U.S. Department of the Interior 

U.S. Geological Survey 

Scientific Investigations Rep
Remote Sensing Characterization of the 

Animas River Watershed, Southwestern 

Colorado, by AVIRIS Imaging Spectroscopy

By J.B. Dalton, D.J. Bove, and C.S. Mladinich 

Scientific Investigations Report 2004-5203

U.S. Department of the Interior

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

Gale A. Norton, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2005

Posted online October 2005, Version 1.0

For more information about the USGS and its products:

Telephone: 1-888-ASK-USGS

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Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Dalton, J.B., Bove, D.J., and Mladinich, C.S., 2005, Remote sensing characterization of the Animas River watershed, 

southwestern Colorado, by AVIRIS imaging spectroscopy : U.S. Geological Survey Scientific Investigations 

Report 2004-5203, 49 p. Available at URL: http://pubs.usgs.gov/sir/2004/5203/
Contents

Abstract ................................................................................................... 1

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

Acknowledgments ....................................................................................... 3

Image Format Considerations and Associated Files ................................................ 3

Image Color Tables and Printing Concerns ...................................................... 3

Image Format Considerations ..................................................................... 3

Brief Geologic History of the Study Area............................................................... 4

Mineralization and Hydrothermal Alteration ................................................... 4

Mo-Cu Porphyry Alteration and Mineralization (26–25 Ma).................................... 4

Acid-Sulfate Alteration and Mineralization (23 Ma) .......................................... 6

Mineralization and Intrusions (<18 Ma) ......................................................... 6

AVIRIS Data Acquisition and Calibration............................................................... 6

Instrument Description ........................................................................... 6

Watershed Coverage and Study Area............................................................ 7

Geometric Considerations ........................................................................ 7

Atmospheric Correction ........................................................................... 8

Ground Calibration Methods ..................................................................... 8

Engineering Correction for Platform Motion ................................................... 8

Spatial Corrections and Orthorectification ...................................................... 8

Classification Methodology: The USGS Tetracorder Algorithm .................................... 9

Infrared Spectral Comparison ..................................................................... 9

Continuum Removal and Band-Fitting Methodology .......................................... 11

Potential Sources of Error ........................................................................ 15

Similar Mineral Signatures: Calcite, Epidote, and Chlorite (CEC).............................. 15

Tetracorder Results for the Animas River Watershed ................................................ 16

Group 1: Iron Oxide Family ........................................................................ 16

Group 2: Alteration Products ..................................................................... 17

Group 3: Snow and Vegetation Distributions ................................................... 18

Groups 4 and 5: Acid-Generating and Acid-Neutralizing Mineral Assemblages............ 18

Imaging Spectroscopy Tour of Study Area ............................................................ 18

Ironton and Red Mountain Scenes ............................................................... 18

Animas Forks and the Animas River Upstream from Silverton .............................. 28

Silverton and Molas Lake Scenes ............................................................... 28

Engineer Mountain and Purgatory Scenes ...................................................... 33

Hermosa and Haviland Lake Scenes ............................................................ 35

Overview and Discussion .............................................................................. 35

Conclusions................................................................................................ 39

References Cited.......................................................................................... 46
Figures

1. Map showing AVIRIS flightlines and geographic features in study area .................. 2

2. Map overview of five specific areas discussed in text .................................... 5

3. Sketch of viewing and illumination geometry 

for a scene incorporating topographic relief ................................................ 7

4–8. Graphs showing:

4. Visible to near-infrared spectra of selected iron-bearing minerals .................. 10

5. Visible to near-infrared spectra of selected alteration products ..................... 10

6. Visible to near-infrared spectra of selected vegetation samples ..................... 12

7. Band-fitting procedure used by Tetracorder algorithm ................................. 13

8. Diagnostic spectral absorptions of calcite, chlorite, 

and epidote in the 2-μm region ......................................................... 15

9. Photograph from Red Mountain # 3 facing northwest toward Imogene Basin............ 19

10. Photograph from Red Mountain # 3 facing north toward Crystal Lake..................... 19

11. Photograph from Red Mountain # 3 facing northeast toward Brown Mountain ......... 20

12–31. Maps showing:

12. Geographic features, Red Mountain scene ............................................. 21

13. AVIRIS Group 1 minerals (1-μm region), Red Mountain scene ........................ 22

14. AVIRIS Group 2 minerals (2-μm region), Red Mountain scene ........................ 23

15. AVIRIS Group 3, snow, soil, and vegetation, Red Mountain scene .................. 24

16. AVIRIS Group 4 minerals, Red Mountain scene ....................................... 26

17. AVIRIS Group 5 minerals, Red Mountain scene ....................................... 27

18. Geographic features, upper Animas River near Animas Forks ........................ 29

19. AVIRIS Group 1 minerals (1-μm region), upper Animas River near Animas Forks... 30

20. AVIRIS Group 2 minerals (2-μm region), upper Animas River near Animas Forks... 31

21. AVIRIS Group 5 minerals, upper Animas River near Animas Forks .................. 32

22. Geographic features, Silverton and Molas Lake scene................................. 34

23. AVIRIS Group 1 minerals (1-μm region), Silverton and Molas Lake scene ......... 36

24. AVIRIS Group 2 minerals (2-μm region), Silverton and Molas Lake scene ......... 37

25. AVIRIS Group 5 minerals, Silverton and Molas Lake scene ........................... 38

26. Geographic features, Engineer Mountain scene ....................................... 40

27. AVIRIS Group 1 minerals (1-μm region), Engineer Mountain scene .................. 41

28. AVIRIS Group 3, snow and vegetation, Engineer Mountain scene .................. 42

29. Geographic features, Hermosa and Haviland Lake scene ........................... 43

30. AVIRIS Group 1 minerals (1-μm region), Hermosa and Haviland Lake scene ...... 44

31. AVIRIS Group 3, snow and vegetation, Hermosa and Haviland Lake scene ......... 45

iv
Abstract

Visible-wavelength and near-infrared image cubes of the 

Animas River watershed in southwestern Colorado have been 

acquired by the Jet Propulsion Laboratory’s Airborne Visible 

and InfraRed Imaging Spectrometer (AVIRIS) instrument 

and processed using the U.S. Geological Survey Tetracorder 

v3.6a2 implementation. The Tetracorder expert system utilizes 

a spectral reference library containing more than 400 laboratory 

and field spectra of end-member minerals, mineral mixtures, 

vegetation, manmade materials, atmospheric gases, and additional 

substances to generate maps of mineralogy, vegetation, snow, 

and other material distributions. Major iron-bearing, clay, 

mica, carbonate, sulfate, and other minerals were identified, 

among which are several minerals associated with acid rock 

drainage, including pyrite, jarosite, alunite, and goethite. 

Distributions of minerals such as calcite and chlorite indicate a 

relationship between acid-neutralizing assemblages and stream 

geochemistry within the watershed. Images denoting material 

distributions throughout the watershed have been orthorectified 

against digital terrain models to produce georeferenced image 

files suitable for inclusion in Geographic Information System 

databases. Results of this study are of use to land managers, 

stakeholders, and researchers interested in understanding a 

number of characteristics of the Animas River watershed.

Introduction

The northern part of the Animas River watershed, 

southwestern Colorado, is the site of a coordinated effort by 

several Federal, State, and local agencies to characterize the 

extent and severity of environmental effects from acid mine 

drainage. This water originates both from numerous 

historical mine sites that date back as much as a century or 

more, and from extensive areas of unmined mineralized 

and hydrothermally altered rocks. The headwaters of the 

upper Animas River, Cement Creek, and Mineral Creek 

are associated with the Tertiary San Juan and Silverton 

volcanic calderas, which were cut by large faults and intense 

ring fractures suitable for later mineralization. As part of 

this study by the U.S. Geological Survey (USGS), the 

Jet Propulsion Laboratory (JPL) was contracted to acquire 

AVIRIS data (region outlined in figs. 1, 2) over the San Juan 

Mountains and Animas River watershed. Methods of infrared 

spectral analysis developed at the USGS Denver Spectroscopy 

Laboratory (Clark and others, 1990, 2003) have been applied 

to characterize the surface distributions of a number of 

materials. These AVIRIS mineral maps have been used, in 

conjunction with field geologic mapping, geochemistry, and 

geophysics, to determine the relative extent of natural and 

anthropogenic sources of acidic and metal-bearing surface 

runoff, and their effect on water quality.

The area covered by the data acquisition is bounded 

approximately by long 108°00' to 107°30' W. and lat 37°15' to 

38°00' N. (figs. 1, 2), mainly in Ouray, San Juan, and La 

Plata Counties, Colo. Also see section, “Watershed Coverage and Study Area.”

As part of a larger effort, the purpose of this report is 

to describe the acquisition, calibration, processing, results, 

interpretation, and relevance of the imaging spectroscopy 

data analysis. The processed images detailing surface 

mineralogy, snow, vegetation cover, and areas of acidgenerating and acid-neutralizing potential are included with 

this report as digital image files. These files are intended to 

be used by land managers, stakeholders, and scientists, and 

so have been generated in standard formats. Familiarity 

with the technology, processing, and interpretation of 

these images will enable the reader to utilize them most 

effectively.

The raw imaging spectroscopy data were acquired as 

image cubes by the high-altitude AVIRIS instrument operated 

by JPL. Each pixel in an image cube contains a spectrum 

rather than a single intensity measurement. AVIRIS image 

cubes cover a visible and near-infrared wavelength range of 

0.35 to 2.5 μm (micrometers). The cubes were converted 

to reflectance using standard atmospheric correction and 

ground calibration techniques (Gao and others, 1997; Clark 

and others, 2002). The calibrated cubes were then processed 

using the USGS Denver Tetracorder 3.6a2 algorithm to create 

Remote Sensing Characterization of the Animas River 

Watershed, Southwestern Colorado, by 

AVIRIS Imaging Spectroscopy

By J.B. Dalton, D.J. Bove, and C.S. Mladinich
Telluride

Durango

Hermosa

Silverton

Ouray

5 km

N

!NIMAS 2IVER

6ALLECITOS

2ESERVOIR

Silverton caldera

approximate boundary

Imogene

Basin

Mineral 

Creek

Red Mtn. Creek

Figure 1. Animas River AVIRIS flightlines (red outline) overlaid upon Landsat Thematic Mapper (TM) band 4 imagery. 

