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Regional Mineral Mapping By Extending Hyperspectral Signatures Using Multispectral Data1,2

Fred A. Kruse Sandra L. Perry Horizon GeoImaging, LLC and Perry Remote Sensing, LLC P.O. Box 4279, Frisco, CO 80443 Englewood, CO 80113 970-668-3607, kruse@hgimaging.com

1 1 1-4244-0525-4/07/$20.00 ©2007 IEEE. 2 IEEEAC paper #1078, Version 4, Updated November 24, 2006

Abstract—Hyperspectral imaging (HSI) data in the 0.4 – 2.5 micrometer (VNIR/SWIR) spectral range allow direct identification of minerals using their fully resolved spectral signatures, however, spatial coverage is limited. Multispectral Imaging data (MSI) (e.g. data from the Advanced Spaceborne Emission and Reflection Radiometer, ASTER)) are spectrally undersampled and may not allow unique identification, but they do provide synoptic spatial coverage. Combining the two data types by modeling hyperspectral signatures to ASTER band passes allows extending HSI mapping results to regional scales and leads to improved mineral mapping over larger areas.

We are using several geologic test sites to establish geologic background and characterize and map human-induced change in the form of mine excavations, mine tailings, mine waste, and acid runoff using HSI and ASTER data. The HSI data are atmospherically corrected using commercial-off-the-shelf (COTS) atmospheric correction software. Data are then analyzed to determine spectral endmembers and their spatial distribution, and validated using field spectral measurements. Spectral modeling is used to convert HSI spectral signatures to the ASTER spectral response. Reflectance-corrected ASTER data are then used to extend the hyperspectral mapping to the full ASTER spatial coverage. Field verification of ASTER mapping results is conducted and accuracy assessment performed. Additional geologic sites are also being assessed with ASTER using the modeling methodology based on scene-external HSI and/or field spectra (but without scene-specific a priori hyperspectral analysis or knowledge). These results are further compared to field measurements and subsequent hyperspectral analysis and mapping to validate the spectral modeling approach. Initial results show that the ASTER multispectral data can successfully map several minerals and/or mineral groups. While some specific minerals are ambiguous, the mineral maps produced using this method, identifying and mapping specific minerals based on their spectral signatures, are significant improvements over previous approaches that expressed simple spectral shape differences on color-composite images or as statistically different (but unidentified) classes.

TABLE OF CONTENTS

1. INTRODUCTION ..................................................... 1 2. BACKGROUND ....................................................... 2 3. APPROACH AND METHODS................................... 3 4. RESULTS................................................................ 5 5. SUMMARY AND FURTHER WORK ....................... 11 6. ACKNOWLEDGEMENTS....................................... 11 7. REFERENCES....................................................... 11 BIOGRAPHIES.......................................................... 14

1. INTRODUCTION

This research uses Advanced Spaceborne Thermal Emmission and Reflection Radiometer (ASTER) data to extend hyperspectral imaging (HSI) mapping results to regional scales for environmental monitoring and geologic mapping. Hyperspectral imaging is currently available from both airborne and satellite platforms. Its utility for detailed materials mapping has been demonstrated for a variety of scientific disciplines [1, 2, 3, 4]. Availability and regional coverage of HSI data continues to be problematic, however, and probably always will be because of the high data volumes generated by these sensors. Thus hyperspectral systems are best used as targeted sensors – looking at small specific regions-of-interest. Multispectral imagery (MSI) systems like ASTER on the other hand are able to provide synoptic coverage, albeit with fewer spectral bands. The spectral information from multispectral instruments is more limited than that from HSI systems because of lower spectral resolution and limited spectral ranges. We are using integration and spectral/spatial scaling of nested HSI/MSI data to model and predict ASTER multispectral signatures. The predicted signatures are then used to extend hyperspectral mapping results to the larger synoptic spatial coverage of ASTER, thus improving geologic mapping and monitoring for areas not covered by hyperspectral data. Concepts and methods are being developed in the context of NASA’s Earth Science Enterprise (ESE) mission and applied to geologic problems to produce case histories in the areas of geologic mapping and baselining, and environmental monitoring of mined areas. Field

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reconnaissance and spectral measurements are being used to validate the ASTER modeling results.

