geological map generation using aster
TRANSCRIPT
www.elsevier.com/locate/rse
Remote Sensing of Environm
Seamless geological map generation using ASTER in the
Broken Hill-Curnamona province of Australia
R.D. Hewson a,*, T.J. Cudahy a, S. Mizuhiko b, K. Ueda c, A.J. Mauger d
aCSIRO Exploration and Mining, ARRC, Western AustraliabERSDAC, Tokyo, Japan
cSumiko Consultants Co., Tokyo, JapandPIRSA Geological Survey of SA, South Australia
Received 20 September 2004; received in revised form 11 April 2005; accepted 28 April 2005
Abstract
The availability of multiple ASTER image acquisitions enables regional-scale geological mapping, though instrument, irradiance,
atmospheric and surface scattering effects can cause problems in generating seamless mosaics of geological information products. These
issues, including shortwave infrared (SWIR) crosstalk, were addressed in producing seamless ASTER geological maps over the Curnamona
Province, associated with the world class Pb–Zn–Ag Broken Hill deposit. Over 35 ASTER scenes covering an area of approximately 52,000
km2 from 14 different overpass dates were acquired. Maps of Al–OH and Mg–OH/carbonate were generated from ASTER SWIR data as
well as a map of quartz content from the thermal infrared (TIR) data. Maps of ferrous iron content were also generated from the SWIR data of
individual ASTER scenes. The SWIR bands also enabled qualitative mapping of the Al–OH composition though garnet and feldspar – rich
units were not well mapped using the TIR. Field sampling and spectral measurements, together with detailed 1 :25,000 mapping and large-
scale HyMap surveying, constrained the accuracy of the ASTER-derived geological products.
Crown Copyright D 2005 Published by Elsevier Inc. All rights reserved.
Keywords: ASTER; Geological mapping; Broken Hill; Curnamona; Multispectral
1. Introduction
The accessibility of inexpensive, satellite-borne, multi-
spectral ASTER data has created new opportunities for the
regional mapping of geological structure and rock types
including alteration products, and regolith. These data have
been used enthusiastically by the minerals industry around
the world. The ASTER sensor was developed by Japan and
launched onboard NASA’s Terra satellite platform. ASTER
acquires imagery within a 60�60 km scene area from 14
different spectral bands with a pixel resolution of between
15 to 90 m, depending on wavelength (Fujisada et al., 1998;
Thome et al., 1998; Yamaguchi et al., 2001). Of particular
interest for remote sensing geoscientists are the inclusion in
ASTER of detectors covering the visible-near infreared
0034-4257/$ - see front matter. Crown Copyright D 2005 Published by Elsevier
doi:10.1016/j.rse.2005.04.025
* Corresponding author. Tel.: +61 8 64368 689; fax: +61 8 64368 555.
E-mail address: [email protected] (R.D. Hewson).
(VNIR), shortwave infrared (SWIR) and thermal infrared
(TIR) wavelength regions offering the potential for discrim-
inating phyllosillicates and also other silicates. Several
examples of generating mineralogical maps using single
ASTER scenes have proved successful (Rowan & Mars,
2003; Hewson et al., 2001).
The area of study encompasses approximately 52,000
km2 of the Curnamona Province from Broken Hill in
western New South Wales to Olary in South Australia and
the surrounding regolith-dominated terrain (Fig. 1). This
study examined the pre-processing issues involved with
handling over 30 ASTER scenes acquired on 14 different
dates within the Curnamona Province (Fig. 1). These pre-
processing issues included SWIR crosstalk (Iwasaki et al.,
2001), which has a significant detrimental effect on SWIR
spectral signatures. Following pre-processing, a number of
quality-control issues for the ASTER-derived geological
maps were also examined with the aid of field measure-
ent 99 (2005) 159 – 172
Inc. All rights reserved.
139° E 140° E 141° E 142° E 143° E
31° S
32° S
33° S
NTQld
NSW
SA
Vic
Tas
WA
Fig. 1. ASTER scenes acquired from fourteen different dates (each colour corresponding to different acquisitions). Blue boundary marks project study area.
(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172160
ments and airborne hyperspectral HyMap data (Robson et
al., 2003).
The main objective of this study was to generate new,
accurate and seamless geological/mineralogical information
from ASTER images acquired within the Curnamona
Province. Specific objectives include:
1. Characterising of the SWIR crosstalk effect and assess-
ment/development of methods that can effectively
remove this instrument problem;
2. Characterising of the effects of the atmosphere, espe-
cially water vapor, in the SWIR;
3. Characterising of cloud/cloud shadows and strategies for
their masking;
4. Generating a method for generating seamless geological
products;
5. Identifying the diagnostic spectral features that can be
targeted for mapping mineral groups within outcropping
geological units and regolith of the Curnamona Province;
6. Devising suitable algorithms for mapping such mineral
groups over multiple mosaiced ASTER scenes;
7. Validating the derived information products using field/
airborne data and scene-based methods;
8. Establishing the credibility or otherwise of the generated
maps by comparison with published geology from the
New South Wales and South Australian Geological
Surveys in conjunction with field observations and
spectral measurements;
9. Contributing more geological mapping detail to the Olary
Domain of the Curnamona Province.