Major geographic features are shown. Animas River flows along right-hand edge of AVIRIS study area. Silverton 

caldera is roughly bounded by upper Animas River, Mineral Creek, and Red Mountain Creek. Highly mineralized 

zones of Silverton caldera dominate northeastern section, including upper Animas River headwaters.

 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
preliminary two-dimensional image maps detailing surface 

distributions of materials on the basis of comparison with 

reference spectra from the USGS Digital Spectral Library 

(Clark, Swayze, Gallagher, and others, 1993). The 

preliminary image maps were then evaluated and field-checked. 

Rock and mineral samples acquired during field studies were 

analyzed and added to the spectral library, and the Tetracorder 

process was repeated to refine the image maps. Once the 

results were found to be satisfactory, the final image maps 

were compiled into color-coded overlay images for minerals 

and other materials of interest (such as snow and vegetation). 

These color images were then corrected for motion of 

the AVIRIS sensor, mosaicked together, registered to a map 

base, and orthorectified using a digital terrain model. The 

orthorectified images are included with this report, and a 

considerably reduced subset of the image set is included 

in the figures.

Acknowledgments

The authors express their appreciation to K. Eric Livo, 

Roger N. Clark, Gregg A.S. Swayze, Raymond F. Kokaly, 

Trude V.V. King, Barnaby W. Rockwell, Noel S. Gorelick, 

Andrea J. Gallagher, Stanley E. Church, Wendy M. Calvin, 

Margaret E. Milman, Robert R. McDougal, J. Sam Vance, 

Randall E. Dailey, Douglas B. Yager, George A. Desborough, 

Winfield G. Wright, Robert P. Kucks, James K. Crowley, 

and Jean L. Ryder for assistance with the field expeditions, 

computer processing, image formatting, and geologic 

understanding needed to complete this study. Special thanks 

go to Barbara Ramsey for help with the figures and regional 

maps. This work was funded by the Mineral Resources 

Program of the U.S. Geological Survey.

Image Format Considerations and 

Associated Files

The large size (>1,200 km2) and small-scale complexity 

(< 1 m) of the study area leads to image manipulation 

considerations which make publication and navigation of the 

imaging spectroscopy results in a traditional format unwieldy. 

In order to present as complete an analysis as possible, the 

images are made available in multiple formats. The most 

effective way to utilize the following sections is to access the 

georeferenced images via any image manipulation software 

that allows zoom and pan functions. The reader may then 

view all of the image detail while reading the text and referring 

to topographic maps. There is far too much detail to present 

all of the map contents within the report; rather, examples 

have been prepared that highlight the types of issues addressed 

in the watershed study and illustrate reasonable approaches to 

interpretation of the mineral maps. The georeferenced images 

can be compared to a map base using a GIS program, or 

overlaid on the USGS 7.5-minute topographic map series by 

printing them out at 1:24,000 scale on a large-format printer. 

Each pixel is 17.5 m (meters) on a side, and each flightline 

is 614 pixels wide. Whereas the entire study area lies within 

the Durango 1:250,000-scale map, this scale is too compact to 

view many of the details in the AVIRIS data. The following 

7.5-minute maps will be most useful: Ouray, Ironton, 

Handies Peak, Ophir, Silverton, Howardsville, Engineer 

Mountain, Snowdon Peak, Electra Lake, and Hermosa 

(U.S. Geological Survey, Denver). Additional neighboring 

maps may be desired to completely cover the margins of the 

study area but are not essential to utilize the information 

presented in this report.

Image Color Tables and Printing Concerns

The colors used for particular material classes in the 

images were arrived at through several years experience 

working with AVIRIS data in many locations. They represent 

a balance between several criteria, including the capabilities of 

display monitors, the need to discriminate adjacent pixels by 

eye, the limitations of human vision, aesthetic concerns, and 

the desire to include as many materials as possible in a scene. 

It was also important that related minerals be represented by 

related colors—or, in some cases, by highly contrasting colors. 

The most demanding requirement was the fact that color 

printers in general are not capable of perfectly duplicating 

the colors seen on a display monitor. The colors used for this 

study produce the closest attainable correspondence between 

display colors and ink colors. At the time of publication, not 

all commercially available color printers faithfully reproduce 

the colors and details required for utilization of these images; 

some printers may produce images with too little contrast or 

color discrimination. The reader is advised to print the color 

keys provided and examine them before printing the full-scale 

images. The page-size mineral maps presented here are also 

compromised in that they have been reduced to half-size to 

fit on the page. The resolution of these images is such that 

adjacent pixels must be averaged together to print them in an 

8.5×11 inch format, and so the printed figures will of a necessity 

be less clear than the digital versions. If a suitable printer cannot 

be located, it is advisable to deal directly with the digital 

images. Many image manipulation software programs allow 

the user to modify image color tables to suit various needs; 

this is recommended only as a last resort and should be 

conducted with caution.