2. BACKGROUND

Imaging Spectrometers, or “Hyperspectral” sensors measuring hundreds of spectral bands provide a unique combination of both spatially contiguous spectra and spectrally contiguous images of the Earth's surface unavailable from other sources [5]. Current airborne sensors provide high-spatial resolution (2-20m), high-spectral resolution (10-20nm), and high SNR (>500:1) data for a variety of scientific disciplines. The two HSI sensors used for this effort are:

AVIRIS: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) represents the current state of the art. AVIRIS, flown by NASA/Jet Propulsion Laboratory (JPL) is a 224-channel imaging spectrometer with approximately 10 nm spectral resolution covering the 0.4 – 2.5 micrometer spectral range [6]. The sensor is a whiskbroom system utilizing scanning foreoptics to acquire cross-track data. The IFOV is 1 milliradian. Four off-axis double-pass Schmidt spectrometers receive incoming illumination from the foreoptics using optical fibers. Four linear arrays, one for each spectrometer, provide high sensitivity in the 0.4 to 0.7 micrometer, 0.7 to 1.2 micrometer, 1.2 to 1.8 micrometer, and 1.8 to 2.5 micrometer regions respectively. AVIRIS is flown as a research instrument on the NASA ER-2 aircraft at an altitude of approximately 20 km, resulting in approximately 20-m pixels and a 10.5-km swath width. Since 1998, it has also been flown on a Twin Otter aircraft at low altitude, yielding 2 – 4m spatial resolution. Key characteristics are shown in Table 1.

EO-1 Hyperion: While airborne hyperspectral data have been available since the early 1980s [5], the launch of NASA’s EO-1 Hyperion sensor in November 2000 marked the establishment of spaceborne hyperspectral mapping capabilities. Hyperion is a satellite hyperspectral sensor covering the 0.4 to 2.5 micrometer spectral range with 242 spectral bands at approximately 10nm spectral resolution and 30m spatial resolution from a 705km orbit [7]. Hyperion is a pushbroom instrument, capturing 256 spectra each with 242 spectral bands over a 7.5km-wide swath perpendicular to the satellite motion along an up to 160km path length. The system has two grating spectrometers; one visible/near infrared (VNIR) spectrometer (approximately 0.4 – 1.0 micrometers) and one short-wave infrared (SWIR)) spectrometer (approximately 0.9 – 2.5 micrometers). Data are calibrated to radiance using both pre-mission and on-orbit measurements. Key Hyperion characteristics are discussed further in [8]. Hyperion data are available for purchase from the U. S. Geological Survey [9]. Thousands of Hyperion scenes have been acquired for a

variety of disciplines. The EO-1 Science Validation Team has evaluated and validated the instrument. Selected results have been presented at team meetings [10] and also published in various venues [11, 12, 13]. Also see [14] for a summary along with associated papers. The instrument remains healthy and additional data can be requested for specific sites. Table 1 shows a comparison of AVIRIS and Hyperion instrument characteristics.

Table 1: AVIRIS/Hyperion Sensor Comparison. HSI Sensor

SpectralBands

Spectral Resolution

Spatial Resolution

SwathWidth

SWIR SNR

AVIRIS 224 10 nm 20 m 12 km ~500:1 Hyperion 242 10 nm 30 m 7.5 km ~50:1

Multispectral Imaging (MSI) sensors usually have only a few spectral bands (<20), cover broad spectral regions and provide synoptic coverage. Examples include Landsat, SPOT, IRS, and ASTER. MSI sensors with high spatial resolution have recently become available (eg: IKONOS and Quickbird). The MSI sensor used for this effort is:

ASTER: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a NASA facility instrument on the Earth Observing System (EOS) TERRA platform that provides visible/near-infrared/shortwave-infrared/long-wave-infrared (VNIR/SWIR/LWIR) earth observation capabilities in a total of 14 total spectral bands (+one backward-looking band) [15, 16, 17, 18], (Table 2). ASTER and/or ASTER-simulated data (MODIS/ASTER Airborne Simulator [19]) have been successfully used for geologic applications, providing basic mapping capabilities using both the VNIR/SWIR and LWIR spectral ranges [20, 21, 22, 23]. The four VNIR bands provide information about iron mineralogy and some rare earth minerals [23]. The six SWIR bands allow mapping of molecular vibration absorption features commonly seen in minerals such as carbonates and clays [20, 22, 23]

Note that MASTER/ASTER VNIR spectra and SWIR spectra don’t fully resolve the VNIR VNIR/SWIR molecular absorption features present for most minerals, however, the bands are generally adequately positioned to determine general shape and feature differences that allow identification of some important minerals [22, 23]. There may be some confusion using MASTER/ASTER between minerals that are distinctly separated using hyperspectral sensors, particularly when mixtures occur (eg: calcite, dolomite; kaolinite, alunite, buddingtonite).

