Automated Mapping of Mineral Groups and Green Vegetation 

from Landsat Thematic Mapper Imagery with an Example 

from the San Juan Mountains, Colorado

Scientific Investigations Map 3252

U.S. Department of the Interior

U.S. Geological Survey
Automated Mapping of Mineral Groups and 

Green Vegetation from Landsat Thematic 

Mapper Imagery with an Example from the 

San Juan Mountains, Colorado

By Barnaby W. Rockwell

Pamphlet to accompany

Scientific Investigations Map 3252

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Suggested citation:

Rockwell, B.W., 2013, Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper 

imagery with an example from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations 

Map 3252, 25-p. pamphlet, 1 map sheet, scale 1:325,000, http://pubs.usgs.gov/sim/3252/.
iii

Contents

Overview..........................................................................................................................................................1

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

Descriptions of Map Sheet and Associated ERDAS IMAGINE Raster Image File Included 

in this Publication.............................................................................................................................2

Map Sheet..............................................................................................................................................2

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

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

Description of Automated Analysis Methodology ...................................................................................3

Mineral Group and Green Vegetation Indices ........................................................................4

Image Processing for Masking and Index Thresholding .....................................................6

Modeling of Mineral Groups Through Boolean Combination of Index Results.................8

Discussion.....................................................................................................................................................10

Deficiencies of the Automated Analysis Methodology.........................................................................23

Conclusions...................................................................................................................................................23

Acknowledgments.......................................................................................................................................23

Disclaimer.....................................................................................................................................................23

References....................................................................................................................................................23

Figures

1. Plots of laboratory reference spectra (Clark and others, 2007) of iron-bearing 

minerals convolved to Landsat TM spectral resolution.........................................................5

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

(Clark and others, 2007) convolved to Landsat TM spectral resolution ..............................7

3. Plots of laboratory reference spectra of vegetation (Clark and others, 2007) 

convolved to Landsat TM spectral resolution .........................................................................8

4. Mineral group map of the Goldfield mining district, Nevada, generated from 

Landsat 7 ETM+ data using the automated algorithm described herein ............................9

5. Seamless map of mineral groups and green vegetation covering the western 

U.S. generated from Landsat 7 ETM+ data using the automated analysis 

algorithm described herein.......................................................................................................11

6. A subset of the map sheet over the southern San Juan Mountains showing 

the mineral group map generated from Landsat 7 ETM+ data using the 

automated algorithm described herein...................................................................................13

7. Mineral group map of part of the Cuprite mining district, Nevada, generated 

from Landsat 7 ETM+ data using the automated algorithm described herein.................15

8. Mineral group map of the Bingham Canyon mine, Oquirrh Mountains, Utah, 

generated from Landsat 7 ETM+ data using the automated algorithm 

described herein.........................................................................................................................17

9. Mineral group map of the Bodie Hills and surrounding region, California/Nevada 

generated from Landsat 7 ETM+ data using the automated algorithm described 

herein............................................................................................................................................19
iv

10. Mineral group map of the northern Marysvale volcanic field, Utah, generated 

from Landsat 7 ETM+ data using the automated algorithm described herein.................21

11. One-meter-resolution imagery from the USGS The National Map Viewer 

(http://raster.nationalmap.gov/ArcGIS/rest/services/TNM_Large_Scale_Imagery_

Overlay/MapServer) showing the Winkleman, Big Rock Candy Mountain, 

and Big Star replacement alunite deposits, Marysvale volcanic field, Utah ...................22

Tables

1. Generalized guide for preliminary interpretation of mineral group maps...........................2

2. Landsat 7 ETM+ reflective spectral data characteristics (NASA, 2011).............................3

3. Boolean logic used to combine Landsat 7 ratio-based index results into the single, 

classified map shown on mapsheet. Blank cells represent NOT operators. The first 

column indicates both the designated number (#) of the output material class and 

the digital number (DN) of that class in the GIS-ready image file provided with this 

map. X = single index used to generate output material class ...........................................10

Map

Map of mineral groups and green vegetation derived from automated analysis of Landsat 7 

enhanced thematic mapper plus satellite data: San Juan Mountains, Colorado 

and Four Corners Region........................................................................................................ link

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

meter (m) 3.281 foot (ft)

micrometer (μm) 0.001 millimeter (mm)
Overview

Ongoing U.S. Geological Survey (USGS) national-scale 

mineral resource and geoenvironmental assessments require 

an efficient and accurate means of mapping and characterizing 

mineral exposures related to mined and unmined hydrother-

mally altered rocks and mine waste. The presence and miner-

alogy of altered rocks are important factors in determining the 

occurrence potential of concealed mineral deposits. Investiga-

tions at the deposit and watershed scales have described the 

importance of sulfide-bearing altered rocks and mine waste 

as diffuse sources of acidic solutions that can transport metals 

into the hydrologic system. These rocks and waste materials 

contain minerals that can be identified using spectral analysis 

of satellite remote sensing data. Analysis of multispectral data 

acquired by the Advanced Spaceborne Thermal Emission and 

Reflection Radiometer (ASTER, http://asterweb.jpl.nasa.gov/) 

sensor carried by the National Aeronautics and Space Admin-

istration (NASA) Earth Observing System Terra satellite is 

currently being employed to generate mineral group maps over 

large areas of the United States (U.S.) and its territories in sup-

port of USGS assessment efforts (Rockwell, 2012). 

Since the launch of the Landsat 4 satellite in 1982, the six 

bands of multispectral data collected by the Thematic Map-

per (TM) sensor have been used to identify and map mineral 

groups and other features on the Earth’s surface. Although 

maps generated by such analysis are less detailed than those 

derived from analysis of the nine reflective bands of ASTER 

data, they are nevertheless useful in identifying hydrother-

mally altered rocks and mine waste bearing oxidizing sulfide 

minerals, and can be very efficiently produced at national 

scales. Landsat data are available at no cost worldwide, and 

one Landsat scene covers approximately the same area as nine 

ASTER scenes. 

This report introduces a new methodology for the auto-

mated analysis of Landsat TM data that has been applied to 

more than 180 scenes covering the western United States. A 

map of mineral groups and green vegetation produced using 

this new methodology is presented that covers the western 

San Juan Mountains, Colorado, and the Four Corners Region. 

The map results can be compared to mineral and vegetation 

maps of the Silverton caldera region produced from analysis 

of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 

data (Dalton and others, 2004; 2007) and ASTER data 

(Rockwell, 2012). 

The map presented here was produced from one full scene 

of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data 

acquired on September 12, 2000. The ASTER data analyzed by 

Rockwell (2012) were acquired on the same day. The Landsat 

data were obtained from the USGS Earth Resources Observa-

tion and Science Center (EROS) in L1T orthorectified format.

Digital mineral and vegetation mapping results are 

presented in GeoPDF and ERDAS IMAGINE format (.img). 

Georeferencing information is included in both file types. 

