ I '

f i \ H

i / //

i / /

1 / '/

 / y/

A. MESQUITE

i i i i i i i

0

CO

^ cc

- UJ

< UJ

il

U. ri

ft J

1 s

LL UJ

O t-

^^-^

O

 n

10 20 30 40 50 60 

RELATIVE NEAR-INFRARED IRRADIANCE, IN PERCENT

70

Average 

number 

of sam 

pled plots

51

31

166 

19

30

34

23

9

6

Range of 

canopy, 

n percent 

of area

75-100

75-100 

50 75

25-50

25-50

75-100

50-75

25-50

Range of 

weight, 

in feet 

(meters)

13+

(4)

13+

(4)

6.5 13 

(2-4)

6.5-13

(2-4) 

13+

(4) 

6.5-13

(2-4) 

0-6.5

(0-2) 

0-6.5

(0-2) 

0.65 

(0-2)

14.0

O 

O 

"- 13.0

LU

cc 

§ 12.0 

O

(n 

a: 11-0

LU

a.

t- 10.0

at

UJ 

LL

0 9-0

CD

0 8.0

Z

lu" 7.0

O

<

-I 6.0

O

LL

> 5.0 

O 

< 4.0

LL

O 3.0

LLJ

2

^ 2.0

O

> i.o

1 i l l i 1 1

\ /" "

: \ / / : \ / / i y j i - -- * i ^___ « _ -=.. =* * r" i \ -~ \~^^" '

\ \ i v \ \ i

\ \ \ tK

} 3 j-\ ]

1 / ^-^"^ ^^ ~^"~^ ^^po°lecl standard deviation of irra-

/ / \ \ canopy classes;4-5-68 data is 

/ / \ \ "*4 percent

<, _____ L v. w. .

\ ' , \v "\>>

B. SALTCEDAR

i i i i i i i

-4

- 3

 2

- 1

0 10 20 30 40 50 60 70

RELATIVE NEAR-INFRARED IRRADIANCE, IN PERCENT

FIGURE 26. Volume of canopy foliage versus relative near-infrared irradiance for photographic flights during 1968, reach 2, Gila

River Phreatophyte Project.

1968. The effect which this time lag had on the data is 

unknown. Several conditions and relationships are illustrated by figure 26. Mesquite volume (A) also seems 

to indicate a nonlinear relationship, although it is difficult to postulate any conclusions based on these data

alone, because of the limited number of canopy classes. 

It is apparent that although the canopy class versus 

irradiance curves are nonlinear, they all consistently 

exhibit the same general shape.

The transition period leading to dormancy, as shown
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O31

by the October 10 flight, shows that the irradiance is 

almost constant and apparently not related to canopy 

volume. But there is a general trend for greater foliar 

cover to produce higher irradiance, particularly in the 

larger volume classes.

A representative standard deviation for each data 

point for April 5 is graphically shown in figure 26. The 

pooled standard deviations (Dixon and Massey, 1957, 

p. 109) of the irradiance for saltcedar and mesquite are 

14 and 7 percent, respectively, which indicates that the 

error is too large for any precise conclusions.

Figure 27 illustrates the relative near-infrared irradiance of three saltcedar foliage classes versus time. 

This illustration shows that an increase in irradiance

accompanies an increase in volume of foliage. The 

pooled standard deviation of the largest class is 12 percent. Figure 28 illustrates a similar relationship for 

mesquite; the pooled standard deviation is 13 percent. 

This large error factor indicates that for the data used, 

it is not practical to use this technique to mearure 

canopy foliage.

FOLIAR COVER

Foliar cover is defined as the ground covered or 

shaded by a vertical projection of plant foliage, ^wo 

techniques of photographic analysis may be used to 

determine the percent of canopy intercept in an 

area visual examination and spectral signature.

100

90

80

Z 70

o

Z

< 60

DC 

DC

Q 50

UJ 

CC

40

30

20

10 -

1 Standard deviation

Jan. Feb. Mar. Apr. May June July 

1968

EXPLANATION

Aug. Sept. Oct. Nov. Dec.

SYMBOL

VOLUME OF CANOPY

FOLIAGE, IN CUBIC

FEET PER SQUARE

FOOT

3.6 (1.1 m 3 /m2 )

8.5 (2.6 m 3 /m2 )

11.4 (3.5 m3 /m2 )

NUMBER OF SAMPLED 

PLOTS

34

166

51

TOTAL ACREAGE 

SAMPLED

125 ( 51 ha) 

608 (246 ha) 

187 ( 76 ha)

FIGURE 27. Relative near-infrared irradiance versus time for three saltcedar volumes of canopy foliage classes, reach 2, Gila R: ver

Phreatophyte Project.
O32

100 

9O

Z 80

til 

U 

DC 

111 

OL

Z 70

U

z

5 60

Q

<

DC

DC

Q 50

£ 40

DC 

<

111

Z 30

20

10

GILA RIVER PHREATOPHYTE PROJECT

EXPLANATION

SYMBOL

VOLUME OF CANOPY

FOLIAGE, IN CUBIC

FEET PER SQUARE

FOOT

O.9 (O.27 m3 /m2 ) 

2.7 (O.82 m3 /m2 ) 

4.4(1.34m3/m2 )

NUMBER OF SAMPLED 

PLOTS

173

45

51

TOTAL ACREAGE 

SAMPLED

635 (257 ha) 

165 ( 67 ha) 

187 ( 76 ha)

Pooled standard deviation of irradiance for 4.4 ft^/ft2 canopy 

class during 1968 is 13 percent

1 Standard deviation

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec!

1968

FIGURE 28. Relative near-infrared irradiance versus time for three mesquite volumes of canopy foliage classes, reach 2, Gila River

Phreatophyte Project.