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Table 2: ASTER Characteristics [24]

Characteristic VNIR SWIR LWIR Spectral Range Band 1:

Nadir looking Band 4: 1.600 - 1.700 µm Band 10: 8.125 - 8.475 µm

Band 2: Nadir looking

Band 5: 2.145 - 2.185 µm Band 11: 8.475 - 8.825 µm

Band 3: Nadir looking

Band 6: 2.185 - 2.225 µm Band 12: 8.925 - 9.275 µm

Band 3: Backward looking

Band 7: 2.235 - 2.285 µm Band 13: 10.25 - 10.95 µm

Band 8: 2.295 - 2.365 µm Band 14: 10.95 - 11.65 µm Band 9: 2.360 - 2.430 µm Ground Resolution 15m 30m 90m Swath Width 60km 60km 60km

3. APPROACH AND METHODS

Our research uses coincident AVIRIS/Hyperion and ASTER data supported by field spectral measurements to allow calibration, atmospheric correction, and modeling/extension of hyperspectral signatures to ASTER data and subsequent mapping/extension using ASTER. We are using several geologic test sites to establish geologic background and characterize and map human-induced change in the form of mine excavations, mine tailings, mine waste, and acid runoff. The overall approach can be summarized as follows.

(1) Acquire spatially nested hyperspectral/ASTER data

(2) Prepare atmospherically corrected spatially nested hyperspectral/ASTER data sets

(3) Analyze the hyperspectral data to determine spectral endmembers and their spatial distribution

(4) Model the predicted ASTER spectral signatures using the hyperspectral data (and/or spectral libraries) and ASTER response functions

(5) Map the distribution of predicted endmembers using atmospherically corrected ASTER data

(6) Compare ASTER mapping results to hyperspectral mapping results in overlapping areas and assess accuracy/determine limitations

(7) Assess the mapping results in extended ASTER mapping areas (outside extent of hyperspectral data)

(8) Use results and lessons learned to conduct and evaluate enhanced mapping for ASTER scenes without hyperspectral data

(9) Use the developed approaches and methods to evaluate human-induced change for selected active and historical mine sites

Atmospheric Correction:

Atmospheric correction is a requirement for this data analysis approach. We used the Atmospheric COrrection Now (ACORN) model-based atmospheric correction method to correct both AVIRIS/Hyperion and ASTER data to apparent reflectance. ACORN is a commercially available, enhanced atmospheric model-based software that uses licensed MODTRAN4 technology to produce high quality surface reflectance without ground measurements [25]. Field spectra measured for targets occurring in the HSI data were also used in some cases to refine the atmospheric correction where possible.

For hyperspectral data, ACORN uses the water-vapor features near 0.9 and 1.1 micrometers (which are fully resolved using HSI data) to estimate water vapor on a pixel-by-pixel basis. The water vapor estimates are used along with data characteristics (band centers, full-width-half-max response) and acquisition parameters (ground elevation, flight altitude, site latitude/longitude, date and time) with an atmospheric model (MODTRAN) to produce a per-pixel reflectance corrected dataset. ASTER data are also converted to reflectance using a simplified MODTRAN model in ACORN and compared to AVIRIS in overlapping areas.

Standardized “Hourglass” HSI Data Analysis

Standardized approaches developed by the 1st author (Kruse) and associates at Analytical Imaging and Geophysics LLC (AIG) for analysis of HSI data [26, 27] are implemented and documented within the “Environment for Visualizing Images” (ENVI) software system (now an ITT commercial-off-the-shelf [COTS] product) [28] (Figure 1). Data are analyzed using an “hourglass” approach [13] to determine unique spectral endmembers their spatial distributions, and abundances, producing detailed mineral maps. These act as the basis for comparison to ASTER mapping results in overlapping datasets.

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Apparent Reflectance

MNF

PPI

n-D

ID

Map Distributionand Abundance

Model-basedMethodsSpectral DataReduction

Spatial DataReduction

Visualization

Identification

Binary, SAMUnmixMF & MTMFSFF

MappingBinary, SAMUnmixMF & MTMFSFF

Mapping

Figure 1: The “Hourglass” HSI analysis approach.

A key point of this methodology is the reduction of data in both the spectral and spatial dimensions to locate, characterize, and identify a few key spectra (endmembers) in the HSI data that can be used to explain the rest of the hyperspectral dataset. Once these endmembers are selected, then their location and abundances can be mapped from the linearly transformed or original data. These methods derive the maximum information from the hyperspectral data themselves, minimizing the reliance on a priori or outside information. The analysis approach consists of the following steps:

(1) Correction for atmospheric effects using an atmospheric model such as ACORN [25]

(2) Spectral compression, noise suppression, and dimensionality reduction using the Minimum Noise Fraction (MNF) transformation [29, 30]

(3) Determination of endmembers using geometric methods (Pixel Purity Index – “PPI”) [26]

(4) Extraction of endmember spectra using n-dimensional scatter plotting [31]

(5) Identification of endmember spectra using visual inspection, automated identification, and spectral library comparisons [32, 33]

(6) Production of material maps using a variety of mapping methods. The “Spectral Angle Mapper” (SAM) produces maps of the spectrally predominant mineral for each pixel by comparing the angle between the image spectra and reference spectra in n-dimensional vector space [34]. “Mixture-Tuned-Matched-Filtering” (MTMF) is basically a partial linear spectral unmixing procedure [35].

