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Spectral indices for lithologic discrimination and mapping by using

the ASTER SWIR bands

Y. YAMAGUCHI* and C. NAITO

Department of Earth and Planetary Sciences, Nagoya University, Furo-cho,Chikusa-ku, Nagoya 464-8602, Japan

(Received 22 December 1999; in final form 12 July 2000 )

Abstract. The Advanced Spaceborne Thermal Emission and Reflection Radio-meter (ASTER) is a research facility instrument launched on NASAs Terra

spacecraft in December 1999. Spectral indices, a kind of orthogonal trans-formation in the five-dimensional space formed by the five ASTER short-wave-infrared (SWIR) bands, were proposed for discrimination and mapping ofsurface rock types. These include Alunite Index, Kaolinite Index, Calcite Index,and Montmorillonite Index, and can be calculated by linear combination ofreflectance values of the five SWIR bands. The transform coefficients weredetermined so as to direct transform axes to the average spectral pattern of thetypical minerals. The spectral indices were applied to the simulated ASTERdataset of Cuprite, Nevada, USA after converting its digital numbers to surfacereflectance. The resultant spectral index images were useful for lithologicmapping and were easy to interpret geologically. An advantage of this method isthat we can use the pre-determined transform coefficients, as long as image data

are converted to surface reflectance.

1. Introduction

The Advanced Spaceborne Thermal Emission and Reflection Radiometer

(ASTER) is a research facility instrument launched on NASAs Terra (originally

called EOS AM-1) spacecraft in December 1999 (Yamaguchi et al. 1998, Fujisada

et al. 1998). The ASTER instrument has three spectral bands in the visible and

near-infrared (VNIR), six bands in the short-wave-infrared (SWIR), and five bands

in the thermal infrared (TIR) regions respectively (table 1, figure 1). The spectral

bandpasses of the SWIR bands were selected for the purpose of surface minera-

logical mapping. Band 4 is centred at the 1.65 mm region, and bands 5 to 9 target

the characteristic absorption features of phyllosilicate and carbonate minerals in the

2.1 to 2.4 mm region.

Many previous studies have proposed various approaches for discrimination

and mapping of surface rock types by using multispectral data; for instance, band

ratio, principal component analysis (PCA), multiband classification, etc. (e.g.

Rowan et al. 1974, Chavez and Kwarteng 1989, Gillespie et al. 1986). A spectral

index is one such approach to quantify multispectral sensor response patterns. The

concept of the spectral index was initiated by Kauth and Thomas (1976), who

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2003 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160110070320

*Corresponding author; e-mail: [email protected]

INT. J. REMOTE SENSING, 20 NOVEMBER, 2003,

VOL. 24, NO. 22, 43114323

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proposed four indices called Brightness, Greenness, Yellowness and Nonsuch using

the four Landsat MSS bands. This method has been widely accepted as the

Tasselled Cap transformation for assessing vegetation (Crist and Cicone 1984).

Richardson and Wiegand (1977) developed a two-dimensional Perpendicular

Vegetation Index (PVI) using two bands of the Landsat MSS. Jackson (1983)

showed that these indices were special cases of a class of spectral indices, formed by

linear combinations of n spectral bands, in n-dimensional space.A spectral index is similar to PCA in the sense that both are orthogonal

transformations of multispectral data. A fundamental difference between these two

methods is that the spectral indices define the transform axes to represent specific

spectral patterns of interest, while PCA determines the transform axes mathema-

tically to maximize variance of multispectral data. PCA can also be used to reduce

dimensionality of multispectral data without significant information loss. Visual

interpretation for discrimination and mapping of surface materials may be enhanced

by a colour composite image of major principal components. However, physical

meanings of colours in a PCA image are not clear in many cases, as a PCA result is

scene dependent, i.e. transform coefficients change from scene to scene.In contrast, as spectral indices uses pre-determined transform coefficients, it is

possible to know physical meanings of a transformed result to some degree (Crist

and Cicone 1984). This means that it is easy to interpret resultant spectral index

images from a geological point of view, if we use spectral indices for discrimination

and mapping of surface rock types. Yamaguchi (1987) proposed spectral indices for

lithologic discrimination by using the three SWIR bands of the Optical Sensor

(OPS) of the Japanese Earth Resources Satellite (JERS-1) launched in 1992. This

paper discusses a similar approach to develop spectral indices for lithologic

mapping by the five SWIR bands of ASTER.

