The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures reflected light in 224 contiguous spectra bands in the 0.4 to 2.45 micron region of the electromagnetic spectrum. Numerous studies have used these data for mineralogic identification and mapping based on the presence of diagnostic spectral features. Quantitative mapping requires conversion of the AVIRIS data to physical units (usually reflectance) so that analysis results can be compared and validated with field and laboratory measurements. This study evaluated two different AVIRIS calibration techniques to ground reflectance: an empirically-based method and an atmospheric model based method to determine their effects on quantitative scientific analyses. Expert system analysis and linear spectral unmixing were applied to both calibrated data sets to determine the effect of the calibration on the mineral identification and quantitative mapping results. Comparison of the image-map results and image reflectance spectra indicate that the model-based calibrated data can be used with automated mapping techniques to produce accurate maps showing the spatial distribution and abundance of surface mineralogy. This has positive implications for future operational mapping using AVIRIS or similar imaging spectrometer data sets without requiring a priori knowledge

Dwyer, John L.

Kruse, Fred A.

English

NASA Technical Reports Server

N95- 23870
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THE EFFECTS OF AVIRIS ATMOSPHERIC CALIBRATION
ON IDENTIFICATION AND QUANTITATIVE MAPPING
MINERALOGY, DRUM MTNS., UTAH
METHODOLOGY
OF SURFACE
Fred A. Kruse !, 2 and John L. Dwyer 2, 3
ICenter for the Study of Earth from Space (CSES), Cooperative Institute for Research in
Environmental Sciences (CIRES), University of Colorado, Boulder
Campus Box 449, Boulder Colorado 80309-0449
2Department of Geological Sciences, University of Colorado, Boulder
3Hughs STX Corporation, Sioux Falls, South Dakota
1. INTRODUCTION
The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures reflected light in
224 contiguous spectral bands in the 0.4 to 2.45 lain region of the electromagnetic spectrum
(Porter and Enmark, 1987). Numerous studies have used these data for mineralogic identification
and mapping based on the presence of diagnostic spectral features. Quantitative mapping requires
conversion of the AVIRIS data to physical units (usually reflectance) so that analysis results can
be compared and validated with field and laboratory measurements. This study evaluated two
different AVIRIS calibration techniques to ground reflectance; an empirically-based method and an
atmospheric model based method to determine their effects on quantitative scientific analyses.
Expert system analysis and linear spectral unmixing were applied to both calibrated data sets to
determine the effect of the calibration on the mineral identification and quantitative mapping
results. Comparison of the image-map results and image reflectance spectra indicate that the
model-based calibrated data can be used with automated mapping techniques to produce accurate
maps showing the spatial distribution and abundance of surface mineralogy. This has positive
implications for future operational mapping using AVIRIS or similar imaging spectrometer data
sets without requiring a priori knowledge.
2. The Drum Mountains Site
The Drum Mountains, located in the semi-arid terrain of west-central Utah were selected
for study because of the good exposure of diverse rock types and alteration mineralogy. The
detailed geology of the area is well documented (Baily, 1974; Lindsey, 1979). Extensive amounts
of remotely sensed imagery and ground-based data including ground spectral measurements have
been collected from all rock units and alteration types, and many rock and soil samples have been
analyzed in the laboratory. Rocks exposed in the study area include limestones, dolomites, and
shales which overlie a heterogeneous sequence of quartzite and argillite. Volcanic rocks,
hydrothermally altered in places, occur in fault contact and overlying the sedimentary units and
some carbonate rocks adjacent to the volcanics have been bleached and recrystallized. Contact
metamorphism has resulted in development of calcsilicate mineralization in the central part of the
study area.
3. Calibration of AVIRIS data
3.1 Empirical Line Method Calibration
The Drum Mountains AVIRIS data were calibrated to apparent reflectance using the empirical line
method (Roberts et al., 1985). AVIRIS radiance spectra for ground targets were used in a linear
regression to calculate coefficients to linearly transform the aircraft spectra to match the ground
spectra. This method requires a priori knowledge in the form of field measurements, which were
acquired for both light and dark ground targets. Gains and offsets were calculated to force the
AVIRIS spectra to match the field spectra. These gains and offsets were then used to calibrate the
Drum Mountains AVIRIS data to apparent reflectance.
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3.2 Atmospheric Model-based Calibration
The other calibration technique applied to the Drum Mountains AVIRIS data involved the use of a
radiative transfer model-based technique (Gao and Goetz, 1990). Apparent reflectance spectra were
first obtained by dividing each AVIRIS spectrum by the solar irradiance curve above the