The Hourglass method described above is not the only way to analyze hyperspectral data, but we have found that it

provides a consistent way to extract spectral information from hyperspectral data without a priori knowledge or requiring ground observations. We have also had good results selectively applying the method to MSI data. The individual steps are described in more detail in [13].

ASTER Data Processing and Analysis

Several processing steps were performed to prepare ASTER data for analysis. The basic goal was to produce a cross calibrated, reflectance corrected dataset that matched the HSI data in overlapping areas. The following steps were followed.

(1) Cross-Talk Correction – The data were corrected for cross-talk between several of the spectral channels using a software algorithm developed by the ASTER science team [36]

(2) Orthorectification – Data were orthorectified using the satellite model and commercial software (SILC) available from Sensor Information Laboratory [37]

(3) Radiance Conversion – Data were converted from digital number (DN) to Radiance using “ASTER Unit Conversion Coefficients” contained in the ASTER HDF data file [18]

(4) Convert formats – BSQ single-band format was converted to stacked, multiband, Integer-scaled, band-interleaved format required by the ACORN atmospheric correction software. SWIR data were scaled to VNIR 15m spatial resolution

(5) ACORN correction – The data were corrected to apparent reflectance using the MODTRAN-based model in the COTS ACORN software [38]. Reflectance corrected data were further compared to AVIRIS reflectance spectra in data overlap areas to verify the reflectance correction

The AVIRIS spectral signatures previously extracted were then converted to the ASTER spectral response using the known ASTER filter functions and used to map mineral signatures in the ASTER data [39]. (Note: we have also used spectral libraries rather than HSI spectra in the modeling, but find that the HSI data provide spectra that are more representative of actual surface conditions – thus performing better in the mapping algorithms). The hourglass processing methods described above were also used on the ASTER data. This standardized approach using reflectance data allows extending the hyperspectral mapping to the full ASTER spatial coverage across space and time (multiple scenes from a variety of dates). Field verification of ASTER mapping results is accomplished through geologic reconnaissance and collection of high-resolution field spectral measurements using an Analytical Spectral Devices (ASD) Fieldspec Pro spectroradiometer.

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4. RESULTS

We present a case history to demonstrate the methods and basic results and show the ability to extend HSI/ASTER-modeled signatures outside areas covered by HSI data and to multiple ASTER scenes.

For a site in Northern Death Valley, California/Nevada, two sets of AVIRIS data were analyzed to provide spectral endmembers for ASTER modeling. We analyzed one flightline of AVIRIS data collected May 11th 2005 for the “Northern Grapevine Mountains” site and a mosaic of 5 flightlines acquired the same date for the “Cuprite, Nevada” site. Both of these are contained in a previously compiled 5-scene ASTER mosaic acquired May 12th, 2004 for the Northern Death Valley area. A third site, Goldfield, Nevada is shown as an example of predicted ASTER-mapped mineralogy verified using pre-existing AVIRIS data and new ASD field spectral measurements.

Northern Grapevine Mountains Site

The northern Grapevine Mountains (NGM) site, located in northern Death Valley, south-central California/Nevada (Figure 2), was designated part of a U.S. Geological Survey Wilderness Study Area in 1982. The USGS was charged with evaluating the economic mineral potential of the area by characterizing the surface geology, alteration, geologic structure, and existing prospects and claims. PreCambrian bedrock in the NGM area consists of limestones, dolomites, sandstones and their contact metamorphic equivalents. Mesozoic plutonic rocks are mapped primarily as granitic-composition and some age-dates are available [40].

