King, T.V.V., Clark, R.N., Ager, C., and Swayze, G.A., 1995, Remote mineral mapping using AVIRIS data at Summitville, Colorado and the adjacent San Juan Mountains. Proceedings: Summitville Forum '95, H.H. Posey, J.A. Pendelton, and D. Van Zyl Eds., Colorado Geological Survey Special Publication 38, p. 59-63.
Most naturally occurring and man-made materials absorb and scatter sunlight at specific wavelengths. The spectral information is a measure of how reflected sunlight interacts with a surface. It is these absorptions that produce the colors sensed by the human eye. For instance, absorption by plants produces the green color observed by the human eye. Just as every human has a characteristic thumb-print, each mineral and manufactured material has a unique spectral signature that is related to chemical composition, grain size, degree of crystallinity, or temperature of formation. Subtle differences in the reflectance spectra of minerals can indicate major differences in chemistry or some physical parameters. Spectral information can be gathered from laboratory samples, remotely sensed by aircraft or satellite systems, therefore providing a powerful mapping tool.
Imaging spectroscopy is a new mapping technique and represents a part of the next generation in remote sensing technology. The narrow spectral channels of an imaging spectrometer form a continuous reflectance spectrum of the Earth's surface, which contrasts with the 4 to 7 channels of the previous generation of imaging instruments, for example the Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) instruments. Systems like Landsat can distinguish general brightness and slope differences in the reflectance spectrum of a surface. However, imaging spectroscopy has the advantage of providing compositional information based on the presence and position of absorption bands, as well as contributing data on brightness and slope.
The system used to collect data for this study is the NASA "Airborne Visible and Infra-Red Imaging Spectrometer" (AVIRIS) instrument. AVIRIS acquires data in the spectral range from 0.4 micron to 2.45 microns in 224 continuous spectral channels. The instrument is flown in an ER-2 aircraft (a modified U-2 spy plane) at 19,800 meters (~65,000 feet). The swath width is approximately 11 kilometers and the swath length can be as great as 1000 kilometers.
The image is composed of many data points, called pixels (614 pixels in a 11 kilometer swath width). Each pixel is a three-dimensional data point consisting of an X-, Y-, and Z- component. Each pixel represents a surface area (the X- and Y-components) approximately 17 meters square and contains information on the chemical and mineralogical character of the material (the Z-, or spectral component). Spectra acquired by remote measurements are interpreted by comparison with laboratory spectra from well characterized samples.
Clark et al., (1990a, 1991) developed a new analysis algorithm that uses a digital spectral library of known reference materials and a fast, modified-least-squares method of determining if a diagnostic spectral feature for a given material is present in the image. This algorithm is called "tricorder." The tricorder analysis compares continuum-removed spectra (Clark and Roush, 1984) from the remotely sensed data, to a database of continuum- removed spectral features from the reference spectral library (Clark et al., 1993). Multiple features from multiple materials are compared and the material with the closest match is mapped. The algorithm does not force a positive match which makes if different from many other algorithms in use. The tricorder algorithm attempts to map only minerals included in the reference database.
IMAGING SPECTROMETER DATA
We have analyzed AVIRIS data for the Summitville mining district and the adjacent San Luis Valley, in Colorado. The data were acquired on September 3, 1993. A combined method of radiative transfer modeling and empirical ground calibration site reflectance were used to correct the flight data to surface reflectance (Clark et al., 1994). This method corrects for variable water vapor in the atmosphere and produces smooth spectra with spectral channel to channel noise approaching the signal to noise of the raw data. Thus, the data can be compared to standard laboratory measurements. The calibration site is a plowed field approximately 18 kilometers SW of Alamosa. The calibration site soil samples were obtained on the day of the overflight and measured on the USGS laboratory spectrometer (Clark et al, 1990b). The spectra of the calibration field are spectrally bland and serve as an ideal calibration standard.
For the present study we mapped minerals based on the presence of absorption features in the ~0.45 micron to ~1.0 micron, 1.5 micron, and 2.2 micron to 2.3 micron wavelength region, which represent the visible and near-infrared portions of the electro magnetic spectrum. In this dataset we looked for 64 different minerals.
Absorption bands in the visible portion of the spectrum (~0.4-0.8 micron) are caused by electronic processes including crystal field effects, charge transfer, color-centers, and conduction bands. The absorptions resulting in the visible portion of the spectrum involve elements of the first transition series which have an outer unfilled d-shell in their electronic distribution. The energy levels are determined by the valence state of the element, its coordination number and its site symmetry. Differences in these parameters are manifested in individual diagnostic absorption bands. Absorptions in this wavelength region are commonly associated with the presence of iron in the mineral structure.
