Comparative Mineral Mapping in the Colorado Mineral Belt 

using AVIRIS and ASTER Remote Sensing Data

Scientific Investigations Map 3256

U.S. Department of the Interior

U.S. Geological Survey
Photo Caption. Bleached, argillized rocks of the Mount Cumulus stock at the summit of Red 

Mountain with the high ridgeline of the Never Summer Mountains in the background to the west.
Comparative Mineral Mapping in the 

Colorado Mineral Belt using AVIRIS

and ASTER Remote Sensing Data

By Barnaby W. Rockwell

Pamphlet to accompany

Scientific Investigations Map 3256

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, Comparative mineral mapping in the Colorado Mineral Belt using AVIRIS and ASTER remote 

sensing data: U.S. Geological Survey Scientific Investigations Map 3256, 8 p. pamphlet, 1 map sheet, scale 1:150,000, 

http://pubs.usgs.gov/sim/3256/.
iii

Contents

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

GeoPDF and ERDAS IMAGINE File Formats..............................................................................................1

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

Descriptions of map sheet and associated ERDAS IMAGINE raster image files...............................2

Map 1 ......................................................................................................................................................2

Title.................................................................................................................................................2

Filename ........................................................................................................................................2

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

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

Map 2 ......................................................................................................................................................3

Title.................................................................................................................................................3

Filename ........................................................................................................................................3

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

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

Comments......................................................................................................................................3

Map 3 ......................................................................................................................................................3

Title.................................................................................................................................................3

Filename ........................................................................................................................................3

Description....................................................................................................................................4

Purpose..........................................................................................................................................4

Map 4 ......................................................................................................................................................4

Title.................................................................................................................................................4

Filename ........................................................................................................................................4

Description....................................................................................................................................4

Purpose..........................................................................................................................................4

Comments......................................................................................................................................5

Map 5 ......................................................................................................................................................5

Title.................................................................................................................................................5

Filename ........................................................................................................................................5

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

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

Additional Data...............................................................................................................................................7

Acknowledgments.........................................................................................................................................7

References......................................................................................................................................................7
iv

Tables

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

2. Boolean logic used to combine results of a) separate iron mineral, vegetation,

and water analysis (mainly VNIR absorptions, see Map 3) and b) clay, sulfate,

mica, carbonate, and hydrous silica mineral analysis (mainly SWIR absorptions,

see Map 4) into a single map of hydrothermal alteration type (Map 5)...............................6

Map

Map of modeled hydrothermal alteration type derived from ASTER data: Comparative 

mineral mapping in the Colorado Mineral Belt using AVIRIS and ASTER remote 

sensing data ............................................................................................................................. 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

This report presents results of interpretation of spectral remote sensing data covering the eastern Colorado 

Mineral Belt in central Colorado, U.S.A., acquired by the 

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 

and Advanced Spaceborne Thermal Emission and Reflection 

Radiometer (ASTER) sensors. This study was part of a multidisciplinary mapping and data integration project that focused 

on long-term resource planning by land-managing entities 

in Colorado. The project supported studies related to understanding the availability of mineral and energy resources, the 

geochemical and environmental effects of mining and undisturbed, hydrothermally altered rocks, the geoenvironmental 

effects of wildfires, geologic controls of groundwater availability and quality, and geologic hazards (Klein, 2007). 

The results relating to mineral occurrence and distribution were an important source of data for studies documenting 

the effects of mining and un-mined, altered rocks on aquatic 

ecosystems at the watershed level (Schmidt and others, 2012). 

The results were also part of a database that integrated geologic data from a variety of sources to identify watersheds in 

which there are hydrothermally altered rocks, mine waste, and 

(or) or tailings containing mineralogy associated with sulfide 

mineral oxidation (Church and others, 2012). These studies 

demonstrated a high correlation between metal concentrations in streams and the presence of hydrothermal alteration 

and (or) pyritic mine waste as determined by analysis of the 

remote sensing data presented herein. The mineral mapping 

results were also used to delineate permissive areas for various 

mineral deposit types.

