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Proceedings of the Second

Airborne Imaging Spectrometer

Data Analysis Workshop

May 6, 7, 8, 1986

Gregg Vane

Alexander F.H. Goetz

Editors

August15,1986

NASA

National Aeronautics and

Space Administration

Jet Propulsion Laboratory

California Institute of Technology

Pasadena California
JPL PUBLICATION 86-35

Proceedings of the Second

Airborne Imaging Spectrometer

Data Analysis Workshop

May 6, 7, 8, 1986

Gregg Vane

Alexander F.H. Goetz

Editors

August 15, 1986

I IASA

National Aeronautics and

Space Administration

Jet Propulsion Laboratory

California Institute of Technology

Pasadena, California
This publication was prepared by the Jet Propulsion Laboratory, California Institute

of Technology, under a contract with the National Aeronautics and Space

Administration.
CONTENTS

io INTRODUCTION

Gregg Vane .....................

CALIBRATION s THE ATMOSPHERE_ DATA PROBLEMS AND TECHNIQUES

o RADIOMETRIC CALIBRATION OF THE AIRBORNE IMAGING SPECTROMETER

Deanne Tucker and Gregg Vane .................... 17

• COMPARISON OF VARIOUS TECHNIQUES FOR CALIBRATION OF AIS DATA

Dar A. Roberts, Yasushi Yamaguchi, and Ronald J.P. Lyon ...... 21

• ANALYSIS OF AIS RADIOMETRY WITH EMPHASIS ON DETERMINATION OF ATMOSPHERIC

PROPERTIES AND SURFACE REFLECTANCE

J. E. Conel, S. Adams, R. E. Alley, G. Hoover, and S. Schultz . . . 31

e AIRBORNE SPECTRORADIOMETRY: THE APPLICATION OF AIS DATA TO DETECTING

SUBTLE MINERAL ABSORPTION FEATURES

Terry D. Cocks and Andy A. Green .................. 52

• ATMOSPHERIC-WATER ABSORPTION FEATURES NEAR 2.2 MICROMETERS AND THEIR

IMPORTANCE IN HIGH SPECTRAL RESOLUTION REMOTE SENSING

Fred A. Kruse and Roger N. Clark .................. 63

e DESTRIPING AIS DATA USING FOURIER FILTERING TECHNIQUES

Chris Hlavka ............................ 74

GEOLOGICAL RESEARCH

• ABUNDANCE AND DISTRIBUTION OF MINERAL COMPONENTS ASSOCIATED WITH MOSES

ROCK (KIMBERLITE) DIATREME

John F. Mustard and Carle M. Pieters ................ 81

• COMPARISON OF THE 1984 AND 1985 AIS DATA OVER THE SINGATSE RANGE

(YERINGTON), NEVADA

R.J.P. Lyon ............................ 86

i0. IDENTIFICATION OF HYDROTHERMAL ALTERATION ASSEMBLAGES USING AIRBORNE

IMAGING SPECTROMETER DATA

Sandra C. Feldman and James V. Taranik ............... 96

Ii. DETECTION OF HYDROTHERMAL ALTERATION AT VIRGINIA CITY, NEVADA, USING

AIRBORNE IMAGING SPECTROMETRY (AIS)

Amy Hutsinpiller and James V. Taranik ............... 102

iii
BOTANICAL AND GEOBOTANICAL RESEARCH

12. PRELIMINARY GEOLOGICAL INVESTIGATION OF AIS DATA AT MARY KATHLEEN,

QUEENSLAND, AUSTRALIA

Jon F. Huntington, Andy A. Green, Maurice D. Craig,

and Terry D. Cocks .........................

109

13. USE OF DIGITAL MUNSELL COLOR SPACE TO ASSIST INTERPRETATION OF IMAGING

SPECTROMETER DATA -- GEOLOGIC EXAMPLES FROM THE NORTHERN GRAPEVINE

MOUNTAINS, CALIFORNIA AND NEVADA

Fred A. Kruse, Daniel H. Knepper, Jr., and Robert N. Clark ..... 132

14. NEAR-INFRARED DETECTION OF AMMONIUM MINERALS AT IVANHOE HOT SPRINGS,

NEVADA

M. Dennis Krohn .......................... 138

15. ANALYSIS OF AIS DATA OF THE BONANZA CREEK EXPERIMENTAL FOREST, ALASKA

Michael A. Spanner and David L. Peterson .............. 144

16. SOIL TYPES AND FOREST CANOPY STRUCTURES IN SOUTHERN MISSOURI: A FIRST

LOOK WITH AIS DATA

Glen M. Green and Raymond E. Arvidson ............... 153

17. GEOBOTANICAL STUDIES AT PILOT MOUNTAIN, NORTH CAROLINA, USING THE

AIRBORNE IMAGING SPECTROMETER

N.M. Milton, P.A. Walsh, and T.L. Purdy .............. 162

18. TRACE ELEMENT-INDUCED STRESS IN FRESHWATER WETLAND VEGETATION:

PRELIMINARY RESULTS

Byron L. Wood and Louisa H. Beck .................. 171

19. PATTERNS OF VEGETATION IN THE OWENS VALLEY, CALIFORNIA

Susan L. Ustin, Barrett N. Rock, and Roy A. Woodward ........ 180

20. AIS SPECTRA OF DESERT SHRUB CANOPIES

Rjay Murray, Dennis L. Isaacson, Barry J. Schrumpf,

William J. Ripple, and Anthony J. Lewis .............. 187

APPENDIXES

i.

2.

3.

4.

ATTENDEES OF THE SECOND AIS DATA ANALYSIS WORKSHOP ......... 195

AGENDA OF THE SECOND AIS DATA ANALYSIS WORKSHOP .......... 199

CATALOG OF TEST SITES FLOWN WITH AIS (FROM 12/82 THROUGH 4/86) . . . 205

LIST OF SLIDES AND FIGURE TITLES .................. 211

iv
ABSTRACT

The Second Airborne Imaging Spectrometer (AIS) Data Analysis Workshop

was held at the Jet Propulsion Laboratory on May 6, 7, and 8, 1986. It was

attended by 100 people from seven countries. Papers were presented by 24

attendees; summaries of 19 of the papers are published in these Workshop

Proceedings. The Second Workshop was divided into three sessions:

Calibration, the Atmosphere, and Data Techniques; Geological Research; and

Botanical and Geobotanical Research. Because many of the AIS researchers

have had an additional year to work with their data, a general advance was seen

in the utilization of the data for solving earth science problems. The major

conclusions that can be drawn from the Workshop are: (1) Amost everyone now

working with AIS data is attempting to take into account in data analysis the

effects of the atmosphere and sensor calibration and performance. As a result,

information extraction techniques are becoming more sophisticated and effective.

(2) The emphasis on work in the geological disciplines is beginning to shift to the

application of AIS data for solving geological problems, now that the efficacy of

the data for mineral identification has been demonstrated. (3) In the botanical

sciences, interpretation of AIS data is becoming more quantitative. Several

statistical techniques are under development for extracting the subtle differences

in plant spectra in the region of the near infrared and short wavelength infrared.

In summary, an overall advance was seen in the ability to effectively utilize the

fundamentally new class of data acquired with imaging spectrometers. There

was also a general feeling at the Workshop however, that much work remains to

be done in remote sensing research before the maximum potential of these data

for solving earth science problems is realized.

V
FOREWORD

In the text and figure captions of several papers in the Proceedings,

reference is made to color slides in a pocket at the end of the Proceedings.

These 35 millimeter slides are color versions of the referenced black and white

figures within the Proceedings. A limited number of copies was printed with the

color slides included.

vi
INTRODUCTION TO THE PROCEEDINGS OF THE SECOND AIRBORNE IMAGING

SPECTROMETER (AIS) DATA ANALYSIS WORKSHOP

GREGG VANE, Jet Propulsion Laboratory, California Institute of Technology,

Pasadena, California

ABSTRACT

An overview of the Second AIS Data Analysis Workshop is

presented and each of the 19 papers from the Workshop published

in the Proceedings is briefly summarized. AIS program activities

during the past year are summarized, and a description of the new

AIS-2 is given. Finally, a discussion of AIS data quality is presented

which includes the effects of vertical striping, horizontal striping and

second order overlap.

WORKSHOP OVERVIEW AND SUMMARY OF CONTRIBUTED PAPERS

The Second AIS Data Analysis Workshop, which was held at JPL on May 6, 7,

and 8, 1986, was attended by one hundred scientists from six countries in addition

to the United States. Twenty-four papers were presented of which nineteen are

represented in these Proceedings. Much of the work presented at the second

workshop was in a preliminary state last year at the first workshop; a significant

advance was seen in the maturity of research in high spectral resolution remote

sensing at this workshop. Research in three broad areas was presented at JPL this

year: in calibration, the atmosphere, data problems and techniques, in geological

research, and in botanical and geobotanical research. From the papers in these

Proceedings and those presented at the workshop, it is clear that most AIS

researchers are taking an active interest in calibration and in understanding sensor

performance. Several papers deal with these issues in considerable detail, and as

a result, the value and limitations of AIS data are becoming better understood. It is

also clear that the geological community is making good progress in moving from

material identification to the application of mineral identification to geological

problems. The vegetation community is also making progress in utilizing high

spectral resolution data. At the first AIS Data Analysis Workshop, emphasis was on

visually inspecting spectral reflectance curves for their information content; at the

second workshop several researchers reported on more quantitative techniques

being developed to gain access to the information. Following is a more detailed

summary of the material that is presented in these Proceedings in the three broad

areas above.
Calibration. The Atmosphere. Data Problems and TechniQues

The Airborne Imaging Spectrometer is an experimental test bed not only for

infrared area array detectors, but in essence for all aspects of high spectral

resolution remote sensing, from sensor design and operation through all phases of

data handling and information extraction. One of the major objectives of the

Imaging Spectrometer Program at JPL in operating AIS as extensively as it has

been operated has been to enlist the science community in helping to assess the

quality of AIS data, as well as to help in devising the best techniques for extracting

the maximum information from the data. There is much work to be done in these

areas requiring more than the capabilities of any one single organization. The

work reported on in the Calibration, Atmosphere, Data Problems and Techniques

Session of the Workshop represents a valuable and significant first step in what will

undoubtedly be an on-going process for several years to come.

The session began with a paper by Tucker and Vane describing the optical

alignment of the AIS detector array and the radiometric calibration of the

instrument, which at present is a spectral relative rather than absolute calibration.

In the area of data preprocessing, Roberts et al. have explored four techniques for

compensating for the effects of decreasing solar irradiance at longer wavelengths

and for the effects of atmospheric absorptions. These are the log residual, the least

upper bound residual, the flat field correction, and calibration using field

reflectance measurements. The field reflectance calibration was superior overall,

while the log residual technique was useful for scenes not containing a dominant

cover type. The least upper bound residual technique was found to be effective in

heavily vegetated areas.

Conel et al. have been working to develop a method of atmospheric correction

for major absorbers using the AIS data themselves. In their paper the radiometric

calibration of AIS is discussed as well as the impact of the second order radiation

contamination beyond the 1.5 micron region. Cocks and Green also discuss the

order overlap problem, as well as the effects of horizontal striping in the data.

Examples of each effect are presented. A technique for removing the effects of

horizontal striping is described by Huntington et al., who also suggest one

approach to minimizing the effects of second order overlap on data interpretation.

Kruse and Clark present results of their work with atmospheric water absorption

features centered at 2.17 and 2.20 microns which under some circumstances can

lead to erroneous conclusions about surface mineralogy. Examples of the effect of

superimposing the atmospheric spectrum on mineral spectra are shown. Finally,

Hlavka discusses a notch filter applied to the Fourier transform of AIS imagery to

remove the noise caused by vertical and horizontal striping in the AIS data.

Geological Research

The geology section of the Proceedings begins with a paper by Mustard and

Pieters who have determined the spatial distribution and relative abundance of
clays, gypsum and serpentine associated with the channels and dispersed

components of kimberlite and blocks of country rocks at the Moses Rock Diatreme,

Utah. Data calibration and information extraction techniques are discussed also.

Lyon reports that while 1985 AIS data over the Yerington, Nevada test site appear

to be noiser than a 1984 data set, O-H absorption bands for sericite and/or kaolinite

can be identified at the correct wavelength. Wavelength calibration techniques

using two water and two carbon dioxide absorption features were used. Feldman

and Taranik have extended work from the previous year by acquiring field and lab

spectra and performing x-ray diffraction analysis on field samples from the Hot

Creek Range, Nevada. Using these, the AIS data and Spectral Analysis Manager

(SPAM), the efficacy of a spectral matching algorithm was tested for separating

kaolinite, montmorillonite and illite. The approach is shown to be more effective

with AIS data than using the location of absorption minima alone.

Hutsinpiller and Taranik have identified areas of strong clay alteration at

Virginia City, Nevada, that can be mapped with AIS data as being zones of

kaolinitic, illitic and sericitic alteration. The effects of mineral mixing within a pixel

and the spectral contribution of vegetation are also addressed. Huntington et al.

present preliminary results of their work in mineral absorption spectra extraction

from AIS data over the Mary Kathleen Uranium Mine in Queensland. Because of

the higher concentration of vegetation in the area than is normally found in the

southwestern US, the second order overlap problem is more serious in the

Australian data. Techniques that attempt to model this effect, used with the log

residual analytical technique, are shown to be effective in recovering spectra that

exhibit characteristics expected of clays, carbonates, amphibole and epidote.

Kruse et al. present the results of a technique using Munsell color transformations

to reduce the dimensionality of AIS data to a single color composite image that

successfully allows the limestone and dolomite roof pendants and sericite-illite and

other clay minerals to be mapped in the quartz monzonite stock in the Grapevine

Mountains of California and Nevada. Field and lab studies verify the mineralogical

distributions mapped from the AIS data. Krohn discusses the nature of

ammonium-bearing minerals at Ivanhoe Hot Springs, Nevada, and shows

preliminary results from AIS data collected over the area. The AIS flight line

missed the intended target by 500 meters and vegetation in the scene presents a

problem for information extraction; hence spectra characteristic of ammonium

minerals such as Buddingtonite have not yet been observed in the AIS Ivanhoe

data.

Botanical and Geobotanical Research

Spanner and Peterson report spectral variations in the 1.4 to 1.8 micron region

that are thought to relate to biological diferences between several forest stands

near Fairbanks, Alaska, overflown with AIS. The data were filtered with the

technique discussed by Hlavka and flat field corrected using portions of the AIS

flight lines that crossed an airport runway and a road. Green and Arvidson discuss

work done with Thematic Mapper (TM) and AIS data from the deciduous
oak-hickory forests in southern Missouri. High reflectance in TM band 4 over xeric

forests as compared to mesic forests was confirmed with AIS data in the same

approximate spectral region. Their preliminary analysis of AIS data, however, does

not indicate the presence of more information in that spectral region than found in

TM band 4. Milton et al. have taken a preliminary look at AIS data over Pilot

Mountain, North Carolina, and found spectral variations in the region between the

1.4 and 1.9 micron water bands related to vegetation cover types. Work is under

way to improve data calibration and normalization, to allow a better study to be

made of these spectral variations.

Wood and Beck find a consistent relationship between AIS spectral response

and plant tissue chemistry in a freshwater wetland in central California. An

increase in trace element concentration in the plant tissue was found to correspond

with an increase in spectral reflectance in the 1.5 to 1.7 micron region. Ustin et al.

report from their work with AIS data in the Owens Valley, California, that shadows

cast by vegetation canopies have a major impact on broad spectral reflectance

under conditions of low canopy cover, especially for xeric shrub communities.

Mesic communities with high vegetation density exhibit greater variations in

spectral signature in the near and short wavelength infrared than do sparse xeric

communities of shrubs. Murray et al. conclude from a preliminary Principal Factor

Analysis (PFA) of AIS data from an area with two sagebrush species and

montmorillonitic and volcanic soils that much information is available for material

discrimination. As many as six factors in the PFA were identified as significant in

contributing to the spectral structure observed in the 1.5 to 1.8 micron region.

AIS PROGRAM ACTIVITIES IN 1985 AND 1986

There has been a wide range of activities on the AIS program this past year at

JPL. In addition to data collection, preprocessing and dissemination, there has

been on-going work to improve the spectral relative calibration of the instrument,

efforts have been increased to assess data quality, and an improved version of AIS

was designed and built which incorporates a new 64 by 64 element detector area

array and also incorporates several other improvements that will enhance data

quality. The issue of data quality will be addressed later in the Introduction to the

Proceedings; AIS calibration is covered in the paper by Tucker and Vane following

the Introduction.

AI$ Fliaht Ooer_tion$

In terms of data collected, the past flight season was the most successful of the

three years that AIS has been in operation. Data were collected for every

approved investigator, and in almost all cases, along the desired flight lines and

under the desired conditions. The success of the season is due primarily to the

professionalism of the Ames C-130 operations staff and to generally good weather

conditions. Approximately 50 test sites were flown in the US, including sites in

Alaska and Hawaii, and another 60 test sites were flown in Australia. In Appendix
3, a complete list is given of all the test sites that have been flown with AIS between

December 1982 and April 1986. Figure 1 shows the geographical distribution of

the US test sites. In the summer of 1986, a major European deployment with the

C-130 will take place. AIS data will be collected over German forests suffering from

acid rain damage, over Austrian forests where heavy metal stress has been

identified, and over the major volcanos of southern Italy. In the US, flights are

planned in the northeastern forests of Vermont and New Hampshire, and in the

southwestern states. AIS will be retired from its active role in data collecting at the

end of the season.

Several improvements were made in data preprocessing during the past year

and new subroutines were added to the SPAM software. Data turnaround times

were very slow, however, due to continuing problems with the aging PDP-11

computer used in the AIS decommutation facility at JPL. In January, the transition

was begun in shifting the decommutation of the AIS high density tapes to Ames,

where a more modern and efficient facility exists to handle remote sensing data

collected by the Ames sensors. Transition of this function to Ames was completed

in May and the decommutated tapes are now sent routinely to JPL for logging,

archival and retrieval processing on the AVIRIS VAX 11-780 computer. The

preprocessed data are sent to investigators from JPL as in the past.

Instrument Im.orovement8

The original AIS, which operated with a prototype 32 by 32 element mercury

cadmium telluride (HgCdTe) area detector array, was completely rebuilt during the

past winter to incorporate a prototype 64 by 64 element HgCdTe array, which is the

next step in the development of detectors which will be used for the Shuttle

Imaging Spectrometer Experiment (SISEX). Both arrays were made by Rockwell

International Science Center as part of an ongoing detector development program

at JPL. The new instrument, called AIS-2 to distinguish it from its ancestor, has the

capability to image a swath that is 64 rather than 32 pixels wide. One hundred

twenty-eight spectral bands are acquired with two steps of the diffraction grating

rather than four in AIS-1, which allows 16 lines per second to be acquired rather

than 12, while still allowing a greater integration time for improved signal-to-noise

performance. AIS-2 incorporates new foreoptics (but the same spectrometer

optics) which have a wider field of view to take advantage of the 64 pixel swath

width capability. The modulation transfer function (MTF) of the foreoptics had be

relaxed somewhat in order to the fit the new foreoptics into the same instrument

housing. Thus while the swath width has been doubled, the effective

instantaneous field of view (IFOV) has been degraded. See Table 1 for a

comparison of AIS-1 and AIS-2.

The two most significant improvements to instrument performance relate to the

new signal chain electronics and to the inclusion of switchable order blocking

filters. The new electronics include additional shielding from the rest of the

instrument (the digital and analog electronics are connected now by opto-isolators,

for example), so electronic signal chain noise is much lower. The instrument is
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also more effectively isolated electrically from the aircraft, which has always been a

major source of noise in the flight data. The other signal chain improvement allows

the full 12 bits of digitized data to be recorded on the high density tape, rather than

a selectable 8 bits with AIS-I. The switchable blocking filters were installed to

avoid the problem of second order overlap at wavelengths beyond 1.6 microns.

This problem with AIS-1 is discussed further in the following section of the

Introduction. The transmission curves for the two blocking filters are shown in

Figure 2. The choice of filter is programmed into the microcomputer which controls

AIS-2 so that when the instrument operator specifies the operating mode ("tree" or

"rock"), a stepper motor automatically moves the correct filter into position at the

entrance to the foreoptics.

Table 1. Comparison of AIS-1 and AIS-2 Performance Parameters

Parameter AIS-1 AIS-2

IFOV (mrad)

Ground IFOV (m, at 6 km altitude)

FOV (degrees)

Swath Width (m, at 6 km altitude)

Spectral Sampling Interval (nm)

Data Rate (kbps)

Spectral Sampling:

"Tree" Mode (microns)

"Rock" Mode (microns)

1.91 2.05

11.4 12.3

3.7 7.3

365 787

9.3 10.6

394 1,670

0.9-2.1 a 0.8-1.6 b

1.2-2.4 a 1.2-2.4 c

a Region beyond 1.6 microns contaminated with second order

radiation. See text.

b An additional approximately 40 spectral bands are taken

beyond 1.6 microns, but they contain second order radiation.

c An additional approximately 10 spectral bands are taken

beyond 2.4 microns, but they contain second order radiation.

AIS DATA QUALITY

Although some impressive results have been achieved with AIS, the quality of

the data has not been as high as an instrument of this type is potentially capable of

producing. Calculations show that an instrument such as AIS, which is based on

infrared area detector array technology, should have a signal-to-noise ratio (SNR)

in excess of 100:1 in the 2.2 micron region for a scene albedo of 0.5. Under these

conditions, SNRs have been measured from AIS-1 in the range of 10:1 to 40:1,
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depending on the data set. It is thought that most of the discrepancy between

observed and predicted SNR performance in AIS-1 was due to signal chain noise

and to instrument/aircraft ground loops. Both of these problems were addressed in

the design of AIS-2. Another class of noise is related to the detector itself. If, for

example, the response of the detector changes between calibrations, data will

appear noisy (in the case of AIS, it will be vertically stripped) because the detector

response equalization was not adequte during the radiometric correction

operation. In addition to electronic and detector noise, there are sources of noise

that are related to the mechanical and optical qualities of the sensor. The effects of

these sources of noise are not seen in the typical signal-to-noise ratio, but they do

affect data quality nevertheless by degrading the purity of the signal in a given

pixel. AIS-1 suffered from two such noise sources which will be discussed below:

"horizontal striping," which degrades the spectral resolution of the sensor, and

second order overlap, which contaminates the spectral signal by mixing light from

two different wavelengths.

Vertical StriDing

Shown in Figure 3 are raw AIS data from Cuprite, Nevada, which have not

been processed in any way. The data were collected on September 23, 1985, in
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rock scan mode; shown are 16 of the 128 spectral images, starting at 1.258

microns and ending at 1.397 microns, inside the 1.4 micron water absorption band.

The top 60 lines of the figure (south on the ground) are alluvium with medium

albedo, followed by another 60 lines of alluvium of lower albedo. Kaolinite Hill

begins at about line 120; its center is the bright spot at about line 145. Below line

200, the effects of aircraft roll are obvious as the C-130 drifted off the flight line. A

number of other features stand out in this raw image: the transition into the 1.4

micron water band beginning at about 1.323 microns and the associated scene

attenuation, the pronounced vertical striping in the individual images, some of

which are bright but most of which are dark, and the horizontal striping that is most

obvious in the transition zone going into the water band. Horizontal striping will be

discussed later. The vertical striping is due to nonuniformity in the response of the

individual detector elements in the 32 by 32 element area array. In the extreme

case, the black vertical lines are due to detector elements that are dead, i.e., which

do not respond to light at all. The bright stripes are due to detector elements that

are more responsive than their neighbors.

The purpose of radiometric correction during AIS data preprocessing is to

remove the effects of detector response nonuniformity by multiplying the response

of each individual detector element by a wavelength-dependent quantity

determined during instrument calibration (see Tucker and Vane), which in effect is

a normalizing factor. In the case of the black stripes which are due to dead detector

elements, the gap in spectral coverage at that position on the detector array is

interpolated across by averaging the radiometrically corrected values of the

detector outputs on either side of the dead detector element. This is done in the

spectral dimension of the detector array. The results of the radiometric correction to

Figure 3 are shown in Figure 4. The first five or so images appear to be clean, the

next few show both dark and light vertical striping (as well as horizontal striping).

The residual vertical striping is thought to be due to a change in instrument

performance after the calibration file was constructed in the lab, before installation

in the airplane. The most likely cause is time-varying detector response, although it

is possible that the noisy electronic environment of the C-130 could be having an

effect, too.

To remove the remaining vertical striping, there

normalize to a portion of the scene with known

uniform across the AIS image. Such an area occu

are several options. One can

reflectance which is spatially

rs in the Cuprite data between

lines 65 and 115. Another approach is to do a log residual correction to the data

(see Roberts et al., these Proceedings). Both these techniques have been used at

JPL, as well as the others discussed by Roberts et al., but since many AIS test sites

do not contain a spatially uniform area of known spectral reflectance, a log residual

correction was applied to these Cuprite data to illustrate the efficacy of this

correction technique. The result is shown in Figure 5, which has been stretched

differently than the preceding two figures to make the residual noise more obvious.

In fact, the vertical noise is gone in Figure 5, leaving only the horizontal striping

which will now be discussed.

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12
Horizontal Strioing

If one looks at the horizontal striping that occurs in various spectral bands

throughout the 128 bands collected by AIS, the distribution at first appears to be

random. On closer inspection, it appears to be most frequently associated with the

edges of the 1.4 and 1.9 micron water absorption bands. This observation led

Cocks and Green (these Proceedings) to speculate that the horizontal striping was

due to wobble in the grating drive which steps the diffraction grating through its

various positions, resulting in successive scan lines being sampled at longer or

shorter wavelengths than they should be. If this were the case, the effect would be

most pronounced at the edges of the water bands where reflectance is most rapidly

changing, which is what is observed.

Two additional pieces of information from AIS-2 have become available since

Cocks and Green advanced their theory, however. During the checkout flight of

AIS-2 it was noted by the instrument operator that (1) the small amount of vignetting

introduced by the new foreoptics (which results in a 50 percent attenuation of the

first and last cross-track pixels) shifted position at about the same rate as the

horizontal striping occurred, i.e., every few scan lines, and (2) the position sensor

on the grating drive indicated that the grating was returning to the same position

throughout the scan line to a precision well in excess of that which would cause

horizontal striping. The conclusion: horizontal striping is caused by the detector

array vibrating around at the focal plane of the spectrometer. The cause was a

poorly designed dewar cold finger on which the detector is mounted. The cold

finger is mounted to the liguid nitrogen reservoir in such a way that it was free to

resonate at the frequency induced into the instrument by the aircraft. To fix the

problem, the detector end of the cold finger was immobilized with a spider mount

connected to the dewar nose piece. The subsequent test flight of AIS-2 with this

modification showed no horizontal striping.

While the cause of horizontal striping has been removed from AIS-2, the effects

remain in AIS-1 data. Cocks and Green give an excellent description of the

spectral manifestations of horizontal striping and Huntington et al. describe a

technique they have developed to remove the spectral effects without degrading

the data. However, there is still a spatial effect since the detector array in AIS-1

was moving in the spatial dimension as well as in the spectral. This results in a

degrade instrument IFOV, which is no longer diffraction limited. It will probably turn

out after further analysis that the effective IFOVs of AIS-1 and AIS-2 are similar, for

while it was not possible to build diffraction limited foreoptics to fit into the available

instrument volume of the old AIS-1 housing, AIS-2 has a very rigid detector array

mount and hence no horizontal striping occurs in the data.

Contamination from S.Dectral Order Overlan

The final effect to be discussed here which has a negative impact on the quality

of AIS-1 data is that of contamination from second order light falling on the detector.

The effect is shown conceptually in Figure 6. Unlike prisms, gratings disperse light

13
m

Z

LIJ

I---

i

I I I I I I ! I I

FIRSTORDER

SECONDORDER

I I I I I I I I I

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6

WAVELENGTH, microns

FIGURE 6. The effect of second order overlap in a grating spectrometer.

in many orders. First order light which appears at one wavelength appears again

in second order at twice the wavelength, in third order at three times the

wavelength, and so on. In the figure a situation is shown in which light at a

wavelength of 0.9 microns passes through the spectrometer. Its second order

appears at the position where first order radiation of wavelength 1.8 microns falls,

at an intensity which depends on the grating efficiency in second order. Thus, if the

spectrometer is allowed to pass light of wavelength 0.9 microns, then the signal at

1.8 microns will be the sum of the second order light at 0.9 microns plus whatever

first order light at 1.8 microns is passing through the spectrometer. The only way to

have light that is purely 1.8 microns in wavelength falling at that position in a

grating spectrometer is to place a blocking filter in front of the spectrometer which

rejects all radiation short of 0.9 microns.

In an early version of AIS, a blocking filter was used which rejected all radiation

short of 1.2 microns in order to keep the spectral region from 1.2 to 2.4 microns

uncontaminated by light of second or higher orders. In an attempt to accommodate

the needs of the botanists for access to the near infrared plateau starting at 0.8

microns, the original blocking filter was replaced with one that only rejected light

short of 0.8 microns. This was done in late 1983, and was done with the

knowledge that some contamination would occur at 1.6 microns and beyond. A

calculation was made of the magnitude of the contamination by extrapolating from

4 points on the grating efficiency curve in first order (which was the only information

available on the grating) to estimate the efficiency of the grating in second order.

The calculation indicated that only a few percent (10 percent or less) of

contamination would occur, which seemed an acceptable level. An experiment

14
was done with a monochromator to measure the amount of contamination and the

result appeared to confirm the calculation. Unfortunately, the calculation was

incorrect, as was the measured value. The monochromator used in the experiment

had been damaged, and this was not discovered until a year later. Now that the

actual grating efficiency in second order has been measured by the manufacturer

and AIS-1 data which were gathered since the change in blocking filters have been

analyzed, it is apparent that the second order contamination is several tens of

percent rather than less than ten percent.

The impact of the contamination depends on the scene and on the observer's

perspective. Obviously for those who are interested primarily in the 0.8 to 1.6

micron region, the impact is positive since the region short of 1.2 microns would not

have been available these past two years. For those interested in the region

beyond 1.6 microns, the impact of the contamination from shorter wavelengths

depends on the scene intensity at the shorter wavelengths and the amount of

structure in the spectral reflectance at those wavelengths. In the southwestern US,

for example, in areas with very little vegetation, the typical effect of the

contamination is to add what is essentially a dc signal to the longer wavelengths,

which has little or no impact on the spectral structure there. However, vegetation is

highly reflective at the shorter wavelengths so scenes containing much vegetation

can be expected to show a much larger contamination at the longer wavelengths.

This is the case at many of the Australian test sites; Huntington et al. are

developing techniques to remove the effects by modeling the contribution expected

at the longer wavelengths from the high vegetation reflectance at the shorter

wavelengths.

Figure 7 shows second order contamination in the AIS-1 Cuprite data set

discussed earlier in this Introduction to the Proceedings. Shown is a spectrum from

a 3 by 3 pixel area centered on Kaolinite Hill. The second order contribution to the

1.8 micron water absorption feature can be clearly seen: the reflectance values in

that region should be zero but in fact are about 60 percent of the total signal level,

which has been normalized. Note, however, that the double absorption feature at

2.2 microns is still quite clear, as it has been in all AIS-1 data over Kaolinite Hill. In

the Cuprite setting, the second order contamination has not affected the ability to

do direct mineral identification.

Thus, data collected with AIS-1 will continue to be useful in spite of their various

shortcomings. In many cases, additional work will be required to extract the

information that lies hidden in the data, while in others the second order

contamination will be found to have minimal effect. However, even in the latter

case the investigator should be cautioned to remember that second order radiation

is present. AIS-2 has been designed to avoid this and the other problems

discussed above (as of course have AVIRIS and SISEX). Each of these

instruments will still have its own "personality," as do all such complex sensors. It

remains incumbent upon all investigators who work with data from any sensor to

learn as much about the sensor as possible, and to approach the interpretation of

data with alert and open minds.

15
8O

76

72

_68

64

60

ILl

z_ 56

_, 52

<_ 48

._1

_: 44

40

36

1.24 1.39 1.54 1.69 1.84 1.99 2.13 2.28 2.43

WAVE LENGTH, microns

ORIGINAL FAGE IS

OF POOR QUALITY

AIS-1 IMAGE

AT 1.24 microns

FIGURE 7. Spectrum of a 3 by 3 pixel area centered on Kaolinite Hill, Cuprite;

AIS-1 data set from September 23, 1985. Data have been corrected with the log

residual technique.

t6
N87-1296g

RADIOMETRIC CALIBRATION OF THE AIRBORNE IMAGING SPECTROMETER

DEANNE TUCKER and GREGG VANE, Jet Propulsion Laboratory, Pasadena,

California

ABSTRACT

The spectral and radiometric calibration of the Airborne Imaging Spectrometer is described.

I NTRODUCTI ON

Several laboratory calibration tests have been developed for the Airborne Imaging Spectrometer (AIS). The goals of these tests are to I) adjust the physical alignment of the detector within the optical system and

set the spectral endpoints, and 2) produce a calibration file of multipliers which equalizes the relative responses of each of the detector

elements in the entire two-dimensional array. Tests are also performed as

part of an ongoing effort to provide absolute radiometric calibration.

This paper describes the tests and discusses the merits and limitations of

each.

TECHNIQUE

Spectral Alignment. The detector is mounted inside a dewar which is

mounted to the optical head of the instrument. This dewar can be positioned

in the spectrometer focal plane to align the detector with respect to the

spectrally dispersed image.

A monochromator and broadband lamp are used to provide illumination of

one spectral channel (approximately I0 nm) on the detector array. The monochromator is calibrated using the visible emission lines from a mercury vapor pen-ray lamp. Since this monochromator has no order blocking filters,

the higher orders of these visible lines are used to calibrate in the

infrared.

The AIS dewar is rotated until the line of illumination falls squarely

on the detector. The dewar is adjusted side-to-side to place the detector

close to the desired position within the spectrum and to minimize any

vignetting. The wavelength endpoints are set by adjusting resistors and

potentiometers in the grating drive circuit. The wavelength end points

are determined by measuring the wavelengths which center the illumination

on the first and last row of the detector. The spectral sampling interval

is a calculated quantity determined by dividing the difference between the

two edge wavelengths by 31 for AIS-I and 63 for AIS-2 (there are N-I increments from pixel I to pixel N).

The accuracy of this method is limited in two ways. The repeat accuracy associated with exactly centering the spectral line on a pixe] is

about 5 to i0 nm, or just under one spectral bandwidth. Also, the center

wavelength which AIS receives depends on the viewing geometry somewhat,

since in order to get enough signal from the monochromator into the instrument, a spectral band wider than I0 nm is used. The monochromator optics

require the light source to be tilted away from the center axis in order to

17
maximize the signal. Changing the incident angle will alter the exit angle

of a particular wavelength.

The combination of these factors induces an uncertainty of about

two spectral channels. Results from several experiments comparing these

measured spectral endpoints with endpoints determined from observing the

carbon dioxide or water absorption features or other known spectral features in AIS data have indicated as much as a two band difference.

A krypton pen-ray lamp has been used both in the lab and as an inflight calibrator. This gas has emission lines in the AIS spectral region.

However, the convolution of the lamp with the instrument response produces

a spectrum whose lines are not necessarily sharp. In AIS-1 the grating

"jitter" smeared the lines considerably making it difficult to assign

wavelengths to the observed emission lines. The krypton data for AIS-I is

most useful as a monitor of calibration stability rather than as an absolute calibration standard. The grating jitter has been eliminated for

AIS-2 and the krypton spectra are much cleaner.

Laboratory calibrations before and after the Australia/Hawaii deployment in October 1985, differ by about one spectral band, which is well

within the measurement accuracy. Comparison of two sets of in-flight

krypton lamp spectra show that no measurable shift in the calibration

occured.

DETECTOR RESPONSE EQUALIZATION

Although the quantum efficiency of the mercury cadmium telluride

detector drops to near zero beyond 2.6 microns, a thermal contribution to

the signal at warm temperatures has been detected. As a consequence, the

detector pixel response equalization tests are performed with AIS inside a

cooled thermal chamber. The test is repeated at several temperatures,

bracketing actual flight temperatures.

As a source of uniform radiance, a standard laboratory flat field

illumination source (light cannon) is used. Two projector lamps are mounted inside a housing which has a hemisphere at the rear and a cone on the

front. The inside of this housing is painted with Kodak white reflectance

paint. On the front of the cone are two diffuse pieces of water-free

quartz.

The light cannon radiance is calibrated using an Optronics Laboratories spectroradiometer. A cooled lead sulfide detector is used in order

to calibrate out to 2.5 microns. First, the spectroradiometer response is

calibrated in units of irradiance (Watts/square cm -nm) viewing a secondary calibration standard lamp whose irradiance is known to within one to

two percent. Second, the light cannon is calibrated also in units of

irradiance with the calibrated spectroradiometer. Radiance units can be

obtained by multiplying the irradiance curve by a constant which depends on

the geometry of the system. Equipment is currently being purchased to

enable us to perform a radiance calibration directly.

18
The AIS optics are placed in the thermal chamber. A flat fold mirror

projects the AIS slit through a hole cut through the side of the chamber

and onto the 11" diameter diffuser plate of the light cannon. Assumingthe

radiance of the light cannon is uniform throughout the region viewed,

variations in digital number output amongthe detector pixels will be due

to detector response variations as well as any off-axis effects from the

optical system. For a radiance level approximately equal to flight levels,

multipliers are generated for each pixel to equalize their signal levels.

These values are then applied to flight data. For any particular scene,

the value of each pixel is adjusted by its multiplier.

Data are taken at ten different radiance levels by adjusting the light

cannon iris. A neutral density filter is placed over the AIS entrance

window such that the detector just saturates when the iris is at the full

open position. For each pixel, a light transfer curve is generated by

plotting signal against wavelength at a relative light level. For AIS-1

the curves depart enough from a linear relationship to warrant using a

piecewise linear fit to the data. Multipliers are generated from a data

set which is at typical flight intensities. When the target intensities

are at different levels from this laboratory data set, the corrected value

for a particular pixel is obtained by a piece-wise linear fit to its light

transfer curve.

The above procedure eliminates most of the striping seen in raw

images; however, some striping remains. One factor which may contribute is

the nonuniformity of the light cannon surface. Ideally, the radiance of

the light cannon should be uniform across the surface of the diffuser

plates. The uniformity has recently been measured with a broad-band spot

photometer, sensitive to visible wavelengths. A thirty percent decrease in

brightness was observed from center to edge. The nonuniformity will alter

the radiance level depending on which portion of the surface an instrument

is viewing. However, it should not alter the spectral shape of the radiance curve. The relative calibration seems quite constant with time.

AIS is out of focus at the distances used for calibration which should

minimize the effect of nonuniformity of the light cannon output. Efforts

are under way to improve the light cannon performance.

A more likely cause for the residual striping is in the detector itself. If the detector array's properties vary with time or environment,

striping will result. If, for example, there is a small change in detector

responsivity after the instrument has been calibrated in the lab, the application of the lab-derived multipliers will not completely equalize the

output of each detector element in the array. The only way to overcome

this situation with AIS is to use an inflight calibration taken at the same

time as the data. Many investigators have successfully used regions of

the flight line which are spatially uniform and have a known spectral

reflectance to take out the effects of the detector variation as well as

some of the atmospheric effects.

PERFORMANCE EVALUATION

There is an on-going effort at JPL to determine instrument performance

characteristics such as the signal-to-noise ratio. One method to determine

these parameters is to image pure rock samples under solar illumination.

19
This is done under flight temperature conditions by enclosing the instrument optics in a styrofoam box and purging with cold nitrogen gas. Data

from kaolinite, montmorillonite, halon, gypsum, alunite, aphrosiderite, and

calcite have been acquired. One such data set gives a signal-to-noise ratio of about ten-to-one for AIS-I. This low number is most likely due to

the electronic signal chain noise. Much of this noise has been removed

from AIS-2. Hence the performance of the instrument should be considerably

improved.

FUTURE PROJECTS

Several activities are under way to further improve the calibration of

AIS. Halon targets are being made at the lab to provide a spatially uniform and spectral known surface which will be illuminated both with sunlight and with calibrated lamps of sufficient intensity to approximate an

AIS scene. An integrating sphere is also being purchased which will have a

uniform radiance across a 16-inch diameter output port. Although this will

be part of the calibration equipment for AVIRIS, it will be used for a

check on AIS calibration at the end of 1986. Finally, improvements are

being made in onboard spectral alignment calibration. Because pen-ray

lamps frequently contain impurities which results in unidentifiable extraneous emission lines, the use of rare earth filters such as holmium oxide

and praseodymium is being investigated. These materials have broad but

deep absorption features throughout the visible and short wavelength infrared, and when used in conjunction with a stable broad band light source,

should provide a more accurate means of assessing spectral alignment in

flight.

2O
N87-12970

COMPARISON OF VARIOUS TECHNIQUES FOR CALIBRATION OF AIS DATA

DAR A. ROBERTS, Stanford University, USA; YASUSHI YAMAGUCHI,

Geological Survey of Japan, Japan; RONALD J.P. LYON, Stanford

University, USA

ABSTRACT

The Airborne Imaging Spectrometer (AIS)

samples a region which is strongly influenced

by decreasing solar irradiance at longer

wavelengths and strong atmospheric

absorptions. Four techniques, the Log

Residual, the Least Upper Bound Residual, the

Flat Field Correction and calibration using

field reflectance measurements were

investigated as a means for removing these

two features. Of the four techniques field

reflectance calibration proved to be superior

in terms of noise and normalization. Of the

other three techniques, the Log Residual was

superior when applied to areas which did not

contain one dominant cover type. In heavily

vegetated areas, the Log Residual proved to

be ineffective. After removing anomalously

bright data values, the Least Upper Bound

Residual proved to be almost as effective as

the Log Residual in sparsely vegetated areas

and much more effective in heavily vegetated

areas. Of all the techniques, the Flat Field

Correction was the noisiest.

INTRODUCTION

The Airborne Imaging Spectrometer (AIS) samples a spectral

region which is severely affected by two factors, decreased solar

irradiance at longer wavelengths and wavelength specific

atmospheric absorptions, such as the 1400 and 1900 nm water

absorptions. These factors result in a reduced brightness and

dynamic range in data collected in these regions, an undesirable

characteristic with regards to data interpretation for geological

or botanical purposes.

The purpose of this study was to investigate a number of

techniques which have the potential for removing atmospheric and

solar effects from the data. Four techniques were investigated.

These have been divided by the authors into two groups,

statistically based techniques which derive various parameters

from the data and use them in the normalization process, and

physically based techniques which utilize some physical/spectral

property derived from the AIS data or from spectra collected in

the field to standardize the data to some known physical quantity.

The study was conducted in the vicinity of the town of

21
Yerington and the adjacent Singatze Range, located approximately

50 miles south of Reno, Nevada. Five flight lines of data in the

"rock mode" (1200 to 2400 nm) were acquired on July 25, 1984. The

five flights, flights 407 to 411, were acquired over a variety of

targets ranging from hydrothermally altered areas and mine dumps

to alfalfa fields. Two areas, one located in a hydrothermally

altered area on flight 407, and one located on flight 411 in a

region dominated by alfalfa, were selected for comparison of the

four techniques.

STATISTICALLY BASED METHODS

The statistically based techniques are the Log Residual and

the Least Upper Bound Residual (Green and Craig, 1985). The Log

Residual is based upon the following multiplicative relationship:

DN_x = T,Rsxl× (i)

Where DNs×= Radiance, equal to the digital number value for

each channel at each pixel in the data, T, = a topographic factor

which takes into account brightness differences due to slope

orientation, Rsx = Reflectance for each channel at each pixel in the

image and Ix = Average irradiance throughout the image.

Equation (i) is divided by a number of logarithmic averages

derived from the data, and rearranged to give the following

equation:

LOG(R,x) = LOG(DNs×)-AVG(LOGx)-AVG(LOG,) (2 )

The average logarithmic spectrum is derived by summing the

logarithmic values for each channel, then dividing these sums by

the total number of samples in the image. The topographic factor

is calculated by summing the logarithmic values for the 128

channels at each pixel, then dividing this sum by 128.

The Least Upper Bound Residual differs from the Log Residual

in only one area. Instead of the average logarithmic spectrum, the

maximum spectrum is used. The equation is as follows:

DN,x = T,R,xLUB× (3)

Where LUBx = the maximum real brightness value for each of the

128 channels. Like equation (I) this equation can be rearranged:

LOG(R,x) = LOG(DN,×)-LOG(MAXx)-AVG(LOG.) (4)

Both the Log Residual and Least Upper Bound Residual values

had one more value added to them. This value was called the image

average and was equal to the average logarithmic value for every

sample and channel in the image. This value was employed as a

means of normalizing each flight line.