Mesozoic units mapped in the field include quartz syenite, a quartz monzonite porphyry stock, quartz monzonite dikes, and a granite intrusion [41]. These rocks are cut by narrow north-trending mineralized shear zones containing sericite

(fine grained muscovite or illite) and iron oxide minerals [41, 42]. Slightly broader northwest-trending zones of disseminated quartz, pyrite, sericite, chalcopyrite, and fluorite mineralization (QSP alteration) ± goethite occur in the quartz monzonite porphyry. There are several small areas of quartz stockwork (silica flooding of the rocks) exposed at the surface in the center of the area. Skarn, composed mainly of brown andradite garnet intergrown with calcite, epidote, and tremolite, occurs around the perimeter of the quartz monzonite stock in Precambrian rocks. The NGM area has many of the characteristics common to porphyry copper deposits, however, there has not been any secondary (supergene) enrichment, and thus economic concentrations of ore do not occur. Remote sensing technology available at the time (Landsat MSS and TM data) was also used as part of this evaluation. Results from the remote sensing analysis, field mapping and field spectral measurements, laboratory analyses, and ancillary data led to removal of the site from consideration as a WSA in 1984 [41, 42].

Because the site was relatively well understood and mapped, repeated overflights of the NGM site with a variety of remote sensing instruments were arranged from 1984 through 2006 to evaluate remote sensing technology for resource assessment and to develop advanced analysis methodologies. Remote Sensing data available for the NGM site include Landsat MSS and TM, Thermal Infrared Multispectral Scanner (TIMS), JPL Airborne Synthetic Aperture Radar (AIRSAR) and SIR-C. Imaging spectrometer (hyperspectral) data flown for the NGM site include GER Spectral Profiler (1982), Airborne Imaging Spectrometer (AIS) (1984 - 1986), Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) (1987, 1989, 1992, 1994, 1995, 2000), Low Altitude AVIRIS (1998, 2005, 2006), and EO-1 Hyperion (2001). The site has been studied in detail using field mapping and the remote sensing [data sets described above (41, 43]. The latest remote sensing work done at this site was validation and demonstration of EO-1 Hyperion mineral mapping [13]. Additional field validation was conducted during September 2006.

For the research reported here, the standardized hyperspectral analysis methods described above were used to extract mineral information from both 2005 AVIRIS and 2004 ASTER data (Figure 3). Spectral endmembers extracted from the AVIRIS data were modeled to the ASTER spectral response and used to map mineralogy over the full 5-scene ASTER dataset. ASTER mineral maps were produced at 1:250,000-scale along with corresponding topographic maps and field reconnaissance conducted. In addition, the Northern Grapevine Mountins area was extracted and examined in more detail. Various mineral occurrences were field verified, GPS tracks and waypoints marked, and ASD spectral measurements collected.

Figure 2: Northern Grapevine Mountains (N. Death Valley) and Cuprite, NV/ site locations.

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Figure 3: AVIRIS mineral endmembers (left) and modeled ASTER mineral signatures (2nd from left) for the Northern Grapevine Mountains site, CA/NV. AVIRIS mineral mapping (2nd from right) and ASTER mineral mapping (right) using the modeled HSI spectra for the Northern Grapevine Mountains site, CA/NV. Note ASTER’s inability to discriminate the “zeolite” (magenta) , “silica” (purple), or multiple sericite signatures (blue).

Figure 3 (left) shows the clear distinctions between spectral signatures for a variety of minerals at the N. Grapevine Mountains, NV site. Slight differences in the positions and shapes of specific features allow identification and mapping of calcite versus dolomite (based on features near 2.3 micrometers), several varieties of muscovite (sericite), a zeolite mineral, and silica. Figure 3 (2nd from right) shows the AVIRIS mineral mapping results using these endmembers. Figure 3 (2nd from left) shows the modeled ASTER signatures and illustrates the difficulties in identifying these same minerals using the ASTER bandpasses. Even so, Figure 3 (right) demonstrates that we can map the differences between the two carbonates with a good degree of success along with the muscovite minerals (as a group). There is significant confusion, however, between the carbonates and the zeolite mineral, and silica is not mapped. The extracted AVIRIS/ASTER-modeled spectra are carried forward to the regional analysis.

Cuprite, Nevada Site

Cuprite, Nevada has been used extensively as a test site for remote sensing instrument validation [44 – 54]. We used the Cuprite site as a second location to extract AVIRIS endmembers for use in extending the ASTER mineral mapping. Five flightlines of AVIRIS data acquired 11 May 2005 were processed using the standardized “hourglass” processing approach described above.

Endmembers extracted included calcite (red), muscovite (2 – blue/green), buddingtonite (cyan), jarosite (magenta), alunite (2 – maroon, purple), dickite (sea green), and kaolinite (orange) (Figure 4). Rowan et al. [54] have obtained similar results with AVIRIS and ASTER.

Figure 4: Cuprite, Nevada 2005 AVIRIS endmembers and mineral map of 5 AVIRIS flightlines.

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Extension to the Full 5-Scene ASTER Mosaic

The AVIRIS spectral endmembers extracted for the Northern Grapevine Mountains, NV and Cuprite, NV sites were examined and combined to form a unique set of endmembers for mineral mapping of the 5-scene ASTER mosaic of the Death Valley, CA/NV region (Figure 5, left). These were modeled to the ASTER spectral response (Figure 5, right).

