Abstract—Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and commercially-available HyMap hyperspectral data were used to study the occurrence and mineralogical characteristics of kimberlite diatremes in the State-Line district of Colorado/Wyoming. A mosaic of five flightlines of AVIRIS data acquired during 1996 with 20-m resolution is being used to locate and characterize the kimberlite diatremes. Higher spatial resolution data (1.6m AVIRIS and 4m HyMap acquired in 1998 and 1999, respectively) are being used to map additional detail. Poor exposures, vegetation cover, and weathering, however, make identification of characteristic kimberlite minerals difficult except where exposed by mining. Minerals identified in the district using the hyperspectral data include calcite, dolomite, illite/muscovite, and serpentine (principally antigorite), however, most spectral signatures are dominated by both green and dry vegetation. The goal of this work is to determine methods for characterizing subtle mineralogic changes associated with kimberlites as a guide to exploration in a variety of geologic terrains. TABLE OF CONTENTS 1. INTRODUCTION
2. COLORADO AND WYOMING KIMBERLITES 3. AVIRIS PROCESSING AND ANALYSIS 4. HYMAP DATA ANALYSIS 5. FIELD RECONNAISSANCE AND LAB SPECTRA 6. PROSPECTS FOR ADDITIONAL WORK 7. CONCLUSIONS
1. INTRODUCTION Kimberlites are the principal source of diamonds and thus of high economic interest and importance. They originate as the result of intraplate alkaline magma, apparently generated at deeper levels in the mantle than most other magmas1. Kimberlites consist of predominantly ultramafic matrix material that has crystallized in situ, associated megacrysts formed in the upper mantle from the kimberlite magma, and mantle-derived xenoliths incorporated during magma transport. Common matrix minerals include olivine, phologopite, perovskite, spinel, chromite, diopside,
monticellite, apatite, calcite, and serpentine (commonly Fe-rich)1. Alteration of some of these minerals to serpentine and calcite is common. Additionally, kimberlites usually contain transported sedimentary and crystalline rocks. Information from deep mining of kimberlites in South Africa has illustrated that the diatremes commonly thought of as the source of diamonds are in fact part of a larger magmatic system including a hypabyssal root zone, the diatreme, and eruptive rocks. Thus three genetic groups of rocks are recognized; hypabyssal facies, diatreme facies, and crater facies1, 2. Kimberlite diatremes are generally vertical or steeply inclined cone-shaped intrusive bodies. They typically cross-cut host rocks, outcrop as approximately circular or elliptical bodies, and decrease in diameter with depth. Host rock contacts are sharply defined with little associated structural and contact metasomatism or metamorphism. The diatremes are typically exposed at the surface as shallow depressions. Hypabyssal facies kimberlites represent the root zones of diatremes and may also occur as dikes and sills. The hyperbyssal facies rocks are characterized by irregular outcrop plan, as the root zone is strongly influenced by the joint and fracture patterns of the host rocks. There are only a few known locations of crater facies rocks, which occur as lavas, pyroclastic rocks, and epiclastic rocks where erosion has been limited2, 3, 4. Imaging spectrometer data (also known as hyperspectral data) are under consideration by several mining companies for kimberlite exploration and characterization. This paper describes the use of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and commercially available HyMap data to study the occurrence and mineralogical characteristics of kimberlite diatremes in the State-Line district of Colorado/Wyoming. Hyperspectral sensors measure reflected light in up to several hundred narrow (approximately 10 nm) bands. Several of the minerals associated with kimberlites, in particular the serpentine and associated calcite are easily detected using the shortwave infrared region of the spectrum. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is a 224-channel imaging spectrometer with
approximately 10 nm spectral resolution covering the 0.4 – 2.5 µm spectral range at 20 m spatial resolution5. Several AVIRIS scenes acquired in August 1996 covering sites in Colorado/Wyoming were analyzed. Selected results were previously reported in Kruse and Boardman (1997)6. HyMap, the most advanced commercial hyperspectral sensor available today, provides high accuracy, calibrated radiance data over 126 channels covering the 0.4 – 2.5 µm spectral range with approximately 15-20 nm spectral resolution and 3-10m spatial resolution7. The system provides precisely geolocated data. Analysis of two 4m-resolution HyMap flightlines acquired for some of the sites is in progress. We expect that this improved spatial resolution may help resolve some of the spectral mixing problems inherent in the larger 20m pixels. The goal of using the combined AVIRIS/HyMap data is to develop methods for characterizing subtle mineralogic changes associated with kimberlites and related diatremes, examining the effect of scaling and spatial resolution on detection, characterization, and mapping of these changes, and developing exploration models valid for a variety of geologic terrains using both airborne and spaceborne hyperspectral sensors. 2. COLORADO AND WYOMING KIMBERLITES The State-Line kimberlite district of Colorado/Wyoming, discovered in the early 1960s, contains a number of Devonian-age diamondiferous kimberlitic diatremes that penetrate the crystalline basement8, 9, 10. Prominent joint sets and faults apparently helped control placement of the diatremes, which occur on an approximately north-south trend roughly paralleling the eastern edge of the Front Range (Figure 1). Diatremes are emplaced in Precambrian igneous and metamorophic rocks. Kimberlites occurring in the State-Line district are similar mineralogically and chemically to other known kimberlites world-wide; a breccia of subrounded to angular clasts in a fine crystalline matrix consisting of serpentine, calcite, dolomite, phologopite, magnetite, perovskite, chlorite, talc, and hematite8. State-line kimberlites also include blocks and fragments of Paleozoic dolomite, congolomerate, and sandstone. The kimberlites themselves are deeply weathered, and alteration typically consists of serpentization and carbonitization.