22
PHYSICALLY BASED METHODS

The physically based techniques employed are the Flat Field

Correction as defined by the Jet Propulsion Laboratory and Field

Reflectance Calibration.

The Flat Field Correction is based upon locating a pixel or a

number of pixels in the imagery which are spectrally flat. This

spectrum is then divided into every pixel in the flight using the

following equation:

R,x = DN,×/F× (5)

Where F× = a spectrally flat 128 channel spectrum.

Reflectance calibration is based upon locating a number of

homogeneous, unvegetated targets in the AIS data. Field spectra

are measured at each site in an attempt to characterize the

spectral reflectance of the target. Next, reflectance, determined

in the field, is compared to the digital number response measured

by the AIS. Theoretically, there should be a strong linear

relationship between these two values. By comparing field

reflectance for a number of targets to thier corresponding

response measured by the AIS the following equation can be

derived:

R,x -----DN,xslopex+interceptx (6)

Whereslopex = the slope of the best-fit equation and interceptx =

the intercept of this equation. These constants can be applied to

the digital number value for each channel at each pixel to

calculate a corresponding percent reflectance value.

Four calibration targets were located in the AIS data. These

targets consisted of the Yerington Mine dump (at 4300 feet on

flight 410), the Bluestone Mine dump (at 4300 feet on flight 407),

a sparsely vegetated tuff (at 5200 feet on flight 407) and a patch

of pavement (at 4300 feet on flight 411).

Six to thirteen spectral measurements were made at each site

using a GER IRIS spectroradiometer which collects 820 channels of

data between the 400 and 2500 nm region at a spectral resolution

of 2 to 4 nm. The spectra were collected between I0:00 A.M. and

3:00 P.M.. All spectra were standardized to a barium sulfate

plate. The number of spectra collected varied depending upon the

homogeneity of the target which was assessed visually at the time

of collection, six were collected over the pavement and the

Bluestone Mine dumps. Thirteen were collected over the tuff. Nine

were collected over the Yerington Mine dumps. In all cases,

spectra were collected randomly in an effort to best characterize

the target.

Field spectra were converted to percent reflectance by dividing

the radiance measurement of the target by the radiance value for

the barium sulfate plate which was measured simultaneously. Next,

spikes, present in the field spectra in the 1400 and 1900 nm

regions were removed by interpolating across the spike, usually a

23
ORIG_,_L F_G_. iS

region 4 to 8 nm wide. Despiked field spectra were averaged

producing a single spectrum for each target, modeling the pixel

response for that target. Finally, the modeled spectra were

convolved to the 9.6 nm AIS spectral equivalent using a gaussian

filter transfer function.

Raw data values were extracted from the AIS imagery over the

four calibration targets. Between 5 and 19 pixels were extracted

for each target and averaged. Next, the averaged pixel value was

plotted against the averaged, convolved field spectra. Leas_

squares regression was used to produce a best fit line. The R E

lues ranged from as low as .874 to as high as .992. The average value was around .95. The slope and intercept of the best fit

lines were used to convert each AIS channel to "percent

reflectance" (Table i).

Table i. Airborne Imaging Spectrometer

channel information

WA%_LENGTH REGRESSION WA%rEI.ENGTH REGRZSSION

CDUkNNZi (urn) EQUATION R 2 CMA/4NKL (Urn.) EQUATION 9 2

1 1.115 .0195x -6.91 .970 65 1.728 .0448x -13,47 .978

2 1.144 .0164x -5.89 .967 66 1.737 .0457x -12.01 .977

3 1.153 ,0149x -4.49 .974 6 _ 1.746 .0476x -10.46 ,975

4 1.162 .0152x -5.94 .976 68 1.755 .0566x -12.10 .969

5 1.172 .0146x -4.31 .97] 69 1.765 .0696x -11.55 .97O

6 1.181 .0146x -4.34 .972 7o 1.774 .099]x -19.31 .97O

7 1.190 .0143x -3.98 .971 71 1.753 .1527x -29.9_ .972

B 1.199 .0137x -3.6O .973 72 1.793 .1_88x -29.77 .958

9 1.208 .0134x -2.76 .973 73 1.802 .2937_ -26.O8 .945

10 1.219 .0139x -3.57 .96_ 74 1.811 .2016x -31.81 .976

11 1.227 .0149x -3.93 .969 75 1.920 .2092x -31.72 .959

12 1.236 .0162x -4.27 .97C 76 1.830 .2161x -28.7O .946

13 1.245 .0161x -3._2 .967 77 1.839 .2691x -35,21 .932

14 1.25_ .018_x -3.81 .96_ 78 1.945 .3049x -3R.25 .93O

15 _.26_ .0164x -4.44 .966 79 1.958 .3006x -34,78 .925

16 1.273 .0179x -4.9? .976 8o 1.967 .2|95x -33,o9 .937

17 1.28_ .0159x -4.18 .969 91 1.$76 .2538x -26.54 .932

18 1.291 °0224x -6.59 .96_ 82 1.995 .2759x -33.78 .942

19 1.3C1 .0268x -8.11 °965 83 1.995 .2146x -22.31 .9O3

2o 1.310 .0363x -12.46 .973 84 _.904 .2100x -2_.73 .938

21 1.319 .0576x -_9.54 ,960 $5 1.913 .1539x -15.10 .9O4

22 1.329 .1034x -29.69 .952 96 1.923 .1398x -14.39 .91_

23 1.33_ .1755x -47.65 .952 87 1.932 .1203x -10.57 .918

24 1.34_ .2099x -55.52 .903 88 1.941 .lOllx -6.29 .903

25 1.356 .199_x -52.O7 ._9_ 99 1.950 .0958x -5.O6 .914

26 1.265 .2097x -5O.66 .874 9o _9_e .07_7x -4.39 .910

2_ 1.374 .2381x -61.28 .921 91 1.969 ,0_58x -_.68 .923

28 1.353 .2192x -_3,8_ .9_4 9_ 1.9_ .0994x -7.2_ .90_

29 1.393 .1948x -42.65 .91_ 93 1.998 .0990x -5.23 .9O8

36 1.402 .l_08x -32.21 .974 94 1.997 .0849x -4.O3 .93O

31 1.411 .11BOx -24.10 .945 95 2.OO6 .0741x -4.66 .936

_2 1.420 .0927x -la.75 .93O 96 2.01_ .0743x -6.27 o951

33 1.432 .0766x -12,12 .9O8 97 2.O28 .0912x -4.74 .948

34 1.442 .0720x -14.15 .933 98 2.O37 oOB2_x -4°90 .9_6

35 1.451 .0606x -10.90 .946 99 2.O46 .0823x -4.8_ .956

36 1.460 .0531x -10.20 .95_ IO0 2.0_5 .0552x -7.23 .962

37 1.469 .0396x -5.12 .948 lOl 2.O65 .O|OOx -5.O7 .962

38 1.479 .0327x -8.26 .950 102 2.O74 .0769x -5.11 .966

39 1.88_ .0289x -3.53 .959 le_ 2.093 .0713x -5°98 .965

4o 1.49_ .0274x -4.08 .963 10_ 2.09_ .0796x -6.O7 .967

41 1.506 .0271x -4.93 .966 105 2°102 .0796x -6.62 .964

42 1._1_ .0211x -_.95 .9_5 106 2.111 .O|04x -6.14 .97O

43 1._25 .02_9x -6.19 .966 107 2.131 .0823x -6._6 .96_

44 Z.53_ .0292x -7.29 .966 10_ 2.130 .0959x -7°19 .965

45 1._43 .0902x -7.93 .963 109 2.119 .0|96x -B.30 .967

46 1._53 .030_x -7.10 .959 110 2.149 .O922× -8.76 .964

87 1.562 .0906x -6.63 .966 lll 2,158 .09_1x -8°83 .965

48 1.571 .032_x -I.03 .967 112 2.16_ .09_9x -9.13 °946

89 1°991 .0314x -6.9o .967 113 2.177 .0962x -9.O6 .956

5o 1.590 .0331x -_.59 .96_ 114 2.186 .1024x -11.24 .963

51 1.999 .0327x -B.33 .969 115 2.195 .0962x -8.74 .950

52 1.60a .03_2x -8°$5 .972 11_ 2.2O5 .1004x -9.26 .961

_3 1.618 .0346x -9.57 .97_ 117 2,214 .1120x -19.31 .939

54 1.627 .O951x -9.93 .979 118 2.223 °11_7x -9°10 ,942

5_ 1.636 .0363x -10.99 .977 Z19 2.232 .1175x -9.31 °93_

56 1.645 o0369x -11°59 .978 120 2.24_ .1204x -9.21 .936

_7 _._55 o036_x -11.52 .97_ 1_1 2.251 .1229x -9,66 .924

$s _.664 .0171x -11.42 .979 1_2 2.260 °125Sx -9.10 .929

59 1.673 .0383x -12.76 .95_ 12_ '2._70 .1303x -10.23 °950

_0 1.612 .0390x -12.96 .98_ 12_ 2°279 .1346x -10.65 .945

61 1.692 .0410x -14.0_ .984 125 2.288 .1436x -Z_.82 .945

62 1.701 .0436x -15.19 .998 12_ 2.299 .1474x -12.82 ,9_7

63 1.710 .0450x -15.20 .986 127 2._07 .1593x -15°2_ °964

64 1.729 .0479x -16.17 .992 125 2.316 .1679x -14.75 .963

Channel wavelengths determined using 1.96, 2.01 and 2.06 CO 2 ab$0rp_ion features

and maximum H2U _b$0rpzions I_ 1.379 end 1._7 (Goldber_, 1954)

24
RESULTS FOR UNVEGETATED TARGETS

Figure 1 shows an uncorrected, unprocessed AIS spectrum

extracted from flight 407 in an area which is hydrothermally

altered. Though, unverified on the

ground, this pixel has been

interpretated as containing

kaolinite. The two most notable

features in this spectral plot are

the strong absorptions in the 1400

and 1900 nm regions and the

decreased brightness at longer

wavelengths; no definitive clay

absorption features are evident.

Figures 2 and 3 show the

results of the Log Residual and

Least Upper Bound Residual for the

same location. Figure 2 has

several notable features. First,

i UNPROCE$SIED DATA

O

14410 IN0 220_1 2,116

'u+nveteullb (am)

Uncorrected AIS spectrum

Hydrothermal alteration

both the atmospheric absorption features and solar irradiance have

been normalized. Second, mineral absorption features have been

markedly accentuated; the resulting spectrum looks like a

kaolinite spectrum. Finally, the resulting spectrum is relatively

noise free and easy to interpret.

Figure 3 demonstrates that the Least Upper Bound Residual

process was not as effective. In this plot, solar irradiance has

been normalized and atmospheric absorption partially normalized.

However, mineral absorption features are not as evident and the

resulting spectrum is very noisy.

641411

LOG |ESI Ol.I AL

..................... ........................................... ! ..................................

11,1141 1401 IIIlt 1111 1311

willlllllll (11)

64141e

LEAST UPPER BOUND

RESIDUAL i

i

IIM 1411 IIIU 31110 13116

wm.elii41lb (ram)

Fig. 2. Log Residual spectrum Fig. 3.

Hydrothermal alteration

Least Upper Bound Residual

spectrum, Hydrothermal

alteration

Many of the problems in the Least Upper Bound Residual

spectrum do not accurately reflect the utility of this technique.

The noise, and poor resolution of mineral absorption features are

the result of anomalously high AIS data values which do not

reflect the true maximum value for each channel. In order to solve

this problem, it became evident that these anomalous data values

had to be removed from the data set before using the Least Upper

Bound Residual technique.

In order to remove these anomalous data values two filtering

25
techniques were employed. The first technique is based upon

removing all data values brighter or darker than a select

percentile. A .01 percentile value was chosen empirically. Based

upon this technique, all data values darker than the first

percentile or brighter than the 99th percentile were assigned the

average brightness value for that channel, calculated from the

data confined by these two bounds.

The second filtering technique was based upon anomalously

large changes in brightness in the down-track direction. These

anomalous data values were located by comparing digital numbers in

one cross-track line to digital numbers in the previous crosstrack line. Two threshold values, one for pixels brighter than

1500 dn, and one for pixels darker than 1500 were selected

empirically. The threshold values used on this data set were 1600

in bright areas and 600 in darker areas. The average brightness

for that channel was assigned to a pixel if the absolute

difference from one line to the next was greater than the

threshold value. The resulting Log Residual and Least Upper Bound

Residual spectra are presented in figures 4 and 5.

6_

FILTERED FILTERED

600Q

LOG RESIDUAL

................................................................ i ..................................

LEAST UPPER BOUND

RESIDUAL

0

IiJ| 1400 II00 | _J)l) 2.111 IIJ| 1410 ItqN 2|N 2111

VlvolOllllh (ira) vlvolOlllk (l|}

Fig. 4. Filtered Log Residual Fig. 5.

spectrum, Hydrothermal

alteration

Filtered Least Upper Bound

Residual spectrum

Hydrothermal alteration

Comparing figure 2 to figure 4, it is evident that filtering

did not result in any marked improvement in the Log Residual

spectrum. However, comparing figures 3 and 5, a very marked

improvement is shown. The filtered Least Upper Bound Residual

spectrum is almost as good as the Log Residual spectrum in terms

of showing mineral absorption features and in terms of noise.

However, though the Least Upper Bound Residual has been improved,

there are some problems with the filtering process. Visual display

of the entire AIS flight demonstrated that a number of bright, non

anomalous pixels were removed, suggesting that a 99th percentile

was too extreme.

Figure 6 shows the resulting Flat Field spectrum for this

same area. Like the other two techniques, the Flat Field

Correction successfully normalized solar irradiance. However, some

atmospheric absorption is still evident in the 1400 and 1900 nm

regions. In addition, the resulting spectrum is not as easy to

interpret as the Log Residual and is much noisier.

Figure 7 shows the percent reflectance spectrum for this same

area. Both solar irradiance and atmospheric absorption have been

26
normalized. In addition, the resulting spectrum is very smooth.

However, mineral absorption features are not as pronounced as they

are in the Log Residual and Least Upper Bound Residual spectra.

FLAT FIELD

i

113,1 I_ IEM0 %NI %_16

wsveleqOII (aim)

CAL,,,AT'6"

IIRi li I| I| %)lib

ws,6tOalEall (mmJ

Fig. 6. Flat Field corrected

spectrum, Hydrothermal

alteration

Fig. 7. Reflectance calibrated

spectrum, Hydrothermal

alteration

RESULTS FOR VEGETATED TARGETS

Figure 8 shows an unprocessed

spectrum of alfalfa located on

flight 411. Comparison to figure 1

demonstrates that there are some

noticeable differences between the

two, especially in the 1200 and

1500 nm regions. However, at

wavelengths longer than 1600 nm,

the differences are not strongly

evident.

Figures 9 and I0 show the Log

Residual and Least Upper Bound

t :NPIIOCESSED DATA I

1_16

walvelullh (aim)

Fig. 8. Uncorrected AIS spectrum

Alfalfa

Residual results for this same

area. Once again solar irradiance and atmospheric absorption have

been partially to completely normalized. In addition, though

unusually shaped, the Log Residual spectrum for the alfalfa is

relatively noise free. The Least Upper Bound Residual, on the

otherhand,is extremely noisy.

Log Residual spectrum

Alfalfa

2NO Ui6 OIi,U

LEAST UPPER IOUND

RESIDUAL i

Fig. I0. Least Upper Bound Residual

spectrum, Alfalfa

27
Figures ii and 12 show the Log Residual and Least Upper Bound

Residual spectra resulting from filtered AIS data (97.5

percentile, threshold values of 1600 and 500). Once more the only

marked improvement occurs in the Least Upper Bound Residual.

6@@0 FILTERED M)O0 FILTERED

LOG RESIDUAL

113_ 14N 1900 2200 2316

wmyelen|t k (urn)

Fig. ii. Filtered Log Residual

spectrum, Alfalfa

i LEAST UPPER BOUND

i RESIDUAL

113S 1400 1900 2200 2316

waovelemlltk (smm)

Fig. 12. Filtered Least Upper Bound

Residual spectrum, Alfalfa

Analysis of an unvegetated area located along flight 411

demonstrates a major problem with the Log Residual when it is

applied to heavily vegetated flight lines. Figure 13 shows the Log

Residual spectrum for an area containing stubble or plowed soil.

Comparison of figure 13 to figure ii demonstrates that figure 13

is the mirror image of figure Ii. This is a product of the average

logarithmic spectrum which is

utilized in the Log Residual

process. Along flight 411, the

average logarithmic spectrum is

strongly influenced by alfalfa.

Therefore, any surface feature

which is not alfalfa will show

departures from this average

spectrum, resulting in a mirror

image.

Figure 14 shows the Flat Field

corrected spectrum for the same

area of alfalfa. Both solar

irradiance and atmospheric

absorption appear to be

LOG RESIDUAL

e

ii3s 1400 1900 2200 22116

waiveletllt h (urn)

Fig. 13. Log Residual spectrum

unvegetated area

normalized. In addition, the resulting spectrum is relatively

noise free. Finally, the spectral shape is unusual for vegetation,

though very similar to the result obtained using the previously

mentioned statistically based techniques.

Figure 15 shows the result obtained using field spectra as a

means for calibrating the AIS data. Of the four techniques, this

result shows the best normalization and has the least amount of

noise. In addition, like the spectra produced by the Log Residual,

Least Upper Bound Residual and the Flat Field Correction, this

spectrum does not have a typical shape for vegetation.

28
FLAT FIELD

Ills 14(m I_ I]M Ul6

vmvelenslb (lira)

Fig. 14. Flat Field corrected

spectrum, Alfalfa

1

1135

CALUIkATIgD

1400 1_00 2200 2316

wmvelenst b (am)

Fig. 15. Reflectance calibrated

spectrum, Alfalfa

The unusual shape of the vegetation may have resulted from

the processing techniques which were employed, or originated in

the AIS data. The fact that this spectral shape is present in all

four processed spectra suggests that the problem may be in the

unprocessed AIS data. In order to investigate this possibility,

uncorrected AIS pixels were extracted from the alfalfa and

compared to the uncorrected AIS values for the pavement which was

measured in the field and found to be approximately 24 percent

reflective. The results showed that the uncorrected AIS values for

alfalfa were up to twice as large as the values for pavement in

the 1800 to 2000 nm region. A typical vegetation spectrum should

be darker than pavement in this region. It was concluded that the

problem was in the original AIS data.

A number of possible explanations, including possible

atmospheric problems were considered. In light of recent findings

at the Jet Propulsion Laboratory, the most likely explanation is

second order overlap from the shorter 800 to 1200 nm regions (G.

Vane, verbal communication). The Airborne Imaging Spectrometer

contains a blocking filter designed to block all wavelengths of

light shorter than 800 nm. Thus, light in the 800 to 1200 nm range

may add to the 1600 to 2400 nm regions in the form of a second

order contribution. This phenomenon would be particularly bad for

vegetated areas because vegetation is strongly reflective in the

800 to 1200 nm region and vegetation is strongly absorbent in the

1800 to 1900 nm region. Based upon the results obtained in the

unvegetated area, this is not a marked problem where vegetation

cover is low. Furthermore, second order overlap is not a problem

in unvegetated pixels located in heavily vegetated flight lines if

the data is calibrated using field reflectance measurements; the

second order contribution is incorporated into the best-fit line

established between percent reflectance measurements and

uncorrected AIS digital numbers.

SUMMARY

The results of this study suggest a number of strategies for

image processing of data with a large number of spectral

dimensions. Of the four techniques investigated, calibration using

field spectra was the best. However, difficulties in selecting

calibration sites, instrumental requirements and the number of

29
hours required to measure the spectra make this technique

impractical from an exploration point of view. In addition, it is

not always possible, or desireable, to collect field measurements

prior to analyzing the data. Therefore, it is necessary to

consider other processing techniques which are not field intensive

or difficult to employ. Of the other three techniques

investigated, the Log Residual proved to be superior in areas

which did not contain a dominant cover type. The Least Upper Bound

Residual, after careful filtering of the data to remove anomalous

data values, proved to be almost as useful as the Log Residual in

these areas. In addition, in an area dominantly composed of one

cover type, the filtered Least Upper Bound Residual proved to be

very successful, whereas the Log Residual was ineffective in these

areas. Of these three techniques, the Flat Field Correction proved

to be the noisiest and least effective. However, the Flat Field

has an advantage over the Least Upper Bound Residual and the Log

Residual in that this technique has some physical basis, and thus

the results may be related back to what is occurring on the

ground.

REFERENCES

Green, A.A., Craig, M.D., Analysis of aircraft spectrometer data

with logarithmic residuals; Proceedings of the Airborne

Imaging Spectrometer Data Analysis Workshop, April 8,9,10,

1985, Gregg Vane and Alexander F.H. Goetz editors, Jet

Propulsion Laboratory, California Institute of Technology,

Pasadena, CA, pp. 111-119

Goldberg, L., 1954, The Absorption Spectrum of the Atmosphere; The

Earth as a Planet, G.P. Kuiper editor, University of Chicago

Press, Chicago, Ii, pp. 434-490

3O
N87-12971

ANALYSIS OF AIS RADIOMETRY WITH EMPHASIS ON DETERMINATION OF ATMOSPHERIC

PROPERTIES AND SURFACE REFLECTANCE

J. E. CONEL, S. ADAMS, R. E. ALLEY, G. HOOVER, and S. SCHULTZ, Jet

Propulsion Laboratory, California Institute of Technology, Pasadena,

California

ABSTRACT

Airborne Imaging Spectrometer data from Mono Lake,

California, were studied in order to establish spectral

radiance of test areas under solar illumination. The

objective is to provide a method of atmospheric correction for major absorbers from the spectrometer data

themselves. Crucial to the analysis is radiometric

calibration of the instrument. Good agreement is found

between calculated and measured radiances for uniform

surface targets (beaches), but simulations of atmospheric properties with LOWTRAN lead to unreasonably

low values of atmospheric precipitable water. Absorptions from carbon dioxide are not detected in the AIS

data, but are strongly present in the LOWTRAN model.

The apparent low contrast of all atmospheric absorption

bands leads to a study of contamination from overlapping

spectral orders in the AIS data. The suspected contamination is shown unambiguously to be present beyond

approximately 1500 nm and consists of an extra radiance

term including atmospheric bands from the %/2 wavelength interval. The magnitude of such contamination

remains uncertain pending determination of second-order

efficiencies of the grating used. The spectral band

filling at 1400 nm cannot be accounted for by order

mixing because of the 800-nm blocking filter used. A

rigorous removal of the unwanted spectral contamination

does not seem possible for any data taken in the rock

mode. Rough estimates for tree-mode observations might

be pieced together if a suitable after-the-fact

radiometric calibration of the instrument can be

formulated.

INTRODUCTION

The Airborne Imaging Spectrometer (AIS) is an instrument with

sufficient spectral resolution (approximately 9.5 nm) to recover most

geologic and geobotanical surface spectral reflectance features. AIS

can also provide data for analyzing important atmospheric absorption

bands from water and carbon dioxide. Use of the full potential of AIS

measurements for surface investigations throughout the spectral interval

of observation (900 to 2400 nm) requires accurate compensation for

atmospheric effects. The principal sources are gaseous absorptions,

but at shorter wavelengths some aerosol scattering may be encountered.

31
This paper discusses the absolute radiometric characteristics of

the AIS data based on laboratory calibration measurements and field

observations. An absolute calibration implies that source radiance may

be deduced from instrumental output, in this case raw counts from the

AIS. For a detailed discussion of the actual laboratory calibration

procedures, see Tucker (1984). We investigated the atmospheric problem

on an absolute basis because both atmospheric and surface parameters

can be derived from simple theory; the numbers obtained can be compared

directly with field observations. The absolute approach also requires

a rigorous understanding of the actual radiometry of the system and

identifies the problem areas that should be addressed in subsequent

instrument designs. Three recommendations emerged from the studies:

(I) the instrument should be calibrated radiometrically, (2) blocking

filters should be included in the optical train to prevent overlapping

spectral orders at infrared wavelengths, and (3) wavelength calibration

should be done on board to allow channel assignment fixing on a

pixel-by-pixel basis for actual flight data.

METHODSOFCALIBRATION

The AIS is an aircraft-mounted system and is thus immersed within

the atmosphere. This complication is usually ignored, however, and the

radiance lij seen by the nadir-looking instrument is assumed to be

given by

lij = Pij + TijRij (1)

where the subscripts refer to image line i and sample j, Pij is the

path radiance, Tij is proportional to atmospheric transmittance of

diffuse downward plus direct components, and R i • is the surface

Lambertian reflectance. All quantities in Eq. (1) 3 are functions of

wavelength and, in the most general case, position. If the atmosphere

is assumed to be horizontally homogeneous and the elevation variation

over the site "small," then the path radiance and transmittance

functions can be taken independently of position (equal, say, to P and

T, respectively), and Eq. (i) takes the form

lij = P + TRij (2)

At infrared wavelengths 1200 nm and beyond, the path radiance is often

negligible, and Eq. (2) reduces approximately to lij = TRij.

Probably the simplest method of estimating the function T at each

wavelength, requiring only manipulation of the image data themselves,

is to form the average of lij over portions of the scene to secure

= TR, where

T

= _ li 1/MN

i=l j=l

(3)

32
(and a similar definition for R) in which M is the numberof lines and N

is the number of samples. Thus

lij/l = Rij/R (4)

and estimates of the surface reflectance are obtained independently of

the transmittance T and scaled according to the average reflectance R.

For Thematic Mapper (TM) data, the average reflectance is not independent of wavelength, even when averages are taken over millions of

pixels. However, the ratio in Eq. (4) may still be useful for isolating

specific features in Ri.] apart from the atmosphere. The question of

structure in such reflectance averages needs to be investigated at a

spectral resolution of i0 nm. Solomon (1984) and Green and Craig

(1985) have discussed atmospheric corrections based on the scene

average method.

Notice in the above discussion that radiances rather than scanner

response (digital number or DN) have been used. For a sufficiently

small range of input energy, the instrument response is nearly a linear

function of input energy (see below), i.e., DN = A + BI, where A is the

dark current response and B is a gain factor, both of which vary from

detector to detector. The constants A and B are determined observationally, and the dark current response is corrected for in the raw

data. If the constants B are assumed to be approximately equal for all

detectors, then forming the ratio in Eq. (4) roughly compensates for

instrumental response as well.

When it is possible to employ field measurements of surface

reflectance, regression plots of the reflectance vs scanner response

can be used on a strictly empirical basis to recover ground reflectance.

This method has often been used and is discussed by Conel et al. (1985,

1986) and Paylor et al. (1985) for Landsat IV observations. An example

of such plots for AIS data over the Mono Lake test site, California

(Conel, 1985), is shown in Figure I. If scanner counts are convqrted

to radiance using radiometric calibration data, then slope and intercept

values obtained from the regression lines provide numerical values for

the path radiance and transmittance functions. These functions can

then be solved on simple atmospheric models to provide estimates of

atmospheric optical depth and single scattering albedo (Conel et al.,

1986; Paylor et al., 1985).

The principal assumption involved in applying Eq. (i) to the

problems discussed is that the radiances lij are actually derivable

from the AIS measurements. The subsequent discussion examines this

problem.

SUMMARY OF RADIOMETRIC CALIBRATION PROCEDURE FOR AIS

The AIS calibration (Tucker, 1984) is performed with a standard

laboratory flat-field illumination source (light cannon) whose spectral

radiance is measured between 750 and 2500 nm with an Optronics Laboratories spectroradiometer. The spectroradiometer is, in turn, calibrated

with a National Bureau of Standards standard irradiance source. The

AIS, mounted in a temperature-controlled chamber, is calibrated at

operating temperature. The standard source, reflected in a mirror, is

33
255 I I I I I 1 --

DN

200

100

0

255_

200

100

255

2OO

100

/_ S,_ 1284 (15)

O PUMICE FLAT #1

[] ASPHALT ROAD

1330 (20)

1376 (25)

1471 (35)

L L I I 1

i I I ] }

1518 (40)

1703 (60)

__[j1748 (65)

L

I I l I I I I --

_j 1841 (75)

1934 (85)

J

--- f _ I I I I

_,_.4,__ 2027 / 2216 (115)

(95)

2123 1105)

_ 2309 1125)

/(

d

o I ( l I L L i

0 01 02 03 0.4 05 0.6 07 0 01 02 03 04 05 0.6 07

REFLECTANCE, %

Fig. i. Examples of empirical calibration curves for the Mono Lake

test site, October 31, 1984. All lines pass through the origin consistent with observation of zero radiance (to at least

one part in 4100) over Mono Lake where surface reflectance is

zero.

viewed through a neutral density filter (transmittance = 0.32 to 0.35

over the spectral interval 900 to 2500 nm). An iris diaphram mounted

internally to the light cannon provides variable illumination to the

instrument and is used to construct curves of the instrument response

as a function of illumination. There are 4096 instrument response

curves, each curve representing one 32 x 32 array detector in each of

34
four grating positions. Examples of such light transfer curves are

shown in Figure 2, in which raw instrument output in digital counts is

plotted as a function of the fraction of maximum light cannon energy at

each wavelength. In all cases, the instrument dark current response is

2.8

2.4

2.0

1.6

1.2

0.8

%

0.4 x

v

u')

t,-

z

0 2.8

U

2.4

<[

m

2.0

_ r F [ I * I * I * I * I * T _/(

GRATING POSITION 1 CH 32 (1498 nm)x,_

r DARK CURRENT /'_ / _'_

,OFFSET _y "

/ jj,_'_ CH 1 11210 rim)'

S

1.6

1.2

0.8

0.4

' I ' I ' I ' I ' r ' I ' I ' I ' I '

GRATING POSITION 3 ) j4

CH 65 (1808 nm

F-DARK CURRENT p,,, J A,.,J"_I /

OFFSET / 11

I , I , I , I , I , I , I , 1 , l ,

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

• , ' * ' ' r *C, 64 ;1,_9'nm_)__

ORAT,NG POSITION 1

F- DARK CURRENT /_ /)/" _ t

/_ _%CH 33 (1508 nm_ 1

T ' I r I ' F ' r ' T ' I ' I T ?

GRATING POSITION 4

CH 97 (2109 nm) _cul

OPFSET

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

FRACTION OF MAXIMUM LIGHT CANNON RADIANCE

Fig. 2. Examples of relative calibration curves used for conversion

of raw digital counts to radiance. Dashed lines in panels

for grating positions i, 2, and 3 represent response of next

highest channel.

indicated on the ordinate of each graph and has been subtracted from

the output at each iris setting. These light transfer curves (obtained

on October ii, 1984) were accepted as representing the radiometric

response of the AIS for the data subsequently acquired on October 31,

1984. These calibration data show several important characteristics:

(l) Except for grating position 2, the instrument response

varies with the channel. For example, in grating position I

the response of channel i is reasonably smooth, but in

channel 32 grating position i assumes a jagged sawtooth

shape for ordinate values greater than about 1300 counts

(500 raw counts is equivalent to approximately 10% ground

reflectance over the wavelength interval represented).

(2) For all grating positions, the response demonstrates a

sawtooth variation in the abscissa interval 0.4 to 0.7.

This behavior may represent missettings of the iris

diaphragm rather than mechanical malfunction of the

instrument. Except for channels in grating position i, a

smooth extrapolation of the curves over the interval would

seem justified.

35
(3)

(4)

The instrument response varies abruptly between channel 32

(last channel of grating position i) and channel 33 (first

channel of grating position 2). This behavior has peculiar

consequences. Converting spectra from raw counts to radiance introduces a discontinuity in the radiance spectrum at

that wavelength representing the boundary between grating

positions 1 and 2.

The instrument response in grating position 1--representing

the highest radiance values--is nonlinear.

CONVERSION OF THE RAW COUNT SPECTRUM TO RADIANCE

Mono Lake, in eastern central California, is a large, saline water

body whose northern shoreline is a series of wide, uniformly bright and

dark contiguous beaches composed of evaporite deposits (trona) and

basaltic lag gravel. The basaltic gravels are almost completely barren

of vegetation. The spectral reflectance (i0 to 15%) is a monotonous,

nearly constant function of wavelength in the visible and near

infrared. Except where these beach deposits are wetted by springs,

absorption bands near 1400 and 1900 nm (representative of OH or HOH)

are absent. Both wet and dry gravel beach sites were studied. Detailed

computations for a wet beach are presented here. A dry beach example

is shown in a LOWTRAN atmospheric simulation described below. Figure 3

gives the raw count spectrum of the wet beach target as a function of

channel number. Intervals corresponding to each grating position are

indicated. The conversion from raw counts to radiance was accomplished

by hand on a point-by-point basis using the instrument response curves

70O

I.-

z

400

0

(,3

300

2O0

I _ I 1 I I i I T I I q

100

J

/

GP1 =--it= GP2 -_- GP3 _l_ GP4 _-

I _ I I L I I [ I I I L

10 20 30 40 50 60 70 80 90 100 110 120 130

CHANNEL NUMBER

Fig. 3. Raw count spectrum of (wet) black beach #i site, Mono Lake,

for October 31, 1984.

36
and light cannon radiance values from the October ii, 1984 c&libration

run. The result of this conversion is shown in Figure 4, where, as

described above, the transformation to radiance units introduces a

prominent discontinuity between grating positions 1 and 2. An addi10" I i I T ] I t T : I [I

K

f 7 TRANSMISSION

;8

10-3

_ 5 -f-

• 4

uJ" _ SOLAR

3- ,_ I I i -'_"_ _ i / (11 '/o GROUND

A,S 1/ REFLECTANCE)

0 ' "rot J i [ [ ,!_-_P" _ I _ L I II l

0 10 20 30 40 50 60 70 80 90 100 110 120 130

CHANNEL NUMBER

1.0

0.9

0.8

0.7

Z

0.6 0

u_

u')

0.5

O9

Z

0.4

0.3

0.2

0.1

Fig. 4. The "absolute" radiance spectrum of black beach #I site

derived from the raw count spectrum of Figure 3, using

laboratory-determined light transfer calibration equations

and measured light cannon spectral radiance distribution.

The spectrum is calibrated in wavelength by comparison with

LOWTRAN transmission spectrum of the atmosphere and is compared to the solar energy expected for zero air mass, solar

elevation of 33 deg, and surface reflectance of ii%, as

determined by field observation. Solar irradiance data from

Arvesen et al. (1969).

tional small discontinuity also appears between grating positions 3 and

4, corresponding to a difference in slope between the light transfer

curves of channels 96 and 97. These discontinuities provide evidence

that the radiometric calibration is imperfect. The noise level in the

data is estimated to be 0.0005 Wm-2nm-lsr -I for a signal/noise

ratio of approximately I0 on a single pixel basis.

To provide a wavelength calibration for this spectrum, a LOWTRAN 6

simulation of the one-way atmospheric transmission spectrum for the

standard mid-latitude winter model was used, degraded to the spectral

resolution of the AIS. Matching the AIS and LOWTRAN spectra on a wavelength basis is uncertain because of noise in the AIS data, but sufficient spectral features representing prominent carbon dioxide and

water absorptions were present and recognizable to secure a tentative

match. Reconciling the wavelength positions between AIS and the LOWTRAN

spectra required shifting the spectrum of the second grating position

three resolution elements to a shorter wavelength with respect to the

first position. A similar displacement was required for the fourth

grating position with respect to the third. The assignment of wavelength to channel number for these flight data is thus secured by

reference to the LOWTRAN spectrum.

37
To provide a tentative check on the absolute energy scale obtained

from the laboratory calibration, a comparison was made between the AIS

spectrum and the solar spectrum expected for zero air mass after

reflection from the surface. The expected solar radiance is Im =

F®PoR where _F® is the solar irradiance, Mo is the cosine of the solar

zenith angle (57 degrees for the October 31, 1984 observations), and R

is the surface reflectance. The seasonally adjusted solar curve for

measured average 11% ground reflectance is shown as solid circles in

Figure 4, where the solar irradiance data of Arvesen et al. (1969) have

been used. The apparent agreement between these energy scales is good;

in fact, a match between the spectra at 1592 nm in grating position 2

(where the atmospheric transmittance reaches 96% for the real atmosphere) can be achieved by changing the ground reflectance to 12%, an

upward adjustment in the surface-measured value of 1%, i.e., 10% of

itself.

While good agreement can apparently be achieved between energy

scales for the AIS and solar spectra in grating positions 2 and 3,

important discrepancies remain. The most significant is in grating

position I, illustrated in Figure 5. Agreement between the AIS and

_ 6

=Es

E

_4

Z

<

5

< 3

n2

I I I

0

0

0 0

u_O0

_e,i ©

II O0

I,,, %

' (_00(_210

I I I L

1200 1300 1400 1500 1600

WAVELENGTH, nm

Fig. 5. The apparent spectrum of 1400-nm water band in grating position i for black beach #I site. Solid triangles represent

spectrum calculated from the instrument light transfer curves

and light cannon spectral radiance data. Open triangles

represent arbitrary upward adjustment of 2.62 radiance units

to obtain continuity between spectral segments of the first

and second grating positions. Open circles represent solar

spectrum for 12% ground reflectance. Solar irradiance data

from Arvesen et al. (1969).

38
solar scales can be obtained by arbitrarily adjusting the AIS curve

upward in one of two ways. In _the first method, the scale is translated

vertically by adding 2.6 Wm-2nm-lsr-i to the AIS spectrum,

thereby producing continuity between grating positions 2 and 3. This

leaves a discrepancy of 2.15 Wm-2nm-lsr-i between the solar and

measured spectra at 1245 nm where the atmospheric transmission is also

high (approximately 94%). In the second method, the AIS and solar

spectra are made approximately equal at 1245 nm by adding 2.15

Wm-2nm-lsr -I to the scale. This produces an AIS spectrum

protruding above the solar spectrum for wavelengths longer than

approximately 1425 nm. These discrepancies are not removed by assuming

a shift of the spectrum between grating positions in the steeply rising

wing of the water band because a horizontal translation of the AIS

spectrum by a few resolution elements is insufficient to produce

agreement with the solar radiance curve in the 1245-nm region. These

two sets of results in the first grating position appear to be

irreconcilable and the responsivity equations evolved by the laboratory

calibration must be in error.

LOWTRAN SIMULATIONS WITH THE 1900-nm WATER BAND

Despite uncertainties in the accuracy of radiance values generated

using the suspect resp0nsivities and in the wavelength calibration of

these spectra, the agreement between calculated curves and the solar

spectral radiance scale encouraged us to compare the precipitable water

and carbon dioxide estimates based on AIS data and those described by

the LOWTRAN atmospheric model. If a close match between aircraftobserved and model spectra could be achieved, a simple way of

eliminating atmospheric absorption effects from the observed spectra

might be available.

We initially worked with the standard LOWTRAN mid-latitude winter

model, specified for the October 31, 1984 overflight conditions. These

calculations were carried out in two steps. First, the solar radiance

incident on the surface of the Mono Lake beach site at an elevation of

2 km was computed for the day and solar elevation. Second, the zenithdirected radiance reflected from the surface and arriving at the instrument (elevation 7.62 km) was computed. The observed AIS spectrum from

a dry basaltic beach gravel (black beach #3 site) was used for the

comparison. Results of the simulations are shown in Figure 6. The top

panel (Figure 6a) shows that the standard model, which is estimated to

contain 0.74 cm of precipitable water (see Figure 5-13 of LaRocca,

1978), produces total absorption in the band center. The prominent

carbon dioxide bands near 1960 and 2000 nm are unobserved in the AIS

data. An approximate match in band depth between model and observation

in Figure 6(b) was produced by decreasing the path-precipitable water

by a factor of 20 (0.037 pr cm). However, the reduction in water

absorption produces a distinct gap between these spectra in the 1750-

and 1830-nm interval and increased the prominent carbon dioxide bands.

Altogether the fit is considered unsatisfactory, and the amount of

water absorber required in the reduced model seems physically

unrealistic.

39
0005 ,_._, (a) -- OBSERVED RADIANCE, USING AIS IMAGE DATA

J _ _,._)_'_t ------ LOWTRAN SIMULATION, USING MID-LATITUDE WINTER

it - v}., _, _,, STANDARDMODE"

0.004_-,, f W.,vV.>.-._'-.

0,003II " '" """ "_'".,

H20 0.74 pr cm

0.002

--I ,. r"_ _, ,-" \', ,,,, ,,

E # I I

C #,,,j

0 _.... ra

E __ _ L . ._ .... J -

_: 0.005 _-_'-'_, '(b) -" ' -- • -

i /_ , -- OBSERVED RADIANCE, USING AIS IMAGE DATA

Z I" _J'_1 ----- LOWTRAN SIMULATION, MID-LATITUDE WINTER WITH 5% H20

=_ooo,! "i" _-_..,.

O003

"\ H20 = 0.037 pr cm

0.002 .

ooo, ii "

" ",""4",I " ,i I l

II I lJ

oL __

1.5 1.6 1.7 1.8 1.9 21.0 2.1

WAVELENGTH, _m

Fig. 6. Observed AIS radiance for the dry black beach #3 site with

radiance from LOWTRAN simulation: (a) for the standard

mid-latitude winter model; (b) for 5% of the precipitable

water present in the standard mid-latitude winter model.

This observation leads us to suspect the existence of spectral

contamination. Loss of contrast in the water absorption bands could be

caused by mixing or smearing one part of the spectrum with another.

This could arise (I) from light scattering by the grating, (2) from

light leaks in the spectrometer, or (3) in mixing between the different

orders of diffraction by the grating. Below we investigate item (3).

For items (i) and (2), independent investigations will be required.

Thus, scattering by the grating results from random surface irregular!ties introduced in the ruling process. This contributes to a

broadening of the instrument profile (Dravins, 1978), a filling in of

spectral lines, and a lowering of the adjacent continuum. Quantitative

evaluation of this possibility, which would be expected to operate

across all of the observed spectrum, must await measurements of line

broadening on the actual grating used.

40
INFLUENCEOF OVERLAPPINGORDERSIN THEDIFFRACTIONGRATINGSPECTRA

The problem of overlapping orders is present in the AIS spectral

data of grating positions 2, 3, and 4 because the blocking filter used

transmits all light with wavelengths longer than about 800 nm. For

example, if light of two different wavelengths %1 and %2is incident

normally upon a grating, the angles corresponding to the principal

maximaof diffraction are given by

sin eI = m%i/2d

and (5)

sin e2 = m%2/2d

where eI corresponds to the m-th principal maximumfor _I and e2 to the

corresponding maximum for %2 (Ditchburn, 1958). Factor d is grating groove spacing. This equation indicates that the spectra of

various orders overlap wherever

ml_I = m2_2 (6)

where mI and m2 are integers. Thus, for example, light of 1800 run

in the first order is diffracted at the same angle as light of 900 nm

in the second order. To secure separation of various orders, blocking

filters are customarily used to restrict the range of wavelengths

received by the instrument. To estimate the possible radiometric and

spectral consequences of such spectral order overlapping, grating

efficiency calculations using scalar diffraction theory (Madden and

Strong, 1958, in Strong, 1958; Bottma, 1981) have been performed for

the AIS grating parameters and angles of incidence appropriate for each

grating position (Vane et al., 1983). The efficiencies as a function

of wavelength for the first and second orders according to these two

approximations are given in Figure 7. Both treatments give similar

results, but the Madden-Strong efficiencies are in general greater.

The effective blaze wavelength where grating efficiency is maximum is

1580 run by the Madden-Strong formula and 1570+ run by the Bottma

approximation. Both values agree with that given in Vane et al.

(1983). In addition, the grating efficiency in the first order is

roughly 50% of the maximum at wavelengths of 2/3 and 3/2 of the blaze

wavelength, corresponding to a rule of thumb for blazed gratings

(Loewen, 1980). Particularly significant is the fact that the calculated efficiency of the grating for the second order is comparable to

that for the first in the critical spectral region.

The Madden-Strong efficiency calculations have been used together

with the blocking filter transmission function and LOWTRAN simulations

of incident irradiance appropriate to the Mono Lake experiment to compute theoretical spectra expected as a consequence of overlapping

orders. Experimental tests using the standard light cannon source and

special filters have been made in a further attempt to isolate possible

effects arising from this source.