Image Format Considerations

Several image formats were utilized in the analysis leading 

to this report. Translating AVIRIS infrared image cubes into 

ordinary images proceeds via several steps, each with its own 

attendant complications. Because of the number of materials 

being considered and the need for many colors to display 

Image Format Considerations and Associated Files 
them, each location must be presented as several images. 

A further complication arises from the fact that the AVIRIS 

flightlines were not acquired in a strictly north-south 

orientation (fig. 1). As a result, the georeferenced image 

mosaics contain a great deal of blank space. In order to 

accommodate all the data in a usable format, the discussion 

that follows will be accompanied by images displaying 

highlights of the study region. The locations of these images 

are shown in figures 1 and 2. 

The classification images are categorized according 

to breakdowns detailed in the section on Tetracorder 

results: Iron Oxide Family (Group 1) minerals, Alteration 

Products (Group 2), Snow and Vegetation (Group 3), 

Minerals Associated with Acid Generation (Group 4), and 

Minerals Associated with Acid-Neutralizing Potential 

(Group 5, Calcite-Chlorite-Epidote-Sericite). Each group 

is presented on a black background for improved visibility; 

the georeferenced images can be imported to a GIS database 

and manipulated accordingly. For field use, navigation, and 

comparison to map bases and other image products, however, 

it is often more useful to have the color-coded material results 

overlaid on a grayscale image of the scene. This shows 

roads, water bodies, and other useful landmarks in a format 

that is more intuitive than the pure classification results. 

Georeferenced grayscale images are included with the pure 

classification results. However, within the text, only the 

grayscale images will be used. This is more than sufficient to 

follow the discussion, and results in a readable paper which 

is still useful and informative without recourse to an image 

display device.

Brief Geologic History of the 

Study Area

The majority of the report focuses on the geology of areas 

A, B, and C (fig. 2), principally the drainage areas of the upper 

Animas River, Cement Creek, and Mineral Creek. This area 

lies in the western part of the mid- to late-Tertiary age San Juan 

volcanic field (Lipman and others, 1976; Bove and others, 2000). 

The geology of areas D and E (fig. 2) is different; only brief 

geologic comments are included herein, in the section, 

“Imaging Spectroscopy Tour of Study Area,” including 

comparisons with the more strongly mineralized areas. The local 

geology is largely lavas and related volcanic rocks associated 

with the San Juan caldera and the younger Silverton caldera 

(Steven and Lipman, 1976). Collapse of these calderas created 

ring fractures that provided conduits for later episodes of 

intense hydrothermal alteration and mineralization commonly 

associated with dacitic to rhyolitic intrusions (Casadevall and 

Ohmoto, 1977; Lipman and others, 1976). More recent geologic 

activity has been dominated by uplifts during the Neogene 

(Steven and others, 1995), followed by downcutting and the 

formation of the upper Animas River and its tributaries, with 

brief episodes of sediment deposition. 

Gold was discovered in the San Juan Mountains in 

1871, and within the next two decades, upwards of a thousand 

mining claims were staked in the upper Animas River and 

Animas River tributaries upstream of the town of Silverton. 

The Denver and Rio Grande Railroad was extended from 

Durango to Silverton in 1882; ore production continued at 

various levels until 1991, when the Sunnyside mine directly 

upstream from Silverton was closed (Bird, 1999). Remediation of private holdings in the region is ongoing, and this 

project is partly concerned with evaluating remediation needs 

of the area, including the thousands of historical prospects 

and mines on both Federal and private properties.

Mineralization and Hydrothermal Alteration

The collapse of the San Juan caldera, the associated 

Uncompahgre caldera, and the Silverton caldera at 28–27 

Ma provided a favorable structural environment for later 

mineralization and hydrothermal activity that postdates these 

calderas by about 5–15 million years (Lipman and others, 

1976; Bove and others, 2001). In general, hydrothermal 

activity temporally associated with caldera development 

caused regional propylitization characterized by the presence 

of chlorite, epidote, calcite, and illite, along with incipiently 

altered primary feldspars. This alteration assemblage, which 

characterizes a large portion (fig. 2, A, B, C) of the study area 

of this report, is generally only very weakly pyritic and is 

important for its acid-neutralizing potential (Bove and others, 

2000). Most mineralization and associated hydrothermal 

alteration in this area are temporally and genetically associated 

with three major episodes of high-level magmatism between 

about 27 and 10 Ma (Bove and others, 2001).