3. AVIRIS PROCESSING AND ANALYSIS Five flightlines of AVIRIS data consisting of approximately 50 scenes were acquired during 1996. Analysis of these data (Figure 2) using standardized methods developed by AIG11, 12 (Figures 3 and described below) is in progress. The 50 Colorado/Wyoming AVIRIS scenes were processed to apparent reflectance using the ATREM software13. ATREM is an atmospheric model-based routine that converts the calibrated radiance measured by the sensor to apparent surface reflectance without requiring in-situ measurements14. A common reference altitude was used for all of the Colorado/Wyoming data to avoid obvious changes
in reflectance across scene boundaries. This, however, introduces elevation-related atmospheric artifacts into some of the data, but these are outside the spectral range used to analyze these data.
Figure 1: Location of the State Line District,
Colorado/Wyoming (From Coopersmith and Schulze)10.
Figure 2: AVIRIS True Color (0.66, 0.55, 0.45 µm, RGB)
composite image of two concatenated AVIRIS scenes (12km x 20 km) showing approximate location of the Kelsey Lake kimberlite pipe along the left edge of the image.
Kelsey Lake
N
Methods being used in the analysis (implemented in the “ENVI” image analysis software) include spectral data reduction using the Minimum Noise Fraction (MNF) transformation15, spatial data reduction using the Pixel Purity Index (PPI)16, an n-Dimensional Visualizer to determine image endmembers16, 17, identification of endmembers using their reflectance spectra18, 19 in the Spectral Analyst , and mineral mapping using several methods20, 21, 22, 23 and also outlined in Kruse (1997)12.
Figure 3 AIG Standardized Processing methods for
hyperspectral data analysis. Operationally, last 60 AVIRIS bands (1.92 – 2.51 µm) were linearly transformed using the MNF transformation, and the top MNF bands, which contain most of the spectral information, were used to determine the most likely endmembers using the PPI procedure. These potential endmember spectra were loaded into an n-dimensional scatterplot and rotated in real time on the computer screen until “points” or extremities on the scatterplot were exposed. These projections were “painted” using Region-of-Interest (ROI) definition procedures and then rotated again in 3 or more dimensions (3 or more bands) to determine if their signatures were unique in the AVIRIS MNF data. Once a set of unique pixels were defined, then each separate projection on the scatterplot (corresponding to a pure endmember) was exported to a ROI in the image. (Note: when trying to locate “rare” or sub-pixel targets (such as kimberlites), selection of the signal vs noise cut-off in the MNF is critical. Care must be taken not to eliminate too many MNF bands or the targets of interest will not be located). Mean spectra were then extracted for each ROI from the apparent reflectance data to act as endmembers for spectral mapping (Figure 4). These endmembers or a subset of these endmembers were used for subsequent classification and other processing. Three digital mapping methods, the Spectral Angle Mapper (SAM)21, Linear Spectral Unmixing16, 17, 22, 23, and Matched filtering24 were used to produce mineral abundance maps (Figure 5). The results are generally presented as gray-scale images with values from 0 to 1.0, which provide a means of estimating mineral
abundance. Brighter pixels in the images represent higher mineral abundances. These can then be combined as color-coded images to show the distribution of principal mineralogies (Figures 6 and 7).