2. Geological setting
The study area for this project encompasses the
Curnamona Province that continues to attract interest for
its potential economic Broken Hill-style deposits of Pb–
Zn base metals and possible Cu–Au systems associated
with hydrothermal alteration. The world-class Broken Hill
Pb–Zn–Ag orebody within the eastern part of the study
area is associated with high-grade metamorphic rocks. A
variety of tightly folded and high-grade metamorphic units
form well-exposed outcrops, including gneiss, schist,
pelite, psammite, amphibolite and granulite lithologies
(Stevens et al., 1988). An equivalent suite of classified
geological units belonging to the Curnamona Province is
also found in the Olary Domain of South Australia
(Conor & Fanning, 2001) although this area lacks the
detailed 1 :25,000 geological mapping of the Broken Hill
Domain.
Of interest for exploration in the Broken Hill area is the
mapping of regional prograde and retrograde metamor-
phism and possible metasomatic alteration, either associ-
ated associated with the Broken Hill type syngenetic or
epigenetic fluids. Previous studies have shown retrograde
alteration associated with the development of muscovite/
sericite and chlorite (Corbet & Phillips, 1981). Rowan and
Mars (2003) have shown that ASTER can map AlOH
abundance and possibly changes related to AlOH chem-
istry (Duke, 1994), associated with metamorphism.
3. Pre-processing issues and strategies for mosaicing
ASTER imagery
The generation of multi-scene ASTER image ‘‘seam-
less’’ products requires consideration of sensor character-
istics (i.e. crosstalk), atmospheric effects (scattering and
transmission), soil conditions and anthropogenic effects.
Chief amongst these issues for the ASTER SWIR bands are
instrumental crosstalk effects and atmospheric transmission.
In this study for the most part, level 1B (L1B) ASTER
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172 161
radiance-at-sensor images were used for the SWIR bands 4
to 9, because of the possibility of inaccurate atmospheric
correction in the surface reflectance standard product,
AST_07, available from the Land Processes Distributed
Active Archive Centre (LPDAAC) (Rowan & Mars, 2003).
Most importantly, however, at the time of this study the
SWIR crosstalk software correction was not routinely
applied to L1B archived data obtained from either the
Earth Remote Sensing Data Applications Centre (ERS-
DAC) or LPDAAC. Version 3.0 beta SWIR crosstalk
software, courtesy of ERSDAC, was applied to all L1B
data acquired for this study. The Level 2 (L2) ASTER
surface emissivity data used in this study were derived
from the L1B radiance data after atmospheric correction
(Thome et al., 1998) and separation of the emissivity
component from the kinetic temperature component (Gil-
lespie et al., 1998).
3.1. SWIR Pre-processing
SWIR crosstalk is an offset or additive error in radiance
due to the leakage of photons from one detector element to
another (Iwasaki et al., 2001). This cross-detector leakage is
most pronounced from band 4 to bands 5 and 9, but it
affects all SWIR bands. For very dark pixels adjacent to
bright pixels, the crosstalk effect will approach 100% of the
input radiance signal. A spatially dependent software
correction for crosstalk has been developed by Iwasaki et
al. (2001) and has since been incorporated by the Japanese
ASTER Ground Data System (GDS) as a part of its L1B
pre-processing. GDS have made this software publicly
available for users to correct their existing ASTER L1B
data of crosstalk effects (http://www.gds.aster.ersdac.or.jp/
gds_www2002/service_e/u.tools_e/set_u.tool_ecross.html).
This software was applied to the Curnamona L1B data to
generate corrected ASTER Hierarchal Data Files (HDF)
files using input parameters listed in Table 1. These input
parameters include the amplitude, a, of the amount of
incident light leaked from band 4 (% units); jx , the size
(pixels) of the applied Gaussian filter function in the across
track direction; and jy , the size (pixels) of the Gaussian
filter function in the along track direction.
An apparent east-west offset between the different
georeferenced SWIR bands along the scene boundaries
results from the inclined descending orbital path and the
Table 1
Input parameters for ERSDAC Crosstalk Software (v. 3.0) applied in
Curnamona Study
ASTER Band a rx ry
5 0.15 42 39
6 0.06 31 40
7 0.04 29 30
8 0.06 31 40
9 0.15 42 39
staggered timing of acquisition for each of the spectral
bands for a given row/pixel. This offset of the SWIR
image boundaries can be of the order of 20 pixels (i.e.,
600 m) although there is no apparent corresponding
spatial offset between bands within the actual image.
Such offsets between SWIR images can generate artifacts
along the east-west boundaries of mosaicked band ratio
images. Corrections were applied to each ASTER scene of
SWIR images using simple conditional algorithms to test
for the presence of the full complement of pixel spectral
data.