Mo-Cu Porphyry Alteration and Mineralization 

(26–25 Ma)

The earliest important mineralizing and related 

hydrothermal event within the study area occurred about 

26–25 Ma and was related to a large swarm of calc-alkaline 

intrusions emplaced over a broad region of the western 

San Juan Mountains (Bove and others, 2001). The only 

known mineralized hydrothermal system of this type and 

age within the study area involves peak 3,792 m, situated 

between the South and Middle Forks Mineral Creek, and 

its immediate area. However, several large zones of weak 

sericite-pyrite (WSP) to quartz-sericite-pyrite (QSP) altered 

rock related to 26 Ma granitic to quartz monzonite 

intrusions are present in a zone south of the upper Animas 

River from Cunningham Creek east to Maggie Gulch. An 

area comprising more than 2 km2 of intensely altered and 

pyritized rock is related to a 25 Ma low-grade porphyry 

Mo-Cu (molybdenum-copper) hydrothermal system. The 

main zone of Mo-Cu anomalies is coincident with intense 

QSP-altered rock, high-density quartz-stockwork veining, 

abundant gypsum, and as much as 5 volume percent 

 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
A B

D C

E

5 km

N

Figure 2. Overview of AVIRIS imagery for Animas River watershed: A, Ironton and Red Mountain; B, 

Animas Forks; C, Silverton and Molas Lake; D, Engineer Mountain; E, Hermosa. This is a mosaic of Group 

1 materials, overlaid on a grayscale and having the same color scheme and explanation as figure 13.

Brief Geologic History of the Study Area   
disseminated sulfides (McCusker, 1982). QSP-altered rock 

grades outward through weak sericite-pyrite and into propylitized 

rock (Ringrose and others, 1986; Bove and others, 1998). 

Unpublished exploration data (McCusker, 1982) indicate 

that oxidation and supergene leaching is present to depths 

of as much as 100 m below the mountain tops. Secondary 

minerals formed within this zone include hematite, goethite, 

ferrimolybdenite, and minor secondary copper sulfides, 

including chalcocite, digeonite, and covellite.

Acid-Sulfate Alteration and Mineralization 

(23 Ma)

The Animas River watershed study area hosts two large 

acid-sulfate hydrothermal systems. The Red Mountain system, 

encompassing an area of roughly 20 km2

, experienced 

significant and economically important Ag-Cu-Au breccia 

pipe and fault-hosted mineralization (Bove and others, 2000). 

The Ohio Peak–Anvil Mountain acid-sulfate system to the 

south comprises about 18 km2 of hydrothermally altered 

materials; however, this system is mostly devoid of important 

mineral deposits. Both are located along the structurally complex 

northwest and southwest margins of the 27 Ma Silverton 

caldera, between Cement and Mineral Creeks.

Some of the richest ores in the Silverton area were mined 

from small silicified breccia pipes associated with the extensive 

acid-sulfate hydrothermal system in the Red Mountains 

district. These high sulfidation deposits, which mainly formed 

within the oxidized portions of the breccia pipes, were 

irregular in shape and typically consisted of carbonates of 

lead and iron, as well as lead sulfates, iron oxides, sphalerite, 

and miscellaneous arsenates (Fisher and Leedy, 1973). Below 

the oxidized zone, sulfide ores of pyrite, enargite and lesser 

chalcopyrite, galena, tetrahedrite, and sphalerite were present 

as irregular masses in altered volcanic rocks.

The acid-sulfate mineral assemblages at both localities 

formed dominantly by the replacement of the original volcanic 

host by low-pH, magmatic SO4-rich solutions, as evidenced 

by the preservation of the original porphyritic rock textures. 

Excellent outcrop exposure in the Red Mountain and Ohio 

Peak–Anvil Mountain areas has allowed detailed zonation 

studies of hydrothermal assemblages within these complex 

acid-sulfate hydrothermal systems (Bove and others, 2000; 

Dalton and others, 2000; Mast and others, 2000). Surface 

mapping and careful paragenetic studies demonstrate that 

structurally controlled zones of quartz-alunite altered rock 

transition outward into pyrophyllite, dickite, smectite, and (or) 

pyrophyllitic assemblages, respectively. Mineralogic data from 

exploration drilling in the vicinity of Red Mountain #3 and 

adjacent Prospect Gulch (Bove and others, 2000; Wirt and 

others, 2001) found that the acid-sulfate alteration zones 

extend at least 300 m beneath the surface.

Mineralization and Intrusions (<18 Ma)

Economically important post-20 Ma vein mineralization 

in the western San Juan Mountains appears to have been closely 

associated with intrusion of high-silica alkali rhyolite (Lipman 

and others, 1973). The Sunnyside, Gold King, Camp Bird, and 

Idarado mines were all sited on these polymetallic 

(Au, Ag, Pb, Zn, Cu) veins, which formed as fracture or 

fissure fillings that commonly range from less than a meter to tens 

of meters in width. Veins within the San Juan and inner nested 

Silverton calderas are either concentric or radial to the ring 

fracture zone, as in the South Silverton district (Cunningham Creek 

to Kendall Mountain; Varnes, 1963), or trend parallel to grabenrelated faults in the resurgent dome of the San Juan–Uncompahgre 

calderas (Burbank and Luedke, 1969). Hydrothermal alteration 

related to the veins consists of narrow (1–5 m) envelopes with 

quartz-sericite-pyrite-kaolinite-calcite-chlorite mineralogy 

superimposed over regionally propylitic altered rock (Ransome, 

1901; Burbank and Luedke, 1969).