atmosphere. A number of theoretical water vapor transmittance spectra for the 0.94 and 1.1 pan
water vapor bands were calculated for varying amounts of atmospheric water vapor using an
approximate radiative transfer code called "Simulation of the Satellite Signal in the Solar Spectrum
(5S)" (Tanre et al., 1986) and the Malkmus (1967) narrow band spectral model. The modeled
spectra are run through the three-channel ratioing method to generate a lookup table of water vapor
concentrations that can be used to convert the AVIRIS apparent reflectance measurements to total
column water vapor. The output of this procedure is an image showing the spatial distribution of
various water vapor concentrations as derived for each pixel of the AVIRIS data. The water vapor
image is then used along with transmittance spectra derived for each of the atmospheric gases CO2,
03, N20, CO, CH4, and 02 using the Malkmus narrow band model and 5S model to produce
scaled surface reflectance (Gao and Goetz, 1990). This model-based technique produces total water
vapor column images and reflectance calibrated AVIRIS data without a priori knowledge.
4. Comparison of Calibration Results and Scientific Validation
4.1 Reflectance Spectra
The results of the two calibration techniques described above were compared by extracting
reflectance spectra firom calibrated AVIRIS images and comparing with field and laboratory spectra.
Figure 1 shows a field spectrum compared to AVIRIS spectra extracted from both the empirical
line (EL) calibrated and atmospheric model (AM) calibrated data. Note that the EL AVIRIS
spectrum matches the position and shape of the laboratory spectrum (particularly at 2.2 tim) more
closely than the AM AVIRIS spectrum. The EL spectra, also were consistently brighter than the
AM spectra and absorption features near 0.9 and 2.2 }am in the AM spectrum were consistently
suppressed as compared to the EL spectrum. These general characteristics were observed for several
different areas using the two AVIRIS calibration methods. Initially, the suppression of the 0.9
lain feature was attributed to model-based calibration error caused by the assumption made in the
AM method that most materials are spectrally flat in the 0.9 region. Subsequently, the AVIRIS
data were recalibrated using only the 1.14 lain water band and the resulting spectra were virtually
indistinguishable. Thus, the suppression of the 0.94 lma band must be attributed to some other
cause. In fact, it appears that perhaps it is the EL method that is enhancing the absorption bands
rather than the AM method suppressing them.
4.2 Automated Feature Extraction and Expert System Analysis
Thematic image products were generated showing the distribution of major mineral
constituents for the Drum Mountains site using an expert system approach. The expert system is
an absorption feature based technique that uses rules built from a spectral library to identify
unknown minerals in imaging spectrometer data (Kruse et al., 1993). The success of this approach
depends on having high-quality, well calibrated spectral data. Once the data are properly calibrated,
the procedure is to treat each pixel individually and sequentially to remove a continuum
(normalize), extract the features, and to compare the features found in the AVIRIS data to the
feature rules. The result of these analyses is a new data cube consisting of a single image for each
endmember showing the degree of match to the rules (Kruse et al., 1993).
Image maps derived from both the EL and AM calibrated AVIRIS data using the expert
system techniques were compared to conventional geologic maps for the Drum Mountains site
(Bailey, 1974, Lindsey, 1979). In general, these images showed good correspondence to known
mineralogy for distinct lithologic units. For example, areas of dolomite could be easily
distinguished from the Prospect Mountain Quartzite, which contains primarily clay minerals. In
addition, previously unmapped mineralogical variation was mapped within the different units.
While the details of these image maps has not been verified in the field, samples from selected
units (Tremper, 1991) indicate that to the first order the mineralogy being mapped using the
imaging spectrometers is accurate. Limitations are likely related to the relatively low signal-to-
noise-ratios of the 1990 AVIRIS data. Very similar mineralogical results were obtained using
both the EL and the AM calibrated data. The expert system results from the AM data were
102
Figure1. Comparison of an EL calibrated and AM calibrated spectrum for the
same pixel to a field spectrum. Portions of the AVIRIS spectra in the
atmospheric water bands have been masked.
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noticeably noisier, however, as would be expected based on the previous descriptions of how this
calibration suppresses absorption features.
4.3 Linear Spectral Unmixing
Spectral mixing is a consequence of the mixing of materials having different spectral
properties within the ground field-of-view (GFOV) of a single image pixel. Boardman (1992)
addressed the mixing problem using singular value matrix decomposition (SVD) to linearly unmix
AVIRIS data. This technique assumes that most mixing is on the macroscopic scale, and thus
linear. To the first order, this model appears to adequately represent the surface geologic conditions.
