Red

Mountain

Lake San Cristobal

Lake 

Gunnison River

Fork

Silverton

Caldera

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Map 3190

Description and Validation of an Automated Methodology 

for Mapping Mineralogy, Vegetation, and Hydrothermal 

Alteration Type from ASTER Satellite Imagery with

Examples from the San Juan Mountains, Colorado
Cover: Three-dimensional perspective view of the Lake City caldera in the San Juan Mountains of Colorado, looking 

toward the northwest. A map showing surficial occurrence of clay, sulfate, mica, carbonate, and other minerals derived 

from spectroscopic analysis of ASTER data provided in this publication is draped over a USGS digital elevation model. 

Background image is derived from National Agriculture Imagery Program aerial photography. Lake San Cristobal along 

the Lake Fork of the Gunnison River is visible at right. Advanced argillic alteration (shown in yellow, red, and orange 

colors) is clearly visible on Red Mountain above the lake and within the Silverton caldera in the distance at upper left.
Description and Validation of an Automated 

Methodology for Mapping Mineralogy, 

Vegetation, and Hydrothermal Alteration 

Type from ASTER Satellite Imagery with 

Examples from the San Juan Mountains, 

Colorado

By Barnaby W. Rockwell

Pamphlet to accompany

Scientific Investigations Map 3190

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Rockwell, B.W., 2012, Description and validation of an automated methodology for mapping mineralogy, vegetation, 

and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, 

Colorado: U.S. Geological Survey Scientific Investigations Map 3190, 35 p. pamphlet, 5 map sheets, scale 100,000.
iii

Contents

Introduction.....................................................................................................................................................1

Map Usage......................................................................................................................................................2

Descriptions of Map Sheets and Associated ERDAS Imagine Raster Image Files............................2

Sheet 1 of 5.............................................................................................................................................2

Description....................................................................................................................................2

Purpose..........................................................................................................................................2

Sheet 2 of 5.............................................................................................................................................2

Description....................................................................................................................................3

Purpose..........................................................................................................................................3

Sheet 3 of 5.............................................................................................................................................3

Description....................................................................................................................................3

Purpose..........................................................................................................................................3

Sheet 4 of 5.............................................................................................................................................5

Description....................................................................................................................................5

Purpose..........................................................................................................................................5

Sheet 5 of 5.............................................................................................................................................5

Description....................................................................................................................................5

Purpose..........................................................................................................................................7

Description of Automated Analysis Methodology ...................................................................................7

Mineral Group and Green Vegetation Indices .................................................................................7

Ferric Iron 1 “Redness” Index ...................................................................................................7

Ferric Iron Index 2........................................................................................................................8

Advanced Argillic-Kandite Index ..............................................................................................8

Argillic-Sericite-Smectite Index................................................................................................9

Carbonate-Propylitic Index ......................................................................................................10

Dolomite-chlorite-epidote index .............................................................................................11

Hydrous Silica-Jarosite-Sericite Index..................................................................................11

Ferrous Iron Index......................................................................................................................12

Green Vegetation Index ............................................................................................................13

Image Processing for Masking and Index Thresholding .............................................................14

Dark Area Masking....................................................................................................................14

Water Masking...........................................................................................................................14

Selective Vegetation Suppression..........................................................................................14

Final Index Thresholding and Clipping ...................................................................................14

Modeling of Hydrothermal Alteration Type and Mineral Groups through Boolean 

Combination of Index Results..............................................................................................16

Comparison and Validation of Automated Results.................................................................................19

Silverton Caldera, San Juan Mountains, Colorado.......................................................................19

Antelope Range, Marysvale Volcanic Field, Utah .........................................................................21

Goldfield Mining District, Nevada....................................................................................................24

Cuprite Mining District, Nevada.......................................................................................................24

Pine Grove and Blawn Mountain Mining Districts, Wah Wah Mountains, Utah .....................29

Deficiencies of the Automated Analysis Methodology.........................................................................29

Conclusions...................................................................................................................................................33
iv

Additional Data.............................................................................................................................................33

Acknowledgments.......................................................................................................................................33

References....................................................................................................................................................33

Figures

1. Plots of laboratory reference spectra convolved to ASTER spectral resolution and 

ASTER pixel spectra of muscovites and a muscovite + chlorite mixture............................4

2. Plots of laboratory reference spectra convolved to ASTER spectral resolution...............8

3. Plots of laboratory reference spectra of alunite, pyrophyllite, and kaolinite 

convolved to ASTER spectral resolution ..................................................................................9

4. Plots of laboratory reference spectra of sericite, smectite, and kaolinite convolved to 

ASTER spectral resolution ........................................................................................................10

5. Plots of laboratory reference spectra of calcite, dolomite, chlorite, and epidote 

convolved to ASTER spectral resolution ................................................................................11

6. Plots of laboratory reference spectra of ferrous iron-bearing minerals convolved 

to ASTER spectral resolution....................................................................................................12

7. Comparison of ferrous iron-bearing minerals identified in the Silverton, Colorado, 

area using AVIRIS data and the automated ferrous iron index ..........................................13

8. Comparison of mineral maps of the area between the Goldfield and Cuprite 

mining districts, western Nevada, produced from AVIRIS and ASTER data....................15

9. Comparison of occurrencesof jarosite + sericite detected in the Silverton, 

Colorado, caldera .......................................................................................................................18

10. Comparison of advanced argillic, argillic, and phyllic alteration in the Silverton, 

Colorado, caldera .......................................................................................................................20

11. Mineral maps of the Antelope Range area of the Marysvale volcanic field, Utah

using A. ASTER data and B. automated ASTER data............................................................22

12. Mineral maps of the Goldfield mining district, Nevada. A, Map produced using 

expert system-based spectroscopic analysis. B, Map produced using expert 

system-based spectroscopic analysis and C, Map produced from orthocorrected 

(AST14OTH) data of the same 2001 ASTER scene ................................................................23

13. Mineral maps of the Montezuma mining district, Nevada ..................................................25

14. Mineral maps of part of the Cuprite mining district in western Nevada produced 

using expert system-analysis of 1998 high-altitude AVIRIS data. A. Map showing 

occurance of iron-bearing minerals, and B. Map showing occurance of clay, 

sulfate, mica, carbonate, Mg-OH and hydrous silica minerals...........................................26

15. ASTER-derived mineral maps of part of the Cuprite mining district, western 

Nevada .........................................................................................................................................28

16. ASTER-derived mineral maps of the Wah Wah Mountains, Broken Ridge, and 

Escalante Desert, southwestern Utah ....................................................................................30

17. Explanations for color-coded mineral and vegetation identifications. 

A, Identifications for map in figure 16A. B, Identifications for map in figure 16B............32
v

Tables

1. Boolean logic used to combine results of separate iron mineral, vegetation, and 

water analysis ...............................................................................................................................6

2. Boolean logic used to combine index results into a single, classified map.....................17

Maps

Maps of mineralogy, vegetation, and hydrothermal alteration type generated from ASTER 

satellite data—San Juan Mountains, Colorado

1. Continuous-tone map of mineral groups ............................................................................. link

2. Map of Iron-bearing minerals, vegetation, and water...................................................... link

3. Map of clay, sulfate, mica, carbonate, Mg-OH, and hydrous silica minerals ............... link

4. Map of modeled hydrothermal alteration type................................................................... link

5. Map of modeled hydrothermal alteration type, mineral groups, and green 

vegetation generated by automated analysis..................................................................... link

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

meter (m) 3.281 foot (ft) 

micron (μm) 0.001 millimeter (mm)

Horizontal coordinate information is referenced to the World Geodetic System 1984 (WGS 84).
Introduction

The efficacy of airborne spectroscopic “hyperspectral” 

remote sensing for geoenvironmental watershed evaluations 

has been demonstrated (Dalton and others, 2004; 2007, 

Rockwell and others, 2005). However, the acquisition, processing, and analysis of such airborne data at regional and 

national scales can be time and cost prohibitive. The Advanced 

Spaceborne Thermal Emission and Reflection Radiometer 

(ASTER, http://asterweb.jpl.nasa.gov/) sensor carried by the 

NASA Earth Observing System Terra satellite was designed for 

mineral mapping and the acquired data can be efficiently used 

to generate uniform mineral maps over very large areas.

Multispectral remote sensing data acquired by the 

ASTER sensor were analyzed to identify and map minerals, 

mineral groups, hydrothermal alteration types, and vegetation 

groups in the western San Juan Mountains, Colorado. This 

mapping was performed in support of multidisciplinary studies 

involving the predictive modeling of surface-water geochemistry at watershed and regional scales. These studies seek to 

determine the relative effects of mining and non-anthropogenic hydrothermal alteration on surface water geochemistry 

and faunal populations.

Detailed maps of minerals, vegetation groups, and 

water (sheets 2 and 3) were produced from an ASTER scene 

using spectroscopic expert system-based analysis techniques 

described by Rockwell (2009) and references therein. This 

publication also presents new methodologies for the modeling 

of hydrothermal alteration type based on the Boolean combination of the detailed mineral maps, and for the automated 

mapping of alteration types, mineral groups, and green vegetation. Results of these methodologies are provided (sheets 4 

and 5) for comparison with the more detailed maps in sheets 2 

and 3 and with previously published mineral mapping results 

derived from analysis of high-resolution spectroscopic data 

acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS, http://aviris.jpl.nasa.gov/) sensor (Dalton and 

others, 2004; 2007). Additional comparisons of these maps 

are provided by Rockwell (2010b). The automated mineral 

group mapping products described in this study are ideal for 

application to mineral resource and mineral-environmental 

assessments at regional and national scales.

Unlike other ASTER data analysis methodologies 

designed for large-area mineral mapping (Mars and Rowan, 

2006), the automated methodologies presented herein 

exploit the full dimensionality of the nine spectral bands of 

ASTER data that measure reflective electromagnetic energy 

(0.4–2.5 μm (microns)). The mapping of iron minerals from 

the visible and near-infrared data offers a means of inferring 

the occurrence of pyritic rocks with high net acid production (NAP) and of mapping iron-bearing lithologic units. 

High concentrations of jarosite derived from pyrite oxidation 

that are unobscured by argillic weathering products are also 

identified. Occurrences of hydrous quartz (for example, opal 

and chalcedony) typically of volcano-hydrothermal origin 

are identified. The identification of acid-neutralizing carbonate rocks and propylitic alteration is also vital for estimating 

surface water pH.

Digital mineral and vegetation mapping results are presented in both portable document format (PDF) and ERDAS 

Imagine format (.img). The ERDAS-format files are suitable 

for integration with other geospatial data in Geographic Information Systems (GIS). The ERDAS files showing occurrence 

of (1) iron-bearing minerals, vegetation, and water (sheet 2); 

(2) clay, sulfate, mica, carbonate, Mg-OH, and hydrous quartz 

minerals (sheet 3), and (3) modeled hydrothermal alteration 

type (sheet 4),and (4) mineral and vegetation groups identified using automated analysis (sheet 5) have been attributed 

according to identified material, so that the material detected 

in a pixel can be queried with the interactive attribute identification tools of GIS and image processing software packages 

(for example, the Identify Tool of ArcMap and the Inquire 

Cursor Tool of ERDAS Imagine).

All raster data have been orthorectified to the Universal 

Transverse Mercator (UTM) projection using a projective 

transform with ground-control points selected from Terra

Server digital orthophoto quadrangles, digital raster graphics, and a digital elevation model from the U.S. Geological Survey (USGS) National Elevation Dataset (NED, 

Description and Validation of an Automated Methodology 

for Mapping Mineralogy, Vegetation, and Hydrothermal 

Alteration Type from ASTER Satellite Imagery with 

Examples from the San Juan Mountains, Colorado

By Barnaby W. Rockwell
2 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

http://ned.usgs.gov/, 1/3 arc second, 10-m resolution). 

Positional errors are commonly less than 60 m, but may be 

locally greater in areas of high terrain relief outside of the 

Silverton caldera where fewer geometric control points were 

collected.

Metadata compliant with Federal Geographic Data 

Committee (FGDC) standards have been included for all 

ERDAS-format files, and contain important information 

regarding geographic coordinate systems, attributes, and 

cross-references.

Map Usage

The image maps were designed primarily for the 

regional mapping and characterization of exposed surface 

mineralogy, including that related to hydrothermal alteration. 

As hydrolytic alteration commonly occurs along faults and 

fractures that serve as conduits for potentially metal-bearing 

fluids, the presence and type of alteration can provide important information for mineral resource investigations. Pyrite 

is a common gangue mineral associated with hydrothermal 

alteration and many types of ore deposits, and produces 

sulfuric acid upon subaerial exposure which can leach and 

transport potentially toxic metals into the ground water 

system. Knowledge of pyrite occurrences and the extent and 

type of alteration is vital in developing predictive models of 

downstream surface water geochemistry in both mined and 

unmined catchments and to evaluate pre- and post-mining 

effects of altered areas on local hydrology.

The maps generated by spectroscopic analysis techniques present the most detailed and accurate ASTER-derived 

information on the spatial occurrence of minerals and mineral 

assemblages. The maps of alteration types and key mineral 

groups modeled from the detailed maps are well-suited for 

use in quantitative, GIS-based modeling for geoenvironmental 

watershed studies in that the values of relative acid producing 

and neutralizing potential can be readily assigned to the data 

elements. The less-detailed map generated using automated 

analysis techniques is designed for regional- and national-scale 

mineral resource and mineral-environmental assessments.

Descriptions of Map Sheets and 

Associated ERDAS Imagine Raster 

Image Files

Sheet 1 of 5

Title: MAP OF MINERALOGY, VEGETATION, 

AND HYDROTHERMAL ALTERATION TYPE FROM 

ASTER SATELLITE IMAGERY WITH EXAMPLES FROM 

THE SAN JUAN MOUNTAINS, COLORADO

Subtitle: CONTINUOUS-TONE MAP OF MINERAL

GROUPS

Filename: sanjuans_aster_minvue468_pt-utm.img

Description

This image consists of three enhanced, shortwave-infrared 

(SWIR) bands (4, 6, and 8) of a single ASTER dataset covering the scene. The three ASTER bands have been saturation 

enhanced and sharpened to 15-m spatial resolution using edgeenhanced ASTER band 2 to modulate intensity (brightness).