Figure 7: AVIRIS Mineral Map produced from the two AVIRIS scenes overlaid on a grayscale image. EM#1 (Serpentine 1) Orange; EM#2 (Calcite) Purple; EM#3 (Serpentine 2) Green; EM#4 (Antigorite [Serpentine3]) Cyan; EM#5 (Dry Grass) Yellow.
There are several things to note when analyzing the hyperspectral data of kimberlite occurrances. First, unless the minerals are very well exposed (for example surface outcrop in an arid terrain, or as here in mined areas), the signatures will be very subdued when compared to the spectral libraries (Figure 8). They may be so subdued in fact that the spectra will appear very similar to those for dry grass or other dry vegetation (Figure 9). In the case history described here for the State Line district, the similarities to dry grass has serious implications, as much of the area is covered by dry grass anyway. Also, note that we have differentiated several serpentines despite not being able to clearly distinguish the exact mineral. EM#4 is clearly identifiable as antigorite (there may be some mixed phlogopite and or lizardite as well, see Figure 8), however, the other serpentines, though clearly serpentine do not match a specific mineral from the spectral library (and may in fact be mixtures).
Figure 8: Comparison of EM#4 (AVIRIS spectrum
identified as Antigorite) to spectra of minerals from the U.S. Geological Survey spectral library25.
Figure 9: AVIRIS EM#4 (Antigorite) and EM#1 (Dry
Grass) compared to Dry Grass spectrum from the U.S. Geological Survey spectral library.
Finally, the AVIRIS data did detect minerals diagnostic of kimberlites, however, these occurred principally where exposed in mined areas. There are a few minor occurrences that do not appear to be associated with the obvious mines and prospects, but these will require additional work with higher spatial resolution hyperspectral sensors and/or field confirmation.
4. HYMAP DATA ANALYSIS The AVIRIS analysis above produced enough information that AIG elected to acquire higher spatial resolution hyperspectral data over several of the serpentine areas. Two flightlines of HyMap data were flown during September 1999 at approximately 4m spatial resolution. HyMap is a state-of-the-art aircraft-mounted commercial hyperspectral sensor developed by Integrated Spectronics, Sydney, Australia, and operated by HyVista Corporation, Sydney, Australia. The sensor provides unprecedented spatial, spectral and radiometric excellence7. The system is a whiskbroom scanner utilizing diffraction gratings and four 32-element detector arrays (1 Si, 3 liquid-nitrogen-cooled InSb) to provide 126 spectral channels covering the 0.45 – 2.5 µm range over a 512-pixel swath. The Y-direction of the imagery is provided by the aircraft motion. HyMap provides high spatial resolution data (3 –5 m), however, has slightly broader spectral bands than AVIRIS (~17nm for HyMap vs ~10nm for AVIRIS in this wavelength range). These are still adequate for identification of specific kimberlite minerals (principally serpentine). The last 29 bands 2.0 – 2.48 µm) were used for this analysis. Figure 10 shows a geocorrected false color-infrared composite (CIR) image of the two flightlines acquired for location purposes. The flightlines cross at their north-west ends over the Kelsey Lake Mine.
Figure 10: Geocorrected False Color Infrared (CIR) composite of two HyMap flightlines covering the Kelsey Lake Mine
and other parts of the Colorado/Wyoming State Line District. North is to the left of the page.
One flightline has been analyzed using the standardized AIG methodology (see Section 3 above and Kruse, 1997 for a description of the methodology)12. Data were corrected to apparent reflectance using ATREM. The spectral dimensionality of the data was reduced using the MNF transform, and the spatial dimensionality using the Pixel Purity Index. Spectral endmember were selected from the data using the n-Dimensional Visualizer and identified using visual inspection and the ENVI Spectral Analyst (Figure 11).
Figure 11: Endmembers extracted from the 1999 HyMap
data. Identifications are based on a combination of visual inspection and comparison to plotted endmember spectra combined with numerical comparison of continuum-removed spectra using the ENVI Spectral Analyst (spectral matching).