The theoretical spectra are computed approximately by assuming (for

given instrument throughput) that the energy arriving at the detector

41
I I T

GPOS 0 GPOS 1 GPOS 2 GPOS 3 GPOS 4

= 31.45 ° _ = 31.87 ° _ = 32.29 ° _ = 32.71 _ = 33.13 °

_. MAX MB = MURK BOTTMA

_ M-S = MADDEN-STRONG

_ _ '_'_ IFIRST

// "" '_" _ORDER

/_:B,p _>= INCIDENCEANGLE

7 SECOND BLAZE=2361.m 2.2 °

f _. I ORDER L =

1.0

> 0.9

o

z

w 0.8

o

0.7

0.6

O

Z

0.5

0.4

0.3

0.2

0.1

O_ _

800 1000 1500 2000 2400

WAVELENGTH, nm

Fig. 7. Grating efficiencies for first and second orders calculated

by the Madden-Strong (1958) and Bottma (1981) approximations. Groove spacing = 23.81 pm, blaze angle = 2.2 deg,

effective blaze wavelength = 1.58 pm (Vane et al., 1983).

at a given diffraction angle consists of contributions from the firstand second-order diffracted light. Analytically this is

Counts = KA_rs[I(X)T(X)E(X)Q(X)dX

+ I(X/2)T(X/Z)E(X/2)Q(X/2)d(X/2)] (7)

where

A = area of the entrance aperture

n = solid angle of detector subtended at the grating

I(X) = radiance incident at wavelength X in order one

I(X/2) = radiance incident at wavelength X in order two

T(%) = transmission of blocking filter at wavelength in order

one

E(%) = efficiency of the grating at wavelength in order one

dX = spectral width of the sample

K = proportionality constant between energy input and

digital counts

r s = lumped reflection coefficient for optical elements,

assumed independent of wavelength

Q(%) = quantum efficiency of HgCdTe detector wavelength X

42
In Eq. (7), as indicated in the definitions, the designation %/2

refers to an appropriate quantity in the second order. From the grating equation, the relationship between d% and the spectral order is

given by

md_ = cos _d_ (8)

where _ is the angle of diffraction. Since _ and d_ remain fixed

between orders, the spectral sample width in the second order (m = 2),

d(_/2) is equal to d%/2, i.e., one-half the spectral sample width

in the first order.

The average function Q(%) over a 64 x 64 HgCdTe detector array

has been used in the calculations since no measurements were available

of the quantum efficiency for the actual AIS detector used. This

function was measured at discrete wavelengths using broad passband

filters (V. Wright, personal communication, 1986) and is shown in

Figure 8. For purposes of calculation, a linear variation of Q with

wavelength has been assumed.

0.T

0.6

)-

O

Z 0.5

uJ

;r 0.4

l,&l

:E 0.3

=)

I--

Z

< 0.2

O

0.1

J

800 1000

E

1 00 2000 2400

WAVELENGTH, nm

Fig. 8. Quantum efficiency of HgCdTe detector IS #17 used in secondgeneration AIS, representing average value over the 64 x 64

detector array. Horizontal bars represent filter passbands.

Data courtesy of V. Wright (April 1986).

The relative contributions of the first order and the first order

+ second order spectra based on Eq. (7) for the LOWTRAN simulation are

given in Figure 9 and are compared there to a plot of the actual incident irradiance. The influence of any second-order contribution to the

spectrum is sharply limited to wavelengths beyond 1600 nm by the

presence of the blocking filter. An additional interesting consequence of the overlap is the appearance in the center of the 1900-nm

water band of another atmospheric water absorption feature actually

located at 950 nm. The water band at 1125 nm appears in the unblocked

spectrum at 2250 nm. None of the original atmospheric bands present

between 1600 and 2400 nm are filled in by the presence of second-order

light. These and later calculations show the second-order contribution

at its extreme because the efficiency calculations assume an ideal

grating whose properties are probably not matched in practice.

To investigate further the overlap problem, calibration experiments

were performed with the light cannon using a test filter, the transmis43
1.2

_;- 1.0

xv

0.8 i

E

0.6

O

Z 0.4

<

O

<

n-

_- 0.2

0

1.0_

0.9

_, 0.8

o

0.7

i

_0.6

_ 0.5

_ O.4

U

z

__ o.3

a

_ 0.2

0.1

Fig. 9.

(a)

/IST + 2ND ORDER

,_IST ORDER , _ ,

r_ (b)

0.75 1

SUN

F>/i ":'°%

_ _ 2.0 km ./////////_/////////_//,

1.25 1.5 1.75 2 2.25 2.5

WAVELENGTH, Fm

Results of LOWTRAN calculations: (a) computed AIS spectra

for mid-latitude winter model and incident irradiance of

(b). Top curve represents contributions of first +

second-order spectra, bottom curve represents first-orderalone spectrum; (b) solar irradiance incidence at the AIS

from the October 31, 1984 Mono Lake simulation.

sion function of which is given in Figure i0. The maximum transmission

is almost 80% at 900 nm and falls abruptly to 5% at 1200 nm. The

throughput is constant to 1800 nm, and thereafter rises almost linearly

to 55% at 2400 nm. This filter does not isolate completely the firstfrom second-order spectral contributions; however, the falloff of light

cannon energy with wavelength is sufficiently steep so that the contribution of first-order light through the filter beyond 2000 nm is

small compared with that from the second-order light originating from

the spectral region short of 1200 nm. The test for second-order light

is decisive, despite this contamination. The assumed light cannon

irradiance distribution is shown in Figure Ii.

44
1.0

0.9

0.8

0.7

_ 0.6

_ 0.5

_ 0.4

I--

0.3

0.2

0.1

0

Fig. i0.

Fig. ii.

I I I I I

BLOCKING FILTER

TEST _ "m'_= _

FILTER

,,.j,

I i I J i

800 1000 1500 2000 2400

WAVELENGTH, nm

Transmittances of the AIS blocking and test filters.

I

7

I

I

700 1000 1500 2000 2400

WAVELENGTH, nm

Assumed light cannon spectral irradiance distribution.

The computed relationships are shown in Figure 12 where the irradiance expected for first order and for first + second-order contributions are depicted separately. As before, the spectral intervals

represented by the zeroth and first grating positions (900 to 1500 run)

are uncontaminated by higher orders. The first-order spectrum alone

contains peaks at 1050, 1350, and 1650 nm, and rises slowly to a

plateau between 2100 and 2400 nm, with a small minimum at 2200 run. All

of these features result from the test filter transmittance or from the

light cannon spectral radiance distribution. The calculated first +

second-order spectrum is identical to that of the first-order spectrum

alone below 1500 run, but rises beyond that wavelength to a plateau

between 1700 and 2000 nm. Thereafter, the combined spectrum falls to a

value near the first order by itself at 2400 nm. The combined spectrum

is roughly three times that of the first order alone, and the peak

radiance at 1900 nm is 30% or so greater than that at 1050 run.

45
0.4

i 0.3 FIRST + SECOND

0.2

_ 0.1

FIRST ORDER

0 I 1 _

800 1000 1500 2000 2400

WAVELENGTH, nm

Fig. 12. Calculated spectra for first-order-alone and first + secondorder contributions for the test filter transmittance of

Figure I0, and the light cannon spectral irradiance distribution of Figure Ii.

The experimentally determined AIS response for the test filter and

light cannon source averaged over all detectors is given in Figure 13.

This spectrum has broad peaks near i000, 1350, 1800, 2000, and 2250 nm,

and sharp peaks at both ends and near the midpoint of the spectral

range for each grating position. In grating position 2, the observed

spectrum rises beyond 1500 nm through the series of broad and sharp

peaks mentioned. The broad maximum centered near 2000 nm is equivalent

in height to that at i000 nm.

- /

Z

a

50

0

900

I [ I

314

213 v

I

Ol // \_ 112

I I I I

1200 1500 1800 2100 2400

WAVELENGTH, nm

Fig. 13. Raw AIS spectrum obtained for light cannon illumination and

the test filter of Figure i0. Sharp peaks indicate boundaries

of spectral regions for various grating positions. Wavelength

scale is approximate.

46
To facilitate comparison between the calculated and measured

results, these spectra are compared in Figure 14 by normalizing the two

at the maximum near i000 nm. This also required a 36-nm shift in the

200

150

S

UJ

-%

-J 100

<

Z

5O_

[ I I 314 I

i

\v I _/_ / ;I/_" 1ST ORDER ONLY,

_ ._.._.,,_ J_ / . .g" CALCULATED WITH -

_ MADDEN-STRONG GRATING

0 , , J J _ I , , I , , I _ , J

900 1200 1500 1800 2100 2400

WAVELENGTH, nm

Fig. 14. Comparison of measured and calculated AIS spectra for test

filter with _ight cannon illumination. Spectra are normalized

at the maximum near i000 run, and the calculated curve shifted

36 nm to the secure agreement of the wavelength scales.

computed curve to shorter wavelengths. The agreement between these two

results is good out to 1600 nm, at which point the experimental curve

assumes 50 to 100% higher values than the observed one, which is the

result expected from the contamination. The calculated curve is an

optimum case because the grating efficiency assumed is the maximum

efficiency expected. Since the real grating has a lower efficiency,

using its values would lower the first-order-alone curve relative to

the first + second-order curve. Thus, the contamination is probably

greater in magnitude than depicted here. Better comparisons between

calculated and measured results will be possible when the calculations

are carried out using efficiencies for the actual grating and detector

used.

ANALYSIS OF INSTRUMENT RESPONSE CURVES

The AIS response curves, examples of which are given in Figure 2,

plot scanner response in digital counts vs the fraction of light cannon

radiance present at a given wavelength in each resolution element. As

part of the radiometric adjustment of these data to remove variations

in responsivity between detectors, a flat-field normalization derived

from assumed uniform illumination of the detector array is applied; the

responsivity values for detectors in the spatial direction are all

rescaled to a constant value, and the wavelength direction is rescaled

according to the wavelength distribution of light cannon radiance (Science Investigators Guide to AIS data, 1985). For example, if the value

47
of light cannon spectral radiance in the first channel (_i) is

arbitrarily assigned the value 2500 digital counts, the spectral

radiance in the n-th channel is 2500[L(Xn)/L(%I)], where L(_) represents

light cannon irradiance. For the next two channels the values obtained

are 2500 × 0.987 = 2468 (X2) and 2500 × 0.977 = 2422 (X3) and so on.

If the AIS system is linear, the responsivity r(x) is defined by

the equation

vCx) = rcx)_(x) (9)

where V(%) is instrument output in counts (or volts) and I(_) is

the incident radiance. The responsivity can be determined from one

light cannon observation, which is assumed to be bright enough to

encompass the entire range expected from natural targets. F is then

determined from

r = Vmax/Ima x (i0)

where the subscript max indicates light cannon values. The value of

F adjusted for the relative spectral radiance of the light cannon is

Fad j = {2500(Vmax/Imax)[L(X)/L(Xl)]} (ii)

The normalized instrumental output is

Vad j = F'adj[l(k)/Imax(k)] (12)

where F'ad j = rad:Imax(k). The calibration data of Figure 2 are plotted

according to Eq. _12). As indicated in Eq. (7), the responsivity F(X)

is proportional to T(X)E(X)Q(X). T(X) and E(X) are roughly comparable

between orders, while Q(X) varies by a factor of seven between 800

and 2400 nm. The dominant variation in r is thus from the variation

in Q(X).

In grating position 2 where second-order diffracted energy is

present, the expression 2 takes on the more complicated form

V(X) = F(X)I(X) + FCX/2)I(X/2) (13)

and the incident flux is comprised of two components I(X) + I(_/2),

where the magnitude of these two terms is comparable. If the system

response were linear, it would again be given by an equation like (9),

but in this instance with an apparent value of the responsivity r,

given by

r,(k) = F(_)I(X)I[I(X) + I(_12)] + F(XI2)I(XI2)I[I(X) + I(_12)] (14)

a weighted sum of first- and second-order contributions. The functional

dependence of F with X, described above, determines that in general

F, < F. An apparent decrease in responsivity between first and

second grating positions would be sufficient to introduce discontinuities into radiance curves produced from raw count spectra if such

spectra were themselves continuous between grating positions.

In addition, a subtler problem must also exist with the calibration

curves of Figure 2 themselves. Consider the curves for channels i and

48
32 (first grating position). The relative falloff in light cannon

radiance between channels i and 32 is i - 1648/2500 = 0.34, but the

relative response of channel 32 is 20% higher, not lower, than for

channel i. The correct relative relationship is shown for the same

detectors in grating positions 3 and 4. For grating position 2, the

response curves are essentially the same. This reversal of position

cannot therefore arise from nonuniformity of illumination during the

tests or from spurious detector response. Without further details of

the test procedure, it is difficult to provide an explanation.

We conclude that the spectral discontinuities observed arise from

abrupt jumps in the apparent response between grating positions I and

2. This behavior can result from the presence of contamination by

second-order light in the second grating position (not a good explanation between 1500 and 1600 run) or from a spurious calibration test.

All of these puzzling details must be resolved before the AIS calibration can be understood.

COMPENSATION FOR SPECTRAL CONTAMINATION FROM THE

DIFFRACTION

SECOND-ORDER

The AIS atmospheric compensation procedures described earlier all

require explicit measurement of the radiances lij of Eq. (I). If

second-order diffracted energy is present in the spectrum, then the

instrumental radiance measured is comprised of two terms lij' and

lij" , representing the first- and second-order contributions. These

separate terms each represent atmospheric and surface contributions at

and _/2 approximately in accordance with Eqs. (i) or (2),

combined according to Eq. 7. Thus, to remove the second-order

contribution, and assuming the relevant instrumental characteristics

are known, the surface and atmospheric properties between 800 and

1200 nm are required. In the short wavelength mode of scanner operation

(the tree mode), the uncontaminated spectrum between 900 and 1200 nm is

taken, and corrections to the longer wavelength (1800 nm and beyond)

may be possible. In the long wavelength mode (the rock mode), the

spectral interval of observation is 1200 to 2400 nm, and no rigorous

correction of the longer wavelength data is possible.

SUMMARY AND DISCUSSION

We studied AIS data for a few pixels from the Mono Lake test site

in California in order to establish the absolute spectral radiance of

the targets under solar illumination. The ultimate objective of the

work is to provide a method of atmospheric correction using the spectral

content of the AIS data themselves. We used actual measurements of the

surface reflectance to aid in the work. Crucial to this analysis and

to other calibration procedures as well is radiometric calibration of

the instrument. We studied the calibration procedure and used the

response curves provided from laboratory calibrations to calculate

"absolute" radiance spectra throughout the 1200- to 2400-nm interval.

We find reasonable agreement between the radiance values observed and

those calculated for the site assuming zero air mass for the spectral

interval 1500 to 2400 nm, but the derived spectrum is 60% or so too low

49
in the first grating position. Based on the reasonable radiometric

comparisons obtained in the 1900-nmregion, we used LOWTRANto simulate

the atmospheric transmission observed. In general, the agreement is

unsatisfactory. The best match model requires a reduction by a factor

of 20 of the precipitable water, which then produces an unsatisfactory

match in the 1700-nmwing of the water band. Prominent carbon dioxide

absorptions present in the simulation are unobserved in the AIS data.

These considerations lead to the study of overlapping spectral orders

in the AIS data. The possibility of serious contamination of the

first-order spectrum beyond 1600 run from second-order diffracted light

between 800 and 1200 nm is introduced by transmission through the

blocking filter, which begins transmission at 800 run. Lacking actual

measurements of the efficiency of the grating used, we calculated

efficiencies for the first and second orders of a hypothetical grating

with the groove spacing and blaze angle of the actual grating. The

efficiencies for first and second order are comparable. Approximate

theoretical spectra were calculated using (i) LOWTRAN simulations and

(2) laboratory light cannon runs with special filters designed to isolate approximately the 800- to 1220-nm spectral contribution. The LOWTRAN simulations show a strong contribution of second-order light in

the first-order spectrum between about 1600 and 2400 nm. In addition

to enhanced energy, atmospheric water absorption bands present at 950

and 1125 nm appear in the spectrum at 1900 and 2250 nm. The laboratory

light cannon simulation with test filter established the presence of

second-order light in the raw spectrum, but unexplained discrepancies

remain between the calculated and observed spectra.

The decallbration procedure involving normalization of the raw

digital count spectra to the scaled light cannon energy profile may

effectively disguise the presence of the second-order energy in all AIS

spectral data subjected to this process. The discrepancies may re-emerge

only when absolute spectra are calculated using the experimental light

transfer curves and may manifest themselves as strong discontinuities

particularly between grating positions i and 2. The calculated spectra

were examined for evidence of the expected extraneous water band contributions, but no such bands have been identified that are distinct

from atmospheric or solar absorptions in this region. The lack of discernible bands is not surprising because (I) the prominent atmospheric

carbon dioxide bands are somehow suppressed in the data (perhaps by the

flat field transform), (2) the signal/noise ratio is low, and (3) the

actual wavelength calibration and resolution of the spectra are

uncertain.

ACKNOWLEDGMENTS

Harold Lang, Mike Abrams, Frank Palluconi, Alex Zak, Deanne Tucker,

Clayton LaBaw, Gregg Vane, Anne Kahle, and Jerry Solomon all gave of

their time and ideas in numerous discussions of the work. Michael Chrisp

contributed a copy of his computer program for the (Murk) Bottma grating

efficiency formula, and Valerie Wright supplied the detector quantum

efficiency data.

50
REFERENCES

Airborne Imaging Spectrometer: Science Investigators Guide to AIS data,

1985, Jet Propulsion Laboratory, Pasadena, CA.

Arvesen, J. C., R. N. Griffin, Jr., B. D. Pearson, Jr., 1969, Determination of extraterrestrial solar spectral irradiance from a research

aircraft, Appl. Opt., 8(11), 2215-2232.

Bottma, Murk, 1981, Echelle efficiencies: Theory and experiment: Comment, Appl. Opt., 20(4), 528-529.

Conel, J. E., 1985, Calibration of AIS data using ground-based reflectance measurements (Abstract), Proceedings of the Airborne Imaging

Spectrometer Data Analysis Workshop, JPL Publication 85-41, Jet

Propulsion Laboratory, Pasadena, CA, 84-85.

Conel, J. E., H. R. Lang, E. D. Paylor, and R. E. Alley, 1985, Preliminary spectral and geologic analysis of Landsat-4 Thematic Mapper

Data, Wind River Basin area, Wyoming, IEEE Trans. on Geosclence and

Remote Sensing, GE-23(4), 562-573.

Conel, J. E., H. R. Lang, and E. D. Paylor, 1986, Derivation of Surface

Reflectance and Atmospheric Properties From Landsat IV Thematic

Mapper Data, Jet Propulsion Laboratory, Pasadena, CA (in

preparation).

Ditchburn, R. W., 1958, _, Interscience, New York.

Dravins, D., 1978, High-dispersion astronomical spectroscopy with holographic and ruled diffraction gratings, Appl. Opt., 17(3), 404-414.

Green, A. A., and M. D. Craig, 1985, Analysis of aircraft spectrometer

data with logarithmic residuals, Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop, JPL Publication 85-41,

Jet Propulsion Laboratory, Pasadena, CA, 111-119.

LaRocca, A. J., 1978, Atmospheric absorption, Chap. 5, in The Infrared

Handbook, W. L. Wolfe and G. J. Zissis, eds., Environmental Research Institute of Michigan, Ann Arbor, MI.

Loewen, E. G., 1980, A review of ruled diffraction gratings, The Industry and Systems Purchasing Directory, 1980, B-327-B-331.

Madden, R. P., and John Strong, 1958, Diffraction gratings, Appendix

P, in Strong, J., Concepts of Classical Optics, W. H. Freeman and

Co., San Francisco, CA, 597-615.

Paylor, E. D., M. J. Abrams, J. E. Conel, A. B. Kahle, and H. R. Lang,

1985, Performance Evaluation and Geologic Utility of Landsat-4

Thematic Mapper Data, JPL Publication 85-66, Jet Propulsion

Laboratory, Pasadena, CA.

Solomon, J. E., 1984, Thoughts on atmospheric "corrections" for AIS imagery, JPL IOM IAS: 384-84-JES102ed (JPL Internal Document), Jet

Propulsion Laboratory, Pasadena, CA.

Tucker, D., 1984, AIS calibration report (unpublished JPL Internal

Document), Jet Propulsion Laboratory, Pasadena, CA.

Vane, G., A. F. H. Goetz, and J. B. Wellman, 1983, Airborne Imaging

Spectrometer: A new tool for remote sensing, Proc. IEEE 1983 Int'l

Geoscience and Remote Sensing Symp., IEEE Cat. No. 83CH1837-4,

I-l-I-15.

51
N87-12972

AIRBORNE SPECTRORADIOMETRY : THE APPLICATION OF AIS DATA TO

DETECTING SUBTLE MINERAL ABSORPTION FEATURES

TERRY D.COCKS and ANDY A.GREEN, CSIRO DIVISION OF MINERAL

PHYSICS AND MINERALOGY, NORTH RYDE, NSW, AUSTRALIA

ABSTRACT

Analysis of AIS data acquired in Australia has revealed

a number of operational problems. Horizontal striping in AIS

imagery and spectral distortions due to order overlap have

been investigated. Horizontal striping, caused by grating

position errors can be removed with little or no effect on

spectral details. Order overlap remains a problem that

seriously compromises identification of subtle mineral

absorption features within AIS spectra. A spectrometric

model of the AIS has been developed to assist in identifiing

spurious spectral features, and will be used in efforts to

restore the spectral integrity of the data.

INTRODUCTION

Prior to the AIS mission,some recent research had been

conducted into the application of high-spectral-resolution

remote sensing in the short-wave infrared (SWIR) to

geological remote sensing in Australia. This work showed

that, in many cases, mineralogically important absorption

features are appreciably weaker in the Australian

environment than in the western USA, where the bulk of the

AIS data has been acquired to date. The inclusion of the AIS

in the C-130 instrument package that came to Australia in

October, 1985 provided an opportunity to assess its

performance in an environment where weathering and

significant vegetation cover place stringent demands on the

radiometric sensitivity and spectral integrity of airborne

instrumentation.

Over the last four years we have conducted a number of

airborne spectroradiometer surveys over selected Australian

sites using spectroradiometers from GER (Geophysical

Environment Research,Inc New York) and CSIRO. In general,

the mineral absorption features used to identify the surface

material are not apparent in the raw, surface-radiance

spectra. As an example,Figure 1 presents raw radiance

spectra (curves A and B) from a flight line at Kambalda,

Western Australia, as recorded by the GER spectroradiometer.

Here spectra A and B are, respectively, from terrain with

little specific mineral content and from terrain containing

appreciable amounts of talc Both curves are dominated by

the absorption features of atmospheric water vapour and

carbon dioxide, and there is no obvious difference in the

radiance spectra.

The inability to distinguish absorption features in

52
radiance spectra led to the development of the logarithmic

residual analysis technique (Green and Craig, 1985).This

technique removes multiplicative spectral effects common to

the whole flight line (e.g. atmospheric absorption, solar

irradiance and detector responsivity), and produces

reflectance spectra, that are normalised by the flight line

average reflectance spectrum. Figures IC and ID illustrate

the respective results of a log residual analysis on Figures

IA and IB, with Figure ID now showing the talc spectral

signature. The log. residual approach has been shown to be a

powerful technique for analysis of highly correlated data,

such as is normally encountered in the SWIR. The very fact

that log residuals successfully isolate subtle absorption

features led us rapidly to suspect the spectral integrity of

the AIS data, and eventually led us to the second-order

overlap problem discussed below. Apart from order overlap,

other problems with AIS data include vertical and horizontal

image striping, and calibration artifacts. In this paper we

limit our discussions to horizontal striping and order

overlap. All data presented here were acquired with the AIS

operating in the 'rock scan' mode, giving a nominal

wavelength range of 1.2-2.4 um.

HORIZONTAL STRIPING -- GRATING WOBBLE

One of the more obvious AIS image defects is horizontal

striping. Usually it appears worse at shorter wavelengths;

some bands may show little or no striping even under a

severe contrast stretch. We suggest that this effect is

caused by grating wobble, i.e there exists some random error

in the precise location of the grating as it cycles through

each of the four positions. Figure 2 (Slide I) presents the

typical appearance of this striping in bands Ii and 29, both

within the first grating position. Figure 3 shows a 128-

channel spectrum from column 16 of the image. If the grating

were positioned such that it diffracted a slightly longer

wavelength onto each detector element than it did in the

previous cycle, then the signal from channel II would be

lower and the signal from channel 29 higher than in the

previous image line. Thus, we expect the brightness of the

stripes in the band ii and band 29 images to be anticorrelated. This is in fact the case, as shown in Figure 2

(Slide I). For bands that occur at wavelengths with small

spectral radiance gradients, the striping is not so

apparent. It also appears as though the striping is worse in

the first grating position , probably enhanced by some

flyback effect.

The magnitude of the line-to-line grating shift was

estimated by fitting a low order polynomial to the cross

correlation of successive spectra (limited to channels of

the first grating position) for a given cross-track pixel or

image column. An example of such estimated shifts for column

8 is shown in Figure 4, where the standard deviation of the

53
shifts over 400 image lines is 0.65 channels ( equivalent

to about 6nm). One would also expect the estimated shifts to

be independent of the image column selected. Thus the solid

dots in Figure 4 show the estimates for column 16. It can be

seen that there is excellent correlation with the estimates

of column 8,in both magnitude and direction.

The removal of horizontal striping in a manner that

took into account its physical cause would involve

determining the wavelength shift, at each grating position,

for each image line, followed by the required correction

obtained from a knowledge of the spectral radiance gradient

at each wavelength. As this approach is likely to be very

cumbersome we applied a procedure developed to remove

aperiodic striping from airborne-line scanner imagery. The

correction algorithm, referred to as DMEAN is described by

Huntington et. al. (this issue). The procedure has been

shown to remove most evidence of horizontal striping without

having meas_reable impact on the spectral integrity of the

data. Channel-shift estimation was performed on this

corrected data with results shown in Figure 3B. The standard

deviation of the estimated shifts in this case was 0.l,a

value probably near the limit of resolution for the

estimation algorithm. Because horizontal striping is due to

a mechanical problem in the AIS, the magnitude of the

grating position errors will to some extent depend on the

vibration experienced by the aircraft during data

acquisition. It may be greater or smaller than the values

presented here.

SPECTRAL DISTORTIONS FROM ORDER OVERLAP

Initial analysis of the AIS data involved computing

log. residuals on a flight line of 128-channel spectra for a

given image column. Figure 5 (Slide 2) presents the results

of this analysis. The first column ( labelled RADIANCE ) is

a density-sliced image of the raw radiance spectra,with

wavelength increasing left to right from 1.23 um to 2.42 um

and flight line spectrum number increasing down the image.

Comparison of Figure 5 (Slide 2) and the spectrum of Figure

6 affords ready identification of various spectral regions

represented in the spectra images (e.g. the location of

atmospheric water vapour and CO2 bands).A density-sliced

representaion of the log. residual spectra appears in column

2 (labelled RAW LR ALL),in which blue represents wavelength

regions where the surface reflectance at that part of the

flight line is lower than the average flight line

reflectance, and Vice versa for red. Absorption bands due to

a particular mineral usually show up as blue regions located

at wavelengths characteristic to that mineral, allowing

unique identification of the surface material. The dominant

spectral patterns in Figure 5 (Slide 2, column 2) are

associated with broad wavelength intervals of the water

vapour bands and the atmospheric windows at 1.6 um and 2.2

54
um. The red or blue colour of the water vapour band is

highly correlated with the surface albedo. Few, or no

localised, narrow absorption features are apparent in the

2.2 um window, the region of prime interest for geological

applications of high-resolution spectroscopy. This atypical

behaviour of the log. residual spectra was diagnosed as

being due to some unexplained error signal. In the water

vapour bands where the signal level is naturally low, such a

spurious signal could well predominate, and would cause the

log. residuals to behave as in Figure 5. A repetition of the

analysis using only channels 1-14, 26-64 and 77-128 (Figure

6, hatched regions) produced log. residual spectra shown in

Figure 5, column 3 (labelled RAW LR W). Now the dominance of

the water vapour bands has been removed and we begin to see

what appear to be more typical mineral absorption bands. One

also notices the blue stripes in channels 1-14 and 26-32,

caused by the aforementioned grating-wobble-induced

horizontal striping. If the log. residuals are computed

after destriping by program DMEAN, the striping in the log.

residual images effectively disappears without any influence

on other spectral features (Figure 5, column 4).

However, two problems still remain in the log. residual

spectra. Firstly, we can see evidence of the CO2 bands in

the log. residual spectra. Secondly, many of the absorption

features occur at wavelengths inconsistent with what is

known of the terrain being observed (see Huntington e__tt

al.,this issue). Examination of the published AIS optical

designs revealed an error in the selection of a cutoff

wavelength for the blocking filter, allowing radiation from

shorter wavelengths to contaminate the AIS spectra at

wavelengths greater than about 1.6 um. Because this shorter

wavelength radiation is added into the spectra and varies

with the surface reflectance , it cannot be removed by the

log. residual process and will generate artificial

absorption features.

To estimate the magnitude of the shorter wavelength

contamination, we developed a simple spectroradiometric

model of the AIS. The grating equation states that, for

given angles of incidence and diffraction, radiation is

diffracted in the first order at one wavelength, and in the

second order at one-half that wavelength. For the grating

used in the AIS, the ratio of first to second order

diffraction efficiencies is near unity, leading to the

possibility of comparable signal levels from both orders. To

avoid contamination of the first-order spectrum by secondorder (or higher) radiation, one usually incorporates in the

optical train a filter that blocks all radiation with

wavelengths less than one-half the longest wavelength in the

first-order spectrum. For the AIS operating in the 'rock'

scan mode, this cutoff wavelength should be near 1.2 um, not

0.8 um as was the case during much of the operational life

of AIS to date.

55
The spectroradiometric model was developed to examine

spectral aspects of the AIS, not its absolute radiometry •

Hence the detector responsivity has been normalised to unity

at 2.4 um, and geometrical factors, such as solid-angle

field-of-view have been neglected. Figure 7 shows the

spectral details of the solar irradiance (A), atmospheric

transmission (B), grating efficiency (C), blocking filter

transmission (D) and detector responsivity (E) over

wavelengths of the first and second orders. Each pair of

curves has been plotted on the same vertical scale to

illustrate the relative contributions to the resultant,

recorded spectrum. Curves A,B,C,D and E were multiplied and

scaled to account for first and second order bandwidths, to

produce the AIS spectral response to a I00% reflector (F).

The recorded spectrum is then the sum of the first and

second order responses as shown in Figure 8. The spectral

distortion predicted with this model can be seen to be

significant for all wavelengths greater than 1.6 um.

Contaminated spectra can be easily recognised by the filling

in of the 1.9 um water band, a step-like feature on the side

of the water band at about 1.82 um, and by a broad feature

centred on 2.28 um (due to the atmospheric water band at

about 1.14 um). The second-order leakage is also present

during calibration hence the magnitudes of some of these

features may be somewhat masked in the data delivered to

users. However, sample spectra published in the 1985 AIS

workshop proceedings ( e.g. pages 77 and 131) do show

features similar to those in Figure 8. The strength of the

2.28 um feature will be dependent on the atmospheric water

vapour content at the time of measurement, and on the

reflectance of the terrain at 1.14 um.

The spectroradiometric model has also been used with

representative surface materials. Figures 9 and 10

show,respectively, the computed responses for green

vegetation and for kaolinite. The reflectance of vegetation

is characterised by a high value on the near-infrared

plateau, and a relatively low value in the SWIR resulting in

an AIS spectrum dominated by second-order leakage from a

wavelength of about 1.8 um. For any terrain containing

appreciable amounts of vegetation, the distortions will

render analysis procedures such as log. residuals of little

value. The computed response to kaolinite shows that, in the

contaminated spectrum, the depth of the characteristic 2.2

um clay absorption band in the radiance spectrum is greatly

reduced by second-order leakage. It should be noted that

the kaolinite spectrum used here was from a laboratory

sample.Actual field spectra of minerals are likely to have

less prominent features, further aggravating the problem of

detecting subtle, but mineralogically significant,

absorption bands.

56
SUMMARY

The analysis of AIS data described above has shown

that horizontal striping can be removed without degradation

of the spectral information. However, the order overlap

problem is far more serious and may render useless data

collected in some environments The paper by Huntington et_____.

al____.(this issue) discusses one method of minimising the

influence of second-order leakage. Other methods such as

using NS001 imagery in conjunction with the

spectroradiometric model, should be further investigated.

However, the AIS will not realise its true potential in the

detection of subtle absorption features until the blocking

filter problem is rectified in the AIS itself.

ACKNOWLEDGEMENTS

The authors greatly appreciated the assistance readily

provided by JPL staff including Greg. Vane, Deanne Tucker,

Valerie Wright and Clayton LaBaw, during the development of

the spectroradiometric model.

REFERENCES

Green, A.A. and Craig,M.D (1985). Analysis of Aircraft

Spectrometer Data with Logarithmic Residuals.

Proceedings of the Airborne Imaging Spectrometer Data

Analysis Workshop, April 8-10, 1985. JPL Publication

85-41, p.lll-l19.

Huntington,J.F., Green, A.A., Craig, M.D., Cocks,T.D.,

1986. Preliminary Geological Investigation of AIS

Data at Mary Kathleen, Queensland, Australia.

Proceedings of the Second Airborne Imaging Spectrometer

Data Analysis Workshop, May 6-8, 1986 (this issue).

57
ORIGINAL PAGE' IS

OF POeR QUALITY

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U

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2"0 2.1 2"2 2"3 2-4

Wavelength (,urn)

Figure i. Surface _adiance spectra (A and B) recorded by the

GER airborne spectroradiometer, with the corresponding

log. residual spectra (C and D).

Figure 2. AIS imagery in bands ii and 29, Showing

horizontal striping. (See color slide number I

in the back cover pocket of this publication.)

58
200

150

o_1001

u_

SO(

Figure 3. A flight line average radiance spectrum

recorded in the 'rock' scan mode, showing

grating position boundaries in channels 32, 64

and 96.

1.0

05

c

0

E

-0"5

tU

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i i !

250 300 350

1"0

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250 300 350

Image line number

Figure 4. A. Estimated channel shifts induced by grating wobble at

wavelengths in the first grating position, for image columns 8

(line) and 24 (dots). B. Estimated channel shifts for column

8 after application of the destriping algorithm, DMEAN.

59
ORIGINAL PAGE IS

POeR qUAU_

Figure 5. Density-sliced radiance and log. Residual

spectra for a variety of processing conditions.

(See color slide number 2 in the back cover pocket

of this publication.)

200C

1500

1000

50(:

I

I 1 I I ! I ! I 1 I I !

32

I

I

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96

I

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I

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10 20 30 40 50 60 70 80 90 100 110 120

Channel

Figure 6. Flight line average radiance spectrum, showing hatched

regions used in a log. residual analysis that excluded water-vapour

band minima.

60
_1500

u

E

_I000

500

E

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.m_ "8

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Figure 7. Components of the AIS spectroradiometrlc model used to assess

spectral distortion from second-order leakage. A. Solar irradlance,

B. Atmospheric transmission, C. Grating efficiency, D. Blocking

filter transmission, E. Detector responsivlty, F. Computed response.

61
2O

RESPONSE TO 100% REFLECTOR

_./'\ \

I I

I

I

I

I

1.'3_T.4m'sT:e 1!7

£

1., 1._ 2_o2'.I 2'-22'.32'.,

Wavelength (pro)

Figure 8. Computed AIS

response to a 100%

reflector. Solid line

shows combined first and

second-order response.

Dashed line shows first

order response.

2O

T

RESPONS£ TO VEGETATION

Figure 9. Computed AIS response

to green vegetation,

presented in the format

above.

50

RESPONSE TO KAOLINITE

l

l

l

\

II

II

I J

I t

1J3 • l_s l'-s 1!? i.e 1.9

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,, ;, _

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2'.o2'._2!2 2'-32'._

Wavelength (_um)

Figure I0. Computed AIS

response to kaolinite,

presented in format above.

62
N87-12973

ATMOSPHERIC-WATER ABSORPTION FEATURES NEAR 2.2 MICROMETERS AND THEIR

IMPORTANCE IN HIGH SPECTRAL RESOLUTION REMOTE SENSING

FRED A. KRUSE and ROGER N. CLARK, Geologic Division, U.S. Geological

Survey, Box 25046, DFC, MS 964, Lakewood, Colorado 80225, USA

ABSTRACT

Selective absorption of electromagnetic radiation by atmospheric

gases and water vapor is an accepted fact in terrestrial remote

sensing. Until recently, only a general knowledge of atmospheric

effects was required for analysis of remote sensing data; however, with

the advent of high spectral resolution imaging devices, detailed

knowledge of atmospheric absorption bands has become increasingly

important for accurate analysis.

Detailed study of high spectral resolution aircraft data at the

U.S. Geological Survey has disclosed narrow absorption features centered

at aproximately 2.17 and 2.20 micrometers not caused by surface

mineralogy. Published atmospheric transmission spectra and atmospheric

spectra derived using the LOWTRAN-5 computer model indicate that these

absorption features are probably caused by water vapor. Spectral

modeling indicates that the effects of atmospheric absorption in this

region are most pronounced in spectrally flat materials with only weak

absorption bands. For example, convolution of a weak montmorillonite

spectrum with a LOWTRAN atmospheric transmission spectrum at Airborne

Imaging Spectrometer (AIS) wavelengths and resolution results in a

composite spectrum that somewhat resembles that of kaolinite. This

example demonstrates that a correction accounting for the atmospheric

effects near 2.2 micrometers must be applied to the data prior to

analysis. Without this correction and detailed knowledge of the

atmospheric effects, accurate mapping of surface mineralogy

(particularly at low mineral concentrations) is not possible.

*** Use of trade and company names in this report is for descriptive

purposes only and does not imply endorsement by the U.S. Geological

Survey.

63
INTRODUCTION

New high spectral resolution remote sensing systems collect data in

many narrow bands, producing a continuous reflectance spectrum for each

image plxel. Analysis of data from these systems has concentrated on

using various techniques to attempt to remove atmospheric and solar flux

effects so that the available spectral information can be extracted

(Vane and Goetz, 1985). These techniques have been only partially

successful, but in several cases individual clay minerals have been

uniquely identified on the basis of absorption features near 2.2

micrometers (Marsh and McKeon, _83; Goetz and others, 1985; Kruse and

others, _85). This report identifies atmospheric absorption features

near 2.2 micrometers that may adversely affect mineral identification.

The importance of correcting for these features prior to data analysis

is discussed below.

IDENTIFICATION OF THE PROBLEM

High spectral resolution spectroradiometer data of an area in

Nevada were processed to reflectance without an atmospheric correction

to compare corrected and uncorrected spectra for the same area.

Analysis of these data revealed kaolinlte-like absorption features at

approximately 2.17 and 2.20 micrometers throughout the field area in the

uncorrected data (Fig. I). Samples collected from this area were

analyzed using laboratory reflectance measurements and did not contain

kaolinite.

A literature search disclosed that many of the published

atmospheric transmission curves in common use today have been

generalized and indicate that atmosoherlc transmittance is essentially

uniform in the 2.2 micrometer re_ion (Sabfns, 1978; Lillesand and

Kiefer, _79; Siegal and Gillespie, ]980; Suits, _83; (Figs. 2a, 2b,

2c). However, other published curves (Wolfe, _65; Kneizvs and others,

1980) show detailed atmospheric transmission spectra measured over

varying path lengths that indicate that narrow atmospheric absorption

features caused by atmospheric water exist in this region of the

spectrum (Fig. 2d, 2e, 2f).

METHODS AND DISOJSSION OF RESULTS

The effect of the atmospheric water absorption bands near 2.2

micrometers on the appearance of mineral absorption features was modeled

using laboratory measurements of selected clay minerals and the LOWTRAN5 computer model (Knelzys and others, 1980; Blake, 1983). The LOWTRAN-5

model produced a high resolution spectrum with absorption minima at2.156, 2._3 and 2.)98 micrometers caused by atmospheric water (Fig. 3)

and is in good agreement with the published atmospheric transmission

curves shown in figure 2. The atmospheric transmission spectrum was

then calculated for an actual AIS path length using mldlatitude

atmospheric characteristics (Kneizys and others, 1980), and the

64
A,

3000.0

2000.0

IZZZ. Z

.Z

2. Z 2. 1 2.2 2.3 2.4 2.5

WAVELENGTH (Dm)

B,

36ZZ.

35ZZ. Z

34ZZ. 0

33BZ. 0

32ZZ. (_

31ZZ. Z

2. 1Z 2. 15 2. 2Z 2. 25

WAVELENGTH (,U,,)

Figure I. Geophysical Environmental Research Inc. high spectral

r_solutlon aircraft spectroradlometer spectrum for an area

showing no strong mineral absorption features. Spectrum "A"

covers the full range of the instrument (1.96 to 2.5

micrometers). Spectrum "B" shows the absorption features near

2.2 micrometers at an expanded scale. The resolution of this

instrument is similar to that of the AIS (Colllns and others,

1981; Goetz and others, 1985). The solar irradtance curve and

the atmospheric transmission function have not been removed;

however, the data have been normalized to 1.0 at 2.13

micrometers.

65
.,- .a o_

an =

_nD

mum,..

Oe H ,

_) JLOliSi_s_vu_

ORIGINAL PAGE 1_

OF POGR QLIALFFY

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66
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z< 2

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Z

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rr

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• 90

2.156

2.183

2.198

2. 11_1 2. 15 2. 213 2. 25

WAVELENGTH (,tim)

Figure 3. Figure 3A shows the LOWTRAN-5 calculated transmission

spectrum for the I. _ to 2.5 micrometer region. Figure 3B shows the

same curve on an expanded scale. This spectrum was calculated for

mldlatltude atmospheric parameters (Knelzys and others, ]980) for a

typical AIS acqulsltlon pathlength.

67
resulting spectrum was convolved to AIS wavelengths and resolution (Fig.

4). Laboratory spectra of selected reference clay minerals were

measured and digitally recorded on a Beckman 5270 laboratory

spectrometer (Clark, 1986, unpublished data).

Spectral processing software (Clark, _80) was used to model the

effect of the atmosphere on the spectra of several different minerals;

one example is shown here. When the atmospheric transmission spectrum

was applied to a montmorillonlte spectrum with a strong absorption

feature at 2.206 micrometers (Fig. 5a, spectrum 2), the position of the

main absorption feature remained unchanged although an additional small

absorption band appeared near 2.16 micrometers (Fig. 5a, spectrum 3).

However, when the atmospheric transmission spectrum was applied to a

montmorillonlte spectrum with a weak absorption feature near 2.2

micrometers (Fig. 5b, spectrum 2), the composite spectrum was grossly

similar in appearance to kaolinite even when no kaolinite was present

(Fig. 5b, spectrum 3, spectrum 4). Comparison of Fig. 5b with Fig. 1

shows that the kaollnlte-llke absorption features occurring in the

aircraft spectra could be explained by the combination of atmospheric

effects and weak mineral absorptions near 2.2 micrometers. Other

combinations of weak mineral absorptions and varying atmospheric water

vapor concentrations might result in an even closer approximation to the

kaolinite spectrum.

CONCLUSIONS

The importance of narrow atmospheric absorption bands near 2.2

micrometers to the analysis of high spectral resolution remote sensing

data is immediately apparent. The assumption that the atmospheric

transmission is relatively constant in the 2.2 micrometer region is not

valid, and it is apparent that a correction accounting for the

atmospheric effects near 2.2 micrometers must be applied to high

spectral resolution remote sensing data prior to analysis. If

atmospheric conditions are not fully known, or if the model does not

account for atmospheric absorption features near 2.2 micrometers, then

the interpreter must be aware of possible atmospheric effects in this

wavelength region and should take care, particularly where absorption

features are weak, to insure that interpretation of absorption features

does not erroneously include atmospheric effects. It is important to

remember that varying atmospheric conditions and particularly mineral

mixtures could add to the complexity of the problem. Additional

atmospheric modeling and on site measurement of atmospheric parameters

are required before complete resolution of atmospheric absorption

problems will be possible.

68
A•

Z

o

(n

co

GO

Z

n_

1.5 2.0

WAVELENGTH (,urn)

Z

o

o3

u')

1"-'4

co

z

.,(

rr

I--

• g8

•96

.g4

• g2

'I ' ' ' ' I ' ' ' ' I ' ' ' ' I

2.206

2.189

0

• I I i I , I i i I I I I I, 1

2. 15 2. 2B 2. 25

WAVELENGTH (_m)

Figure 4. Figure 4A shows the LOWTRAN-5 calculated atmospheric

transmission from figure 3A convolved to AIS wavelengths and

resolution. Figure 4B shows the same curve on an expanded scale.

