Thermal Infrared Multispectral Scanner (TIMS) data were acquired over the McDowell Mountains northeast of Scottsdale, Arizona during August 1994. The raw data were processed to emphasize lithologic differences using a decorrelation stretch and assigning bands 5, 3, and 1 to red, green, and blue, respectively. Processed data of alluvium flanking the mountains exhibit moderate color variation. The objective of this study was to determine, using a quantitative approach, what environmental variable(s), in the absence of bedrock, is/are responsible for influencing the spectral properties of the desert alluvial surface

Christensen, Philip R.

Hamilton, Victoria E.

Wenrich, Melissa L.

English

NASA Technical Reports Server

N95- 33800
A FIELD- AND LABORATORY-BASED QUANTITATIVE ANALYSIS OF ALLUVIUM:
RELATING ANALYTICAL RESULTS TO TIMS DATA
Melissa L. Wenrich, Victoria E. Hamilton, and Philip R. Christensen
Department of Geology, Arizona State University
Tempe, Arizona 85287-1404
1. INTRODUCTION
Thermal Infrared Multispectral Scanner (TIMS) data were acquired over the
McDowell Mountains northeast of Scottsdale, Arizona during August 1994. The raw data
were processed to emphasize lithologic differences using a decorrelation stretch and
assigning bands 5, 3, and 1 to red, green, and blue, respectively (Gillespie et al., 1986).
Processed data of alluvium flanking the mountains exhibit moderate color variation. The
objective of this study was to determine, using a quantitative approach, what
environmental variable(s), in the absence of bedrock, are responsible for influencing the
spectral properties of the desert alluvial surface.
2. ANALYTICAL METHODS AND RESULTS
2.1 FIELD
To quantify the surface properties of the alluvium, two linear field traverses
were defined along which data were collected at individual points and appropriately
averaged. By averaging the data, the effect of small-scale surface variability is removed,
thus the dam better reflect the overall character of the surface. The positions of the
transects were defined to cross alluvial surfaces displaying the maximium color variation
in the TIMS image. The boundaries of the adjacent distinctly colored areas are washes,
as verified during fieldwork. Large-scale vegetation such as creosote, palo verde,
saguaro, and low scrub constitutes 10-15% of the surface area surrounding the transecks.
Because this value applies to both transect locations, macro-vegetation was ruled out as a
controlling factor for the variation in the TIMS data. At twenty-foot intervals along the
transects, three-foot square sample sites were examined and rock samples were collected
for laboratory spectral analysis. Prior to sample collection at each site, the areal
percentage of soil and the percentage of each rock type were quantified (Figures 1 and 2).
The average percentage of soil over the cntire length of transects 1 and 2 (ignoring the
data points within washes) is 11.8% and 0.8%, respectively. When sample points within
a given color unit are averaged, the soil percentage deviates from the average by a
maximum of 2.9% (transect 1) and 0.6% (mmsect 2); these values are too small to
account for the image color variation along each transect. Two dominant rock types were
identified as various quartzites and phyllitic schists. Figures 2a and 2b display the
systematic variation in rock type percentage along each transect. Transect 1 is segmented
by two washes and transect 2 is segmented by one wash. For each transect segment the
average percentage of rock compositions was calculated. From the first segment (A) to
the last segment (C) of transect 1, the percentage of qu:krtzite increases from 20.6% to
66. 1% as the percentage of phyllitic schist decreases from 58.6% to 19.1%. A similar
trend occurs along transect 2, along which the percentage of quartzite increases from
1.4% to 70.2% as the percentage of phyllitic schist decreases from 97.8% to 26.8%. The
shifts in dominant mineralogy correlate well to the areas defined by the TIMS colors,
suggesting that the rock composition of the surface alluvium determines the TIMS
spectral signature and swamps the effects of vegetation or soil.
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https://ntrs.nasa.gov/search.jsp?R=19950027379 2020-06-16T06:58:08+00:00Z
2.2 LABORATORY
Thermal infrared spectra of nineteen rock samples were acquired in emission
over the range of 1400 to 450 cm "1 (-7 to 22 I.tm) using a modified Mattson Cygnus 100
interferometer/spectrometer. These vibrational spectra were separated into two groups
based on the most apparent distinction which is overall absorption feature depth.
Subsequently, these two groups were found to correspond to the two general rock types
identified in the field. The nine quartzite and eleven schist laboratory spectra were
averaged to produce a representative endmember spectrum for each rock group; the
resulting quartzite and phyllitic schist spectra are shown in Figure 3. The spectrum of the
average quartzite exhibits deep absorptions at frequencies similar to those of quartz,
suggesting a quartz composition with minor impurities. The spectral positions and shapes
of the average phyllitic schist absorptions suggest that the schists are silicic and similar to
the quartzites; however, the absorptions are 45% shallower than the quartzite features and
the schist spectrum exhibits an additional feature within the TIMS wavelength range at
1040 cm -1 (9.6 l.tm) which was determined by normalizing the two average endmember
qu_tzite and phyllite spectra over the range of 770 to 750 cm "t (~ 13.0 to 13.3 p_m) and
differencing them. The difference spectrum (Figure 3) is indicative of clay mineralogy.
No further spectral analysis was performed to identify the specific type of clay. The