The TerraGo Toolbar (http://www.terragotech.com/products/

field-data-collection/terrago-toolbar/), a no-cost extension 

for Adobe Acrobat Reader, is required for viewing geographic 

coordinates in the GeoPDF files. With or without the toolbar, 

multiple, geographically referenced map layers can be turned 

on and off for viewing and printing. A map of hydrothermal 

alteration types, mineral groups, and green vegetation derived 

from automated analysis of ASTER data (Sheet 5 of Rockwell, 

2012) is included in the GeoPDF file, as is the 1:500,000-scale 

geologic map of Colorado (Tweto, 1979; Green, 1992). The 

GeoPDF contains a bookmark that will download the ASTER-

derived mineral group map when clicked. Subsidiary map lay-

ers showing administrative boundaries, roads, surface hydrol-

ogy, and landmarks (for example, National Parks) are also 

included. These layers are provided for user convenience and 

may not conform to USGS map standards. The layer of surface 

hydrology is scale-dependent, with smaller, lower-order water-

courses appearing when viewed at larger scales. The ERDAS-

format file is suitable for integration with other geospatial data 

in Geographic Information Systems (GIS). The ERDAS file 

showing occurrence of mineral and vegetation groups identi-

fied using automated analysis has been attributed according to 

identified material, so that the color-coded material detected in 

a pixel can be queried with the interactive attribute identifica-

tion tools of GIS and image processing software packages (for 

example, the Identify Tool of ArcMap and the Inquire Cursor 

Tool of ERDAS IMAGINE). For the attribute information to 

appear in the ArcMap Table of Contents, open the ERDAS 

Automated Mapping of Mineral Groups and Green 

Vegetation from Landsat Thematic Mapper Imagery with 

an Example from the San Juan Mountains, Colorado

By Barnaby W. Rockwell
2 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

file in ArcMap and symbolize using Unique Values for the 

“Materialid” value field. Metadata compliant with Federal Geographic Data Committee (FGDC) standards have been included 

for the ERDAS-format file, and contain important information 

regarding geographic coordinate systems, attributes describing 

identified materials, and cross-references. 

Map Usage

The image map was designed primarily for the regional 

mapping and characterization of exposed surface mineralogy, including that related to hydrothermal alteration and 

supergene weathering of pyritic rocks (Rockwell, 2012). 

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. Prospective areas for epithermal and porphyry-type mineralization in 

which alteration is identified are, in general, more likely to 

host near-surface deposits. 

Pyrite is a common gangue mineral important for 

geoenvironmental studies that is associated with hydrothermal 

alteration and many types of ore deposits. Weathering of pyrite 

produces sulfuric acid upon exposure to atmospheric oxygen 

and water that can leach and transport potentially toxic metals 

into the groundwater system. Knowledge of pyrite occurrence 

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. Ferric 

iron minerals including jarosite, copiapite, melanterite, and 

goethite are also produced during the weathering process, and 

concentrations of undifferentiated ferric iron-bearing minerals 

can be identified using Landsat TM data. Mine waste and tailings in which no mineral groups are mapped are less likely to 

represent exposed, nonpoint sources of acidic solutions, or are 

too small in size to be mapped using Landsat data. The maps 

can also be used to evaluate mineral exposure at mine sites 

that have undergone remediation prior to the acquisition of 

the Landsat data. It is important to note that the Landsat-based 

mineral group mapping will preferentially identify potentially 

acid-producing assemblages over acid-neutralizing assemblages including carbonates and chlorite because of the weaker 

spectral absorption features related to the CO3

2– and Mg-OH 

molecules at Landsat TM spectral resolution. Mineral group 

maps similar to those described herein and generated from the 

same Landsat scene were used in a GIS-based statistical analysis of the Silverton mining area in the San Juan Mountains that 

identified the variables influencing water quality (Yager and 

others, 2013).

The Landsat TM-derived mineral group map can be 

best interpreted in conjunction with geologic maps in a GIS 

environment. The character and origin of an identified mineral 

group occurrence can begin to be understood by recognizing the host rock lithology or material type (table 1). Most 

occurrences of known hydrothermal alteration contain the 

clay-sulfate-mica-marble mineral group, but a vast majority of the occurrences of all mineral groups are unrelated to 

alteration. Within volcanic rocks and mine waste, the presence 

of alteration and (or) supergene weathering of pyrite can be 

inferred if both ferric iron and the clay-sulfate-mica-marble 

mineral groups are present, especially in the same pixel. The 

maps may aid in delineating lithologic and alluvial units in 

which minerals identifiable using spectral remote sensing exist 

in abundance. 

Table 1. Generalized guide for preliminary interpretation of 

mineral group maps.

Host rock or 

material type

Highest probability origin of

mineral group occurrence

volcanic hydrothermal alteration

plutonic lithology (commonly mica) or alteration

sedimentary lithology (commonly mica and (or) clays),

diagenesis, or alteration (rare)

metamorphic lithology, weathering, metamorphic grade and 

type, or alteration (rare)

alluvium weathering

mine waste alteration and (or) supergene weathering of 

pyritic rocks

Descriptions of Map Sheet and 

Associated ERDAS IMAGINE Raster 

Image File Included in this Publication

Map Sheet:

Title:

MAP OF MINERAL GROUPS AND GREEN VEGETATION 

DERIVED FROM AUTOMATED ANALYSIS OF LANDSAT 7 ENHANCED THEMATIC MAPPER PLUS SATELLITE DATA: SAN JUAN MOUNTAINS, COLORADO AND 

FOUR CORNERS REGION

Filename:

le70350342000256pac01.tar_msi_aa11_merge.img

Description:

This image shows the results of a new methodology designed 

for the automated identification and mapping of mineral 

groups and green vegetation from Landsat ETM+ data at 

regional to national scales. Detailed description of this 
Description of Automated Analysis Methodology 3

methodology is provided in the following section. These 

results were modeled using Boolean algebra from the raw 

results of the ratio-based analysis algorithm. The 10 output 

classes are ordered relative to decreasing inferred acidproducing potential in non-sedimentary rocks (commonly 

referred to as net acid production, or NAP) from top to 

bottom. 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 that can 

have substantial ANC (Yager and others, 2008). 

Purpose:

This image shows the occurrence of 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 or 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

Landsat 7 ETM+ multispectral data consist of 6 bands 

measuring reflected electromagnetic energy (bands 1–5 and 7, 

0.45–2.35 μm, table 2) and one band measuring emissive thermal infrared energy (band 6, 10.4–12.5 μm) (NASA, 2011). 

Only the reflective bands were used for this study, and have a 

ground instantaneous field of view (GIFOV, or ground pixel 

size), of 30 meters. Band ratios, which highlight variation in 

reflectance between wavelength regions measured by the various bands, or channels, of the sensor, have been the simplest 

and most widely used analysis method applied to TM data 

(Crippen, 1989; Knepper, 1989, 2010; Rockwell, 1989). 

To efficiently generate uniform maps of minerals and 

hydrothermal alteration over very large areas using Landsat TM 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 Landsat scene in any 

climate or terrain with no operator interaction. Suitable scenes 

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

cover and maximal solar illumination (high solar elevation 

angle during late spring to early autumn). 

In the northern hemisphere, scenes acquired in late 

May through early July are optimal to avoid the presence of 

senesced cheatgrass and other dry vegetation that are more 

abundant in mid-summer through autumn (Kokaly, 2011). 

Such dry vegetation typically has deep absorptions in the 

shortwave-infrared (SWIR) region caused by leaf biochemicals such as lignin and cellulose (Rockwell, 2009), and is 

spectrally similar at Landsat TM spectral resolution to minerals having strong SWIR (TM band 7) absorption features 

(Elvidge and Lyon, 1985; John and others, 2010). This spectral 

similarity of dry vegetation and many clay, sulfate, and mica 

minerals can lead to false identifications of this mineral group 

when Landsat scenes acquired in mid-July through October 

are analyzed, especially over areas in which cheatgrass is 

abundant, such as the northern Great Basin (northern Nevada 

and surrounding region). 

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 Landsat TM 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 Landsat 

TM bands). If another surface material, material mixture, or 

feature has deep absorption at a given wavelength interval, 

a false detection of a mineral group having a similar absorption feature 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 

described the technique “relative band depth” mapping. 

Prior to computing the material indices described below, 

the uneven edges of the Landsat TM bands were equalized by 

setting pixels to zero if that pixel had a digital number (DN) 

value of zero in any band in the raw data. To mitigate effects 

of Rayleigh (gaseous) scattering (Kaufman, 1989), a darktarget subtraction was applied in the form of:

DNhaze= (DNraw + (constant – 

(non-zero minimum value per band)))

A constant value of five was used to offset values to 

avoid divisions by zero in the ratio-based analysis. Similar 

atmospheric corrections using dark-target subtraction are discussed by Chavez (1988) and Song and others (2001). 