2. Test site and data

Cuprite in western Nevada, USA was selected as a test site because of the

availability of an appropriate simulated ASTER dataset. This area has been a

famous test site for evaluating mineralogical mapping capabilities of various

Table 1. ASTER baseline performance requirements.

SubsystemBand

no.Spectral

range (mm)Radiometric

resolutionAbsolute

accuracy (s)Spatial

resolutionQuantization

levels

1 0.520.60

2 0.630.69 NEDrf0.5% f4% 15 m 8 bitsVNIR 3N 0.780.86

3B 0.780.86

4 1.6001.700 NEDrf0.5%5 2.1452.185 NEDrf1.3%

SWIR 6 2.1852.225 NEDrf1.3% f4% 30 m 8 bits7 2.2352.285 NEDrf1.3%8 2.2952.365 NEDrf1.0%9 2.3602.430 NEDrf1.3%

10 8.1258.475 f3 K(200240 K)11 8.4758.825 f2 K(240270 K)

TIR 12 8.9259.275 NEDTf0.3K f1 K(270340 K) 90 m 12 bits13 10.2510.95 f2 K(340370 K)14 10.9511.65

4312 Y. Yamaguchi and C. Naito

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airborne and spaceborne remote sensors. Sections of the Tertiary volcanics in this

area were intensively altered in mid- to late-Miocene times. The most highly altered

rocks to the least altered are referred to as silicified, opalized, and argillized.

Dominant minerals are quartz in the silicified areas, opal, alunite and kaolinite in

the opalized areas, and kaolinite and montmorillonite in the argillized areas,

respectively (Abrams et al. 1977, Hook and Rast 1990, Kruse et al. 1990, Abrams

and Hook 1995, Resmini et al. 1996).

A simulated ASTER dataset for the Cuprite area was produced from an

Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) dataset by the

Earth Remote Sensing Data Analysis Center (ERSDAC) in Japan. The AVIRIShas 224 spectral bands in the 0.4 to 2.45 mm region with 20 m spatial resolution in

this case. The AVIRIS data were spectrally resampled to the appropriate ASTER

bands by using the ASTER band response functions. The ASTER maximum input

radiance, quantization level, and gain factors were also considered to generate

Figure 1. Spectral bandpasses of the ASTER VNIR and SWIR, and the reflectance spectraof typical minerals, rocks, and vegetation: (a) kaolinite, (b) montmorillonite, (c)alunite, (d) calcite, (e) andesite, (f) granite and (g) green leaves. Note that the SWIRbands 5 to 9 are targeting the characteristic absorption features of these minerals.

Spectral indices for lithologic discrimination and mapping 4313

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digital numbers (DNs) of the simulation dataset. Finally, the simulated data were

spatially resampled to produce the appropriate pixel size; 15 m for the VNIR and

30 m for the SWIR.

3. Methods

3.1. Overview

Spectral indices in n-space can be defined as values measured by projecting data

points onto axes with appropriate unit vector directions. It is a kind of orthogonal

transformation, and the transform axes are determined to represent specific spectral

patterns (Jackson 1983). In general, the m-th spectral index in n-space (mfn) for

the i-th pixel (mYi) can be given by the following formula:

mYi~mA1X1izmA2X2iz zmAnXni 1

where n is the total number of the spectral bands, mAn is the transform coefficient of

n-th band data for the m-th spectral index, and Xni is n-th band data of an i-th

pixel. By using the five ASTER SWIR bands in the 2 mm region (bands 5 to 9), the

following five spectral indices, from lower to higher orders, are proposed in this

study:

Brightness Index

Alunite Index

Kaolinite Index

Calcite Index

Montmorillonite Index

We can produce spectral index images by simply applying the transform

coefficients to surface reflectance datasets. Please note that there are two very

important points in this method (figure 2). One is generation of the transform

Figure 2. Data processing workflow used in this study.