Endmembers identified are typical for weathered kimberlites. Two slightly different antigorite spectra were extracted, along with two different illites. Calcite is present, however, the spectrum marked as “Epidote?” is problematic. It really doesn’t match anything in the library very well and is likely a mixture of carbonate minerals and something else, probably not epidote. The spectrum extracted for dry vegetation is typical, and similar in some ways to the mineral spectra of interest. The “green vegetation” spectrum is typical for healthy vegetation in this spectral region, but has no individual diagnostic spectral features. Comparison of the HyMap “Antigorite#2” endmember spectrum to a spectrum of Antigorite from the USGS Spectral Library25 (Figure 12) demonstrates a close match, however, there are some differences. The spectral feature near 2.3 µm is somewhat subdued in the HyMap spectrum, as are the secondary features. These are likely explained by the fact that first, this is a spectral average for several spectra from the n-D Visualizer. Additionally this spectrum is a linear mixture of all materials occurring within the approximately 4m HyMap pixel.
Figure 12: Comparison of HyMap Antigorite#2 endmember
spectrum to USGS Spectral Library Antigorite spectrum. The sharp fall-off at long wavelengths in the HyMap spectrum is probably caused the low energy levels available at these wavelengths when using natural solar illumination.
Although the complete HyMap flightline was analyzed using the above methodology, and there appear to be several areas of serpentine occurrences, this HyMap analysis work is in progress. Therefore, only a small area within the Kelsey Lake Mine itself is used here for illustrative purposes. Figure 13 is a HyMap image subset around the Kelsey Lake Mine and workings. Figure 14 is the HyMap mineral map for the same image subset.
Figure 13: HyMap CIR composite of the Kelsey Lake Mine.
Figure 14: HyMap Mineral map of the Kelsey Lake Mine.
Figure 15: Oversize materials piles at Kelsey Lake Mine. Ore from KL-2 pit and currently located at site “A” seen in Figure 13.
5. RECONNAISSANCE AND LAB SPECTRA Field reconnaissance was conducted following image processing and analysis in conjunction with a GSA field trip to the Kelsey Lake Mine on 24 October 1999. Access was granted by Diamond Company N.L., the current owner of Kelsey Lake Mine, and several grab samples were selected based on hyperspectral-mapped mineralogy and returned for laboratory spectral measurements. Figure 15 shows one of the areas mapped as Antigorite #2 using the HyMap data (area “A” marked on Figure 13 and a semi-circular yellow ring opening to the west shown on Figure 14 at the same location). Field reconnaissance and information from the property owner show this area to be a holding pile for oversize material from the mine. A wide
variety of materials were observed, most appearing to correspond to relatively fresh kimberlite cobbles, boulders, and rock fragments on the order of about 15cm across (Figure 16) occurring on the sloped sides of the piles.
Figure 16: Sample of “Antigorite #2” from the Kelsey Lake
Mine.
There is also a significant amount of fine-grained sand to gravel matrix material supporting the larger material and covering the flat tops of the piles. These flat areas map as “Antigorite #1” in the HyMap data, corresponding to the magenta center to the yellow semi-circular area on Figure 14. There doesn’t appear to be any obvious compositional difference between the two antigorites, only that one occurs as finer grained material and possibly slightly more weathered. This affects the shape and depth of the principal spectral band for antigorite near 2.3 µm. Samples returned from the mine were measured in the laboratory using an Analytical Spectral Devices FieldSpec FR visible/infrared spectrometer. The FieldSpec covers the range 0.4 – 2.5 µm at high spectral resolution. Figure 17 compares the Hymap-measured antigorite spectrum to the FieldSpec-measured antigorite spectrum. Note that HyMap, though lower resolution, resolves the key spectral features.
Figure 17: HyMap spectrum of the Antigorite #2 area
compared to a laboratory-measured FieldSpec FR spectrum (left), and the FieldSpec spectrum compared to a spectrum of Antigorite from the USGS Spectral Library (right).
6. PROSPECTS FOR ADDITIONAL WORK
Known occurrences of undisturbed/developed kimberlites in the State Line District are also being studied using AVIRIS and HyMap. The nature of kimberlites, and in particular the kimberlites of this district, however, makes practical use of hyperspectral technology for exploration extremely difficult. This is principally because of the nature of the exposures. Kimberlite at the surface typically is heavily weathered. In the State Line district, its presence can be diagnosed by an experienced geologist based on its geomorphic expression as small depressions (often between granite outcrops), the general absence of trees, the presence of specific grass associations, and fragments of kimberlite brought to the surface by biological activity. There is typically no outcrop, and if any material occurs at the surface at all, the distribution of diagnostic kimberlite materials is quite small (Figure 18).