GeoPDF and ERDAS IMAGINE File 

Formats

Digital mineral and vegetation mapping results are 

presented in GeoPDF and ERDAS IMAGINE format 

(.img) (Intergraph, 2012). 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-format map sheet. With or without the toolbar, 

multiple, geographically referenced map layers within the 

GeoPDF file can be turned on and off to generate custom 

maps for viewing and printing using the Layers tool on the 

left side of the Acrobat Reader window. The GeoPDF file 

contains five layers of AVIRIS- and ASTER-derived mineral 

and vegetation maps (identified as Maps 1–5 in the Layers 

list), color composite images of the three AVIRIS flight 

lines, and a layer showing areas that were excluded from the 

ASTER data analysis because of data errors, snow, clouds, 

and cloud shadow. Map 5, showing modeled alteration type 

and key mineral groups derived from Boolean combination 

of Maps 3 and 4, is displayed in the GeoPDF by default when 

the file is opened. Explanations of the colors assigned to 

material detections and modeled alteration classes for the five 

maps are provided at the bottom of the map sheet. Subsidiary 

map layers showing the 1:500,000-scale geologic map of 

Colorado (Tweto, 1979; Green, 1992), administrative boundaries, roads, surface hydrology, and landmarks (for example, 

National Parks) are also included. These layers, derived from 

ArcGIS Online (Esri, 2012), are included to make the maps 

more useful by displaying geographic features of interest, but 

may not conform to U.S. Geological Survey (USGS) map 

standards, and in some cases may interfere with viewing the 

AVIRIS- and ASTER-derived mineral and vegetation maps. 

Several of these geographic map layers are scale-dependent; 

for example, more detailed, lower-order watercourses in the 

“Water” layer appear when viewed at larger scales.

The ERDAS-format files are suitable for integration with 

other geospatial data in Geographic Information Systems (GIS). 

The files have been attributed according to identified or modeled 

material (mineral assemblage, vegetation class, or alteration 

type), which enables the color-coded pixels to be interactively 

queried with the 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). 

For the attribute information to appear in the ArcMap Table of 

Contents, open the ERDAS file in ArcMap and symbolize using 

Unique Values for the “Materialid” or “Alteration” value fields. 

Metadata compliant with Federal Geographic Data Committee 

(FGDC) standards have been included for the ERDAS-format 

files, and contain important information regarding geographic 

coordinate systems, attributes describing identified materials, 

and bibliographic cross-references. 

Comparative Mineral Mapping in the Colorado Mineral 

Belt using AVIRIS and ASTER Remote Sensing Data

By Barnaby W. Rockwell
2 Comparative Mineral Mapping in the Colorado Mineral Belt using AVIRIS and ASTER Remote Sensing Data

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, a common gangue mineral important for geoenvironmental studies, is associated with hydrothermal alteration and many types of ore deposits. Weathering of pyrite 

produces sulfuric acid that can leach and transport potentially toxic metals into the surface water and groundwater 

systems. 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. Clay and ferric 

iron minerals including jarosite, copiapite, melanterite, and 

goethite are also produced during the supergene weathering 

process. Such iron minerals can be identified and differentiated using spectroscopic remote sensing data such as that 

acquired by the AVIRIS sensor, but the low spectral resolution of ASTER data permit only the detection of undifferentiated ferric iron (Rockwell, 2009). Anomalous concentrations 

of ferric iron minerals in volcanic rocks and mine waste can 

be used as a proxy for pyrite occurrence. Mine waste and 

tailings in which no minerals were detected are less likely to 

represent exposed, nonpoint sources of acidic solutions, or 

are too small in size to be mapped using the remote sensing 

data. The maps can also be used to evaluate mineral exposure 

at mine sites that have undergone remediation prior to the 

acquisition of the image data. 

The mineral maps can be best interpreted in conjunction with geologic maps in a GIS environment. The mineral 

mapping results can be compared to the regional geologic 

map included in the GeoPDF-format map sheet to determine 

the lithologic or alluvial unit(s) in which an identified mineral 

occurrence is located. An explanation for the lithologic units 

on this geologic map is available at http://geology.about.com/

library/bl/maps/n_statemap_COexp.htm. The origin of an 

identified mineral occurrence can begin to be understood by 

recognizing the host rock lithology or material type (table 1). 

Most occurrences of hydrothermal alteration contain phyllosilicate and (or) sulfate minerals with or without iron, but a vast 

majority of identified mineral occurrences shown on the maps 

are unrelated to alteration. Within volcanic rocks and mine 

waste, the presence of hydrolytic alteration and (or) supergene 

weathering of pyrite can be inferred if both ferric iron and 

phyllosilicate and (or) sulfate minerals are present, especially 

in the same pixel. Acid-neutralizing propylitic alteration can 

be inferred if calcite, chlorite, epidote, and ferrous ironbearing minerals are detected (Yager and others, 2008). The 

maps may aid in the refinement of geologic maps by 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 maps.