69
Z A. 1 • ' ' ' I " ' " ' I • ' " ' lJ

1

Iz

i- 9

_1

0 ,B

.J

.7

L i i i , , | , i • ,

2. ]0 2. ]5 2.2_ 2.25

WAVELENGTH (_um)

Figure 5A. Spectrum l Is the LONTRAN-5 atmospheric spectrum convoLved

to AIS wavelengths and resolution. Spectrum 2 is a laboratory spectrum

for montmorillonlte convolved to AIS wavelengths and resolutlon.

Spectrum 3 results when the atmospheric function shown in spectrum ] Is

applled to spectrum 2. All spectra have been scaled to approxlmately

1.0 at their highest points and spectra 2 and 3 have been vertlcally

offset for clarity.

7O
z 1. BB

0

(n

z

(

.95

1."so

w

w

.J

w

_: . 95

0

hi

u

• 90

I. BO

Iv .95

• 9B

2.,.. -

2.188 _

2.206

4

2.1_6

I • • I I I I I I I I I I " "

2. IB 2. 15 2. 2B 2. 25

WAVELENGTH (.urn)

Figure 5B. Spectrum I is the LOWTRAN-5 atmospheric spectrum convolved

to AIS wavelengths and resolution. SpectrL_n 2 shows a typical "weak"

montmorillonlte absorption. This absorption is similar to a

montmorillonite absorption feature when the montmorillonlte is mixed

with other minerals that do not have absorption features in this

region. Spectrum 3 results when the atmospheric function from spectrum

I is applied to spectrum 2. Spectrum 4 shows a typical "weak" (poorly

crystalline) kaolinite spectrum similar to that which might be seen in

mineral mixtures. Note the gross similarities between spectra 3 and

4. All spectra have been scaled to approximately 1.0 at their highest

points.

71
REFERENCES

Blake, P. L., _83, Analytical removal of atmospheric absorptions from

remotely sensed near-infrared data of geologic targets: M.S.

thesis (unpublished), University of Hawaii, 96 p.

Clark, R. N., 1980, A large-scale interactive one-dlmenslonal array

processing system: Publications of the Astronomical Society of the

Pacific, v. 92, p. 221-224.

Collins, W., Chang, S. H., and Kuo, J. T., _81, Infrared airborne

spectroradlometer survey results in the western Nevada area:

Columbia Univ., Aldrldge Lab. Appl. Geophysics, Final Rept. to

NASA, Contract JPL 955832, 61P.

Goetz, A. F. H., Vane, Gregg, Solomon, J. E., and Rock, B. N., 1985,

Imaging spectrometry for earth remote sensing: Science, v. 228, p.

1147-1153.

Kneizys, F. X., Shettle, E. P., Gallery, W. 0., Chetwynd, J. H. Jr.,

Abreu, L. W., Selby, J. E. A., Fenn, R. W., and McClatchey, R. A.,

1980, Atmospheric transmittance/radiance: Computer code LOWTRAN 5:

AFCRL Environmental Research Paper, No. 697, AFCRL-80-0067, 233 p.

Kruse, F. A., Ralnes, G. L., and Watson, Kenneth, _85, Analytical

techniques for extracting geological information from multichannel

airborne spectroradiometer and imaging spectrometer data: in

Proceedings, International Symposium on Remote Sensing of

Environment, Thematic Conference on Remote Sensing for Exploration

Geology, 4th, San Francisco, California, _4 April, 1985,

University of Michigan, Ann Arbor, p. 309-324.

Lillesand, T. M., and Kiefer, R. W., 1979, Remote sensing and image

interpretation: John Wiley & Sons, New York, 6_ p.

Marsh, S. E., and McKeon, J. B., 1983, Integrated analysis of highresolution field and airborne spectroradiometer data for alteration

mapping: Economic Geology, v. 78, no. 4, p. 6 18-632.

Sabins, F. F. Jr., 1978, Remote sensing prlnciDles and interpretation:

W. H. Freeman and Company, San Francisco, _. 6.

Santa Barbara Research Center, 1975, The SBRC Brochure; Goleta,

California.

Siegal B.S. and Oillespie, A. R., (eds.), 1980, Remote Sensing in

Geology: John Wiley & Sons, New York, 702 p.

Suits, G. H., _83, The nature of electromagnetic radiation, in

Colwell, R. N. (ed), Manual of Remote Sensing: American Society of

Photogrammetry, Falls Church, Virginia, p. 40.

72
Vane, Gregg, and Coetz, A. F. H., (eds), 1985, Proceedings of the

Airborne Imaging Spectrometer Data Analysis Workshop, JPL

publication 85-4 I, Jet Propulsion Laboratory, Pasadena, California,

173 p.

Wolfe, W. L., (ed.), 1965, Handbook of military infrared technology:

Office of Naval Research, Department of the Navy, Washington, D.

C., p. 252, 255, 256.

Yates, H.W., and Taylor, J. H., 1960, Infrared transmission of the

atmosphere, NRL report 5453, U. S. Naval Research Laboratory,

Washington, D. C., ASTIA AD 256952.

73
" r4

DESTRIPING AIS DATA USING FOURIER FILTERING TECHNIQUES

CHRIS HLAVKA,

Field,CA,USA.

NASA/Ames Research Center, Moffett

ABSTRACT

Airborne Imaging Spectrometer (AIS) data

collected in 1984 and 1985 showed pronounced

striping in the vertical and horizontal

directions. This striping reduced the signal to

noise ratio so that features of the spectra of

forest canopies were obscured or altered by noise.

This noise was removed by application of a notch

filter to the Fourier transform of the imagery in

each waveband.

BACKGROUND

Remote sensing data are being studied as a potential

source of information about nutrient cycling in

forests(Peterson et al., 1985). A process model is being

developed to describe the cycling of carbon, nitrogen, and

phosphorus in forest ecosystems (Running et al., 1985).

High resolution infrared imagery will be used to estimate

concentrations of nitrogen and carbon compounds in leaf

canopies for input to the model. The leaf concentrations of

some of these compounds, in particular lignin and nitrogen,

have been correlated with reflectances at several IR

wavelengths measured in the laboratory (D. Card et

ai.,1986). AIS spectra will be analyzed for similar

correlations. Also, subtle differences in the structure of

spectra will be analyzed by correlating measures of shape,

such as first and second derivatives, with chemical

concentrations, as has been done with lab spectra ( Williams

et ai.,1983).

The presence of noise, such as striping, in AIS spectra

can be expected to decrease correlations with chemistry

(Brownlee, 1965). If R' is a large sample correlation

between reflectance in a single AIS channel, or a linear

combination of reflectances in several channels, and the

concentration of a chemical, and R is the correlation that

would be observed if the data were noise-free, then

R' = R/SQRT(I + I/SNR).

Therefore, it is clearly an advantage to improve the signal

to noise ratio (SNR) of the AIS data. This can be achieved

with processing techniques which reduce noise content more

than information content.

74
ORIGI_.,_L P_GE iS

OF POeR QUALITY

a)

b)

Figure i. Blackhawk Island AIS data before (top) and after

(bottom) filtering, a) Channels 1-16 of grating position O.

b) Channels 1-16 of grating position i.

Figure 2. The IFTI of band 1 of grating position O,

enhanced by the logarithm transformation to show detail.

75
VHF

VHFF

- frequencies DC

I

+ frequencies VHF

I I

VHFF

Figure 3. A schematic drawing of the notch filter. Note

that the horizontal axis is for vertical frequencies and the

vertical axis is for horizontal frequencies.

RRW SPECTRRL. PL_T$: RED ORK

--.--.4t0 _

zbso I_za Ib)o ikso sk_ • Ikw ib*o ]kso abso

NAVI[LENGTH (lIMI

F'DURI_ _ FZJ..TERE_. R[S DRTA** RED OAK

zbR 1_1o n_lso Ikso zk_o iklo I]l*o z_alo

HRVELENGTN (NHI

Figure 4. Plots of AIS spectra for sites

Island. Left: Before filtering. Right:

within Blackhawk

After filtering.

DESTRIPING TECHNIQUES

Visual examination of AIS imagery revealed striping both in

the along track (vertical) and across-track (horizontal)

directions in imagery collected in 1984 and 1985, as can be

seen in a portion of a flightline shown in Figure i.

Vertical striping, caused by small differences in

calibration among the 32 sensors for each waveband, was

particularly prominant. In addition to the striping, the

imagery contained drop-out lines in some channels. These

lines contained spurious grey levels of 4096, usually in

every pixel in a row, in every band in a grating position.

A straightforward technique for removing striping in

both directions was desired to satisfy the following

requirements: i) reduction of noise (striping in both

76
directions) with retention of information content, and 2)

minimal software development for implementation.

Histogram normalization techniques were considered.

This type of technique involves collection of histogram

statistics, such as the mean and standard deviation

(Barker,1983) or bin frequencies (Dykstra and Segal, 1985)

for each row and/or column in the image. A transformation

for each row and/or column is applied to equalize the

statistics. Dykstra and Segal applied histogram

normalization to AIS data to remove vertical striping. They

noted that the success of the technique depended upon the

condition that "over the course of a complete flight line,

each of the 32 detectors has covered, on the average, very

similar surface materials". Such an assumption was

plausable for many of our flight lines, but a similar

condition clearly could not be assumed for rows only 32

pixels wide. "Flat field" radiometric corrections

(Conei,1985) have been used to destripe data, while also

removing effects due to the solar spectrum and atmospheric

absorption and radiance. This technique requires an area of

spectrally flat and homogenous material across the flight

line, a condition not met in all of our flight lines.

Fourier filtering was considered as a means for destriping

because the technique does not require the special

conditions required by the techniques described above.

Improvement in SNR with Fourier filtering was expected

because the striping patterns were in two very specific

directions, the vertical and horizontal, therefore the

energy associated with these patterns would be confined to

small portions of the two-dimensional Fourier transform,

namely the axes associated with zero frequency in the

horizontal and vertical directions. These portions can be

removed by notch filtering, .i.e. zeroing out the Fourier

transform over selected areas and then taking the inverse

transform. The striping pattern is thus removed with little

reduction in information content.

FOURIER FILTERING AIS DATA

Fourier filtering techniques were applied on two

flightlines of AIS imagery. One flightline, acquired in

9/84, contained the portion of Blackhawk Island, Wisconsin

shown in Figure i. This is a forested island preserve, with

stands of pines near its shores, and mixed deciduous

hardwoods in the interior. The island is surrounded by

farmland. The other flightline was acquired over areas of

spruce forests, birch forests, and marshes in Alaska in

8/85.

As a first step, drop-out lines were located in the

image by visual inspection. Each drop-out line was then

"repaired" by replacement with the average of the average

values in the two lines directly above and below it. The

magnitude of the Fourier transform (IFTI) of the 256 line

portion of the Blackhawk Island flightline was created, and

several individual bands, such as the IFTI of band 1 of

77
grating position 1 shown in Figure 2, were visually examined

to determine a design for the notch filter. As expected,

the grey levels in the axes associated with zero frequencies

in the horizontal or vertical axes were high compared with

grey levels in most other areas. High magnitudes were

spread out along the axes, rather than concentrated in a few

spatial frequencies. The other pixels on the IFTI image

with high grey levels were clustered around diagonal lines,

and were associated with the patterns made by the river

around the island, or were clustered around the DC point

(the origin), and were associated with the pattern of

forests and fields, which were primarily low frequency

patterns as are most patterns on remotely sensed imagery

(Moik,1980). A few (two or three) notch filters that were

low pass filters in the horizontal and vertical directions

were tested on the image of Blackhawk Island. The filtered

images were examined on an interactive display. The "best"

filter was then applied to a portion of the Alaska

flightline.

Fourier filterin_ implementation - AIS images were

filtered using programs in a commercial image processing

package, IDIMS, on a VAXlI/780. The technique can be

implemented with VICAR software developed at JPL as well,

and probably with other image processing packages.

Figure 3 shows the notch filter that was most

successful in destriping the Blackhawk Island image. A

binary image was created with ones in the white areas, and

zeroes in the black areas by creating a 17 row by 256 column

image of ones and inserting three small images of zeroes.

All non-zero frequencies in the vertical axis (rows 2-17)

were zeroes, and all frequencies above .43 cycles/pixel

(columns I-ii0 and 147-256). An image containing the

complex two dimensional FT of each of the 128 channels of

AIS was created and each FT was multiplied by the binary

notch filter. The inverse FT was then applied to each

channel in the product image to obtain the destriped AIS

imagery.

Results - The results of filtering the Blackhawk Island

image are shown in Figure i, for selected channels.

Vertical striping was completely removed. This indicates

that the striping was due to differences in bias among the

32 detectors associated with each band, because zeroing out

all non-zero frequencies along the central vertical axis of

the FT is mathematically equivalent to adding a constant to

AIS grey levels to make the average grey level of each

column equal to the average grey level for the channel.

Most of the horizontal striping was also removed. Some

sudden apparent changes in detector response level were

noted on AIS data, for example after row 190, shown in

Figure ib). The filtered image showed a dark band around

this row, an artifact of the filter. An image containing

1024 rows of the Alaska flightline was processed using the

same filter(the same in terms of frequency, the notch filter

78
dimensions were 17 rows by 1024 columns overall, with zeroes

in columns 1-440 and 585-1024). A visual examination of the

filtered image indicated successful removal of striping in

both directions.

Plots of spectra of 2x2 pixel areas on the filtered

images were much smoother after filtering, and features such

as the position of the NIR plateau were more consistant from

site to site, as can be seen in plots of sites with red oak

canopy shown in Figure 4.

CONCLUSIONSAND FUTUREWORK

The Fourier filtering procedure that has been developed

appears to remove most of the vertical and horizontal

striping in AIS imagery, and it does so with minimal

decrease in information content. The removal of vertical

striping is an indication that miscalibrations among the 32

sensors in each band have been corrected in the imagery.

Occasionally, the filter distorts the imagery when there is

a sudden change in the apparent response level in the AIS

instrument. Some modifications in the filter will be tested

in an attempt to correct this problem.

When a final filter has been selected, all 1984 and

1985 AIS imagery cover study sites to be used for research

at Ames Research Center will be destriped as a first step in

processing. Other radiometric corrections for solar spectra

and atmospheric corrections can then be applied uniformly to

output from all sensors within a band, because relative

miscalibration has been corrected.

REFERENCES

Barker, J.L. 1983. Relative Radiometric Calibration of

Landsat-4 Thematic Mapper Reflective Bands. In:

Landsat-4 Science Characterization-Early Results. NASA

Conference Publication 2355, III:i-220.

Brownlee, K.A. 1965. The Effects of Errors of Measurement.

In: Statistical Theory and Methodology-Second Edition.

(John Wiley and Sons, Inc., New York), pp.414-418.

Card, D., D. Peterson, N. Swanberg, and W. Westman.

1985. Personal communication.

Conel, J.E. 1985. Calibration of AIS Data Using

Ground-Based Spectral Reflectance Measures. In:

Proceedings of the Airborne Imaging Spectrometer

Data Analysis Workshop, JPL 85-41 (Pasadena,

California: The Jet Propulsion Laboratory), pp.84-85.

Dykstra, J.D. and D.B. Segal. 1985. Analysis of AIS Data

of the Recluse Oil Field, Recluse Wyoming.

Proceedings of the Airborne Imaging Spectrometer

Data Analysis Workshop, JPL 85-41(Pasadena, California:

The Jet Propulsion Laboratory), pp.86-91.

Moik, J.G. 1980. Unitary Transforms. In:

Digital Processing of Remotely Sensed Images. NASA

SP-431. pp. 15-33.

Peterson, D.L., P.A. Matson, J.G. Lawless, J.D. Aber,

79
P.M. Vitousek, S.W. Running. 1985. Biogeochemical

Cycling in Terrestial Ecosystems: Modeling,

Measurement, and Remote Sensing. Presented at

The 36th International Astronautical Federation

oL_-n-gress, Stockholm, Sweden, October 9-12, 1985.

Running, S.W., J.D. Aber, D.L. Peterson, P.A. Matson, and

P.M. Vitousek. 1985. A Simulation Model Integrating

Carbon, Water, and Nitrogen Cycles in Forests. In:

Proc. Sm_. IUFRO Whole Pla____ntPhysiology, Oak Ridge,

TN.

Spanner, M.A., D.L. Peterson, W. Acevedo, and P. Matson.

1985. High Resolution Spectrometry of Leaf and Canopy

Chemistry for Biogeochemical Cycling. In:

Proceedings of the Airborne Imaging Spectrometer

Data An_aalysis Worksho_-JPL 85-41(pasadena, California:

The Jet Propulsion Laboratory), pp. 92-99.

Williams, P.C., K.H. Norris, C.W. Gehrke, and K.

Berstein. 1983. Comparison of Near-Infrared Methods

for Measuring Protein and Moisture in Wheat. In:

Cereal Foods World, Vol. 28, No. 2, pp. 149-152.

80
• p

N87-12975

ABUNDANCE AND DISTRIBUTION OF MINERAL COMPONENTS ASSOCIATED

WITH MOSES ROCK (KIMBERLITE) DIATREME

MUSTARD, JOHN F. and PIETERS, CARLE M., Brown University, Department of

Geological Sciences, Box 1846, Providence, Rhode Island 02912

ABSTRACT

The surface mineralogy in and around Moses Rock diatreme, a kimberlite-bearing dike in

SW Utah, was examined using internally calibrated AIS data. Distinctive near-infrared

absorption characteristics of jclays, gypsum, and serpentine (a key marker for kimberlite

concentration) allowed the surface units containing these components to be identified spatially

and the relative abundance of each component measured. Within the dike itself, channels and

dispersed components of kimberlite and blocks of country rocks were accurately determined.

GEOLOGY OF MOSES ROCK.

Moses Rock kimberlite-bearing diatreme is one of several tertiary age diatremes on the

Colorado plateau. At the present level of exposure, the diatreme is in contact with three

members of the Permian Cutler formation: The Halgito tongue composed of reddish brown

siltstone and sandstone with minor limestone, the Cedar Mesa sandstone consisting of buff

sandstone, sfltstone, grey limestone and gypsum, and the Organ Rock tongue, a sequence of

reddish brown sfltstone, variegated sfltstone and sandstone. A simplified geological map of the

region is shown in Figure 1. The geology and geochemistry of the dike have been described in

detail by McGetchin (McGetchin, 1968; McGetchin and Silver 1970, 1972). The contact

between the dike and sediments is sharp and the vent walls are steep. The dike itself is

composed largely of blocks up to 100 meters across derived from the units exposed in the vent

walls. The interstices between these blocks contain fragments of the larger blocks as well as

limestone, crystalline rock fragments and maflc and ultramafic constituents. Kimberlite in

the dike is characterized as a highly serpentinized ultramafic microbreccia and spatially

comprises about 1% of the dike. Additional kimberlitic material (about 12%) is found

dispersed throughout the dike.

DATA ACQUISITION AND CALIBRATION.

Airborne Imaging Spectrometer (AIS) data were acquired over Moses Rock dike and

surrounding area in July of 1984 and 1985. Field work was conducted in the summer of

1985 to become familiar with the surface geology as well as 1) to collect samples for

bidirectional laboratory reflectance measurements 2) to assess the nature of surface material

and extent of vegetation coverage and 3) to measure reflectance spectra in the field with a

portable field spectrometer (IRIS). The summary presented here is discussed in more detail

in Mustard and Pieters (1986a).

AIS data were processed using VICAR and SPAM on a Vax 750. The data were

calibrated by first dividing the raw data by a carefully selected internal standard. The

standard area chosen is a region of low topography consisting of windblown sand. Field

spectra were obtained of this area and surface samples were collected. High precision bidirectional reflectance spectra of these samples measured in the laboratory are in excellent

agreement with the field measurements (see below). A homogeneous region within the

standard consisting of about 60 lines of AIS data was averaged along track maintaining the

independence of individual lines. Each line of the AIS scene was divided by the average

radiance of the standard. The resulting AIS relative reflectance spectra were then multiplied

by the laboratory spectrum of the standard collected from the field area and measured at the

same phase angle as the AIS data.

This calibration procedure not only removes the effects of the solar irradiance spectrum,

but also corrects for overall system response and, since no across track averaging was done,

helps to correct for across track system or detector variations. To first order these calibrated

81
AIS data are true reflectances. The AIS data in the atmospheric water bands, however, still

contain very low signal to noise ratio; these lower quality data in the wavelength regions

1.31-1.51 ttm and 1.78 to 2.03 t_m were thus not used in the analyses for mineral detection

and abundance measurements.

MINERAL IDENTIFICATION.

From the calibrated true reflectance AIS data, three prominent spectral units were

identified and interpreted mineralogically. These are a serpentine-bearing unit, a gypsiferous

unit and a clay bearing unit. Additional surface compositions (such as desert varnish,

carbonate) are discussed in the more detailed Mustard and Pieters (1986a) manuscript.

Diagnostic spectral features of these three types of materials are shown in Figures 2-4.

There is very good agreement between the spectral character of calibrated AIS spectra, field

(IRIS) spectra, and laboratory (RELAB) bi-directional spectra.

AIS spectra of the serpentinized kimberlite are shown in Figure 2 along with field spectra

measured with the IRIS field spectrometer and a laboratory spectrum of a surface sample

collected from this unit. The strong 2.33 ttm absorption band is evident in all these spectra

as well a 2.2 ttm reflectance peak and overall increase in reflectance from the visible towards

longer wavelengths. X-ray diffraction of the field samples confirmed that serpentine is the

dominant mineral in these samples.

In Figure 3 are shown AIS, IRIS and laboratory spectra from the standard area. The

overall spectrum is generally flat in the wavelength region covered by AIS with a small (5%)

absorption at 2.2 ttm. This absorption feature is due to a minor clay component in the sand,

interpreted to be illite.

Spectra of a unit containing gypsum are shown in Figure 4. Shown in this figure are

laboratory spectra of a pure gypsum sample (sub-surface), a gypsum bearing soil collected at

Moses Rock, and AIS spectra from a gypsiferous unit. The features to note are the overall

decrease in reflectance toward longer wavelength, a 1.68 t_m reflectance peak and a 2.21

absorption band centered between asymmetric reflectance peaks, the higher one at 2.08 ttm

and a lower one near 2.3 tam.

The laboratory spectrum of the gypsiferous soil shown in Figure 4, clearly shows there

are multiple m__n_ral species contributing to the observed reflectance spectrum of this soil.

The strong Fe _ absorptions throughout the visible wavelengths indicates the presence of

ferric oxide bearing sand or siltstone; the presence of gypsum is equally well indicated by the

diagnostic gypsum features near 1.4, 1.68, 1.9 and 2.2 tam.

APPROACH TO MINERAL MIXTURES.

Important geologic information relating to the proportions or abundances of minerals is

contained within the reflectance spectra of mineral mixtures. One of the objectives in

examining Moses Rock dike with mapping spectrometer data is to identify and determine the

distribution of the mantle derived mafic kimberlite component in the dike. Spatial

concentrations of kimberlite indicate that the eruption of this primary mantle material was

confined to channels during emplacement. On the other hand, homogeneous kimberlite

distribution throughout the dike would imply an entirely different style of eruption. To obtain

this mineral abundance information from reflectance spectra of multicomponent surface soils

requires the development of analytical tools to deconvolve complex spectra into contributions

from individual components, or endmembers. Reflectance spectra of intimate or microscopic

mixtures, however, cannot be modeled using a simple linear relationship between the endmember spectra.

The complex interactions of reflected light with particulate media have been modeled by

Hapke (1981) with a rigorous application of radiative transfer models. In this model for

bidirectional reflectance it is predicted that mixing systematics are linear if reflectance is

expressed as single scattering albedo, SSA, a fundamental optical property of a material.

Reflectance is a function of SSA, as well as viewing geometry, particle size, porosity, etc.

However, to derive complete solutions to Hapke's (1981) equations for bidirectional

reflectance, multiple viewing geometries are required. Mustard and Pieters (1986b) have

simplified Hapke's equation to be applicable to a fixed viewing geometry and tested this

82
approach with laboratory measurements of well characterized mixtures of igneous rock

forming minerals and mixtures of naturally occuring soils.

In this analytical approach the reflectance spectrum of a mixture is converted to single

scattering albedo and the proportion of each endmember contributing to the mixture is

calculated by a least squares fit of endmember SSA spectra to the measured mixture

spectrum. The abundance coefficient calculated for each component is called the relative

geometric cross section, or F parameter, and is directly proportional to mass fraction and

inversely proportional to particle diameter of the specific mineral component. The results of

this laboratory test of the analytical model (see Mustard and Pieters, 1986b) indicate that this

technique accurately predicts the abundance of endmember components from the reflectance

spectrum of a mixture, provided strongly opaque components (reflectance < 10%) are not

present.

MINERAL ABUNDANCE DERIVED FROM AIS DATA.

This approach to derive mineral abundance information for naturally occurring surface

intimate mixtures (Mustard and Pieters, 1986b) was implemented on the JPL SPAM

software package. The routine MIXTURE was modified to convert the calibrated AIS

reflectance data to single scattering albedo prior to a least squares regression of the

endmember SSA spectra to the AIS SSA spectra. The modified routine produces the desired

relative geometric cross-section, or F-parameter, for individual components (endmembers) in

the mixture.

An important consideration prior to deconvolving the AIS image spectra is the

determination of appropriate endmembers to be used in the analyses. Initial choices of

endmembers for abundance determination in the Moses Rock region include standard soil

(sand dunes), illite, a sample from the Cedar Mesa sandstone, gypsum and kimberlite

(serpentine). Spectra for these endmembers are shown in Figure 5. All these carefully

selected components except illite were collected from the field area.

The results of deconvolving spectra from an AIS flight line (indicated in Figure 1) into the

abundances of the endmember components is shown in Figure 6 (BROWN U. Slide 1). The

first two endmembers (standard soil and illite) dominate the abundance calculations. This

result is consistent with the geology these endmembers represent; the reddish brown fine

grained derivative of sandstones and siltstones are a pervasive component of all soils. Since

they are fine grained, the F-parameter values, which are inversely proportional to particle

diameter, are high. The other mineral abundance maps correspond well with known geology.

The Cedar Mesa unit occurs at moderate values (0.35-.4) throughout the area of the Cedar

Mesa sandstone covered by the flight line. The gypsum unit can be seen to have two primary

occurences midway down the strip. The F-parameter values are low because the typical

gypsum grain size is large compared to the fine grained oxide and clay bearing component

found intimately mixed with the gypsum. These F-parameter values of close to 0.3 thus

correspond to actual gypsum abundances of up to 70%.

From analyzed field samples, it is known that serpentine is a direct indicator of

kimberlite occurence and concentration. It occurs in local concentrations within the dike as is

shown in Figure 6. With the current spectral coverage of AIS an ambiguity sometimes arises

between serpentine and carbonate (which contains an absorption centered near 2.33 _m).

Therefore the occurrences of "serpentine" in the sedimentary units actually correspond to

known limestone beds. The greater spectral range of AVIRIS should allow these two

components to be distinguished. Nevertheless, in the body of Moses Rock diatreme the

distribution" of serpentine corresponds directly with the results of detailed mapping of

kimberlite by McGetchin (1968).

The distribution and abundance for serpentine was analyzed in more detail and is shown

in Figure 7 (BROWN U. Slide 2). The F-parameter values in the oval shaped region of high

concentration range from 0.4 to 0.7 and the peak values occur for the region mapped by

McGetchin as pure kimberlite. On the opposite side of the dike is another patch containing a

significant concentration of serpentine. These areas are clearly primary channels for the

kimberlite. It is apparent from figures 6 and 7 that this approach also maps the diluted

83
kimberlite of the dike quite efficiently. The vacant (non-serpentine) regions in the dike

correspond to large breccia blocks of the Cutler formation.

It is clear even from this initial AIS study that a wealth of geological and geochemical

information is contained in mapping spectrometer data. We look forward to the coming era of

advanced airborne and spacebased sensors which will certainly open valuable opportunities

for geological applications of remotely acquired data.

ACKNOWLEDGEMENTS: NASA funding (NASW-4048, NAGW-748) for this research is

gratefully acknowledged. We greatly appreciate the loan of IRIS from ARCO during the field

season and the valuable help of Dr. L. Silver and USGS, Flagstaff. We thank the Navajo

nation for permission to do field work at Moses Rock.

REFERENCES

Hapke, B. 1981. Bidirectional reflectance spectroscopy 1. Theory, J. Geophys. Res., 86,

3039-3054.

McGetchin, T.R. 1968. The Moses Rock dike: Geology, petrology, and mode of emplacement of

kimberlite-bearing breccia dike, San Juan County, Utah, Ph.D. thesis, 405 pp., Calif. Inst.

of Technol., Pasadena.

McGetchin, T.R. and Silver, L.T. 1970. Compositional relations in minerals from kimberlite

and related rocks in ilhe Moses Rock Dike, San Juan County, Utah. The American

Mineralogist, Vol. 55, 1737-1771.

McGetchin, T.R. and Silver, L.T. 1972. A crustal-upper mantle model for the Colorado

Plateau based on observations of crystalline rock fragments in the Moses Rock Dike. J.

Geophys. Res., 77, 7022-7037.

Mustard, J.F. and Pieters, C.M. 1986a. Characterization of eruption channels in Moses Rock

kimberlite diatreme with mapping spectroscopy, to be submitted, J. Geophys. Res.

Mustard, J.F. and Pieters, C.M. 1986b. Quantitative abundance estimates from bidirectional

reflectance measurements. Prec. Lunar Planet. Sci. Conf. 17th, J. Geophy. Res., submitted.

Pche

Pcm

AIS

Strip,

I

i

I

I

i

i

i

Q

_ Figure 1

(_l - Quaternary deposits

Mrd -Moees Rock kimberlite diatreme

Pcd -ca c.elh/ sandstone Member "_

t

Pco -Organ Rock Member _l-

/

PcfTI -Cedar Mesa Sandstone Member

I

Pchs- Halgito Member ,..J

Cutler

Formation

.... COlttOOt, doahed whore OoEtoeeled

.............. Limit of quorterfllry deposits

..... F eolor dike

0 1

I I

km

84 C " "_-"
0.80 t

KZ_RLITE

0.80

RELAB

IRIS

0.00 .... t

0.80 1.10

ILl

0

Z

4C

)" 0.40

0

W

--I

U.

1.00

Cyp'_url

0.80

RELAB

(Pure)

o.8o

0.40

RELAB

(Soil)

0.20

O.OO , , , ,

0.60

AIS COVERAGE ::

ORmlNAL PAGE 13

OF POOR CU_Lr_

2

1.80 2.10 |.00

WAVELENGTH IN MICRONS

r

0.80 t I i//_;_ _] , "._//////////_/_ i I

upper gratings i

O RE

Z

O)'" 0.40

ATMOSPHERIC N 0.20 I _ WATER BANDS_

ooo....I

1.60 1.10 1,80 2.10 2.80

WAVELENGTH IN MICRONS

1.00

5t

0.80

D I

°W

"J 0.40 O -t U.

m _

0.20 ! _ m -1

I! I

0.00 I

0.80 1. I 0 t.60 2.10 2.60

WAVELENGTH IN MICRONS

a

i •

1.10 1.80 2.10 2.80

WAVELENGTH IN MICRONS

Figure 2, 3, 4 Laboratory (RELAB), field (IRIS), and AIS spectra (small arrows) of Moses

Rock materials. Figure 5. Laboratory (RELAB) bidirectional reflectance spectra of samples

used as endmember for deconvolution of AIS spectra: a (gypsum), b (illite), c (standard soil),

d (kimberlite soil), and e (Cedar Mesa samples).

Figures 6 and 7 Deconvolution of AIS spectra into mineral abundance images (F-parameter

for endmembers). (See color slides numbers 3 and 4 in the back cover pocket of this publication.)

85
N87-12976

COMPARISON OF THE 1984 & 1985 AIS DATA OVER THE SINGATSE

RANGE (YERINGTON) NEVADA.

R.J.P.LYON, Applied

University,USA

Earth Sciences Department,Stanford

ABSTRACT

The Singatse Range is composed

of a series of 53 types of volcanic,

plutonic, metamorphic and sedimentary rocks.

In addition the Jurassic plutonic rocks are

also of economic interest for their copper

mineralization which is contained in a

porphyry dike swarm. This paper contrasts

and compares the 1984 and the 1985 flight

results from the AIS instrument flown in the

NASA/JPL C-130 aircraft. Due to the long thin

nature of the economically-interesting target

(hydrothermal alteration) the flights were

flown as parallel lines, with only one flight

crossing at right angles. The 1984 data are

less noisy than the 1985, in which many sets

of vertical stripings from bad detectors can

be seen. Significantly however, enough of the

hydrothermal alteration patterns can be seen

in each line at the mutual crossing points

that one can say that the specific targets

were detected in both year's flights. The

spectra of both years are corrupted by the

"second-order" effect from the grating, but

O-H bond absorptions at essentially-correct

wavelengths for sericite and/or kaolinite can

be seen. Internal adjustments for wavelength

calibrations were made using initially the HO-H water vapor absorptions at 1.386 and

1.868 um, and with refinement by using the

CO 2 bands at 2.006 and 2.053 um respectively.

AIS-II flights in 1987, and especially

AVIRIS in 1987/88 would help to extend this

detailed, site-specific mapping to much

broader, regional mapping experiments along

the whole length of the Singatse Range.

INTRODUCTION

The porphyry complexes of the Singatse Range at

Yerington and Ann Mason, NV have been the target of

extensive studies by our laboratory at Stanford for the past

fifteen years. Because both geological and remote sensing

studies have taken place ( 4 Ph.D. theses in geology; 4

Ph.D. and 3 MS theses in remote sensing) this geological

site has a breadth of ground information unlike many in the

US. In 1985/86 six remote sensing papers alone will have

86
been published which deal with the aspects of

geological-remote sensing connections of this site.

the

Geoloqv of the Site.

Geologically the Singatse Range area ( 12 Km N-S by 6

E-W) contains over 53 rock types-- 15 sedimentary, 20

volcanic ,17 igneous plutonic, and 4 metamorphic-- of which

all have formation names, indicating their differentiation (

and significance) in conventional ground-based mapping.

Table 1. Rock types in the Singatse Range (Yerington) site

( from Proffett & Dilles, 1984)

PLUTONI C VOLCANIC SEDIMENTARY

(17) (20) (15)

Ja Tba Qal

Jr Tha Qls

Shamrock Jqp Thai Ta

" Jsa Tb Tws

" Jqms Td Tcg

" Jgdp Tbs Jq

Yerington Jqmp Tbm Jgu

" Jpq Ts Jl

" Jpqm Tsl JTrCl

" Jgd Trt JT_cv

" Jqm Twh JT_ib

Jbqm Tru Tr_ ,$

" Jdb Tgm TrVl

Strosnider Mzqp Tei T_la

Ranch Mzap Teb Trll

" Mzqm Jaf

" Mzdi Trad

Trr

Tra

TrV

METAMORPHIC

(4)

Skarns-

(Ludwig)

(Mason V)

(Bluestone)

Hornfels

Ages range from earliest Triassic for the sedimentary

rocks through Upper Jurassic for the plutonic rocks.

Cover-rocks range from Eocene to MiddleMiocene for the ashfall tuffs ("ignimbrites") up to Quaternary alluvium. Over

50 rocks have been age-dated from this range. Those

which are published were often the average of a series of

datings, all performed for the Anaconda Co. (Proffett &

Proffett,1976). Estimates of the geological

efforts of the Anaconda Company's geological staff indicate

that about i00 man-Mears of professional mapping has been

done on this range alone. Because of the personal

experience with this mapping effort of several of our

Faculty and students (especially M.T.Einaudi,J.Dilles,

etc.), the Remote Sensing Laboratory is in an excellent

position to assess the geological-remote sensing

relationships.

87
Drilling by Anaconda in the range totals over 250,000

feet and thus provides excellent vertical control on the

cross-sections which appear in the recently published maps

at a 1:24,000 scale (Dilles & Proffett, 1984)

Structurally this range is of great geological

significance as one of the first, well-documented examples

in which listric (curved,concave-upward) faulting forms a

dominant feature of the blocks which were rotated around

sub-horizontal axes increasing with time (from 28 M.Y. to

the present) (Proffett, 1977). Originally deposited as

flat-lying ash-fall and -flow tuffs, the o_dest ( 27.5 M.Y.)

tuffs of the Guild Mine member now have 70 W dips, with th_

middlemost (TI0) rhyolitic tuff of 26 M.Y. age having a 50v

W dip. _he youngest in this sequence, the 17-19

M.Y. old Lincoln Flat hornblende andesite has only about 15°

W dip.

As both the Triassic /Jurassic rocks and the Miocene

turfs have rotated together to the west, the oldest Triassic

beds dip almost vertically with a well developed fold axis

which has a 700 W plunge. The Jurassic rocks( of

economic interest) plunge 200 Eas_ towards their

batholithic base,having being rotated 70 v W from vertical.

Thus one can walk across outcrop, from an original -2Km

depth downwards to -8Km depth merely by walking towards the

SE.

Local Description Pertinent to AIS

Detailed descriptions of the terrain overflown by the

AIS appear in last year's report ( Lyon & Lanz, 1985).

Other parallel studies will be reported elsewhere in 1986

(IGARSS,Zurich & ERIM,Reno,1986). In addition the range

site will be the first major stop on the field trip for the

ERIM, Reno Conference, October 3, 1986.

RATIONALE FOR AIS SITE SELECTION

The following summarizes the rationale for selection of

this site for the AIS flights;

I. The site is well mapped geologically and

mineralogically at 1:4800 scale (Dilles, 1983) and this is

also published at a scale of 1:24,000 (Dilles &

Proffett,1984).

2. An extensive set of remote sensing studies

performed by our laboratory exist, with coverages by

Landsat MSS,TM,TMS and now AIS ('84 & '85), together with

SKYLAB (S-191), HCMM, etc.

88
3. Large areas of man-made homogeneous materials

exist for calibration targets,as mine dumps, tailings

ponds,etc, as well as large, natural, alluvial fans which

have been shown to be spectrally-flat ( for "flat-field"

corrections to AIS ).(Roberts,et.al.,1985).

4. Over 300 high-resolution VIRIS spectra

(Collins Visible Infrared field spectrometer) have been

collected to date (5/86) and more will be taken in the upcoming field season this Summer.

SPECIFIC AIS TARGETS-1984/85 FLIGHTS

Detailed studies of the hydrothermal rock alteration

(Dilles,1983)together with our field mapping indicated that

while the Singatse Range trended N-S, the quartz monzonite

dike swarms with their attendant rock alterations ( both the

"Early-hypogene" and the "Late-stage" geothermally-induced)

followed a S_-NW direction,starting at the N-S Bluestone

Fault, for over 5 Km (roughly 4 Km NE of the small town of

Mason on the Walker River). _ Patterns repeated by

extensive, roughly N-S block-faulting interrupt this

continuity, but the trend can be followed 2-4 Km across the

Mason Pass road to the Blue Hills, and on over to the

Buckskin Ranges 5-6 Km to the NE. This total distance of 22

Km represents (vertically) about 8-9 Km of the original

cogenetic dike-extrusive volcanic pile, fed by the dikes

from the deeper-seated batholithic rock, now to the SE,

almost 100% extension due to faulting, (Proffett,1977).

The outcrop of the alteration of interest ( "Latestage", or geothermal) is quite narrow starting at the crest

of the Singatse Range where it covers about 400m. As one

progresses NW ("upwards" in the section ). the northernmost

edge becomes hidden under the ignimbrites lying over the

(now) flat-lying Singatse Fault. At the Mason Pass road ( 5

Km to the NW ) the altered zone broadens out (tree-like) to

cover about 1-2 Km. In the Blue Hills the zone is 2-3 Km

wide and in the uppermost zone (in the cogenetic lavas) at

the Buckskin Range the zone widens to about 6 Km in the N-S

direction.

89
8 km

DEPTH F

',_\ = BATHOLITH

ADVANCED

ARGILLIC

ARGILLIC

ALTERATION

POTASSIC

ALTERATION

Figure i. Cartoon-style vertical cross-section of the

original plutonic-volcanic pile, showing the relative

position of the Ann Mason dikes, the Blue Hills alteration

and the Buckskin Range volcanics (heavily altered "advanced

argillic" zone). F-F ; breaks due to faulting.

The outcrop of the alteration of interest ( "latestage",geothermal) is quite narrow, starting at the central

spine of the Singatse Range covering about 400-600 m, with

its northern edge hidden under the ignimbrite cover which

lies over the (now) flat-lying Singatse Fault. At the

Mason Pass road ( 5 Km to the NE ) it rapidly broadens, tree

-like up to a 1-2 Km width. In the flat lying Blue Hills

the altered zone is 4 Km wide, and at the upper-most level

in the Buckskin Range the width reaches 6 Km.

AIS flight lines were directed mostly in a SE-NW

direction so as to maximize the coverage along this thin

alteration zone, and to look for specific mineral changes

( originally vertical) in the volcanic-geothermal

hydrothermal plumbing system. Operationally this meant

vectoring the C-130 in to hit a target only 400 m wide but

over 6 Km in length. Three parallel flights ( #

407,409,508) up the structure, with one flight crossing at

9O
about 600 (# 410) from the SW-NE direction completed the

strategy. Ten Km to the north another " faulted-off slice"

of the structure (MacArthur) was traversed by two parallel

lines ( # 408,509) also flown in the basic SE-NW direction.

AIS INSTRUMENT--WAVELENGTHCALIBRATIONS

1984 Flight Calibrations ( I0 July, 1984)

In the 1984 data it was clear from inspection of the

relatively broad H20 vapor absorptions, that the wavelength

calibration originally provided by JPL was somehow in error,

and that the O-H absorptions also were displaced. By

carefully studying the deepest absorptions near 1.386 um in

the raw data set we were able to shift the channel-lambda

relationship a offset of several channels to a better fit.

When the corrected Channel-lambda values were later supplied

our empirically-derived values were found to be quite

close. The O-H absorptions in the AIS data over sites of

known O-H-bearing mineralogy were found to agree. (Roberts,

et.al,in press).

As with the 1985 data we retroactively applied the

further correction based upon the 2.006 and 2.053 um CO2

absorptions, although the latter appeared to be in the

middle of a grating change.( See companion paper by Roberts,

et.al., in this 1986 issue).

1985 Flight Calibrations.

Again we obtained our flight data set without

calibration data. It was soon apparent that quite

different grating settings had been used (relative to those

of 1984), and that about a seven channel shift to shorter

wavelengths was involved ( see Table I). This analysis

again was based upon the water vapor lines ( in the raw-data

mode). In addition, at the suggestion of Dr.A.A.Green

( 4/21/86) we looked for the presence of (and used) the

2.006 and the 2.053 um CO 2 peaks in both the raw- and

processed-data sets, even though in "log-residual" data

these should have been removed.

Most of the pixels we examined in the log-residual data

carried very small peaks with the correct separation of 47

nm. These were used to " correct" the lambda-channel

relationships in our table (Table i). What did worry us

however, was that these peaks oscillated about 1-2 channels

down the flight line, and produced a wavering line in the

imagery--no explanation for this was available, but it

appears to be a grating wobble".