Purpose

The purpose of the image is to enable the visual discrimination of mineral groups and other surface features. When 

the provided bands (4, 6, and 8) are displayed as a color composite (468/RGB, or 123/RGB for this dataset) the colors can 

be interpreted according to the guide given below.

Magenta: minerals with Al(Fe)-OH or bound water 

SWIR absorptions (clays, sulfates, and white micas).

Red: minerals with Al(Fe)-OH or bound water SWIR 

absorptions + ferric iron ± minor carbonate ± dry vegetation. 

Dark red indicates green vegetation (brightness reduced by 

using the red wavelengths of ASTER band 2, in which green 

vegetation shows deep chlorophyll absorptions and is thus 

dark, to modulate intensity).

Orange to reddish orange: dry vegetation ± minor 

Al-OH minerals ± minor carbonate.

Brown to reddish brown: green vegetation.

Yellow/green: carbonate minerals (for example, calcite 

and dolomite) will more likely have a yellowish hue; chlorite, 

epidote and (or) amphiboles with ferric/ferrous iron absorptions are more likely to have a greenish hue. Yellow indicates 

strong absorption in ASTER band 8, typical of unaltered 

carbonate rocks devoid of clay, mica, and ferric iron minerals. 

Green indicates absorption in ASTER bands 4 and 8 relative 

to band 6, typical of Mg/Fe-OH minerals such as chlorite and 

epidote which are common components of propylitic alteration 

(often intimately mixed with calcite). The absorption in band 

4 of epidote and chlorite is caused by ferric and (or) ferrous 

iron. Dense, dry vegetation typically has less absorption (is 

brighter) in band 4 than chlorite and epidote, although it also 

has deep absorptions in band 8.

White: High-albedo surfaces such as playas which may 

contain clays, micas, and (or) other minerals.

Sheet 2 of 5

Title: MAP OF MINERALOGY, VEGETATION, 

AND HYDROTHERMAL ALTERATION TYPE FROM 

ASTER SATELLITE IMAGERY WITH EXAMPLES FROM 

THE SAN JUAN MOUNTAINS, COLORADO
Descriptions of Map Sheets and Associated ERDAS Imagine Raster Image Files 3

Subtitle: IRON-BEARING MINERALS, VEGETATION, AND WATER

Filename: sanjuans_aster_fe-veg-water_pt-utm.img

Description

This image shows iron-bearing minerals, vegetation 

groups, and water identified using ASTER data based on spectral similarity between image spectra and laboratory reference 

spectra of well-characterized materials and material mixtures. 

Calibration and analysis of ASTER data were performed using 

methodologies described by Rockwell (2009). Colluvium (see 

http://static.panoramio.com/photos/original/4408546.jpg) on 

the northern and western flanks of Engineer Mountain derived 

primarily from unaltered mid-Tertiary intrusive rocks on the 

mountain summit was used for reflectance calibration of the 

ASTER data.

Purpose

The purpose of the image is to display the spatial distributions of iron-bearing minerals, vegetation groups differentiated 

by variations in chlorophyll content and dryness, and volatiles 

(water and snow). Most ferric iron associated with hydrothermal alteration or oxidized pyritic mine waste is shown in 

shades of red. Minor ferric iron associated with lithology and 

(or) weathering of iron-bearing minerals is shown in shades 

of orange. The image has been attributed by pixel value with 

material identification data that can be queried in most image 

processing and GIS software packages. Metadata includes pixel 

values associated with each identified material.

Sheet 3 of 5

Title: MAP OF MINERALOGY, VEGETATION, 

AND HYDROTHERMAL ALTERATION TYPE FROM 

ASTER SATELLITE IMAGERY WITH EXAMPLES FROM 

THE SAN JUAN MOUNTAINS, COLORADO

Subtitle: CLAY, SULFATE, MICA, CARBONATE, 

MG-OH, AND HYDROUS QUARTZ MINERALS

Filename: sanjuans_aster_swir_pt-utm.img

Description

This image shows clay, sulfate, mica, carbonate, 

Mg-OH, and hydrous quartz minerals identified using ASTER 

data based on spectral similarity between image spectra and 

laboratory reference spectra of well-characterized materials 

and material mixtures. Analysis of ASTER scene performed 

using methodologies described by Rockwell (2009).

Purpose

The purpose of the image is to display the spatial distributions of clay, sulfate, mica, carbonate, Mg-OH, and hydrous 

quartz minerals. The image has been attributed by pixel value 

with material identification data that can be queried in most 

image processing and GIS software packages. Metadata 

includes pixel values associated with each identified material.

Comments: Pixels colored light purple were spectral 

matches to an iron-rich muscovite (sample GDS116 Tanzania

see http://speclab.cr.usgs.gov/spectral.lib06/ds231/PLOTS/M/

muscovite_ gds116.15173.w1.gif, Clark and others, 2007), and 

have been labeled as “Fe/Mg sericite, or muscovite + chlorite 

mixture.” Figure 1, line a, shows the reference spectrum of 

this muscovite convolved to ASTER spectral resolution. The 

iron in the crystal structure shifts the position of the primary 

Al-OH absorption near 2.2 μm (micrometers) to somewhat 

longer wavelengths (2.215 μm and longer) than in aluminumrich muscovites, and also produces minor absorption near 

2.245 μm that results in an asymmetric shape of the Al-OH 

feature. At ASTER resolution, these spectral characteristics 

are manifested by reduced reflectance near 2.27 μm (ASTER 

band 7) relative to that of ASTER band 5 (see black arrows in 

fig. 1), and generally low reflectance of bands 6–9 compared 

to that of band 5. Figure 1 line d shows an average spectrum 

of several ASTER pixels that were matched to the GDS116 

muscovite reference spectrum. ASTER and AVIRIS spectra of 

iron-rich muscovites are compared in Rowan and Mars (2003).

The laboratory spectrum of a chlorite + muscovite 

mixture convolved to ASTER resolution is shown in line b 

of figure 1. This spectrum also shows reduced reflectance 

at ASTER band 7 and is very close in overall SWIR shape 

to that of the ASTER spectrum in line d. The iron- and (or) 

magnesium-related absorptions in chlorite near 2.25 μm also 

result in decreased reflectance at ASTER band 7. The similarities in SWIR shape of iron-rich muscovites (figure 1line a) 

and chlorite + muscovite mixtures (figure 1, line b) at ASTER 

spectral resolution prevent accurate discrimination from each 

other. Spectral similarities between chlorite + muscovite 

mixtures and low-aluminum (iron- and magnesium-bearing) 

muscovites have also been observed in AVIRIS data (Rockwell and others, 2005).

The pixels matched to iron-rich muscovite (fig. 1, line d) 

are located along the western edge of the Silverton caldera 

in rocks that have undergone regional propylitic alteration 

to sericite and chlorite (± minor calcite) associated with lava 

degassing and caldera development (Bove and others, 2007; 

Yager and others, 2005). This alteration corresponds to the 

chlorite-illite-calcite, or CIC, type of propylitic alteration 

characterized by Bove and others (2008) which is commonly 

epidote-poor and in some cases represents a younger overprint 

of weak sericite-pyrite alteration on regionally propylitized 

rocks. The sericite (illite) commonly replaces chlorite in such 

cases. The lack of substantial absorption at ASTER band 7 

indicates a lower concentration of calcite, chlorite, and epidote 

relative to sericite, in contrast to higher-grade, proximal 
4 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

propylitic alteration (chlorite-epidote-calcite or CEC alteration 

of Bove and others, 2008) surrounding hydrolytic alteration 

located near the center of the caldera. Aluminum-rich sericite 

associated with phyllic (quartz-sericite-pyrite) alteration (fig. 

1, line c) shows higher reflectance in ASTER band 7 relative 

to that of band 5, an effect which is made more pronounced by 

trace amounts of supergene kaolinite which reduce reflectance 

in band 5 (fig 1, orange arrow).

The AVIRIS-based mapping of Dalton and others (2004) 

does not discriminate sericites based on composition, as all 

identified sericites not mixed with clays, carbonates, chlorite, 

or epidote were combined into a single sericite class. The 

ASTER-based mapping shown here demonstrates that distal, 

regional propylitic alteration with abundant sericite and minor 

chlorite and calcite can be distinguished from higher-grade, 

proximal propylitic alteration with more abundant calcite, 

chlorite, and epidote. The acid neutralizing capacity (ANC) of 

the latter, proximal type of propylitic alteration is most likely 

greater than that of the former, distal type. Sheet 3 shows that 

mixtures of calcite (± minor chlorite and epidote) and sericite 

(or smectite) are directly identified and shown in bluish green, 

in addition to “pure” calcite (bright green) and dolomite (dark 

green). Calcite + dolomite mixtures may be similar in SWIR 

spectral shape to epidote, and are thus not differentiated (pixels matched to epidote are shown in blue but labeled as either 

epidote or calcite + dolomite mixture) (Rockwell, 2009). Not 

many pixels of “pure” chlorite (shown in faded light green) 

were identified because the deep ferric and (or) ferrous iron 

absorptions of chlorite in the visible and near-infrared (VNIR) 

were included in the spectral matching process, meaning that 

both the VNIR and SWIR features had to match the reference 

spectrum for a pixel to be identified as chlorite. Such VNIR 

absorptions are not present in calcite and are not included 

in the spectral matching of epidote, making identifications 

of those minerals more likely than chlorite. The iron-related 

VNIR absorptions of epidote are relatively weak compared to 

those of chlorite, and are thus easily masked in remote sensing 

data by vegetation and other spectral mixing effects.

The smectite group mineral montmorillonite occurs as 

a primary or diagenetic constituent in various rock types, 

including ash-flow tuffs which are common in the San Juan 

Mountains. Montmorillonite also occurs in peripheral parts of 

argillic and phyllic alteration zones from hydrothermal fluids of 

near-neutral pH and lower temperatures. Sericite in low abundance may be misidentified as montmorillonite, as the Al-OH 

absorption feature of sericite at ASTER band 8 (2.336 μm) may 

Wavelength μm (microns)

Reflectance (offset for clarity)

ASTER—Regional propylitic alteration: muscovite + chlorite 

ASTER–Phyllic alteration: Al sericite ± trace kaolinite + trace ferric iron 

chlorite + muscovite (CU93–65A) 

Fe muscovite (GDS116 Tanzania) 

ASTER bands 

1.0 1.5 2.0

a

b

c

d

1 2 3 4 5 6 7 8 9

Laboratory Spectrum of a Chlorite + Muscovite Mixture Convolved to ASTER Resolution

Figure 1. Plots of laboratory reference spectra (Clark and others, 2007) convolved to ASTER spectral resolution and 

ASTER pixel spectra of muscovites and a muscovite + chlorite mixture. ASTER band centers are shown along the bottom. 

Click on figure for more detailed image.
Descriptions of Map Sheets and Associated ERDAS Imagine Raster Image Files 5

be too shallow to be detectable using ASTER SWIR data. In 

the Silverton caldera, most low-abundance sericite is associated 

with weakly propylitized volcanic rocks, although it is common in many un-altered rock types including shales, quartzites, 

and granites.

ASTER SWIR data become unstable at low surface 

albedos (for example, terrain and cloud shadow) and spurious 

spectral shapes are present in such areas which may mimic 

those of minerals and other surface features. The spectral 

shapes common to iron-rich muscovites and sericite + chlorite 

mixtures are common in such low-albedo areas. Masking of 

low-albedo areas prior to analysis is vital to remove false 

muscovite detections in these areas.

Several pixels of hydrous silica (chalcedony or opal) 

were identified within a silicified breccia body (Bove and 

others, 2007) immediately southwest of the summit of Red 

Mountain No. 3.

Sheet 4 of 5

Title: MAP OF MINERALOGY, VEGETATION, 

AND HYDROTHERMAL ALTERATION TYPE FROM 

ASTER SATELLITE IMAGERY WITH EXAMPLES FROM 

THE SAN JUAN MOUNTAINS, COLORADO

Subtitle: MODELED HYDROTHERMAL ALTERATION TYPE

Filename: sanjuans_aster_alteration5_pt-utm.img

Description

This image shows the results of a new methodology 

designed to simplify and combine the mineral occurrence 

information contained in sheets 2 and 3 into a single map 

showing the spatial distribution of hydrothermal alteration 

types and (or) carbonate rocks. Although carbonate rocks can 

be isolated from propylitic alteration through integration with 

a digital lithologic map, carbonate rocks are included because 

of their strong acid neutralizing capacity importance to geoenvironmental modeling. In the map explanation, alteration types 

and key mineral assemblages have been grouped by relative 

NAP and ANC. These groups are ordered by decreasing NAP 

from upper left to lower right.

Hydrothermal alteration types are defined based on the 

presence of mineral assemblages, and diagnostic minerals for 

such definitions are remotely identified from both the visible/

near-infrared (VNIR, sheet 2) and SWIR (sheet 3) spectral 

regions which are analyzed separately. Therefore, a suite of 

Boolean equations was developed that combines the mineralogic information from the two spectral regions (table 1).

As aluminum-rich sericite associated with hydrothermal 

alteration is commonly present in greater spectral abundance 

(greater absorption feature depth given equivalent grain size) 

than sericite in unaltered sedimentary and metamorphic rocks, 

sericite identified using the ASTER data was subdivided 

into groups of high and low abundance. Sericite with greater 

feature depths was assigned to the high abundance group, and 

sericite with lesser feature depths was assigned to the low 

abundance group. Low-abundance sericite was included in the 

Boolean formulae used to compute alteration types only when 

in the presence of ferric iron, in which case the combination 

was classified as “regional propylitic to weak QSP—minor 

sericite + ferric iron” (“QSP” refers to quartz-sericite-pyrite, 

or phyllic, alteration).