Host rock or

material type

Highest-probability origin of

mineral occurrence

volcanic fresh and weathered hydrothermal alteration

plutonic lithology (commonly mica) or alteration

sedimentary lithology (commonly mica +/- clays +/- 

carbonates), diagenesis

metamorphic lithology, weathering, metamorphic grade 

and type

alluvium erosion and weathering

mine waste and 

tailings

alteration and (or) supergene weathering of 

pyritic rocks

Descriptions of map sheet and 

associated ERDAS IMAGINE raster 

image files

The map sheet contains various data layers that are 

referred to as Maps 1 through 5 in the GeoPDF layer list. 

These maps are also provided in GIS-ready ERDAS IMAGINE format with associated metadata, and are described below.

Map 1:

Title:

AVIRIS-DERIVED MAP OF IRON-BEARING MINERAL 

ASSEMBLAGES AND WATER

Filename:

aviris00_mosaic_1um_arcspline_utm_rgbclus_zfix_rle.img

Description:

This image shows the spatial occurrence of iron-bearing minerals and water identified using AVIRIS data based on spectral 

similarity between image spectra and laboratory reference 

spectra of well-characterized materials and material mixtures. 

These materials have diagnostic spectral absorption features 

primarily in the visible and near-infrared (VNIR) regions of 

the electromagnetic spectrum. 
Descriptions of map sheet and associated ERDAS IMAGINE raster image files 3

The image is a mosaic of three flight lines of high-altitude 

AVIRIS data acquired from 20 km altitude on June 21, 

2000. These data have a ground instantaneous field-of-view 

(GIFOV) of approximately 17 m/pixel, although this value 

will increase slightly with altitude of the ground surface. The 

data were calibrated to reflectance using techniques described 

by Rockwell and others (2002a; 2002b) and field spectral 

data of a parking lot at the Copper Mountain ski area located 

10 km west of Breckenridge. The data were analyzed using 

a modified expert system (Rockwell, 2002) for comparing 

the spectral shapes of image spectra to laboratory reference 

spectra using least-squares curve fitting (Rockwell and others, 

2005, 2006; Rockwell, 2009).

The AVIRIS flight lines were georeferenced to orthoimagery using control points and a cubic spline algorithm before 

mosaicking. The image file uses lossless run-length encoding 

compression to reduce file size.

Purpose:

The high spectral resolution of AVIRIS data permits the 

identification of specific species of iron-bearing minerals 

that is impossible with ASTER data (Rockwell, 2004; 2009). 

Identified areas of jarosite are most likely to contain oxidizing pyrite and (or) other sulfide minerals that generate sulfuric 

acid during weathering. Identified goethite may indicate rocks 

with less abundant or fully oxidized pyrite. The image has 

been attributed by pixel value with material identification 

data that can be queried in most image processing and GIS 

software packages. This attribute information, including pixel 

values and assigned colors, is described in the accompanying 

metadata. 

Map 2:

Title:

AVIRIS-DERIVED PHYLLOSILICATE, SULFATE, 

CARBONATE, SOROSILICATE, AND NESOSILICATE 

MINERALS

Filename:

aviris00_mosaic_2um_arcspline_utm_rgbclus_zfix_rle.img

Description:

This image shows clay, sulfate, mica, carbonate, Mg-OH, 

and nesosilicate (topaz) minerals identified using AVIRIS 

data based on spectral similarity between image spectra and 

laboratory reference spectra of well-characterized materials and material mixtures. These minerals have diagnostic 

spectral absorption features primarily in the shortwave 

infrared (SWIR) region of the electromagnetic spectrum. 

See description of Map 1 for information on AVIRIS data 

processing and analysis. 

Purpose:

The purpose of the image is to display the spatial distributions 

of pure and mixed clay, sulfate, mica, carbonate, Mg-OH, and 

nesosilicate (topaz) minerals. Mineral assemblages containing 

kaolinite, pyrophyllite, topaz, and jarosite are most likely to be 

related to hydrothermal alteration and (or) supergene weathering of pyritic rocks. The image has been attributed by pixel 

value with material identification data that can be queried in 

most image processing and GIS software packages. This attribute information, including pixel values and assigned colors, 

is described in the accompanying metadata.