Table 2. Landsat 7 ETM+ reflective spectral data 

characteristics (NASA, 2011).

[μm, micrometer]

Band

Landsat 7 ETM+ 

wavelength 

range (μm)

Landsat 7 ETM+ 

band center 

(μm)

Wavelength region

1 0.45–0.52 0.485 Blue

2 0.52–0.60 0.560 Green

3 0.63–0.69 0.66 Red

4 0.76–0.90 0.83 Near-infrared

5 1.55–1.75 1.65 Shortwave-infrared

7 2.08–2.35 2.215 Shortwave-infrared
4 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

All analytical processing described below was performed in one pass, producing the output file provided with 

this map. This processing takes 3 minutes per full Landsat 

7 ETM+ scene on an Intel® CoreTM i7 Gulftown processor 

running at 3.7 GHz, including the computation of reducedresolution pyramid layers for fast display.

When analyzing large areas using many Landsat TM 

scenes, inter-scene variability can be reduced by converting 

the data to exoatmospheric, or “top of atmosphere (TOA),” 

reflectance (Chander and others, 2009). This process involves 

the radiometric calibration of the data to at-sensor spectral radiance followed by corrections for variance in exoatmospheric 

solar irradiance as a function of wavelength, earth-sun distance, 

and solar zenith angle. Such conversion is particularly useful when analyzing data from different sensors, for example 

Landsat 4, 5, and 7. Although TOA reflectance conversion has 

not provided significant advantages in generating seamless 

mineral group maps, future research will experiment with the 

incorporation of the conversion into the processing stream. 

Coverage of Landsat 7 ETM+ data acquired between 1999 

and 2003 for which the Scan Line Corrector was functioning (“SLC-On”) is excellent for the western U.S., so there has 

been no need to use data from other sensors to map that area. 

However, as work proceeds into the more temperate and alpine 

regions of the eastern U.S., Alaska, and Hawaii, the need to use 

data collected by the Landsat 4 and 5 sensors may arise, making TOA reflectance conversion more applicable and necessary.

Mineral Group and Green Vegetation Indices

Five ratio-based spectral indices were generated that 

exploit diagnostic absorption feature characteristics derived 

from published spectral libraries to highlight specific mineral 

groups. Information regarding the spectral and spatial characteristics of Landsat TM data is provided in table 2. In the 

formulas for the indices, Landsat TM (or ASTER) bands are 

indicated in bold. 

1. Ferric iron 1 “redness” index

A Landsat TM 3/1 ratio was used to highlight areas 

that have absorption in Landsat band 1 (blue wavelengths) relative to band 3 (red wavelengths) (fig. 1). 

Ferric iron minerals are characterized by deep 

charge-transfer absorptions in the ultraviolet (< 0.4 

micrometers) (Clark, 1999). The long-wavelength 

edge 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 ASTER 2/1 ratio 

described by Rockwell (2012), and is commonly 

used to highlight ferric iron minerals (Knepper, 

1989, 2010; Rockwell, 1989).

Jarosite will generally have deeper absorption at 

band 1 compared to goethite (fig. 1); both minerals are common in weathered pyritic rocks. Most 

hematites will also show less band 1 absorption 

compared with 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.

2. Ferric iron index 2

A Landsat TM (3/1) × ((3+5)/4) compound ratio was 

used to highlight ferric iron-bearing areas in which 

crystal-field absorptions in the near infrared (Landsat band 4) are present in addition to the charge 

transfer absorptions previously described (3/1 ratio). 

The index is similar to the ASTER (2/1) × (0.5 × 

((2+4)/3) ferric iron index described by Rockwell 

(2012). Figure 1 shows that the crystal-field absorptions in jarosite are significantly deeper than those 

of goethite, although the visible to near-infrared 

(VNIR) spectral characteristics of ferric iron minerals vary greatly relative to abundance and grain 

size (Rockwell, 2004). Since this ratio contains the 

3/1 operator of ferric iron index 1, pixels with high 

index values will be well correlated with those of 

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.

3. Ferrous iron – coarse-grained ferric iron – fire ash 

index

Ferrous iron produces a broad VNIR absorption at 

Landsat bands 3 and 4 relative to 2 and 5 (fig. 1). A 

Landsat TM (2+5)/(3+4) compound ratio was used 

to highlight areas with this spectral shape, and is 

similar to the ASTER (1+4)/(2+3) ratio described 

by Rockwell (2012). In the Silverton caldera area, 

chlorite associated with propylitic alteration is the 

mineral most commonly identified using this index.

This index also highlights concentrations of coarsegrained ferric iron that are typically characterized 

by deep crystal-field absorption in band 4 relative 

to band 5, as exhibited by the spectrum of hematite GDS27 in figure 1. Hematite-bearing oxidized 

basalts on cinder cones in young volcanic fields 

will typically be identified using this index. Coarsegrained goethite having deep and broad crystal-field 

absorptions near 1.0 micrometer (Rockwell, 2004) 

will also be highlighted.
Description of Automated Analysis Methodology 5

The index can also be expected to highlight rocks 

having a general greenish color (for example, greenstones), blue-gray shales (including the Cretaceous 

Mancos Shale on map sheet), moist soils, and fire 

ash. The spectral properties of forest fire ash and its 

detection using AVIRIS data analysis are described 

by Kokaly and others (2006).

4. Clay-sulfate-mica-marble index

Deep absorption in the SWIR (Landsat TM band 7) 

relative to band 5 is characteristic of Al-OH- and 

(or) bound water-bearing phyllosilicates, sulfates, 

and amphiboles (fig. 2), and the Landsat 5/7 ratio 

is used to highlight this broad mineral group 

Figure 1. Plots of laboratory reference spectra (Clark and others, 2007) of iron-bearing minerals convolved to 

Landsat TM spectral resolution. Plots are annotated with circles indicating Landsat bands used in the material 

indices. Orange circles show bands used in the numerator of an index, and light blue circles show bands used in the 

denominator. Landsat band centers are shown along the bottom. See text for explanation. Click on figure for more 

detailed image. 

1.0 1.5 2.0

Wavelength, in micrometers

0.5

1 2 3 4 5 7

Landsat 7 band centers 

Reflectance (offset for clarity)

jarosite GDS99 K-y 200C 

chlorite SMR-13.c

 45-60 micrometers 

actinolite HS116.3B 

illite IMt-1.a 

hematite GDS27 

hematite GDS76 2%+98% qtz

goethite WS222 3/1: ferric iron (“redness”) index

3/1 × (3+5)/4: major ferric iron index

(2+5)/(3+4): ferrous iron index
6 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

(Knepper, 1989, 2010; Rockwell, 1989). Carbonate minerals such as calcite and dolomite, Fe-OHbearing minerals such as epidote, and Mg-OHbearing phyllosilicate minerals such as chlorite, 

talc, and serpentine (“CEC” mineral group) have 

SWIR absorptions at slightly longer wavelengths 

that are only partially covered by the bandpass 

of Landsat band 7 (fig. 2 and table 1). As such, 

chlorite, epidote, and unaltered carbonate minerals 

typically have less band 7 absorption than clays, 

sulfates, and micas (fig. 2), and are not typically 

highlighted by the 5/7 ratio. However, the ratio can 

readily detect carbonate minerals in marbles where 

recrystallization has concentrated the minerals and 

increased grain sizes, resulting in deeper absorption 

bands. Varieties of the Mg-OH phyllosilicate mineral serpentine can have a wide absorption feature 

near 2.33 μm that can be highlighted by the 5/7

ratio, especially when the abundance of the mineral 

has been enriched by alteration and (or) metamorphic processes, typically in ultramafic rocks. As 

the carbonate absorption band of dolomite centered 

near 2.32 μm lies at shorter wavelengths than the 

calcite band (2.34 μm) and thus closer to the center 

of the Landsat TM band 7 bandpass (table 2), 

dolomitic marbles are slightly more likely to be 

identified using the 5/7 ratio. In areas where no 

phyllosilicates, sulfates, or amphiboles with deep 

band 7 absorption exist, the 5/7 ratio may highlight 

the CEC mineral group even in the absence of 

recrystallization or other enrichment.