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coefficients for the spectral indices as discussed in 3.3 and 3.4, and the other is

reflectance conversion of the dataset before applying the coefficients as discussed in

3.2. In summary, the transform coefficients of the first axis, Brightness Index, were

determined by PCA to indicate the average brightness of the five SWIR bands of

the simulated ASTER dataset of Cuprite. The second and higher order axes werechosen to direct the response patterns of the four target minerals, which exhibit

different diagnostic response patterns (figure 3). The reflectance spectra used for

calculation include alunite (11 samples), calcite (10 samples), kaolinite (22 samples),

and montmorillonite (15 samples). The calculation processes were based upon

Jackson (1983), which utilized the Gram-Schmidt orthogonalization method.

3.2. Reflectance conversion

The DNs of the simulated ASTER dataset were converted to surface reflectance

by using two calibration targets: Stonewall Playa in the eastern part of the image as

a bright target with high reflectance and a Miocene basalt flow in the northern partof the image as a dark target with low reflectance (figure 4). Spectral reflectance of

the samples collected at these two targets was measured in the laboratory. An

assumption was made that the recorded DNs are linearly related to the surface

reflectance. By using the linear regression coefficients between the image DNs and

the measured reflectance of the two calibration targets, the whole simulated ASTER

dataset was converted to surface reflectance.

Original DNs of Alunite Hills, three small hills located in the centre left of the

image (figure 4), had a spectral pattern in which band 8 was the highest among the

four SWIR bands in the 2mm region (figure 5(a)). After reflectance conversion,

band 7 became higher than band 8 (figure 5(c)). This pattern is similar to a simulated

SWIR response from reflectance spectra of alunite (figure 5(b)). Thus, the authors

assumed that the reflectance calibration was performed correctly. The reflectance-

Figure 3. Simulated response patterns of the four typical minerals for the ASTER SWIRbands.


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calibrated ASTER simulation dataset was used for further processing in this study.

An ASTER surface reflectance dataset will be provided as an ASTER standard

data product (Yamaguchi et al. 1998), so that we will not need the reflectance

conversion in the future.

3.3. Brightness Index

The transform axis for the Brightness Index was determined by PCA applied to

the reflectance-calibrated ASTER simulation dataset. The first principal component

Figure 4. Alunite Index image of the simulated ASTER dataset of Cuprite, western Nevada,USA. Top of the image is north, and the imaged area is about 10 km (EW) by8.3 km (NS) in size.

Figure 5. (a) Original DN values of the SWIR bands for Alunite Hills, (b) a simulatedresponse of the SWIR bands from a reflectance spectrum of alunite, and (c) DNsafter reflectance conversion to surface reflectance by using ground targets.


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contains the most common information, which mainly relates to average albedo

and topography. Thus a brightness image generally exhibits topography of the area.

The brightness (1Yi) can be given by the following formula:

1Yi~0:446X1iz0:449X2iz0:453X3iz0:447X4iz0:441X5i 2

where X1i represents values of the first spectral band of the i-th band (note that X1iis for the ASTER SWIR band 5, X2i for band 6, X3i for band 7, X4i for band 8, and

X5i for band 9). The transform coefficients are also shown in tables 2 and 3. In this

particular case, the brightness contains 99% of the total DN variance. In the near

future when the actual ASTER data become available, we will be able to define the

transform coefficients more accurately based upon the real ASTER data products.

However, the authors expect that the transform coefficients shown in this paper are

not likely to be changed greatly by this improvement.

3.4. The second and higher spectral indicesJackson (1983) and Yamaguchi (1987) showed that the second and higher

spectral indices were deviations of each data point from the brightness axis. The

transform axes of the higher order indices are chosen to be perpendicular to

the brightness, and thus should have little relationship to topography. As a result,

the spectral indices of the higher orders can be used for lithologic discrimination

and mapping by suppressing the topographic effect. The transform axis of the

second index was defined by two conditions: (1) the axis must be perpendicular to

the brightness axis, and (2) the axis must pass through the mean of the particular

mineral response (mineral A in figure 6).

There are two ways to determine the transform axes for the third and higherorder spectral indices. The third index axis must be perpendicular to the brightness

axis, but can be chosen as either non-orthogonal (mineral B in figure 6) or

orthogonal (mineral B in figure 6) to the axis of the second spectral index (mineral

A in figure 6). These two approaches have been tested in order to generate the

Table 2. Transform coefficients of the spectral indices for the ASTER SWIR bands for thecase where the transform axes of these minerals are perpendicular to the brightnessaxis, but are not orthogonal to each other.