Mixture-Tuned-Matched-Filtering (MTMF) mapping was used to map the location and spatial distribution of the AVIRIS/ASTER-modeled endmembers. Figure 6 shows the mapping results for the 5-scene mosaic covering the Death Valley region (the Goldfield, Death Valley, and Trona 1:250,000 UGSG Topographic Quadrangles).

Figure 5: Combined AVIRIS endmembers from analysis of Northern Grapevine Mountains, NV and Cuprite, NV AVIRIS data acquired 11 May 2005.

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Figure 6: ASTER mineral mapping using modeled HSI spectra and extended to full 5-scene ASTER ortho-mosaic for the N. Death Valley site, CA/NV. Mosaic is approximately 60 km wide by 300 km long. Mapping colors match endmember colors and names on Figure 5.

ASTER regional mapping results demonstrate that the AVIRIS endmembers can be used to extend the mineral mapping to large areas using the ASTER data. We have conducted field reconnaissance across much of the image-map and made selected ASD field spectral measurements. Field verification at several sites illustrates repeated success in mapping calcite versus dolomite, clay minerals as a group, and in some cases discriminating between various clay minerals (alunite, kaolinite, muscovite).

Selected Field-verified examples include:

(1) N. Grapevine Mtns, NV: Calcite vs Dolomite vs muscovite

(2) Goldfield, NV: Alunite vs kaolinite vs muscovite/illite

(3) Cuprite, NV: kaolinite vs alunite vs muscovite

(4) Grapevine Mtns, NV: Calcite vs Dolomite, muscovite

(5) Talc City, CA: Calcite vs dolomite vs 2 varieties of muscovite

(6) Darwin City, CA: Calcite vs dolomite vs muscovite

(7) Racetrack Valley, CA: Calcite vs Dolomite vs altered (sericite-rich) granite

(8) Last Chance Range, CA: Calcite vs Dolomite

(9) West Eureka Valley, CA: Two varieties of muscovite

N. Grapevine Site

Goldfield

Cuprite

Grapevine Mtns

Talc City

Racetrack Valley

Darwin City

West Eureka Valley

Last Chance Range

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Goldfield, Nevada Site (ASTER Mineral Predictions)

A case history of the ASTER mapping results at the Goldfield, NV site is provided to illustrate the ASTER regional mapping results above in more detail and the predictive nature of the ASTER mineral mapping.

The Goldfield mining district is a volcanic center thought to be a resurgent caldera [55, 56, 57]. At least two periods of volcanism occurred and the hydrothermal alteration present in the district was caused by convective circulation of hydrothermal solutions along a zone of ring fractures and their linear extensions. Rocks exposed at the surface include air-fall and ash-flow tuffs, flows, and intrusive bodies. Hydrothermal alteration is extensive [44, 55, 56, 57]. The district exhibits a zoned alteration pattern. The rocks in the area have extensive exposures of alteration minerals including alunite, kaolinite, microcrystalline silica, illite, and montmorillonite.

Figure 7 (left) shows an excerpt from the 5-scene ASTER mineral map covering the Goldfield site. Colors and mineral names are the same as for Figure 5. Previous unpublished reconnaissance mineral mapping for Goldfield by the authors using 1995 AVIRIS data (Figure 7, right) show a variety of minerals, including alunite, kaolinite, illite, and calcite. This image-map was previously published in Sabins [57] as a comparison of mineral identification using HSI data versus a Landsat color composite (MSI data result). Comparison of apparent level of detail in the ASTER mineral map to the AVIRIS result is a little confusing, as the previous AVIRIS mineral mapping only looked at a the specific mineral endmembers described above. Further, more detailed analysis of the AVIRIS data, or field spectral measurements are required to verify this mapping. It does, however, point out the potential of the approach. Figure 8 is a comparison of the same AVIRIS mineral map to a consolidated ASTER mineral map. Here we have combined specific similar minerals and color-coded them the same as the AVIRIS image.

Figure 7: Comparison of extended ASTER mineral mapping using ASTER-modeled AVIRIS endmember signatures (left) versus previous AVIRIS reconnaissance mineral mapping (right).

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Note the general high correspondence between the two image-maps. Also note, however, the confusion between some of the kaolinite and illite/muscovite when mapping on the ASTER data.

Figure 8: Comparison of extended ASTER mineral mapping using combined ASTER-modeled AVIRIS endmember signatures (left) versus AVIRIS mineral mapping (right). Similar minerals have been grouped for the ASTER data and color-coded the same as the AVIRIS data.