High-silica rhyolite intrusions such as those present 

near Houghton Mountain and California Mountain are 

petrologically very similar to Climax-type high-silica rhyolites 

(Lipman and others, 1976; White and others, 1981) that are 

associated with large molybdenum porphyry systems in the 

western United States. In some instances these volatile-rich 

intrusions formed large haloes of intense quartz-sericite-pyrite 

altered rock and are associated with dense stockwork 

quartz veinlets.

AVIRIS Data Acquisition 

and Calibration

The application of remote sensing technology to 

problems of mine site remediation and acidic drainage has 

been shown to provide cost-effective solutions to large-scale 

remediation problems (Swayze, Smith, and Clark, 2000; 

Swayze, Smith, Clark, Sutley, and others, 2000). Effective 

utilization of the acquired information is best achieved with 

some understanding of the method and its limitations. The 

following section is intended to briefly familiarize the reader 

with the AVIRIS instrument, its mode of operation, and the 

methods employed to calibrate the data and present them in a 

useful form for geophysical studies.

Instrument Description

The AVIRIS instrument (Porter and Enmark, 1987; 

Green and others, 1988, 1998) utilizes a rotating mirror to 

sweep the field of view perpendicular to the direction of 

motion. The foreoptic system focuses light from each pixel 

in the ground track onto a set of fiber optics, which transmit 

the light to four spectrometers. Within each spectrometer, 

light is dispersed via a grating to a linear array of detectors. 

   Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Each spectrum thus acquired features 224 channels covering 

the spectral range from 0.35 to 2.5 μm, with a spectral 

sampling of 10 nm (nanometers) and 10 nm full-width at 

half maximum (Green and others, 1998). For this study, 

the instrument was flown at approximately 20 km altitude 

aboard the NASA ER-2 airplane, a modified version of the 

high-altitude U-2 plane. 

Watershed Coverage and Study Area

The AVIRIS data were acquired on June 18, 1996, under 

cloudless skies. Two northeast-southwest-trending, parallel, 

10-km wide flightlines with an overlap of approximately 2.5 

km were flown (fig. 1). The image is bounded approximately 

by long 108°00' to 107°30' W. and lat 37°15' to 38°00' N. 

The first flightline runs from the confluence of Red Mountain 

Creek and the Uncompahgre River to the Hermosa Cliffs just 

south and east of the East Fork Hermosa Creek. The second 

line runs from just upstream of the town of Hermosa, Colo., 

to the headwaters area of the upper Animas River at Animas 

Forks, Colo. These two flightlines cover the Animas River 

itself from its headwaters to just upstream of Hermosa Creek, 

as well as the entire source area for two major Animas River 

tributaries, Cement and Mineral Creeks (including South Fork 

Mineral Creek). These two creeks originate within the caldera 

and are strongly affected by acidic mine drainage. All but the 

most distant reaches of the other tributaries in the watershed 

upstream of Hermosa Creek are part of the AVIRIS data set, 

including Cunningham, Cascade, Elk, and Lime Creeks. Most 

of the acidic drainage has been shown to originate within 

the limits of the overflight (Church and others, 1997). The 

towns of Durango and Telluride lie just outside the study area, 

whereas the town of Silverton is within the second flightline, 

between the confluences of Cement and Mineral Creeks with 

the Animas River.

The region outlined in figure 1 is covered by the Durango 

1:250,000-scale topographic map (U.S. Geological Survey, 

1976) and numerous maps at larger scales. Molas Lake, near 

Silverton, and the much larger Electra Lake north of Hermosa 

both lie within the study area. The Vallecito Reservoir in the 

lower right of the Landsat image is not included in this study, 

nor are the mostly roadless San Juan National Forest and 

Weminuche Wilderness to either side of the AVIRIS flightlines. 

The colors within the AVIRIS overflight area correspond to 

minerals associated with hydrothermal alteration zones and 

to iron-bearing precipitates detected downstream from the 

caldera regions. 

Geometric Considerations

Interference from spectral absorptions and scattering in 

the atmosphere, inherent motion of the sensor platform, and 

the orientation and topography of the study region all affect 

the interpretation of AVIRIS data. The rugged San Juan 

Mountains present some of the most extreme topographic 

relief encountered in AVIRIS studies so far, with elevation 

changes exceeding 1.8 km in some places. Because the 

intensity of light decreases as the square of distance traveled, 

and also because atmospheric absorption is proportional to 

path length, the reflectance signal from high-elevation ground 

is stronger than that from lower elevations. An additional 

complication is illustrated in figure 3: rays of incident light 

from the sun are reflected specularly at an emission angle (e) 

which is equal to the incidence angle (i). Diffuse reflection 

travels in all directions from the illuminated surface; however, 

the diffuse light from the surface still peaks near the emission 

angle. As the sensor picks up light reflected from surfaces 

whose orientation changes relative to the line-of-sight, the 

received intensity varies as a complex function of illumination 

angle, surface orientation, emission angle, and sensor orientation 

(Hapke, 1993). Finally, sunlight striking a hillside at a grazing 

angle provides less illumination than sunlight striking level 

ground at a high angle of incidence; shadowing at low incidence 

results in additional lost signal. The most intuitive way to 

regard all of these effects is to consider the final results of 

spectral analysis. As the signal level decreases and noise 

remains constant, the relative quality of the signal degrades. 