Comments:

A small occurrence of topaz was mapped in the center of the 

Climax molybdenum mine north of Leadville, and is shown 

on the map in a dark red color. More extensive exposures of 

quartz-sericite-pyrite (QSP) alteration were identified to the 

north of the topaz exposure, and are expressed on the map as 

the “jarosite + sericite +/- gypsum” mineral mixture shown in 

cyan. Topaz occurs as an accessory mineral in the ore assemblages of many Climax-type porphyry molybdenum deposits 

(Ludington and Plumlee, 2009), and in fluorine-rich rhyolites 

and granites (Haapala, 1997).

A northeast-trending fracture zone containing pyrophyllite, 

kaolinite, sericite, and jarosite (after pyrite) was identified on 

a high ridge forming the southern end of Grizzly Gulch, 8.7 

km south of Saint Elmo and 1.6 km east-northeast of Pomeroy 

Mountain in the Sawatch Range. The fracture zone is intensely 

iron stained and occurs within intermediate composition 

rocks of the Mount Princeton pluton in an area permissive for 

porphyry molybdenum deposits. The ASTER data analysis 

identified ferric iron of the highest abundance (Map 3) and 

low-abundance Al sericite (Map 4) in this area. The argillized 

fracture zone is too narrow to be identified using ASTER 

SWIR data having a 30-m GIFOV (ground pixel size).

Map 3:

Title:

ASTER-DERIVED IRON-BEARING MINERAL ASSEMBLAGES, VEGETATION, AND WATER

Filename:

cmb_fe-veg_bootcal_8scene_mosaic_02top_3-10_geofix_

rgbclus_filt3focmaj_rle.img
4 Comparative Mineral Mapping in the Colorado Mineral Belt using AVIRIS and ASTER Remote Sensing Data

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. 

These materials have diagnostic spectral absorption features 

primarily in the visible and near-infrared (VNIR) regions of 

the electromagnetic spectrum.

The image is a mosaic of eight ASTER scenes from two overpasses, or paths, of the Terra satellite. The four scenes from 

the western path were acquired on September 2, 2002, and the 

four scenes from the eastern path were acquired on September 

14, 2003. Reflectance-calibrated AVIRIS data over Georgetown to the east of the study area were used as a reference 

base to calibrate the ASTER data to reflectance using a “bootstrap” method described by Rockwell and others (1999). The 

ASTER scenes were analyzed using methodologies described 

by Rockwell (2009). An example of a seamless, two-scene 

mosaic of ASTER-derived mineral mapping results produced 

by the same analysis methodology is provided by Rockwell 

(2010). A different contrast stretch in which green vegetation 

appears in hues of slightly brighter green was applied to the 

northeasternmost scene to highlight the geographic coverage 

of a single ASTER scene.

The mosaic has been georeferenced to orthoimagery using 

control points and a cubic spline algorithm. The scenes from 

the western path were acquired with an ~8.5° off-nadir pointing angle, and thus had positional errors of up to 400 m. The 

image file uses lossless run-length encoding compression to 

reduce file size. The results were simplified using a 3 × 3 focal 

majority filter to reduce the occurrence of non-contiguous, 

single-pixel material identifications. 

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 

oxidation of hydrothermal pyrite in unmined rocks or 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. Brighter shades of color indicate 

increased least-squares fit × depth of diagnostic absorption 

features, which correlates with mineral group abundance 

given a constant grain size. Identified green, dry and green, 

and dry vegetation can be used to evaluate vegetative cover 

at the times of ASTER data acquisition (Rockwell, 2009). 

The image has been attributed by pixel value with material 

identification data that can be queried in most image processing and GIS software packages. This attribute information, 

including pixel values and assigned colors, is described in the 

accompanying metadata.

Map 4:

Title:

ASTER-DERIVED MAP OF PHYLLOSILICATE, SULFATE, CARBONATE, SOROSILICATE, AND SILICA 

MINERALS

Filename:

aster_cmb_swir_mosaic_2-08_3199gf_overlapfix2.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. These minerals have diagnostic spectral absorption 

features primarily in the shortwave infrared (SWIR) region of 

the electromagnetic spectrum. Identification of jarosite was 

based on analysis of diagnostic ferric iron and Fe-OH absorptions in the VNIR and SWIR, respectively (Rockwell, 2009). 