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5. SUMMARY AND FURTHER WORK

The AVIRIS/ASTER case histories presented highlight the importance of spectral resolution for mineral mapping, however, they show that MSI data actually can perform quite well given image-specific endmembers modeled to the MSI bandpasses. Using ASTER and signatures from areas identified using the hyperspectral data allows mapping of similar areas using the multispectral data. Results indicate that HSI endmember spectra modeled to the ASTER bandpasses can be used with ASTER SWIR bands for predicting general mineral groups (i.e., clays, and carbonates), and potentially identifying some minerals (calcite vs dolomite, kaolinite vs alunite, varieties of muscovite). Higher spectral resolution HSI data are essential, however, for the greatest level of detailed mineral mapping. ASTER analyzed in tandem with HSI data and spectral libraries have proven that detailed mineral mapping and exploration is possible with MSI data. While some specific minerals are ambiguous, the mineral maps produced using this method, identifying and mapping specific minerals based on their spectral signatures, are significant improvements over previous approaches that expressed simple spectral shape differences on color-composite images or as statistically different (but unidentified) classes.

Future work will include analysis of AVIRIS/ASTER data for additional geologic sites (and mineral assemblages) using the modeling methodology based on scene-external HSI and/or field spectra (but without scene-specific a priori hyperspectral analysis or knowledge). These results will be further compared to field measurements and subsequent hyperspectral analysis and mapping to further validate the spectral modeling approach and determine limitations in mapping specific minerasl.

6. ACKNOWLEDGEMENTS

This manuscript describes selected research performed by the authors as part of the NASA ASTER Science Team. The work was funded by NASA Contract NNH05CC10C. Exceptional support was provided by both the AVIRIS and MASTER aircraft/instrument teams. Particular thanks are due Ian McCubbin, Mike Eastwood, and Rose Domingues for their efforts in acquiring these essential data.

7. REFERENCES

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[13] Kruse, F. A., Boardman, J. W., and Huntington, J. F., Evaluation and Validation of EO-1 Hyperion for Mineral Mapping: in Special Issue, Transactions on Geoscience and Remote Sensing (TGARS), IEEE, v. 41, no. 6, June 2003, p. 1388 – 1400, 2003.

[14] Ungar, Stephen, Jay Pearlman, Jeffrey Mendenhall and Dennis Reuter, Overview of the Earth Observing One (EO-1) Mission: in Special Issue, Transactions on Geoscience and Remote Sensing (TGARS), IEEE, v. 41, no. 6, June 2003.

[15] Kahle, A.B., F.D. Palluconi, S.J. Hook, V.J. Realmuto and G. Bothwell, The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER). International Journal of Imaging Systems and Technology, 3: 144-156, 1991.

[16] Fujisada, H., Design and performance of the ASTER instrument: in Proceedings of SPIE, the International Society for Optical Engineering, 2583, p. 16-25, 1995.

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[19] Hook, S. J., Myers, J.J., Thome, K.J., Fitzgerald, M., and Kahle, A. B., The MODIS/ASTER airborne simulator (MASTER) – a new instrument for earth science studies: Rem Sens. Env., v. 76, p. 93-102, 2001.

[20] Hook, S. J.; The combined use of multispectral remotely sensed data from the short wave infrared (SWIR) and thermal infrared (TIR) for lithological mapping and mineral exploration: in Proceedings, Fifth Australasian Remote Sensing Conference, Perth, Western Australia, v. 1, p. 371 – 380, 1990.

[21] Kruse, F. A., Mapping Active and Fossil Hot Springs Systems Using AVIRIS, HYMAP, TIMS and MASTER (Abst): in Proceedings, 14th Thematic Conference, Applied Geologic Remote Sensing, 6-8 November 2000, Las Vegas, NV, Environmental Research Institute of Michigan (ERIM), Ann Arbor, MI, p. 122, 2000.

[22] Kruse, F. A., Combined SWIR and LWIR Mineral Mapping Using MASTER/ASTER: in Proceedings, IGARSS 2002, 24 – 28 June 2002, Toronto, Canada,

Published on CD ROM –Paper Int1_B15_04, ISBN: 0-7803-7537-8. Also in hardcopy, v. IV, p. 2267 – 2269, IEEE Operations Center, Piscataway, NJ, 2002.

[23] Rowan, L. C., and Mars, J. C., Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Spectrometer (ASTER) data: Remote Sensing of Environment, 84, p. 350-366, 2003.