Even so, there is some potential for mapping undisturbed kimberlites, or even locating unknown ones in the State Line district using hyperspectral data. Distinct vegetation associations merit further study and could lead to vegetation-based mapping methods. Surface outcrop, though small, has been observed at some locations (Figure 19) and there are indications from preliminary image analysis that serpentine minerals occur at the surface in some areas where there is no obvious mining or prospecting.
Figure 19: Small outcrop exposure of kimberlite materials
at the Sloan#1 pipe in another part of the State Line district.
7. CONCLUSIONS A hyperspectral study of selected diatremes in the Colorado/Wyoming State Line District has established the viability of hyperspectral data for mapping diagnostic
kimberlite-related minerals. Several AVIRIS scenes have been analyzed using a standardized procedure consisting of atmospheric removal, selection and identification of spectral endmembers, and spatial mapping of endmember occurrence and abundance. Regional mineral mapping using a total of 50 AVIRIS scenes to produce maps showing the distribution of minerals associated with kimberlites and surrounding host-rocks is underway. Several prospects identified from the AVIRIS mapping have been flown using higher spatial resolution HyMap data and detailed mineral studies are also in progress. The hyperspectral remote sensing data provides the advantage of being able to cover large segments of ground, providing a synoptic view and simultaneously obtaining high-quality reflectance spectra of diagnostic minerals. The goal of this research is to determine methods for characterizing subtle mineralogic changes associated with kimberlites and developing exploration models valid for a variety of geologic terrains. The methods described here may or may-not lead to improved understanding of State Line District kimberlites or new kimberlite discoveries in this district, but as a minimum, these approaches, based on first principals, can be used as part of an integrated exploration strategy.
8. ACKNOWLEDGMENTS This research was supported by Analytical Imaging and Geophysics IR&D funds. AVIRIS data were provided by JPL. HyMap data were acquired under a cooperative agreement between HyVista Corporation, Sydney, Australia and AIG. Access to the Kelsey Lake property was courtesy of Diamond Company N. L., and particular thanks are due to Howard Coopersmith for his assistance and discussions during the GSA field trip. Laboratory spectral
Figure 18: Typical kimberlite exposures at KL-1, undeveloped occurrences approximately 1 km northeast of current Kelsey
Lake Mine. Left: Grassy area in the foreground is slight depression corresponding to part of the KL-1 kimberlite pipe. Right: Narrow extension of KL-1 in the grass-covered area between the trees in the center of the photo, surrounded on both sides by granite covered with ponderosa pine.
Kimberlite
Granite
Kimberlite Outcrop
measurements were performed at the University of Colorado with the assistance of Bruce Kindel. HyMap is a trademark of HyVista Corporation. ENVI is a registered trademark of BSCLLC, Lafayette, Colorado. Pixel Purity Index (PPI), n-Dimensional Visualizer, Spectral Analyst, and Mixture-Tuned Matched Filter (MTMF) are all trademarks of BSCLLC. FieldSpec is a trademark of Analytical Spectral Devices, Boulder, Colorado. REFERENCES 1] Mitchell, R. H., 1986, Aspects of the petrology of kimberlites and lamproites: some definitions and distinctions: in Proceedings, Fourth International Kimberlite Conference, Perth, 1986, GSA Special Publication No. 14, p. 7- 45. 2] Clement, C. R., and Skinner, E. M. W., 1985, a textural-genetic classifcation of kimberlites: Trans. Geol. Soc. S. Afr., v. 88, p. 403-409. 3] Hawthorne, J. B., 1975, Model of a kimberlite pipe: Phys. Chem of the Earth, v 9, p. 1-15. 4] Reid, A. M., Donaldson, C. H., Brown, R. w., and Ridley, W. I., 1975, The Igwisi Hills estrusive “kimberlite”: Phys. Chem. of the Earth, V. 9, p. 199- 5] Porter, W. M., and Enmark, H. E., 1987, System overview of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), in Proceedings, Society of Photo-Optical Instrumentation Engineers (SPIE), v. 834, p. 22-31 6] Kruse, F. A., and Boardman, J. W., 1997, Characterization and mapping of kimberlites and related diatremes in Utah, Colorado, and Wyoming, USA, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): in Proceedings, 12th Thematic Conference, Applied Geologic Remote Sensing, 17-19 November 1997, Environmental Research Institute of Michigan (ERIM), Ann Arbor, MI, p. I-21 – I-28. 