As previously mentioned, dry vegetation has deep 

SWIR absorptions at Landsat TM band 7 related 

to lignin and cellulose that become more visible as 

leaf moisture decreases during senescence (fig. 3). 

Typical green, moist vegetation also shows absorption in band 7, in addition to high reflectance by 

mesophyll plant tissue in the near-infrared (TM 

band 4) and deep chlorophyll absorption in the 

visible red wavelengths (TM band 3). A Landsat 4/3 ratio highlights areas of green vegetation 

with abundant chlorophyll content, similar to 

the ASTER (1+3)/2 ratio described by Rockwell 

(2012), and is used to generate the green vegetation 

index described below. As these SWIR vegetation 

absorptions can be highlighted by the Landsat 5/7

ratio (Elvidge and Lyon, 1985), results of a Landsat 

4/3 ratio are subtracted from the 5/7 ratio results 

to generate the clay-sulfate-mica-marble index. 

This technique will largely eliminate the influence 

of green vegetation in the 5/7 ratio results so that 

only the clay-sulfate-mica-marble mineral group is 

identified. Dense, dry vegetation with low chlorophyll content (such as senesced cheatgrass) will not 

be substantially affected by this technique, and will 

still be observed in the index results, necessitating 

the selection of Landsat scenes acquired during late 

spring and early summer when most vegetation is 

green and relatively moist. 

An effect of this green vegetation suppression is that 

mineral group occurrences partially covered with 

green vegetation will be removed from the index 

results, focusing the results on those mineral exposures that are free of vegetation. Most occurrences 

of intense advanced argillic and argillic alteration, 

especially those with acidic surface runoff related to 

sulfide oxidation, are well exposed with little to no 

green vegetation cover. Carbonate-bearing rock and 

soil are more likely to support vegetative growth, and 

are thus more likely to be removed from the claysulfate-mica-marble index by the green vegetation 

suppression process, although carbonates (and minerals with Mg-OH SWIR absorptions) are less likely to 

be detected by the index as described above.

5. Green vegetation index

A Landsat 4/3 ratio is used to highlight areas with 

strong absorption in the red wavelengths (band 3) 

relative to band 4. Chlorophyll-related absorptions 

in green vegetation are the most common source of 

such absorption (fig. 3). 

Image Processing for Masking and Index 

Thresholding 

1. Dark Area Masking

Landsat ratio results can be spurious in areas of low 

surface albedo, including those with poor solar illumination caused by terrain shadow (Knepper, 2010). 

Such areas are masked from analysis using thresholds of Landsat TM band 5 determined through 

examination of many scenes from different climates, 

land cover, and terrain relief. Any pixel with a raw 

band 5 DN value less than 80 is removed from all 

index results other than the green vegetation index. 

2. Water Masking

A Landsat TM 1/5 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 Landsat scenes. A mask 

is created by applying the threshold to the 1/5 ratio 

result. The mask is then applied to index results 

other than the green vegetation index to remove 

data from areas of snow and standing water. Turbid 

water, shallow water with a bright substrate, and 
Description of Automated Analysis Methodology 7

mixtures of thin or melting snow and vegetation 

have reduced absorption in the SWIR and may not 

be entirely removed by this masking process.

3. Final index thresholding and clipping

The final indices are created by isolating the highest 

values using a common standard deviation threshold, followed by an index-specific clip (removal) of 

the lowest index values based on extensive testing 

of scenes from a variety of climates, underlying 

bedrock and surficial geology, 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. The amount of low-end index values that are 

Figure 2. Plots of laboratory reference spectra of alunite, pyrophyllite, and kaolinite (Clark and others, 2007) convolved 

to Landsat TM spectral resolution. Plots are annotated with circles indicating Landsat TM bands used in the claysulfate-mica-marble index. Orange circles show bands used in the numerator of an index, and light blue circles show 

bands used in the denominator. Landsat band centers are shown along the bottom. See text for explanation. Click on 

figure for more detailed image.

1.0 1.5 2.0

Wavelength, in micrometers

0.5

1 2 3 4 5 7

Landsat 7 band centers 

Reflectance (offset for clarity)

dolomite HS102.3B 

pyrophyllite PYS1A fine grained 

alunite GDS84 Na03 

kaolinite KGa-1 (wxyl) 

muscovite GDS107 

montmorillonite CM27 

talc GDS23 74-250 micrometers

actinolite HS116.3B

calcite WS272 

(5/7 - 4/3): clay-sulfate-mica-marble index
8 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

Figure 3. Plots of laboratory reference spectra of vegetation (Clark and others, 2007) convolved to Landsat TM spectral 

resolution. Spectra are ordered from bottom to top relative to decreasing depth of the chlorophyll absorption feature at 

Landsat TM band 3. Red arrow shows position of this chlorophyll absorption, and black arrow shows position of SWIR 

absorption related to lignin-cellulose and (or) leaf moisture. Although chlorophyll content decreases and dryness increases 

from bottom to top, absorption in band 7 is variable because of changes in lignin-cellulose content by species, moisture 

content, and sample characteristics. Plot at bottom is annotated with circles indicating Landsat TM bands used in the green 

vegetation index. Orange circle shows band used in the index numerator, and light blue circle shows band used in the 

denominator. Landsat band centers are shown along the bottom. Click on figure for more detailed image. 

removed increases in the following order from low to 

high: (1) ferric iron index 2, (2) ferric iron index 1 and 

green vegetation, (3) clay-sulfate-mica-marble, and (4) 

ferrous iron – oxidized basalt – fire ash. The relatively 

high degrees of clipping of the latter two indices reflect 

a lower degree of confidence in the accuracies of results 

given the spectral similarities between these mineral 

groups and other surface features at Landsat TM spectral 

resolution. 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, which will focus the results on outcrops 

rather than distal alluvium derived from those outcrops.

Figure 4 shows a subset from the regional, Landsat-based 

mineral group mapping over the Goldfield mining district 

in west-central Nevada. As discussed by Rockwell 

(2012) regarding the automated analysis of ASTER 

data, the clipping of the clay-sulfate-mica-marble 

index removes most of the alunite-bearing alluvium 

weathering southeastwards toward Stonewall Flat 

in which alunite (and kaolinite) are present in less 

abundance than in the intensely altered rocks from 

which the alluvium is derived. 

Modeling of Mineral Groups Through Boolean 

Combination of Index Results

For the purposes of combining the five indices into a 

more easily visualized single map and assigning GIS-ready 

descriptive attributes, Boolean algebra was used to examine 

pixel occurrence in one or more indices and generate 10 

output classes of mineral groups and green vegetation (map 

sheet). Table 3 shows the logic of the algebraic expressions.