Visual examination generally requires mapping of 

canopy-intercept classes from aerial photography and 

field reconnaissance, planimetering, and computing 

the mean percent of canopy intercept in the area 

(Griffith and Howe, 1960). Spatial computer evaluation 

of imagery can aid greatly in the use of this technique, 

as shown in the recent studies by Denny, Morrison, 

Worthman, and Lucht (1971).

Photographic analysis using the spectral signature 

technique can probably be best accomplished by nearinfrared photographic sensing. Figure 29 illustrates 

the relationship between foliar cover and the £ signature for the 50 plots within grids 2-24 and 2-25 of the 

Gila River Phreatophyte Project. The regression equation describing the relationship is

C7=1.54(£)-29.9, (39) 

where

CI=foliar cover, in percent. 

The coefficient of correlation is 0.85. The variations

within the individual grids as shown in figure 29 are 

due primarily to two reasons.

1. Each plot has a different percentage of water 

showing through the canopy, which greatly affects the 

spectral signature registered on the film.

2. The actual distribution of vegetation types 

(saltcedar, mesquite, and grasses) is different in each 

plot, therefore yielding variability in the signatures.

Interception of precipitation by vegetal cover is a 

difficult hydrologic parameter to measure, but photographic remote sensing can be used to estimate both 

the spatial and temporal variability of interception. Interception is defined as the amount of rain or snow 

stored on leaves and branches that eventually evaporates back into the atmosphere. It is equal to the precipitation on the vegetation minus stemflow and 

throughfall (Langbein and Iseri, 1960, p. 12) and is 

basically a storage characteristic of the hydrologic 

cycle; it also is a function of other hydrologic, climatologic, and botanical variables, such as vegetation type,
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY 

100 i i i i i i l / 1 / 1 /

mm» <a>/oo /

EXPLANATION

O33

a. 

Z 60

CI = 1.54 #-29.9

Sy.x =17.7

r = 0.85

Plots located in grid 24-2

(fig. 1)

 

Plots located in grid 25-2

(fig- D

Relative near-infrared irradiance, in percent

CI 

Foliar cover, in percent

Syx

Standard error of estimate

r 

Coefficient of correlation

50

40

30

20

10

0 10 20 30 40 50 60 70 80 90 100 

RELATIVE NEAR-INFRARED IRRADIANCE, IN PERCENT

All data from photography 

of May 31, 1968; altitude 

3,600 feet

FIGURE 29. Foliar cover versus relative near-infrared irradiance for 50 3.67-acre (1.49 ha) 

plots, subreach 2b, Gila River Phreatophyte Project.

leaf area, wind velocity, and rainfall intensity and duration. As precipitation strikes a vegetative canopy, 

some is retained and distributed over the exposed surface area. If there is adequate precipitation, a quasiequilibrium is reached, whereby interception storage 

becomes stable and stemflow plus throughfall equals 

precipitation. The intercepted precipitation is then retained (stored) on the leaf and stem surface until after 

the storm or until the vapor-pressure gradient is high 

enough to initiate evaporation. For 100 percent canopy 

cover, interception of precipitation from an individual 

storm may be estimated using the curves of Kittridge 

(1948, fig. 12) or from the interception data of Molchanov (1960). Photographic remote sensing then can 

be used to approximate the percent of canopy cover the macroleaf count on a watershed, 

which is then used to determine the interception 

storage.

TECHNIQUES FOR ANALYSIS OF 

COLOR-INFRARED PHOTOGRAPHY

In the 5-year period 1967-71, 38 color-infrared photographic missions were flown over the Gila River 

Phreatophyte Project. During the summer of 1971, six 

color-infrared photographic missions were also flown 

over the Cibecue Ridge Watershed Study. The photographic missions were flown at altitudes between 1,500

and 60,000 feet (460 and 18,000 m) above land surface 

under many different climatic conditions using a variety of cameras, films, and filter combinations. This 

is shown in figure 30. The quantitative utilization of 

these diverse data required the development of both 

deterministic and empirical techniques of film standardization.

Figure 30 shows both the adjusted and unadjusted £ 

for subreach 2b from 1967 through 1971. The unadjusted £ is the parameter described in the section 

"Data Standardization and Sources of Variability." 

The adjusted ft has been adjusted for the variability 

due to the inconsistency in flying altitude, filter combination, processing techniques, or other contributirg 

factors. The effect of adjustment is illustrated by th« 

signature difference in unadjusted ^ between th? 

summer of 1968 and 1969. The difference between unadjusted signatures is not related to the vegetation 

condition because the phreatophytes were equally 

dense during both periods. The adjusted values for this 

same period are nearly equal, as would be expected if a 

correlation exists with ground conditions. Most of th« 

photographic flights during the 5-year period (1967- 

71) were flown at an altitude of 8,500 ft (2,600 m) above 

land surface; however, six flights were flown at 3,600 ft 

(1,100 m), and two at 60,000 ft (18,000 m); therefore, 

the adjustments described above are necessary.
IRRADIANCE, NEAR-INFRARED RELATIVE 

PERCENT IN 

Z O 

fapi 

3

T - 3 

o 

S SI 

viio HSAIH axAHdOxvanHd OHd xoar 
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O35

COST OF PHOTOGRAPHIC ANALYSIS

The cost in 1968 of using conventional techniques to 

assess the vegetation on the Gila River Phreatophyte 

Project (the technique is outlined in the section "Vegetation Data Collection") was approximately $6,600, 

while the cost of a color-infrared photographic mission 

and a complete computer analysis of the photographic 

data cost approximately $650.

BASIC PHOTOGRAPHIC CONCEPTS

The analysis of photographic remote sensing data 

requires a basic knowledge of the fundamentals of photographic science. Spectral sensitivity, density versus 

log exposure, and analytic optical density are photographic topics necessary for the type of analysis 

utilized in this report.