ASTER’s default projection datum, WGS 84, is effec-
tively identical to the Australian datum, GDA94, used for
geological mapping and results presented in this study. A
comparison of the ASTER L1B SWIR data to the 1 :25,000
mapping available in the Broken Hill area, indicated that
the image data spatial accuracy was better than 100 m. An
analysis of the spatial accuracy for seven ASTER scenes
(SWIR bands) acquired at four different pointing angles of
�8.58-, �2.87-,+2.88- and +8.57-, revealed average
residual error distances of 45.2 m, 70.8 m, 34.3 m and
98.4 m, respectively. The overall geometric accuracy from
25 ground control points over the seven ASTER scenes
was 63.7 m and regarded as sufficiently accurate for this
study.
To maximise the dynamic range of the 8-bit SWIR data
and process the L1B data into calibrated radiance at the
sensor (W/m2/sr/Am), a set of gains (unit conversion
coefficients) were applied after the crosstalk correction
(Abrams et al., 2002). The calibration to spectral radiance
units of the L1B ASTER data were then obtained using the
equation, Radiance=(DN – 1) * Gain.
Variable illumination conditions of the ASTER radiance
data resulting from different solar angles can also be a
significant seasonal effect related to the cosine of the solar
incident angle (Schowengerdt, 1997). At the latitude of the
Curnamona study area (¨32- S) and for the ASTER
acquisition time (¨10:30 a.m.), the range of solar incidence
angles for flat ground varies from 60 to 23 degrees (from
winter to summer solstice), producing almost a twofold
change in spectral irradiance. However this effect is
cancelled if spectral normalisation (e.g., band ratios) is
employed in the information-extraction strategy (Abrams et
al., 1983).
Another major issue involved with processing mo-
saicked ASTER SWIR data into seamless maps is the
variability of atmospheric water vapor between different
acquisitions. The lack of ASTER spectral bands over key
water absorption bands and the situation where atmospheric
information from other Terra atmosphere instruments were
not used routinely for ASTER corrections at the time of
this study, means that standard climate models have been
used for ASTER atmospheric corrections. Thus towards the
deep atmospheric absorptions at wavelengths longer than
2.5 Am, L2 surface reflectance data are prone to errors,
particularly for ASTER band 9 (e.g. 2.360–2.430 Am).
Fig. 2. MODTRAN 4 atmospheric radiative transfer model results at
ASTER SWIR spectral resolution (bands 4 to 9) indicating effects of
variable climates and associated water vapor on radiation.
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172162
The variation of atmospheric transmissivity at ASTER
SWIR spectral resolution was estimated, using MOD-
TRAN4 (Berk et al., 1999). This atmospheric modeling
indicated that radiance measurements by ASTER bands 4
and 7 are insensitive to changes in water vapor associated
with different modeled climatic conditions (Fig. 2). Bands
8 and 9 are the most affected by water vapor absorption
(Fig. 2). The effects of the changes in the atmospheric
water vapor during different ASTER L1B data acquis-
itions from the Curnamona region become obvious for
mosaicked ASTER band ratios b4 /b7 and b7 /b9 using
L1B data within the Broken Hill region (Fig. 3a,b,c). Fig.
3a shows the different acquisitions of ASTER at different
dates (i.e., different shades). A mosaic of ASTER
radiance data as a simple ratio of bands 4 and 7 yields
Fig. 3. a) ASTER acquisitions for seven different dates for the eastern Broken Hill
b7. Broken Hill Domain outcrop in red. c) ASTER L1b band ratio b7 /b9. Broken H
colour in this figure legend, the reader is referred to the web version of this artic
a seamless mosaic of overlapping imagery (Fig. 3b). By
comparison, the ratio of bands 7 and 9 show major
differences across different acquisition dates (Fig. 3c).
Although path radiance is predominantly occurring within
VNIR wavelengths, any significant additive aerosol
scattering effect within the SWIR would likely to show
up within the b4 /b7 mosaic. The importance of these
results is that differences between ASTER_s SWIR
radiance data acquired at different dates, under varying
atmospheric and solar illumination conditions, appear
effectively multiplicative in nature. In this study ASTER
L1B radiance data were mosaicked assuming linear gain
factors to adjust for variable acquisition conditions.
Further MODTRAN modeling in the future could usefully
also be undertaken to derive aerosol scattering effects and
path radiance. If ASTER SWIR responses to variable
atmospheric conditions are predominantly multiplicative,
path radiance and atmospheric scattering effects should be
near zero.
The significance of the combined errors associated with
crosstalk and inaccurate atmospheric correction can be
demonstrated by the comparison between the ASTER L2
surface reflectance signature and ASD (Analytical Spectral
Devices Inc.) VNIR–SWIR spectral measurements, col-
lected at the Broken Hill Airport and resampled to ASTER
spectral resolution (Fig. 4). The large contrast between the
field reflectance measurements of the gravel and bitumen
runways is not reproduced by the ASTER L2 data (Fig.