Spectral index Band 5 Band 6 Band 7 Band 8 Band 9

Brightness 0.446 0.449 0.453 0.447 0.441Alunite 20.694 20.219 0.562 0.389 20.048Kaolinite 0.012 20.763 0.505 0.372 20.124Calcite 0.156 0.522 20.388 20.647 0.365Montmorillonite 0.696 0.069 20.364 0.185 20.589

Table 3. Transform coefficients of the spectral indices for the ASTER SWIR bands for thecase where all the transform axes of these minerals are orthogonal to each other.

Spectral index Band 5 Band 6 Band 7 Band 8 Band 9

Brightness 0.446 0.449 0.453 0.447 0.441Alunite 20.694 20.219 0.562 0.389 20.048Kaolinite 0.528 20.795 0.212 0.174 20.119Calcite 20.087 20.212 0.322 20.659 0.640Montmorillonite 0.138 0.284 20.134 0.499 20.796


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higher order indices. If we employ the former method, the order of the mineral

indices has no effects on the transform coefficients nor the index images. On the

other hand, if we use the latter method, the order of choice of minerals for higher

spectral indices is very important, and thus can greatly affect the resultant spectral

index images. The authors tried several combinations, and found that selection of

the second index next to the brightness strongly affects the result, but the order of

the third and higher indices seems not so significantly to affect the results. However,

more systematic investigation is needed for clear criteria in this point of view.Alunite was chosen for the second index mineral, as alunite is not only an important

mineral, but also it has a peculiar response pattern, with a high reflectance in band 7, an

intermediate one for band 8, and low for the other bands (figure 3). Kaolinite was

selected for the third spectral index in this study, because kaolinite is the second most

important mineral in this area, and also has diagnostic reflectance patter (figure 3). As

mentioned above, there are two ways to choose the Kaolinite Index axis, i.e. orthogonal

to the brightness axis and non-orthogonal to the Alunite Index axis, or orthogonal to

both the Brightness and Alunite Index axes (figure 7). The Calcite and Montmorillonite

Indices are the fourth and fifth, respectively.

Figure 6. A schematic diagram showing the relationship of the brightness axis and mineralaxes for spectral indices.

Figure 7. Relation of the transform axes of the spectral indices generated in this study.


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The transform coefficients for the case where the transform axes of these

minerals are perpendicular to the Brightness Index axis, but are not orthogonal to

each other, are shown in table 2. The transform coefficients for the case where all

the transform axes for these minerals are orthogonal to each other are shown in

table 3. The spectral index images were generated by simply applying these

transform coefficients to the reflectance-calibrated ASTER simulation dataset of

Cuprite. Differences of the resultant spectral index images generated by these

two different sets of transform coefficients are discussed in the following

chapter.

4. Results and discussion

Based on the simulated ASTER dataset, the five proposed mineral index images

were produced. The two approaches for generation of the higher order indices were

also tested. These spectral index images and a colour composite of indices were

compared with the mineralogical mapping by previous workers (e.g. Abrams et al.1977, Hook and Rast 1990).

The Alunite Index image presents three bright patches in its centre left (figure 4).

These correspond in fact to three mounds called the Alunite Hills with high

concentration of alunite. In the north-east of the Alunite Hills, there are fans with

moderately high Alunite Index values, and those fan deposits contain alunite-

bearing rocks. Therefore, we can conclude that the Alunite Index image shows

distribution of alunite in this area, and also indicates relative concentration of

alunite to some degree. Note that black and white small dots in the image are

processing errors due to abnormal index values.