The kaolinite + smectite mixture was assigned to the phyllic alteration type if it occurred in the presence of ferric iron 

(shaded cell in table 1) even though the mixture is an argillic 

assemblage. Supergene weathering of pyritic rocks typically 

produces a poorly crystallized argillic overprint containing 

kaolinite ± smectite (Rockwell and others, 2005; Cunningham 

and others, 2005) and abundant ferric iron minerals including 

jarosite and goethite. Rockwell (2009) shows that a kaolinite + 

smectite mixture and abundant ferric iron (in addition to gypsum) were detected in weathered, highly pyritized, phyllicallyaltered dacite lava flows on Big Rock Candy Mountain in the 

Marysvale volcanic field, Utah, using ASTER data.

Purpose

This image shows the occurrence of hydrothermal 

alteration types and carbonate rocks. The image provides an 

effective, more easily interpretable tool for geologists, watershed modelers, and land managers, The data can be readily 

integrated with other data layers in a GIS environment for 

the purposes of geoenvironmental watershed modeling. Each 

alteration type or mineral group can be attributed with values 

representing relative net acid production (acid producing 

potential) and acid neutralizing capacity.

Comments: This map is compared to alteration maps 

produced from integrated AVIRIS data analysis and field mapping (Bove and others, 2007) by Rockwell (2010b).

Sheet 5 of 5

Title: MAP OF MINERALOGY, VEGETATION, 

AND HYDROTHERMAL ALTERATION TYPE FROM 

ASTER SATELLITE IMAGERY WITH EXAMPLES FROM 

THE SAN JUAN MOUNTAINS, COLORADO

Subtitle: MODELED HYDROTHERMAL ALTERATION TYPE, MINERAL GROUPS, AND GREEN VEGETATION GENERATED BY AUTOMATED ANALYSIS

Filename: sanjuans_aster_autoalt55_merge_pt-utm.img 

Description

This image shows the results of a new methodology 

designed for the automated identification and mapping of 
Table 1. Boolean logic used to combine results of separate iron mineral, vegetation, and water analysis (mainly VNIR absorptions, see sheet 2) and carbonate, and hydrous silica mineral analysis (mainly SWIR absorptions, see sheet 3) into a single map of hydrothermal alteration type (see sheet 4). Iron mineral, vegetation, and water analysis VNIR-SWIR mineral analysis major ferric iron minor ferric iron copiapite (possible) sericite + chlorite kaolinite + smectite alunite alunite + kaolinite natroalunite, or alunite + minor kaolinite pyrophyllite kandite clay kaolinit + sericite Al sericite Fe/Mg sericite,or muscovite + chlorite hydrous silica (or jarosite + sericite or smectite) jarosite smectite calcite 

Advanced argillic OR OR OR OR Argillic (or clay- bearing rocks) NOT NOT NOT OR OR OR OR Argillic + ferric iron OR OR OR OR OR Hydrous silica NOT NOT NOT AND Phyllic (QSP) OR OR OR OR OR OR (high abundance) Highly pyritic QSP +/- hydrous silica OR OR OR AND Pyritic (jarosite) X Propylitic or carbonate rocks NOT NOT NOT OR 

Propylitic + sericite NOT NOT NOT 

Propylitic + ferric iron OR OR OR OR 

Propylitic + sericite + ferric iron OR OR OR 

Regional propylitic to weak QSP OR OR OR AND (low abundance) Regional propylitic X
Description of Automated Analysis Methodology 7

mineral groups and green vegetation from ASTER data at 

regional to national scales. Detailed description of this methodology is provided in the following section. These results 

were modeled using Boolean algebra from the raw results of 

the ratio-based analysis algorithm, which are also provided 

(sanjuans_aster_autoalt54_pt-utm.img). The 23 output classes 

are ordered relative to decreasing assumed net acid production 

from upper left (high) to lower right (low). Note that although 

ferrous iron itself has little to no acid neutralizing capacity 

(ANC), it is often associated with chlorite-, epidote-, and (or) 

carbonate-bearing rocks which can have substantial ANC 

Yager and others, 2008).

Purpose

This image shows the occurrence of alteration types, mineral groups, and dense green vegetation. The image provides 

an easily interpretable tool for geologists, watershed modelers, and land managers engaged in geoenvironmental watershed modeling and mineral resource assessment at regional 

to national scales. The image has been attributed by pixel 

value with material identification data that can be queried in 

most image processing and GIS software packages. Metadata 

includes pixel values associated with each identified material.

Description of Automated Analysis 

Methodology

To efficiently generate uniform maps of minerals and 

hydrothermal alteration over very large areas using ASTER 

data, automated processing and analysis techniques are 

required. As such, the analysis must accurately identify 

exposed minerals or mineral groups (minerals having similar spectral properties given the spectral resolution of the 

data being analyzed) from any suitable ASTER scene in any 

climate or terrain with no operator interaction. Suitable scenes 

are those with minimal cloud, haze, dust, and snow cover and 

maximal solar illumination (high solar elevation angle during 

late spring to early autumn).

The methodology presented here involves the use of compound band ratios to identify areas where one or more specific 

spectral absorption features are present. Ratios were developed 

to identify mineral groups based on diagnostic absorptions 

of pure and mixed mineral species in laboratory reference 

spectra convolved to ASTER spectral resolution. The technique assumes that only a particular mineral or mineral group 

(or green vegetation) will have deep absorption at a particular 

wavelength interval (one or more adjacent ASTER bands). If 

another surface material, material mixture, or feature has deep 

absorption at a given wavelength interval, a false detection of a 

mineral group will occur. The utility of a ratio of type (x + y)/z 

to identify absorption at band z relative to adjacent bands x and 

y was first demonstrated by Crowley and others (1989), who 

termed the technique “relative band depth” mapping. Some of 

the same ratios have been used previously to map minerals and 

mineral groups using ASTER data (Rowan and Mars, 2003).

Prior to computing the material indices described below, 

the ASTER VNIR data are resampled to 30-m spatial resolution 

to match that of the SWIR data, and all data are converted to 

scaled, 16-bit radiance units through multiplication of gain values that are documented in the scene metadata. No further corrections are applied to the data. Cross-talk corrections (Iwasaki 

and Tonooka, 2005) are not applied to the data for four reasons. 

First, the cross-talk error does not cause significant spectral 

corruption except in areas where there is extreme variation 

in surface albedo, such as in transitions between bright land 

surfaces and large bodies of water. Second, correction software for this error available from the Earth Remote Sensing 

Data Analysis Center (http://www.gds.aster.ersdac.or.jp/gds_

www2002/service_e/release_e/set_release_ecrs.html) does not 

fully correct the crosstalk effects, and introduces random noise 

to the SWIR data. Third, the software is designed to operate on 

ASTER Level 1B data in Hierarchical Data Format (HDF), and 

the use of orthocorrected Level 3 AST14OTH data, which is in 

geotiff rather than HDF format, is required for efficient product 

generation over large regions. And fourth, the software is difficult to integrate with the automated processing stream being 

developed for regional- and national-scale analysis.

All analytical processing described below is performed in 

one pass, producing two output files. The first is a nine-band 

binary file containing the material indices, and the second is 

a single-band, GIS-ready, attributed file of the merged results 

(sheet 5). This processing takes 75 seconds per ASTER scene 

on an Intel Core i7 Gulftown processor running at 3.7 GHz.

Mineral Group and Green Vegetation Indices

The nine indices described below are contained in the 

nine bands of the file “sanjuans_aster_autoalt54_pt-utm.img” 

provided with this publication. Information regarding the 

spectral and spatial characteristics of ASTER data is provided 

by Rockwell (2009). In the formulas for the indices, ASTER 

bands are indicated in bold. Rockwell (2010b) compares the 

results of several of the indices to detailed mineral maps produced from ASTER (sheets 2–4) and AVIRIS data.

Ferric Iron 1 “Redness” Index

An ASTER 2/1 ratio is used to highlight areas that have 

absorption in ASTER band 1 (green wavelengths) relative 

to band 2 (red wavelengths) (fig. 2). Ferric iron minerals 

are characterized by deep charge-transfer absorptions in the 

ultraviolet (< 0.4 μm) (Clark, 1999). The long-wavelength side 

of this absorption extends into the visible, absorbing blue and 

some green light and creating the characteristic red, orange, 

and yellow colors of such minerals. This ratio is similar to 

the red/blue ratio commonly used to identify ferric iron with 

Landsat Thematic Mapper data (3/1 ratio). The utility of the 

2/1 ratio for mapping ferric iron occurrence was demonstrated 

by Rowan and Mars (2003).
8 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

Jarosite will generally have deeper absorption at band 1 

compared to goethite (fig. 2), both minerals are common in 

weathered pyritic rocks. Most hematites will also show less 

band 1 absorption than jarosite. However, this index will highlight areas in which any ferric iron mineral (including jarosite, 

goethite, and hematite) occurs pervasively or as coatings. 

Ferric iron-bearing surfaces of hydrothermally altered rocks, 

sedimentary rocks such as red beds and arkoses, metamorphic 

rocks containing weathered, iron-bearing mafic minerals such 

as hornblende and biotite, sand deposits, and alluvium derived 

from such rocks will be identified with this index. The index is 

sensitive to ferric iron even in low concentrations.

Ferric Iron Index 2

An ASTER (2/1) × (0.5 × ((2+4)/3) compound ratio is 

used to highlight ferric iron-bearing areas in which crystalfield absorptions in the near infrared (ASTER band 3) are 

present in addition to the charge transfer absorptions previously described (2/1 ratio). Figure 2 shows that the crystalfield absorptions in jarosite are significantly deeper than those 

of goethite, although the VNIR spectral characteristics of ferric iron minerals vary greatly relative to abundance and grain 

size (Rockwell, 2004). Since this ratio contains the 2/1 operator of ferric iron index 1, pixels with high ratio values will be 

a subset of those highlighted by ferric iron index 1. Ferric iron 

index 2 will identify areas in which ferric iron minerals are 

present in greater abundance than ferric iron index 1, and these 

areas will in most cases be surrounded by pixels highlighted 

by ferric iron index 1.

Advanced Argillic-Kandite Index

Alunite and pyrophyllite are common components of 

advanced argillic alteration, and have vibrational Al-OH 

absorption in the SWIR at ASTER band 5 (fig. 3). Kandite 

1.0 1.5 2.0

Wavelength μm (microns)

Reflectance (offset for clarity)

Green vegetation

(fir tree)

Jarosite

Goethite

Hydrous silica

(chalcedony)

ASTER bands

Band 2

Band 1

Band 1 + Band 3

Band 2

Green vegetation index:

Ferric iron index 2:

Band 5 + Band 8

Band 6 + Band 7

Hydrous silica-jarosite-sericite index:

Band 2 + Band 4

Band 3

Ferric iron index 1:

Band 2

Band 1

1 2 3 4 5 6 7 8 9

×

Laboratory Reference Spectra Convolved to ASTER Spectral Resolution

Figure 2. Plots of laboratory reference spectra (Clark and others, 2007) convolved to ASTER spectral resolution. 

Plots are annotated with circles indicating ASTER bands used in the material indexes. Green circles show bands 

used in the numerator of an index, and cyan circles show bands used in the denominator. ASTER band centers 

are shown along the bottom. Click on figure for more detailed image.
Description of Automated Analysis Methodology 9

clay minerals such as kaolinite and dickite may occur mixed 

with and peripheral to alunite and pyrophyllite in advanced 

argillic alteration zones from fluids of higher pH and lesser 

temperature (Rockwell and others, 2006). Kandite clay minerals also have absorption ASTER band 5, although of a lesser 

depth (Rockwell, 2009). An ASTER (4+7)/5 ratio is used to 

highlight these absorptions.

Dry vegetation has deep lignin-cellulose absorptions at 

ASTER bands 5 and 7 and 8 (Rockwell, 2009 and references 

therein). When sparse dry vegetation is present on surfaces 

underlain by kaolinite- or sericite-bearing rocks, the vegetation 

absorption mixes with the mineral absorptions and produces 

spectra that can be similar in appearance to that of alunite 

and pyrophyllite, creating severe problems for the accurate 

mapping and characterization of advanced argillic and argillic 

alteration (John and others, 2010). To mitigate the deleterious 

spectral effect of dry vegetation, the (4+7)/5 ratio is divided 

by the (6+9)/(7+8) ratio (which highlights absorption in bands 

7 and 8), resulting in the highlighting of areas in which only 

band 5 absorption occurs.

Argillic-Sericite-Smectite Index

Sericite, smectite, and kandite clay minerals such as 

kaolinite have vibrational Al-OH absorption features near 

ASTER band 6 (fig. 4). Kandite clay minerals have weaker 

absorption at band 6 compared to sericite and smectite. An 

ASTER (5+7)/6 ratio is used to highlight these absorptions. 

The utility of this ratio for mapping aluminum-rich muscovites was demonstrated by Rowan and Mars (2003). Figure 3 

shows that kaolinite also has absorption at ASTER bands 5. 

Therefore, kandite clay minerals will typically be highlighted 

by both the advanced argillic-kandite and argillic-sericitesmectite indices.