1 2 3 4 5 7

Landsat 7 band centers 

1.0 1.5 2.0

Wavelength, in micrometers

0.5

Reflectance (offset for clarity)

golden-hued dry grass 

fir tree needles (green) 

oak leaf (dry and green) 

lodgepole pine needles

 (dry and green) 

willow leaves (dry) 

sagebrush leaves

lodgepole pine needles (dry) 

4/3: green vegetation index
Description of Automated Analysis Methodology 9

Figure 4. Mineral group map of the Goldfield mining district, 

Nevada, generated from Landsat 7 ETM+ data using the 

automated algorithm described herein. A, exposure of spectrally 

pure jarosite (Rockwell, 2012) identified as class 9; B, hematitebearing, oxidized Tertiary basalts mapped as class 4; C, alluvium 

containing alunite and kaolinite derived from weathering of 

intense advanced argillic alteration in the Goldfield Hills east 

of the town of Goldfield. Only alluvium in which the alteration 

minerals are present in highest abundance is shown, as most 

of the alluvium has been removed by the automated clipping 

process. The full extent of the alluvium is shown by Rockwell 

(2012); D, weakly propylitized rhyolite and quartz latite flows 

containing muscovite and chlorite in which the Landsat data 

analysis identified only sparse exposures of mineral groups, 

although several pixels of class 5 (ferric + ferrous iron) are 

present, representing abundant chlorite; E, Diamond (Peak).

Click on figure for more detailed image.

37°45'

117°15' 117°10' 117°05'

37°40'

Modeled mineral groups and vegetation

clay, sulfate, mica, and /or marble + major ferric iron

clay, sulfate, mica, and/or marble+ moderate to major ferric iron

clay, sulfate, mica, and/or marble + minor ferric iron

major feric iron

major ferric iron (high redness)

ferric + ferrous iron

clay, sulfate, mica, and/or marble

clay, sulfate, mica, and/or marble + ferrous iron

ferrous or course-grained ferric iron (may include oxidized

 basalts, fire ash, some moist soils, and any blue/green rocks)

dense, green vegetation

C

B

E

A

B

B

D

9

8

7

3

1

5

6

10

4

11

0 1 2 3 KILOMETERS

EXPLANATION 0 1 2 3 MILES ESMERALDA NYE
10 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

Table 3. Boolean logic used to combine Landsat 7 ratio-based index results into the single, classified map shown on mapsheet. Blank 

cells represent NOT operators. The first column indicates both the designated number (#) of the output material class and the digital 

number (DN) of that class in the GIS-ready image file provided with this map. X = single index used to generate output material class.

Output Class 

# and DN 

Value

Class Color Class Name

(Materialid attribute)

Material Index

3/1 3/1 × (3+5)/4 (2+5)/(3+4) 5/7 - 4/3 4/3

1 light brown minor ferric iron (high redness) X

3 magenta major ferric iron AND AND

4 dark cyan ferrous iron or oxidized basalts (may 

include any blue-green rocks, fire 

ash, and some moist soils)

X

5 purple ferric + ferrous iron AND/NOT AND AND

6 green clay, sulfate, mica, and (or) marble X

10 cyan clay, sulfate, mica, and (or) marble + 

ferrous iron

AND AND

7 yellow clay, sulfate, mica, and (or) marble + 

minor ferric iron

AND AND

8 orange clay, sulfate, mica, and (or) marble + 

moderate to major ferric iron

AND AND/NOT AND

9 red clay, sulfate, mica, and (or) marble + 

major ferric iron 

AND AND AND/NOT AND

11 dark green dense, green vegetation X

Discussion

As a means of validating the results of the automated 

analysis methodology, derived mineral group maps of several 

well-known mineralized and (or) hydrothermally altered areas 

in the western United States are presented for comparison 

with field-verified maps generated from detailed spectroscopic 

analysis of AVIRIS and (or) ASTER data (Rockwell, 2012). 

These maps are subsets of a seamless mineral group map of 

the southwestern U.S. (fig. 5).

Pixels in which both ferric iron indices and the clay-sulfate-mica-marble index occur are assigned the color red on the 

map sheet (output class 9 in table 3). Most advanced argillic, 

argillic, and phyllic alteration within volcanic rocks in the Silverton caldera area, (Bove and others, 2007; Rockwell, 2012) 

have been identified with this output class, as the high-sulfidation, magmatic-hydrothermal advanced argillic and phyllic 

alteration typically contain abundant pyrite that weathers to 

ferric iron minerals. The hydroxyl-bearing iron sulfate mineral 

jarosite, a relatively insoluble product of pyrite oxidation, has 

strong visible-near-infrared (VNIR) absorptions related to 

ferric iron in addition to deep Fe-OH absorption in the SWIR 

(fig. 1). As such, spectrally pure jarosite will be highlighted in 

the ferric iron and clay-sulfate-mica-marble indices if present 

in sufficient abundance and areal extent to be detected in a 

30-m Landsat ETM+ pixel. Occurrences of jarosite + sericite 

on Rolling Mountain 15 km west-southwest of the town of 

Silverton identified using AVIRIS (Dalton and others, 2004) 

and ASTER (Rockwell, 2012) data were mapped as output 

class 9 (map sheet). A small area of spectrally pure jarosite 

surrounded by alunite located east of Diamond (Peak) in the 

Goldfield mining district, Nevada, identified using AVIRIS 

and ASTER data (Rockwell, 2012) was identified as class 9 

using the Landsat analysis algorithm presented here (fig. 4). 

Large scars from wildfires, including those in the Mesa 

Verde area southwest of Mancos, have been identified primarily as class 4 because of VNIR absorptions of ash (map sheet). 

Ferric iron minerals were detected across large areas in the 

southwestern part of the Landsat scene within Quaternary 

eolian dune sands and silts, and Peoria Loess (unit Qe of Green, 

1992 and Tweto, 1979), the Cretaceous Dakota Sandstone and 

Burro Canyon Formation (unit Kdb), and locally within the 

Cretaceous Mancos Shale (Km, including Mesaverde Group or 

Formation, Frontier Sandstone and Mowry Shale), and shales 

and siltstones of the Jurassic Morrison Formation, Summerville 

Formation, and Entrada Sandstone (Jmse). In Arizona, ferric 

iron minerals mapped within the red-colored Wingate and red 

to buff Navajo Sandstones (Jurassic).

Mineral exposures related to alteration and (or) lithology 

were identified within quartzites, slates, and phyllites of the 

Proterozoic Uncompahgre Formation on Peaks One, Two, and 

Three and White Dome between the Grenadier Range and Elk 

Creek 15 km southeast of Silverton (A, fig. 6). The Landsat 

TM-based mapping identified mineral groups 6, 8, and 9, and 
Figure 5. Seamless map of mineral groups and green vegetation covering the western U.S. generated from Landsat 7 ETM+ data using the automatedescribed herein. Click on figure for more detailed image.

125°00' 120°00' 115°00' 110°00' 

45°00'40°00'

35°00'

Modeled mineral groups and vegetation

clay, sulfate, mica, and /or marble + major ferric ironclay, sulfate, mica, and/or marble+ moderate to major ferric ironclay, sulfate, mica, and/or marble + minor ferric ironmajor feric ironmajor ferric iron (high redness)ferric + ferrous ironclay, sulfate, mica, and/or marbleclay, sulfate, mica, and/or marble + ferrous ironferrous or course-grained ferric iron (may include oxidized basalts, fire ash, some moist soils, and any blue/green rocks)dense, green vegetation

EXPLANATION

0 100 200 300 400 KILOMETERS0 100 200 300 400 MILES
12 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

the ASTER-based mapping of Rockwell (2012) identified 

primarily ferric iron minerals and sericite with local kaolinite 

+ sericite and kaolinite. While the sericite is most likely lithologic in character, representing primary or authigenic mica 

in the quartzite groundmass or phyllites, the presence of clay 

minerals in localized areas within the formation suggest the 

presence of fracture-controlled argillic hydrothermal alteration. The mineral mapping results in this area have not been 

field verified.