SPECTRAL SENSITIVITY

Spectral sensitivity, S(X), for color-infrared film is 

defined as follows:

S(X) = 1/E(X), (40)

where E(X) is the energy in ergs/cm2 of monochromatic 

radiation required to produce in the individual dye 

layer an equivalent neutral density, usually 1.0 (N.L. 

Fritz, 1969, written commun.). Equivalent neutral 

density (END) is defined as the visual density a dye 

layer would have if it were converted to a neutral gray 

by superimposing the minimum necessary amounts of 

dye in the other two dye layers (Todd and Zakia, 1969, 

p. 215-219).

The relationship between wavelength sensitivity 

and color formulation of color-infrared film was discussed in the section "Color-Infrared Film" and was demonstrated in figures 2 and 3.

Multigeneration photography such as received from 

NASA during the study refers to the fact that the 

color-infrared photography has been duplicated on 

color film for data analysis. A quantitative analysis of 

the data must include consideration of the sensitivity 

of the duplicated and duplicating films.

FILM DENSITY VERSUS EXPOSURE

The relationship between film density and exposure 

was discussed in the section "Color-Infrared Film" and 

shown in figure 4.

BASE PLUS FOG DENSITY

Base plus fog density (Z)0 ) may be referred to as the 

fog level of the film. It is the optical density obtained 

after processing film which has received no exposure 

and is the result of chemical reduction by the developer 

on unexposed grains. For example, the base plus fog

level for the cyan dye layer in figure 4 is an optical 

density of approximately 3.6. The actual base plus fog 

densities of the dye layers of film type 8443 analyzed 

with our equipment are shown below:

Cyan Do =2.76,

Magenta Do =1.38, and

Yellow Do = 2.22.

GAMMA

Gamma (y) is the slope of the linear portion of the 

characteristic curve:

AOD y=

Alog E

(41)

A perfect tone reproduction of a scene is obtained 

when the gamma is equal to 1 (for positive film), and 

a gamma less than -1 means that the scene is reproduced at a higher contrast. (This discussion applies to 

positive film only; a negative film would have a positive gamma.) Aerial films, such as color-infrared film, 

which are used for high-altitude photography usually 

have gammas of approximately -2.0. The reason for 

the low gamma of aerial films is because the original 

scene contrast is greatly reduced by atmospheric attenuation, and the lower gamma compensates for this. 

The gammas for the dye layers of film type 8443 as 

analyzed by our equipment were:

Cyan y= -2.00, 

Magenta y= -0.81, and 

Yellow y= -2.82.

DATA STANDARDIZATION AND SOURCES OF VARIABILITY

The use of color-infrared photography as a tool for 

the quantitative monitoring of ground-scene changes 

with time requires the standardization of the data 

taken from the film. Certain sources of variability from 

one photographic flight to another and within a roll of 

film can be controlled by consistent practices and careful planning. Controllable variables are camera, film, 

filter combinations, flying altitude, and certain procedures in the film processing. Variability in the data 

from these sources can to some degree be controlled by 

consistency, or if necessary, by mathematical adjustments. Other sources of variability, such as atmcspheric changes, film-emulsion variation, and filmprocessing changes and inconsistency, are less deterministic in nature, and the investigator has very little 

control over these sources of error. Variability in the 

data from these sources are very difficult to correct 

without elaborate instrumentation and calibration 

and, to a large degree, must be adjusted by an empirical approach if precise calibration is not available.

Theoretically, the standardizations of photographic
O36 GILA RIVER PHREATOPHYTE PROJECT

data for such variables as atmospheric attenuation, 

solar altitude, and filter transmittance should be made 

as exposure adjustments for the film gamma. This was 

not done for two reasons: (1) the high cost involved in 

precisely measuring the energy radiated from the 

ground scene and subsequently sensed by the film and 

(2) the high error involved in measuring hydrologic 

phenomenon such as evapotranspiration.

As pointed out previously (in the section "Photographic Data Collection"), each dye layer of the film 

was viewed as a relative radiometer, and the irradiance sensed in each dye layer was related to the sum of 

the irradiance sensed by the three dye layers. For 

clarification the reader should refer back to equations 

3, 4, 5, 6, and 7.

ANALYTIC OPTICAL DENSITY

Analytic optical density is defined as the optical density within the individual dye layers of a multidye 

layer film. The necessity for isolating the individual 

dye-layer spectral contribution is illustrated in figure 2 

by the spectral sensitivity curves for color-infrared 

film. It should be borne in mind that these curves (fig. 

2) are only for the film and do not show the effect of the 

instrumentation (filters, lenses, densitometer, and so 

forth) used for analysis. The color density measured by 

the densitometer in each selected wavelength band 

(fig. 7) is the sum of the radiation absorbed by all three 

of the dye layers.

Several techniques are available for isolating the 

exposure in each of the three dye layers. The process 

used in the following analysis involves selective exposure yielding a step tablet in this case a film strip 

graduated in transparency from one end to the other, 

for each dye layer of the film. The relative contribution 

of each dye layer in the three sensing bands was then 

isolated algebraically through the use of a matrix inversion (Evans and others, 1952, p. 441-447).