91
* 25 JUL 84 ** 23 SEP 85 25 JUL 84

GRATING GRATING GRATING

SETTINGS SETTINGS SETTINGS

I 1.155 I 1.229 49 1.601 49

2 1.164 2 1.238 50 1.610 50

3 1.174 3 1.247 51 1.620 51

4 1.183 4 1.257 52 1.629 52

5 1.192 5 1.266 53 1.638 53

6 1.201 6 1.275 54 1.647 54

7 1.210 7 1.284 55 1.657 55

8 1.220 8 1.293 56 1.666 56

9 1.229 9 1.303 57 1.675 57

10 1.238 10 1.312 58 1.684 58

11 1.247 11 1.321 59 1.694 59

12 1.257 12 1.330 60 1.703 60

13 1.266 13 1.339 H20 61 1.712 61

14 1.275 14 1.349 H20 62 1.721 62

15 1.284 15 1.358 H20 63 1.731 63

16 1.293 16 1.367 H20 64 1.740 64

17 1.303 17 1.376 MAX-H2 65 1.748 65

18 1.312 18 1.386 MAX'H2 66 1.757 66

19 1.321 19 1.395 H20 67 1.767 67

20 1.330 20 1.404 H20 68 1.776 68

21 1.339 H20 21 1.413 H20 69 1.785 69

22 1.349 H20 22 1.422 H20 70 1.794 70

23 1.358 H20 23 1.432 H20 71 1.804 71

24 1.367 H20 24 1.441 H20 72 1.813 H20 72

25 1.376 MAX'H 25 1.453 H20 73 1.822 H20 73

26 1.386 MAX-H 26 1.462 H20 74 1.832 H20 74

27 1.395 H20 27 1.471 75 1.841 H20 75

28 1.404 H20 28 1.481 76 1.850 H20 76

29 1.413 H20 29 1.490 77 1.859 H20 77

30 1.422 H20 30 1.499 78 1.869 MAX'H2 78

31 1.432 H20 31 1.508 79 1.878 MAX'H2 79

32 1.441 H20 32 1.518 CHANGE 80 1.887 H20 80

33 1.453 H20 33 1.536 81 1.897 H20 81

34 1.462 H20 34 1.545 82 1.906 H20 82

35 1.471 35 1.554 83 1.915 H20 83

36 1.481 36 1.564 84 1.925 H20 84

37 1.490 37 1.573 85 1.934 H20 85

38 1.499 38 1.582 86 1.943 H20 86

39 1.508 39 1.591 87 1.952 H20 87

40 1.518 40 1.600 88 1.962 88

41 1.527 41 1.610 89 1.971 89

42 1.536 42 1.619 90 1.980 90

43 1.545 43 1.628 91 1.990 91

44 1.555 44 1.637 92 1.999 92

45 1.564 45 1.646 93 2.008 93

46 1.573 46 1.656 94 2.017 94

47 1.583 47 1.665 95 2.027 95

48 1.592 48 1.674 96 2.036 96

* D.TUCKER LTR ** D.TUCKER PHONE CALL

6/26/84 5/2/86

23 SEP 85

GRATING

SETTINGS

1.683

1.692

1.702

1.711

1.720

1.729

1.738

1.748

1.757

1.766

1.775

1.784

1.794

1.803

1.812

1.821 H20

1.835 CHANGE

1.844 H20

1.853 H20

1.863 H20

1.872 H20

1.881 MAX-H2

1.890 MAX'H2

1.899 H20

1.909 H20

1.918 H20

1.927 H20

1.936 H20

1.945 H20

1.955 H20

1.964 H20

1.973

1.982

1.991

2.001

2.010

2.019 C02

2.028

2.037

2.047

2.056

2.065 C02

25 JUL 84

GRATING

SETTINGS

97 2.048

98 2.057

99 2.067 CHANGE

100 2.076 GYPSUM

101 2.085

102 2.095

103 2.104

104 2.113

105 2.123

106 2.132

107 2.141

108 2.150

109 2.160

110 2.169

111 2.178

112 2.188

113 2.197

114 2.206

115 2.216

116 2.225

117 2.234

118 2.244

119 2.253

120 2.262

121 2.272

122 2.281

123 2.290

124 2.300

125 2.309

126 2.318

127 2.327

128 2.337

2006

2053

2.074 GYPSUM,MONT

2.083

2.093

2.102

2.111

2.120

23 SEP 85

GRATING

SETTINGS

97 2.134

98 2.143

99 2.152

100 2.162 KAOLINITE

101 2.171 PYROPHYLLITE

102 2.180

103 2.189

104 2.198

105 2.208 KAOL,SER,MONT,ALUN.

106 2.217

107 2.226

108 2.235

109 2.244

110 2.254

111 2.263

112 2.272

113 2.281

114 2.290

115 2.300

116 2.309

117 2.318

118 2.327

119 2.336 CALCITE

120 2.346 SERICITE

121 2.354

122 2.364

123 2.373

124 2.382

125 2.391

126 2.400

127 2.410

128 2.419

Table 2 Comparison of Lambda-Channel relationships, 1984/85.

92
ORIGtNAL PAC_ IS

roe. 

JOINT CROSSINGS OF AIS FLIGHTS.

One method of testing the repeatability of the AIS

flight data is to cross the same area several times. Because

it is difficult to hit the same flight path twice due to the

normal uncertainties of aircraft navigation, we chose to fly

parallel, N-S offset flight lines. As our target at Ann Mason

was long and thin this seemed to be a better plan.

In _uly 25,1984 we placed one of our lines ( # 410) at

about 60 to the others, and produced two (2) crossings ( A

and B) with lines 407 and 409 over the most-altered area.

In September 23,1985 we only were able to have two lines

due to a tight flight-time schedule, and elected one

( # 508) to be parallel with the main trend again, crossing

the older #410 at crossing C.

Fig 2. (Left) B/W copy of a color composite of three

channels (107,51,3) showing the strong O-H absorption

of sericite (black).

Fig 3. (Right) Graphical form of the same data, showing the

spectra with pronounced O-H absorptiona at 2.206 um

(vertical line).

1984 Crossings A & B

Crossing A, with # 409 & 410 is 1.0 Km to the SE of the

4 x 4 Km altered zone of interest, missing it entirely.

Accordingly neither line in Crossing A show marked O-H

absorption areas, although some patchy areas can be

discerned.

93
Crossing B, of # 407 & 410, is of much more interest,

as the two lines intersect over an ESE extension of the main

altered zone in the quartz monzonite and quartz monzonite

porphyry system. Here it is possible to define

geographically-similar areas of O-H absorption common to

both lines.

1985/84 Crossing C

As with many well-planned things in experimental

studies, this crossing of the 1984 line with the new 1985

line took place exactly over a small valley filled with

alluvium and wind-blown sand of very recent origin. Portions

of the 32-pixel swath do intersect parent rock, and the

presence of the O-H absorptions can be used to define the

altered ground. Examples of the patches of O-H

absorbing clay can be well seen in Figures 2 & 3 taken from

Run 508 at line 800.

CONCLUSIONS.

A. Despite the variability of the wavelength

settings, it is possible to establish the " correct "

positions using the atmospheric bands at 1.386, 1.868 2.006

and 2.53 um.

B. Comparable spectral evidence exists from repeated

flights over the same terrain, that the spectral patterns

can be repeated in the 1984 and the 1985 data. In addition

continuing ground spectral measurements, using the Collin's

VIRIS spectrometer confirm the types of O-H bearing minerals

seen in the flight data.

C. Flights in 1986/87 with the AIS-II instrument

would be extremely significant, given the degree of ground

spectral work being performed in the Singatse Range.

D. AVIRIS flights ( with the markedly wider swath

width ), flown N-S along the crest of the Singatse Range in

the 1987/88 time period, and extending both to the south and

to the north, would allow the REGIONAL MAPPING of these same

rock types, presently intensively studied in detail only in

this smaller ( Ann Mason )site.

REFERENCES

Dilles, J.H., 1983, The petrology and geochemistry of the

Yerington batholith and the Ann-Mason porphyry copper

deposit, western Nevada, Unpublished Ph.D. thesis, AES

Department, Stanford University.

94
Einaudi, M.T.,1977,Petrogenesis of the copper-bearing skarn

at the Mason Valley mine,Yerington district, Nevada:

Econ.Geol. 72,769-795

Green,A.A., Craig,M.D., 1985,Analysis of aircraft

spectrometer data with logarithmic residuals: Proc. AIS

Data Analysis Workshop,April 8-10,1985,G.Vane & A.F.H.

Goetz, eds. Jet Propulsion Lab,Cal.Tech.,Pasadena, CA,

111-119

Lyon,R.J.P. and Lanz, K, 1985, Field utilization of AIS

128-Channel imagery using microcomputers: Application

to Yerington,NV, field: Proc. AIS Data Analysis

Workshop,April 8-10,1985,G.Vane & A.F.H. Goetz, eds.

Jet Propulsion Lab,Cal.Tech.,Pasadena, CA, 35-40

Proffett,J,M, 1977, Cenozoic geology of the Yerington

district, Navada, and implications for the nature and

origin of basin and range faulting: Geol. Soc. Amer.,

Bull 88:247-266

Proffett,J.M.Jr., and Dilles,J.H., 1984, Geological Map of

the Yerington, District, Map 77, published by the

Nevada Bureau of mines & Geology,University of

Nevada,Reno,NV. One page map in folder.

Proffett,J.M.Jr. and Proffett,B.H.,1976, Stratigraphy of the

Tertiary ash-flow tuffs in the Yerington, district,

Nevada: Nevada Bureau of mines and Geology, Report 27,

28p.

Roberts,D.A.,Yamaguchi,Y. and Lyon,R.J.P., 1985, Calibration

of airborne imaging spectrometer data to percent

reflectance using field spectral measurements: Proc.

Nineteenth Int. Symp. Rem.Sens.Environ.,Ann Arbor ,MI,

Oct 21-25,1985 (in press)

Roberts,D.A.,Yamaguchi,Y. and R.J.P.Lyon, 1986, Calibration

of various techniques for calibration of AIS Data:

Proc. Second AIS Data Analysis Workshop, May 6-8, 1986,

G.Vane & A.F.H.Goetz, eds. Jet Propulsion

Lab,Cal.Tech.,Pasadena, (in this issue).

95
N87-12977

IDENTIFICATION OF HYDROTHERMAL ALTERATION ASSEMBLAGES USING AIRBORNE

IMAGING SPECTROMETER DATA

SANDRA C. FELDMAN and JAMES V. TARANIK, Mackay School of Mines,

University of Nevada-Reno, Reno, NV 89557, USA

ABSTRACT

Airborne Imaging Spectrometer (AIS) data, field and laboratory

spectra and samples for x-ray diffraction analysis were collected in

argillically altered Tertiary volcanic rocks in the Hot Creek Range,

Nevada. From laboratory and field spectral measurements in the 2.0 to

2.4 um range and using a spectroradiometer with a 4 nm (0.004 um)

sampling interval, the absorption band centers for kaolinite were

located at 2.172 and 2.215 urn, for montmorillonlte at 2.214 um and for

illlte at 2.205 urn. Based on these values and the criteria for

resolution and separation of spectral features, a spectral sampling

interval of less than 4 nm (0.004 um) is necessary to separate the

clays. With an AIS spectral sampling interval of 9.3 nm (0.0093 um),

a spectral matching algorithm is more effective for separating kaolinite, montmorillonlte and illlte in the Hot Creek Range than using the

location of absorption minima alone.

INTRODUCTION

Airborne Imaging Spectrometer (AIS) data were collected in an

area of hydro thermally altered and unaltered carbonate rocks and

rhyolitic ash-flow tuffs in the southern Hot Creek Range, Nevada.

Atmospherically corrected AIS data were used to separate argilllcally

altered Tertiary ash-flow ruffs from unaltered turfs. The separation

was possible because of spectral absorption features near 2.2 um

caused by molecular vibrations related to the hydroxyl ion and structural water (Feldman and Taranlk, 1985). Although this work demonstrated that altered rock could be differentiated from unaltered rock,

it did not determine whether kaolinite, montmorillonlte and illite,

found in the altered ash-flow tuffs, could be spectrally and spatially

separated from each other using AIS data. This paper addresses the

identification of clays by resolution of narrow absorption features in

their reflectance spectra and by spectral curve-matchlng procedures.

Clay identification was accomplished by comparing AIS spectral signatures with those acquired in the field and laboratory with a Geophysical Environmental Research (GER) IRIS spectroradlometer and also

by computer processing AIS imagery using the Spectral Analysis Manager

(SPAM) software developed by the Jet Propulsion Laboratory (JPL).

Detailed field mapping and x-ray diffraction analysis supplement the

spectral methods.

96
COLLECTIONOF FIELD AND LABORATORY SPECTRA

Alteration mineral spectra are most distinctive in the portion of

the spectrum between 2.0 and 2.4 um where overtone and combination

features related to anion vibrations occur (Hunt and Ashley, 1979;

Goetz and Rowan, 1981). The followlng discussion will be limited to

this portion of the spectrum.

The spectra of Hot Creek rocks were measured in the field and

laboratory with a GER IRIS spectroradlometer. The instrument measures

the radiance of the sample and the Fiberfrax standard simultaneously

using a dual beam system. The radiance measurements can then be

converted to percent reflectance of the target relative to the standard. In the 2.0 to 2.4 um region, the instrument spectral bandwidth

is 4 nm.

Kaolinite, montmorillonlte and illlte spectra recorded with the

spectroradlometer, possess absorption features which are similar in

form and very close in wavelength (Figures i and 2). The kaolinite

spectrum shows a characteristic doublet (the first absorption feature

will be referred to as kaolinite A and the second absorption feature

as kaolinite B). The illlte spectrum has a minimum at a shorter wavelength than the dominant kaolinite minimum. The absorption minimum

for montmorillonlte has a wavelength close to the second kaolinite

minimum and is similar in form to illlte. The montmorillonlte absorption feature, however, is broader, more asymmetrical and the depth of

the feature is not as great as in illlte even in pure samples.

Sensor spectral sampllng interval greatly influences the detection of absorption features. Using kaolinite as an example, at a

spectral sampling interval of 20 nm, the first kaolinite absorption

feature begins to lose definition and becomes a shoulder in the

spectral curve (bottom curve, Figure 3). Resolution of these spectral

absorption features is dependent on both the width and depth of the

feature and the separation between two absorptions. In order to

resolve an absorption feature, a spectral sampling interval equal to

no more than one half the width of the feature is necessary (Nyquist

criterion). For kaolinite, the separation between the two absorptions

is about 40 nm (less if the points of inflection on the curves rather

than minima are considered). The kaolinite absorption features should

be resolved at a 20 nm or smaller spectral sampling interval. Since

the GER bandwidth is 4 nm and AIS spectral sampling interval is 9.3

nm, the kaolinite doublet should be resolved with both the field and

airborne instruments. For AISI the sampling interval is the same as

the instrument resolution because the system is diffraction limited.

In the resolution of two absorption features in the same mineral or,

two or more minima from different clays in mixtures, the feature being

resolved is actually the peak or portion of the spectrum between the

minima.

Using the digital data acquired with the IRIS spectroradlometer,

the average absorption minimum and difference between minima were

calculated for kaollnlte, montmorillonite and i111te samples (Tables I

and 2). Only samples which showed Just one clay group in the x-ray

diffraction pattern were used in the calculation of the clay statistics.

97
Figure 1. GER spectroradiometer curves for kaolinite and illite.

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98
Figure 3. Kaolinite spectra at 4 to 20 nm spectral

sampling intervals (JPL, 1983).

EFFECT OF SAMPLING INTERVAL ON KAOLINITE REFLECTANCE SPECTRUM

J_

'_12 nm

 ,,oo

I_1i _20 nm

I O I I I I I | I I I I I I [ 1 I I 0.40 0.80 1.00 1.20 1,40 1.60 1.80 2.00 2.20 2,40

microns

Table I. Band center of clay absorption features recorded wlth

GER spectroradlometer; Hot Creek Range, Nevada samples

Wavelength (urn)

Kaoltnlte 2.172 2.215

Montmorlllonlte 2.214

1111te 2.205

Table 2. Difference between clay spectral absorption features

from spectrometer measurements; Hot Creek Range,

Nevada samples

Difference (nm)

Kaolinite A - Kaolinite B 43

Kaolinite B - Montmorlllonlte

Kaollnlte A- Montmorlllonlte

42

I

Kaolinite A - lllite

Kaolinite B - lllite

33

10

Montmorlllonlte- lllite

99
PROCESSINGOFAIS DATA

Using the 32 spectral bands between 2.0 and 2.4 um to which a

flat field correction has been applied, a spectral slgnature-matchlng

algorithm from the SPAH software was employed to find picture elements

in the AIS imagery with spectral signatures similar to kaolinite,

montmorillonite and illlte library spectra. The algorithm does not

rely on resolution of spectral minima but on the comparison of binary

encoded spectral curves (Goetz, Vane, Solomon, and Rock, 1985).

Pixels in a scene which have signatures similar to each of the clays

are located and colored on the 32-plxel wide image.

Two areas on the Hot Creek AIS flight lines were examined using

this curve-matchlng algorithm, Red Rock Canyon and the Falrbanks

Canyon area. In Red Rock Canyon, the montmorillonite-altered Shingle

Pass Tuff and the kaollnitlcally altered Tuff of Orange Lichen Creek

were correctly identified using the SPAM algorithm. In Falrbanks

Canyon, one tuff hydrothermally altered to kaolinite and another

altered to illlte were correctly identified. A shale was also identified as illlte. Areas with another tuff were matched with a montmorillonlte spectrum. X-ray diffraction analysis of rocks from areas

of matched signatures confirmed the presence of the clay minerals with

the exception of the area of montmorillonlte in Falrbanks Canyon, yet

to be sampled.

SUMMARY

Based on the Nyqulst criterion, the spectral sampling interval

must be no more than one half the difference in wavelength between the

two absorption minima (or strictly speaking, the inflection points on

the curve) for resolution or separation of features. Based on the

distance between clay spectral absorption features, the following

absorption features can be classified as "separable" or "inseparable"

in AIS data, based on location of minima alone:

Separable on Basis of Wavelength of Minima on AIS

Kaolinite A - Kaolinite B

Kaolinite A - Hontmorillonite

Kaolinite A - llllte

Inseparable on Basis of Wavelength of Minima on AIS

Kaolinite B - Hontmorillonlte

Kaolinite B - llllte

Hontmorillonite - llllte

To effectively separate kaolinite, montmorillonlte and illlte

absorption features near 2.2 um, a sensor spectral sampling interval

of less than 4 nm would be necessary. With the current AIS sensor

(Vane, Goetz and Wellman, 1985) and family of instruments, without

increasing spectral resolution, the most productive approach to the

I00
identification of clay mlnerals _s to use signature matching algorithms. In the Hot Creek Range, spectral matching procedures have

identified the location of kaolinite, montmorillonlte and llllte.

ACKNOWLEDGEMENTS

The authors would llke to acknowledge the assistance of JPL's

Image Processing Laboratory staff for assistance during processing of

the data and ARCO 011 and Gas Company for the use of the GER

spectroradlometer in the study area.

REFERENCES

Feldman, S. C., and Taranlk, J. V., 1985. A first look at Airborne

Imaging Spectrometer (AIS) data in an area of altered volcanic

rocks and carbonate formations, Hot Creek Range, south central

Nevada. In Proceedings of the Airborne Imaging Spectrometer Data

Analysls Workshop, 1985. Jet Propulslon Laboratory Pub1. 85-41,

p. 56-61.

Goetz, A. F. H., and Rowan, L. C., 1981. Geologic remote sensing.

Science, V. 211, p. 781-791.

Goetz, A. F. H., Vane, G., Solomon, J. E., and Rock, B. N., 1985.

Imaging spectrometry for earth remote sensing. Science, v. 228,

p. 1147-1153.

Hunt, G. R., and Ashley, R. P., 1979. Spectra of altered rocks in the

visible and near infrared. Economic Geology, v. 74, p. 1613-1629.

Jet Propulsion Laboratory, 1983. Shuttle Imaging Spectrometer

Experiment (SISEX), A proposal to the National Aeronautics and

Space Administration for a Shuttle Experiment, July 18, 1983

(Pasadena, California: California Institute of Technology).

Vane, G., Goetz, A. F. H., and Wellman, J. B., 1983. Airborne Imaging

Spectrometer: A new tool for remote sensing. In Proceedings IEEE

1983 Int'l Geosclence and Remote Sensing Symp., IEEE Cat. No.

83CH1837-4, 5p.p

i01
N87-12978

DETECTION OF HYDROTHERMAL ALTERATION AT VIRGINIA CITY,

NEVADA,USING AIRBORNE IMAGING SPECTROMETRY (AIS)

AMY NUTSINPILLER and JAMES V. TARANIK, Mackay School of

Mines,University of Nevada-Reno, Reno, Nevada 89557

ABSTRACT

Airborne imaging spectrometer (AIS) data were collected

over Virginia City, Nevada; an area of gold and silver

mineralization with extensive surface exposures of altered

volcanic rocks. The data were corrected for atmospheric

effects by a flat-field method, and compared to library

spectra of various alteration minerals using SPAM. We were

able to identify areas of strong clay alteration on the AIS

images that were mapped as kaolinitic, illitic, and sericitic alteration zones. Kaolinitic alteration is distinguishable in the 2.1 to 2.4 and 1.2 to 1.5 micrometer wavelength

regions. Montmorillonite, illite, and sericite have absorption features similar to each other at 2.2 micrometers

wavelength. Montmorillonite and illite also may be present

in varying proportions within one GIFOV. Therefore, it is

difficult to distinguish montmorillonite, illite, and

sericite from each other on the AIS images. In propylitically altered areas, the spectral contribution of vegetation leads to ambiguous identification of chlorite, calcite,

and epidote in the 2.1 to 2.4 micrometer region. In general,

AIS data is useful in identifying alteration zones that are

associated with or lie above precious metal mineralization

at Virginia City.

INTRODUCTION

Virginia City is located 30 kilometers southeast of

Reno in the Virginia Range. From the 1850's to the present,

over 260 million grams (9 million ounces) of gold and 6

billion grams (200 million ounces) of silver have been

produced from the Comstock Lode district. Extensive surface

exposures of bleached and altered rocks are associated with

the precious metal mineralization. Zones of hydrothermal

alteration are identifiable on Landsat Thematic Mapper (TM)

and Thermal Infrared Multispectral Scanner (TIMS) images due

to the presence of iron-oxide and silicate alteration

minerals.

The purpose of acquiring AIS data at Virginia City is

to use the finer spatial and spectral resolution to try and

identify specific alteration mineral assemblages and their

distributions, and to differentiate the volcanic host rocks

and their derivative soils. Also, the contribution of

vegetation spectral response to the discrimination of rock

and alteration types will be assessed.

102
This paper presents our initial results on the identification of specific alteration minerals and the spectral

contribution of vegetation along portions of three AIS

flight lines.

GEOLOGYAND ALTERATION

The oldest rocks exposed in the district are Mesozoic

metasediments and metavolcanics that have been intruded by

Cretaceous granodiorite and granite. Unconformably overlying

the Mesozoic rocks are a series of sili¢ic ash-flow turfs.

These are overlain by thick andesite flows and breccias of

the Miocene Alta Formation, the main host of orebodies in

the district. Dikes and stocks of Davidson Diorite intrude

the Mesozoic rocks and the Alta Formation. Overlying the

Alta Formation are andesitic to dacitic flows and associated

dikes and stocks of the Kate Peak Formation. During emplacement of the lower member of the Kate Peak Formation, normal

faulting, mineralization, and alteration occurred. The Late

Miocene or Pliocene Knickerbocker Andesite unconformably

overlies the Kate Peak Formation.

Mineralization and hydrothermal alteration in the

Comstock Lode district are mostly confined to the northerlytrending Comstock, Silver City, and Occidental fault zones,

although some ore was mined in hanging-wall faults. The

faults dip moderately to the east but are nearly vertical at

the surface. Post-mineral faulting has displaced the

hanging-wall ore bodies as well as portions of ore bodies

within the fault zones. Hudson (1984,1986) has recently

studied hydrothermal mineral assemblages at Virginia

City. He defines seven major alteration types based on field

mapping, petrography, and x-ray diffraction:

i. PROPYLITIC - albite + chlorite ± epidote ± calcite ±

white mica ± quartz ± pyrite.

2. ILLITIC (+ MONTMORILLONITE) - illite + quartz +

pyrite ± mixed-layered illite-montmorillonite.

3. KAOLINITIC - kaolinite + quartz + pyrite.

4. ALSIC - pyrophyllite + quartz ± diaspore ±

kaolinite ± pyrite.

5. ALUNITIC - quartz + alunite + pyrite.

6. SERICITIC - sericite + quartz + pyrite.

7. SILICIFICATION and STOCKWORK VEINING - quartz ±

adularia ± calcite.

Alteration assemblages vary with depth from the paleosurface

and horizontal distance from major faults. Stockwork veining

and sericitic alteration associated with the deeper-level

ore horizon are exposed at the surface in the footwall of

the Comstock Lode because of post-mineral faulting. At the

surface in the hanging wall, alunitic and alsic ledges grade

laterally into kaolinitic alteration. The kaolinitic zones

in turn grade into illitic and finally into propylitic

103
alteration. The alunitic, alsic, kaolinitic, and illitic

mineral assemblages represent near-surface and intermediate

levels of deeper alteration types that are associated with

ore. The deeper alteration assemblages include the minerals

sericite, quartz, calcite, adularia, anhydrite, illite, and

kaolinite (Hudson, 1986).

Propylitic alteration is by far the most widely exposed

in the district. Illitic and kaolinitic assemblages occur in

smaller zones from several meters to over 500 meters in

width. Narrow alunitic and alsic ledges occur within the

illitic and kaolinitic zones. Patches of sericitic alteration up to 800 meters wide occur west of Virginia City.

DATA COLLECTION AND ANALYSIS

Three flight lines of AIS data were collected in 128

continuous spectral bands between 1.2 and 2.4 micrometers

(9.3 nm bandwidth) over the Virginia City area on 2 July

1985 at ii:00 am local time. The altitude of the sensor

varied from 5.0 to 5.3 kilometers above mean terrain. The

ground instantaneous field of view (GIFOV) averaged 10.4

meters and the swath width averaged 330 meters.

Two types of atmospheric corrections were applied to

the data. First, the effects of the solar spectral irradlance curve were removed from the data using an algorithm

developed at the Jet Propulsion Laboratory (JPL). Secondly,

a flat-field method was used to correct for solar spectral

irradiance, atmospheric absorption, and detector calibration

errors. Spectrally flat areas (400 to 500 meters by 330

meters in size) were found at the end of each flight line

away from alteration at Virginia City. The spectrally flat

areas were used to normalize the remainder of the flight

lines. Figure 1 compares spectral curves for raw data (RAW)

to data that has been corrected using a solar irradiance

method (SOLAR) and a flat-field method (FF). The upper and

lower plots show spectra derived from 5x5 pixel areas of

unaltered and altered volcanic rocks, respectively. The

flat-field method appears to better resolve spectral

features, and was applied all the AIS data collected at

Virginia City.

A spectral analysis program (SPAM) developed at the JPL

was used to analyze the data. Most spectral comparisons were

made with only the last 32 bands (grating position 4) of the

AIS data. This was done because i.) absorption features of

many important alteration minerals occur in this wavelength

region, 2.) there were less spectral noise and line drops in

grating position 4 than in other grating positions, and

3.) the spectral data had slight discontinuities where two

grating positions met (bands 32/33, 64/65, 96/97), making it

difficult to compare these spectral regions with library

spectra. AIS spectra and library spectra were compared using

FIND (a SPAM function). The comparisons were made with

spectral amplitude and not slope. When spectral slope

104
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WAVELENGTH (microns)

Figure i. Spectral curves for raw data (raw), and data that

has been corrected using a solar irradiance method

(solar) and a flat-field method (ff). Upper plot

is for unaltered volcanic rocks and lower plot is

for altered volcanic rocks.

105
similarities were compared, irregularities in the spectra of

the AIS data resulted in erroneous mineral matches.

RESULTS

Segments of the three flight lines that cover known

areas of alteration in andesitic volcanic rocks were chosen

for initial analysis. Propylitic, kaolinitic, illitic, and

sericitic alteration assemblages are present, as well as

disturbed ground, mine dumps, alluvium, roads, and buildings.

Identification of Alteration Minerals

To identify alteration minerals on the AIS images, SPAM

library spectra were compared to spectra from individual

pixels in the 2.1 to 2.4 micrometer wavelength region. Clay

minerals which exhibit strong reflectance minima near 2.2

micrometers, including kaolinite, montmorillonite, muscovite, and illite, were identified on the images. The locations of these pixels correlate with kaolinitic, illitic,

and sericitic alteration zones, and portions of mine dumps

and disturbed areas. Scattered pixels with strong 2.2

micrometer absorption features also occur in propylitic

alteration zones.

Pixels with kaolinite-like spectra are distinguishable

from other pixels whose spectra show a single absorption

feature near 2.2 micrometers. Concentrations of kaoliniticpixels correlate with mapped kaolinitic alteration and

portions of mine dumps. The spectra of kaolinite-like pixels

can also be identified in the 1.2 to 1.5 micrometer wavelength region, based on the characteristic absorption

doublet at 1.4 micrometers (see figure 2).

In the 2.1 to 2.4 micrometer wavelength region, pixels

identified as montmorillonite, illite, or muscovite show

considerable overlap. That is, the same pixel may be

classed as montmorillonite, illite, and/or muscovite,

depending on which library spectrum is called upon for the

comparison. This is because montmorillonite, illite, and

muscovite have similar absorption features near 2.2 micrometers, and because of mixed-pixel problems. Both illite and

montmorillonite may be present in the illitic alteration

zone, and there is a gradation between illitic and other

types of alteration. The result is that illite, montmorillonite, sericite, and/or kaolinite may be present within a

single GIFOV.

yegetation Contribution

An attempt was made to _ind pixels with spectra similar

to calcite, chlorite, and epidote, which are common in the

propylitically altered volcanics. These minerals exhibit

absorption features near 2.3 micrometers. The lower-wave106
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Figure 2. Area-normalized spectral curves for library minerals and flatfield corrected AIS data. Upper plot is library spectrum of

kaolinite and spectrum from 3x3 pixel area of kaolinitic alteration (PLOT I) in 1.2-1.5 micrometer wavelength region. Lower

plot shows library spectra of kaolinite and montmorillonite

(MONT), haolinitic alteration (PLOT I), and spectrum from 3x3

pixel area of mine dump with clay-altered volcanics (PLOT 2) in

2.1 - 2.4 micrometer wavelength region.

107
length side of the absorption feature coincides with the

shoulder of a broad reflectance maxima for vegetation that

occurs at about 2.2 micrometers.

Most undisturbed ground at Virginia City is sparsely to

moderately vegetated with grasses, sage, juniper, and

pinyon. Propylitically altered rocks were not extensively

prospected and are relatively undisturbed and therefore

vegetated. The result is mixed-pixels of vegetation and

propylitically altered andesites.

The mixed-pixel problem combined with similarities

between the spectral features of vegetation and propylitic

alteration minerals, makes identification of calcite,

chlorite, and epidote ambiguous. Pixels that had chloritelike spectra proved in the field to be propylitized andesite

with epidote-coated fractures and grassy areas (one was a

baseball field). Some of the calcite-like spectra, however,

were indeed small areas of abundant calcite within propylitic alteration zones.

CONCLUSIONS

The AIS spectra of kaolinitic, illitic, and sericitic

alteration in volcanic rocks are distinguishable from

unaltered or weakly propylitized rocks in the 2.1 to 2.4

micrometer spectral region. Strong 2.2 micrometer absorption

features in the AIS spectra may be attributable to montmorillonite, illite, and/or sericite. Pixels with kaolinitelike spectra can be identified and correlate with mapped

kaolinitic alteration zones. Vegetation influences spectral

identification of the minerals calcite, chlorite, and

epidote associated with propylitically altered volcanic

rocks.

At Virginia City, AIS data is useful in identifying

alteration zones that are associated with or lie above

precious metal mineralization. The ability to discriminate

individual alteration minerals depends on the amount of

mineral mixing within a pixel and the amount of vegetation.

We hope to quantify these factors in future work with the

AIS data.

REFERENCES

Hudson,D.M., 1986, The Comstock District, Storey County,

Nevada: Geologic Society of Nevada Field Trip Guidebook

#4, 13 p.

Hudson,D.M., 1984, Geology of the Comstock District, Storey

County, Nevada: report prepared for United Mining

Company of Nevada, Virginia City, Nevada, 29 p.

Whitebread,D.H., 1976, Alteration and Geochemistry of

Tertiary Volcanic Rocks in Parts of the Virginia City

Quadrangle, Nevada: U.S.Geol. Sur. Prof. Paper 936, 43p.

108
N87-12979

PRELIMINARY GEOLOGICAL INVESTIGATION OF AIS DATA AT MARY

KATHLEEN, QUEENSLAND, AUSTRALIA

JON F. HUNTINGTON, ANDY A. GREEN, MAURICE D. CRAIG, AND

TERRY D.COCKS, CSIRO DIVISION OF MINERAL PHYSICS

MINERALOGY, NORTH RYDE, NSW, AUSTRALIA

AND

ABSTRACT

The Airborne Imaging Spectrometer (AIS) was flown over

granitic,volcanic and calc-silicate terrain around the Mary

Kathleen Uranium Mine in Queensland, in a test of its

mineralogical mapping capabilities. Previous airborne

spectrometer surveys over this terrain identified amphibole,

carbonate, marble, epidote, kaolinite, smectite, and garnet

minerals and provided new lithological subdivisions. An

analysis strategy and new restoration and enhancement

techniques have been developed to process the 128 band AIS

data.

A preliminary analysis of one of three AIS flight lines

has shown that the data contains considerable spectral

variation but that it is also contaminated by second-order

leakage of radiation from the near-infrared region. This

makes the recognition of expected spectral absorption shapes

very difficult. The effect appears worst in terrains

containing considerable vegetation. Techniques that try to

predict this supplementary radiation coupled with the log

residual analytical technique show that expected mineral

absorption spectra of some of the above minerals can be

derived. The techniques suggest that with additional

refinement correction procedures for the Australian AIS data

may be devised.

Application of the log residual analysis method has

proved very successful on the Cuprite, Nevada data set, for

highlighting the alunite,kaolinite and SiOH mineralogy.

INTRODUCTION

As part of the US/Australia Joint Scanner Project (a

NASA/CSIRO/BMR multi-agency and company remote sensing

mission), some 54 separate Australian test sites were

surveyed with the AIS aboard NASA's AMES C130 aircraft in

October, 1985. Approximately 1597 kms of AIS data were

acquired. This paper along with Cocks and Green (this

volume) represents the first detailed analysis of the

Australian AIS data.

Four AIS flight lines (one north-south and three eastwest) were flown over the Mary Kathleen site in Queensland

(Fig. i) on October 19th. Sky conditions were generally

clear, though a few small clouds in the vicinity implied a

moist and varying atmospheric environment. NS001 and TIMS

scanner data were acquired concurrently with three of the

109
four AIS lines, along with 9 x 9 inch colour infrared (CIR)

photography and 35 mm black and white tracking photography.

The NS001 and CIR data are invaluable for location

and for determination of vegetation conditions as well as

for the usual photogeological interpretation. The AIS 35 mm

photography is important for exact ground track recovery,

particularly when the aircraft was yawing, but is of

generally very poor quality.

Flight details of the survey are given in Table I. This

test site was chosen for the initial Australian evaluation

because of two previous, highly successful airborne

profiling spectrometer surveys that showed the considerable

potential for imaging spectrometry.

GEOLOGICAL SETTING

The Mary Kathleen test site is centred around the now

mined-out Mary Kathleen Uranium-Rare Earth mine Situated at

latitude 20 45' S, longitude 140 00'30" E,some 55 kilometres

east of the city of Mt Isa (Fig.l).

The major rock groups and structures of the area are

depicted on Figure 2. The most complete presentation of the

regional geology is given by Derrick et al. (1971 & 1977),

whilst Scott and Scott (1985) provide the most recent

account of the detailed geology of the Mary Kathleen

syncline and the uranium mineralisation.

Two major rock units occur in the area: a Lower

Proterozoic to Carpentarian central,crystalline, basement

block which is flanked by the Carpentarian metasedimentary

eastern succession. The western end of the AIS flight-line

covers basement granite, and acid volcanics of the

basement,Tewinga Group (Fig.2) represented by the Argylla

Formation.

Resting unconformably on the basement complex is the

arenaceous and carbonate-rich Mary Kathleen Group. Local

depressions in the carbonate shelf contain areas of

sulphide-bearing black shales. In the Rosebud and Mary

Kathleen Synclines (Fig.2), the Group is represented by the

lower arenaceous Ballara Quartzite and the upper,calcareous

and skarn-rich Corella Formation. East-west compression

formed folds and major north-south trending high-angle

reverse faults, and major northeasterly and northwesterly

trending conjugate faults in the basement and cover.

Metasedimentary bedding is steep to near-vertical. Granite

intrusion and low pressure greenschist and amphibolite

facies metamorphism accompanied the deformation. Numerous

dolerite dyke swarms intrude both the basement and the

metamorphic cover.

A more detailed view of the geology and the individual

ii0
litholgies and minerals in the vicinity of the AIS flightline is given in Fig.3. The Mary Kathleen Uranium deposit

occurs half-way along the flight-line and is associated with

an Allanite,Garnet,Diopside Skarn (Pkc2g on Fig.3). Apart

from the clay and muscovite bearing granites and clay

weathering acid volcanics it can be seen that the major

minerals present are those that have primarily 2.3 to 2.4

micrometre absorption features.

PREVIOUS SPECTROMETER SURVEYS

Two previous spectrometer surveys have been conducted

by the authors over the same terrain as flown with the AIS

at Mary Kathleen. In 1982 two parallel, across strike,

flight lines were flown with the Geophysical Environmental

Research (GER) Mark II airborne spectrometer system

(Huntington, 1984). This instrument collected 512-channel,

visible and near infrared (VNIR) spectra and 64-channel,

shortwave infrared (SWIR) spectra. The SWIR spectra covered

the range 1.9 to 2.45 micrometres with 9 nanometre sampling.

In 1984 the area was re-flown with an airborne, circular

variable filter (CVF) spectrometer developed by the CSIRO.

This instrument has 128 spectral channels of approximately

I_ wavelength resolution covering the range 1.4 to 2.45

micrometres. Both surveys were flown with a ground pixel of

approximately 20 metres. The ground tracks crossed the same

geological sequences but were, on average, separated by

about 500 metres.

These two data sets provide an excellent background

against which to evaluate the AIS. In both cases the

spectral data were processed to logarithmic residuals (Green

and Craig, 1985) to enhance the subtle mineral absorption

features that were not evident in the raw radiance data.

Both surveys demonstrated excellent mineralogical

discrimination, producing interpreted geology that matched

the published 1:50,000 and I:I0,000 mapping and provided

improved lithological subdivision of some formations. In

particular, identification was achieved of the minerals,

amphibole,epidote,kaolinite,calcite, marble,andradite garnet

and smectite. Results from the two instruments matched

extremely well, particularly for the calc-silicate

lithologies.

Comparisons of typical log residual spectra from the

two instruments for various lithologies are shown in Figs. 4

and 5. The spectra are averages of blocks of terrain varying

from 4 to 50 samples. Apart from the epidote/amphibole

spectra (Fig. 4) these surveys also picked up unusual

absorptions at 2.24 micrometres associated with a calcium

rich smectite in the Mary Kathleen open pit(Fig.5A). Also,

unusual 2.28 to 2.25 micrometre absorptions were located,

assocated with andradite garnet/diopside quartzites and

garnetites east of the mine (Figs.5A & 5C). Substantial

spectral measurement of field samples (backed-up by XRD

iii
analyses) confirmed the mineralogy and demonstrated the

close correspondence between the bi-directional reflectance

characteristics of field spectra and log residual

reflectance characteristics of airborne samples.

AIS PROCESSING AND DATA RESTORATION

Figure 6 illustrates schematically the processing steps

developed to analyse the AIS data. They allow parallel

analysis of the data in several different formats. These

include three band composited images, "fanned" terrain

images spread out laterally with respect to wavelength

(Fig.7), "fanned" spectral images spread out according to

the 32 pixel columns, or graphical analysis of individual or

averaged spectral curves (Fig.12). Results can be converted

between the various display formats and data base types by

programs AIX and FAN (Fig. 6).

Three steps of data restoration were implemented for

this paper. The first was a replacement of "bad" data

records. The second was a correction of the vertical

detector striping by forcing all image columns to have the

same mean as the central column (Program SHAD1). A

normalization to the mean of all columns would be better to

avoid particularly noisy detectors. Subsequent data analysis

revealed that residual, vertical detector striping still

exists and varies in intensity along the flight line. This

finding suggests some time-dependent variation in the

instrument's aircraft operating environment that is not

fully corrected for by the ground-based calibration. A test

of these restoration techniques on the 1983 Cuprite data set

(Goetz and Srivastava,1985) worked particularly well and did

not show variations along the flight line, though it was a

much shorter line than any at Mary Kathleen.

The third correction was to remove the horizontal

striping, particularly strong in some wavelength bands, and

believed to be due to grating wobble causing variable

starting wavelengths of each spectral scan(Cocks and Green,

this volume). This correction (Program DMEAN) forces all

lines to have a mean defined by the smoothed response of a

moving window of lines. Atypical lines and pixels can be

skipped in this process to avoid "outliers" affecting the

window statistics. The two correction processes, for

vertical and horizontal striping, were run independently and

sequentially on each of the 128 spectral bands. A comparison

of log residuals run on both the raw and corrected data

produced the same results, confirming that the correction

procedures do not appear to introduce artifacts into the

data.

MARY KATHLEEN AIS DATA ANALYSIS AND RESULTS

Given our previous

spectrometer data in

experience with airborne SWIR

Australia,that little or no

112
mineralogical information is visible in the raw radiance

data (Fig. 7), the log residual technique (Green and Craig,

1985) was applied to the AIS data to enhance potential

mineral absorption features.

Initially a new set of 128 log residual bands were

computed using all 128 input bands, including those covering

the 1.4 and 1.9 micrometre atmospheric water absorption

bands. Fig. 8 illustrates the results, which proved to be

unsatisfactory. This figure shows the dominating influence

of the water bands. They are inversely correlated with the

atmospheric windows in which there is no mineralogical

discrimination , particularly between 2.1 and 2.4

micrometres where it was expected. It should also be

noticed that the last eight or so spectral bands, between

2.35 and 2.4 micrometres, behave like the water absorption

bands and are positvely correlated with them. These channels

are indeed approaching the edge of the next water

absorption band at 2.7 micrometres and their low response

may imply a marked drop-off in the sensitivity of the AIS

detectors at these wavelengths.

The log residuals were computed again utilizing only

bands in the three atmospheric windows, 1.23 - 1.35, 1.49 -

1.77 and 1.96 - 2.40 micrometres. Residuals were computed

for each of these windows quite separately. The resulting

"fanned" wavelength images are displayed in Fig. 9 which now

shows considerable absorption variation (the zones of light

and dark across the image). Residual effects from the 1.4

micrometre water absorption band are seen in the pattern of

residuals on each side suggesting that a greater number of

bands should be omitted from the calculation in this region.

Though considerable spectral variation is visible in

various parts of the flight line in the central 1.5 - 1.8

micrometre window, we have yet to interpret the significance

of this information. We have long held this to be a

significant information window that deserves greater

attention. In this paper most attention has been directed to

the 2.0 - 2.4 micrometre region.

Figure I0 shows a detailed,

residuals in the 1.96 - 2.45 window,

wavelength end of Fig. 9.

annotated image of log

equivalent to the long

Detailed analysis of Figs. 8, 9 and I0 illustrates

several unexpected characteristics of the data that bear on

a) the instrument performance and b) the geological

interpretation. These characteristics are:

. Signs, and variations along the flight line,of

the two C02 absorption bands centred near 2.00

and 2.06 micrometres (Fig. 10).

2. General, broad absorptions at unusual wavelengths

113
centred near 2.28 micrometres (Fig.10).

o A sharp spectral boundary at 2.35 micrometres

whether residuals alternate between positive

and negative.

. A lack of clear and expected absorptions due to

the calc-silicate minerals, particularly near

2.34 micrometres. This result is no doubt

related to point 3 above.

The CO2 bands, being present in the ubiquitous

atmospheric spectrum, should be completely removed by the

log residual process. Their continued presence implies an

unpredictable and varying spectral response along the flight

line that we suggest is due to additive components described

by Cocks and Green, (1986, this volume). These components

appear to be an unexplained offset and supplementary,terrain

dependent radiation from shorter wavelengths, resulting from

an order overlap problem in the instrument.

The order overlap effect means that observed radiances,

at 2.0 micrometres, for example, are supplemented by terrain

dependent radiation from 1.0 micrometres. The addition of

this shorter wavelength energy to the region of the CO2

bands means that they cannot be adequately corrected by

the log residual process, which assumes a multiplicative

model of the data. We believe that the unusual absorptions,

seen particularly near 2.28 micrometres (dark zones on

Fig.10),can be explained by this same order overlap problem.

Modelling of the addition of first and second-order

radiances by Cocks and Green, (1986,this volume) for the

vegetation case (Fig.ll) shows a very pronounced, broad

absorption in the spectrum centered on 2.28 micrometres. This

is similiar to the observed 2.28 micrometre absorption seen

in the data in Fig. i0. It is a relatively unusual, though

not unheard of, absorption wavelength and is not expected in

the locations indicated. We believe this feature to result

from the addition of energy from the 1.13 to 1.15 micrometre

region, which straddles a water absorption band. The high

reflectance of vegetation at these wavelengths (the infrared

plateau) causes this 1.14 micrometre water absorption to

have a particularly strong relative effect at 2.28

micrometres, where vegetation is otherwise quite dark.