compositional dissimilarity between the schists and the quartzites provides the basis for
distinguishing the alluvial surfaces in the TIMS data. The overall spectral similarity of
the rock samples prompted thin section preparation and compositional analysis to verify
the presence of clay in the phyllitic schists. We anticipate results of thin section analysis
by the time of the conference.
3. CORRI._LATION OF TIMS DATA WIT1! FIELD AND LABORATORY RESULTS
,3.1 PROCEDURE
Spectral endmembers derived from 77MS image
Quartzite and phyllitic schist endmember compositions of the alluvial rock suite
(Figure 4) were selected from the digital TIMS image using the bedrock compositions
nearest the transects. We have assumed that the primary sources of the alluvial materials
are proximal because the rock fragments are angular. To evaluate the method of mixing
endmember spectra derived from TIMS data and determine accurate areal percentages of
mixed pixcls, endmember spectra derived from the TIMS image were linearly summed
according to Ramsey and Christensen [1992] using the average percentage of quartzite
and phyllitic schist observed for each distinctly colored area along the transects. The
resultant mixed spectra were then compared to spectra averaged over 4 by 4 pixel
squm'cs for each distinct color area in the TIMS image along the transects.
The mixed spectra derived from the TIMS endmembers have emissivities that
plot within 2% of the spectral emissivities derived directly from the transect segments m
the TIMS image. This degree of accuracy suggests that the rock percentages were
correctly quantified in the field.
Spectral endmembers derived from laboratory analysis
Laboratory spectra were linearly mixed [Ramsey and Christensen, 1992]
according to the average rock percentages observed for each segment along the transects.
Because the laboratory spectra were acquired at 4 cm -1 resolution, the mixed spectra were
dcconvolved to TIMS resolution (Figure 5).
Subsequent comparison of the lab spectra with TIMS spectra of the same alluvial
areas shows two primary differences. Firstly, the emissivities of the TIMS spectra are
greater than .96, whereas the laboratory spectral emissivities are as low as .72. Secondly,
quartzite-dominated spectra in both data sets exhibit a distinct quartz absorption feature in
band 3; however bands 1, 5, and 6 in the TIMS data show additional absorptions not
present in the laboratory spectra. We believe these additional TIMS features do not result
from vegetation (assuming unit emissivity) but may be due to absorptions from a soil
component which was not included as a laboratory endmember.
40
4. CONCLUSIONS
In order to determine the effects of vegetation, soil, and rock mineralogy on the
thermal emission spectral properties of two alluvial surfaces flanking the McDowell
Mountains, AZ, a quantitative survey of the areal extent of these environmental variables
was conducted. Our study suggests that vegetation and soil do not strongly affect the
TIMS image and that the composition of rock fragments is the primary control on the
spectral signature. The two main lJthologies present along the field transects are
quartzites and phyllitic schists derived from separate upslope bedrock sources. Effective
mixing of these lithologies is inhibited by washes that intersect the transects and act as
natural barriers to talus migration. Washes in the field area correlate with the boundaries
of the various colored areas in the TIMS data confirming that mixing is inhibited and that
TIMS color differences result from dominant rock type. Mixing TIMS-derived
endmember (bedrock) spectra according to the average percentage of each rock type for
the five transect segments produced spectral emissivities which lie within 2% of the
emissivities derived from the same transect areas in the TIMS image.
NOTE: A color slide of the processed TIMS data of the field area may be obtained from
the authors by request.
5. ACKNOWLEDGEMENT
We would like to thank the Ames Research Center C-130 crew for allowing us
to participate in the overflights. We greatly appreciate being so closely involved during
the data collection.
6. REFERENCES
Gillespie, A.R., A.B. Kahle, and R. Walker, 1986, "Color enhancement of highly
correlated images: I. Decorrelation and HSI contrast stretches", Remote Sensing
Environ,. vol. 20, pp. 209-235.
Ramsey, M.S. and P.R. Christensen, 1992. "Ejecta patterns of Meteor Crater, Arizona
derived from the linear un-mixing of TIMS data and laboratory thermal emission
spectra", in Proc. of the Third TIMS Workshop: Abbott, E.A., ed., JPL PuN. 92-14,
JPL, Pasadena, Calif., pp. 34-36.
2-
-10
--'-(2--- Transect l
30 -----!!--- Transect 2
20
o 100 200 300 400 500
Transect Dist. (ft)
Figure 1. Areal percentage of soil at each sample site along transects 1 and 2.
41
Tr:nseet I
1O0 I ' i::: _ i:
:0il
0 100 200 300
% Quartzite
% Phylliti¢ Schist
&
Transect Dist. (ft)
400 5OO
lo0
80
60
Trans¢ct 2
=0
0 25 50 75 i00 125 l-CO 175 200
Trc*ns¢ct Dist. (It)
Figure 2. Areal percentage of rock type at each sample site along transect 1 (a) and
transect 2 Co). Stippled areas represent data points acquired in washes.
,-
! , I ]
' I L -i
,J ,, L r
12e_ _ooo soo _c,o
Figure 3. Averaged laboratory quartzite, phyllitic schist, and differenced clay spectra.
TIMS bands are labeled I-6.
°
'i
s, '
, _ , _°, _
- /
J') I-'- ' \_', 4/'
4
_[. , , , i , , : , , :oleo
Figure 4. Quartzite and phyllitic schist endmember spectra derived from TIMS data.
Bedrock proximal to the transects was used.
Figure 5.
S
3 [ ""
v 1-
,i
]
,[ 1
_'4II t I,)o iol Ill _.ii
Mixed laboratory spectra deconvolved to TIMS resolution.
42


A field- and laboratory-based quantitative analysis of alluvium: Relating analytical results to TIMS data

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