The step tablets were analyzed using the transmittance densitometer (Macbeth TD 402) by recording the 

three optical densities (R0d, God, B0d) transmitted at 

each exposure step of each step tablet. These were then 

plotted graphically with the major optical density (Rc 

for the cyan dye layer) versus the two minor optical 

densities (Gc and Bc for the cyan dye layer) for each 

step tablet (the slope of the major optical density of the 

dye layer taken as 1, fig. 31). The slopes of the major 

versus minor optical densities for each dye layer were 

then entered into a 3x3 matrix (A). The inverse (A" 1 ) 

of the matrix (A) was then taken. CA" 1 ) was then used 

for the three equations to convert the three integral 

optical densities (R, G, B) to their respective analytic 

optical density (C, M, Y). Matrices (A) and (A~ l ) and 

the resulting analytic optical density equations for film

type 8443 analyzed with our equipment are given below:

7? 7? ]

fl c fl m L£

(A)8443 = Gc Gm Gy =

Bc Bm Bv

and

C

M

Y

where

(A )g443 =

Therefore

« 8443

= (A8443

1.012

-0.185

-0.011 ^ 

1.000 0.065 0.01

0.184 1 .000 0.10

0.046 0.192 1.00

^

'8443 ^

-0.063

R

G

B

-0

5

8443

.008

1.032 -0.107

-0.195 1 .021

C8443 = 1.012CR)-0.063(G)-0.008(B),

5

5 (42)

0

__ 8443

(43)

(44)

8443

(45)

= -0.185CR) + 1.032(G)-0.107(B), end (46) 

= -0.0111R)-0.195(G) + 1.021(S). (47)

FILM TYPE CORRECTION

During the period of study for this report, two colorinfrared films were used. Film type 8443 wa? used in 

1967-69; after 1969, film type 2443 was used. The 2443 

film has three advantages over its predecessor: (1) better infrared color balance; (2) increased overall speed; 

and (3) improved consistency from emulsion to emulsion. The quantitative use of the photographic data required that the data from film type 2443 be corrected to 

the same spectral response as film type 8443. The 

technique for accomplishing this was to premultiply 

the inverse type 8443 [(A"1 ^443] by a conversion matrix, [CA)844s], which was supplied by Kodak (N. L. 

Fritz, written commun., 1972):

where

'2443

x8443. 

J2443"

2443] X [(A

1.094 -0.024 -0.009

0.000 0.997 0.003

0.011 -0.029 1.043

(48)

(49)

It must be emphasized that the technique illustrated

by equations 48 and 49 is not precise but is a usable

approximation.

The solution for (A"1 ^443 is given below:

)2443 =

1.112 -0.092 -0.015

-0.184 1.028 -0.104

0.005 -0.240 1.065

(50)

2443

The analytic optical densities (C, M, Y) for film type 

2443 were derived as shown below:

therefore,

C

M 

Y

2443

(51)

2443
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O37

0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1-80 2.00 2.20 2.40 2.60 

MAJOR OPTICAL DENSITY, BLUE

2.PQ

0.60

0.40

0.20

Magenta dye leyer

0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80

MAJOR OPTICAL DENSITY, GREEN

2.00 2.20 2.40 2.60 2.80

0.80

0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 2.20

MAJOR OPTICAL DENSITY, RED

EXPLANATION

D 

Blue minor optical density

A 

Green minor optical density

O 

Red minor optical density

FIGURE 31. Major versus minor optical densities for the three dye layers of film type 8443.

2.40 2.60 2.80

= 1.112CR)-0.092(G)-0.015(B), (52)

= -0.184(72) +1.028(G)-0.104(B), and (53)

^2443 = 0.005(72)-0.240(G) + 1.065(B). (54)

These analytic optical densities were then converted to

relative irradiance by the technique already discussed 

in the section "Photographic Data Collection" and by 

use of the equations shown in that section equations 

3, 4, 5, 6, and 7.
O38 GILA RIVER PHREATOPHYTE PROJECT

FLIGHT-ALTITUDE CORRECTION

The atmospheric attenuation of radiation varies as a 

function of wavelength and altitude. An atmosphericattenuation model based on the U.S. Standard Atmosphere, 1962 (Elterman and Toolin, 1965), can be used 

to determine the transmittance of the atmosphere as a 

function of both wavelength and altitude. With vertical 

photographs an attenuation factor may be described by 

the following equation (Elterman and Toolin, 1965):

r__/i=exp[-(TAl rfcj)], (55) 

where

T ^ atmospheric transmittance of a particular 

wavelength, between the ground-surface 

elevation and the camera altitude, 

Thi extinction optical thickness at the camera

altitude, and

r/,2 = extinction optical thickness at the groundsurface elevation; extinction optical 

thickness is defined as the mean value of 

the sum of the Rayleigh scattering coefficient, and the ozone absorption coefficient 

at a particular altitude.

Applying this equation to the peaks of the dye-layer 

sensitivity curves (fig. 2) at 0.55 jam, at 0.65 jam, and 

0.72 (Jim, the atmospheric transmittances at 3,600, 

8,500, and 60,000 ft (1,100, 2,600, and 18,000m) for 

these peaks of the sensing ranges are computed. These 

are shown on table 3. The peaks were used because 

they are the wavelengths where the greatest exposure 

results for a given unit of radiant energy. The altitude 

correction needed to convert 3,600- and 60,000-ft (1,100 

and 18,000 m) flights to the standard 8,500-ft (2,600 m) 

flight is then tabulated in table 3.

The values obtained from equations 6, 7, and 8 are 

therefore corrected (for the photographic missions 

which were flown at altitudes other than 8,500 ft 

(2,600 m)) by their respective atmospheric correction 

factors as follows:

R =RVAC/(RVAC + GvAm + BvAy ),

& =GvAm/(RvAc + GvAm + BvAy), and

B =BvAy/(RvAc + GvAm + BvAy),

(56)

(57)

(58)

where

v = subscript indicating that the relative irradiance data (R, Cr, or B) was from a 

nonstandard flight altitude or filter combination and that the data are corrected to 

standard values by the equation,

Ay = altitude correction factor for yellow dye 

layer,

Am = altitude correction factor for magenta dye 

layer, and

Ac = altitude correction factor for cyan dye layer.