The two approaches for generation of spectral indices were compared by usingthe two Kaolinite Index images (figure 8), which were generated by different sets of

transform coefficients (tables 2 and 3). The upper image in figure 8 was generated by

using the transform coefficients in table 2, that is, the Kaolinite Index axis non-

orthogonal to the Alunite Index axis. The lower image was generated by using the

transform coefficients in table 3, namely, the Kaolinite Index axis orthogonal to

the Alunite Index axis. Distribution of kaolinite is less clear in the upper image than

the lower image. The upper one seems to be influenced by alunite distribution, as

the response patterns of alunite and kaolinite are somewhat similar (figure 3). Band

7 responses of both alunite and kaolinite form a peak with downward slopes to

band 6 to the left and bands 8 and 9 to the right. However, the spectral contrast ofthe kaolinite pattern is smaller than that of alunite, i.e. the reflectance difference

between the maximum and minimum is 11% for alunite and 8% for kaolinite,

respectively (figure 3). As a result, the pixels including alunite are also strongly

responding to the Kaolinite Index in this case.

On the other hand, the lower image was generated by using the transform axis

orthogonal to the Alunite Index axis, and the alunite areas do not respond to the

Kaolinite Index. This is probably due to the reason that each of the two orthogonal

axes only picks up a different part of a response pattern from the other one. The

distribution of kaolinite in the lower image looks reasonable compared with the

previous work by Abrams et al. (1977). Therefore, we can conclude that the spectralindices should be defined by transform axes orthogonal to each other in order to

enhance spectral response patterns of different minerals. Specifically, we should use

the transform coefficient shown in table 3, not those shown in table 2.

The transform coefficients for the Calcite and Montmorillonite Indices were


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determined as fourth and fifth axes respectively. We can recognize a large area with

high Calcite Index values in the south-west part of the Calcite Index image

(figure 9), and it coincides well with the limestone-dominant area in the existinggeological map. On the other hand, the opalized and argillized areas with high

concentration of alunite and kaolinite do not respond to the Calcite Index. Namely,

carbonate minerals which have absorption features at 2.35 mm can be distinguished

from hydrothermal alteration zones containing clay minerals, which have

Figure 8. Upper: Kaolinite Index image whose transform axis is not orthogonal to theAlunite Index axis. Lower: Kaolinite Index image whose transform axis is orthogonalto the Alunite Index axis.


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absorption at 2.2 mm. There is Stonewall Playa in the eastern edge of the image,

where the Montmorillonite Index is relatively high (figure 9). It is also consistent

with the field evidence that the playa material contains montmorillonite or other

clay minerals that include molecular water in their structure.

Finally, a colour composite image was generated by assigning the three spectralindices to three colour primitives, red, green, and blue, respectively (figure 10). It is

evident that the mineral distributions are clearly shown by different colours, while

the topographic effect is suppressed. This colour image is particularly useful for

geologic interpretation and mapping of the test area.

Figure 9. Upper: Calcite Index image. Lower: Montmorillonite Index image.


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5. Conclusions

Transform coefficients of the spectral indices for the ASTER SWIR bands weredetermined by using both the simulated ASTER dataset of Cuprite and the ASTER

SWIR response patterns simulated from reflectance spectra of typical minerals. We

can generate a spectral index image by simply applying the transform coefficients to

reflectance-calibrated ASTER data. It was proven by the simulated ASTER dataset

of Cuprite that the spectral index images were useful for lithologic mapping and

were easy to interpret geologically.

An advantage of this method is that we can use the pre-determined transform

coefficients. In other words, data processing for spectral indices is not scene dependent,

as long as image data are converted to surface reflectance. It can also greatly reduce the

processing effort required. It is planned that surface reflectance will be provided as an

ASTER standard data product to the general user community on a non-discriminatory

basis (Yamaguchi et al. 1998). Therefore, we do not need to worry about conversion of

ASTER DNs to surface reflectance including atmospheric correction, since an

atmospherically corrected ASTER surface reflectance data product can be obtained

routinely. We can simply apply the transform coefficients to the ASTER surface

reflectance dataset to get a quick image depicting the surface mineral distribution. The

spectral indices proposed in this paper should be a candidate to be used as a standard

processing method for ASTER data.

Acknowledgments

The authors would like to express sincere thanks to Earth Remote Sensing Data

Analysis Center (ERSDAC) for providing the simulated ASTER dataset, and to Jet

Propulsion Laboratory (JPL) for providing the AVIRIS data of Cuprite. They are

Figure 10. A colour composite image generated by assigning the Alunite Index to red, theCalcite Index to green, and the Kaolinite Index to blue, respectively.


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also grateful to the ASTER Science Team members for their useful discussions and

comments.

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