ASTER data processing and analysis procedures are identical 

to those used to generate the data shown in Map 3 other than 

the majority filtering, which was not applied.

Mineral identifications on north-facing alpine slopes can be 

erroneous because of poor solar illumination and SWIR detector instability over dark (low albedo) surfaces. North-facing 

slopes, many of which were in shadow at the time (approximately 10 a.m.) of data acquisition, were isolated using an 

aspect image derived from a digital elevation model (DEM) of 

30-m resolution. A mask was made of surfaces with azimuths 

between 270° and 73° and elevations greater than 3,199 m. 

Mineral identifications in Maps 4 and 5 that occurred within 

the mask area were attributed to indicate their potentially erroneous nature. For example, high-abundance sericite occurring 

on these poorly-illuminated surfaces was renamed “primarily 

false detections of high-abundance sericite on shadowed, 

north-facing slopes” on Map 4. 

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. This attribute information, including pixel values and assigned colors, is described 

in the accompanying metadata. 
Descriptions of map sheet and associated ERDAS IMAGINE raster image files 5

Comments:

Kaolinite was verified on the upper, south face of Radial 

Mountain west of the Never Summer Mountains (18 km northnorthwest of the town of Grand Lake) within intermediatecomposition rocks of the Radial Mountain stock; on the summit of Red Mountain in the Never Summer Mountains within 

felsic rocks of the Mount Cumulus stock; on the southwestern 

flank of Jenkins Mountain in the Sawatch Range above the 

Pieplant mine in Taylor Park, within felsic, leucocratic rocks 

of the Twin Lakes stock; and within poorly-consolidated 

alluvial and colluvial deposits of the Dry Union Formation 

(Miocene to Pliocene) 2 km west of the town of Granite in the 

Arkansas River Valley.

Sericite (fine-grained muscovite) and minor chlorite were 

identified using laboratory spectroscopy in cobbles of porphyritic intrusive rock within Holocene to Pleistocene alluvium 

8.7 km north-northwest of Granite in the Arkansas River Valley. The ASTER SWIR data of this area are characterized by 

absorptions at 2.20 and 2.34 micrometers (caused by sericite 

and calcite, respectively), and the area was identified in Map 4 

as “sericite (or smectite) + calcite (and/or chlorite, epidote).”

No advanced argillic alteration (alunite, pyrophyllite) identified using the ASTER data has been verified in the field or 

laboratory. Pixels identified as alunite and (or) pyrophyllite 

with the ASTER SWIR data are most likely areal mixtures of 

well-crystallized kaolinite and dry vegetation. Such mixtures 

can result in a spectral response similar to advanced argillic 

alteration minerals (Rockwell, 2009; John and others, 2010), 

although the presence of these minerals cannot be ruled out 

without field verification.

Tailings at the mill located 26 km north of Silverthorne 

associated with the Henderson molybdenum mine have deep 

spectral absorption at 2.17 micrometers consistent with alunite 

and pyrophyllite, but it is highly unlikely that these minerals 

exist there. These tailings have not been field checked. Lowabundance ferric iron minerals were also identified in the 

tailings deposit.

Pixels having a similar spectral response were identified at 

the western end of the Twin Lakes southwest of Leadville, 

and are shown in red on Maps 4 and 5. The water level in the 

lakes was low at the time of ASTER data acquisition, and the 

western end of the western lake was exposed. This area was 

covered by water in 2011. Ferric iron minerals (Map 3) identified there may be related to oxidation of pyrite derived from 

intense phyllic alteration in the Grizzly Peak caldera 12–20 

km to the west-southwest of the Twin Lakes. Acid rock drainage derived from this alteration is known to have affected the 

geochemistry of South Fork Lake Creek that drains eastward 

into the Twin Lakes (Neubert and others, 2011). 

Map 5:

Title:

ASTER-DERIVED ALTERATION TYPE AND KEY 

MINERAL ASSEMBLAGES GROUPED BY RELATIVE 

NET ACID PRODUCTION AND ACID NEUTRALIZING 

CAPACITY

Filename:

aster_cmb_mosaic_alteration-merge_rle.img

Description:

This image shows the results of a methodology designed to 

simplify and combine the mineral occurrence information 

contained in Maps 3 and 4 into a single map showing the 

spatial distribution of hydrothermal alteration types and (or) 

carbonate rocks. This methodology is similar to that described 

by Rockwell (2012, sheet 4). 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 and importance to 

geoenvironmental modeling.