The Landsat TM-based mapping identified class 4 (ferrous iron) in Proterozoic felsic and hornblendic gneisses, 

metabasalts, and metatuff (unit Xfh of Green, 1992) mainly 

derived from low-grade metamorphism of volcanic rocks 

(B, fig. 6). ASTER-based mapping identified extensive 

exposures of calcite, chlorite, and possibly epidote in this 

area, often mixed with sericite or smectite. The ferrous 

iron mineral group identified by the Landsat TM mapping 

likely reflects the presence of chlorite, but the carbonate and 

Mg-OH SWIR absorptions of calcite and chlorite are too 

weak to be detected by the Landsat analysis, as discussed 

above. Chlorite and mixed-layer chlorite/smectite are common in low-grade metabasalts; epidote will appear at higher 

metamorphic grades (Robinson and others, 1993).

Steam-heated alunite formed in near-surface environments from neutral-pH fluids derived from oxidation of H2

S 

typically does not contain pyrite (Rockwell and others, 2006). 

Such alteration should map as classes 6 or 7 (table 3), in 

which the clay-sulfate-mica-marble mineral group is abundant, but ferric iron derived from pyrite oxidation is present in 

low abundance, or not at all. Figure 7 shows a mineral group 

map of part of the Cuprite mining district, Nevada, in which 

steam-heated alunite without significant ferric iron minerals 

have been mapped as classes 6 and 7 (green and yellow, 

respectively). Maps produced from spectroscopic analysis of 

AVIRIS data (Rockwell, 2012) confirm that these areas consist 

of alunite + kaolinite with sparse to absent ferric iron minerals. 

Hematite may co-occur with and (or) overlie the steam-heated 

alunite, and wall rocks surrounding or underlying the alunite 

may contain abundant pyrite, which will weather to jarosite 

and (or) goethite. Alunitic rocks and alluvium in which hematite, jarosite, or goethite are present mapped as classes 8 or 9 

in figure 7.

In typical pyritic rocks undergoing supergene alteration 

related to weathering, clay minerals such as kaolinite and 

smectite are produced from the breakdown of feldspars in the 

rocks, in addition to abundant ferric iron minerals (for example alunogen, copiapite, melanterite, jarosite, goethite; Cunningham and others, 2005). Such clays will tend to mask the 

Fe-OH absorption feature of jarosite in TM band 7 (Rockwell 

and others, 2005), yet the clays themselves will be identified 

by the clay-sulfate-mica-marble index if present in sufficient 

abundance and areal extent. Weathered, pyritic mine waste 

and tailings with abundant supergene weathering products will 

typically be identified as class 9. Waste and unmined, pyritic 

rock with weak or absent clay minerals, micas, or spectrally 

pure jarosite will typically map as ferric iron alone (classes 1 

or 3). Figure 8 shows the weathered, highly pyritic waste rock 

along the east flank of the open pit at the Bingham Canyon 

porphyry-copper mine in Utah mapped as class 9. 

The Landsat-based analysis methodology is capable of 

delineating most zones of intense advanced argillic, argillic, 

and phyllic alteration. Figure 9 shows a map of the Bodie Hills 

and surrounding area just north of Mono Lake, California, 

and can be compared to mineral maps of the area generated 

from spectroscopic analysis of ASTER data (Rockwell, 2010). 

Zones of intense quartz-alunite-kandite alteration along Red 

Wash (A1, fig. 9), a west-northwest-trending fault zone north 

of the Ramona mining district in the Wassuk Range (A2, 

fig. 9), and in an altered rhyolite plug 3 km west of the Aurora 

mining district (A3, fig. 9) are clearly seen, although the claysulfate-mica-marble mineral group is identified only where 

alunite and clay minerals are most abundant. In altered areas 

where alunite and clay minerals are less spectrally abundant 

and ferric iron minerals are also present, only the ferric iron 

mineral group is identified (A4, fig, 9). The lower spectral 

abundance of the clay-sulfate-mica-marble mineral group in 

these areas is controlled by less intense argillization and (or) 

the small size of more fracture-controlled zones relative to the 

30-m GIFOV of a Landsat 7 ETM+ pixel. In smaller zones, 

areal mixing with unaltered rocks, soils, or vegetation in adjacent pixels will decrease the value of the 5/7 ratio and make 

removal of such pixels from the clay-sulfate-mica-marble 

index by the automated clipping process more likely.

The degree of correlation between mineral and mineral group mapping derived from analysis of ASTER and 

Landsat TM data over some extensively altered areas of the 

Marysvale volcanic field in Utah is shown in figure 10. Most 

altered areas and mill tailings mapped using AVIRIS (Rockwell and others, 2005; 2006) and ASTER (Rockwell, 2009) 

data were identified as classes 1, 3, 6, 8, or 9. Altered rocks 

of the Winkleman replacement alunite deposit in the foothills of the Tushar Mountains 2 km west of Big Rock Candy 

Mountain were not mapped by the Landsat data analysis. The 

Winkleman area lies at an elevation 215–315 meters higher 

than the alteration in the Antelope Range, and therefore 

receives greater precipitation, resulting in thicker stands 

of vegetation (fig. 11). The use of the Landsat 4/3 ratio for 

vegetation suppression removed pixels from the clay-sulfate-mica-marble index in this area. The automated analysis 

of ASTER data (Rockwell, 2012) did detect advanced argillic 

alteration in the Winkleman area.

Other than thick alluvial deposits along the west bank 

of the Sevier River derived from weathering of intense 

alunitic alteration on Alunite Ridge and Deer Trail Mountain 

(H, fig. 10), most alluvium containing alunite and kaolinite 

were not mapped, focusing the user on altered rock outcrops.
Discussion 13

Figure 6. A subset of the map sheet over the southern San 

Juan Mountains showing the mineral group map generated 

from Landsat 7 ETM+ data using the automated algorithm 

described herein. Embedded digital layers include a 1:500,000-

scale geologic map (Green, 1992), two ASTER-derived mineral 

maps (Rockwell, 2012), and various geographic basemaps 

(ESRI, 2012). A, possible argillic alteration in metamorphosed 

quartzites and metapelites of the Uncompahgre Fm; B, 

ferrous iron (most likely from chlorite) identified in primarily 

metavolcanic rocks. Like the map sheet, the high-resolution 

version of this figure is a GeoPDF file containing multiple 

layers that can be visualized using the TerraGo® Toolbar 

plugin for Adobe Acrobat Reader. Click on figure for more 

detailed image.

37°40'

107°40' 107°35' 107°30' 107°25'

37°35'

Modeled mineral groups and vegetation

clay, sulfate, mica, and /or marble + major ferric iron

clay, sulfate, mica, and/or marble+ moderate to major ferric iron

clay, sulfate, mica, and/or marble + minor ferric iron

major feric iron

major ferric iron (high redness)

ferric + ferrous iron

clay, sulfate, mica, and/or marble

clay, sulfate, mica, and/or marble + ferrous iron

ferrous or course-grained ferric iron (may include oxidized

 basalts, fire ash, some moist soils, and any blue/green rocks)

dense, green vegetation

9

8

7

3

1

5

6

10

4

11

0 2 4 KILOMETERS

EXPLANATION 0 1 2 3 4 MILES

A

B

Vallecito Creek

LA PLATA

SAN JUAN

HINSDALE
14 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

Figure 7 (following page). Mineral group map of part of the 

Cuprite mining district, Nevada, generated from Landsat 7 ETM+ 

data using the automated algorithm described herein. A, alunite 

(+/- kaolinite) containing hematite (or local goethite) mapped as 

class 9; B, alunite and kaolinite in which ferric iron is absent or 

present in only low abundance, mapped as classes 6 or 7; C, green 

phyllitic siltstones of the Lower Cambrian Harkless Formation 

bearing chlorite and muscovite mapped as ferrous iron (class 

4, from chlorite) +/- clay-sulfate-mica-marble (class 10). Class 5 

(ferric + ferrous iron) was mapped immediately to the east where 

the Harkless Fm is adjacent to ferric-iron bearing argillically 

altered rocks; D, Mule Spring Limestone in which the clay-sulfatemica-marble mineral group was not mapped; E, Stonewall Playa 

surface containing montmorillonite + goethite (Rockwell, 2012), 

mapped as major ferric iron (class 3) surrounded by minor ferric 

iron (class 1). Low-abundance montmorillonite was not identified 

because of automated index clipping. Like the map sheet, the 

high-resolution version of this figure is a GeoPDF file containing 

multiple layers that can be visualized using the TerraGo® Toolbar 

plugin for Adobe Acrobat Reader. Click on figure for more detailed image.
Discussion 15