TABLE 3. Atmospheric transmittance at different flight altitudes for 

the peak wavelengths of dye-layer sensitivity of color^infrired film

Peak wavelength 

Dye sensitivity 

layer (in micrometers)

Percent of atmospheric

transmittance at flight

altitude

Correction factors for

conversion of nonstandard fl;ght altitudes 

to standard flight 

altitude of 8,500 ft

3,600 ft 8,500 ft 60,000 ft 3,600 ft 60,000 ft 

(1,100ml (2600ml (18,000ml (1,100ml (18,000ml

Yellow _

Magenta ______

0.55

.65

.72

88.5

90.1

90.9

84.3

86.7

87.8

77.7

82.7

84.9

0.953

.962

.966

1.085

1.048

1.034

This correction as well as the corrections in the following sections could be improved by vectorially multiplying the atmospheric transmittance by the spectral 

sensitivity at small wavelength increments (0.01 ju,m) 

to achieve a spectral sensitivity at a specific altitude 

(U.S. Air Force, 1968, p. 91-96). However, this procedure does not compensate for the effect of increased sky 

luminance with height on aerial photography. Sky 

luminance affects both the highlights and shadows 

equally, thus in effect causing a lower contrast ratio 

and a narrower density range (Smith and Anson, 1968, 

p. 312). An expansion of the density scale (fig. 4) to 

compensate for sky luminance was not necessary because the relationship between the illuminance sensed 

by each dye layer was not affected significantly.

FILTER CORRECTION

The filters chosen as a standard for vegetation differentiation with color-infrared film were the Wratten 

numbers 12, CC20B, and CC30M (Culler, 1970). Filter 

combination was chosen as a result of testing procedures suggested by Fritz (1967). The testing procedure 

required a series of photographs with various combinations of filters and exposures. A cliff lookir? over a 

collection of plants typical of the Gila River flood plain 

was used as a platform to simulate aerial photographic 

conditions. A Graflex XL camera with a 100 mm Zeiss 

Tessar lens, aperature to f/3.5, shutter speed to 1/500, 

and 70 mm film type 8443 was used for this photography. Photographic testing equipment was not readily 

available, and comparisons were made by visual inspection. A filter combination of Wratten numbers 12, 

CC20B, and CC30M provided the best visual color resolution for identifying flood-plain vegetation. This 

"standard" filter pack was adapted beginning: with the 

1968 photography. Figure 32 shows the transmittance 

curves for the W12, CC20B, and CC30M filters and the 

product curve for this combination. Optical filters obey 

the Bouguer-Lambert Law, which states that the 

transmittance of a series of filters is the product of the 

transmittance of each filter.

The need for color correction filters to enhance the 

spectral signature on color-infrared photography was 

explored by Pease and Bowen (1969). Figure 2 shows
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O39

100

90

80

70

- 60

<

I- 50

H

5 

(/)

< 40

CCIcc

HI 30

20

10

W12 Filter

/

Product filtration

t '/

Principal sensing

range of the _^J^

yellow dye laye3r H

Principal sensing 

range of the 

magenta dye layer

Principal sensing 

range of the 

cyan dye layer

0.50 0.60 0.70

WAVELENGTH, IN MICROMETERS

0.80 0.90 

Data from Kodak (1970) and N. L. 

Fritz (written commun., 1971)

FIGURE 32. Filter transmittance versus wavelength for the standard filter pack used in aerial photography in the study areas. .

lower sensitivity relative to the other layers of the cyan 

dye layer of color-infrared film. The purpose of the color 

correction filters used as a standard in this study is to 

decrease the sensitivity of the magenta and yellow dye 

layers and therefore proportionally increase the cyan 

sensitivity, which has the lowest sensitivity as shown 

in figure 2. In this study the two spectral ranges of 

interest (because of the physiological and plant chemical relationship) these sensed by the cyan and the 

magenta dye layer must be accentuated to yield a 

signature more useful for the determination of plant 

condition. Figure 32 shows that the filter pack transmits approximately 65 percent in the cyan sensing 

range, 45 percent in the magenta, and 30 percent in the 

sensing region of the yellow dye layer (all percentages 

refer to the percent radiation transmitted which is incident to the filter pack in each of the three different 

spectral ranges); the filters thereby compensate for the 

lower sensitivity of the cyan and increase the magenta 

dye layer sensitivity with respect to the other two dye 

layers. This filter pack accentuates the two dye layers 

used as indices of plant vigor. This can be visualized by 

noting figure 4; the differential shift resulting from

this filter pack would diminish the yellow dye la}Ter 

image with respect to the other two.

During 1967 only a number 12 filter was used. The 

two NASA flights (September 30, 1969, and September 

11, 1970) included in this analysis used a number 15 

only. Filter corrections were therefore necessary to 

convert to the standard filter pack used during most of 

the study.

The transmittances of the two color correction filters 

(CC20B, CC30M) were multiplied at 0.01 /mi intervals, 

and these products were summed over the high sensitivity wavelength range of the particular dye layer. 

The mean transmittance of the filters for each d;re 

layer was then calculated. These filter corrections were 

then used to adjust the nonstandard photographic 

flights by the same method used for atmospheric attenuation. No compensations were made for transmittance differences between the filter numbers W12 

and W15 because of similar transmittance properties 

(fig. 2).

The correction equations used for the filters is illustrated below for the near-infrared irradiance:

m +&vF), (59)
O40 GILA RIVER PHREATOPHYTE PROJECT

where

Fy = filter correction factor for the yellow dye

layer, 

Fm = filter correction factor for the magenta dye

layer, and

Fc = filter correction factor for the cyan dye layer. 

The actual values for the correction factors for the 

specific filters cited are given below:

Fc =0.73, 

Fm = 0.50, and 

Fy =0.35.

This filter correction could be improved by the vectorial 

multiplication of the filter correction and the spectral 

sensitivity at small increments of wavelength as proposed for atmospheric attenuation, but it was not done 

because of the limitations of the study.