In the map explanation, alteration types and key mineral 

assemblages have been grouped by relative net acid production 

(NAP) and acid-neutralizing capacity (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 identified from both the VNIR (Map 3) and 

SWIR (Map 4) spectral regions that are analyzed separately. 

Therefore, a suite of Boolean equations was developed that 

combines the mineralogic information from the two spectral 

regions (table 2) into a single map. 
Table 2. Boolean logic used to combine results of a) separate iron mineral, vegetation, and water analysis (mainly VNIR absorptions, see Map 3) ancarbonate, and hydrous silica mineral analysis (mainly SWIR absorptions, see Map 4) into a single map of hydrothermal alteration type (Map 5). Logic b) are considered separately and are linked by an inferred AND operator. For example, a pixel will be modeled as Phyllic (QSP) alteration if minor or midentified AND either kaolinite + smectite, kaolinite + sericite, or high-abundance sericite. Logic in blank cells was ignored. a) Iron mineral, vegetation, and water analysis b) VNIR-SWIR mineral analysis major ferric iron minor ferric iron kaolinite + smectite alunite alunite + kaolinite kandite clay kaolinite + sericite Al sericite Fe/Mg sericite, or muscovite + chlorite jarosite smectite calcite dolomit

Advanced argillic OR OR Argillic (or clay-bearing rocks) NOT NOT OR OR OR OR Argillic + ferric iron OR OR OR OR Phyllic (QSP) OR OR OR OR OR (high abundance)* Pyritic (jarosite) X Propylitic or carbonate rocks +/- sericite or smectite NOT NOT OR OR Propylitic or carbonate rocks +/- sericite or smectite + ferric iron OR OR OR OR 

Regional propylitic to weak QSP OR OR X (low abundance) Possible regional propylitic (sericite + chlorite) X Sericitic (mainly lithologic) NOT NOT X (high abundance) AND *Modeled phyllic alteration on north-facing alpine slopes where SWIR mineral detections may be erroneous has been attributed as “potential phyllic (QSP) on north-facing slop
References 7

Where they co-occur with ferric iron minerals from Map 3, 

potentially false occurrences of high-abundance Al sericite on 

north-facing alpine slopes (see Map 4 description above) are 

labeled as “potential phyllic (QSP) on north-facing slopes” on 

Map 5. See description of Map 4 for procedures used to isolate 

north-facing, high-elevation slopes.

Purpose:

The image shows the occurrence of alteration types, key 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. This attribute 

information, including pixel values and assigned colors, is 

described in the accompanying metadata. 

Additional Data

A raster image that indicates areas excluded from the 

ASTER data analysis because of corrupted SWIR data (detector “scratch,” Rockwell, 2009; 2012), snow at high elevations 

(eastern ASTER path only), clouds, and cloud shadow is 

included in the GeoPDF map sheet and in ERDAS IMAGINE 

format. The image is named “aster_mosaic_nodata3_all_edit3-

lvcld_rle.img,” and has been attributed for querying by most 

image processing and GIS software packages. Metadata 

includes pixel values associated with the excluded areas.

Acknowledgments

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

Land Processes Distributed Active Archive Center (LP DAAC), 

the National Aeronautics and Space Administration (NASA) 

Earth Observing System Data and Information System (EOSDIS), and Japan’s Earth Remote Sensing Data Analysis Center 

(ERSDAC) for providing the ASTER data used in this study. 

The author would also like thank the AVIRIS team at NASA Jet 

Propulsion Laboratory for providing the AVIRIS data. Special 

thanks go to K. Eric Livo of the USGS for providing the field 

spectral data of the parking lot at Copper Mountain ski area and 

reflectance-calibrated AVIRIS data of the Georgetown area that 

were used to calibrate the AVIRIS and ASTER data, respectively, to reflectance prior to analysis. Robert McDougal and 

Douglas Yager of the USGS provided helpful suggestions for 

the manuscript, maps, and digital data. 

References

Church, S.E., San Juan, C.A., Fey, D.L., Schmidt, T.S., Klein, 

T.L. DeWitt, E.H., Wanty, R.B., Verplanck, P.L., Mitchell, 

K.A., Adams, M.G., Choate, L.M., Todorov, T.I., Rockwell, 

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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—Comparative Mineral Mapping in the Colorado Mineral Belt using AVIRIS and ASTER Remote Sensing Data—Scientific Investigations Map 3256