Regional Mineral Mapping By Extending Hyperspectral Signatures

Regional Mineral Mapping By Extending Hyperspectral
Signatures Using Multispectral Data
1, 2
Fred A. Kruse
Horizon GeoImaging, LLC
P.O. Box 4279, Frisco, CO 80443
970-668-3607, [email protected]
and
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.
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
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 commercialoff-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 sceneexternal 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.
1
1
2
Sandra L. Perry
Perry Remote Sensing, LLC
Englewood, CO 80113
1-4244-0525-4/07/$20.00 ©2007 IEEE.
IEEEAC paper #1078, Version 4, Updated November 24, 2006
1
reconnaissance and spectral measurements are being used to
validate the ASTER modeling results.
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.
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:
Table 1: AVIRIS/Hyperion Sensor Comparison.
HSI
Sensor
AVIRIS
Hyperion
Spectral
Bands
224
242
Spectral
Resolution
10 nm
10 nm
Spatial
Resolution
20 m
30 m
Swath
Width
12 km
7.5 km
SWIR
SNR
~500:1
~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:
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.
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/shortwaveinfrared/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]
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
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).
2
Table 2: ASTER Characteristics [24]
Characteristic
Spectral Range
Ground Resolution
Swath Width
VNIR
Band
1:
Nadir looking
Band
2:
Nadir looking
Band
3:
Nadir looking
Band
3:
Backward looking
15m
60km
SWIR
Band 4: 1.600 - 1.700 µm
LWIR
Band 10: 8.125 - 8.475 µm
Band 5: 2.145 - 2.185 µm
Band 11: 8.475 - 8.825 µm
Band 6: 2.185 - 2.225 µm
Band 12: 8.925 - 9.275 µm
Band 7: 2.235 - 2.285 µm
Band 13: 10.25 - 10.95 µm
Band 8: 2.295 - 2.365 µm
Band 9: 2.360 - 2.430 µm
30m
60km
Band 14: 10.95 - 11.65 µm
90m
60km
Atmospheric Correction:
3. APPROACH AND METHODS
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.
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.
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 pixelby-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.
(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
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.
(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
3
Apparent Reflectance
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].
Model-based
Methods
MNF
Spectral Data
Reduction
PPI
Spatial Data
Reduction
n-D
Visualization
ID
Identification
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.
Mapping
Map Distribution
and Abundance
Binary, SAM
Unmix
MF & MTMF
SFF
(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]
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
atmospheric model such as ACORN [25]
using
(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, bandinterleaved format required by the
ACORN
atmospheric correction software. SWIR data were
scaled to VNIR 15m spatial resolution
an
(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
(2) Spectral compression, noise suppression, and
dimensionality reduction using the Minimum Noise
Fraction (MNF) transformation [29, 30]
(3) Determination of endmembers using
methods (Pixel Purity Index – “PPI”) [26]
geometric
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.
(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 ndimensional vector space [34]. “Mixture-TunedMatched-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
4
(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].
4. RESULTS
We present a case history to demonstrate the methods and
basic results and show the ability to extend HSI/ASTERmodeled 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
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.
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 graniticcomposition and some age-dates are available [40].
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.
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
5
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.
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.
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.
Figure 4: Cuprite, Nevada 2005 AVIRIS endmembers and
mineral map of 5 AVIRIS flightlines.
6
Extension to the Full 5-Scene ASTER Mosaic
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).
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).
Figure 5:
Combined AVIRIS endmembers from analysis of Northern Grapevine Mountains, NV and Cuprite, NV
AVIRIS data acquired 11 May 2005.
7
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 imagemap 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:
muscovite
Goldfield
West Eureka Valley
Calcite vs Dolomite vs
(2) Goldfield, NV: Alunite vs kaolinite vs muscovite/illite
Cuprite
(3) Cuprite, NV: kaolinite vs alunite vs muscovite
N. Grapevine Site
(4) Grapevine Mtns, NV: Calcite vs Dolomite, muscovite
Last Chance Range
(5) Talc City, CA: Calcite vs dolomite vs 2 varieties of
muscovite
Grapevine Mtns
(6) Darwin City, CA: Calcite vs dolomite vs muscovite
(7) Racetrack Valley, CA: Calcite vs Dolomite vs altered
(sericite-rich) granite
Racetrack Valley
(8) Last Chance Range, CA: Calcite vs Dolomite
Talc City
(9) West Eureka Valley, CA: Two varieties of muscovite
Darwin City
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.