4). In addition, the shape of the ASTER SWIR signatures,
portion of the Curnamona study area (blue). b) ASTER L1b band ratio b4 /
ill Domain outcrop boundary in red. (For interpretation of the references to
le.)
Fig. 4. Comparison between ASD field VNIR-SWIR measurements and ASTER L2 surface reflectances for Airfield 1 and Airfield 2 validation sites at
Broken Hill.
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172 163
particularly at bands 5 and 9, shows a significant
difference compared with the field measurements, due
largely to the uncorrected crosstalk effect (Iwasaki et al.,
2001). These results emphasize the importance of correct-
ing crosstalk in ASTER SWIR radiance data.
Gain factors used for mosaicking Level 1B SWIR
radiance-at-sensor data in this study, were empirically
derived from the band means of overlapping ASTER scene
areas acquired during different satellite orbits. For most
scenes, band means were derived automatically from
overlapping areas using image-derived statistics. However,
areas with clouds and associated shadows required manual
definition of the overlapping scene areas to extract reliable
band-mean statistics. Gains were subsequently calculated
relative to a chosen reference image (e.g., Broken Hill
ASTER scene) from the ratio of band means and applied to
each scene acquired from the same orbit and date. This
simple method effectively yielded seamless images across
14 different ASTER acquisition dates for each SWIR band,
having adjusted the L1B data into ‘‘apparent’’ radiance units
relative to a reference scene. The same set of SWIR gains
could generally be used for all ASTER scenes collected
along the same orbit for most cases, although an exception
was observed for ASTER acquisition straddling the Barrier
Ranges at Broken Hill with likely localized water vapor
variations.
Clouds represent a potential problem for seamless
geological mapping for several reasons. Firstly, both
clouds and their shadows can obscure the underlying
surface. Secondly, clouds can affect those mosaicking
procedures that rely on scene statistics. Hence, clouds
should first be identified and then masked out. It was also
observed that residual crosstalk effects could still be
present in high- and low-albedo areas, including those
associated with clouds, and especially within their shadows
(Fig. 5a–e). These residual effects produced ‘‘false
anomalies’’ in SWIR ratio image products. Although the
ASTER operator, GDS, uses an automatic cloud identi-
fication algorithm that attempts to screen all Level 1A
scenes with greater than 20% cloud cover, this process is
still undergoing improvements and can sometimes be
problematic in areas of limited outcrop, such as in the
Curnamona Province.
An algorithm for masking clouds and their shadows
was developed in this study using thresholded ASTER
L1B bands 10 and 3 radiance data, respectively. The low
albedo observed from cloud shadows in VNIR band 3
images, and the low radiant temperatures of thick cloud
tops observed from TIR band 10 images, enabled the
successful mask development of this ASTER data. It was
found, however, that manual rather than automated histo-
gram thresholding was required to limit the masking to
cloud-related features instead of possible geological or
topographic-related effects (i.e., shadowed areas from
sharp relief). In the example shown, band 3 and band 10
(Fig. 5a,b) were thresholded to produce a mask (Fig. 5c).
Crosstalk-corrected L1B SWIR images were processed to
generate AlOH (including muscovite) abundance maps
using band combination [(b5+b7)/b6] (Rowan & Mars,
2003); however shadowed areas are falsely highlighting
high AlOH content (Fig. 5d). This is predominantly the
result of uncorrected residual crosstalk additively contri-
buting to the ASTER signal. Application of the cloud
mask (Fig. 5c) to the AlOH abundance image produces an
improved map result, removing most of the cloud-induced
artifacts but still highlighting small outcrops in the extreme
south east of the scene (Fig. 5e).
3.2. TIR Pre-processing
A comparison between ASTER emissivity data and the
average of TIR spectral field measurements using Design
Fig. 5. a) ASTER L1B band 3 highlighting cloud and associated shadow; b) ASTER L1B thermal band 10 highlighting cloud; c) cloud mask generated from
thresholded bands 3 and 10; d) ASTER L1B generated AlOH abundance anomalies. Light areas indicate interpreted high AlOH abundance; e) ASTER L1B
generated AlOH abundance imagery masked for clouds and associated shadows. Note the interpreted AlOH anomalies that were associated in Fig. 5d with
cloud covered areas.
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172164
and Prototypes’s microFTIR 101 (Hook & Kahle, 1996),
resampled to ASTER spectral resolution, showed similar
signatures for several validation sites including the currently
unused North Broken Hill Mine dump (Fig. 6a). The
consistency of the ASTER-derived surface emissivity
signatures for different acquisition dates (i.e., acquired at
different temperatures and/or atmospheric conditions) was
also examined using two ASTER overpasses (Fig. 6b).
Overall the ASTER derived emissivities, under different
conditions, essentially showed similar signatures though
small spectral variations can be observed in detail,
especially in band 14 (¨11.3 Am) (Fig. 6b).