At Mary Kathleen and many arid Australian test sites

there is enough desert grass present to generate this

"pseudo" absorption at 2.28 micrometres. It is then due not

to any mineralogy but to the mixing of vegetation that has a

major spectral absorption contrast at one-half this

wavelength.

Previous spectrometer surveys, without the order

overlap problem, do not show this 2.28 feature. They have

114
also shown that the calc-silicate mineralogy can be detected

even in the case of up to 40_ grass cover.

Because the amount of Australian AIS data is so

large,a serious attempt must be made to correct for this

supplementary radiation. A complete removal of the effect is

probably impossible as it would require a knowledge of the

ground cover in every pixel and its spectral

characteristics: i.e. just the information that we are

trying to elucidate. However, we believe a partial

correction is feasible.

A number of correction procedures may be imagined that

might use the uncontaminated AIS region between 1.2 and 1.6

micrometres or the NS001 data to correct the longer

wavelength AIS bands.

One initially promising technique has been tested.

Using bands 1-14 and 29 - 60, some of which are free from

order overlap and some of which are affected, we used a

multiple linear regression method to predict bands 80 - 128.

We argue that these predicted bands contain the common,

supplementary energy resulting from the order overlap and

that component of the true longer wavelength radiation which

can be predicted from the shorter wavelengths. Subtraction

of these predicted bands from the raw data yields a new set

of residual bands (not log residuals !) that we believe are

freer of the short wavelength contamination. These new,

corrected, residual AIS bands for the 1.96 - 2.40 are still

correlated with each so were used as input to the log

residual process to yield, equivalent reflectance-like

spectra.

This process essentially removes the ambiguous 2.28

micrometre feature and generates spectra (Figs. 12 and 13),

that though still very noisy, approximate the shapes and

absorption wavelength positions predicted from the known

mineralogy and the previous surveys. Comparisons of Figs. 4

A & 4B with Fig 12C for carbonate/epidote targets and of

Fig. 5A with Figs. 13A & 13B, for the mine smectite,

illustrate that the AIS data do, after correction, have the

expected characteristics.

However,some spectra are still unusual in their

absorption wavelengths, hence further work is clearly

required. The AIS garnetite spectra have more of a dry grass

(cellusose-type) spectrum (Fig.13D) than the previously

located, garnetite characteristics (Figs.5B & 5E). In

addition, the noisy nature of the AIS spectra, even after

averaging within similar spectral blocks and after all the

processing that has been necessary, attests to a need to

apply more thorough noise removal techniques at the outset.

This pre-processing we intend to do in future analyses.

Colour composites of the three AIS bands indicated in

115
Table If, made prior to the removal of the order overlap

effect by the least-squares prediction, gave colours that

were totally the reverse to those expected. For example,

known clay-rich alluvium appeared blue rather than red. The

same composites made after the correction process yield the

predicted colours of clays in orange-red, and of carbonates

and calc-silicates in blue.

CONCLUSIONS

From the

data carried

conclusions.

preliminary analysis of the Mary Kathleen AIS

out so far, we can draw the following

l.As expected from previous surveys, no

mineralogical differences were detectable in the

raw radiance data. This finding is due to

diminution of the mineralogical signatures by

weathering and mixing with grass cover.

2.Mineralogical absorption features appear only

after removal of backgound and atmospheric

effects by computation of log residual spectra.

Log residuals should be computed separately for

each atmospheric window and should exclude the

atmospheric water absorption regions.

3.Greatest attention has been given to the 2.0 -

2.4 micrometre window,but significant spectral

variation evident in the 1.5 - 1.8 micrometre

window is deserving of further attention.

4.General spectral pattern boundaries defined by

the pattern of log residuals match some of the

known geological boundaries.

5.Raw log residual spectra do not, however, match

the spectral absorption shapes expected from

laboratory studies and earlier spectrometer

surveys.

6.The raw AIS data appear to be degraded by

addition of first and second-order radiation

that generates "pseudo" absorption features at

incorrect wavelengths. This effect is worst in

the case of pixels containing appreciable amounts

of vegetation. The effect also masks true

mineralogical absorption features.

7.Unexpected variations in the intensity of the CO2

bands along the flight line atest to a problem

related to item 6 above.

8.A multiple linear regression technique has been

applied prior to the calculation of log

116
residuals that appears to correct, to some

extent, the second order radiation leakage for

the 1.96 - 2.4 micrometre region.

9.After correction, spectra can be extracted that

exhibit the expected spectral characteristics of

clays, epidote, amphibole and carbonates.

10.The region 2.35-2.40 micrometres exhibits very

low sensitivity and often responds poorly to the

log residual method. In this environment this

fact is significant because of the dominance of

mineral absorptions near 2.35 micrometres.

II.A software strategy for AIS data restoration and

analysis has been devised using relatively

conventional image processing methods blended

with some novel techniques. It has been shown to

work well on the 1983 Cuprite data set in which

no order overlap effect existed.

12.Additional strategies need to be explored for

correction of the order overlap problem, and

greater attention given to noise removal

techniques.

ACKNOWLEDGEMENTS

We thank NASA and the AMES/JPL team, who conducted the

1985 Australian deployment, for their considerable efforts.

We thank also the other members of the Australian consortium

whose joint involvement made the mission possible. Conzinc

Riotinto of Australia, and Mary Kathleen Uranium kindly

assisted with provision of geological data. Bob Moore

(Bureau of Mineral Resources) and other members of the

CSIRO Division of Mineral Physics and Mineralogy Remote

Sensing group, particularly Tom Cudahay, provided valuable

assistance.

REFERENCES

Cocks,T.C. and Green,A.A. (1986). Airborne

Spectroradiometry: The Application of AIS Data to the

Detection of Subtle Mineral Absorption Features.

Proceedings of the Second Airborne Imaging

Spectrometer Data Analysis Workshop, May 6-8, 1986

(this volume).

Derrick,G.M.,et.al.(1971). Geology of the Marraba I:I00,000

sheet area, Queensland,BMR Record 1971/56

(unpublished).

Derrick,G.M.,et.al.(1977).Geology of the Mary Kathleen

i:i00,000 sheet area, northwest Queensland,,BMR

117
Bulletin 193, 114 pages.

Goetz,A.F.H. and Srivastava,V., (1985). Mineralogical

Mapping in the Cuprite Mining District,Nevada.

Proceedings of the Airborne Imaging Spectrometer Data

Analysis Workshop,April 8-10,1985, JPL Publication 85-

41,pp. 22-31.

Green,A.A. and Craig,M.D. (1985). Analysis of Aircraft

Spectrometer Data with Logarithmic Residuals.

Proceedings of the Airborne Imaging Spectrometer Data

Analysis Workshop, April 8-10, 1985, JPL Publication

85-41, pp. 111-119.

Huntington,J.F., (1984). Interpretation of visible,near and

short wavelength infrared,field and airborne spectra

from Mary Kathleen, Queensland,Australia,Proceedings of

the Int. Symp. on Remote Sensing of Environment,Third

Thematic Conference: Remote Sensing for Exploration

Geology, April 16-19, Colorado Springs,USA,

Abstract, p.227.

Scott,A.K. and Scott,A.G., (1985). Geology and genesis of

uranium-rare earth deposits at Mary Kathleen, Northwest

Queensland, Bull. Proc. Austral. Inst.Min. and Metal.,

Voi.290, No.l, pp.79-89.

TABLE I

AIS AND C130 FLIGHT DETAILS

MARY KATHLEEN TEST SITE, QUEENSLAND, AUSTRALIA

SITE # S723, RUN 2, LINE i, ACQUIRED 19 OCT 1985

AIS MODE: Rockscan

1.4 to 2.4 micrometres

PIXEL SIZE: 8.3 metres (approx)

DETECTOR No: AISI

MEAN ALTITUDE ABOVE TERRAIN: 14,300 feet (4358 metres)

AIRSPEED:

FLIGHT DIRECTION:

TOPOGRAPHIC VARIATION:

No. OF SAMPLES (lines):

LINE LENGTH:

LINE WIDTH:

167 knots

East to west (flipped west

to east for analysis)

180 metres

2560

18 kilometres (approx)

266 metres (approx)

118
TABL,E_,IT

COLOUR COMPOSITE CHARACTERISTICS

SPECTRAL

BAND

(micrometres)

ASSIGNED

COLOUR

MAJOR ABSORBING

MATERIALS

IMAGE

COLOURS

2.22

2.27

2.34

Blue Clays

Granitic Terrain

Green Epidote

Garnet

Red Carbonates

Garnetite -

Epidote -

Calc-silicates

Red

Blue

119
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121
ORIGINAL PAGE _3

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AIS

1985 GER

1982

CSIRO

1984

eea

_em

Bkb

E'kb2

E'kc

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Eke 2

EkC2c

BkC2g

Eke21

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Quartz feldspar porphyry, rhyolitic to dacitic

crystal tuff (?), quartzite, schist, minor

limestone.

Metabasalt, amphibolite, basalt-quartzite breccia,

agglomerate.

Corella Formation

Quartzite minor conglomerate.

Quartzite, micaceous quartzite, minor limestone

and granofels, cordierite and andalusite schist.

Calcareous scapolite granofels, limestone,

scapolite, cordierite and andalusite schist,

quartzite.

Fine feldspathic quartzite, metasiltstone,

limestone, calcareous granofels.

Calcareous sandstone, slate, schist, marl, calcsilicate rocks.

Cordierite - biotite schist, mica schist.

Garnetite, garnet-diopside skarn.

Limestone, calcareous granofels with diopside,

vesuvianite, wollastonite and garnet.

metacongl omerate

Quartzite

Calcareous breccia, limestone, hematite pods.

Won_a Granite

Coarse foliated granite, megaporphyritic granite,

medium leucogranite, aplite, minor orthogneiss

Burstall Granite

Medium to coarse granite, microgranite, aplite.

Lunch Creek Gabbro

Olivine - pyroxene gabbro, biotite, diorite, hybrid

diorite and tonalite.

Other Rocks

Do Dolerite, metadolerite.

Cz Cainozoic alluvium.

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122
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I I

Fig. 7. 128 "fanned" radiance images for the Mary Kathleen flight line.

Wavelengths are 1.2 on the left edge to 2.4 micrometres on the right

edge. The two dark bands are the atmospheric water absorption bands.

Note: a) The variation in some individual band calibration levels

and b) The absence of any apparent absorption features at any specific

wavelengths. All grey level variations are due to albedo and water

absorption.

Fig. 8. 128 "fanned" log residual images computed with incorporation of

the 1.4 and 1.9 water absorption bands. Dark equates to negative and

white equates to positive residuals relative to the flight line mean.

126

ORIGINAL PAC-iE:IS

OF POOR QUALITY
ORIGINAL PAGE IS

OF POOR QUALITY

Fig. 9. "Fanned" log residual images computed on the three

atmospheric windows separately. Wavelengths extremes as in Figure

6. The two black columns are the zeroed atmospheric water

absorption bands. Note the considerable high frequency pattern

of light and dark residuals that represent absorption features

now evident down the flight line. The vertical dimension represents

flight line direction.

127
Fig. i0. Detail of "fanned" log residuals for the 1.96 -2.4 micrometre

window illustrating the CO^bands at 2.00 and 2.06 micrometres, and

broad, intense absorption _eatures at 2.28 micrometres (for example,

between lines 667 and 943). (See color slide number 5 in the back

cover pocket of this publication.)

ORIGINAL PAGE rs

OF POeR QUALfI'Y

128
ORIGINAL P,_,G_ tS

OF POOR QU,_,L'TY

CSIRO-MINERAL PHYSICS

AIS SPECTRORAOIOMETRIC HODEL

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a vegetated target (lower curve) with the modelled addition of first

and second-order energies (upper curve). Note the strong absorption

centred on 2.28 micrometres in the combined spectrum.

129
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131
N87-12980

USE OF DIGITAL MUNSELL COLOR SPACE TO ASSIST INTERPRETATION OF IMAGING

SPECTROMETER DATA a GEOLOGIC EXAMPLES FROM THE NORTHERN GRAPEVINE

MOUNTAINS, CALIFORNIA AND NEVADA

FRED A. KRUSE, DANIEL H. KNEPPER JR., and ROGER N. CLARK, Geologic

Division, U.S. Geological Survey, Denver Federal Center, Mail Stop 964,

Box 25046, Denver, Colorado 80225, USA

ABSTRACT

Techniques using Munsell color transformations have been developed

for reducing _8 channels (or less) of Airborne Imaging Spectrometer

(AIS) data to a single color-composite-image suitable for both visual

interpretation and digital analysis. Using AIS data acquired in _84

and _85, limestone and dolomite roof pendants and sericite-illite and

other clay minerals related to alteration have been mapped in a quartz

monzonite stock in the northern Grapevine Mountains of California and

Nevada. Field studies and laboratory spectral measurements verify the

mineralogical distributions mapped from the AIS data.

SUMMARY

Four flightlines of Airborne Imaging Spectrometer (AIS) data over a

hydrothermally altered stock in the northern Grapevine Mountains,

California and Nevada, were analyzed primarily to detect and map

carbonate rocks possibly containing skarn deposits and clay minerals

associated with hydrothermal alteration of the stock. Initially, the

AIS data were processed to relative reflectance using procedures

described by Kruse and others (1985a; 1985b); data processed in this

fashion can be used to generate near-laboratory-quality reflectance

spectra. Additional algorithms were then developed to automatically

detect and characterize the most prominent absorption feature in each

spectrum according to band position, width, and depth and to display

this spectral information in a single color-composite-lmage suitable for

visual interpretation. The color-composlte-image may also be analyzed

digitally and used to prepare thematic maps of selected absorption

features (minerals) by classifying the Munsell color characteristics of

the image data.

_se of trade and company names is for descriptive uses only and does

not imply endorsement by the U.S. Geological Survey.

132
A key element to this analysis procedure is to reduce the

dlmenslonallty and complexity of the AIS data to manageable

proportions. This task was accomplished by limiting the extracted

spectral information to the three absorption feature parameters--band

position, band depth, and band wldth--for only the dominant absorption

feature in each spectrum (Fig. ]); to avoid water absorption bands, only

the wavelength interval 2.0-2.4 micrometers (436 channels) was

considered. Band position is defined as the lowest point of the

absorption feature after a continuum is removed. Band depth is defined

as one minus the ratio of the reflectance at the band position to the

reflectance of the continuum (Clark and Roush, |984). Band width is

defined as the absolute difference of the wavelengths at one-half the

band depth. These three parameters were then mapped into digital

Munsell color space (Ralnes, _77; Kruse and Ralnes, 1984) such that hue

represents the band position, value (intensity) represents the band

depth, and saturation represents the band width (Fig. 2). Digital

analysis of Munsell parameters was used to classify the hue, saturation

and value images (band position, depth, and width) and produce thematic

mineralogical maps (Kruse, _84; Ralnes and Knepper, |983) based on the

known spectral characteristics of the carbonate and clay minerals (Fig.

3). In addition, the Munsell classified data can be inverted to

trlstlmulus color space (red, green, blue) and used to prepare a color

composite image that allows the direct visual interpretation of mineral

distribution based on the observed colors.

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reflectance spectrum after continuum removal.: - A, band

position (wavelength); B, band depth; and C, band width.

133
A. BAND POSITION (HUE)

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B. BAND DEPTH (VALUE)

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digital Hunsell coordinates.

134
OF POOR QUALITY

A B C

_ Carbonate

Sericite-illite alteration

_ Argillic (other clay minerals) alteration

Figure 3. Thematic mineralogical images (note that these images consist

of two 32-pixel flightlines concatenated to form a single 64-

pixel-wide image) and thematic map interpreted from the image

and individual spectra. A, Overlay of pixels identified as

carbonates (white) on a single band image; B, overlay of

pixels identified as clay (white) on a single band image; C,

Map interpreted from images A and B and individual spectra.

(See color slide number 6 in the back cover pocket of this

publication.)

135
Thematlc mineral maps of carbonate rocks and clay alteration of the

quartz monzonlte stock were produced from the AIS data (Fig. 3).

Further subdivision of the broadly mapped mineral groups was

accomplished by examining individual spectra (Fig. 4). The

identification and mapping of mineral distribution from the AIS data was

verified by field and laboratory reflectance measurements and field

alteration mapping. The mapping of the primary absorption band

position, depth, and width on the AIS images accurately reflects the

predominant mineralogy of the surficlal materials. Additional

techniques are being developed that will allow secondary absorption

features to be examined and mapped and mineralogical mixture problems to

be addressed.

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Figure 4. Individual spectra from the AIS images. A, carbonate

spectra; B, comparison of an AIS clay spectrum with

laboratory spectra. The QSP (quartz-serlcite-pyrlte)

spectrum is from a sample collected in the area of serlciteillite alteratlon in Figure 3.

136
REFERENCES

Clark, R. N., and Roush, T. L., _84, Reflectance spectroscopy:

0uantitative analysis tehnlques for remote sensing applications, J.

Geophys. Res., v 89., p. 6329-6340.

Kruse, F. A., _84, Munsell color analysis of Landsat color-ratiocomposite images of limonitic areas in southwest New Mexico: in

Proceedings, International Symposium on Remote sensing of

Environment, Third Thematic Conference, "Remote Sensing for

Exploration Geology", Colorado Springs, Colorado, _- _ April 1984,

University of Michigan, Ann Arbor p. 76 _773.

Kruse, F. A., and Ralnes, G. L., 1984, A technique for enhancing digital

color images by contrast stretching in Munsell color space: in

Proceedings, International Symposium on Remote Sensing of

Environment, Third Thematic Conference, "Remote Sensing for

Exploration Geology", Colorado Springs, Colorado, 16-_ April 1984,

University of Michigan, Ann Arbor, p. 755-760.

Kruse, F. A., Raines, G. L., and Watson, Kenneth, 1985a, Analytical

techniques for extracting mineralogical information from

multlchannel airborne imaging spectrometer data (Abs.): in

Proceedings, AIS workshop, 8-10 April, 1985, Pasedena, California,

JPL Publication 85-41, Jet Propulsion Laboratory, Pasadena,

California, p. 105.

Kruse, F. A., Ralnes, G. L., and Watson, Kenneth, 1985b, Analytical

techniques for extracting geologic information from multlchannel

airborne spectroradlometer and airborne imaging spectrometer

data: i__n_nProceedings , International Syposlum on Remote Sensing of

Environment, Fourth Thematic Conference, "Remote Sensing for

Exploration Geology", San Francisco, California, I-4 April,

1985, University of Michigan, Ann Arbor, p. 309-324.

Raines, G. R., 1977, Digital color analysis of color ratio composite

Landsat scenes: i__nnProceedings, Eleventh International Symposium

on Remote Sensing of Environment, University of Michigan, Ann

Arbor, p. 1463-1472.

Ralnes, G. R., and Knepper, D. H., Jr., _83, A hue-saturatlonintensity transform to improve hydrothermal alteration mapping: in

International Geosclence and Remote Sensing Symposium Digest,

1983: Institute of Electrical and Electronic Engineers, v. 2, se.

FP-6, p. I. _1.3.

137
N87-12981

NEAR-INFRARED DETECTION OF AMMONIUM MINERALS AT IVANHOE HOT SPRINGS,

NEVADA

M. DENNIS KROHN, U.S. Geological Survey, Reston, VA 22092

ABSTRACT

Airborne Imaging Spectrometer (AIS) data were collected over

the fossil hot spring deposit at Ivanhoe, Nev. in order to

determine the surface distribution of NH4-bearing minerals.

Laboratory studies show that NH4-bearing minerals have

characteristic absorption features in the near-infrared (NIR).

Ammonium-bearing feldspars and alunites were observed at the

surface of Ivanhoe using a hand-held radiometer. However, first

look analysis of the AIS images showed that the line was about

500m east of its intended mark, and the vegetation cover was

sufficiently dense to inhibit preliminary attempts at making

relative reflectance images for detection of ammonium minerals.

INTRODUCTION

Minerals having NH 4 bound in their crystal structure are reported

from several economically important geologic environments. Ammonium

minerals are observed in Hg-bearing hot springs, undersea "black smoker"

hydrothermal vents, disseminated Au-deposits, exhalative Pb-Zn-Ag

deposits, in coal- and oil-shale-bearing formations and in several other

crystalline and sedimentary llthologles. Yet little is known about the

detailed distribution and role of ammonium in geologic system, primarily

due to difficulty of analyzing for NH4-bearing minerals in the

laboratory. NH 4 minerals typically resemble their K-bearing analogs can

usually only be defined by small differences in X-ray diffraction

patterns or by mld-infrared analysis.

Recently, NH4-bearing mineral were shown to have diagnostic

spectral absorption bands in the near-infrared (0.7 - 2.5 um) (Krohn and

Bethke, 1984; Krohn, 1986) and were observed remotely using the Airborne

Imaging Spectrometer (Goetz and Srivastava, 1985). The near-infrared

appears to be the analysis technique of choice for detecting ammonium in

minerals and its discovery at Cuprite, Nev. is one of the earliest

achievements of high-resolutlon airborne spectrometry.

SPECTRAL FEATURES

Ammonium has three diagnostic absorption bands in the near-lnfrared

located at 1.56 um, 2.02 um, and 2.12 um (Figure I). These bands are

overtones and combination tone of N-H stretching and bending vibrational

modes that occur in the mld-lnfrared near 3 um and 7 um. The two bands

near 2 um are broad spectral features and are the primary means for

discriminating NH 4 in minerals. In spectrally flat minerals, such as

feldspars, the NH 4 spectral features are the only absorption features

seen. In phyllosilicates and other OH-bearlng minerals, the NH 4

spectral features are observed as shoulders to the primary AI-OH

absorption band near 2.2 um or as a doublet band of equal intensity.

138
Although the band centers of the NH 4 spectral features shift wlth

different mineralogies, the ammonium bands are readily distinguished

from OH bands in the laboratory samples examined to date.

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Figure I. Laboratory near-infrared reflectance spectra of buddingtonite

from several hot spring and sedimentary localities in the

western U.S.

SPECTRAL CONTRAST

Since the NH 4 absorption features are relatively broad bands, the

depth of the spectral band or the spectral contrast becomes a key

parameter for distinguishing ammonium minerals remotely. Mid-infrared

(2.5 to 25 um) intensities in transmittance are directly proportional to

the concentration of NH 4 in the mineral and band depth of the 7 um band

maybe used to estimate the amount of ammonium in mineral samples.

However in near-infrared reflectance, spectral contrast is a functLon of

several factors including NH 4 concentration, particle size, and

absorption coefficients of other sample constituents, all of which have

a large role in determining the NIR detectability of NH 4.

Figure 1 shows laboratory reflectance spectra of buddingtonite from

several localities. The spectral contrast varies greatly both within

139
and between localities. Smaller amounts of NH 4 are observed as small

shoulders in spectra of micas (Krohn, 1986). Experience with AIS and

AVIRIS systems will be needed to determine the minimum spectral contrast

of NH4-mlnerals needed to be observed by airborne systems.

AMMONIUM AT IVANHOE

Buddingtonite was initially identified at Ivanhoe, Nev. in drill

core by x-ray diffraction (Bloomstein, 1984). Fieldwork has shoal that

NH 4 is stable or metastable in the oxidizing environment at the surface

at Ivanhoe and can readily be identified using a hand-held ratioing

radiometer. Both buddingtonite and NH4-bearing alunite are observed.

Most of the NH4-bearing minerals at Ivanhoe are found in a zone of

intensely hydrothermally-altered basalt. This lithology sits below a

leach cap of porous silica and chalcedony, a zone of argillic alteration

consisting primarily of kaolinite, and hydrothermally-altered rhyolitic

turfs that overlie and surround the basalt outcrops. NH4-bearing

minerals are observed around the periphery of the deposit rather than in

the central silicified area. On the ground, the distribution of NH 4

minerals is shown to be highly variable rather than forming broad

continuous halos. NH 4- bearing minerals appear localized along fault

and fracture surfaces, suggesting NH 4 is transported and deposited by

hydrothermal fluids of the hot spring system.

AIS OVERFLIGHT

On July 24, 1985, the C-130 flew both the Carlin and Ivanhoe sites

on its first leg of a major two month deployment. Flight lines were

chosen in a N-S and E-W transect intersecting at the hot spring. The NS line covered Antelope Creek, some of the Tertiary basalt, and Big

Butte, a flowdome that is a major landmark of the area. The E-W line

covered a barite mine, that is part of a trend of minera[ization near

the Carlin mine. Several large brush fires had occurred in the previous

month and can be seen as large blackened areas on the ground. In-situ

spectral measurements were gathered the previous day on the

hydrothermally-altered rocks and on one of the burned areas. Partial

cloud cover hindered the gathering of in-situ spectral data.

The aircraft was flown at ma×imum altitude of 19,000 f t (5800 m) to

cover as much ground area as poss[ble. AIS instruments appeared to be

working nominally during the mission. However, the NS001 scanner

experienced intermittent data interrupts which worsened as the fllght

progressed.

FIELD RESULTS

initial field e_amination of the AIS line showed [t to be located

500 m east of the proposed line. The line covers a portion of

hydrothermally altered and bleached rhyolitic tuff and a siliceous

opaline leach cap of the hot spring system. However, neither of these

lithologles are believed to be favorable for hosting NH4-beariqg

minerals because of the lack of K-feldspar suitable Eor substitution.

Laboratory NIR spectra of rock samples showed that both kaolini_e and

alunkte are present, although no NH 4 was observed in the alunite

samples. Identification of alunite and kaolinite minerals at Cuprite by

140
the AIS led to the detection of buddingtonite, so that analogous

processing at Ivanhoe should also reveal other localites of NH4-mlnerals

if present. The flightline did cover the inactive Butte #I Hg-mine.

The mine site consists of a trench and tailings. The mine site plus the

burn areas should make a good light and dark targets for calibration.

Fieldwork also showed that large mining cuts and bulldozer swaths about

8 m wide were visible, but smaller mining roads and jeep trails were not

distinguishable. Presumably the jeep trails constitute a small enough

fraction of the pixel and are spectrally similar to surrounding terrain

not to effect the DN values of the AIS.

PRELIMINARY DATA CHARACTERISTICS

AIS data were wavelength calibrated using a LOWTRAN atmospheric

model (Fig. 2) Strong CO 2 features were observed in the wavelength

range between 2.0 and 2.4 urn. Analysis showed that the data needed to

be offset approximately 70 nm to shorter wavelength from the 1984

calibration specifications. Calibration performed on the last 32

channels seemed to hold for tile rest of 96 channels, indicating that the

inter-grating calibration performed well.

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Figure 2. Average AIS spectrum for fllghtline compared with LOWTRAN

atmospheric model to show alignment of carbon dioxide bands

near 2 um. Upper spectrum - LOWTRAN model; lower spectrum

AIS average reflectance from pixel column 16.

141
A relative reflectance spectrum was created along a single pixel

column to test spectral properties of the data set. Such techniques

have worked well at defining alteration minerals at the Grapevine Mt,

Nev. (Kruse, this volume). Spectra of data points located at Butte and

Ivanhoe mine sites (Fig. 3) showed different patterns, but were

sufficiently noisy to be unrecognizable spectrally. This type of

fluctuating spectral pattern appears to be characteristic of sites with

moderate vegetation cover. The variability appears to be a combination

of mixed pixel and second-order effects (resonance spectral energy from

shorter wavelength due to absence of a blocking filter on the AIS)

(Terry Cook, this volume). Refined processing techniques need to be

developed to compensate for this spectral variability.

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2 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40

WAVELENGTH (MICRONS)

Figure 3. Relative reflectance AIS spectra for the Ivanhoe and Butte no.

1 mine site localities at Ivanhoe, Nevada. Variability is due

to defining average using areas with a vegetation cover.

CONCLUSIONS

Near-infrared detection of ammonium minerals is feasible using

hand-held and airborne instruments. Detection o@ammonium minerals

depends upon finding a suitable host rock with K_ or other cation site

for substition, in addition to the presence of ammonium and a proper

geochemical environment. Unlike O-H-bearing minerals, NH 4 is not

essential to the structure of its constituents minerals and can vary in

concentration, giving rise to additional variability in band depth not

present in O-H bearing minerals. Initial field work has shown the AIS

line passed to the east of known NH4-minerals , but did encounter

spectrally-diagnostlc minerals kaolinite and alunite. Vegetation

density and second-order effects have hindered initial spectral

processing of AIS data at Ivanhoe, Nev. and processing techniques need

to be developed for spectral variability.

142
REFERENCES

Bloomstein, E.I., 1984, Ammoniaalteration as a geochemical link in gold

deposits of the Carlin-Midas belt (abs.): Symposium of the Assoc.

of Exp. Geochem., Exploration for ore deposits of the North

American Cordillera, March 1984, Reno, p. 27.

Goetz, A.F.H., and Srivasta, V., 1985, Mineralogical mapping in the

Cuprite mining district, Nevada: in Vane, G. and Goetz, A.F.H,

Proceedings of the Airborne Imaging Spectrometer Data Analysis

Workshop, Jet Propulsion Laboratory Publication 85-41, pp. 22-31.

Krohn, M.D., and Bethke, P.M., 1984, Near-infrared spectral features of

ammonium minerals: applications for remote sensing of hot spring

deposits (abs.): Geol. Soc. Am. Abstracts with Programs, v. 16, p.

566.

Krohn, M.D., 1986, Spectral properties (0.4 to 25 microns) of selected

rocks associated with disseminated gold and silver deposits in

Nevada and Idaho: J. Geophys. Res., v. 91, B. I, pp. 767-783.

143
ANALYSIS OF AIS

FOREST, ALASKA

DATA OF THE BONANZA

N8 -lZ982

CREEK EXPERIMENTAL

MICHAEL A. SPANNER, TGS Technology, Inc., NASA Ames

Research Center, Moffett Field, CA, USA 94035; DAVID L.

PETERSON, NASA Ames Research Center, Moffett Field, CA, USA

94035.

ABSTRACT

Airborne Imaging Spectrometer (AIS) data were

acquired in 1985 over the Bonanza Creek

Experimental Forest, Alaska for the analysis of

canopy characteristics including biochemistry.

Concurrent with the AIS overflights, foliage from

fifteen coniferous and deciduous forest stands

were analyzed for a variety of biochemical

constituents including nitrogen, lignin, protein

and chlorophyll. Preliminary analysis of AIS

spectra indicates that the wavelength region

between 1450-1800 nanometers (nm) displays

distinct differences in spectral response for some

of the forest stands. A flat field subtraction

(forest stand spectra - flat field spectra) of the

AIS spectra assisted in the interpretation of

features of the spectra that are related to

biology.

INTRODUCTION

The taiga forests of interior Alaska are composed of a

variety of vegetation types including pure and mixed stands

of white and black spruce, paper birch, aspen, balsam

poplar, alder and willow. At the Bonanza Creek Experimental

Forest near Fairbanks, Alaska, a good understanding of the

productivity and nutrient cycling of taiga forest ecosystems

has been developed (Van Cleve et al., 1983; Viereck et al.,

1983; Yarie and Van Cleve, 1983; Van Cleve et al., 1981).

Environmental factors including large seasonal variations in

day length and temperature, and the occurrence of permafrost

result in generally lower rates of primary production and

nutrient cycling compared to temperate forest ecosystems.

At NASA Ames Research Center, we are investigating the

use of remote sensing data to study the biogeochemical

cycling of forest ecosystems (Peterson et al., 1985).

Various organic compounds present in foliage absorb visible

and infrared radiation. The absorption features correspond

to fundamental stretching frequencies of organic bonds.

These organic compounds are of importance to nutrient

cycling because they are composed of key nutrients (carbon,

nitrogen, phosphorous, etc.) that are required for plant

144
growth. Quantification of the concentration of the various

organic compounds in the canopy may be possible using high

spectral resolution remote sensing data obtained from

instruments like the Airborne Imaging Spectrometer. Because

of the understanding of the biogeochemical cycling of the

taiga forests developed in Alaska, we initiated a study at

the Bonanza Creek Experimental Forest to determine the

sensitivity of high spectral resolution remote sensing data

to leaf and canopy biochemistry.

BACKGROUND

Last year we reported laboratory and AIS analysis of

leaf and canopy chemistry from Blackhawk Island, Wisconsin

(Fownes and Aber, 1985; Spanner et al., 1985). A fertility

gradient trends east-west across the mixed deciduous forests

of Blackhawk Island. Total canopy nitrogen prior to

senescence does not vary substantially across the gradient,

but the lignin content of the canopy does vary with

increased lignin in the less fertile west and less lignin in

the more fertile east. The green state lignin to nitrogen

ratio is strongly related to the underlying mineralization

rate. Laboratory analyses of ground, dried leaves from this

gradient using spectro-photometers demonstrated that the

reflectance spectra is related to variations in nitrogen and

lignin content with high predictability (Card, personal

communication 1986; Wessman, personal communication 1986).

Analysis of the 1984 AIS data indicated the presence of

considerable spatial noise in the data (Figure ib). During

the past year, a Fourier filtering algorithm was developed

to reduce spatially correlated noise (Hlavka, 1986) (Figure

la). The influence of solar irradiance and atmospheric

water absorption on these spectra are still dominant.

However, absorption features of these spectra now occur at

the same wavelength and the spectra are much smoother.

Analysis of total canopy biochemical contents of six stands

on Blackhawk Island indicated that there is a strong inverse

relationship between wet foliage biomass and AIS data for

most wavelengths outside the atmospheric water absorption

bands (Figure ic). Lignin content is also inversely related

to the AIS data at wavelengths near 1330 nm and from 1500 to

1700 nm (Figure id).

STUDY AREA AND METHODS

Forest stands at the Bonanza Creek Experimental Forest,

Alaska vary from relatively highly productive aspen, paper

birch and white spruce on south facing well-drained slopes,

to black spruce forests of low production on north facing

slopes underlain by permafrost. In the Tanana River

floodplain, forest stands of balsam poplar and white spruce

are productive where permafrost is absent; conversely, less

145
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productive black spruce stands are located on river terraces

underlain by permafrost. Twenty-one forest stands have been

established by researchers in the Bonanza Creek Experimental

Forest. Detailed measurements of tree and foliage biomass,

production and nutrient cycling were made at a number of

these stands.

During August 1985 we collected foliage at fifteen of

the stands for biochemical analysis. At each stand foliage

was obtained from three trees of each species. Using a

shotgun, foliage was sampled from four canopy layers

(dominant, upper codominant, lower codominant and

suppressed). Samples were also obtained from understory

vegetation consisting mostly of mosses and lichen. A

subsample of the foliage was freeze dried and analyzed for

nitrogen, chlorophyll A, chlorophyll B, total chlorophyll,

protein, amino acids, lignin, cellulose, starch, sugar and

phosphorus. The freeze-dried samples were scanned on a

Perkin-Elmer spectro-photometer that measured reflectance

from 400-2450 nm, with 2 nm resolution at 5 nm intervals.

Stepwise multiple regression analysis will be performed on

these data to determine relationships between biochemical

content and spectral response.

The Airborne Imaging Spectrometer onboard a C-130

aircraft obtained data from nine flightlines over the

Bonanza Creek Experimental Forest on August 28, 1985. The

AIS data were acquired in rock scan mode covering

wavelengths from approximately 1220 to 2400 nm. The spatial

resolution of the AIS data was approximately 8 meters. High

diffuse clouds at the time of overpass resulted in variable

illumination conditions. Twelve of the fifteen forest

stands sampled were imaged. A calibration flightline was

flown perpendicular to a runway, road and highway to allow

for flat field corrections.

ANALYSIS AND DISCUSSION

Raw spectral plots of the forest stands were generated

from the AIS data and analyzed. Presented in Figure 2 are

spectral plots of four forest stands extracted from

flightline i. The spectra are mean AIS intensities for

forest stands of at least 3 by 3 pixels. Of particular

interest is the region between 1450 and 1800 nm. Intensity

increases for all four stands from 1400 nm to beyond 1700

nm, and then declines. The pattern of increase and decline

varies between the four stands. These curves exhibit large

differences in reflectance between deciduous and coniferous

stands. Noise in the form of intensity variations between

grating positions is evident on these plots.

Compare the difference between the biologically derived

spectra in Figure 2 with the spectra from a runway, highway

and road in Figure 3. The spectra in Figure 3 were

147
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149
extracted from the calibration flightline. Each target

(runway, highway and road) extended across the full width of

the flightline (32pixels_ _' and varied from 2-4 lines in

length. These are termed flat field spectra because they

are essentially spectrally flat without major absorption or

increased reflectance features. Note that between 1500 nm

and 1700 nm the spectral is fairly flat, compared to the

biological spectra. The dip at approximately 1660 nm is a

sensor artifact.

The biological spectra were subtracted by a flat field

spectrum under the assumption that atmospheric water vapor

and solar irradiance effects of these spectra are similar.

Thus, the "relative difference" signal should represent a

residual biological signal without first order atmospheric

and solar effects. An example of subtraction of the stand

spectra from flightline 1 by the runway spectrum is

presented in Figure 4. The magnitude of the reflectance is

not of interest, instead we are interested in the slope

(positive or negative) of each curve. A decrease indicates

absorption relative to a flat field spectrum. An increase

represents increased reflectance with respect to the flat

field.

In Figure 4 dramatic differences between the paper

birch stands and the spruce stands are evident that are not

as noticeable on Figure 2. For the birch stands,

reflectance increases relative to the runway from 1400 nm to

approximately 1680 nm and then drops off. For the spruce

stands, there is an absorption relative to the runway from

1400 nm to about 1500 nm. The spectra level off until about

1580 nm and then increase until approximately 1800 nm.

Structural and morphological differences between coniferous

and deciduous stands probably account for the coarse

differences in intensity seen in Figure 2. The differences

observed in Figure 4, however, are due to characteristics of

the canopy that we need to understand.

Figure 5 displays a flat field subtraction for a

variety of forest stands. It is clear from this figure that

the trend in reflectance relative to the runway flat field

spectrum are different for each vegetation type. These

variations occur near and within the region of Thematic

Mapper band 5 (l.53um - 1.73um), indicating that there is

fine spectral information within this broad region. A

relative difference minima occurrs at 1400 nm for the birch

stand corresponding to atmospheric water absorption. For

the aspen, alder and willow stands, however, the relative

difference minima is closer to 1500 nm. The alder stand

composed mostly of nitrogen fixing alder has a strong

absorption centered at 1520 nm. The relatively flat signal

from the willow site is probably dominated by the exposed

soil because the vegetation cover is sparse and of low

stature. It is premature to attribute these features to

specific vegetation characteristics such as biochemical

150
absorption, but we are continuing to examine this aspect

using stand estimates of biochemical content from foliage

samples that are currently being analyzed. Additional

analysis is required to account for second order optical

errors in the AIS instrument (Conel, this volume), for solar

spectral irradiance variations, and for atmospheric

absorption and scattering to permit direct comparisons of

biological spectra.

Our data indicate that conifer and deciduous stands

have spectral variations that are not solely related to

intensity differences. There is evidence of absorption that

can be seen in the "relative difference" plots for many of

the forest stands. Once our field data is extrapolated to

the canopy level, we should have an improved understanding

of the sensitivity of AIS data to the biochemistry of taiga

forest canopies. Advanced high spectral resolution remote

sensing instruments like the Airborne Visible and Infrared

Imaging Spectrometer (AVIRIS) should provide improved data

for the analysis of canopy biochemistry.

ACKNOWLEDGEMENTS

We thank Joan Foote and Les Viereck (Institue of

Northern Forestry) and Keith Van Cleve (University of

Alaska) for their assistance in locating the field sites and

providing production and nutrient cycling data for these

stands; Pam Matson and Cindy Berger for analyzing the

biochemistry of the foliage samples; and Don Card for

assisting our field data collection efforts. This work was

supported by the NASA Land Processes Branch of the Earth

Science and Applications Division under RTOP 677-21-35.

REFERENCES

Hlavka, C., 1986, Destriping AIS data using fourier

filtering techniques, Proceedings of the 1986 Airborne

Imaging Spectrometer Data Analysis Wor-ksho_, this volume.

Pastor, J., J.D. Aber, C.A. McClaugherty and J.M.

Melillo, 1984, Aboveground production and N and P cycling

along a nitrogen mineralization gradient on Blackhawk

Island, Wisconsin, Ecology, 65:256-268.

Peterson, D.L., J.G. Lawless, P.A. Matson, J.D. Aber,

P.M. Vitousek and S.W. Running, 1985, Biogeochemical

Cycling in Terrestrial Ecosystems, 36th International

Astronautical Federation Congress, Stockholm, Sweden.

Spanner, M.A., D.L. Peterson, W. Acevedo and P. Matson,

1985, High resolution spectrometry of leaf and canopy

chemistry for biogeochemical cycling, P[oceedin_s of th___e

198____55Airborne _ Spectrometer Data Analysis Workshop,

151
pp. 92-99.

Van Cleve, K., i. Oliver, R. Schlentner, L.A. Viereck and

C.T. Dyrness, 1983, Productivity and nutrient cycling in

taiga forest ecosystems, Canadian Journal of Forest Research

13:747-766.

Van Cleve, K., R. Barney and R. Schlentner, 1981, Evidence

of temperature control of production and nutrient cycling in

two interior Alaska black spruce ecosystems, Canadian

Journal of Forest Research, 11:258-273.

Viereck, L.A., C.T. Dyrness, K. Van Cleve and M.J. Foote,

1983, Vegetation, soils and forest productivity in selected

forest types in interior Alaska, Canadian Journal of Forest

Research, 13:703-720.

Yarie, J. and K. Van Cleve, 1983, Biomass and productivity

of white spruce stands in interior Alaska, Canadian Journal

of Forest Research, 13:767-772.

152
N87-12983'

SOIL TYPES AND FOREST CANOPY STRUCTURES IN SOUTHERN MISSOURI:

A FIRST LOOK WITH AIS DATA

GLEN M. GREEN and RAYMOND E. ARVIDSON, McDonnell Center for the Space

Sciences, Washington University, St. Louis, Missouri 63130

ABSTRACT

Spectral reflectance properties of deciduous oak-hickory

forests covering the eastern half of the Rolla Quadrangle

were examined using Thematic Mapper data acquired in

August and December, 1982 and AIS data acquired in August,

1985. For the TM data distinctly high relative

reflectance values (>0.3) in the near infrared (Band 4,

0.73-0.94 micrometers) correspond to regions characterized

by xeric (dry) forests that overlie soils with low water

retention capacities. These soils are derived primarily

from rhyolites. More mesic forests characterized by lower

TM band 4 relative reflectances are associated with soils

of higher water retention capacities derived predominately

from non-cherty carbonates. The major factors affecting

canopy reflectance appear to be the leaf area index (LAI)

and leaf optical properties. The Suits canopy reflectance

model predicts the relative reflectance values for the

xeric canopies. The mesic canopy reflectance is less well

matched and incorporation of canopy shadowing caused by

the irregular nature of the mesic canopy may be necessary.

Preliminary examination of high spectral resolution AIS

data acquired in August of 1985 reveals no more

information than found in the broad band TM data.

INTRODUCTION

The relationship between forest canopies in the Missouri Ozarks

(Figure i) and underlying soil types are explored in this paper. In

particular we develop explanations for the variation in canopy

characteristics sensed by Thematic Mapper and the Airborne Imaging

Spectrometer as a function of soil water retention capacities. The

intent is to eventually utilize reflectance data from aircraft or from

space to determine canopy characteristics and to then infer soil

characteristics. We stress TM observations over a number of control

sites in the Ozarks, use of the Suits canopy reflectance model, and

whether there is additional information to be gained from AIS data.

A Thematic Mapper image (band 4) acquired in August, 1982 for the

eastern half of the Rolla Quadrangle is shown in Figure 2A. A color

slide using bands 4 (red component), 5 (green) and 7 (blue) processed to

radiance values is inserted in the plastic holder at the end of these

proceedings. Agricultural fields appear as white, while other regions

which are covered by oak-hickory forests show a variety of colors. Most

of the color variety is driven by variations in band 4 radiance values.

A winter TM band 4 scene of the same area is shown in Figure 2B. While

fields are light, the bland brightness patterns demonstrate that most

forested lands are spectrally homogeneous in band 4 in the winter season.

153
ORICdNAI.. PAC_ IS

OF POOR QUALITY

4, _

MISSOUR(

3_ _

_ _° 92" 90"

STUDY AR A

zoo

, ;,f

Fig. I. Study location:

the eastern half

of the Rolla

Quadrangle.