[24] JPL ASTER website: http://asterweb.jpl.nasa.gov/characteristics.asp

[25] Kruse, F. A., Comparison of ATREM, ACORN, and FLAASH Atmospheric Corrections using Low-Altitude AVIRIS Data of Boulder, Colorado: In proceedings 13th JPL Airborne Geoscience Workshop, Jet Propulsion Laboratory, 31 March – 2 April 2004, Pasadena, CA., 2004.

[26] Boardman, J. W., and Kruse, F. A., Automated spectral analysis: A geological example using AVIRIS data, northern Grapevine Mountains, Nevada: in Proceedings, Tenth Thematic Conference, Geologic Remote Sensing, 9-12 May 1994, San Antonio, Texas, p. I-407 - I-418, 1994.

[27] Kruse, F. A., and Boardman, J. W., Characterization and Mapping of Kimberlites and Related Diatremes Using Hyperspectral Remote Sensing: in Proceedings, 2000 IEEE AeroSpace Conference, 18 – 24 March 2000, Big Sky, Montana, 2000.

[28] Research Systems Inc (RSI), ENVI User’s Guide, Research Systems Inc, Version 4.3, 2006.

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[30] Boardman, J. W., “Automated spectral unmixing of AVIRIS data using convex geometry concepts,” in Summaries, Fourth JPL Airborne Geoscience Workshop, JPL Publication 93-26, v. 1, p. 11-14, 1993.

[31] Boardman, J. W., F. A. Kruse, and R. O. Green, Mapping target signatures via partial unmixing of AVIRIS data: in Summaries of the5th JPL Airborne Earth Science Workshop, JPL Publication 95-1, Vol. 1., Jet Propulsion Laboratory, California Institute of Technology, Pasadena, Calif., pp. 23 – 26, 1995.

[32] Kruse, F. A., and Lefkoff, A. B., Knowledge-based geologic mapping with imaging spectrometers: Remote Sensing Reviews, Special Issue on NASA Innovative

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Research Program (IRP) results, v. 8, p. 3 – 28, 1993.

[33] Kruse, F. A., Lefkoff, A. B., and Dietz, J. B. 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, 1993.

[34] Kruse, F. A., Lefkoff, A. B., Boardman, J. B., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., and Goetz, A. F. H., The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging Spectrometer Data: Remote Sensing of Environment, Special issue on AVIRIS, May-June 1993, v. 44, p. 145 – 163, 1993.

[35] Boardman, J. W., “Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering,” in Summaries of the Seventh Annual JPL Airborne Geoscience Workshop, Pasadena, CA, p. 55, 1998.

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[37]Sensor Information Laboratories website: http://www.silc.co.jp/en/index.html

[38]Analytical Imaging and Geophysics LLC (AIG), “ACORN User's Guide, Stand Alone Version,” Analytical Imaging and Geophysics LLC, 64 p., 2001.

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BIOGRAPHIES

Dr Fred A. Kruse has been involved in scientific research and the practical application of multispectral, hyperspectral, and SAR remote sensing for over 25 years in positions with the U. S. Geological Survey, the University of Colorado, and private industry. He holds a B.S. (1976, Geology) from the University of

Massachusetts, Amherst, and the M.S. (1984) and Ph. D. (1987), in geology, both from the Colorado School of Mines. Dr. Kruse has been on several NASA Science Teams, including the Shuttle Imaging Radar-C (SIR-C) Science, the EO-1 Hyperion Science Validation Team, and the Advanced Spaceborne Thermal Emmission and Reflection Radiometer (ASTER) Science Team. He is currently Principal Scientist, Horizon GeoImaging, LLC, Frisco, Colorado. His primary scientific interests are in the characterization and mapping of the geology of the Earth’s surface using combined analysis of VNIR/SWIR/MWIR/LWIR, and SAR remote sensing data

and Data Fusion. Additional areas of expertise and ongoing research include the development and application of analysis and visualization techniques for multispectral and hyperspectral data, and the use of knowledge-based artificial intelligence (AI) techniques to identify and map Earth-surface materials for geologic mapping, environmental monitoring, and military and homeland security activities. Dr. Kruse is also one of the scientists that originally developed the image analysis software, "ENVI".

Sandra Perry is a consulting geologist and partner at Perry Remote Sensing LLC of Englewood, Colorado working on applications of satellite-based remote sensing to geologic exploration for oil, natural gas, metals, and groundwater. She has two degrees in geology: a B.Sc.

from Indiana State University, and a M.S. from the Colorado School of Mines. Previous positions include Cities Service Corporation, U.S.G.S., Barringer Geoservices Inc., and Johnson Controls World Services for a combined 26 years of industry experience. Her application and research interests include rock/soil composition prediction, mineral mapping, and structural geologic interpretation using satellite multispectral systems.

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