1.6 1.8 2.0 2.2 2.4

Wavelength μm (microns)

Reflectance (offset for clarity)

Alunite

Well-crystallized

kaolinite

Band 4 + Band 7

Band 5

Band 6 absorption will 

increase with increasing 

kaolinite, sericite, and (or)

Na content in alunitic rocks, 

increasing argillic-sericitesmectite index value

Pyrophyllite

ASTER bands

4 5 6 7 8 9

Laboratory Reference Spectra of Alunite, Pyrophyllite, and Kaolinite

convolved to ASTER Spectral Resolution

Figure 3. Plots of laboratory reference spectra of alunite, pyrophyllite, and kaolinite 

(Clark and others, 2007) convolved to ASTER spectral resolution. Plots are annotated with 

circles indicating ASTER bands used in the advanced argillic-kandite index. Green circles 

show bands used in the numerator of an index, and cyan circles show bands used in the 

denominator. ASTER band centers are shown along the bottom. Click on figure for more 

detailed image.
10 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

Absorption at band 6 will increase with increasing 

kandite clay, sericite, and (or) Na content in alunitic rocks, 

increasing the argillic-sericite-smectite index value (fig. 3; 

Rockwell 2009). Therefore, advanced argillic alteration with 

significant kandite clay, sericite, and (or) Na content will 

likely be highlighted by both indices, whereas K-rich alunite 

and pyrophyllite with minimal clay or sericite will most likely 

be highlighted only by the advanced argillic-kandite index.

In clay- and (or) mica-bearing rocks, band 5 absorption 

will decrease with decreasing kandite clay content, increasing 

the argillic-sericite-smectite index value and decreasing the 

advanced argillic-kandite index value (fig. 4). Kandite-poor 

surfaces with sericite and (or) smectite will thus be unlikely 

to be highlighted by the advanced argillic-kandite index.

Carbonate-Propylitic Index

Carbonates, chlorite, epidote, magnesium-bearing phyllosilicates such as talc and serpentine, and most amphiboles 

(for example, tremolite, actinolite, hornblende, cummingtonite) have vibrational absorptions near ASTER bands 7 and 

8 related to bonds in Fe-OH (band 7), and CO3

 and Mg-OH 

(band 8) molecules (Clark, 1999). Figure 5 shows laboratory reference spectra of some of these minerals convolved 

to ASTER spectral resolution. An ASTER (6+9)/(7+8) ratio 

is used to highlight these absorptions. Carbonate-bearing 

sedimentary and metamorphic rocks and propylitically-altered 

igneous rocks will be highlighted with this index. Most of 

the minerals identified using this index have at least some 

acid neutralizing capacity, including some of the amphiboles 

(Jambor and others, 2002).

As explained above, dry vegetation with abundant 

lignin and (or) cellulose leaf biochemicals is characterized 

by prominent absorptions at ASTER bands 5 and 7–8. To 

minimize spectral confusion between dry vegetation and 

carbonate-propylitic minerals, the (6+9)/(7+8) ratio is divided 

by a (4+7)/5 ratio to highlight areas in which only band 7–8 

absorption occurs.

1.8 2.0 2.2 2.4

Wavelength μm (microns)

ASTER bands

Reflectance (offset for clarity)

Band 5 + Band 7

Band 6

Well-crystallized

kaolinite

Sericite 

(K muscovite)

Smectite

(montmorillonite)

Band 5 absorption will decrease 

with decreasing kaolinite content 

and (or) crystallinity in clay/micabearing rocks, increasing

argillic-sericite-smectite index value

4 5 6 7 8 9

Laboratory Reference Spectra of Sericite, Smectite, and Kaolinite

Convolved to ASTER Spectral Resolution

Figure 4. Plots of laboratory reference spectra of sericite, smectite, and kaolinite (Clark and others, 

2007) convolved to ASTER spectral resolution. Plots are annotated with circles indicating ASTER bands 

used in the argillic-sericite-smectite index. Green circles show bands used in the numerator of an index, 

and cyan circles show bands used in the denominator. ASTER band centers are shown along the bottom. 

Click on figure for more detailed image.
Description of Automated Analysis Methodology 11

Dolomite-chlorite-epidote index

Figure 5 also shows that dolomite has absorption at 

ASTER band 7 that is deeper than in other carbonate-propylitic 

minerals. Of these minerals, band 7 absorption depth increases 

from calcite (low), to epidote, chlorite, and dolomite (high). 

The position and shape of the Fe-OH absorption in chlorites 

near ASTER band 7 vary significantly relative to Fe/Mg ratios. 

The absorption position shifts from 2.241 μm in intermediate 

magnesium chlorites to 2.247 μm in intermediate iron chlorites (Jones and others, 2005). It can be assumed that iron-rich 

chlorites will have slightly greater absorption at ASTER band 

7 than magnesium-rich chlorites, as the absorption position in 

iron-rich chlorites is closer to the momentum center of band 7 

(2.266 μm).

A (6+9)/(7+8) × (2 × ((6+8)/7)) compound ratio is used 

to highlight areas with significant band 7 absorption within 

carbonate and (or) propylitically-altered rocks. Such areas 

most likely correspond to dolomite in sedimentary rocks, and 

to chlorite and (or) epidote in metamorphic or propyliticallyaltered igneous rocks.

As with the carbonate-propylitic index, the ratio for the 

dolomite-chlorite-epidote index is divided by a (4+7)/5 ratio to 

minimize spectral confusion with dry vegetation.

Hydrous Silica-Jarosite-Sericite Index

Hydrous silica minerals such as opal and chalcedony 

have vibrational Si-OH absorptions at ASTER bands 6 and 7, 

and jarosite has Fe-OH absorptions in the same wavelength 

interval (figure 2). A (5+8)/(6+7) ratio is used to highlight 

areas in which such absorptions are present. Nonhydrous 

quartz in advanced argillic and phyllic alteration, quartzites, 

cherts, sand deposits, and perlitic volcanic rocks can be identified through analysis of the ASTER thermal infrared data 

(Rockwell and Hofstra, 2008).

Band ratios are an imprecise method of quantifying 

absorption feature shape compared to spectroscopic methods 

such as least-squares curve fitting (Clark and others, 1990), and 

matched-filtering (Harsanyi and Chang, 1994). When coupled 

with the low spectral resolution of ASTER data compared to 

that of laboratory and imaging spectrometers, band ratio results 

1.8 2.0 2.2 2.4

Wavelength μm (microns)

Reflectance (offset for clarity)

Chlorite

Calcite

Dolomite

Epidote

ASTER bands

Band 6 + Band 9

Band 7 + Band 8

Band 6 + Band 9

Band 7 + Band 8

Band 6 + Band 8 ( Band 7 ×

Carbonate-propylitic:

Dolomite-chlorite-epidote:

× ) 2

4 5 6 7 8 9

Laboratory Reference Spectra of Calcite, Dolomite, Chlorite, and Epidote

Convolved to ASTER Spectral Resolution

Figure 5. Plots of laboratory reference spectra of calcite, dolomite, chlorite, and epidote (Clark 

and others, 2007) convolved to ASTER spectral resolution. Plots are annotated with circles 

indicating ASTER bands used in the carbonate-propylitic and dolomite-chlorite-epidote indices. 

Green circles show bands used in the numerator of an index, and cyan circles show bands used 

in the denominator. ASTER band centers are shown along the bottom. Click on figure for more 

detailed image.
12 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

can show significant confusion between surface materials with 

similar spectral properties. The (5+8)/(6+7) ratio highlights 

not only areas with hydrous silica minerals or jarosite, but 

also areas having spectral shapes with significant decreases in 

reflectance between ASTER bands 5 and 6–7 related to ironrich muscovites and (or) sericite + chlorite mixtures (fig. 1). 

However, such sericite-bearing areas will also be highlighted 

by the argillic-sericite-smectite index (band 6 absorption), 

whereas hydrous silica minerals will not, as band 6 absorption 

relative to adjacent bands, will be weak. Similarly, jarositic 

areas will always occur with ferric iron, unlike hydrous silica 

in most tuffaceous and (or) hydrothermally-altered rocks. The 

spectral confusion present in the individual index results is 

largely mitigated by examining pixel occurrence in multiple 

indices using Boolean algebra as presented below.

Based on examination of many scenes during the development of the automated technique, the hydrous silica-jarosite-sericite index is the least accurate of any of the indices, and results 

should be interpreted with caution. A majority of pixels identified as hydrous silica in sheet 5 are incorrect, and hydrous silica 

identified using the expert system-based analysis of the ASTER 

data (sheet 3) in a silicified breccias body near Red Mountain 

no. 3 was not identified using the automated technique.

Ferrous Iron Index

Ferrous iron produces a broad VNIR absorption at 

ASTER bands 2 and 3 relative to 1 and 4 (fig. 6). An ASTER 

(1+4)/(2+3) ratio is used to highlight areas in which this 

absorption is present.

Wavelength μm (microns)

Reflectance

Chlorite

Actinolite

Jadeite

Augite

Biotite

Cummingtonite

Band 1 + Band 4

Band 2 + Band 3

Ferrous iron index:

ASTER bands

0.6 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 0.8 1.0 1.2 1.4 1.6

1 2 3 4

Laboratory Reference Spectra of Ferrous Iron-Bearing Minerals

Convolved to ASTER Spectral Resolution

Figure 6. Plots of laboratory reference spectra of ferrous iron-bearing minerals 

(Clark and others, 2007) convolved to ASTER spectral resolution. The iron 

absorptions in chlorite are related to both ferric and ferrous iron. Plots are annotated 

with circles indicating ASTER bands used in the ferrous iron index. Green circles 

show bands used in the numerator of an index, and cyan circles show bands used 

in the denominator. ASTER band centers are shown along the bottom. Click on figure 

for more detailed image.
Description of Automated Analysis Methodology 13

The results of the ferrous iron index correlate well with 

ferrous irons identified in the Silverton area using AVIRIS 

data (fig. 7). Such ferrous iron-bearing minerals correspond 

mainly with chlorite-bearing, propylitically altered rocks. 

The ferrous iron index has also been found to highlight 

some carbonate rocks in other areas. It is unknown if ferrous iron is present in these carbonates, or if the ferrous 

iron index is highlighting the rocks because of their bluishgray color. Ferrous iron is known to occur in recrystallized 

dolomitic marble (see http://speclab.cr.usgs.gov/spectral.

lib06/ds231/descript/M/dolomite_hs102.html) (Clark and 

others, 2007). The index can also be expected to highlight 

rocks having a general greenish color (for example, greenstones), and other areas with strong decreases in reflectance 

between the green (ASTER band 1) and red/near-infrared 

spectral regions (ASTER bands 2 and 3) such as blue-gray 

shales and moist soils.

Green Vegetation Index

An ASTER (1+3)/2 ratio is used to highlight areas with 

strong absorption in the red wavelengths (ASTER band 2). 

Chlorophyll-related absorptions in green vegetation are the 

most common source of such absorption (fig. 2).

0 1

1

2

2

3

3

4

4

5

5

6 MILES

0 6 KILOMETERS

107°45' 107°40' 107°35'

37°55’

37°50’

37°55’

37°50’

Edge of AVIRIS

coverage

ASTER

AVIRIS

550

550

A

B

Figure 7. Comparison of ferrous iron-bearing minerals identified in the Silverton, Colo., area using A. expert 

system-based analysis of AVIRIS SWIR data (modified from Dalton and others, 2004), and B. the automated ferrous 

iron index. Approximate extent of Silverton caldera is indicated in red. Click on figure for more detailed image.
14 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

Absorption in the red wavelengths has been observed on 

low-albedo surfaces in the arid Great Basin, including some 

underlain by young basalts, some of which have low-density 

grass and (or) sage cover. Such absorption is typically weak, 

and has been observed with ASTER and AVIRIS data. However, the vegetation may not be the only source of the band 2 

absorption, which could be related to zeolite minerals or other 

greenish- or bluish-colored minerals or coatings on the basalts. 

Image Processing for Masking and Index 

Thresholding

Dark Area Masking

As previously mentioned, ASTER SWIR data are 

unstable in areas of low surface albedo. Such areas are masked 

from analysis using thresholds of ASTER band 4 determined 

through examination of many scenes from different climates, 

land cover, and terrain relief. Different threshold values are 

used for semi-arid and temperate, alpine climates. Thresholds 

for additional climate and terrain relief types may be added as 

more scenes are analyzed. Based on the mean scaled radiance 

value of band 4, a band 4 threshold value is assigned and a 

mask created which highlights dark areas. This mask is then 

applied to all bands to remove data from dark areas. 

Water Masking

An ASTER 1/4 ratio is used to highlight areas having a 

steep decrease in reflectance between the green and SWIR 

wavelengths. Water and snow are characterized by intense 

absorption in the SWIR relative to the visible spectral regions. 

A threshold value for the ratio was chosen based on examination of standing water in many ASTER scenes. A mask is created by applying the threshold to the 1/4 ratio result. The mask 

is then applied to all bands to remove data from areas of snow 

and standing water. Turbid water, shallow water with a bright 

substrate, and mixtures of thin or melting snow and vegetation 

have reduced absorption in the SWIR and may not be entirely 

removed by this masking process.

Selective Vegetation Suppression

The argillic-sericite-smectite index is divided by the 

green vegetation index results to reduce the influence of green 

vegetation. This operation reduces false detections of clays 

and micas in highly vegetated areas. The operation is also 

performed on the carbonate-propylitic and dolomite-chloriteepidote indices in temperate and alpine areas having locally 

abundant vegetation. As in the dark area masking process 

described above, if the mean scaled radiance of band 4 is below 

an empirically-derived value, the scene is determined to be of 

the alpine and (or) temperate climate type, and the vegetation 

suppression operation is performed on the selected indices.