Linear spectral unmixing was applied to each of the calibrated Drum Mountains AVIRIS
data sets using endmember spectral libraries derived from the two calibrated data sets using the
expert system. The output of the unmixing process was an image data cube of endmember
abundances, an abundance sum image, and a root-mean-square (rms) error image. Interactive
analysis of these images showed that each library explained most of the spectral variation in the
Drum Mountains AVIRIS data in its respective calibrated data set. In general, similar results were
obtained using both the EL and the AM calibrations. The locations and relative abundances of
specific minerals were very similar (see Slide 4). There were some obvious differences between
the images. For example, the two "illite" images show differences in absolute abundances. The
EL image shows a crescent shaped area of high concentrations (yellow) along the right edge of the
image. The AM image shows a similar shaped area, however, the concentrations are somewhat
lower (purple and blue). These differences are attributable to degeneracy of the AM spectral
endmember library caused by increased similarity of spectra by suppression of spectral features.
5. DISCUSSION AND SIGNIFICANCE OF THE RESULTS
Comparison of reflectance spectra and derived thematic maps from the empirical- vs
model-based calibrations demonstrated that both data sets contained basically the same
mineralogical information about the ground surface. The empirical line method, however,
emphasized mineral absorption features, thus it was better suited to analysis using absorption-
feature-based techniques. In particular, more consistent results were obtained when this reflectance
data was analyzed using an expert-system approach. While similar endmember spectra were
103
identified using the atmospheric model, suPl_ssion of tbe absorption bands in the AM calibrated
data (relative to the EL data) caused less effective mapping of spectral variation. Once unique
endmembcr spectra were identified, however, linear spectral unmixing worked equally well on both
calibrated data sets, producing very similar results.
The significance of this research is that it demonstrates that a model-based calibration
method for imaging spectrometer data requiring no a priori knowledge can be used to produce
reflectance data for quantitative scientific analysis. This achieves the goal of being able to extend
analysis techniques developed for analysis of one area to analysis of a second, unknown area. With
the advent of imaging spectrometers, model-based calibration methods, and the quantitative
techniques outlined here, imaging spectrometers can be used to extract ground surface
characteristics without having to first conduct a field survey.
6. ACKNOWLEDGMENTS
This research was funded by U. S. Geological Survey Cooperative Agreement 14-08-0001-A0746.
7. REFERENCES
Bailey, G. B., 1974, The occurrence, origin, and economic significance of gold-bearing jasperoids
in the central Drum Mountains, Utah: Unpublished Ph.D. Dissertation. S_ford
University. 300 p.
Boardman, J. W., 1992, Sedimentary Facies Analysis Using Imaging Spectrometry: A
Geophysical Inverse problem: Unpublished Ph. D. Dissertation. University of Colorado.
Boulder. 212 p.
Gao B. and Goetz, A. F. H., 1990, Column atmospheric water vapor and vegetation liquid water
retrievals from airborne imaging spectrometer data: Journal of Geophysical Research, v.
95, no. D4, p. 3549-3564.
Kruse, F. A., Lefkoff, A. B., and Dietz, J. B., 1993, Expert System-Based Mineral Mapping in
northern Death Valley, California/Nevada using the Airborne Visible/Infrared Imaging
Spectrometer (AVIRIS): Remote Sensing of Environment, Special issue on AVIRIS,
May-June 1993, p. 309 - 336.
Lindsey, D. L., 1979, Geologic map and cross-sections of Tertiary rocks in the Thomas Range and
northern Drum Mountains, Juab County, Utah: U. S. Geological Survey Map I-1176,
Washington, D.C.
Malkmus, W., 1967, Random Lorentz band model with exponential-tailed S line intensity
distribution function: J, Opt. Soc. Am., v 57, p. 323-329.
Porter, W. M., and Enmark, H. T., 1987, A system overview of the Airborne Visible/Infrared
Imaging Spectrometer (AVIRIS): in Proceedings. 31st Annual International Technical
Symposium. Society of Photo-Optical Instrumentation Engineers. v. 834, p. 22-31.
Roberts, D. A., Yamaguchi, Y., and Lyon, R. J. P., 1985, Calibration of Airborne Imaging
Spectrometer Data to percent reflectance using field spectral measurements: in
Proceedings. Nineteenth International Sym_nosium on Remote Sensing of Environment,
Ann Arbor, Michigan, October 21-25, 1985.
Tanre, D., Deroo, C., Duhaut, P.. He _rrnan- M.. Morchrette. J. J.. Perbos. J.. and Deschamps. P.
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(U. S. T. de Lille, 59655 ViUeneuve d'ascq, France: Laboratoire d'Optique
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Tremper, C. W., 1991, The mineralogy and chemistry of rocks from the Drum Mountains, west-
central Utah: Internal report to the U. S. Geological Survey (P.O # 2451234).
Department of Geology, Northern Illinois University, DeKalb, Illinois, 76 p.
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The effects of AVIRIS atmospheric calibration methodology on identification and quantitative mapping of surface mineralogy, Drum Mountains, Utah

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