SOLAR ANGLE CORRECTION

The effect of solar angle (<I» should also be considered 

when interpreting color-infrared photography taken at 

different times of year or day. The solar angle, the angular departure from the local vertical, can be calculated at solar noon as

<!>=latitude -solar declination. (60) 

The effect of solar angle on the amount of radiation 

reaching a reflecting source can be computed as (Elterman and Toolin, 1965)

T'=exp(-TA2 sec $), (61) 

where

T" = atmospheric transmittance of radiation at a

particular wave length through the total

atmosphere and 

Th2 extinction optical thickness at a particular

wavelength through the total atmosphere

to the ground-surface elevation. 

The relationships between object reflectance and 

solar angle, incidence look angle, and azimuth look 

angle are treated in Egbert and Ulaby (1972), while 

solar angle versus exposure is explored in Sprecht, 

Fritz, and Sorem (1966). The Gila River Phreatophyte 

Project is at latitude 33°10', and the noon solar angle 

that occurs during summer and winter solstice (June 

21, December 22) are 9.88° and 55.62°, respectively.

A computation of the transmittances at the peak of 

each dye-layer sensing range for June 21 and December 22 are given in table 4. Table 4 shows that the 

maximum possible solar angle effect on the three 

wavelength regions of interest would be significant if 

all other effects were constant. During the period of 

plant activity (April through September) this effect is 

not significant at this latitude when the photographic 

data is analyzed as a ratio. Further evidence of this is 

shown in figure 33, which shows that the spectral sigTABLE 4. Atmospheric transmittance for the peak sensitivity of dye 

layers of color-infrared film at summer and winter solstice

Dye layer

Peak wavelength

sensitivity 

(in micrometers 1

Percent of 

atmospheric transmittance

Winter 

solstice

Summer 

solstice

Ratio of

vinter solstice

to summer

solstice

Yellow _ 

Magenta 

Cyan _

0.55 

.65 

.72

65.9

74.0

78.9

78.6

84.1

87.2

0.84

nature parameters of plant activity (&, &} are largely 

independent of solar angle. Figure 33A is the bare 

ground site shown in figure 1, while 33B is a part of the 

dense saltcedar site in 25-2 also shown in figure 1.

The decrease of$ and increase of & in figure 33B at 

1200 hours results primarily from the high sun angle 

which allows irradiance from the ground between the 

trees to register on the photographs. The increase in $ 

at 1400 hours in figure 33A is more difficult to explain, 

but this is perhaps due to an increase in temperature of 

the soil which may cause a depletion of the surface soil 

moisture and a change in signature.

STANDARD CORRECTION

Many photographic variables, such as atmospheric 

changes, processing inconsistencies, and shifts in film 

color balance due to aging or improper storage, are 

very difficult to correct. Elaborate ground ins^rumentation data and the use of complicated sensitometric 

methods are necessary to derive such corrections. 

However, a simple empirical correction for this type of 

variability was attained by using the spectral signature of a highway bridge as a standard calibration. The 

necessity of a standard correction is indicated by the 

uncorrected signature of reach 2b during 1968 versus 

1969 (fig. 30).

The basic theoretical assumptions inherent in the 

bridge correction procedure are given below.

1. The bridge is assumed to be a gray surface, such 

that the film (using the "standard" filter pack) registers equal quantities of energy in the three wavelength 

regions sensed by the individual dye layers; the photographic mission flown on June 27, 1968, was used as 

the standard photographic flight for this reason.

2. The characteristic curves (fig. 4) are superimposed at the analytic densities of the bridge readings, 

achieving an equivalent neutral density.

The accuracy of this empirical method is considered 

adequate for this'study, and although it is recognized 

that neither of these conditions is precisely met, the 

effect of error is overshadowed by the need for a usable 

inexpensive correction technique.

Densitometric readings of the highway bridge located on the Gila River were taken on each photographic flight,convertedto $, &, andB by the technique
CALCULATION OF EVAPOTRANSPIRATION USING COLOR-INFRARED PHOTOGRAPHY O41

40 I

g 30 J

O

DC

UJ

0.

Z 20

Q

I 10

A. Bare ground (all standard deviation less than 0.01)

R

0900 1000 1100 1200 1300 

TIME OF DAY, IN HOURS

EXPLANATION

R 

Relative near-infrared irradiance

G 

Relative red irradiance

1400 1500

100 ,

Q 20

Z

10 

0900 1000 1100 1200 1300 1400 1500

TIME OF DAY, IN HOURS 

FIGURE 33. Relative near-infrared irradiance and relative red irradiance versus time of day for bare ground and dense saltcedar.

illustrated in equations 4, 5, and 6, and corrected for 

film type, altitude, and filter combination when necessary. All bridge readings were then corrected relative 

to the bridge reading flown June 27, 1968, which conformed to the assumption (1) given above. The June 27, 

1968, irradiance was

RS =&S =BS =Q.33, (62) 

where

s = subscript standard bridge reading June 27,

1968, photography.

The standard correction factors for each photograph 

run derived from the bridge reflectance are

<Jc =Rq/Rs, (63) 

Jm =Gq/Gs , (64) 

and

Jy =Bq/Bs , (65)

where

q=subscript bridge reading for any photographic

mission.

The data for a specific flight was then corrected as 

demonstrated by the following equation for relative 

near-infrared irradiance:

R =(RVJC )/(RVJC +GvJm +BVJV ), (66)

It should be realized that in this specific instance 

equations 63, 64, and 65 may be simplified (Jc =3Rq , 

Jm =3Crq , and Jy = 3&q \ but the technique is presented 

here for use if the condition of equation 62 is not met.

This technique is only usable during the summer 

months (May through August) because the correction 

factors tend to overcompensate the cyan layer by as 

much as a factor of ten during the winter. The cause of 

this high overcompensation is that the skylight con-
O42 GILA RIVER PHREATOPHYTE PROJECT

tribution to the cyan dye layer is much lower in the 

winter than in the summer, the vegetation adjacent to 

the bridge becomes dormant during the winter, and the 

effect of the scattered near-infrared radiance from this 

source is greatly reduced, therefore reducing the quantity of near-infrared irradiance received by the film 

sensing the bridge.