Final Index Thresholding and Clipping

The final indices (see file sanjuans_aster_autoalt54_

pt-utm.img) are created by isolating the highest values using a 

common standard deviation threshold, followed by an indexspecific clip (removal) of the lowest index values based on 

extensive testing of scenes from a variety of climates, terrain 

relief, and alteration types. These clips are minor in scope, 

and were determined based on detailed comparisons of the 

index results to mineral maps created from spectroscopic 

analysis of ASTER and AVIRIS imagery. In arid and semi-arid 

climates, the low end of the green vegetation index is clipped 

to reduce influence of ASTER band 2 absorptions on lowalbedo surfaces not related to green vegetation. The amount 

of low-end index values which are removed increase in the 

following order from low to high: (1) argillic-sericite-smectite, 

(2) ferric iron and carbonate-propylitic, (3) dolomite-chloriteepidote, and (4) hydrous silica-jarosite-sericite and advanced 

argillic-kandite. The relatively high degrees of clipping of 

the latter two indices reflect a lower degree of confidence in 

the accuracies of results. The areal mixing of clay minerals 

and vegetation having ASTER band 5 absorption related to 

leaf biochemicals can result in false detections of minerals 

associated with advanced argillic alteration that also have 

absorption at band 5 (Rockwell, 2009; John and others, 2010). 

Absorptions at ASTER bands 6 and 7 related to hydrous silica 

minerals are typically shallow at the spatial and spectral scale 

of ASTER SWIR data, and false detections may occur that are 

caused in part by areal mixing of sericites or smectite (band 6 

absorption) with certain types and densities of dry vegetation 

and (or) carbonate- or gibbsite-bearing rocks and soils (band 

7 absorption). Increased clipping of low-end values will result 

in fewer detected pixels in an index, and increased confidence 

in those that remain. However, occurrences of the particular 

mineral group in lower abundance will be removed.

For example, alunite occurs in lower abundance in alluvium derived from an advanced argillic alteration zone than 

in the zone itself, and much of that alluvium can be removed 

by higher clipping values. Clipping values were determined 

through testing in well-known altered areas including the 

Goldfield mining district in Nevada, the site of extensive 

alunite- and pyrophyllite-bearing alteration and derived 

alluvium (fig. 8). Resultant clipping values for the advanced 

argillic-kandite index remove most alunite-bearing alluvium, 

andhighlights the altered areas themselves. Such tradeoffs 

between completeness and accuracy of results are required for 

regional- and national-scale mapping using automated, ratiobased techniques with ASTER data.
Alunite + kaolinite ± sericite Alunite + pyrophyllite ± jarosite Alunite ± minor kaolinite ± minor jarosite Alunite/natroalunite + pyrophyllite ± sericite Calcite Calcite + sericite Dickite Epidote High-Al sericite Jarosite —Lowest fit × depth Alunite, intermediate Na/K composition, or alunite/natroalunite areal mix — Highest fit × depth Hydrous quartz: chalcedony (mainly in tuffaceous units), or opal Kaolinite + pyrophyllite Kaolinite ± smectite Kaolinite 75% + alunite 25% areal mix Kaolinite and(or) halloysite Low-Al sericite or muscovite + chlorite mixture Natroalunite Natroalunite + kaolinite Natroalunite ± dickite, other clays Pyrophyllite Pyrophyllite + sericite Sericite ± kaolinite 1—Lowest fit × depth Sericite ± kaolinite 2 Sericite ± kaolinite 3 Sericite ± kaolinite 4 Sericite ± kaolinite 5—Highest fit × depth Smectite (montmorillonite) Al sericite (± kaolinite) Alunite (± pyrophyllite) Alunite + kaolinite Calcite Calcite (or chlorite/epidote) + sericite (or smectite) Chlorite (Mg-OH SWIR + ferrous/ferric iron absorptions Dolomite Epidote, or calcite + dolomite mixture Fe/Mg sericite or muscovite + chlorite mixture Gypsum ± clay minerals Kaolinite ± sericite Kaolinite and (or) dickite/nacrite Natroalunite, or alunite + minor kaolinite Opal and (or) chalcedony Pyrophyllite or alunite Jarosite ± gypsum, or ferric iron + opal/chalcedony Smectite (montmorillonite) or low-abundance sericite Kaolinite ± smectite (typically supergene) EXPLANATION EXPLANATION 

Argillic-sericite- smectite index 

Typical argilli

(kandite clay ± sericit

± smectite) ± mino

advanced argilli

0 1 1 2 2 3 3 4 MILES 0 4 KILOMETERS

AVIRIS Expert System Mapping ASTER Expert System Mapping ASTER 

Goldfielddistrict

Cupritedistrict

117°10'

37°40'

37°35'

117°10'

37°40'

37°35'

1

37°40'

37°35'
16 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

Modeling of Hydrothermal Alteration Type and 

Mineral Groups through Boolean Combination of 

Index Results

For the purposes of (1) combining the nine indices into 

a more easily-visualized single map, (2) assigning GISready descriptive attributes to the map, (3) applying geologic 

knowledge of the alteration types and mineral groups identified by the indices, and (4) mitigating the mixing of mineral 

groups in single indices as discussed above, Boolean algebra 

is used to examine pixel occurrence in one or more indices 

and generate 23 output classes of alteration type, mineral 

groups, and green vegetation (sheet 5). Table 2 shows the 

logic of the algebraic expressions.

The modeling of the index results into output mineral 

groups does not account for all possible combinations of pixel 

occurrence in multiple indices, and the raw index results themselves can be consulted to obtain information on additional 

mineral assemblages. For example, the co-occurrence of the 

sericite and (or) smectite mineral group with the ferrous iron 

mineral group is not modeled. Most mineral groups applicable 

to hydrothermal alteration characterization and lithologic discrimination that are identifiable using ASTER data have been 

defined in the modeling logic.

The logic used to generate the “jarosite ± sericite ± 

smectite, or hydrous silica + ferric iron” output class (#8) is 

different than that used for other classes because most jarosites 

occur as coatings on a variety of rock and rock-alteration 

types. The logic states that the class will be mapped as long 

as a pixel is highlighted by both ferric iron indices and the 

hydrous silica–jarosite–sericite index, regardless of its occurrence in other indices. Figure 9 shows that the class generally 

correlates generally well with a jarosite + sericite mixture 

directly identified using AVIRIS data (Dalton and others, 

2004), although significantly fewer occurrences of the mixture 

were detected using the ASTER data. Jarosite derived from the 

subaerial oxidation of pyrite commonly occurs as coatings on 

a supergene overprint of kaolinite ± smectite, and this overprint typically obscures the Fe-OH absorption of jarosite near 

2.27 μm but not ferric iron absorptions in the VNIR (Rockwell 

and others, 2005). Therefore, most jarosite will be highlighted 

by one or both of the ferric iron indices, and only the most 

abundant, clay-free occurrences will be identified in the modeled “jarosite ± sericite ± smectite, or hydrous silica + ferric 

iron” output class.

Class 11 (table 2) is named “argillic + ferric iron or 

weathered phyllic” and may include (1) argillic alteration with 

ferric iron (for example, hypogene kaolinite + hematite, or 

hypogene kaolinite + supergene jarosite derived from pyrite 

oxidation) and (2) weathered phyllic alteration with a supergene argillic overprint. Class 12 is named “sericite and (or) 

smectite + ferric iron (may include fresh phyllic)” and may 

include (1) “fresh” phyllic alteration without a supergene argillic overprint (caused by either recent exposure or low pyrite 

content), (2) iron- and smectite-bearing playa surfaces, and 

(3) sericite with a coating of ferric iron derived from weathering of mafic, iron-bearing minerals.

In carbonate and propylitically-altered rocks, the inclusion of logic to isolate band 7 absorptions associated with 

dolomite, chlorite, and epidote, and the modeling of the 

“carbonate-propylitic + ferrous iron” output class which 

will likely correspond to chlorite, and (or) epidote, result 

in increased likelihood that pixels within the “carbonatepropylitic” output class correspond to calcite, which, when 

pure, has nearly no band 7 absorption (fig. 5).

Figure 8 (previous page). Comparison of mineral maps of 

the area between the Goldfield and Cuprite mining districts, 

western Nevada, produced from AVIRIS and ASTER data. Zones 

of magmatic-hydrothermal, advanced argillic alteration in the 

southern and southwestern parts of the Goldfield district occur in 

an east-west belt along the top of the maps. Alunite- and kaolinitebearing alluvium derived from these zones has been transported 

southeastwards by fluvial processes toward Stonewall Flat. The 

northern tip of the Cuprite eastern center is visible at center 

bottom. The map on the left was produced using expert systembased spectroscopic analysis (Rockwell and others, 2005; 2006) of 

1998 AVIRIS data. Alunites of varying composition are shown in red 

and blue. The map at center was produced using expert systembased spectroscopic analysis (Rockwell, 2009) of 2001 ASTER 

data. Alunite-bearing rocks are shown in red and orange, kandite 

clay minerals (mainly kaolinite) in yellow. The map at right was 

produced from orthocorrected (AST14OTH) data of the same 2001 

ASTER scene using the automated, ratio-based analysis described 

herein. The advanced argillic-kandite, argillic-sericite-smectite, 

and carbonate-propylitic indices are shown as red, green, and 

blue, respectively. Pixels that occur in both the advanced argillickandite and argillic-sericite-smectite indices are shown in yellow 

(red + green additive color mixing), and correspond to argillic 

alteration. Note how most of the alunite-bearing alluvium in the 

center of the maps at left is not shown in the map at right. If 

clipping values were optimized for the Goldfield area alone, the 

areal extent of alunite-bearing alluvium would be comparable 

to those in the maps at left. White and yellow lines indicate the 

limits of the AVIRIS and ASTER data coverages, respectively. In 

the explanation for the ASTER-based automated mapping at lower 

right, black text indicates which indices are assigned red, green, 

and blue. Blue text indicates typical mineralogic compositions for 

selected colors in the map relating to argillic minerals. Click on 

figure for more detailed image.
Advanced argillic– Argillic–sericite– Carbonate– Dolomite–chlorite– Hydrous silica– 

Output Ferric iron 1 Ferric iron 2 

Band Absorption kandite smectite propylitic epidote jarosite–sericite 

Class #

1 1 and 3 5 6 7 and 8 7 and (7 and 8) 6 and 7 

1 minor ferric iron

X 

2 major ferric iron

X 

3 advanced argillic (± ferric iron) AND/OR/NOT AND/OR/NOT AND 

argillic (kandite clay ± sericite ± 4 AND AND 

smectite)

5 sericite and (or) smectite

X 

6 carbonate - propylitic AND 

7 dolomite AND AND AND/NOT 

jarosite ± sericite ± smectite, or 8 AND AND AND/NOT AND/NOT AND/NOT AND/NOT AND 

hydrous silica + ferric iron

9 hydrous silica

X 

sericite + chlorite or Fe/Mg 10 AND AND 

sericite

argillic + ferric iron or weathered 11 AND/OR AND/OR AND AND 

phyllic

sericite and (or) smectite + ferric 12 AND/OR AND/OR AND 

iron (may include fresh phyllic)

carbonate - propylitic + sericite 13 AND AND 

and (or) smectite

14 green vegetation 

15 carbonate - propylitic + ferric iron AND/OR AND/OR AND 

sericite + chlorite or Fe/Mg 16 AND/OR AND/OR AND AND 

ericite + ferric iron

carbonate - propylitic + sericite 17 AND/OR AND/OR AND AND AND/NOT 

and (or) smectite + ferric iron

18 dolomite + ferric iron AND/OR AND/OR AND AND AND/NOT 

carbonate - propylitic ± hydrous 19 AND AND 

silica

carbonate - propylitic ± hydrous 20 AND/OR AND/OR AND AND 

silica + ferric iron

21 ferrous iron

carbonate - propylitic + ferrous 22 AND 

iron

23 dolomite + ferrous iron AND AND AND/NOT 

Table 2. Boolean logic used to combine index results into a single, classified map (see sheet 5). Blank cells represent NOT operators. The ASTER badetect absorption relative to adjacent bands are indicated in the first row.
18 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

Edge of AVIRIS

coverage

AVIRIS

ASTER

550

550

Ouray County

Ouray County

A

B

0 1

1

2

2

3

3

4

4

5

5

6 MILES

0 6 KILOMETERS

107°45' 107°40' 107°35'

37°55'

37°50'

37°55'

37°50'

107°45' 107°40' 107°35'

Figure 9. Comparison of occurences of jarosite + sericite detected in the Silverton caldera in Colorado using A. 

expert system-based analysis of AVIRIS SWIR data (modified from Dalton and others, 2004), and B. the automated 

modeling of the ASTER-derived mineral group indices (output class no. 8 described in table 2). Approximate extent 

of Silverton caldera is indicated in yellow. Click on figure for more detailed image.
Comparison and Validation of Automated Results 19

Comparison and Validation of 

Automated Results

As a means of validating the results of the automated 

analysis methodology, derived maps of material groups are 

compared with those generated from detailed spectroscopic 

analysis of AVIRIS and (or) ASTER data .These data cover 

the Silverton caldera in Colorado, the Antelope Range in the 

Marysvale volcanic field in south-central Utah, the Goldfield 

and Cuprite mining districts in Nevada, and the Wah Wah 

Mountains in southwestern Utah, the site of the concealed Pine 

Grove porphyry molybdenum deposit, the NG alunite deposit, 

and other Miocene epithermal systems.

Silverton Caldera, San Juan Mountains, 

Colorado

Mineral mapping results derived from analysis of 

AVIRIS and ASTER data over the Silverton caldera are 

shown in figure 10. The map at top shows selected alteration 

types produced by combining mineral assemblages identified 

by spectroscopic analysis of AVIRIS data (Dalton and others, 

2004). All spectrally pure alunites and pyrophyllite were combined into the advanced argillic alteration class (red), and all 

kaolinite- and dickite-dominant assemblages were combined 

into the argillic ± sericite class (yellow). Directly-identified 

mixtures of alunite or phyrophyllite + kaolinite have been 

colored orange, and represent smaller or more weakly altered 

advanced argillic zones. Polygons showing phyllically-altered 

rocks produced by interpretation of the AVIRIS-derived maps 

and field mapping (Bove and others, 2007) are overlain in 

cyan. The map at bottom shows selected results from the 

automated analysis of ASTER data (sheet 5). The two maps 

are not entirely equivalent in that the ASTER-derived map 

incorporates ferric iron identifications, and the AVIRISderived map does not.