37°35'

117°15' 117°10'

37°30'

Modeled mineral groups and vegetation

clay, sulfate, mica, and /or marble + major ferric iron

clay, sulfate, mica, and/or marble+ moderate to major ferric iron

clay, sulfate, mica, and/or marble + minor ferric iron

major feric iron

major ferric iron (high redness)

ferric + ferrous iron

clay, sulfate, mica, and/or marble

clay, sulfate, mica, and/or marble + ferrous iron

ferrous or course-grained ferric iron (may include oxidized

 basalts, fire ash, some moist soils, and any blue/green rocks)

dense, green vegetation

9

8

7

3

1

5

6

10

4

11

EXPLANATION

B

D

A

A

E

C

A

B

B

B

NYE

ESMERALDA

0 1 2 KILOMETERS

0 1 2 MILES
16 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

Figure 8 (following page). Mineral group map of the Bingham 

Canyon mine, Oquirrh Mountains, Utah, generated from Landsat 

7 ETM+ data using the automated algorithm described herein. A, 

open mine pit in which micas, biotite, and ferrous iron minerals 

associated with potassic alteration in freshly exposed rocks 

(Rockwell and others, 2005) have not been mapped because 

of 1) poor solar illumination, and (or) 2) associated absorption 

features are weak because of low mineral abundance and other 

factors, and thus identified mineral groups have been removed 

by the automated index clipping. Most phyllic alteration lies on 

the fringes of the pit distal to the potassically altered core of the 

deposit; B, site of remediated International Smelter; C, barren, 

iron-bearing surfaces denuded by historic acidic emissions 

from the smelter (Rockwell and others, 2005); D, talc in wall 

rock in which no mineral groups were identified, indicating the 

weaker band depth of Mg-OH SWIR absorptions compared to 

those of micas and clays in this area; E, highly pyritic waste 

rock containing locally abundant jarosite; F, waste and wall 

rock bearing clays, micas, and ferric iron minerals not mapped 

because of north-facing aspect and associated poor solar 

illumination; G, alluvial soils bearing ferric iron minerals derived 

from in-situ weathering of iron-bearing minerals and (or) deflation 

of weathered pyritic waste from mine site; H, argillic alteration 

and ferric iron associated with Barneys Canyon and Melco distaldisseminated gold deposits. AVIRIS-derived mineral maps of the 

area (Rockwell and others, 2005) are available for comparison 

at these links: iron-bearing minerals, http://pubs.usgs.gov/

sir/2004/5241/graphics/f35.pdf; clay, sulfate, mica, Mg-OH, and 

carbonate minerals, http://pubs.usgs.gov/sir/2004/5241/graphics/

f36.pdf. Background image is from ESRI World Imagery base layer 

(ESRI, 2012). Click on figure for more detailed image.
Discussion 17

0 1 2 3 KILOMETERS

0 1 2 3 MILES

40°35'

112°10' 112°05'

40°30'

Modeled mineral groups and vegetation

clay, sulfate, mica, and /or marble + major ferric iron

clay, sulfate, mica, and/or marble+ moderate to major ferric iron

clay, sulfate, mica, and/or marble + minor ferric iron

major feric iron

major ferric iron (high redness)

ferric + ferrous iron

clay, sulfate, mica, and/or marble

clay, sulfate, mica, and/or marble + ferrous iron

ferrous or course-grained ferric iron (may include oxidized

 basalts, fire ash, some moist soils, and any blue/green rocks)

dense, green vegetation

9

8

7

3

1

5

6

10

4

11

EXPLANATION

C

H

B

E

A

D

C C

E

E

E

G F
18 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

Figure 9 (following page). Mineral group map of the Bodie 

Hills and surrounding region, California/Nevada generated from 

Landsat 7 ETM+ data using the automated algorithm described 

herein. Embedded digital layers include statewide geologic maps 

(Ludington and others, 2005; Crafford, 2007), two ASTER-derived 

mineral maps (Rockwell, 2010), and various geographic basemaps 

(ESRI, 2012). The map also contains automated mapping results 

from two overlapping Landsat 7 ETM+ scenes that can be 

compared to each other. The northern scene was acquired on 

August 18, 2002, and the southern scene was acquired on July 

25, 1999. A, quartz-alunite-bearing advanced argillic alteration 

mapped using ASTER data (Rockwell, 2010) and field work, 

including: A1, Red Wash; A2, along fault zone on western flanks 

of Wassuk Range; A3, rhyolite plug west of Aurora mining district; 

A4, advanced argillic alteration in which primarily ferric iron and 

a few pixels of the clay-sulfate-mica-marble mineral group were 

detected; B, site of Cinnabar Canyon sulfur deposit; C, intense, 

kaolinite-bearing argillic alteration, including: C1, kaolinized sinter 

with local ferric iron (mapped as classes 3, 8, and 9) east of East 

Brawley Peak and associated alunite; C2, Sweetwater Mountains; 

D, low-sulfidation deposits of the Bodie mining district and 

associated mill tailings; E, argillized Cretaceous granite intrusion 

(mapped as ferric iron) surrounded by intense propylitic alteration 

(not mapped); F, hematite-bearing, oxidized basalts mapped as 

class 4; G, travertine hot springs with local nontronite (Rockwell, 

2010) in which classes 1, 3, 6, and 7 were identified; H, jarosite and 

smectite in pyritic rocks of the Borealis mine of Ramona mining 

district mapped as classes 3, 8, and 9. Like the map sheet, the 

high-resolution version of this figure is a GeoPDF file containing 

multiple layers that can be visualized using the TerraGo® Toolbar 

plugin for Adobe Acrobat Reader. Click on figure for more detailed image.
Discussion 19

Modeled mineral groups and vegetation

clay, sulfate, mica, and /or marble + major ferric iron

clay, sulfate, mica, and/or marble+ moderate to major ferric iron

clay, sulfate, mica, and/or marble + minor ferric iron

major feric iron

major ferric iron (high redness)

ferric + ferrous iron

clay, sulfate, mica, and/or marble

clay, sulfate, mica, and/or marble + ferrous iron

ferrous or course-grained ferric iron (may include oxidized

 basalts, fire ash, some moist soils, and any blue/green rocks)

dense, green vegetation

9

8

7

3

1

5

6

10

4

11

EXPLANATION

A

A1

A3

A2

A2 A2

A4

A

B

A4

A

A

C 2

A

A

C 1

D

E

A

F

A4 G

WASSUK RANGE

BODIE HILLS

C

H

LYON

MINERAL

MONO

CALIFORNIA

NEVADA

38°30'

38°20'

38°10'

119°20' 119°10' 119°00' 118°50' 118°40'

0 5 10 KILOMETERS

0 5 10 MILES
20 Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery

Figure 10 (following page). Mineral group map of the northern 

Marysvale volcanic field, Utah, generated from Landsat 7 ETM+ 

data using the automated algorithm described herein. Embedded 

digital layers include a geologic map (Rowley and others, 2002), 

mineral maps generated by spectroscopic analysis of ASTER 

data (Rockwell, 2009), a map of mineral groups and green 

vegetation generated by automated analysis of ASTER data 

(Rockwell, 2012), and various geographic basemaps (ESRI, 2012). 