STATISTICAL ANALYSIS OF VARIABLES

Extensive statistical analyses have been performed 

on the equations presented in this report. For the use of 

those interested in these evaluations, the statistical 

parameters are presented in table 5.

To attain significance in a quantitative study of 

spectral data requires a rigorous statistical approach. 

This is of particular importance in the study of individual plant signatures, where the two primary 

parameters used (R,Cr) are elements of a closed system, 

as shown by equation 7 in the text, $+G+£ = 100 percent. The interrelationship between variables in a 

closed system such as expressed in equation 7 yields a 

spurious correlation. For example, an increase in R 

dictates an equal decrease in the sum of &+B. In addition, the fact that the sum of the parameters is a constant destroys the independence of both the variance 

and covariance and induces a bias toward a negative 

correlation. Therefore, the correlation coefficient of a 

closed system, such as that used in this report, may not 

be related to the conventional null hypothesis, p = 0. 

Chayes (1960, 1971) proposed that the expected value 

for a null hypothesis with this "closure restraint" may 

be approximated byp=(l n)" 1 , where n is the number 

of interrelated variables in the closed system. This 

value can then be utilized in the Fisher Z transformation (Fisher, 1950, paper 3, p. 125) to determine the 

distribution of the coefficient of correlation. A pictorial 

representation of the distribution of this transformation is shown by Natrella (1963, 20- le).

The normal sampling distribution at a particular 

confidence level can be approximated by the equation 

for Z given below; for ease of calculation a table ofZ 

values has been published in Dixon and Massey (1957, 

table A-30b). The equation used to determine the confidence levels of the coefficient of correlation is

Z'r =Zr±(t')(SD), 

where

Z'r the maximum and minimum values of Z for 

the coefficient of correlation as determined 

by the t' distribution,

SD = 1/Vn 3, where rc=number of samples,

t' = "student" t value of a particular significance 

level, and

r = coefficient of correlation.

This procedure was used in this report to solve for the 

^max and ''min by the inverse transformation,

Similarly, the corresponding confidence levels for the 

null hypothesis were computed as

where

Z' p the maximum and minimum values of Z for 

the null hypothesis as determined by the t' 

distribution,

p = value of the null hypothesis,

and the values for pmax and pmin were derived by the 

transformation,

p=Z-l(Z'p).

This was done for both the null hypothesis and the 

coefficient of correlation at the 95 percent confidence 

level and is presented along with the value for p and r 

in table 5.

The percent confidence level of the computed r when 

related to the null hypothesis was computed by

Zp=Zr-(t')(SD\ 

which is then solved for t',

t' =

SD

The confidence level for the t' for each computed correlation coefficient as related to the null hypothesis is 

also given in table 5.

The statistical parameters shown in table 5 which 

have not been discussed were evaluated from the definitions given in Ezekiel and Fox (1963).

SUMMARY

In the 5-year period 1967-71, 38 color-infrared 

photographic missions were flown over the Gila River 

Phreatophyte Project in southeastern Arizona. Data 

from these missions were analyzed to determine the 

possibility of identifying and measuring vegetative 

parameters and their associated hydrologic variables 

by spectral analysis of the photography. Dur; ng the 

summer of 1971, additional data from six colorinfrared photographic missions flown over the Cibecue 

Ridge Watershed Study in central Arizona we^e used 

to test the validity of some of the techniques developed 

in this study.

A transmittance densitometer was used to obtain a
TABLE 5. Statistical index of variables in equations used in this report