Fig. 2A, 2B. August (2A) and December (2B), 1982, TM band 4

images of the eastern half of the Rolla Quadrangle.

(Figure 2A is color slide number 7 in the back cover

pocket of this publication.)

154
Color composites of the winter scene corroborate this conclusion.

Spectral variations in the August scene are thus controlled by structures

and/or optical properties of the foliated oak-hickory canopy.

GEOLOGIC CONTROL

In general, soils in the study area are derived as weathering

products from the underlying bedrock and as a consequence they preserve

signatures of the underlying geology. Geologically the central region of

the eastern half of the Rolla quadrangle is underlain by Precambrian

granites and rhyolites of the St. Francois uplift. These rocks are

surrounded by flat-lying Paleozoic carbonate sedimentary rocks. The box

in Figure 2A includes outcrops of both rhyolites and carbonates. This

area is shown enlarged in Figure 3, and Figure 4 shows a simplified

geologic map for the region. A color slide of this area, processed in

the same way as the slide described previously, is inserted in the

plastic holder at the end of these proceedings.

The rhyolites and granites typically develop thin rocky soils, while

most carbonates weather to produce a thick clay-rich residuum. This

contrasting soil texture can produce a significant impact on perennial

vegetation through a process linked to the local climate (Green et al.,

1985). Specifically, spring rainfall is high and all soils are brought

to water holding capacity; however summers are hot and dry so the soil

water retention capacity of the soil controls the survival of perennial

plants. The thin, rocky soils on rhyolites have low water retention

capacities that produce local seasonally xeric (dry) environments. Xeric

forests and glades (naturally treeless areas) tend to develop in these

area. On the other hand, the higher clay content of carbonate derived

soils causes water to be retained through the summer and thus supports

more mesic (moist) forests. Some carbonates have a high chert content

that forms a soil with an impermeable residual layer (fragipan) that

restricts water infiltration. These fragipan soils also create a

seasonally xeric environment. The rhyolites of Figure 4 are usually

associated with a high August TM band 4 reflectance shown as lighter

tones in Figure 3 and as an orange color in the accompanying slides. The

carbonates are generally associated with lower August TM band 4

reflectance for the detailed study area except for a region near the

western margin of Figure 3. Ground observations in this region reveal an

abundance of chert pebbles within stream beds and as float on the ground

surface.

To explore the vegetative control on the August TM reflectances

within the study area (Figure 3), fifteen study sites were chosen for

detailed study. These sites are also along an AIS flightline where data

were acquired in August, 1985. At each of these sites 3 permanent chart

quadrats were laid out along transects in which all tree species were

identified and measured. These sites were chosen to encompass the

majority of the August TM spectral variations.

Three broad Oak-Hickory forest types were identified in the study

sites: mesic, xeric, and very xeric% ....These three endmembers differ

markedly in species composition and car_opy structure (see Figure 5). The

mesic forests, predominantly overlying carbonate rocks, exhibit trees of

the largest diameter, tallest height, and lowest density. These forests

are dominated by the moist favoring white oak (quercus alba) and scarlet

155
GEOLOGIC MAP

d o

PRECAMBRIAN RHYOLITES

[_PALEOZOIC CARBONATES

Fig. 5. Measured cross-sections of

three oak-hickory forest

types.

ORIGINAL PAGE IS

OF PO_R QUALITY

Fig. 3. August, 1985

band 4 image

of the dedetailed

study area.

(~25km wide)

The white

line marks

the AIS

flightline.

(See color

slide number

8 in the back

cover pocket

of this publication.)

Fig. 4. Simplified

geologic map of

study area.

MEASURED CANOPY X-SECTIONS

3o

2O

5

0 . ,

MESIC FOREST

'!i

METERS

VERY XERIC FOREST

156
oak (Quercus coccinea). Aerial photographs acquired along the AIS

flightline reveal that these forests usually develop an irregularly

topped canopy. In contrast, xeric forests underlain mainly by rhyolites

generally develop flat topped canopies. These forests support smaller

diameters and lower height trees of the dry favoring species black oak

(Quercus velutina) and mochernut hickory (Carya tomentosa). Very xeric

for--_ develop a very short, sparse canopy of small diameter post oak

(Quercus stellata) and blackjack oak (quercus marilandica) and hickory

species which favor very dry environments. These forests are associated

with glades, where perennial plant growth is absent. Glades offer the

most extreme xeric environments, since perennial plant growth cannot

survive the dry Augusf season. These glades are generally covered by

herbaceous growth and lichen-covered rock. The very xeric forests and

glades are associated with high TM band 5 and 7 reflectances (1.5-1.8 and

1.9-2.4 micrometers respectively) that produce light blue colors in the

August TM color slides.

The spectral characteristics of these forest types as measured by TM

were examined at locations A (two sites) and B (one site) along the study

line. These three sites are shown enlarged in Figure 6. The

corresponding TM reflectances for these study sites extracted from 3x3

pixel areas are plotted in Figure 7, using standard procedures to reduce

TM data to Lambertian reflectance. Figure 7 reveals that the xeric

forest stand exhibits the highest reflectance (>0.30) in band 4, though

these forests generally have lower biomass (as revealed in basal area

measurements) than mesic forests. In the visible (TM band 3, 0.56-0.74

micrometers) both xeric and mesic forests exhibit low reflectances.

These observations call into question the use of simple vegetation

indices such as the ratio of TM 4 over 3 or the normalized difference

vegetation index. These indices may give erroneously high biomass

estimates to the xeric forests.

SUITS CANOPY REFLECTANCE MODEL

To quantitatively understand the radiative transfer characteristics

of xeric and mesic forests that give rise to the distinctive TM

signatures, the Suits (1972) canopy reflectance model was used with data

obtained for the study sites at A and B. The Suits canopy reflectance

model is based on Duntley's (1942) diffuse-scattering radiative transfer

theory. The model predicts the bidirectional reflectance properties of

the canopy that are traceable to specific geometric and optical

characteristics, using a number of infinitely extended horizontal layers.

Leaves and branches are idealized as a combination of vertical and

horizontal flat panels. The Suits model also takes into account the

lighting and viewing geometry, factors that can have great effects on

canopy reflectances.

While many of the parameters used by botanists and foresters were

recorded in this study, such as tree height and diameter, these

measurements may not directly indicate the causative factors controlling

the spectral properties of these forest types. Thus, we also collected

data that directly measure more relevant structural and optical

parameters, data that can be incorporated into the Suits model.

Hemispherical reflectance measurements of the leaves of all dominant

species as well as those of branches and trunks were acquired. Sunlit

leaves were collected from the top of the canopy by means of an

157
Fig, 6, Forest study

sites along AIS

flightline at

locations A and

B, (see figure

3.)

.4

3

uJ

(.3

Z

0.2

W

I_.

W

cr

.I

o

FOREST CANOPY REFLECTANCE

AUGUST TM

I I l I I I I I 1 I

-- MESlC

---- XERIC

,_ --- VERY XERIC

\

I I I I I I i 1 I I

.4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.02.2 2.42.6

WAVELENGTH (JJm)

Fig. 7. Canopy bidirectional

reflectances of forest

study sites from

August, 1982 TM image.

I.O

FOREST CANOPY REFLECTANCE

SUITS MODEL

I I I I I I I I I I

MESIC

-- XERIC

.8 _ ___VERY XERIC

\

k\

.2

0 ...... ' '.0 '. '

.4 .6 .8 LO LZ L4L6 L82 222.42.S

WAVELENGTH (Rm)

uJ

Z.6

<.)

UJ

hi

n_

Fig, 8, Predicted canopy

bidirectional reflectance of forest study

sites using the

Suits (1972) model,

ORIG_,,%L PAGE IS

OF POOR Olm,:.;_;

158
expandable pole. The horizontal and vertical leaf area indices of both

leaves and branches were measured by means of point counting 35 mm slides

taken up through the canopy at various angles (Suits, 1976).

The results of the first of these canopy model reflectance plots are

shown in Figure 89 for the three sites referred to earlier. Although the

magnitudes are different from TM data, the Suits model successfully

predicts the relative canopy bidirectional reflectance for both xeric and

very xeric forests. It appears that the reflectance of the very xeric

forest is controlled in large part by its low leaf area index (less than

I) which exposes ground material that is typically bright at TM band 5

and 7 wavelengths. Both the LAIs of the xeric and mesic forests are high

enough to minimze the contribution from ground materials.

The slight difference in modeled canopy reflectance between the

xeric and more mesic canopies (Figure 8) is traceable to higher near IR

leaf hemispherical reflectance in the xeric species. At longer

wavelengths hemispherical reflectance measurements of all leaves are

similar. This difference in near IR leaf reflectance is probably

explained by a thicker wax coating and the double layer of palisade cells

typical of xeromorphic leaves (Kramer and Kozlowski, 1960). These

differences contribute to more internal reflections and greater leaf

reflectances. The differences in near IR leaf hemispherical

reflectances, however, do not appear to be enough to account for the low

August TM reflectance in band 4 (Figure 7) for the mesic forests. Mesic

forests commonly have a more irregular canopy surface than do xeric

forests and, thus, more canopy shadowing. This shadowing is not taken

into account in the Suits model, which uses plane parallel layers.

Incorporation of shadowing would probably lower the canopy reflectance of

mesic forests, bringing the modeled results closer to actual data.

AIS DATA

To examine the added information higher spectral resolution AIS data

may provide, AIS radiance values from 5x5 pixel averages of the 3 study

sites are plotted in Figure 9. The AIS data were acquired on August 2,

1985 in the tree mode (0.80 to 2.20 micrometers). The plot of a

predominantly gladed area is also included. Unfortunately, that portion

of the spectrum that contains most of the TM variation (band 4) and that

can be used to distinguish mesic and xeric forests is not included in the

wavelength interval acquired with the AIS.

Figure 9 reveals considerable differences in radiances between

forest types and glades, although the shapes of all curves appear

similar. To enhance the spectral differences the curves were normalized

to equal areas using the JPL SPAM software. The results are shown in

Figure i0. Both Figures 9 and I0 show that the AIS observations are in

general accord with those from TM data. At wavelengths greater than 1.4

micrometers, glades exhibit significantly higher relative radiances than

the forest types. At shorter wavelength glades exhibit low relative

radiances. Very xeric forest show these same trends to a lesser extent.

As discussed, the small LAI in the gladed and very xeric forests

apparently exposes materials, dead vegetation, and bare rock (rhyolite),

which are spectrally similar and much brighter than leaves at wavelengths

greater than 1.4 micrometers. Xeric and mesic forests have similar AIS

spectra, although xeric forests do show somewhat higher values at shorter

]59
189

171

153

135

117

99

81

63

45

27

9  ̧

I

llll IVi

l /1

i/Vl

- I

0.90

I

m

I

0.90

I

1.20

AIS DATA

I I I I 1

-- MESIC

-- XERIC

.... VERY XERIC -

........ GLADE

1.50 1.80 2.10

WAVELENGTH (pm)

I

AIS DATA

I I I I

EQUAL AREA -

NORMALIZATION u

-- MESIC

-- XERIC

.... VERY XERIC

....... GLADE

I I I I I I

1.20 1.50 1.80 2.10

WAVELENGTH (jJm)

Fig. 9. Raw AIS data for

the forest sites

plus glade using

5 x 5 pixel areas.

Fig. I0. AIS data

normalized to

equal area under

the curves.

160
wavelengths, consistent with TM observations.

Since no destriping was applied to the AIS data and in the light of

the second order overlap, interpretation of the more subtle features,

especially those greater than 1.6 micrometers, may not be warranted. At

this early stage in our analysis the AIS data do not seem to contain more

information than TM images. This may be caused by the inherent diffusing

nature of leaves and of forest canopies and by the complex mixtures of

vegetative components present, which would tend to dampen specific

spectral features.

CONCLUSIONS

Spectral variations in TM and AIS data are caused by differing

forest types. Three oak hickory forest types were identified based on

field observations: mesic, xeric and very xeric stands. Glades form the

most extreme xeric environment. Forest type distribution is controlled

primarily by variation in the soil water retention capabilities of the

soils, which in turn are controlled by bedrock, primarily the presence of

rhyolite or carbonate. Canopy bidirectional reflectances in xeric and

very xeric areas can be predicted by a reflectance model developed by

Suits. In mesic forests, canopy texture and shadowing may have to be

incorporated into this model, since the first order trends are not

predicted. AIS data reveal essentially the same trends as observed in TM

images.

BIBLIOGRAPHY

Duntley, S.O., 1942, The optical properties of diffusing materials, Opt.

Soc. Am., v.32, pp.61-70.

Green, G., R. Arvidson, M. Sultan, and E. Guinness, 1985, Geobotanical

information contained in Landsat Thematic Mapper images covering

southern Missouri. In Proceedings of the Fourth Thematic Conference:

"Remote Sensing for Exploration Geology", San Francisco, CA, April

1-4, 1986, ERIM, pp.371-380.

Kramer, P.J. and Kozlowski, T.T. (1960), Physiology of Trees,

McGraw-Hill, New York.

Suits, G., 1972, The calculation of the directional reflectance of a

vegetative canopy, Remote Sensing of Environment, v.2, pp. I17-125.

Suits, G., 1976, Instructions for making vegetation canopy measurements

suitable for reflectance modeling. In Suits monograph available

through ERIM.

161
N87:129s4

GEOBOTANICAL STUDIES AT PILOT MOUNTAIN, NORTH CAROLINA, USING THE AIRBORNE

IMAGING SPECTROMETER

N.M. MILTON, P.A. WALSH, and T.L. PURDY, U.S. Geological Survey, Reston,

Virginia 22092.

ABSTRACT

Airborne Imaging Spectrometer (AIS) data were acquired of several vegetation

types within the humid temperate eastern United States. The spectral region

covered, 0.9_m to 2.1j_m, has been little used in vegetation studies. A

preliminary analysis of spectral curves suggests that variations between

vegetation spectra may be useful for discriminating plant communities.

Calibration and normalization procedures must be refined to compensate for

cloud cover, detector and other system noise, and possible second-order

effects.

INTRODUCTION

The distributions of plant communities composed of deciduous and coniferous

trees in the Piedmont of the eastern United States are associated with

llthologic variations. Remotely sensed data from the Landsat MSS and TM

satellites have been used to determine some of these distributions (Krohn and

others, 1981; Milton and Krohn, 1982), but little attention has been given to

the high resolution spectral characteristics of tree species in the 0.9-2.1_m

region. Data from the Airborne Imaging Spectrometer (AIS) were evaluated to

determine the significance of this spectral region for discrimination of

different plant communities.

STUDY AREA

The AIS data were acquired over Pilot Mountain, a hydrothermally altered

monadnock in the Piedmont physiographic province of North Carolina.

Enrichments of Cu, Mo, Sn, and Au are associated with the alteration. The

vegetation around the crest of the monadnock, where concentrations of metals

are highest, is composed primarily of chestnut-oak (_uercus prinus) forest.

MIxed oaks and pines (Pinus) comprise the forested slopes; the area to the

west within and outside the alteration zone is pine plantation. The geology,

vegetation, and results of an analysis of data from an earlier hlgh-resolution

airborne radiometer experiment have been discussed in Milton and others

(1983). Fields, pasture, and lowland forests surround the monadnock and were

also imaged during the AIS surveys.

NEAR-INFRARED REFLECTANCE OF PLANTS

Spectral reflectance of plants in the 0.9 to 2.1_m region is controlled by

scattering from cell walls and absorption by water (Knipllng, 1970). This

portion of the electromagnetic (EM) spectrum has been less intensively studied

for plants than the visible portion of the spectrum. Spectral features which

appear to be species specific must still be considered tentative. Wickland

(1985) reported variations in an absorption feature at 1.19j_m in deciduous

versus pine forests. In broadband spectral data, spectral changes due to

plant water content have been used to separate plant communities or to mask

vegetation for rock discrimination in images.

162
AIS DATA

AIS and NS001data were acquired over the study area on August II,

1985. Two flightlines were requested, one oriented SE to NWand the other SW

to NE, crossing over Pilot Mountain. The latter line was flown as requested,

but the former, which would also have passed over two additional monadnocks,

was flown approximately 1 mile to the southwest, thus missing all

monadnocks. The NASA C-130 aircraft flew at a maximum of 4500 m, resulting in

a ground swath width of approximately 300 m. Scattered cloud cover was

present throughout the flights.

Preliminary processing of the AIS data was done using software developed

by F. Kruse, U.S. Geological Survey, Denver and modified by D. Krohn, U.S.

Geological Survey, Reston. _The spectrometer seemed to be functioning well, as

data from both flightlines are clean with relatively little noise, line-drops,

etc. Whereas the spectrometer was operating in "tree mode" (0.9-2.1 _m), the

processing parameters were still set for "rock mode" (l.2-2.4jnm). To

compensate, calibration was done using a LOWTRAN atmospheric model, matching

HgO and CO_ absorption features. The offset was determined to be

approximately 210 nm from "rock mode" (90 nm from "tree mode"), and beginning

and ending wavelengths were computed to be 0.99Aun and 2.19jum. Calibration

appeared to be correct for all grating intervals.

An enlarged gray-level raster plot of the AIS data was made for each

flightllne. The plots proved useful in plotting the precise location of each

flightllne and in delineating specific field or forest areas from which to

derive spectra (figure I). Average spectral curves were derived for both

flightlines, and relative reflectance images were produced. These initial

images were noisy because the average spectrum was computed from the entire

flightline, including clouds and cloud shadow, so that artifacts were

introduced and noise obscured any useful information. Possible interference

from second order effects will also need to be identified and corrected before

the data can be used reliably (Terry Cook, this volume). Average (3 x 3

plxel) spectral curves were derived for several vegetation types and then

compared to the average spectrum for the flight line.

AIS SPECTRA

Derived spectra from deciduous and coniferous forests, cropland, pasture,

and water are shown in figures 2-6. The spectral variations reported by

Wlckland at 1.19 _m are not observed in the raw spectral data (figures 2 and

3).

Differences in the shape of the curves between the two water bands at 1.4

jum and 1.9jum appear to provide a more promising region for discrimination of

vegetation types. Deciduous forests are more reflective than coniferous

forests and less reflective than cropland. Pasture is the least reflective of

the vegetation types. The pasture curve is closest to the average curve,

though, as mentioned earlier, the average was derived from clouds and shadow

as well as plant cover. The slope of the curve in this region also varies,

but more analysis of the data will be needed to discern patterns.

163
CONCLUSIONS

I • Airborne Imaging Spectrometer data provide a high-resolutlon look in the

spectral region 0.9j_m to 2.1 _m, a part of the electromagnetic spectrum

not previously well studied for plants.

2. Processing of the AIS data requires calibration and normalization, as well

as compensation for the effects of second-order reflections•

• Spectral variations at 1.19 _m which were previously reported were not

observed, but variations between 1.4jum and 1.9j_m may allow

discrimination of major vegetation types•

REFERENCES

Knipling, E.B., 1970. Physical and physiological basis for the reflectance of

visible and near-infrared radiation from vegetation: Remote Sensing of

Environment, v. I, p. 155-159.

Krohn, M.D., Milton, N.M., Segal, D.B., and England, A.W., 1981.

Discrimination of a chestnut-oak forest unit for geologic mapping by

means of a principal component enhancement of Landsat multl-spectral

scanner data: Geophysical Research Letters, v. 8, p. 151-154.

Milton, N.M., Collins, W., Chang, S.H., and Schmidt, R.G., 1983. Remote

detection of metal anomalies on Pilot Mountain, Randolph County, North

Carolina: Economic Geology, v. 78, no. 4, p. 605-617.

Milton, N.M., and Krohn, M.D., 1982. Chestnut-oak (Quercus prinus L.)

distribution in the Virginia Piedmont as an aid to geologic mapping

(abs.): Virginia Journal of Science, v. 33, no. 3, p. 128.

Wickland, D.E., 1985• AIS spectra for stressed and unstressed plant

communities in the Carolina slate belt: in Vane and Goetz, eds.,

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop,

JPL Publication 85-41, p. 46-50.

164
1

ORIGINAL PAGE IS

OF POOR QUALITY

Mixed coniferous/deciduous

forest

Planted field

Deciduous forest

Plowed field

Coniferous forest

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Route 2607

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portion of the NE-SW

flightline. When

used with the colorinfrared aerial

photograph, location

and identification of

features is facilitated.

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line) and average of all spectra along the flightline (dashed line).

166
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line) and average of all spectra along the flightline (dashed line).

167
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average of all spectra along the flightline (dashed line).

168
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average of all spectra along the flightline (dashed line).

169
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line] and creek [dashed line]).

170
N87-12985

TRACE ELEMENT-INDUCED STRESS IN FRESHWATER WETLAND VEGETATION: PRELIMINARY RESULTS

BYRON L. WOOD, TGS Technology, Inc., NASA/Ames Research

Center, Moffett Field, CA, USA; LOUISA H. BECK, Remote Sensing Research Program, University of California, Berkeley,

CA, USA.

ABSTRACT

Airborne Imaging Spectrometer (AIS) data were acquired

over an area of freshwater wetlands in Central California on

September 23, 1985. Plant samples were subsequently collected along the flight line with the goal of relating plant

tissue chemistry to spectral reflectance in the nearinfrared region. It was determined that a consistent relationship existed between spectral response and plant tissue

chemistry. This was especially evident in the 1500 to 1700

nm region.

INTRODUCTION

For a number of years the authors have been conducting

research on the use of remote sensing data to identify and

map freshwater wetlands in California. The results of this

research have shown that some types of remote sensing data

can be used to characterize wetlands in terms of species

composition, as well as plant morphology and phenology.

These differences can, in turn, be related to environmental

factors such as soil and water quality. The wetlands previously studied were found on similar basin soils and maintained by good quality irrigation water. As such, they are

considered to be representative of healthy freshwater wetland ecosystems. Many wetlands in California are, however,

maintained by agricultural drainage water which often contains a variety of potentially toxic pollutants.

Studies by the United States Bureau of Reclamation and

Fish and Wildlife Service have shown that unusually high

trace element levels are found in food chain organisms at

Kesterson National Wildlife Refuge (Blum, 1984). For nearly

a decade, Kesterson, a freshwater wetland in California's

Central Valley, has served as a disposal site for agricultural drainage water. This water contains abnormally high

concentrations of such trace elements as boron, molybdenum,

selenium, and lead, which are known to cause plant stress.

Therefore, the Kesterson wetlands provided the authors with

an opportunity to extend their previous research to the

characterization of stressed and non-stressed wetlands.

Based on a review of management practices at wetlands surrounding Kesterson, the nearby Volta Wildlife Refuge was

C- _ 171
selected for control purposes. A key consideration in the

selection process was that, unlike Kesterson, the wetlands

at Volta have been maintained using good quality irrigation

water.

Although trace elements are essential to healthy plant

growth, deficiencies or excesses of these same elements can

result in plant stress (Kabata-Pendias and Pendias, 1984).

A frequent non-specific stress response is chlorosis, or

yellowing of leaves due to decreasing chlorophyll concentration in a plant's mesophyll cells. The relationship between

chlorophyll concentration and leaf spectral reflectance has

been demonstrated by several researchers, including Thomas

and Gausman (1977), Myers (1975), and Horler et al.

(1983a,b). Although many factors affect leaf chlorophyll

content, including senescence (Tucker, 1977), nutrient

stress (Kabata-Pendias and Pendias, 1984), soil salinity

(Ward, 1969), and disease (Colwell, 1956; Suits and Safir,

1972; Wildman, 1979; Murtha, 1982), it is possible to track

changes in leaf chlorophyll content by examining reflectance

measures (Benedict and Swidler, 1961; Thomas and Gausman,

1977).

OBJECTIVES

The objectives of this research are to determine if

field and laboratory spectral data can be used to characterize stressed and non-stressed wetland vegetation. Specific

goals are to (a) determine which wetland plant species bioaccumulate high levels of trace elements; (b) determine if

these high concentrations affect moisture and/or chlorophyll

content, and therefore, spectral response; and (c) test the

feasibility of using airborne- or satellite-based remote

sensing data for the identification, monitoring, and mapping

of these effects.

APPROACH

Achieving the stated objectives required a multilayered procedure. Figure 1 illustrates the general flow of

the approach, which involves the initial analysis of laboratory data, and the extension of these results to a

regional-scale remote sensing system. Remote sensing data

used in this research were acquired with the following systems: (a) 1000-channel Barringer field spectrometer; (b)

1000-channel Perkin-Elmer laboratory spectrophotometer; (c)

12-channel Daedalus Thematic Mapper Simulator (TMS); and (d)

128-channel Airborne Imaging Spectrometer (AIS).

172
Laboratory Biochemlcal/High Resolution

Spectral Relationships

Nigh Resolution/Broad Band

Spectral Relationships

Field

HR* Spectra_

(green veg| _

Biochem,l ---J

Analyses _

Laboratcry ]

HR*

Spectra

(dry veg)

Statistical

Analyses

Statistical

Analyses

Chemical/Spectral

Analyses

?

IDENTIFICATION

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MONITORING AND MAPPING

Figure 1. General flow of the research approach.

(I)

The data analysis procedure is as follows:

Identify the relationships between trace element content and high resolution visible and infrared spectrometer data

a) collect and prepare plant tissue samples from both

a 'stressed' and a non-stressed area

b) obtain high resolution spectrometer data

i) from fresh plant samples (using the Barringer

spectrometer)

ii) from dried plant samples (using the PerkinElmer spectrophotometer)

c) perform biochemical analysis on plant samples to

measure trace element and chlorophyll content

d) correlate biochemical and spectrometer data using

statistical analyses

173
(2) Extend correlations to a broad-band remote sensing system

a) isolate wavelengths in high resolution data critical for diagnosing plant stress, and analyze the

equivalent channels in Thematic Mapper Simulator

(TMS) 12-channel data

b) extend site-specific correlations to a regional

scale

c) perform an accuracy assessment.

In addition to the laboratory spectrometer data and the

TMS data, we are also using digital data from the Airborne

Imaging Spectrometer (AIS). Since this instrument records

in the 900 to 2100 nm region (tree mode), it cannot be used

for analyzing shifts in the chlorophyll absorption edge, the

"red edge"*, located at approximately 700 nm. Research by

Tucker (1980) has shown that changes in the geochemical

environment can affect a plant's water availability. Therefore, a chemically-stressed plant may also exhibit water

stress symptoms which would show up in the near-infrared

region between 1600 and 2200 nm, where reflectance levels

represent an accurate indication of leaf water content.

Work by Wood and Wrigley (1985) in the near-infrared region

using AIS spectra from agricultural crops, determined that

the shape and amplitude of the reflectance curves in the

1550 to 1700 nm region were related to crop condition rather

than crop type. Their findings suggest that the curve slope

in the 1550 to 1700 nm region might be related to crop

senescence or moisture stress. Perkin-Elmer laboratory

spectra for stressed and non-stressed 2X_ spp. and

spp. samples indicated that, within the range of the AIS

data, spectral differences were primarily ones of curve

amplitude rather than shape. It was, however, noted that

the stressed samples generally had less well-defined absorption features than the non-stressed samples. The shape of

the curves in the near-infrared region suggests that AIS

spectra can be utilized for characterizing stressed and

non-stressed plants.

DATA ACQUISITION

Remote sensing data were collected over the study area

throughout the summer of 1985. These included six Daedalus

TMS acquisitions between June and October, three dates of

* Geobotanists and mineral exploration groups often use

shifts in the red edge as an indicator of metal- or

mineral-induced plant stress. For a discussion of this

topic, see Goetz et al. , 1983.

174
color-infrared high altitude photography, and a single date

of AIS data. The AIS data were acquired from the NASA C-130

aircraft on September 23, 1985. Additional data acquired

during this flight included 8-channel NS-001 digital data,

35mm black-and-white photography, and VHS video imagery.

Immediately following the AIS flight, the video imagery

was viewed in order to locate the flight line on the ground;

this was done for purposes of directing field data collection. Plant sample collection at Kesterson and Volta was

undertaken on September 27-28. A total of 98 samples,

representing ten species, were collected for chemical and

laboratory spectral analyses. For purposes of comparison,

the same plant species were sampled at both Kesterson and

Volta. The sample sites at Kesterson were selected from a

set previously used by the U.S. Bureau of Reclamation for

analyzing soil and water chemistry; samples at Volta were

randomly selected.

DATA ANALYSES

Prior to oven drying, all fresh plant samples were

scanned with the Barringer spectrometer, which records in

the 450 to 2450 nm range at 2 nm resolution. Both hardcopy

and digital output products were generated for each sample.

Individual samples were weighed before and after oven

drying to determine percent plant water content. All dried

samples were taken to the Laboratory of Biomedical and

Environmental Biology at the University of California, Los

Angeles, for chemical analysis. Samples were separated into

leaf, stem, and flower parts, which were then individually

ground to a uniform size using a Wiley mill with a 40-mesh

screen. A total of 138 samples was prepared for analysis.

All samples were analyzed for twenty-eight elements using

optical emission spectrometry and X-ray fluorescence techniques.

Generation of the AIS spectral reflectance curves was

performed on a VAX 11/780 using IDIMS image processing

software and additional programs developed specifically for

the analysis of AIS data. No pre-processing (i.e., atmospheric correction) was attempted prior to the generation of

spectral curves. The dominant plant species found within

the AIS flight line was 2Y_ spp. Approximately seventyfive two-by-two pixel blocks of pure _ spp. stands

growing in standing water were selected for curve generation

so as to represent a range of water quality and plant tissue

chemistry conditions throughout the study area.

175
DISCUSSION

As stated previously, our objectives are to (a) determine which wetland plant species bio-accumulate high levels

of trace elements; (b) determine how these concentrations

affect spectral response; and (c) examine the feasibility of

using airborne- or satellite-based remote sensing data for

identifying, monitoring, and mapping these effects. To

date, our research efforts have focused on the first two

objectives.

The plant chemistry analyses have shown that plants

from Kesterson had significantly higher concentrations of

most of the trace elements (notably boron and selenium) than

the same species collected at Volta. This difference was

found for all species.

The results of the plant chemistry analyses also showed

that levels of trace elements differed by species. _jZp_

spp. had the highest concentration of selenium among the

species sampled. The highest boron levels were found in

spp., _ spp., and _ spp. Molybdenum levels were highest for _ spp. and D__ spp. It

should be noted that _ spp. and _ spp. are the

dominant species in the permanently-flooded portions of Kesterson and Volta. _ spp. and D__ spp. are

found only in the seasonally-flooded areas of these wetlands. In addition, plant chemistry data varied by plant

part, with leaf tissue generally having the highest concentrations.

Analysis of the spectral data taken from fresh plant

samples (Barringer spectrometer data) may be inconclusive

because of problems relating to instrument calibration.

These data, however, suggest that as plant moisture content

decreases, reflectance in the 600 to 700 nm region

increases. Laboratory reflectance data (Perkin-Elmer spectrophotometer) from _ spp. samples with different leaf

moisture and trace element levels appear to be similar to

the Barringer data in this same 600 to 700 nm region.

Further analysis of the Barringer and Perkin-Elmer reflectance data in relation to plant chemistry is currently

underway.

The seventy-five AIS reflectance curves were generated

in order to represent a range of plant chemistry and/or

water quality conditions found throughout the Kesterson and

Volta wetlands. Based on a preliminary comparison between

these curves and plant chemistry data, it appears that there

is a significant spectral difference between areas with high

and low concentrations of trace elements. These differences

are primarily in terms of curve amplitude rather than shape.

Figure 2 illustrates this difference. The lower curves are

representative of Volta where _ spp. had low levels of

176
Kesteraon pond t3

Volta pond 03

Figure 2. AIS spectral reflectance curves for _ spp.

from Kesterson and Volta. The lower two curves represent

samples containing low concentrations of selected trace elements; the upper two contain high concentrations.

o

o

g

u_

o

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oo

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Figure 3. AIS spectral reflectance curves for T_ spp.

from Kesterson and Volta. This series of curves represent

increasing levels of trace element concentration in plant

tissue from Volta (low) to Kesterson (high).

177
boron, selenium, and lead; the upper curves represent the

same species from Kesterson, where these elements were

present in abnormally high concentrations. In addition, it

was found that the slope of the reflectance curve in the

1500 to 1700 nm region was significantly steeper for the

spectra from Kesterson. This phenomenon is also illustrated

in Figure 3 which shows a series of curves representing a

range of plant chemistry conditions. These show the amplitude shift from low to high trace element concentrations, as

well as the increasing change in slope angle in the same

spectral region. Although the biological cause of the

reflectance shifts shown in Figures 2 and 3 is not entirely

understood, it appears to be consistent for all AIS plots

generated; the shifts may be related to changes in plant

tissue chemistry. At the present time we are completing an

analysis of plant tissue chlorophyll content in hopes of

further understanding the phenomenon illustrated by the AIS

curves.

CONCLUSIONS

Results of this research suggest that there exists a

consistent relationship between plant tissue chemistry and

spectral reflectance in the near-infrared. Preliminary

analysis of the data shows that an increase in trace element

concentration often corresponds to an increase in spectral

reflectance in the 1500 to 1700 nm region. Ongoing research

into the spectral-chemical-biological relationships suggested by these results is being conducted by the authors.

REFERENCES

Benedict, H.M. and R. Swidler. 1961. Nondestructive method

for estimating chlorophyll content of leaves. Science

, vol. 133:2015-2016.

Blum, J.R. 1984. Testimony of J.R. Blum (U.S. Fish and

Wildlife Service) before the State Water Resources

Control Board. Sacramento, California.

Colwell, R.N. 1956. Determining the prevalence of certain

cereal crop diseases by means of aerial photography.

, vol. 26(5):223-86.

Goetz, A.F.H., B.N. Rock, and L.C. Rowan. 1983.

sensing for exploration: an overview. Econ.

, vol. 78(4):573-590.

Remote

Geol.

Horler, D.N.H., J. Barber, J.p. Darch, D.C. Ferns, and A.R.

Barringer. 1983a. Approaches to detection of

geochemical stress in vegetation. Adv. _ Res. ,

vol. 3(2):175-179.

178
Horler, D.N.H., M. Dockray, and J. Barber. 1983b. The red

edge of plant leaf reflectance. Intl J. Rem. Sens.

, vol. 4(2):273-288.

Horler, D.N.H., M. Dockray, J. Barber, and A.R. Barringer.

1983c. Red edge measurements for remotely sensing

plant chlorophyll content. Adv. _/_G__Res. , vol.

3(2):273-277.

Kabata-Pendias, A. and H. Pendias. 1984. Trace elements in

soils and plants. CRC Press, Boca Raton, Fla.

Murtha, P.A. 1982. Detection and analysis of vegetation.

In : Remote Sensing for Resource Management, Chapter

13. C.J. Johannsen and J.L. Sanders, ed. Soil

Conservation Society of America, Ankeny, Iowa.

Myers, V.I. 1975. Crops and soils. In : Manual of Remote

Sensing. L.W. Bowden, ed. American Soc. Photogram.,

Falls Church, VA, pp.1715-1813.

Suits, G.H. and G.R. Safir. 1972. Verification of a

reflectance model for mature corn with applications to

corn blight detection. Rem. Sens. _ , vol.

2:183-192.

Thomas, J.R. and H.W. Gausman. 1977. Leaf reflectance

versus leaf chlorophyll and carotenoid concentrations

for eight crops. _ J. , vol. 69:799-802.

Tucker, C.J. 1980. Remote sensing of leaf water content in

the near-infrared. Rem. Sens. _ , vol.

10:23-32.

Tucker, C.J. 1977. Spectral estimation of grass canopy

variables. Rem. Sens. _ , vol. 6(1):11-26.

Ward, J.M. 1969. The significance of changes in infrared

reflectance in sugar maple ( Acer saccarum Marsh),

induced by soil conditions of drought and salinity.

In : Proc. of the 6th Intl Symp. on Rem. Sens. of

Environ., ERIM, Ann Arbor, Michigan, pp.1205-1226.

Wildman, W.E. 1979. Color infrared: a valuable tool in

vineyard management. In : Proc. of the 7th Biennial

Workshop on Color Aerial Photography in the Plant

Sciences and Related Fields. Amer. Soc. of

Photogram., Falls Church, VA.

Wood, B.L. and R.C. Wrigley. 1985. AIS investigation of

agricultural monocultures. In : Proc. of the

Airborne Imaging Spectrometer Data Analysis Workshop,

April 8-10, 1985. Jet Propulsion Laboratory,

California Institute of Technology, Pasadena,

California.

179
N87-12986

PATTERNS OF VEGETATION IN THE OWENS VALLEY, CALIFORNIA

SUSAN L. USTIN, Remote Sensing Research Program, Space Sciences

Laboratory, University of California, Berkeley, CA 94720, USA, and

Department of Botany, University of California, Davis, CA 95616 USA;

BARRETT N. ROCK, Jet Propulsion Laboratory, Pasadena, CA 91109, USA; and

ROY A. WOODWARD, Remote Sensing Research Program, Space Sciences

Laboratory, University of California, Berkeley, CA 94720, USA, and

Department of Botany, University of California, Davis, CA 95616 USA

ABSTRACT

Spectral characteristics of seml-arld shrub

communities were examined using AIS data collected in

the "tree" mode on 23 May 1985. Mesic sites with

relatively high vegetation density and distinct zonatlon

patterns exhibited greater spectral signature variations

than sites with more xeric shrub communities. Spectral

signature patterns were not directly related to

vegetation density or physiognomy, although spatial

patterns were consistent with ecological changes, and

maps derived from an 8-channel maximum likelihood

classification were supported by photo-interpreted

surface features. In AIS data, the principal detected

effect of shrub vegetation on the alluvial fans is to

lower reflectance across the spectrum. These results

are similar to those reported during a period of minimal

physiological activity in autumn, indicating that

shadows cast by vegetation canopies are an important

element of soil-vegetation interaction under conditions

of relatively low canopy cover.

INTRODUCTION

The primary objective of our study has been to evaluate the

contribution of vegetation to pixel spectra under semi-arld conditions,

where total canopy leaf area and surface cover are low. Owens Valley was

chosen as a test site because of its diverse seml-arld shrub communities

and substrate types (Ustin and Rock, 1985; Ustln et al., 1986).

Last year, preliminary results of late autumn AIS data showed that

vegetation had little apparent effect on the spectral signatures of pixels

from several different rock/soll types having shrub canopy covers varying

between i0 and 65% of the ground surface (Ustln and Rock, 1985).

Regardless of substrate type (sand, granitic alluvium, or basalt) or

vegetation type (Great Basin sagebrush or saltbush communities), the only

detectable response to the presence of vegetation was a change in surface

albedo consistent with increasing shadows cast by the plant canopies as

density increased (Ustin et al., 1985; Ustin and Rock, 1985). Because

these data were obtained when most of the species in the shrub communities

were dormant, or nearly dormant, we suggested that more spectral

differences attributable to vegetation would be apparent during periods

180
of greater physiological activity. Here we report spectral responses

that exemplify the range of patterns observed on a spring date coinciding

with peak growth and physiological activity at these localities.

AIS DATAACQUISITIONANDCALIBRATIONPROCEDURES

Although several flights have been conducted over our sites

(Science Investigators Guide to AIS Data, Suppllment, 1986), only two

dates appear to have reasonably high quality data. No AIS data were

obtained on 2 April 1985becauseof equipmentmalfunction; data on i0 July

1984are of questionable value becauseof atmospheric haze and smoke, and

on 22May 1985, becauseof clouds and atmospheric moisture. However, data

acquired on 30 October 1984are cloud-free and 23 May 1985 data are cloudfree on most flight lines.

Data reported here are from flightlines 507 and 509 on 23 May

1985. The flight occurred between 11.30 and 12.00 hours PSTand flight

altitude was about 5.0 km above the terrain. The data were analysed at

the Space Sciences Laboratory, University of California, Berkeley using

the SPAM(Spectral Analysis Manager) developed by the Jet Propulsion

Laboratory. The data were rectified for radiometric differences and

corrected for solar-irradiance by the JPL prior to our analyses. Spectra

of pixels selected from Diaz lake showpeak reflectance dn values of 55 to

80 at I.i0 and 1.30 um, indicating a significant non-zero offset. A

vertical stripe is pronounced between the 22nd and 32nd pixels.

Eight channels of the AIS data were selected for further analysis

using an unsupervised clustering algorithm (derived from ISOCLAS)

followed by a maximum likelihood classification of the scene using the

ELAS program. These channels (7, 19, 32, 40, 64, 72, 88, 117) were

selected from near infrared and shortwave infrared bands which exhibited

significant spectral brightness variation when examined with SPAM.

Vertical striping was pronounced and was removed prior to clustering

analysis by calculating the means of each pixel column and the mean of the

512 by 32 pixel line segment. The difference between each column mean and

the scene mean was determined and this difference was added or subtracted

to each pixel in the column.

RESULTS AND DISCUSSION

On most of the flight lines examined, there were few apparent

differences in spectral signatures from shrub communities differing in

species composition or plant density. These results are similar to those

reported for these communities during autumn when physiological activity

was minimal (Ustin and Rock, 1985). In contrast, sites with available

soil water, where plant cover and green biomass tend to be relatively

high, have more diverse spectral responses (Figure i). Vegetated

surfaces have distinct spectral signature patterns with different slopes

in the 1.4 to 1.8 um spectral region and brightness differences between

the near infrared and shortwave infrared spectral regions. These

spectral responses resemble those reported from agricultural and marsh

habitats (Gross and Klemas, 1985; Wood and Wrigley, 1985; Vanderbilt,

1985). However, it is not clear how surface interactions are affecting

the spectral responses, since slope differences and brightness changes

are not clearly related to vegetation density or to specific plant

physiogonomlc types, such as grasses or shrubs. Soils are fine-grained

181
OF POOR QUALITY

alkaline quartz sands and do not account for the spectral variation; such

patterns are not evident in field-collected soil spectral data. Although

second order energy overlap is affecting reflectance at wavelengths

longer than 1.6 um, the spectral patterns we observed are evident also at

shorter wavelengths, and thus are not explained by this source of

contamination.

Figure i. Reflected radiance curves for vegetation types near Dlaz Lake.

Upper curve is from an irrigated grassland on a golf course, the next

lower curve a saltbush community, a dry meadow community, and a wet meadow

community, respectively.

Figure 2. Above, a 3-color composite of the scene in Figure I, after

removal of most vertical striping. Below, the 8-band maximum likelihood

classification map. (See color slide number 9 in the back cover pocket

of this publication.)

182
Despite the uncertainties in interpreting the physical basis for

the spectral patterns they appear to be clearly related to ecological

differences between plant communities. Figure 2 shows the spatial

distribution in spectral classes near Diaz Lake in the southern Owens

Valley. The upper image (refer to appendix for color slide) is a three

color composite (channels 19, 32, 64) and the 8-band classification map

(below). These classes represent an irrigated and clipped grass golf

course (red) in the triangular unit on the left margin, an unirrigated

saltgrass (Distichlis) meadow (turquoise), a denser meadow with

cottonwoods (Populus) (red, cream), lake (pink), a sand pit (green), a

saltbush scrub (Atriplex) community (blue) and other disturbed areas

having low (less than 5% canopy cover) saltbush vegetation (yellow). The

speckled area near the right of the llne is a trailer court. The wavy

line nearby is U.S. Route 395. Spectral patterns clearly differentiate

ecologically distinct spatial units on the ground, with most spectral

differences apparently due to changes in the vegetation type or to

variation in canopy density related to soll moisture patterns rather than

soll morphology.

Figure 3 shows representative spectra from 3 x 5 pixels from a

line segment crossing the Sierra Nevada bajada west of the town of

Independence. The left third of this llne segment crosses an older

alluvial fan surface, which is overlain by a younger fan surface covering

the remainder of the flight line. A sagebrush-blackbrush (ArtemlslaColeosyne) community of comparable canopy cover is found on both sol1

types. In addition to the sol1 changes, the llne crosses two old wildfire

sites where the vegetation canopy has not yet recovered to the level

ovserved on the adjacent unburned surfaces. Pixels were selected from

each of these surface types. It can be seen that all vegetation and soll

surfaces have a similar spectral signature pattern. Only the burned

surface having the least canopy cover (near the right margin of the llne),

having the least canopy cover, has a sufficiently higher albedo to

distinguish it consistently from the other surfaces. Plant cover at this

site is only half that on the surrounding surfaces (17% versus 33%).

These signature patterns are llke those previously obtained during autumn

(Ustln and Rock, 1985), again suggesting that the principal effect of

semi-arld shrub vegetation on the AIS plxel spectra is to lower brightness

nearly equally in all bands, probably as a consequence of the relatively

sparse, spreading canopies shading the ground.