Initial attempts at generating seamless, accurate, geo-
logical information products derived from ASTER L2 TIR
surface emissivity data yielded images that showed no
apparent residual temperature. Some discontinuous line-
striping, related to systematic drift in instrument response,
was apparent; however the minor nature of this problem,
despite the relatively low signal to noise of these TIR
data, indicated that the final geological products were not
severely compromised by this problem.
4. Strategies for generating ASTER seamless geologic
maps
An overview of the various pre-processing steps devised
for this study dealing with the issues described above is
illustrated in Fig. 7. These steps are time-consuming and
automation of this methodology is needed, particularly
Fig. 6. a) Comparison between field mFTIR and ASTER L2 emissivity signatures at Broken Hill Pit Dump, b) repeatability of ASTER L2 surface emissivity
signatures for two different acquisitions (solid vs dashed lines) at Broken Hill.
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172 165
involving the calculation of gain factors for the adjustment
of variable illumination and atmospheric conditions between
different acquisitions.
5. Mineral group spectroscopy at ASTER spectral
resolution
The ability of laboratory-based VNIR, SWIR and TIR
spectroscopy to measure and enable identification of
minerals and mineral groups has already been established
for several decades (Hunt & Ashley, 1979; Lyon & Burns,
1963; Vincent et al., 1975; Vincent & Thomson, 1972). In
particular OH-bearing minerals and other silicates have
been shown to display diagnostic spectral features within
the SWIR and TIR wavelength regions, respectively (Clark
et al., 1990; Grove et al., 1992; Salisbury & D’Aria,
1992). Resampling of VNIR-SWIR and TIR mineral
library spectra (Clark et al., 1990; Grove et al., 1992,
Salisbury & D’Aria, 1992) to ASTER spectral resolution
provides a basis for understanding the potential limit of
extracting mineral (group) information from ASTER (Fig.
8a and b). In particular, MgOH minerals such as chlorite
and hornblende, have limited diagnostic SWIR spectral
Fig. 7. Overview of pre-processing strategies for mosaicking ASTER
imagery at Curnamona.
absorption features at ASTER resolution that can be
potentially confused with carbonate (Fig. 8a). Garnets
(e.g., almandine, spessartine) are common high-grade
metamorphic minerals within certain units at Broken Hill
are important indicators of Broken Hill-style base metal
mineralisation (Spry & Wonder, 1988). Garnets display
broad VNIR and TIR features at ASTER spectral
resolution, especially from bands 3 to 4 and 12 to 13,
though possible confusion with the spectral features of
green vegetation at VNIR wavelengths and some mafic
silicates at TIR wavelengths needs to be considered (Fig.
8a,b). Feldspars (e.g., albite, anorthite) are common and
important for indicating alteration associated with albitisa-
tion in the Curnamona Province. However, the lack of a
spectral band in the 9.6 Am region, because of strong
atmospheric ozone absorption experienced from a satellite
platform, renders feldspar mapping difficult at ASTER
spectral resolution.
The modeled ASTER SWIR spectra of AlOH minerals
(e.g., kaolinite, Al-poor and Al-rich mica) displayed in Fig.
8a indicate changes in the symmetry of the AlOH
absorption feature centered at 2.2 Am, or ASTER band
6. Previous work by Duke (1994) has shown that white
mica chemistry (e.g., muscovite/illite, phengite), particu-
larly its Al content, can be inferred by the wavelength of
its 2.2 Am absorption feature. Duke (1994) showed that Al
poor micas (e.g., phengite) display a AlOH absorption
feature with a longer wavelength than Al-rich micas. On
the basis of this absorption feature, observed by ASTER
bands 5, 6 and 7, an estimate of AlOH abundance was
estimated by the ASTER band combination, (b5+b7)/b6
(Rowan & Mars, 2003), and inferred white mica compo-
sition by band ratio indexes b5/b6, b7/b6 and b7/b5.
Resampling of muscovite library spectra into ASTER
equivalent spectra suggested that a high b5/b6 and low
b7/b6 can represent a longer wavelength mica absorption
feature compared to the converse situation of a low b5/b6
Fig. 8. a) Laboratory (solid lines) and ASTER equivalent resampled (dashed) VNIR–SWIR mineral library reflectance signatures; b) laboratory and ASTER
equivalent resampled TIR mineral library emissivity signatures.
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172166
and high b7/b6 result. It is also suggested by these
resampled spectra, that kaolinite may be discriminated
from white mica using b7/b5. In a similar way, abundances
of MgOH (e.g., chlorite, hornblende) and carbonate (e.g.,
calcite) group minerals were estimated by the ASTER
band parameter, (b6+b9)/b8, based on their 2.33–2.35 Am(band 8) absorption feature. The presence of ferrous iron in
MgOH silicates can also display a steady rise in the SWIR
spectral reflectance signature approximately from 1.0 to
2.0 Am. This trend can be preserved at ASTER spectral
resolution as indicated for the chlorite and hornblende
spectra (Fig. 8a). Estimates of the ferrous iron content
using the ASTER band ratio b5 /b4 were also examined in
this study.