This reduces the quality of a spectral match between observations 

and reference spectra in the spectral library. The final result is 

decreased confidence in accuracy of identifications for areas of 

low signal-to-noise. The areas most affected by these geometric 

variations are the edges of the flightlines, and portions of steep 

slopes far from and facing away from the aircraft travel path. 

When interpreting the AVIRIS results, the operator must make 

allowances for such complications.

e i

Figure 3. Viewing and illumination geometry for a scene incorporating topographic relief; e, emission angle; i, incidence angle.

Aviris Data Acquisition and Calibration 
Atmospheric Correction

Interferences arising from atmospheric absorptions and 

wavelength-dependent solar spectral response were corrected 

using the ATREM atmospheric correction program (Gao and 

others, 1993, 1997). The ATREM program uses the ratios 

of water absorption lines in each pixel spectrum to derive an 

atmospheric column abundance for water vapor. Other species 

with infrared absorptions such as carbon dioxide and ozone 

are scaled on the basis of the water values and then ratioed 

from the final data. Correction of the Animas River watershed 

data set utilized an average elevation of 3.2 km and a visibility 

of 200 km. Total ozone column abundance was inferred to be 

0.34 atm-cm. The ATREM algorithm estimates the effects of 

Rayleigh and aerosol scattering in the radiance path, and performs 

a first-order path length correction accordingly. Although 

significant water absorption remains in the spectral data at the 

major water absorption feature locations of 1.49 and 1.9 μm, the 

remainder of the spectral range appears virtually water-free and is 

suitable for mineral identifications and mapping.

Ground Calibration Methods

Subsequent to atmospheric removal, the AVIRIS data 

were corrected to absolute reflectance using a path radiance 

correction (Clark and others, 1990, 1991; Clark, Swayze, 

Heidebrecht, and others, 1993; Dalton and others, 1998) and 

the known spectrum of a ground calibration site (Clark and 

others, 2002). Review of the AVIRIS data set revealed that 

the most suitable site for this correction was a spectrally 

bland, 300×100 m talus slope at 3.6 km elevation on the 

southeast slopes of Engineer Mountain near Coal Bank Pass. 

This talus slope is composed primarily of white porphyritic 

andesite (93–96 percent by area) and a small amount 

(3–5 percent) of a red sandstone, with sparse (1–2 percent area) 

lichen and virtually no appreciable absorption features in the 

AVIRIS spectral range. Field expeditions were conducted 

to collect upward-facing rock samples from the slope for 

laboratory analysis. Laboratory measurements were made for 

39 collected specimens of talus from Engineer Mountain 

using a modified Beckman 5270 spectrophotometer. Estimates 

of rock shadowing (≈15 percent by area) were made from 

photos taken during the field expedition. Talus spectra were 

averaged together with a “shadow” component to produce a 

representative composite spectrum. Additional in-place spectra 

were later collected using an Analytical Spectral Devices 

ASD FR-653-1 portable field spectrometer. Three horizontal 

traverses of the slope were done at different locations on both 

trips in order to accurately characterize the site. Reflectance 

values of all measurements were found to be in close agreement. 

The “path radiance,” an estimate of scattered light intercepted 

by the AVIRIS sensor, was derived using spectra from the 

AVIRIS image cubes over heavily vegetated, north-facing 

slopes that were essentially black to the spectrometer. A 

path radiance correction was derived which brought the 

AVIRIS spectra of the Engineer Mountain site into agreement 

with both the laboratory and field spectra of the site. This 

path radiance correction was then applied to the entire 

AVIRIS data set.

Engineering Correction for Platform Motion

Imaging spectrometers mounted on both spacecraft and 

aircraft must contend with spatial distortions introduced by the 

motion of the platform. Roll, pitch, and yaw of the platform 

each introduce specific spatial distortions (Richards, 1994). 

Velocity variations in the direction of travel, and to a lesser 

extent, buffeting by crosswinds as well, cause additional distortions 

for airborne sensors (Sabins, 1987; Richards, 1994). Although 

the AVIRIS sensor is stabilized by gyroscopes, these distortions 

still affect the data. The addition of Global Positioning System 

(GPS) technology to the AVIRIS system has greatly facilitated 

the process of correcting these geometric distortions. Another 

geometric problem is known as “smile”: since the AVIRIS 

sensor uses a rotating mirror to direct the field-of-view across a 

linear strip, there is a difference in path length for light traveling 

from the edges of the scene as compared to light traveling 

from the center of the scene, directly below the aircraft. The 

inverse-square attenuation of light coupled with absorption 

proportional to path length causes differences in the strength of the 

received signal and consequent changes in the signal-to-noise 

ratio proportional to a cosine function across each strip of 

the image cube. 