Near-infrared radiation (1-2.45 microns, in this study) absorbed by a mineral is converted into molecular vibrational energy. The frequency or wavelength of the absorption depends on the relative masses and geometry of the atoms and the force constants of the bonds. There are two main types of molecular vibrations: stretching and bending. A stretching vibration is a movement along the bond axis which either increases or decreases the interatomic distances. Bending vibrations consist of a change in the angle between bonds with a common atom or the movement of a group of atoms with respect to the remainder of the molecule, but without movement of the atoms in the group with respect to one another (Silverstein et al., 1981). Only vibrations that result in a change in the dipole-moment of the molecule will be infrared active.
Absorption features in the 2.2 to 2.3-micron region result from a combination of the OH-stretching fundamental with either the AL-O-H bending mode absorbing at approximately 2.2 micron, or Mg-O-H bending mode absorption at 2.3 micron. At high resolution these bands also appear as characteristic multiple, complex absorption features. Based on previous work (King and Clark,1989, Clark et al., 1990b, Clark et al., 1993), it is known that the strength, position and shape of these features is a function of the mineral chemistry.
In this study we searched for 22 minerals with absorption features at wavelengths near or less than 1.0 micron. We successfully mapped 7 of these 22 potential minerals. The minerals we detected include: amorphous iron-hydroxide, ferrihydrite, goethite, hematite, K-jarosite, Na-jarosite, and an Fe-bearing material that spectrally matches the processed sludge removed from the Reynolds Tunnel. Figure 1 shows their distribution at, and near, the Summitville mine. Comparison of spectra of these minerals extracted from the remotely sensed data with our laboratory standards shows no differences. We have detected spectral difference that allow us to discriminate between the amorphous iron-hydroxide and ferrihydrite, based on our laboratory standards, however, it is possible that these two materials are chemically similar. It should be noted that the most reliable method of identifying amorphous iron oxides is mossbauer spectroscopy.
Figure 1: 69K GIF
Samples of the processed Reynolds Tunnel sludge, collected at the mine site, were used as laboratory standards to identify this material in the remotely sensed data. In Figure 1, its distribution is depicted in yellow. In this case the algorithm maps solid Fe-bearing material and red-stained water puddles in the mine pit. Several days of rain occurred prior to the day of data acquisition, thus it is likely there was standing water in the pit. Other investigators have reported the occurrence of red-puddles (for example, Black Strap and Son of Black Strap; see Plumlee et al., this volume) in the pit resulting from precipitation or melting snow.
To detect the presence of minerals that have absorption features in the 2.2-2.3 micron wavelength region we used 48 mineral standards. These 48 standards included phyllosilicates, sulphates, carbonates, and cyanide compounds. Of these 48 standard minerals we detected 8 different phases of significant areal extent. Figure 2 shows the distribution of 6 of the 8 mineral phases detected (not shown are muscovite and dickite). Subtle spectral differences allow for the discrimination between K and Na alunites and poorly-crystalline and highly- crystalline kaolinites. The occurrence of Na- montmorillonite is depicted in yellow in this image. However, because of spectral similarities and limitations of the mapping algorithm, some of the material mapped as Na-montmorillonite may be muscovite or sericite. Recent modifications to the mapping algorithm have eliminated this inconsistency.
Figure 2: 35K GIF
Spectral data contained in the AVIRIS pixels are very similar to the spectral standards measured in the laboratory. Figure 3 shows the spectrum of a montmorillonite pixel detected near Alum Creek compared to a laboratory spectrum of a standard montmorillonite. Breaks in the spectra of the AVIRIS data occur at the wavelengths where absorptions from atmospheric gases occur. The absorption features in the montmorillonite spectrum from the AVIRIS data are identical to the diagnostic absorption features in the laboratory standard at wavelengths near 2.2 micron. The AVIRIS data shows that the montmorillonite is mixed with an Fe-bearing mineral phase because of an absorption near 0.8 micron (this absorption feature would be mapped with the Fe-bearing minerals). Similarly, Figure 4 shows the spectrum of a goethite mixed with alunite from the AVIRIS data and laboratory spectra of alunite and goethite. The diagnostic absorption features associated with the alunite occur near 1.5 micron, 1.7 micron, and between 2.1 micron and 2.4 micron, whereas the absorption from goethite is near 0.9 micron.