Last summer (1985), a new wildfire removed nearly 100% of the

surface vegetation in a 5000 ha area, leaving intact only a few isolated

stands of the unburned shrub community and the formerly youngest burn

site. New AIS data were obtained for this area 16 May 1986. To gain

greater insight into the influence of vegetation on these pixel data, we

plan to examine in detail the spectral response characteristics on the

three dates, comparing the physiologically dormant and active periods

with the substrate surface lacking vegetation. Because of the wider path

width of AIS-2, at least a portion of each of the flight lines can be

coregistered.

183
ORIG A1- PAGE IS

OF poeR QUALITY

Figure 3. Selected spectra of alluvial fan sites differing in soil or

vegetation characteristics. Upper curve is from the younger burn site on

the younger soil surface, next lower curve from the older burned site also

on the younger soll surface, lowest two curves from the unburned sagebrush

community on the younger and older soil surfaces, respectively.

Figure 4. Alluvial fan region depicting an unsupervised 8-band maximum

likelihood classification map before destriping (top), a 3-band color

composite (middle) after removal of vertical striping, and the maximum

likelihood classification of the region before destriping (middle) and

after destriping (bottom). (See color slide number i0 in the back cover pocket of this publication.)

184
The Thematic Mapper had been previously used to classify

ecological units at this site (Ustln et al., 1986). In the TM

classification, the differently aged alluvial surfaces were found to be

spectrally distinct in the infrared region. Figure 4 shows the results of

a maximum likelihood classification of the bajada AIS sequence. Clearly,

prior to removing the vertical banding, most of the mapping relates to

noise. However, destrlping produces a significant improvement and bajada

features are seen in considerable detail. Although neither of the two

soll surfaces are clearly delineated in the AIS classification map, the

numerous stream channels and riparian communities stand out as west-east

trending features; colors appear to relate to the size and amount of

riparian vegetation present (red=shallow washes; blue=deeper washes with

riparian vegetation). The open fan surface is generally green (33% shrub

cover), although the two burns are distinguished by belonging to the

turquoise (28% cover) and yellow (17% cover) classes. Dirt roads are

visible as the cream-colored class. Despite the similarity of spectral

features, the small albedo differences, and the presence of instrument

noise even after destrlping, the AIS data appear to be valuable in mapping

surface features at considerable spatial detail.

Despite the usefulness of data enhancement techniques, such as

log-resldual transformations, and spectral signature comparisons of

specific absorption features for geologic applications, these techniques

appear to have limited success for vegetation analyses. Further

vegetation studies should concentrate on understanding the physical basis

for signature differences in AIS data and on developing new analytical

techniques better suited to the relative spectral changes observed among

differing vegetation types. Developing techniques for modeling spectral

mixtures, such as described by Smith and Adams (1985), may be useful.

Acknowledgements. We would llke to thank Dr. Jerry Solomon and Alex Zak

at JPL and Paul Ritter at U. C. Berkeley for their assistance in analysing

the AIS data, and the staff at the White Mountain Research Station for

their assistance in the field research.

REFERENCES CITED

Gross M.F., and V. Klemas. 1985. Discrimination of coastal vegetation

and biomass using AIS data. in Proceedings of the Airborne

Imaging Spectrometer Data Analysis Workshop, April 8-10, 1985.

Gregg Vane and Alexander F.H. Goetz, eds. JPL Publ. No. 85-41, p.

129-133.

Smith, M.O. and J.B. Adams. 1985. Interpretation of AIS images of

Cuperite, Nevada using constraints of spectral mixtures, in

Proceedings of the Airborne Imaging Spectrometer Data Analysis

Workshop, April 8-10, 1985. Gregg Vane and Alexander F.H. Goetz,

eds. JPL Publ. No. 85-41, p. 62-67.

Ustin, S.L., and B.N. Rock. 1985. Preliminary analysis of AIS spectral

data acquired from seml-arid shrub communities in the Owens

Valley, California. in Proceedings of the Airborne Imaging

185
Spectrometer Data Analysis Workshop,April 8-10, 1985. GreggVane

and Alexander F.H. Goetz, eds. JPL Publ. No. 85-41, p. 41-45.

Ustln, S.L., B.N. Rock, and R.A. Woodward. 1985. Analysis of substrate

and plant spectral features of seml-arid shrub communities in the

Owens Valley, California. Proceedings of the International

Symposiumon Remote Sensing of Environment. Fourth Thematic

Conference: Remote Sensing for Exploration Geology, San Francisco, CA, April 1-4, 1985, p.347-360.

Ustln, S.L., J. B. Adams, C.D. Elvidge, M. Rejmanek, B.N. Rock, M. O.

Smith, R.W. Thomas, and R.A. Woodward. 1986. Thematic mapper

studies of semiarid communities. BioSclence. (in press)

Vanderbilt, V.C. 1985. Urban, forest, and agricultural AIS data: Fine

spectral structure, in Proceedings of the Airborne Imaging

Spectrometer Data Analysis Workshop, April 8-10, 1985. Gregg Vane

and Alexander F.H. Goetz, eds. JPL Publ. No. 85-41, p. 158-165.

Wood, B.L., and R.C. Wrigley. 1985. AIS investigation of agricultural

monocultures, in Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop, April 8-10, 1985. Gregg Vane and

Alexander F.H. Goetz, eds. JPL Publ. No. 85-41, p. 134-140.

186
=J

AIS SPECTRA OF DESERT SHRUB CANOPIES

N87-12987

RJAY MURRAY, Milne Computer Center, DENNIS L. ISAACSON, BARRY J.

SCHRUMPF, WILLIAM J. RIPPLE, ANTHONY J. LEWIS, Environmental Remote

Sensing Laboratory, Oregon State University, Corvallis, OR, USA

ABSTRACT

Airborne Imaging Spectrometer (AIS) data were collected 30 August

1985 from a desert shrub community in central Oregon. Spectra from

artificial targets placed on the test site and from bare soil, big

sagebrush (Artemesia tridentata wyomin_ensis), silver sagebrush

(Artemesia cana bolanderi) and exposed volcanic rocks were studied.

Spectral data from grating position 3 (tree mode) were selected

from 25 ground positions for analysis by Principal Factor Analysis

(PFA). In this grating postion, as many as six factors were

identified as significant in contributing to spectral structure.

Channels 74 through 84 (tree mode) best characterized between-class

differences. Other channels were identified as non-discriminating and

as associated with such errors as excessive atmospheric absorption and

grating position changes.

The test site studied was relatively simple with the two species

( A__.tridentata and A. cana) representing nearly 95 percent of biomass

and with only two mineral backgrounds, a montmorillonitic soil and

volcanic rocks. If, as in this study, six factors of spectral

structure can be extracted from a single grating postion from data

acquired over a simple vegetation community, then AIS data must be

considered rich in information-gathering potential.

INTRODUCTION

Eight transects of AIS data were collected 30 August 1985 from

flights crossing Papoose Lake, a dry lakebed in central Oregon. A

typical data line traversed an A___.tridentata-dominated desert shrub

community on the approach to the lake, a sparse A. cana-dominated

shrub canopy on the lakebed of bright soil ( a montmorillonitic,

frigid, aquic palexeralf) and A_:.tridentata again on the overrun.

Exposed outcoppings of basalt ring the lakebed in the A_:.

tridentata-dominated community (Fig. I).

Ancillary data were collected coincidentally with AIS coverage.

NSOOI spectra, black and white 35mm photographs (Fig. I), and 9x9 inch

CIR photographs were collected aboard the NASA C-130, and large scale

natural 70mm natural color photographs were collected on 8 September

1985. A. cana biomass data were collected 27-28 August 1985 and

canopy-held water estimates were derived.

For preliminary investigations, AIS data from selected areas were

processed. Artificial targets on the test site were used in local

registration of features, and representative data sets were chosen.

Spectral traces, selected graymaps and files of raw radiance values

were generated for first-order inspection of the data.

Principal factor analysis has been used to characterize spectral

data (Murray 1983) and was the basis for the extraction and comparison

of information from AIS data. PFA is useful for isolating causal

187
0R_GINAL PAGE IS

or

Figure I. Enlarged 35mm photograph of Papoose Lake acquired during

NASA C-130 overpass. Scale is about I" = 500', and each square target

is the area-equivalent of two pixels.

("real") factors, marking unique sites, and indicating important

spectral variables; morever, most results can be presented

graphically.

SITE AND SPECTRAL DATA DESCRIPTIONS

AIS data from 25 ground sites were selected: six big sage and

four outcrop sites had low radiance values, seven silver sage sites

had intermediate radiance values, and seven bare soil sites were

highly reflective. These four classes span the reflectivity range for

surface features in the Papoose Lake area. The spectra of 4 to 22

pixels were averaged and the mean site radiance was used in subsequent

analyses. Big sage sites are more elevated and have dense canopies.

Silver sage sites are lower and are surrounded by bare soil. The soil

areas have standing water during spring run-off and dry to a highly

reflective surface. The 6-to-8 feet high rock outcrops have scree and

scattered vegetation at the foot. Letter designations for figures and

color designations for the accompanying slides are:

O = Outcrop(black), B = Big sage(blue), C = Silver sage(red), and

SL = Bare soil(green).

188
Outcrop spectra were selected from one flight line and from a

small area. The other sites were selected from any of three flight

lines, and because the overpasses were made under high cloud cover,

incoming solar radiation and ground shadows are sources of variation

in the recorded radiance. Moreover, no corrections were made for the

usual errors in AIS data, e.g., vertical and horizontial striping.

For visual comparison (Fig. 2) the spectra were equal-area normalized

(AIS 1985); however, for PFA the original, unaltered data were used

(Fig. 3). Only 32 channels in the 1.5-1.8 micrometer (tree mode)

region are used in the following PFA. Thus, the data matrix is a

25-row (sites) by 32-column (spectral variables) matrix.

PRINCIPAL FACTOR MODEL

In PFA, observed responses in a data matrix are modeled as an

optimized linear combination of n cofactors. For AIS data:

n

d(i,j)= Z r(i,k) x c(k,j); i=1,2, ... ,rows; j=1,2, ... ,columns.

I<=I

where d(i,j) is the response of the ith site in the jth channel. A

matrix row contains the spectra from one of r sites and a column

contains the spectral response in one of c channels over all sites.

Because of the variance optimizing design of PFA only n independent

factors will be needed to approximate the data matrix. In matrix

no tat ion:

[V"] = R(1)*C(1) + R(2)*C(2) + ... +R(n)*C(n) : [Rt']*[C"] (I)

where " denotes the n-factor approximation and * defines an outer

product. The cofactor R(i) is generated by projecting each row in the

original data matrix, [D], onto the eigenvector associated with the

Ith eigenvalue (ordered largest to smallest) computed from the

covariance matrix Z (=[D]T[D]). The cofactor C(i) is the ith column

eigenvector transposed. Thus,

[R"] : [D][Q"]; [C] : [Q,,]T; and [D"] : [R"][C"]

where columns of [Q"] contain eigenvectors corresponding to the n

largest eigenvalues of [Z]. The designations R and C are used to

emphasize that the R cofactors are associated with the row (site)

variance, and the C cofactors are associated with the column (spectral

channel) variance. No centering or standardization was applied prior

to forming the covariance matrix. Consequently, the R and C cofactors

are based on a "covariance about the origin" (a CO matrix) which also

is called a scatter matrix.

Measures for estimating n proposed by Malinowski (1977a; 1977b)

have been used previously for site-spectra matrices (Murray 1983).

The approximation [D"] is the matrix sum of R(i)*C(i) cofactors

[Equation (I)]. When n matrices have been summed, the remaining (c-n)

matrices (assuming r > c) will only add random error. Determining n

exactly requires a distribution of random measurement error rarely, if

ever, found in real data. Nevertheless, the estimates are useful and

easily computed.

RESULTS

In Figure 4, Malinowski's Indicator function is shown for the

25-site by 32-channel AIS data and, for comparison, the responses for

two matrices with known characteristics (Malinowski 1977b; data sets A

and C). For a well-behaved error distribution the Indicator function

189
2ZOO

bJ 2100

o

Z

(I

!-4

Q LO00

<Z

n,

0 900 LI,J

N

l-i

-,I

800

nO

_' 700

Black--Outcrop (0)

Blue--big sage (B)

Red--silver sage (C)

Green--bare soil (SL)

GO0

500 ERSAL

Oregon State University

400 1,986 !

60 70

, I , I , I

80 90 100

AIS CHANNEL(TREE)

Figure 2. Equal-area normalized spectra for Papoose Lake sites.

Wavelength and wavenumber scales are approximate. (See color

slide number ii in the back cover pocket of this publication.)

Ld

o

z

<E

i-i

Q

mr

1200

1100

2000

900

S00

700

GO0

500

400

300

200

100

GO

G..qOO GG01 G28S GOOi; VF.NUI48(RklPt 5747 1/04

1.45 1.52 1`5S 1`G7 t74

..... : .... : ..... " I_VE"LENG';H "'*" :

C

St.

g

C

o

E Blue--big sage (B)

Oregon State University Red--silver sage ICL) !986 Green--bare sola )

, I * I , I '

70 80 90

I

100

AZS CHANNEL

Figure 3. Original spectra for Papoose Lake sites. Letters

indicate general position of ground classes. Wavelength and

wavenumber scales are approximate. (See color slide number

12 in the back cover pocket of this publication.)

190
has a minimum at the correct n value. In the matrix of random numbers

only locally equal amounts of random variation are added with each

factor, and the error minimum occurs with the first factor. For AIS

data, the broad minimum implies unique factors for a single site or a

single channel, or large, isolated measurement errors, or all three.

For smaller eigenvalues, real factors are mixed with a singular

factor, and always with some error.

The combined RI*CI and R2"C2 approximations account for the major

differences in site radiance and in canopy characteristics. In Figure

5, the RI and R2 cofactors indicate separation between classes is

good. Re-examination of photographs indicates class overlap results

as much from inexact site location and description as from spectral

confusion. Based on the R3 plot, site 12 has a spectral response

differing significantly from the other silver sage sites. Also,

cofactor R8 has a strong pattern of variance associated with

individual sites rather than with classes.

Figure 6 is a plot of cofactors CI and C2 with two additional C3

values for channels 93 and 94. Because the square of an eigenvalue

component comprising a C cofactor is directly proportional to the

spectral variance spanned by the vector, Figure 6 depicts spectral

discrimination by channel where channels near the origin contain

little discriminating power. Of the three clusters located away from

the origin, cluster I ( channels 74 to 84) best discriminates the real

differences between classes. Clusters 2 and 3 are associated with

erroneous values caused by grating position changes {channels

65,66,95,96) and atmospheric absorption peaks (88-94). For these

latter channels, error is the superimposition of inexact radiance

values and possibly wavelength ranges on a sharply dropping wing of a

water absorption peak. In particular, the occurrence of dead cell

nearest-neighbor interpolation in channels 93 and 94 generates severe

bias in the averaged site spectral curve. As an example, 40 percent

of the variance spanned by C3 is associated with channels 93 and 94,

indicating the R3"C3 product ( cf. Fig. 4) is accounting primarily

for the errors caused by the dead cells in site 12 data. In Figure 2

the curve for site 12 is the left-most trace in region G, but the

other traces for silver sage sites are to the extreme right.

In Figure 7 a similar result is shown for channel 65 which

accounts for 39 percent of C5 variance. For channels 65 and 66 not

only do dead cells bias the spectral curves, but also there apparently

is a skidding effect of grating change: the response often is flat

for the first two or three channels. The C5 cofactor heavily weights

the channel 65 site responses in order to differentiate the flat

curves and the otherwise increasing radiance response (Fig. 2).

Additional analysis of real and error interactions in the first

nine principal factors indicates that in factors three through six, a

pattern of real factor and error mixing occurs, and factors numbered

greater than six are the result of noise in the AIS data. For

example, the R8"C8 matrix adds mostly small random adjustments to the

individual sites {Figs. 5 and 7). Six factors produce a better

representation of real site-spectra responses and are acceptable if

the influence of errors are recognized. The first two factors will

separate the four major cover classes. Additional between-class and

within-class differences are associated with the real parts of factors

three through six.

191
ORIGINAL

OF POBR

IND FUNCTION

Random

i our1234 56 78

__rriAIS Data

NUMBER OF FACTORS USED IN [R] [C] HODEL

Figure 4. Indicator Function responses. Indicator values

are proportionally correct but with different scales. Four

factor and random matrices are from Malinowski (1977b).

PAGE IS

QUALn'Y

RI SCALE

-2000 -4000

L i

0-I°

RI

0-2"

0-3"

0-4'

B-5,

B-7

B-10

B-9

B-8

B-6 (_R2

RI

C-11

C-15

C-14

C-17

C-I;

C-13

C-18

c-Ig

SL-16

SL-20

SL-21

SL-22

SL-23

SL-24

SL-25

R3 SCALE

-6000 -100 +100

• i I

i

R2

P

' 20 '

-i00 0 -i00 - +40

R2 SCALE R8 SCALE

Figure 5. Variation of selected R cofactors by site.

The arbitrarily scaled ordinate designates the class

(O=rock outcrop; B-big sage; C=silver sage; SL-soil )

and site n_mber. Note scele change_'- f._r R's.

C2

•. ; %, 85-87

-0.i

_go 96 '

o

0.3

.,)

C2

C1

J !

0,25

' "69

.67

C2

-0,3

Figure 6. Distribution of spectral channel weights fr(w_ column factors

CI, C2, and C3. The channel number denotes the position in the CI, C2, or

C3 plane. Show, in parentheses are the C3 values for channels 93 and 94.

0.4

0.2

•_ o

-0.2

-0.4

-0.6

.,-C5

65

Figure 7.

_" C5

_-._C8

i ,

70 75 80 85 90 95

AIS CHANNEL (TREE MODE)

Responses of cofactors C5 and C8 for 32-site data.

192
CONCLUSIONS

This preliminary study used a limited data set; nevertheless, the

results appear valid for other sites and other AIS spectral regions.

Therefore the following results are expected:

I. In spite of variable scene radiance, valuable information can be

extracted from the raw values (DNs). Note that this information is

lost in equal-area normalization (Figs. 2 and 3).

2. Spectral selectivity is concentrated in a small number of

wavelength bands (Fig. 2, region A). Identification of appropriate

channels will reduce computational requirements and facilitate

interpretation, including the assignment of spectral characteristics

to basic physical properties and chemical structure.

3. AIS channels at starting and stopping grating positions contain

excessive noise and should not be included in most analyses (Fig. 2,

regions E and D).

4. Measurements on the shoulders of strong absorbance peaks should be

examined with healthy skepticism. In particular, dead cell

interpolation can severely bias responses for a specific channel if

pixel spectra are averaged (Fig. 2, region G).

5. Because of real factor and error mixing, fine spectral details

(e.g., peak wavelength shifts, changes in slope) will require

extensive corrections, assumptions, and ancillary data. In the end,

the results still may be suspect.

6. Imaging spectrometer data contains more information than that of

broad-band sensors. Using only the first-order spectral responses of

AIS data will provide more spectral selectivity and subsequent site

discrimination of vegetated surface features than previous systems.

REFERENCES

AIS 1985. Airborne Imaging Spectrometer: Science Investigator Guide,

Jet Propulsion Laboratory, Pasadena, CA, Appendix III, p. 2-4.

Malinowski, E.R. 1977a. Theory of error in factor analysis, Analytical

Chemistry_ Vol. 49, 606-612.

Malinowski, E.R. 1977b. Determination of the number of factors and

experimential error in a data matrix, Analytical Chemistry_ Vol.

49, 612-617.

Murray, R. 1983. Abstract Factor Analysis of multispectral data, Proc.

Internat. Conf. Renewable Resource Inventories for Monitoring

Change and Trends_ 15-19 August 1983, Corvallis, Oregon, 348-252.

(College of Forestry, Oregon State University, Corvallis, OR)

193
APPENDIX i

ATTENDEES OF THE

SECOND AIS DATA ANALYSIS WORKSHOP

MAY 6-8, 1986

_q_F..OEDINGPAGE Bt.ANK NOT

195
Name

1 Abbott, Elsa

2 Abrams, Michael

3 Adams, Steven

4 Albee, Arden

5 Anderson, Jim

6 Arvidson, Ray

7 Balick, Lee

8 Banninger, Cliff

9 Barge, Lisa

10 Bolef, Larry

11 Cimino, JoBea

12 Cocks, Terry

13 Conel, Jim

14 Cox, Bill

15 Curlander, John

16 Curtiss, Brian

17 Eliason, Patricia

18 Elvidge, Chris

19 Evans, Carla S.

20 Evans, Diane

21 Feldman, Sandra C.

22 Gerstl, Sigfried

23 Goetz, Alexander

24 Green, Glen

25 Green, Robert O.

26 Hayashi, Joan

27 Herring, Mark

28 Heyada, Jan

29 Hlavka, Chris

30 Hoffer, Roger M.

31 Hollinger, Allan

32 Hoover, Gordon

33 Hunt, Gavin A.

34 Huntington, Jon

35 Hutsinpiller, Amy

36 Kahle, Anne

37 Klemas, Vic ._.._,

38 Koch, Barbara

39 Kohno, Itoshi

Representinq

JPL

JPL

JPL

California Institute of Technology

NASA/NSTL

Washington University

EG & G Energy Measurements Inc.

Research Center Joanneum, Austria

JPL

University of Colorado

JPL

CSIRO, North Ryde, Australia

JPL

Autometric

JPL

California Institute of Technology

University of Colorado

JPL

University of Maryland

JPL

University of Nevada, Reno

Los Alamos National Lab

University of Colorado

Washington University

Unocal, International Division

University of Hawaii

JPL

JPL

Ames Reseach Center

Purdue University

Moniteq Ltd., Canada

JPL

Open University, United Kingdom

CSIRO, North Ryde, Australia

University of Nevada, Reno

JPL

University of Delaware

University of Munich

ERSDAC, Japan

196
Name

40 Kowalick, William S.

41 Krohn, Dennis

42 Kruse, Fred

43 Lang, Harold

44 Lawrence, Geoff

45 Lee, James

46 Lee, Meemong

47 LeVine, Catherine

48 Lozano, Fabian

49 Lyon, Ron J. P.

50 Mahoney, Colin

51 Martin, Miki

52 Matson, Dennis

53 Maughan, Paul M.

54 Miller, Edward A.

55 Milne, Tony

56 Milton, Nancy

57 Molinari, Lou F.

58 Mouat, David

59 Mouginis-Mark, Pete

60 Munday, Tim J.

61 Murray, RJay

62 Mustard, John

63 Norris, Dave

64 Ocampo, Adriana C.

65 Paley, Helen

66 Palluconi, Frank D.

67 Paylor, Earnest D.

68 Pieters, Carle M.

69 Prelat, Alfredo

70 Press, Harry

71 Rand, Robert

72 Reimer, John

73 Ridd, Merrill

74 Riggs, GeorgeA.

75 Roberts, Dar

76 Rock, Barry

77 Running, Steven W.

78 Ruzek, Martin

Representinq

Chevron Oil Field Reseach Co.

USGS, Reston

USGS, Denver

JPL

BP Petroleum Development Ltd, England

BHP Central Research Lab, Australia

JPL

JPL

Purdue University

Stanford University

JPL

JPL

JPL

Space Development Systems

JPL

University of New South Wales, Australia

USGS, Reston

JPL

University of Nevada, Reno

NASA Code EEL

University of Durham, England

Oregon State University

Brown University

JPL

JPL

JPL

JPL

JPL

Brown University

Unocal, Reseach Division

JPL

USAETL

JPL

University of Utah

University of Montana

Stanford University

JPL

University of Montana

JPL

197
79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

Name

Samson, Scott A.

Satterwhite, Melvin

Schultz, Stan

Slater, Philip

Sleigh, Bill

Solomon, Jerry

Spanner, Michael

Staenz, Karl

Stanich, Chuck

Strahler, Alan

Taranik, James V.

Tucker, Deanne

Ustin, Susan

Vane, Gregg

Vural, Kadri

Walker, Richard

Walsh, Jerry

Wickland, Diane E.

Wood, Byron

Zak, Alex

Zall, Linda

Zamudio, Joe

Representincl

University of Nebraska, Lincoln

USAETL-RI

JPL

University of Arizona

JPL

JPL

Ames Research Center

University of Zurich-lrchel

Daedalus Enterprises

Hunter College

University of Nevada, Reno

JPL

University of California, Berkeley

JPL

Rockwell International Science Center

JPL

Great Falls, Virginia

NASA Code EEL

Ames Reseach Center

JPL

Bethesda, Maryland

University of Colorado

198
APPENDIX2

AGENDAOFTHE

SECONDAIS DATAANALYSISWORKSHOP

MAY6-8, 1986

199
The Second Airborne Imaging Spectrometer

Data Analysis Workshop

May 6, 7, and 8, 1986

Building 167 Conference Room

Jet Propulsion Laboratory

Pasadena, California

AGENDA

TUESDAY, MAY 6

8:00 Shuttle Bus Leaves Holiday Inn for JPL

8:30 Registration at JPL Visitor Control Center

9:00 WELCOMING REMARKS: Moustafa Chahine, 3PL Chief Scientist

9:15 THE NASA HEADQUARTERS PERSPECTIVE ON IMAGING

SPECTROMETRY: Diane Wickland, NASA Terrestrial Ecosystems

Program Manager

9:30 THE SISEX/HIRIS PROJECT: OVERVIEW AND STATUS: Alex Goetz,

University of Colorado

AVIRIS STATUS UPDATE: Gregg Vane, JPL

BREAK

AIS OPERATIONS IN 1985 AND PLANS FOR 1986: Gregg Vane, JPL

AIS DATA HANDLING AND ANALYSIS: Jerry Solomon, JPL

LUNCH

SESSION: CALIBRATION, THE ATMOSPHERE, DATA PROBLEMS AND

TECHNIQUES

1:00 RADIOMETRIC CALIBRATION OF THE AIRBORNE IMAGING

SPECTROMETER: Deanne Tucker and Gregg Vane, JPL

1:30 COMPARISON OF VARIOUS TECHNIQUES FOR CALIBRATION OF

AIS DATA: Dar A. Roberts, Stanford University, Yasushi Yamaguchi,

Geological Survey of Japan, and Ronald J. P. Lyon, Stanford University

20O

10:00

10:30

11:00

11:30

12:00
2:00

2:3O

3:00

3:3O

4:O0

4:3O

5:15

6:00

ANALYSIS OF AIS RADIOMETRYWITH EMPHASIS ON

DETERMINATION ATMOSPHERIC PROPERTIES AND SURFACE

REFLECTANCE: J. E.Cone[, S. Adams, R. E. Alley, G. Hoover, and S.

Schultz, JPL

BREAK

AIRBORNE SPECTRORADIOMETRY: THE APPLICATION OF AIS DATA

TO DETECTING OF SUBTLE MINERAL ABSORPTION FEATURES:

Terry D. Cocks and Andy A. Green, CSIRO, Australia

ATMOSPHERIC WATER ABSORPTION FEATURES NEAR 2.2 um AND

THEIR IMPORTANCE IN HIGH SPECTRAL RESOLUTION REMOTE

SENSING: Fred A. Kruse and Roger N. Clark, USGS, Denver

LINEAR TRANSFORMS AND IMAGING SPECTROMETRY ANALYSIS:

Jerry Solomon, JPL

DESTRIPING AIS DATA USING FOURIER FILTERING TECHNIQUES:

Chris Hlavka, NASA/Ames Research Center

Shuttle Bus Returns to Holiday Inn

Workshop Reception at the Holiday Inn

WEDNESDAY, MAY 7

8:00 Shuttle Bus Leaves Holiday Inn for JPL

SESSION: GEOLOGICAL RESEARCH

9:00 ABUNDANCE AND DISTRIBUTION OF MINERAL COMPONENTS

ASSOCIATED WITH MOSES ROCK (KIMBERLITE) DIATREME: John F.

Mustard and Carle M. Pieters, Brown University

9:30 COMPARISON OF THE 1984 AND 1985 AIS DATA OVER THE

SINGATSE RANGE (YERINGTON), NEVADA: R. J. P. Lyon,

Stanford University

10:00 IDENTIFICATION OF HYDROTHERMAL ALTERATION ASSEMBLAGES

USING AIRBORNE IMAGING SPECTROMETER DATA: Sandra C.

Feldman and James V. Taranik, University of Nevada-Reno

10:30 BREAK

11:00 DETECTION OF HYDROTHERMAL ALTERATION AT VIRGINIA CITY,

NEVADA, USING AIRBORNE IMAGING SPECTROMETRY: Amy

Hutsinpiller and James V. Taranik, University of Nevada-Reno

201
SESSION: BOTANICAL AND GEOBOTANICAL RESEARCH

11:30 ANALYSIS OF AIS DATA OF THE BONANZA CREEK EXPERIMENTAL

FOREST, ALASKA: Michael A. Spanner and David L. Peterson,

NASA/Ames Research Center

12:00 LUNCH

1:00 SOIL TYPES AND FOREST CANOPY STRUCTURES IN SOUTHERN

MISSOURI: A FIRST LOOK WITH AIS DATA: Glen M. Green and

Raymond E. Arvidson, Washington University

1:30 GEOBOTANICAL STUDIES AT PILOT MOUNTAIN, NORTH CAROLINA,

USING THE AIRBORNE IMAGING SPECTROMETER: N. M. Milton, P. A.

Walsh, and T. L. Purdy, U. S. Geological Survey, Reston

2:00 TRACE ELEMENT-INDUCED STRESS IN FRESHWATER WETLAND

VEGETATION: PRELIMINARY RESULTS: Byron L. Wood, NASA/Ames

Research Center, and Louisa H. Beck, University of California - Berkeley

2:30 BREAK

3:00 PRELIMINARY ANALYSIS OF FLUORESCENCE LINE IMAGER (FLI)

DATA ACQUIRED FOR FOREST DECLINE SITES IN VERMONT: Barry

Rock, JPL

3:30 PATTERNS OF VEGETATION IN THE OWENS VALLEY, CALIFORNIA:

Susan L. Ustin, University of California - Berkeley and University of

California- Davis, Barrett N. Rock, JPL, and Roy A. Woodward, University -

of California - Berkeley and University of California - Davis

4:00 FURTHER OBSERVATIONS ON THE SUCCESS OF AIS DATA FOR THE

GEOBOTANICAL DISCRIMINATION OF ROCK TYPES IN SOUTHWEST

OREGON: David Mouat, University of Nevada

4:30 AIS SPECTRA OF DESERT SHRUB CANOPIES: RJay Murray, Milne

Computer Center, Dennis L. Isaacson,William J. Ripple, Barry J. Schrumpf

and Anthony J. Lewis, Oregon State University

5:15 Shuttle Bus Returns to Holiday Inn

THURSDAY, MAY 8

8:00 Shuttle Bus Leaves Holiday Inn for JPL

202
SESSION: GEOLOGICAL RESEARCH (RESUMED)

9:00

9:30

10:00

10:30

11:00

11:30

12:00

1:00

5:15

DELINEATION OF SOIL BOUNDARIES IN CENTRAL NEBRASKA: Scott

Samson, University of Nebraska

PRELIMINARY GEOLOGICAL ANALYSIS OF AIS DATA AT MARY

KATHLEEN, QUEENSLAND, AUSTRALIA: Jon F. Huntington, Andy A.

Green, Maurice D. Craig and Terry D. Cocks, CSIRO, Australia

USE OF DIGITAL MUNSELL COLOR SPACE TO ASSIST INTERPRETATION OF IMAGING SPECTROMETRY DATA: GEOLOGIC

EXAMPLES FROM THE NORTHERN GRAPEVINE MOUNTAINS,

CALIFORNIA AND NEVADA: Fred A. Kruse, Daniel H. Knepper, Jr. and

Roger N. Clark, USGS, Denver

BREAK

APPLICATION OF AIS DATA TO STRATIGRAPHIC STUDIES, WIND

RIVER/BIGHORN BASIN, WYOMING: Harold Lang, Ernie Paylor, and

Jim Conel, JPL

NEAR INFRARED DETECTION OF AMMONIUM MINERALS AT IVANHOE

HOT SPRINGS, NEVADA: M. Dennis Krohn, USGS, Reston

LUNCH

SPAM Demonstrations at IPL

Shuttle Bus Returns to Holiday Inn

203
APPENDIX3

CATALOGOF TESTSITESFLOWNWITHAIS

(FROM12/82 THROUGH4/86)

PREenING PAGE BLAI_K NOT

205
CATALOG OF TEST SITES FLOWN WITH AIS

(FROM 12/82 THROUGH 4/86)

DATE

12/2/82

to

12/3/82

4/25/83

to

5/24/83

8/5/83

to

8/15/83

10/18/83

10/20/83

10/21/83

10/27/83

10/27/83

10/28/83

10/28/83

10/28/83

10/30/83

4/25/84

4/25/84

4/27/84

4/27/84

4/27/84

5/05/84

SITE NAME

Mammoth, CA

Long Valley, CA

Mud Lake, NV

Goldfield, NV

Cuprite, NV

Pico Anticline, CA

Mt. Pass, CA

Mud Lake, NV

Cuprite, NV

Death Valley, CA

Mt. Pass, CA

Silver Bell, AZ

Imperial Valley, CA

Los River, W VA

Klamath Forest, CA

Josephine County, OR

Cuprite, NV

Boreal Forest, MN

Sleeping Bear Dunes, MI

Saginaw Forest, MI

Konza Prairie, KS

Lost River, W VA

Washington N.F., VA

Mineral, W VA

Klamath N. F., CA

Slate Belt, NC

Vermont, NH

New Hampshire

Virginia

Slate Belt, NC

Virginia

Lost River, W VA

Virginia

Wind River, WY

Cuprite, NV

Goldfield, NV

Death Valley, CA

Jasper Ridge, CA

Stanford, CA

Virginia

INVESTIGATOR

Gillespie

Gillespie

Goetz

Goetz

Goetz

Rock

Kahle

Goetz

Goetz

Kahle

Kahle

Goetz

Rock

Rock

Strahler

Rock

Goetz

Pitts

Olson

Olson

Blad

Rock

Labovitz

Labovitz

Strahler

Wickland

Vogelmann

Vogelmann

Bell

Wickland

Labovitz

Rock

Masuoka

Lang

Goetz

Adams

Kahle

Mouat, Peterson

Mouat

Bell

206
Date Site Name Investigator

5/O5/84

5/O9/84

5/11/84

Virginia

Slate Belt, NC

Slate Belt, NC

Masuoka

Wickland

Wickland

7/10/84

7/10/84

7/10/84

7/10/84

Cuprite, NV

Owens Valley, CA

Death Valley, CA

Sequoia, CA

Goetz

Rock

Kahle

Peterson

7/18/84

7/19/84

7/20/84

Boreal Forest, MN

Sleeping Bear, MI

Monument Valley, UT

Pitts, Star

Olson

Pieters

7/25/84

7/25/84

7/25/84

7/26/84

7/26/84

7/26/84

7/31/84

Ubehebe& Owlshead, NV

Hot Creek, NV

Yerington, NV

Oregon Transect

Josephine Cty, OR

Josephine Cty, OR

Stockton, CA

Raines

Feldman

Lyon

Peterson

Mouat

Gillespie

Wrigley

8/07/84

8/10/84

8/16/84

8/17/84

9/07/84

9/08/84

9/09J84

9/09/84

10/30/84

10/30/84

10/31/84

10/31/84

10/31/84

Sand Hill, NE

Purdue, IL

Delaware Bay, DL

Vermont, NH

Blackhawk Island, Wl

Vermont

PowderRiver, WY

Wind River, WY

Owens Valley, CA

Cuprite, NV

Mono Lake, CA

Bridgeport Basin, CA

Sierra Nevada, CA

Blad

Vanderbilt

Klemas

Rock-Vogelmann

Peterson

Rock-Vogelmann

Dykstra, Segal

Lang

Rock, Ustin

Goetz, Vane

Conel

Gillespie

Gillespie

4/2/85

4/2/85

Owens Valley, CA

Cuprite, NV

Rock, Ustin

Goetz, Vane

5/22/85

5/22/85

5/23/85

Owens Valley, CA

Jasper Ridge, CA

Owens Valley, CA

Rock, Ustin

Mouat

Rock, Ustin

5/23/85 Cuprite, NV Goetz, Vane

5/23/85 Grapevine, NV Kruse

6/16/85

6/16/85

Cement, OK

Sand Hills, NB

Settle, McKeon

Blad

2O7
Date Site Name Investigator

6/18/85

7/2/85

7/24/85

7/24/85

7/25/85

7/25/85

7/26/85

7/27/85

7/30/85

7/31/85

8/2/85

8/2/85

8/6/85

8/9/85

8/11/85

8/11/85

8/12/85

8/12//85

8/13/85

8/13/85

8/23/85

8/28/85

8/28/85

8/30/85

8/30/85

9/23/85

9/23/85

9/23/85

9/23/85

9/23/85

9/23/85

10/1/85

10/1/85

10/1/85

10/1/85

10/1/85

10/1/85

10/2/85

10/2/85

Sand Hills, NB

Virginia Range, NV

Ivanhoe, NV

Carlin, NV

Ruby Range, MT

Little Falls, ID

Wind River, WY

Monument Valley, UT

Marysvale, UT

Lake City, CO

Ozark Mtns., MO

Purdue, IN

Blackhawk Island, WI

Oak Ridge, TN

Pilot Mtn, NC

Ringwood, NC

Vermont

New Hampshire

Virginia

Central Virginia

Bonanza, Alaska

Bonanza, Alaska

Poker Caribou, Alaska

Oregon Transect, OR

Squaw Butte, Oregon

Gustine, CA

Sequoia, CA

Cuprite, CA

Mono Lake, CA

Yerington, NV

Virginia Range, NV

AUSTRALIA

Temora

Junction Creek

Dicks Creek

Yarralaw

Braidwood

Goulburn

Wycanna

Inverell

Blad

Feldman

Krohn

Podwysocki

Rowan

Gillespie

Lang

Pieters

Podwysocki

Lee

Arvidson

Vanderbilt

Peterson, Aber

Balick

Milton

Bell

Rock

Rock

Masuoka

Bell

Peterson

Peterson

Peterson

Peterson

Schrumpf

Peterson

Peterson

Vane

Conel

Lyon

Feldman

Laughton

Logan

Gallaway

Williams

Atkinson

Kalma

Jupp

Atkinson

208
Date Site Name Investigator

10/3/85

10/3/85

10/7/85

10/7/85

10/7/85

10/7/85 1O/7/85

10/8/85

10/8/85

10/8/85

10/8/85

10/1 O/85

10/10/85

10/10/85

10/11/85

10/11/85

10/11/85

10/11/85

10/14/84

10/14/85

10/15/85

10/15/85

10/15/85

10/15/85

10/15/85

10/16/85

10/16/85

10/16/85

10/19/85

10/19/85

10/19/85 I 0/19/85

10/21/85

10/21/85

10/21/85

10/21/85

10/21/85

Puckapunyal

Adelaide

Loxton

Broken Hill

Fowlers Gap

Wilkawillina

Pine Creek

Cascades

Norseman

St. Ives

Yindarlgooda

Murrin Murrin

Agnew

Skull Creek

Dangin

York

Yalanbee

Dwellingup

Jillawarra

Port Hedland

Sundown

Blina

Halls Creek

EllendaleB

EllendaleA

Munni Munni

Coppins Gap

Broadhurst

Mount Isa

Mary Kathleen

Gorge Creek

Phosphate Hill

Palm Valley

Stairway

Allambi-Amadeus

Riddock-Mordor

Hatches Creek

Morgan

Douglas

Douglas

O'Sullivan

Milne

Huntington

Hussey

Houghton

Burgess

Simpson

Burgess

Huntington

Gregory

Burgess

Hick

Hick

Hick

Houghton

Huntington

Morgan

Williams

Williams

Otani

O'Sullivan

O'Sullivan

Simpson

Huntington

Swarbrick

Simpson

Huntington

Simpson

Simpson

Simpson

Simpson

Taylor

Horsfall

Horsfall

209
Date Site Name Investigator

10/22/85

10/22/85

10/22/85

1O/22/85

10/22/85

Kidston

Bald Mountain

Newcastle Range

Chillagoe

Cairns

Logan

Gabell

Maffi

Otani

Kelly

10/24/85

10/24/85

Townsville

Burdekin River

Morgan

Pearce

10/26/85

1O/26/85

10/26/85

10/26/85

1O/26/85

Mt. Leyshon

Burdekin River

Bimurra

Plateau

Newcastle Range

Fraser

Pearce

Maffi

Munday/Swarbrick

Maffi

10/28/85

10/28/85

Mt. Leyshon

Gregory

Fraser

Gray

11/1/85

11/2/85

11/3/85

11/7/85

Hawaii

Hawaii

Hawaii

Hawaii

Kahle

Kahle

Kahle

Kahle

11/9/85 Kahoolawe, Hawaii Elliot

210
APPENDIX 4

LIST OF SLIDES AND FIGURE TITLES

Slide No.

. Cocks and Green, AIRBORNE SPECTRORADIOMETRY: THE APPLICATION

OF AIS DATA TO DETECTING SUBTLE MINERAL ABSORPTION FEATURES

(Figure 2, page 58) AIS Imagery in bands Ii and 29, showing

horizontal striping.

Q Cocks and Green

(Figure 5, page 60) Density-sliced radiance and log. residual

spectra for a variety of processing conditions_

o Mustard and Pieters, ABUNDANCE AND DISTRIBUTION OF MINERAL

COMPONENTS ASSOCIATED WITH MOSES ROCK (KIMBERLITE) DIATREME

(Figure 6, page 85) Deconvolution of AIS spectra into mineral

abundance images (F-parameter for endmembers).

. Mustard and Pieters

(Figure 7, page 85) Deconvolution of AIS spectra into mineral

abundance images (F-parameter for endmembers).

o Huntington, Green, Craig, and Cocks

PRELIMINARY GEOLOGICAL INVESTIGATION OF AIS DATA AT MARY

KATHLEEN, QUEENSLAND, AUSTRALIA

(Figure I0, page 128) Detail of "fanned" log residuals for the

1.96 -2.4 micrometre window illustrating the C02 bands at 2.00

and 2.06 micrometres, and broad, intense absorption features

at 2.28 micrometres (for example, between lines 667 and 943).

o Kruse, Knepper, and Clark

USE OF DIGITAL MUNSELL COLOR SPACE TO ASSlST INTERPRETATION

OF IMAGING SPECTROMETER DATA--GEOLOGIC EXAMPLES FROM THE

NORTHERN GRAPEVINE MOUNTAINS, CALIFORNIA AND NEVADA

(Figure 3, page 135) Thematic mineralogical images (note that

these images consist of two 32-pixel flightlines concatenated to

form a single 64-pixel-wide image) and thematic map interpreted

from the image and individual spectra. A, Overlay of pixels

identified as carbonates (white) on a single band image;

B, overlay of pixels identified as clay (white) on a single

band image; C, Map interpreted from images A and B and

individual spectra.

211
. Green and Arvidson

SOIL TYPES AND FOREST CANOPY STRUCTURES IN SOUTHERN

MISSOURI: A FIRST LOOK WITH AIS DATA

(Figure 2A, page 154) August, 1982, TM band 4 images of the

eastern half of the Rolla Quadrangle.

. Green and Arvidson

(Figure 3, page 156) August, 1985 band 4 image of the detailed

study area. (-25km wide) The white line marks the

AIS flightline.

. Ustln, Rock, and Woodward

PATTERNS OF VEGETATION IN THE OWENS VALLEY, CALIFORNIA

(Figure 2, page 182) Above, a 3-color composite of the scene in

Figure I, after removal of most vertical striping. Below, the

8-band maximum likelihood classification map.

i0. Ustin, Rock, and Woodward

(Figure 4, page 184) Alluvial fan region depicting an

unsupervised 8-band maximum likelihood classification map before

destriping (top), a 3-band color composite (middle) after

removal of vertical striping, and the maximum likelihood

classification of the region before destriping (middle) and

after destriping (bottom).

Ii. Murray, Isaacson, Schrumpf, Ripple, and Lewis

AIS SPECTRA OF DESERT SHRUB CANOPIES

(Figure 2, page 190) Equal-area normalized spectra for Papoose

Lake sites. Wavelength and wavenumber scales are approximate.

12. Murray, Isaacson, Schrumpf, Ripple, and Lewis

(Figure 3, page 190) Original spectra for Papoose Lake sites.

Letters indicate general position of ground classes. Wavelength

and wavenumber scales are approximate.

212