7] Cocks T., R. Jenssen, A. Stewart, I. Wilson, and T. Shields, 1998, The HyMap Airborne Hyperspectral Sensor: The System, Calibration and Performance. Proc. 1st EARSeL Workshop on Imaging Spectroscopy (M. Schaepman, D. Schläpfer, and K.I. Itten, Eds.), 6-8 October 1998, Zurich, EARSeL, Paris, p. 37-43. 8] McCallum, M. E., and Mabarak, C. D., 1976, Diamond in State-Line kimberlite diatremes, Albany County, Wyoming, Larimer County, Colorado: Report of Investigation No. 12, The Geological Survey of Wyoming, 36 p. 9] McCallum, M. E., and Waldman, M. A., 1991, The diamond resources of the Colorado-Wyoming State Line District: Kimberlite indication mineral chemistry as a guide
to economic potential: in Frost, B. R., and Roberts, S., eds., Wyoming Geological Association Forty-Second Field Conference Guidebook, Mineral Resources of Wyoming, p. 77-90. 10] Coopersmith, H. G., and Schulze, D. J., 1996, Development and geology of the Kelsey Lake diamond mine, Colorado: In Diamonds to Gold, I. State Line Kimberlite District, Colorado: Society of Economic Geologist guidebook Series, V. 26, p. 1-19. 11] Kruse, F. A., Huntington, J. H., and Green, R. O, 1996, Results from the 1995 AVIRIS Geology Group Shoot: in Proceedings, 2nd International Airborne Remote Sensing Conference and Exhibition: Environmental Research Institute of Michigan (ERIM), Ann Arbor, v. I, p. I-211 - I-220. 12] Kruse, F. A., 1997, Regional Geologic Mapping Along the Colorado Front Range from Ft Collins to Denver Using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): in Proceedings, 12th Thematic Conference, Applied Geologic Remote Sensing, 17-19 November 1997, Environmental Research Institute of Michigan (ERIM), Ann Arbor, MI, (this volume). 13] 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. 14] CSES, 1992, Atmosphere REMoval Program (ATREM) User’s Guide, Version 1.1, Center for the Study of Earth from Space, Boulder, Colorado, 24 p. 15] Green, A. A., Berman, M., Switzer, B., and Craig, M. D., 1988, A transformation for ordering multispectral data in terms of image quality with implications for noise removal: IEEE Transactions on Geoscience and Remote Sensing, v. 26, no. 1, p. 65 - 74. 16] Boardman, J. W., Kruse, F. A., and Green, R. O., 1995, Mapping target signatures via partial unmixing of AVIRIS data: in Summaries, Fifth JPL Airborne Earth Science Workshop, JPL Publication 95-1, v. 1, p. 23-26. 17] Boardman, J. W., 1993, Automated spectral unmixing of AVIRIS data using convex geometry concepts: in Summaries, Fourth JPL Airborne Geoscience Workshop 18] Kruse, F. A., Lefkoff, A. B., and Dietz, J. B., 1993a, 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, v. 44, p. 309 - 336.
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AUTHOR BIOGRAPHIES
Dr. Fred A. Kruse received the Ph. D. in Geology from the Colorado School of Mines, Golden, Colorado (1987). He has been involved in multispectral, hyperspectral, and SAR remote sensing research and applications for over 18 years in positions with the U. S. Geological Survey, the University of Colorado, and private industry. His primary
scientific interests are in the application of remote sensing technology to exploration and understanding of ore deposits and the development of knowledge-based techniques for spectral identification and mapping of geology, man-made materials, vegetation, and near-shore marine environments. Dr. Kruse is also co-founder of BSC, the company that developed the remote sensing analysis software package ENVI, “The Environment for Visualizing Images.” Dr. Joseph W. Boardman received the Ph. D. degree in Geophysics from the University of Colorado in 1991, followed by a 2 year Postdoctoral research fellowship with the CSIRO, Sydney, Australia. From 1993 to 1995 he was a Research Associate at CSES, University of Colorado, Boulder. His current research as Senior Geophysicist, Analytical Imaging and Geophysics LLC, Boulder, Colorado, applies convex geometry and n-dimensional methods to automatically determine endmembers for sub-pixel analysis (spectral unmixing). Dr. Boardman is also co-founder of BSC, the company that developed the remote sensing analysis software package ENVI, “The Environment for Visualizing Images”.