Quartz has a pronounced reststrahlen TIR spectral feature
within the 8 to 9.2 Am region producing a diagnostic
emissivity signature that can be also observed by ASTER
bands 10 to 12 (Fig. 8b). Phyllosilicates (e.g., muscovite,
kaolinite) by comparison generally display longer TIR
wavelength spectral features between 8.6 to 9.6 Am or
ASTER bands 11 and 12. Consequently in this study
ASTER band ratio b13 /b10 was used to map quartz-rich
units and regolith (Fig. 8b).
6. ASTER seamless geologic maps for the Curnamona
Province
Seamless images were generated from the ASTER L1B
SWIR radiance data to map major geological units rich in
Al–OH, Mg–OH/carbonate and quartz abundances (Fig.
9a–d). In these images, brighter areas represent surface
materials with deeper absorption features which are
assumed to be associated with higher abundances of AlOH,
MgOH-carbonate and quartz. In particular, the brightest
areas highlighted in Fig. 9b correlate with the mica-rich
outcrops and associated colluvium within the Broken Hill
and Olary Domains (‘‘A’’ and ‘‘B’’, respectively) while
quartz-rich areas tend to be associated with alluvial outwash
and accumulations within the Lake Frome Basin (‘‘C’’) (Fig.
9d). The MgOH-carbonate abundance image product high-
lights Adelaidean carbonate-rich units south of the Olary
Domain (‘‘D’’) and to a lesser extent, amphibolite-rich units
within the Broken Hill Domain (Fig. 9c).
Comparisons between HyMap (Robson et al., 2002) and
ASTER data for the AlOH abundance image, [(b5+b7)/b6]
in the Broken Hill Domain (Area I and Area II, Fig. 9a),
show the accuracy of the mosaicked ASTER SWIR data
product (Fig. 10a,b). The ASTER and HyMap derived
AlOH abundance (inverted) maps both show that the
northerly and northeasterly AlOH-rich units (dark areas)
decrease in white mica abundance towards the southwest
(Fig. 10a, b). There is also a correspondence between the
ASTER derived AlOH abundance image and the radio-
metric potassium concentration obtained from airborne
geophysical surveying (Fig. 10c) (Robson & Spencer,
1997). No explanation is available as yet for these spatial
patterns of K-mica abundance although it appears to be
associated with trends also associated with metamorphic
retrograde alteration at Broken Hill (Corbet & Phillips,
1981). It is also interesting to note that the Broken Hill type
deposits are located in areas relatively poor in muscovite.
a) b)
c) d)
III
III
IVA
B
C
D
Fig. 9. a) Study area for the ASTER Curnamona Project (cyan) showing the geological outcrops (grey) (AGSO, 2000) and 1 :250,000 map sheets
encompassing the Curnamona Province; b) ASTER derived AlOH abundance imagery; c) ASTER derived MgOH-carbonate abundance imagery; d) ASTER-
derived quartz abundance imagery. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 10. a) ASTER derived AlOH abundance within the Broken Hill Domain and surrounding regolith areas (Area I—Fig. 9 a) Broken Hill Mine indicated by
‘‘X’’ ; b) HyMap derived AlOH abundance; c) Airborne geophysics derived radiometric potassium concentration.
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172 167
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172168
Further investigation is required to investigate its relevance
for the alteration history of the Broken Hill deposit.
The ability of ASTER band ratio b5 / b4 to map
lithologies rich in ferrous iron silicates was examined and
Fig. 11. a) Olary Domain geological outcrop boundaries (Area II—Fig. 9 a) (PI
derived ferrous iron silicate index; d) MNF bands 1, 2 and 3 of ASTER TIR su
emissivity data.
studied in Area III (Figs. 9a and 11a) within the units of the
Olary Domain (Fig. 11c) containing several MgOH/
carbonate anomalies (Fig. 11b). In particular amphibolite/
calcalbite units (396000E, 6434800N; dark green Fig. 11a)
RSA, 2000); b) ASTER-derived MgOH/carbonate abundance; c) ASTER-
rface emissivity data; e) MNF bands 2, 3, and 4 of ASTER TIR surface
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172 169
are discriminated by both the ferrous iron silicate (Fig. 11c)
and the MgOH/carbonate image (Fig. 11b) products. Using
both of these products appears to offer the potential to
discriminate between MgOH group minerals and some
carbonate minerals. In this region, the amphibolite-rich
units at 399500E, 6437200N are highlighted in both image
products (Fig. 11b,c), whereas the Skillogalee and Auburn
Dolomites, within the Burra Group (401800E, 6437600N;
burgundy, Fig. 11a), are highlighted only by the MgOH/
carbonate product (Fig. 11b). Note that this interpretation
and discrimination could be complicated by ferrous-bearing
carbonates (e.g., ankerite, siderite and ferroan dolomite) in
other geological settings. Attempts at generating ASTER-
derived mosaics, representing ferrous iron content across
the Curnamona, proved problematic and showed significant
differences between image acquisition dates. This is likely
to be the result of residual crosstalk contributions for bands
4 and 5 used in the ratio product b5 /b4. In particular this
product would be more sensitive to crosstalk, reducing
band 4 and increasing band 5 in a non-linear manner
related to average radiance levels present at each acquis-
ition. Attempts were also made to discriminate these
MgOH and carbonate units using the Minimum Noise
Fig. 12. a) Geology of the Broken Hill Domain–Mundi Mundi Escarpment (Are
AlOH composition; d) HyMap-derived AlOH wavelength index from short (blue:
signatures. (For interpretation of the references to colour in this figure legend, th
Fraction transformation (Green et al., 1988) applied to the
L2 surface emissivity TIR product. However the emissivity
data proved noisy with limited dimensionality and pro-
duced no clear discrimination of the carbonate and MgOH-
rich units (Fig. 11d,e).