Most altered areas identified using AVIRIS data were also 

identified using the automated ASTER analysis methodology. 

The spectroscopic analysis of ASTER data (sheet 3) mapped 

and characterized these areas with significantly greater accuracy 

and detail than the automated analysis, as expected. In the automated ASTER mapping results, much of the advanced argillic 

alteration (alunites and pyrophyllite) in the Red Mountain area 

near the top center of the maps is included in the “argillic + 

ferric iron or weathered phyllic ± minor advanced argillic” class 

rather than the advanced argillic class, although the east-west 

belt of the most pervasive alteration is discernable (red pixels). 

Advanced argillic pixels (red) that occur along the edges of the 

altered areas in some cases most likely represent areal mixtures 

of kandite clay + dry vegetation (Rockwell, 2009).

Comparison of sheets 3 and 4 with the maps shown in 

figure 10 indicates that both the spectroscopic and automated 

analysis of ASTER data identified less advanced argillic 

alteration in the northern part of the Red Mountain no. 1 area 

than did the analysis of AVIRIS data. In this area, a small 

zone of mixed-composition Na-K alunite 100 m × 50 m 

in size occurs near the ridgeline, surrounded by a 141-m 

diameter zone of alunite + kaolinite. Float and colluvium 

consisting of minor alunite and kaolinite derived from the 

small central advanced argillic zone account for much of the 

alunite-bearing rock in the area. The majority of alteration in 

the area is argillic or phyllic in nature (kandite clay + ferric 

iron ± sericite ± smectite). The abundance of alunite in this 

area is too low to be detected using ASTER SWIR data having a ground resolution of 30 m.

The AVIRIS-derived map in figure 10A shows that most 

phyllic alteration has a mixed kaolinite (yellow) and sericite (purple) surface signature (with abundant goethite and 

(or) jarosite which are not shown). Kaolinite, in most cases 

of supergene origin, is present in nearly all phyllic alteration, although in varying amounts. Some phyllic alteration is 

primarily kaolinitic. A vast majority of the exposed sericite in 

the Silverton caldera is either lithologic or related to lowgrade regional propylitic alteration where it often occurs with 

chlorite. Therefore, the presence of sericitic signatures alone 

cannot be used as an accurate proxy for phyllic alteration.

A discrepancy in alteration characterization between the 

results of the automated ASTER data analysis and those generated by spectroscopic analysis of the AVIRIS and ASTER 

data is shown in figure 10. The area marked “A” in figure 10 

is located on an east-west-trending ridge 1.3 km east-southeast of the alteration center at Red Mountain no. 3. AVIRISbased mapping identifies this area primarily as pyrophyllite 

with local zones of pyrophyllite + kaolinite and alunite + 

pyrophyllite (Dalton and others, 2004). These highly altered 

zones grade outwards to the east and west to pyrophyllite 

+ sericite and (or) kaolinite with local zones of kaolinite + 

sericite. The AVIRIS-based mapping also identified goethite 

with local, northeast-trending jarositic zones, indicating abundant pyrite. A majority of the pyrophyllite-bearing rocks in 

this area are colluvium derived from small outcrops near the 

ridgeline. The ASTER-based spectroscopic mapping (sheet 3) 

identifies this area as primarily alunite with local pyrophyllite 

and kaolinite grading outwards to alunite + kaolinite and then 

kaolinite + sericite. The low spectral and spatial resolution of 

ASTER SWIR data make it difficult to accurately differentiate alunite and pyrophyllite, which can occur as intimate and 

areal mixtures in magmatic-hydrothermal alteration systems 

(Rockwell, 2009). Abundant ferric iron was also identified 

(sheet 2). The automated ASTER-based mapping (sheet 5, 

and fig. 10B) identified this area as the “sericite and (or) 

smectite + ferric iron (may include fresh phyllic)” class, 

indicating that the area was not detected by the advanced 

argillic-kandite index (table 2). Even if the clipping value for 

the advanced argillic-kandite index is set lower, the area is 

not identified as advanced argillic alteration.
20 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

Advanced argillic (± ferric iron)

Argillic (kandite clay ± sericite ± smectite) ± minor advanced argillic

Argillic + ferric iron or weathered phyllic ± minor advanced argillic

Sericite and (or) smectite

Sericite and (or) smectite + ferric iron

(may include fresh phyllic)

EXPLANATION

ASTER

(automated)

A

Argillic ± sericite (smectites not included)

Advanced argillic + kaolinite

Advanced argillic (alunites and pyrophyllite) Sericite

Phyllic alteration (unit qsp)

EXPLANATION

Edge of AVIRIS

coverage

AVIRIS Red Mountain 

no. 1

A

0 1

1

2

2

3

3

4

4

5

5

6 MILES

0 6 KILOMETERS

550

550

A

B

107°45' 107°40' 107°35'

37°55'

37°50'

A

A

37°55'

37°50'

107°45' 107°40' 107°35'

Regions affected by

ASTER SWIR

detector "scratch"
Comparison and Validation of Automated Results 21

ASTER reflectance spectra of this area indicate that the 

band 5 absorption features associated with alunite and pyrophyllite have a continuum-removed band depth of 6–9 percent, 

and kaolinite ± sericite spectra have a band 6 feature depth of 

4 percent. Spectra from the intense quartz-alunite alteration on 

the flanks of Red Mountain no. 3 to the west (where advanced 

argillic alteration was detected using the automated mapping) 

have typical band 5 absorption depths of 17–18 percent, indicating significantly greater alunite abundance given a constant 

grain size. It is therefore assumed that the advanced argillickandite index will not detect pixels in which band 5 absorption 

is less than approximately 10–15 percent.

A narrow zone of alunite + pyrophyllite, a signature 

assemblage for magmatic-hydrothermal advanced argillic 

alteration, occurs along an arcuate, or ring, fracture system 

that crosses area “A” (northern white line in AVIRIS-derived 

map in figure 10). Alunite-free pyrophyllite occurs along 

another, sub-parallel fracture system immediately to the south 

that extends south and west through intense phyllic alteration 

situated immediately adjacent to and beneath area “A” along 

Prospect Gulch. Vertical fracture zones and localized, pipelike structures of pyrophyllite ± illite, with natroalunite and 

(or) dickite ± alumino-phosphate sulfate minerals at higher 

levels, have been recognized within phyllic alteration situated 

beneath or adjacent to the Big Rock Candy Mountain (BRCM) 

and Big Star alunite deposits of the Marysvale volcanic field 

(Rockwell and others, 2005; 2006, Cunningham and others, 

2005). Pyrophyllite replaces sericite at the margins of quartzalunite centers upon outward transition to phyllic alteration 

in the lower part of the relict hydrothermal systems at Red 

Mountain no. 3 (Bove and others, 2007). The relatively low 

abundance of advanced argillic minerals in area “A,” the 

prevalence of pyrophyllite over alunite, abundant pyrite, 

close proximity to mapped phyllic alteration, and the presence of sericite in its peripheral zones suggest that the area 

represents transitional advanced argillic-phyllic alteration. 

Such a mixed alteration type (qap-s) was mapped immediately 

adjacent to area “A” to the east and north, where sericite has 

been replaced by pyrophyllite. The identification of this area 

as “sericite and (or) smectite + ferric iron (may include fresh 

phyllic)” by the automated ASTER mapping associates it 

mineralogically with the phyllic alteration in Prospect Gulch 

rather than the quartz + alunite ± pyrophyllite ± kaolinite of 

the main advanced argillic centers in the area.

Antelope Range, Marysvale Volcanic Field, Utah

Figure 11 shows ASTER-derived maps of the replacement alunite deposits in the Antelope Range area of the 

Marysvale volcanic field in south-central Utah (Rockwell 

and others, 2005; 2006). The map produced by spectroscopic 

analysis (fig. 11A, Rockwell, 2009) shows high correlation 

with the map produced by the automated analysis (fig 11B). 

The primary difference between the two maps is the more 

extensive occurrence of sericite and (or) smectite (cyan 

colors) shown in latter map. Sericite and smectite having low 

fit × depth of diagnostic absorption features identified using 

spectroscopic techniques were removed from figure 11A to 

enhance accuracy and simplicity of the results. Mineral detections with low fit × depth values correspond to lower mineral 

abundance (reduced feature depth) and (or) lower degrees 

of least-squares fit (and thus confidence) to reference spectra. Such detections are often removed from mineral maps 

to reduce complexity and show only results with high confidence. Also note that sericite and smectite are not differentiated by the automated analysis.

In addition, the map produced using the automated analysis shows less extensive calcite-chlorite-epidote-bearing propylitic alteration in the Hoover pluton and other monzonites 

of the Central Intrusion located 1–8 km east of BRCM than do 

the results of the spectroscopic analysis. This effect is caused 

by the dark area masking performed during the automated 

analysis which removed the darkest (lowest band 4 radiance 

values) prior to analysis. The propylitically-altered intrusive 

rocks generally have low surface albedo.

Note that the phyllically-altered dacite lava flows on the 

eastern and northeastern flanks of BRCM were mapped as the 

“argillic + ferric iron or weathered phyllic ± minor advanced 

argillic” class (orange), and not as a sericite-bearing class. The 

supergene overprint on these highly pyritic and illitic rocks 

(with local vertical zones of pyrophyllite) is well developed 

and consists of natrojarosite, gypsum (fig. 11A), kaolinite, 

smectite, and local copiapite and epsomite (Cunningham and 

others, 2005; Rockwell and others, 2005).

Figure 10 (previous page). Comparison of advanced argillic, 

argillic, and phyllic alteration in the Silverton caldera in Colorado 

identified and mapped using A. expert system-based analysis of 

AVIRIS data (modified from Dalton and others, 2004), and B. the 

automated analysis of ASTER data (results modified from sheet 5). 

Cyan polygons in A are the primary phyllic alteration unit mapped 

by Bove and others (2007). These polygons do not show the full 

extent of phyllic alteration, as much is combined into other mixed 

alteration units (for example, qsp-wsp, wsp, wsp-hyd, wsp-pr) 

which are not shown (“qsp” refers to quartz-sericite-pyrite, or 

phyllic, alteration). Note false detections of advanced argillic 

alteration within the area of degraded SWIR data associated 

with the ASTER detector “scratch” (Rockwell, 2009). Approximate 

extent of Silverton caldera is indicated in yellow. Click on figure 

for more detailed image.
22 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

ASTER

(automated)

Marysvale

ASTER

(detailed)

White Horse 

alunite deposit

Big Rock

Candy Mountain

Marysvale

White Horse 

alunite deposit

Big Rock

Candy Mountain

Al sericite

Alunite

Alunite + kaolinite

Calcite

Calcite + sericite or smectite

Chlorite

Dolomite

Epidote, or calcite + dolomite mix

Gypsum

Hydrous quartz—Opal and (or) chalcedony

Kaolinite + sericite

Kaolinite + smectite

Natroalunite, or alunite + minor kaolinite

Pyrophyllite and (or) alunite

Fe/Mg sericite, or mixture of muscovite 

and chlorite, carbonate, and (or) dry vegetation

Jarosite, or hydrous quartz + ferric iron 

near mapped hydrous quartz

EXPLANATION

Smectite (montmorillonite) Kandite clay

0 1

1

2

2

3

3

4

4

5

5

6 MILES

0 6 KILOMETERS

A

B

112°20' 112°15 112°10' 112°05'

38°30’

38°30’

112°20' 112°15 112°10' 112°05'

Figure 11. Mineral maps of the Antelope Range area of the Marysvale volcanic field, Utah. A. Results of detailed, expert 

system-based spectroscopic analysis of ASTER data (Rockwell, 2009). See sheet 5 for color explanation. B. Results 

of automated, ratio-based methodology described herein (see sheet 5 for color explanation). Click on figure for more 

detailed image.
Alunite + kaolinite ± sericite Alunite + pyrophyllite ± jarosite Alunite ± minor kaolinite ± minor jarosite Alunite/natroalunite + pyrophyllite ± sericite Calcite Calcite + sericite Dickite Epidote High-Al sericite Jarosite —Lowest fit × depth Alunite, intermediate Na/K composition, or alunite/natroalunite areal mix —Highest fit × depth Hydrous quartz—Chalcedony (mainly in tuffaceous units), or opal Kaolinite + pyrophyllite Kaolinite ± smectite Kaolinite 75% + alunite 25% areal mix Kaolinite and (or) halloysite Low-Al sericite or muscovite + chlorite mixture Natroalunite Natroalunite + kaolinite Natroalunite ± dickite, other clays Pyrophyllite Pyrophyllite + sericite Sericite ± kaolinite 1—Lowest fit × depth Sericite ± kaolinite 2 Sericite ± kaolinite 3 Sericite ± kaolinite 4 Sericite ± kaolinite 5—Highest fit × depth Smectite (montmorillonite) Al sericite (± kaolinite) Alunite (± pyrophyllite) Alunite + kaolinite Calcite Calcite (or chlorite/epidote) + sericite (or smectite) Chlorite (Mg-OH SWIR + ferrous/ferric iron absorptions Dolomite Epidote, or calcite + dolomite mixture Fe/Mg sericite or muscovite + chlorite mixture Gypsum c clay minerals Kaolinite ± sericite Kaolinite and (or) dickite/nacrite Kaolinite and (or) dickite/nacrite Natroalunite, or alunite + minor kaolinite Opal and (or) chalcedony Pyrophyllite or alunite Jarosite ± gypsum, or ferric iron + opal/chalcedony Smectite (montmorillonite) or low-abundance sericite Kaolinite ± smectite (typically supergene) EXPLANATION A EXPLANATION B Advanced argillic (± ferric iron) Carbonate-propylitic Carbonate-propylitic + ferric iron Argillic + ferric iron or weathered phyllic ± minor advanced argillic Argillic (kandite clay ± sericite ± smectite) ± minor advanced argillic Carbonate-propylitic + sericite and (or) smectite + ferric iron Carbonate-propylitic ± hydrous silica (may include some dolomite) Carbonate-propylitic ± hydrous silica + ferric iron (may include some dolomite) Dolomite (may include chlorite or epidote in volcanic terranes) Dolomite + ferric iron (may include chlorite or epidote in volcanic terranes) Dolomite + ferrous iron (may include chlorite or epidote in volcanic terranes) Carbonate-propylitic + ferrous iron Carbonate-propylitic + sericite and (or) smectite 

EXPLANAT

ASTER Automate

C

ASTER Expert System Mapping

B

AVIRIS Expert System Mapping

B

C

D

E

B

A

A

0 1

1

2

2

3

3

4 MILES

0 4 KILOMETERS

117°10'

37°40'

37°35'

117°10'

37°40'

37°35'

95 95

117°10'

37°40'

37°35'

95
24 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

Scattered pixels of hydrous silica were identified across 

this area with the ASTER data using both analysis techniques. 