Parameters

Equation Equation Equation Equation Equation Equation Equation Equation 

11 12 18 20 21 22 23 24

Equation Equation Equation Equation Equation Equation Equation Equ

25 26 27 28 29 30 31 

Number of photographic nights

Number of sample points per 

flight ______________________ 472

Mean of independent variable __ 208.4 

Variance of independent variable ... _____ _ _____ 4,248.7

Standard deviation of independent variable ._ .. 65.2

Mean of dependent variable____ 153.5 

Variance of dependent variable _ 2,242.1 

Standard deviation of dependent

Standard deviation of slope ___. .22 

Student t' test of slope . 2.9

Intercept of regression line __-_ 20.5 

Standard deviation of intercept __ 31.4 

Student t' test of intercept ____ .65 

Coefficient of correlation ______ .88 

Coefficient of determination- _ .77 

P test of coefficient of correlation__ ___ _ ___ . ____ . 37.1

95 percent confidence levels of .64, 

coefficient of correlation ____ .96 

Confidence level of coefficient of 

correlation when related to

Value of the null hypotheses __ .00 

95 percent confidence levels of .55,

Standard error of estimate

Standard error of the standard 

error _ _ . _. _ .20

13

727 

261.4

17,621.6

132.8 

337.7 

18,506.7

136.0 

15,466.2 

.88 

.31 

2.8 

108.2 

45.3 

2.4 

.86 

.74

30.3 

.59, 

.96

.99 

.00 

-.55, 

.55

71.0 

.20

7

5 

69.9

317.5

17.8 

.23 

.03

.16 

2.4 

0075 

002 

3.5 

-.29 

17.3 

-.017 

.84 

.71

12.2 

.23, 

.97

.95 

.00

-.75, 

.75

.09 

.29

7

5 

75.6

931.0

30.5 

.23 

.03

.16 

4.6 

.0049 

.0008 

5.9 

-.14 

12.9 

-.010 

.93 

.86

34.35 

.59, 

.99

.98 

.00

-.75, 

.75

.06 

.29

1

14 

53.1

55.1

7.3 

11.6 

48.6

7.0

-.64

.21 

-3.1 

45.4 

11.6 

3.9 

-.67 

.45

9.55 

-.22, 

-.88

.80 

.00 

-.53, 

.53

5.4 

.20

25

727 

47.6

411.1

20.3 

30.7 

297.2

19.9 

-373.4 

-.91 

.08 

-11.4 

73.9 

4.2 

17.7 

-.92

129.8 

-.82, 

-.96

.99 

-.50 

-.13, 

-.75

7.9 

.14

25

'375 

49.0

,240.1

15.5 

25.4 

252.3

15.9 

-217.7 

-.91 

.10 

-9.1 

69.9 

5.0 

14.0 

-.89

82.7 

-.78, 

-.95

.99 

-.50 

-.13, 

-.75

7.6

.14

25

'271 

48.1

303.2

17.4 

28.0 

301.6

17.4 

271.0

-.89 

.09 

-9.7 

71.0 

4.7 

15.0 

-.90

93.6

-.78, 

-.95

.99 

-.50 

-.13,

-.75

7.9 

.14

24

18 

49.4

179.4

13.4 

23.8 

161.6

12.7 

-145.5

-.81 

.11 

-7.72 

63.9 

5.7 

11.3 

-.86

59.5 

-.68, 

-.93

.99 

-.50 

-.12,

-.75

6.8

.15

24

25

51.8

213.3

14.6 

23.2 

169.7

13.0 

-154.8 

-.73 

.11 

-6.57 

60.7 

6.7 

9.1 

-.81

43.1 

-.60, 

-.91

.98 

-.50

-.12,

-.75

7.7 

.15

15

6

48.3

364.7

19.1 

33.3 

456.0

21.4 

369.6

-1.01 

.13 

-7.7 

82.3 

6.1 

13.6 

-.91

59.9 

-.74, 

-.96

.99 

-.50 

.01, 

-.80

9.4 

.19

15

5 

23.1

830.4

28.8 

72.5 

874.8

29.6

-849.7 

-1.02 

.02 

-45.9 

96.2 

.79 

122.4 

-.99

2,108.0 

-.97, 

-.99

.99 

-.50 

-.08, 

-.80

2.4 

.19*

6

5

44.8

27.8

5.3 

50.3 

26.7

5.16 

-23.5 

-.85 

.25 

-3.4 

88.3 

11.3 

7.8 

-.86

11.9 

-.20, 

-.98

.60 

-.50 

.53, 

-.93

2.9 

.32

6

10 

60.1

174.8

13.2 

31.9 

80.1

8.9 

-111.1 

-.64 

.07 

-8.6 

70.1 

6.7 

10.5 

-.94

74.4

-.98

.99 

-.50 

.10, 

-.83

3.2

.21

6

30 

54.6

144.9

12.0 

32.3

55.8

7.5 

-61.3 

-.42 

.08 

-5.3 

55.4 

7.2 

7.7 

-.68

27.7 

-.44, 

-.83

.80 

-.50 

-.19,

-.71

5.5

.12

1

-6

-

4

'Owing to clearing during the period of analyses, the number of sampled points for equation 23 changed from 375 to 218, while those for equation 24 changed from 271 to 52.
O44 GILA RIVER PHREATOPHYTE PROJECT

14 percent sample of the study area. Optical density 

readings in each of three primary colors of the positive 

transparencies were taken. The irradiance (defined in 

this report as energy recorded on the film) sensed from 

the dye concentration in each of these primary colors 

was determined and related to the total energy sensed 

by the film, achieving three parameters of relative irradiance (near-infrared, red, and green) which were 

functionally related to spectral regions indicative of 

plant activity. These parameters were then corrected 

to a "standard photo flight" by adjusting the densitometric data for flight altitude, filter combination, 

film type, and a standard correction based on the 

spectral signature of a highway bridge located in the 

project area.

Remote sensing calculations of evapotranspiration 

from 13 photographic missions flown during 1968 were 

related to water-budget measurements for a 1,700-acre 

(689-ha) area cleared of vegetation and a 2,200-acre 

(891-ha) phreatophyte-covered area of the Gila River 

flood plain. The coefficients of correlation between the 

water-budget measurements and the remote sensing 

calculations were 0.88 for the cleared area and 0.86 for 

the phreatophyte-covered area. Evapotranspiration 

calculated from seven photographic missions flown 

during 1968 over a spatially homogeneous grain sorghum field gave a coefficient of correlation of 0.93 when 

related to evapotranspiration computed by the 

Blaney-Criddle equation.

Determining depth to the ground-water level spectrally was not practical, although depths to water of 

less than 30 ft (9 m) had a standard error of 5.4 ft 

(1.6 m). Moisture content of the soil could not be determined spectrally, but plant stress due to moisture 

content deficiencies was detectable. Plant-type discrimination was possible between eight different plant 

communities, which included both deciduous and perennial species. A spectral evaluation of near-infrared 

versus red irradiance of these plant communities 

showed that each had a standard error of less than 10 

percent. Computer maps of the spectral signature distribution for each photographic mission flown of the 

Gila River flood plain were found useful for spatial and 

temporal evaluation of vegetation. The spectral signatures for different types of vegetation were harmonically modeled and used as a means of recognizing seasonal trends and anomalies in the vegetation. When an 

increase in volume of canopy for both mesquite and 

saltcedar was noted, there was a corresponding increase of near-infrared irradiance, but the measurement errors of both irradiance and canopy volume were 

very large. However, foliar cover versus irradiance had 

a high coefficient of correlation, 0.85.

During this study it was determined that a colorinfrared photographic mission and a computer analysis 

of the photographic data for the Gila River 

Phreatophyte Project area cost about a tenth of the 

amount of conventional species classification and 

canopy-measurement techniques ($650 versus $6,600). 

A short discussion of the derived spectral eiuations 

and a table of 24 statistical parameters describing the 

spectral and hydrologic variables is included to aid the 

reader in evaluating the significance of the study.

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