A comparison between the AlOH absorption wavelength
measured using the hyperspectral airborne data (Fig. 12d)
and the ASTER RGB band ratio AlOH imagery (Fig. 12c)
was undertaken along the north-western margins of the
Broken Hill Domain (Area IV, Figs. 9a and 12a). The area
straddles the Mundi Mundi fault line and associated
escarpment where an apron of alluvium and colluvium
flanks the uplifted Broken Hill Domain to the southeast
(Fig. 12a). Spectral profiles of the hyperspectral HyMap
imagery reveals that areas of blue (‘‘A’’), green (‘‘B’’) and
red (‘‘C’’) shown in Fig. 12d correspond to a measurable
increase in the wavelength of the main AlOH spectral
feature (Fig. 12e). The ASTER RGB AlOH image on the
other hand shows a qualitative similarity only to the HyMap
results (Fig. 12c). Also in some areas mapped by the
HyMap data as AlOH poor (e.g., ‘‘D’’, Fig. 12d), the
processed ASTER results wrongly highlighted areas of
high-abundance Al-poor mica (Fig. 12b,c).
a III—Fig. 10 a); b) ASTER derived AlOH abundance; c) ASTER derived
2.195 Am) to long (red: 2.207 Am) wavelength; e) HyMap SWIR spectral
e reader is referred to the web version of this article.)
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172170
A series of field transects were conducted as part of the
validation of ASTER image products at Broken Hill. These
transects involved a series of closely spaced measurements
at 1-m intervals for approximately 150 m across the
geological strike of contrasting Broken Hill outcropping
units. Spectral reflectance measurements of soil and rock
outcrop were measured along these transects with the ASD
Fieldspec Pro VNIR-SWIR spectrometer to compare with
ASTER spectral signatures. Along one of the transects,
ASD field measurements and samples were collected across
several narrow (~20–50 m) amphibolite, retrograde schist
and psammite units (Fig. 13a). The ASD field spectral
Fig. 13. a) Darling Creek field traverse with geology (Area IV—Fig. 9 a) and AS
acquired from traverse; c) HyMap SWIR sample signatures corresponding to traver
Am); e) AlOH composition interpreted from ASTER SWIR log residuals; f) Ca
intervals along field traverse listed. (For interpretation of the references to colour i
measurements (Fig. 13b) were shown to compare favour-
ably with the HyMap image signatures (Fig. 13c) within the
SWIR wavelength region. The ASD and HyMap signatures
corresponding to retrograde shear schists, amphibolites, and
psammite units are highlighted by grey, green and blue
spectra respectively (Fig. 13b and c). Comparisons between
the New South Wales Geological Surveys published
1 :25,000 geology (Fig. 13a) and the ASD and HyMap
signatures were reasonable, showing AlOH (i.e., 2.2 Am)
absorption features for the mica-rich schists and psammite/
psammopelite units (Fig. 13b and c). The ASD and HyMap
signatures also identified the MgOH’s spectral feature
D measurements (white squares) along traverse; b) ASD SWIR signatures
se; d) HyMap-derived AlOH wavelength index (blue=2.197 Am, red=2.202
librated ASTER SWIR spectral signatures (*=ASTER band centers) for
n this figure legend, the reader is referred to the web version of this article.)
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172 171
between 2.3 to 2.33 Am associated with the amphibolite
units. Discrepancies between some of the geological
boundaries and field/image signatures were apparent;
however this may also possibly be a result of inaccuracies
within the 1 :25,000 mapping or the presence of colluvial
float material.
A wavelength index image was generated from the
HyMap data to represent shifts in the wavelength position of
the AlOH 2.2 Am feature (Fig. 13d). This wavelength index
represents possible changes in the white mica chemistry as
suggested by Duke (1994) where blue areas indicate shorter
wavelength (i.e., 2.197 Am) Al-rich mica compared to red
areas representing longer wavelength Al-poor mica (i.e.,
2.202 Am) (Fig. 13d). ASD field measurements along the
transect shown (Fig. 13b) indicate a change for the AlOH
absorption feature from 2.203 Am to 2.199 Am northwards
as suggested by the HyMap derived AlOH wavelength
index image (Fig. 13d). The ASTER derived AlOH
composition RGB imagery was also compared with the
ASD signatures and geology along the Darling Creek
Traverse. However it was clear that the coarser spatial
nature of the SWIR data (i.e. 30 m) limited ASTER’s
discrimination and usefulness for mapping these narrow
geological units (Fig. 13a).