These occurrences have not been field verified, although 

hydrous silica (opal or chalcedony) within the Siebert Tuff 

and other rhyolitic airfall tuffs, tuff breccias, and agglutinate 

(Albers and Stewart, 1972) was identified using both ASTER 

data analysis techniques in the surrounding area (fig. 13).

The discrimination of limestones and dolomites by the 

automated analysis is also demonstrated in figure 13. The 

coarsely-crystalline Precambrian Reed Dolomite on the western slopes of the Montezuma Range is clearly differentiated 

from the mixed limestone and dolomite of the Precambrian 

Deep Spring Formation, and from calcite-rich rocks of the 

Lower Cambrian Mule Spring Limestone to the east. Both 

ASTER-based analysis techniques identified potentially dolomitic zones within the Mule Spring Limestone. These zones 

are shown in blue in both maps and likely represent a calcite + 

dolomite mixture. Ferrous iron was detected in isolated areas 

within the Mule Spring Limestone, including several of the 

more dolomitic zones, using the automated analysis.

Cuprite Mining District, Nevada

Figures 14 and 15 compare AVIRIS- and ASTER-derived 

mineral maps of a part of the Cuprite mining district located 

25 km south of Goldfield. Late Miocene, advanced argillic 

alteration in Tertiary rhyolite and latite tuff with intercalated 

sedimentary rocks (mainly the Siebert Tuff; Abrams and others, 1978; Rowan and others, 2003) is present in two centers 

bounded on the west by the Lower Cambrian Harkless Formation (dominantly green phyllitic siltstones) and on the south by 

the Mule Spring Limestone.

The maps in figures 14 and 15 all show the distribution of 

opaline sinter, alunite-bearing advanced argillic, and kaolinitedominated argillic alteration. Propylitic alteration in the area, 

although present, is generally weak and poorly exposed with 

the exception of associated calcite + sericite immediately 

north and northwest of the western center. The expert systemderived maps show how most alunite zones grade outwards to 

mixed kaolinite and then sericite-bearing assemblages.

The most recent hypothesis for the genesis of the two 

centers is that they formed independent of each other and 

represent two distinct hydrothermal systems (Swayze, 1997). 

The eastern center is younger and more well-preserved than 

the western center, which has been eroded more substantially, 

exposing the fracture-controlled plumbing system of the 

deposit. More abundant and spectrally pure alunite (and native 

sulfur deposits) occur along a north-trending, inferred fracture 

Figure 12 (previous page). Mineral maps of the Goldfield mining 

district, Nevada. A. Map produced using expert system-based 

spectroscopic analysis (Rockwell and others, 2005; 2006) of highaltitude AVIRIS data acquired on June 18, 1998 (http://aviris.jpl.

nasa.gov/ql/98qlook/f980618t01p02_r02.ql.jpg). B. Map produced 

using expert system-based spectroscopic analysis (Rockwell, 

2009) of ASTER data acquired on August 24, 2001. C. Map produced 

from orthocorrected (AST14OTH) data of the same 2001 ASTER 

scene using the automated, ratio-based analysis. White lines 

indicate the limits of the AVIRIS data coverage. Click on figure for 

more detailed image.

Goldfield Mining District, Nevada

Mineral maps of the Goldfield mining district produced from AVIRIS and ASTER data are compared in figure 

12.Abundant advanced argillic alteration minerals identified in bedrock outcrops show excellent correlation between 

the maps (red colors). Highly abundant quartz + alunite ± 

pyrophyllite alteration on Preble Mountain (“A”, in fig. 12A), 

and spectrally pure jarosite (“B” in fig. 12A), were identified using both data types and analysis techniques. The map 

produced using the automated methodology has not identified 

all occurrences of these minerals where they occur in lower 

abundance within alluvium derived from the alteration zones, 

as explained above (fig. 8).

The AVIRIS-based mapping identified several pixels 

of calcite ± sericite on the western flanks of Diamond 

(Mountain) at “C,” and epidote just west of Black Butte at 

“D,” both in propylitically-altered andesites. This alteration 

was also identified with the ASTER data using both analysis 

techniques, although the automated technique identified it 

over a wider area (green).

Weakly propylitized, compact quartz latite tuffs and 

lapilli tuffs near the center of the district at “E” contain illite 

and chlorite (verified by X-ray diffraction) and were identified as low-aluminum (iron- or magnesium-rich) sericite or 

muscovite + chlorite mixture by the expert system analysis 

using both AVIRIS and ASTER data (see description of 

this mineral mixture in comments for sheet 3 above). The 

automated analysis mapped these rocks as sericite and (or) 

smectite surrounded by (and occurring with) ferrous iron, 

which is indicative of chlorite.
Comparison and Validation of Automated Results 25

Mule Spring

Limestone

Reed Dolomite

ASTER

(expert system)

Hydrous silica in rhyolitic

airfall tuffs, tuff breccias,

or agglutinate

Deep Spring Fm.

limestones and dolomites

ASTER

(automated)

Mule Spring

Limestone

Reed Dolomite

Hydrous silica in rhyolitic

airfall tuffs, tuff breccias,

or agglutinate

Deep Spring Fm.

limestones and dolomites

0 1

1

2

2

3

3

4

4

5

5

6 MILES

0 6 KILOMETERS

117°25' 117°20' 117°15'

37°40’

37°40’

95

95

Figure 13. Mineral maps of the Montezuma mining district, Nevada. The town of Goldfield and advanced argillic 

alteration associated with the Goldfield mining district are visible in the northeastern corners of the maps. The map at 

top was produced using expert system-based spectroscopic analysis (Rockwell, 2009) of ASTER data acquired on Aug. 

4, 2001. See figure 12B for explanation of color-coded mineral identifications. The map at bottom was produced from 

orthocorrected (AST14OTH) data of the same 2001 ASTER scene using the automated, ratio-based analysis described 

herein. See figure 12C (and sheet 5) for explanation of color-coded mineral identifications. Click on figure for more 

detailed image.
A B Chlorite + goethite Chlorite + sericite Explanation for high-altitude AVIRIS mapping: iron-bearing minerals Coarse-grained jarosite Iron sulfate minerals Iron Hydroxide Minerals Hematite—Fine grained Hematite—Med. to coarse grained Iron oxide minerals With goethite and sericite Fe -bearing Minerals 2+ Other iron oxides and (or) hydroxides Goethite—Medium grained Goethite—Fine grained Goethite—Coarse grained Goethite—Thin coating Chlorite-bearing Assemblages Generic C CC C C Occurance of Iron-Bearing Minerals C—Continuous stretch applied Kaolinite + sericite Kaolinite 

Jarosite Kaolinite ± smectite Calcite + sericite Kaolinite Group Minerals Dickite Sulfa

Klunit

Alunite—

compos

Alunite Muscovi

Low-Al sHigh-Al se

 and (o

Medium-A

Natroalun

Carbonate Minerals Explanation for high-altitude AVIR

phyllosilicates, sulfates, carbonate

Opal/chalcedony Montmorillonite (Ca + Na) Calcite Katroalun

Kaolinite (poorly crystallized and (or) mixed float) Kaolinite (well crystallized) Buddingto

Kaolinite ± smectite or halloysite Ammoniu

Illite + ch

K

Occurance of Clay, Sulfate, Mica, Car

and Hydrous Silica Mine

0

0 1

1

2 3 MILES

2 3 KILOMETERS

117°14’ 117°12’ 117°10’

37°34’

37°32’

37°30’

117°14’ 117°12’ 
Comparison and Validation of Automated Results 27

Figure 14 (previous page). Mineral maps of part of the Cuprite 

mining district in western Nevada produced using expert systemanalysis (Rockwell and others, 2005; 2006) of high-altitude 

AVIRIS data acquired on June 18, 1998 (http://aviris.jpl.nasa.gov/

ql/98qlook/f980618t01p02_r02.ql.jpg). A. Map showing occurrence 

of iron-bearing minerals. B. Map showing occurrence of clay, 

sulfate, mica, carbonate, Mg-OH, and hydrous silica minerals. 

Bright shades of color indicate high values of fit × depth, and 

successively darker shades indicate lower values. Fit × depth 

is proportional to both the least-squares fit of image spectra to 

reference laboratory spectra and the depth of the diagnostic 

absorption feature(s) in the image spectra. Low feature depths may 

indicate lower abundance of the mineral type in the pixel, given a 

constant grain size. Click on figure for more detailed image.

system which crosses the western center (magenta arrows in 

fig. 15). Abundant quartz-alunite also occurs along another, 

parallel system ~850 m to the west which forms the highest 

ridge in the Cuprite Hills. Natroalunite ± dickite (Rockwell, 

2002) generally occurs distal to alunite along these fracture 

systems, particularly at their southern mapped extent near the 

periphery of the western center alteration. This occurrence 

relationship of hypogene alunite and natroalunite has been 

recognized in the Goldfield district (fig. 12), on East Brawley 

Peak near the Aurora mining district (Rockwell, 2010a), and 

on Alunite Ridge in the Tushar Mountains near Marysvale, 

Utah (Rockwell and others, 2005). The traces of these fracture 

systems are visible even in the results derived from the automated analysis. Alunite in the eastern center occurs generally 

beneath and peripheral to the central cap of leached silica, 

reflecting more replacement-dominated, near-surface solfataric 

hydrothermal processes of the paleovadose zone.

Most alunites in the district are white to light gray in 

color, and figure 14 shows that most alunite zones in the 

district have little to no ferric iron content. Argillic assemblages with minor alunite (including float) surrounding the 

alunite-rich zones often contain more abundant ferric iron 

minerals. Alunite of mixed Na-K composition typically has 

more abundant ferric iron than potassium-rich alunite. In the 

western center, goethite and jarosite are more abundant than 

hematite, reflecting the deeper level of erosion which has 

exposed jarosite derived from subaerial oxidation of pyrite 

which occurs in underlying phyllically-altered rocks formed 

in reducing conditions below the paleowater table. Jarosite 

is particularly abundant in kaolinitic wall rocks surrounding 

selvages of dickite or kaolinite ± natroalunite at the southwestern edge of the western center, suggesting pyritization of wall 

rocks in that area. Some of the jarosite in the western center 

occurs with alunite, and could represent a hypogene, vadosezone assemblage as has been recognized in steam-heated 

replacement alunite deposits near Marysvale (Cunningham 

and others, 1984; Rockwell and others, 2005).

Within the Harkless Formation at left center, the AVIRISbased mapping (fig. 14) identified low- and medium-aluminum 

sericite (illite), suggesting increased iron and (or) magnesium 

content relative to aluminum. Large exposures of an illite + 

chlorite mixture were also identified. Ferrous iron related to the 

chlorites and (or) illites was mapped throughout the Harkless 

and locally within the Mule Spring Limestone. Abundant 

calcite surrounded by calcite + sericite mixtures was mapped 

in the adjacent Mule Spring Limestone, the latter indicating 

areal mixtures with sericites from the Harkless Formation and 

derived alluvium. A calcite-rich area within the Harkless Formation (“C” in fig. 15) was identified by both data types and 

analysis techniques, and most likely represents either a local 

limestone member of the Harkless or an erosional remnant of 

the overlying Mule Spring Limestone that has not been recognized in local geologic mapping (Crafford, 2007).

The Harkless Formation was identified as predominantly 

sericite and (or) smectite with strong local ferrous iron and 

“sericite + chlorite or Fe/Mg sericite” by the automated analysis (fig. 15). The expert system-based analysis of ASTER data 

also identified this Fe/Mg sericite or sericite + chlorite mixture 

in the Harkless, surrounded by wide exposures of “muscovite 

± calcite or chlorite,” representing a spectral match to a reference spectrum of a calcite + muscovite mixture (Rockwell, 

2009). Ferrous iron was also identified locally within the 

calcite of the Mule Spring Limestone.

Exposures of undifferentiated chalcedony and opal were 

identified in the silica cap of the eastern alteration center using 

both data types and analysis techniques, although fewer were 

mapped using the ASTER-based automated technique. This 

discrepancy is directly related to the relatively high clipping 

value applied to the hydrous silica-jarosite-sericite index to 

reduce inaccurate identifications in regional mapping. A small 

area (one 30-m ASTER pixel) of hydrous silica near the northern edge of alteration in the eastern center which was correctly 

identified using both data types and analysis techniques is 

indicated in figure 15 (“HS”).