A, advanced argillic (alunite, pyrophyllite), argillic (kaolinite, 

dickite), and phyllic alteration associated with Big Rock Candy 

Mountain replacement alunite deposit; B, Big Star natroalunite 

deposit; C, Yellow Jacket alunite deposit; D, Al Kee Mee alunite 

deposit; E, White Hills alunite deposit; F, White Horse alunite 

deposit; G, alunitic waste rock at loading site (approximately 

80–100 m in diameter) on outskirts of town of Marysvale; H, 

alluvium-bearing alunite, kaolinite, and ferric iron minerals 

derived from weathering of altered areas on Alunite Ridge and 

Deer Trail Mountain (Rockwell and others, 2005); I, Alunite Ridge 

magmatic steam alunite deposit; J, argillic alteration on summit 

of Deer Trail Mountain (mapped as ferric iron classes 1 and 3 

only); K, ferric iron identified in tailings from mill at Old Deer 

Trail mine; L, propylitically altered quartz-monzonite intrusion 

(Hoover Pluton) containing calcite, chlorite, and epidote that is 

unmapped by the Landsat data analysis except for two pixels of 

class 4 (ferrous iron) related to chlorite; M, alunite and kaolinite 

associated with Monroe hot springs and related alteration; 

N, Red Narrows kaolinite mines with hematite (mined for fire 

brick) mapped as classes 1, 3, and 9; O, Marysvale Peak alunite 

deposit; P, Aluminum Queen alunite deposit; Q, one pixel of 

class 1 (ferric iron) mapped in tailings from mill, Kimberly mining 

district; R, Box Creek kaolinite deposit; S, sericite, smectite, 

and minor kaolinite associated with weak argillic and regional 

propylitic alteration of Big John caldera fill; T, argillic alteration 

(kandite clay) associated with Saturday uranium prospect 

in northern Bullion Hills; U, altered rocks of the Winkleman 

replacement alunite deposit that were not identified; V, smectite 

in lacustrine deposits of the Sevier River Formation identified as 

classes 6 and 8. Like the map sheet, the high-resolution version 

of this figure is a GeoPDF file containing multiple layers that 

can be visualized using the TerraGo® Toolbar plugin for Adobe 

Acrobat Reader. Click on figure for more detailed image.
Discussion 21

38°35'

38°20'

38°25'

112°20' 112°15' 112°10' 112°05' 112°00'

Modeled mineral groups and vegetation

clay, sulfate, mica, and /or marble + major ferric iron

clay, sulfate, mica, and/or marble+ moderate to major ferric iron

clay, sulfate, mica, and/or marble + minor ferric iron

major feric iron

major ferric iron (high redness)

ferric + ferrous iron

clay, sulfate, mica, and/or marble

clay, sulfate, mica, and/or marble + ferrous iron

ferrous or course-grained ferric iron (may include oxidized

 basalts, fire ash, some moist soils, and any blue/green rocks)

dense, green vegetation

9

8

7

3

1

5

6

10

4

11

EXPLANATION

WASSUK RANGE

Clear Creek Canyon 

 

 

B

A

V

D

E

F

G

I H

J K

L

M

M

N

O

P

Q

R

S

Sevier River

T

U

Antelope Range

Sevier Plateau

C

Tushar Mountains

BEAVER

70

89

118

89

SEVIER

PIUTE

0 2 4 6 KILOMETERS

0 2 4 6 MILES

Marysvale

Monroe
Figure 11. One-meter-resolution imagery from the USGS The National Map Viewer (http://raster.nationalmap.gov/ArcGIS/rest/services/TNM_Large_MapServer) showing the Winkleman, Big Rock Candy Mountain, and Big Star replacement alunite deposits, Marysvale volcanic field, Utah. Altered rodeposit are outlined approximately in yellow. Note increased density of vegetation cover over Winkleman deposit related to increased elevation and procks of the Big Rock Candy Mountain deposit; B, Big Star deposit; C, note vegetation growing on upper parts of propylitically altered quartz-monzonitin which only two pixels of class 4 (ferrous iron absorptions in chlorite) were detected using the Landsat data. Click on figure for more detailed image.

112°18' 112°17' 112°16' 

38°31'

38°30'

A

A

B

C

Sevie

89

0 0.5 1 KILOMETERS0 0.5 1 MILES
References 23

Deficiencies of the Automated 

Analysis Methodology

For the regional mapping effort, Landsat 7 scenes with 

minimal cloud cover and optimal solar illumination were 

selected for analysis. However, scenes with unclear atmospheres caused by dust, haze, clouds, fog, and smoke will show 

uncertain analysis results. Suitable Landsat data may not be 

available for all areas. False mineral group detections can 

occur in areas where seasonally dry, brown vegetation such as 

cheatgrass is present, especially in the northern Great Basin.

Because of the spectral coverage of Landsat TM band 7 

with respect to the diagnostic absorptions of carbonate and 

Mg-OH minerals, Landsat TM data analysis will not generally identify exposures of these minerals, except in cases 

of recrystallization where carbonate mineral abundance is 

increased (marbles), or when phyllosilicate minerals are also 

present. In addition, as vegetative cover is more common over 

carbonate-bearing rocks and soils than over argillically and 

pyritically altered rocks, the vegetation suppression logic will 

be more likely to remove pixels from the clay-sulfate-micamarble index in areas where carbonate and (or) Mg-OH 

minerals are present. These features of Landsat data and the 

analysis technique will tend to skew results toward potentially 

acid-producing argillic and phyllic alteration. 

In relatively rare cases, undisturbed, hydrolytically 

altered rocks containing clay, sulfate, and (or) mica minerals 

are partially covered by green vegetation. If the vegetative 

cover is sufficiently dense, the vegetation suppression logic 

may remove most or all pixels from the clay-sulfate-micamarble index. Therefore, if any pixels of this index are 

mapped in an area with vegetative cover, field checking and 

(or) additional data analysis should be performed to understand the true extent of altered rocks. Customized Landsat 

analysis without vegetation suppression, or ASTER data 

analysis, can be used as verification tools in such cases.

As described by Rockwell (2012) regarding the automated ASTER data analysis technique, any ratio-based 

spectral analysis algorithm will present “answers” for every 

material 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. Therefore, the results of ratio-based analysis algorithms 

represent “high likelihood” maps where a particular material is 

most likely to occur. Substantial false mineral group detections 

related to the algorithm itself have not been observed in the 

testing performed for this study, as the mineral groups being 

identified are quite common on the Earth’s surface.

As Landsat scenes are analyzed on a scene-by-scene basis, 

and as scene-wide statistics are used to calculate the values used 

for clipping off the low-end values of each index, material identifications across scene boundaries will not always be continuous. In addition, seasonal and annual variation between adjacent 

scenes may cause visible variation in mapped materials in scene 

overlap areas related to changes in vegetation abundance and 

dryness, soil moisture content, and atmospheric conditions.

Conclusions

Despite deficiencies in the quality and availability of 

Landsat TM data, the products generated by the automated 

process are ideal for efficient application to mineral resource 

and geoenvironmental assessments at regional and national 

scales. It is recommended that the Landsat TM-derived results 

be integrated with geologic maps and mineral maps generated 

from analysis of data from ASTER and (or) hyperspectral sensors to yield an optimal, hybrid map of hydrothermally altered 

areas. Such integration will improve accuracy and allow for 

characterization of alteration type.

Acknowledgments

The author would like to thank the USGS Earth 

Resources Observation and Science Center (EROS) for providing the Landsat 7 data, and Robert McDougal and Douglas 

Yager of the USGS for their helpful reviews of the manuscript, 

maps, and digital data.

Disclaimer

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government 

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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 and Environmental Resources Science 

Center Web site at:

http://minerals.cr.usgs.gov/
Rockwell—Automated Mapping of Mineral Groups and Green Vegetation from Landsat Thematic Mapper Imagery—Scientific Investigations Map 3252