Figure 3: 11K GIF
Figure 4: 10K GIF
The mapping of the halloysite or kaolinite-smectite mixtures from the AVIRIS data has been less certain because of inconsistencies in both the spectral data and supporting X-ray analysis. Initially, the areas in red (Figure 2) were mapped as halloysite, based on laboratory standards. However, X-ray data indicated that the material contained kaolinite and other unidentified phases, but did not contain either halloysite or illite (Stephen Huebner, personal communication, 1994). The spectral data of the material (collected in the field) clearly indicates that crystalline kaolinite is not present and that absorption features very similar to illite and halloysite are present. Thus, we believe that the material is likely to be a supergene weathering product, a mineral for which we do not have either spectral or X-ray standards, or a new mineral. Therefore, caution should be applied in interpreting the presently-mapped areal distribution of this phase.
Imaging spectroscopy data of the Summitville mine and the Iron, Alum and Bitter Creek basins were used to identify minerals associated with alteration. Hydroxyl-bearing materials, including clays, show discrete distribution patterns at both the mine site and within the Iron, Alum and Bitter Creek basins. Mineralogic differences between the open pit and the heap leach pile at the mine site can be distinguished (Figure 2) and discrete mineralogical boundaries in the Iron, Alum and Bitter Creek basins can also be detected.
Perhaps the most interesting observation is that the Summitville mine apparently does not contribute OH-bearing minerals via the Wightman Fork to the Alamosa River. In contrast the mineralized area in Iron, Alum and Bitter Creek basins do contribute OH-bearing minerals to the Alamosa River. This observation is based on the spectral characteristics of the exposed fluvial sediments along Alum Creek and Bitter Creek and lack of OH-bearing fluvial sediment along the Wightman Fork. The unmined mineralized areas are believed to contribute OH-bearing materials to the Alamosa River due to the porous character of the well-exposed rock, which allows altered materials to be eroded easily and deposited along the stream banks. If hydroxyl-bearing materials, and associated contaminants, are being supplied to the Wightman Fork by the Summitville mine, the material must be carried as a very fine-grained aqueous suspension which cannot settle onto the creek banks.
Images show that both the Summitville mine and Iron, Alum and Bitter Creek basins are sources of iron-bearing sediments to the Alamosa River (Figure 1). These sediments give a reddish-brown color to stream banks, a characteristic typically associated with acid drainage, and are potential carriers of heavy metals to locations downstream. Consequently, in assessing the environmental impact of mining at Summitville, it is an important to recognize that both the Summitville mine site and the Iron, Alum and Bitter Creek basins contribute this type of sediment to the Alamosa River.
In summary, we have demonstrated the unique utility of imaging spectroscopy in mapping mineral distribution. In the Summitville mining region we have shown that the mine site does not contribute clay minerals to the Alamosa River, but does contribute Fe-bearing minerals. Such minerals have the potential to carry heavy metals. This application illustrates only one specific environmental application of imaging spectroscopy data. For instance, the types of minerals we can map with confidence are those frequently associated with environmental problems related to active and abandoned mine lands. Thus, the potential utility of this technology to the field of environmental science has yet to be fully explored.
Clark, R.N. and T.L. Roush, Reflectance Spectroscopy: Quantitative Analysis Techniques for Remote Sensing Applications, J. Geophys. Res., 89, 6329-6340, 1984.
Clark, R.N., A.J. Gallagher, and G.A. Swayze, Material absorption band depth mapping of imaging spectrometer data using a complete band shape least-squares fit with library reference spectra: Proceedings of the Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, JPL Publication 90-54, p. 176-186, 1990a.
Clark, R.N., T.V.V. King, M. Klejwa, G. Swayze, and N. Vergo, High Spectral Resolution Reflectance Spectroscopy of Minerals: J. Geophys Res. 95, 12653-12680, 1990b.
Clark, R.N., G.A. Swayze, A. Gallagher, N. Gorelick, and F. Kruse, Mapping with Imaging Spectrometer Data Using the Complete Band Shape Least-Squares Algorithm Simultaneously Fit to Multiple Spectral Features from Multiple Materials, Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, JPL Publication 91-28, 2-3, 1991.
Clark, R.N., G.A. Swayze, A. Gallagher, T.V.V. King, and W.M. Calvin, The U. S. Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 micron, U.S. Geological Survey, Open File Report 93-592, 1340 pages, 1993b. (Also being published as a USGS Bulletin, 1300+ pages, 1994 in press.)
Clark, R.N., G.A. Swayze, K. Heidebrecht, R.O. G.A.F.H. Goetz: Calibration to Surface Reflectance of Terrestrial Imaging Spectrometry Data: Comparison of Methods, Applied Optics in review, 1994a.
King, T.V.V. and R.N. Clark, Spectral Characteristics of Chlorites and Mg-Serpentines Using High- Resolution Reflectance Spectroscopy. J. Geophys. Res., 94, 13,997-14,008, 1989.
Silverstein, R.M., G.C. Bassler, and T.C. Morrill, Spectrometric Identification of Organic Compounds. John Wiley, New York, New York, 442p., 1981
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