An altered Tertiary rhyolite dike extends south from the 

dickite-bearing southwestern edge of the western alteration 

center along the contact between the Harkless Formation and 

Mule Springs Limestone (fig. 15). The dike contains sericite + 

kaolinite in rather low abundance (fig. 14; Rowan and others, 

2003), and the sericite is aluminum rich (as indicated by the 

position of the Al-OH feature at 2.199 μm absorption). The 

ASTER-derived maps in figure 15 show that the dike was 

mapped as aluminum sericite with small kaolinitic zones using 

the expert system analysis and as “sericite and (or) smectite 

± ferric iron” with several pixels of argillic alteration near its 

northern end by the automated analysis. The low abundance 

of the argillic minerals in the altered dike make it difficult to 

discriminate using the automated analysis.
H S MSHR C HS ASTER Expert System Mapping ASTER Automated M

0 1 1 2 2 3 3 4 MILES 0 4 KILOMETERS A B Al sericite (± kaolinite) Calcite Gypsum ± clay minerals Kaolinite ± sericite Natroalunite, or alunite + minor kaolinite Smectite (montmorillonite) Kaolinite ± smectite (typically supergene) Opal and (or) chalcedony Pyrophyllite or alunite Alunite + kaolinite Kandite clay (mainly kaolinite) Muscovite ± calcite or chlorite Alunite Fe/Mg sericite or sericite + chlorite EXPLANATION 117°14' 117°12' 117°10' 37°34' 37°32' 37°30' 117°14' 117°12' 
Deficiencies of the Automated Analysis Methodology 29

Pine Grove and Blawn Mountain Mining 

Districts, Wah Wah Mountains, Utah

Mineral maps generated by expert system and automated 

analysis of ASTER data covering the Wah Wah Mountains in 

southwestern Utah are compared in figure 16. Explanations 

of the color-coded mineral identifications in both maps are 

shown in figure 17.

Both analysis techniques have identified minerals associated with alteration in many areas where alteration had been 

previously mapped (Ken Krahulec, written commun., Utah 

Geological Survey, 2010), and in several undocumented 

areas, including the northwestern San Francisco Mountains 

(Rockwell and Hofstra, 2009a) and in quartzites surrounding the Pine Grove porphyry molybdenum deposit (Rockwell 

and Hofstra, 2009b; Rockwell, 2010b). Argillic and advanced 

argillic alteration in lower abundance in alluvium has not 

been mapped using the automated techniques because of 

the clipping of low-end index values, as mentioned above. 

Especially when dry vegetation with leaf biochemical absorption at ASTER band 5 is present, overdetection of advanced 

argillic alteration minerals can occur at the expense of kandite 

clay minerals, such as in the altered rocks surrounding the 

Pine Grove deposit.

Carbonate minerals in low abundance, including those 

in alluvium and in formations with silty, impure limestones 

and interbedded shales (for example, Fillmore and House 

Formations in the northern part of fig. 16) are also not identified using the automated technique. The spectroscopic analysis of the ASTER data is more sensitive to minerals in low 

abundance, although the degree to which such exposures are 

identified using the automated technique is directly related 

to the clipping values applied to the indices. Such clipping 

values were optimized for accuracy and completeness over 

many tested ASTER scenes, but can be adjusted for specific 

areas as needed.

Figure 15 (previous page). ASTER-derived mineral maps of part 

of the Cuprite mining district, western Nevada. A. Map produced 

using expert system-based spectroscopic analysis (Rockwell, 

2009) of ASTER data acquired August 24, 2001. B. Map produced 

from the same ASTER scene using the automated ratio-based 

analysis described herein. See figure 12C (and sheet 5) for 

explanation of color-coded mineral identifications. U.S. Route 95 

crosses the center of the maps from south to north between the 

two alteration centers. Magenta arrows show trace of inferred 

fracture system along which more abundant and spectrally 

pure alunite occurs. H, Harkless Fm; C, calcite-rich area within 

Harkless Fm; MS, Mule Spring Limestone; S, Stonewall Playa; 

R, Altered rhyolite dike (illite + kaolinite); HS, one of several 

exposures of hydrous silica identified using AVIRIS data (fig. 14) 

and both expert system and automated analysis of ASTER data. 

Click on figure for more detailed image.

Detection of dolomite within carbonate units using the 

automated technique shows good correlation with the expert 

system analysis results and with mapped dolomitic units 

(Steven and others, 1990). However, vegetation growing on 

dolomitic rocks can weaken absorption at ASTER band 7, as 

can be seen in the Simonson Dolomite just north of the NG 

Alunite area where most of the dolomite has been identified 

as “carbonate-propylitic.” Therefore, the detection of dolomite 

within carbonate rocks using the automated technique should 

not be considered to be comprehensive, and should only serve 

as a guide to the presence of dolomite within carbonate rocks.

Deficiencies of the Automated 

Analysis Methodology

The most serious drawbacks for the use of ASTER-based 

mineral mapping are related to the data themselves. Although 

ASTER scenes with minimal cloud cover and optimal solar 

illumination will be selected for analysis, scenes with unclear 

atmospheres caused by dust, haze, thin clouds, fog, and smoke 

will show uncertain results. Suitable ASTER data may not be 

available for all areas. Spatial misregistration of the ASTER 

VNIR and SWIR data in some scenes is not currently recognizable during scene selection, and can also cause severe 

degradation of the results. Crosstalk-related problems in 

areas adjacent to large bodies of water and other instances of 

extreme variation in surface albedo will necessitate the use 

of scenes in which the problems have been corrected, and 

will thus require additional processing and custom geometric 

correction. Degraded data associated with the SWIR detector 

“scratch” create areas of erroneous mapping results in every 

scene, and necessitate the development of an efficient means 

of highlighting these areas in large numbers of scenes. In addition, despite logic to reduce the effect of areal mixing of dry 

vegetation with surface minerals, false detections of argillic 

and advanced argillic alteration minerals can occur in areas 

where seasonally dry vegetation such as cheatgrass is present, 

especially in the northern Great Basin.

The primary problem with a ratio-based spectral analysis 

algorithm is that there will always be an “answer” to every 

index. Even if no minerals of a type for which an index was 

designed to highlight are present in a scene, the high-end 

values of that index will be shown as a detection result. This 

effect could be mitigated by the empirical development of 

quantitative thresholds of index values below which accurate 

identifications cannot occur. In addition, fuzzy logic could 

be applied to the index values to model confidence levels of 

results. However, it would be difficult and time-consuming 

to develop thresholds that will generate accurate detections 

over very large regions given the variability of ground cover, 

atmospheric visibility and moisture content, solar illumination 

relative to season of data acquisition, and ASTER data quality. 

Substantial false mineral group detections related to the algorithm itself have not been observed in the testing performed 

for this study.
30 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

B

A

B

D E

F

C

I

H

J

K

L

Escalante Desert

G

H

J

L

B

0 5 10

10

15 MILES

0 5 15 MILES

113°40' 113°20'

38°40’

38°20’

38°00’

37°40’

113°40' 113°20'

Escalante Desert

A B
Deficiencies of the Automated Analysis Methodology 31

Figure 16 (previous page). ASTER-derived mineral maps of 

the Wah Wah Mountains, Broken Ridge, and Escalante Desert, 

southwestern Utah. Orange outlines show extent of four 

overlapping ASTER scenes from July 31, 2000 (right-hand path) 

and August 27, 2004 (left-hand path). A. Map produced using 

expert system-based spectroscopic analysis (Rockwell, 2009) of 

ASTER data calibrated to reflectance using laboratory spectra 

of smectite-bearing alluvium from Wah Wah Wash in the central 

Wah Wah Valley. Areas of 2004 data affected by the SWIR 

detector “scratch” are indicated by translucent white polygons 

in the upper left quarter of the map. Kaolinitic assemblages 

identified in Pine Valley west of the Wah Wah Mountains that 

occur in the left-hand “scratch” are erroneous in both A and B. B. 

Map produced from the same ASTER scenes using the automated, 

ratio-based analysis described herein. See figure 17 for 

explanation of color-coded mineral identifications. A, Concealed 

Pine Grove porphyry molybdenum deposit; B, Advanced argillic 

and argillic alteration (pyrophyllite and dickite with local alunite) 

associated with the Pine Grove deposit in folded Late Proterozoic 

and Lower Cambrian quartzites (Rockwell and Hofstra, 2009b); 

C, Unaltered Prospect Mountain Quartzite (sericite, Lower 

Cambrian); D, NG Alunite area in Blawn Mountain mining district; 

E, Alteration associated with Shauntie Hills hydrothermal system; 

F, Bible Springs fault zone (magenta line) and associated quartzalunite alteration; G, rhyolite vent above inferred porphyry 

molybdenum deposit in Broken Ridge area (Duttweiler and 

Griffitts, 1989); H, Simonson, Sevy, Laketown, and Fish Haven 

Dolomites (Ordovician – Devonian); I, Advanced argillic and 

argillic alteration in Late Proterozoic quartzites (Mutual and Inkom 

Formations), San Francisco Mountains (Rockwell and Hofstra, 

2009a); J, Wah Wah Summit Formation and other Middle and 

Upper Cambrian limestones and dolomites; K, Fillmore Formation 

(silty limestone and shales) and House Limestone (Lower 

Ordovician); L, Notch Peak Formation (interbedded silty limestones 

and massive dolomite, Lower Ordovician and Upper Cambrian), 

recrystallized to marble in southern San Francisco Mountains. 

White polygons show altered areas compiled from a variety 

of published and unpublished sources (Ken Krahulec, written 

commun., Utah Geological Survey, 2010). Lithologic units from 

Steven and others (1990). Click on figure for more detailed image.
Carbonate-propylitic ± hydrous silica (may include somCarbonate-propylitic ± hydrous silica + ferric iron (may 

Dolomite (may include chlorite or epidote in volcanic t

Dolomite + ferric iron (may include chlorite or epidote 

Jarosite ± sericite ± smectite, or hydrous silica + ferric Sericite + chlorite or Fe/Mg sericite + ferric iron Attribute name: Materialid Advanced argillic (± ferric iron) Carbonate-propylitic Carbonate-propylitic + ferric iron Carbonate-propylitic + sericite Carbonate-propylitic + sericite + ferric iron Ferrous iron Green vegetation Hydrous silica Major ferric iron Minor ferric iron Sericite + chlorite or Fe/Mg sericite Sericite and (or) smectite Sericite and (or) smectite + ferric iron Argillic and weathered phyllic ± minor alunite and (or) Argillic and weathered phyllic + ferric iron ± minor alu

Carbonate-propylitic + ferrous iron Dolomite + ferrous iron (may include chlorite or epidot

B Explanation for Automated ASTER mine

* These minerals, which may co- occur as areal and intimate mixtures, are often difficult to distinguish reliably using ASTER SWIR data because of spectral similarities and (or) sensor noise. They also may be incorrectly identified in areas of sparse, dry vegetation. Epidote, chlorite, or calcite + dolomite mixture Kaolinite Kaolinite ± sericite Jarosite ± gypsum, or ferric iron + hydrous silica Kaolinite ± smectite (high- albedo surfaces with minor and (or) impure kaolinite) Clay-bearing Assemblages Sulfate-bearing Assemblages Alunite + kaolinite** Alunite ± pyrophyllite** Sericite-bearing Assemblages Al/Fe illite/muscovite or muscovite + chlorite mixture Al illite/muscovite Carbonate, Mg-OH, and Fe-OH Minerals* Explanation for spectroscopic, expert system-based ASTER mineral mapping Hydrous silica (chalcedony or opal), or jarosite/gypsum + clay/mica near mapped occurrences of jarosite Montmorillonite/smectite, or low-abundance sericite Calcite Calcite (or chlorite/epidote) + sericite or smectite Gypsum ± clays Natroalunite, or alunite + minor kaolinite** ** these assemblages may represent areal mixtures of clay/mica + dry vege- tation or areas of sensor instability related to low or high surface albedo Possible pyrophyllite (± alunite)** Dolomite AFigure 17. Explanations for color-coded mineral and vegetation identifications shown in figure 16. A. Identifications for map in figure 16A made us

of ASTER data. B. Identifications for map in figure 16B made using automated analysis of ASTER data. Explanation is identical to that of sheet 5 and for more detailed image.
References 33

Conclusions

Despite deficiencies in the quality and availability of 

suitable ASTER data, the products generated by the automated process are suitable for efficient application of mineral 

resource and geoenvironmental assessments at regional and 

national scales. Hydrothermal dolomitization of limestone 

units and subtle argillic alteration in quartzites and quartz 

arenites, both potentially important indicators of concealed 

mineral deposits, can be recognized. Potentially acidgenerating altered areas as small as 100 m in diameter can be 

identified and characterized as to clay-sulfate-mica-hydrous 

silica mineralogy. Acid-neutralizing carbonate units and 

propylitically-altered areas can also be accurately identified.

The more time-intensive spectroscopic analysis of 

ASTER data can be applied to selected areas and (or) permissive tracts for certain deposit types where more detailed 

mapping and mineralogical characterization of alteration 

would be beneficial.

Additional Data

An ESRI polygon shapefile indicating areas of corrupted 

ASTER shortwave-infrared data is also provided in a .zip file 

(sanjuans_aster_scratch_geodrg_shapefile.zip).

Acknowledgments

The author thanks the U.S. Geological Survey (USGS) 

Land Processes Distributed Active Archive Center (LP 

DAAC), the National Aeronautics and Space Administration 

Earth Observing System Data and Information System (EOSDIS), and Japan’s Earth Remote Sensing Data Analysis Center (ERSDAC) for providing the ASTER data that were the 

foundation of this research. The author also thanks Robert 

R. McDougal and Douglas B. Yager of the USGS for their 

helpful reviews of the manuscript, maps, and digital data. 

Gregg A. Swayze of the USGS provided the field spectrum of 

Stonewall Playa that was used to calibrate the Goldfield and 

Cuprite AVIRIS data to reflectance.

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34 Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery

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Publishing support provided by:

Denver Publishing Service Center

For more information concerning this publication, contact:

Center Director, USGS Central Mineral and Environmental Resources 

Science Center

Box 25046, Mail Stop 973

Denver, CO 80225

(303) 236-1562

Or visit the Central Mineral Resources Team Web site at:

http://minerals.cr.usgs.gov/
Rockwell—Methodology for Mapping Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite Imagery—SIM 3190