Crosstalk-corrected ASTER L1B radiance data encom-
passing this area were calibrated using ASD field spectral
measurements and processed into log residuals (Green &
Craig, 1985) for comparison with ASD and HyMap
spectra. The resulting ASTER data produced SWIR
reasonable signatures with no obvious distortion of bands
5 and 9 (Fig. 13f). There is the suggestion of a shift to
more left symmetric AlOH absorption feature (i.e., from
red to yellow, Fig. 13e) corresponding to possibly shorter
wavelength mica chemistry north along the traverse (Fig.
13e). However the spectral resolution of ASTER SWIR
bands precludes accurate estimation of the AlOH 2.2 Amabsorption feature’s wavelength (Fig. 13f). These ASTER
transect results and their comparison with field and
hyperspectral data indicate that ASTER has limited
potential to provide compositional information for small
changes in AlOH chemistry as observed in Broken Hill,
even assuming well-calibrated SWIR radiance data.
7. Conclusions
Several pre-processing steps were required to generate
seamless imagery before band combination processing was
applied to target specific mineral absorption features. These
steps included correction for additive SWIR crosstalk
effects, SWIR band image offsets and also gain adjustments
for variable atmospheric/illumination conditions during
different acquisitions. As part of validation studies under-
taken for this study, field spectral measurements revealed
that ASTER’s SWIR crosstalk effect was a significant
factor for the ASTER L2 surface reflectance data, parti-
cularly for bands 5 and 9. Although ERSDAC’s crosstalk
correction software (Version 3.0) alleviated much of the
crosstalk problem for ASTER L1B data, artifacts were still
apparent, especially for areas of low albedo (e.g., cloud
shadows) and but also for areas of high albedo. As a
consequence, masking for clouds (and water) is a critical
pre-processing step.
Atmospheric radiative-transfer modeling revealed that
variable water vapor associated with different climatic
models, is a significant issue for the ASTER L1B SWIR
data, particularly for bands 8 and 9. The effects of variable
atmospheric conditions and from variable solar illumina-
tion conditions appeared to be multiplicative from band
ratio results of different ASTER acquisitions. Successive
mosaicking of ASTER SWIR data was subsequently
derived by the application of gains upon overlapping
ASTER SWIR L1B images from 14 different acquisitions.
The resulting mosaicked ASTER L1B SWIR images were
successfully processed using band ratios to measure the
abundance of mineral groups including AlOH and MgOH/
carbonate within outcropping and/or regolith units of the
Broken Hill-Curnamona Province. ASTER derived AlOH
abundance imagery compared well with large-scale air-
borne hyperspectral HyMap survey results, providing
confidence in the application of multiplicative gains to
adjust ASTER scenes acquired on different dates. During
this study, it was also found that discrimination between
MgOH- and carbonate-rich units was possible using
ASTER if ferrous iron products were generated to assist
the discrimination of MgOH group minerals. Partial
success was also achieved in generating seamless maps
qualitatively representing AlOH composition from ASTER
SWIR images. However, its reliability was significantly
reduced in areas of low albedo and in the presence of
chlorite-rich units. Despite the application of crosstalk
correction software, some residual crosstalk effects still
proved problematic.
ASTER TIR L2 surface emissivity data compared
favourably with field spectral measurements and also
produced reasonably consistent signatures, independent of
acquisition conditions. As a consequence, ASTER emissiv-
ity data were mosaicked and processed to generate quartz-
abundance images and were found to highlight regolith
accumulations of alluvial quartz and some units of quartzites
and sandstones.
Acknowledgement
This work was financially supported and encouraged by
the Geological Survey of South Australia within the
Department of Primary Industries (PIRSA), as part of
PMD*CRC activities. Several individuals, particularly
within PIRSA, played a key part in this project including
Paul Heithersay, and Stuart Robertson and others within the
Curnamona Team. Japan’s ERSDAC provided ASTER data,
R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172172
crosstalk-correction software, and support during field
validation activities in Broken Hill. LPDAAC of the United
States Geological Survey also provided ASTER data.
Support was also gratefully received from CSIRO’s Glass
Earth Project, its coordinator, Joan Esterle and co-worker,
Joanna Parr. US and Japanese members of the ASTER
Science Team provided technical feedback during this
research. Andrew Rogers modelled the effects of variable
atmospheric conditions upon ASTER data. New South
Wales Department of Mineral Resources and Geoscience
Australia granted permission to publish radiometric data
from the Discovery 2000 geophysical database. Processed
HyMap data of Broken Hill were gratefully received from
Peter Hausknecht of HyVista.
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