8
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 colorcoded them the same as the AVIRIS image.
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: Comparison of extended ASTER mineral mapping using ASTER-modeled AVIRIS endmember signatures (left)
versus previous AVIRIS reconnaissance mineral mapping (right).
9
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.
10
[2] Clark, R. N., Swayze, G. A., Rowan, L. C., Livo, K.E.,
and Watson, K., 1996, Mapping surficial geology,
vegetation communities, and environmental materials in
our national parks: The USGS imaging spectroscopy
integrated geology, ecosystems, and environmental
mapping project: in Summaries of the 6th Annual JPL
Airborne Earth Science Workshop, JPL Pub. 96-4, Vol.
1. AVIRIS Workshop, Jet Propulsion Laboratory,
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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 colorcomposite images or as statistically different (but
unidentified) classes.
[3] Green, R. O., and Dozier, J., 1996, Retrieval of surface
snow grain size and melt water from AVIRIS spectra: in
Summaries of the 6th Annual JPL Airborne Earth Science
Workshop, JPL Pub. 96-4, Vol. 1. AVIRIS Workshop,
Jet Propulsion Laboratory, California Institute of
Technology, Pasadena, Calif., p. 127-134.
[4] Hamilton, M. K., Davis, C. O., Rhea, W. J., Pilorz, S. H.,
and Carder, K. L., 1993, Estimating chlorophyll content
and bathymetry of Lake Tahoe using AVIRIS data:
Remote Sensing of Environment, v. 44, nos. 2-3, p. 217 230.
[5] Goetz, A. F. H., Vane, G., Solomon, J. E., and Rock, B.
N., Imaging spectrometry for earth remote sensing:
Science, 228, 1147-1153, 1985.
[6] Green, R. O., B. Pavri, J. Faust, and O. Williams,
“AVIRIS radiometric laboratory calibration, inflight
validation, and a focused sensitivity analysis in 1998,” in
Proceedings of the 8th JPL Airborne Earth Science
Workshop: Jet Propulsion Laboratory Publication 99-17,
p. 161 – 175, 1999.
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.
[7] Pearlman, J, Stephen Carman, Paul Lee, Lushalan Liao
and Carol Segal,, Hyperion Imaging Spectrometer on the
New Millennium Program Earth Orbiter-1 System: In
Proceedings, International Symposium on Spectral
Sensing Research (ISSSR), Systems and Sensors for the
New Millennium, published on CD-ROM, International
Society for Photogrammetry and Remote Sensing
(ISPRS), 1999.
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.
[8] Green, R. O., T.G. Chrien, and B. Pavri: On-orbit
determination of the radiometric and spectral calibration
of Hyperion using ground, atmospheric and AVIRIS
underflight measurements, IEEE Trans. Geosci. Remote
Sensing, 41 (6), 1194- 1203, 2003.
[9] USGS E0-1 Website: http://eo1.usgs.gov/
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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.
[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
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[22] Kruse, F. A., Combined SWIR and LWIR Mineral
Mapping Using MASTER/ASTER: in Proceedings,
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[32] Kruse, F. A., and Lefkoff, A. B., Knowledge-based
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Grapevine Mountains, Nevada,” in Proceedings of the
8th JPL Airborne Earth Science Workshop: Jet Propulsion
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258, 1999.
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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
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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".
[53] Swayze, G.A., The hydrothermal and structural history
of the Cuprite Mining District, southwestern Nevada: an
integrated geological and geophysical approach, Ph. D.
Dissertation, University of Colorado at Boulder, 399 p.,
1997.
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.
[54] Rowan, L.C.; S.J. Hook; M.J. Abrams; and J.C. Mars,
Mapping hydrothermally altered rocks at Cuprite, Nevada,
using the Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER), a new satellite-imaging
system, Economic Geology and the Bulletin of the Society
of Economic Geologists, 98(5): 1019-1027 , 2003.
[55] Ashley, R. P., Goldfield Mining District: Nevada
Bureau of Mines and Geology, Reno, NV, NBMG Rep.
19, pp. 49 –66, 1974.
[56] Ashley, R. P., Relation Between Volcanism and Ore
Deposition at Goldfield, Nevada, Nevada Bureau of
Mines and Geology, Reno, NV, NBMG Rep 33, pp. 7786, 1979.
[57] Sabins, F. F., Remote Sensing Principles and
Interpretation, Third Edition, W. H. Freeman and
Company, New York, 494 pp., 1997.
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
14
15