The AVRECGEN and AVRECTFY programs 

(Clark and others, 1998) were used to correct all of these 

distortions. AVRECGEN and AVRECTFY operate on the 

Tetracorder-processed mineral map images, not on the image 

cubes themselves. The correction method applies GPS data 

recorded in flight to each pixel to determine its exact latitude 

and longitude, and reprojects the final images using a nearestneighbor interpolation. Because averaging of adjacent spectra 

destroys the original signal and its associated diagnostic 

absorption bands, application of the rectification procedures 

to the image cubes themselves would result in loss of spectral 

information. Instead, the image cube data are analyzed and 

interpreted, and the resultant processed images reprojected to a 

standard mapping base.

Spatial Corrections and Orthorectification

Following the just-mentioned engineering corrections for 

effects of aircraft motion, the images are much closer to a flat 

x-y plane. Some residual spatial distortions still remain, and 

the effects of topography must be taken into account. Terrain 

rectification is needed for the extreme relief displacement 

found throughout the study area to accurately map the areal 

extent and location of the various materials found. 

Traditional orthorectification processes cannot be used 

for AVIRIS airborne scanner data because image acquisition is 

not instantaneous as in aerial photography. Each pixel of each 

   Remote Sensing Characterization of the Animas River Watershed by AVIRIS
scan line has a unique geometric model. The traditional 

orthorectification processes such as polynomial transformations 

rely on information about camera lens distortions and accurate 

ground control point locations in x, y, and z. Radial basis 

functions, such as multiquadric and thin-plate spline, have 

produced the best results for orthorectification of airborne 

scanner data (Wiemker and others, 1996; Ehlers, 1994; Ehlers 

and Fogel, 1994). Two principal characteristics distinguish 

the multiquadric and thin-plate spline from the polynomial 

approach: (1) the functions interpolate (the control points 

match exactly between images) and (2) their behavior is 

mathematically constrained between control points (Ehlers 

and Fogel, 1994).

Thin-plate spline functions (Bookstein, 1989) interpolate 

the values at all ground-control points (GCPs), and a new 

GCP can be added or deleted where the transformation is not 

satisfactory. Unlike polynomial transformations that require 

a small number of points depending on the order of the 

polynomial, the thin-plate spline may require hundreds 

of points in rough terrain. There should be a GCP at every 

extreme of the terrain, peak or valley, and along break lines. 

Whereas this process requires a large number of control 

points, there is a fine line between having too many points 

and not enough. Too many points in a location can actually 

introduce localized geometric distortions.

A two-step process was used to correct the 17.5-m 

resolution AVIRIS data using the PCI OrthoEngine v. 6.3 

software package. For this project, there were two flightlines 

to be corrected. Prior to merging, the western flightline was 

636 pixels by 3,034 lines and used 234 GCPs. The eastern 

flightline was 636 pixels by 2,520 lines and used 131 GCPs. 

Portions of fifteen USGS 1-m digital orthophoto quadrangles 

(DOQs) were used for reference points. These DOQs formed 

the foundation for the revision of the hydrography and 

transportation vector layers and any subsequent fieldwork. 

Terrain elevations were obtained from USGS 10- and 30-m 

digital elevation models (DEMs). The bulk of the elevations 

came from the 10-m DEMs. The first step defined the GCPs 

using a second-order polynomial transformation which was 

done to evaluate the selection of the points. Root-mean-square 

(RMS) errors were normally kept in the 3- to 4-m range with 

no RMS errors exceeding 10 m. During the second step, each 

GCP was assigned an elevation from the DEM. After the 

elevations were assigned to the points, the image correction 

using the thin-plate spline transformation was performed. This 

two-step process was similar to the methods used by Ehlers 

(1994), which followed a global second-degree polynomial 

transformation with a registration using multiquadric interpolation 

and a radial basis function. 

The registration was checked by selection of points 

throughout the AVIRIS image and comparison with the 

same points in the DOQ image. Comparisons of the AVIRIS 

image were also checked against revised hydrography and 

transportation vectors. The distances between the revised 

vector features and DOQs, and the rectified AVIRIS 

imagery were generally within one to two pixels. The fully 

orthorectified and georeferenced AVIRIS imagery is suitable 

for fieldwork, mapping, runoff modeling, and inclusion in 

GIS databases.

Classification Methodology: The USGS 

Tetracorder Algorithm

The USGS Tetracorder algorithm is an expert system 

(Clark and others, 1990, 1991; Clark, Swayze, and Gallagher, 

1993; Clark and Swayze, 1995; Dalton and others, 1998; 

Dalton, 2000; Dalton and others, 2000; Swayze, Smith, and 

Clark, 2000; Swayze, Smith, Clark, Sutley, and others, 2000; 

Clark and others, 2003 and references therein) for classification of spectral image cubes on the basis of comparison of 

unknown spectra with those in a library of reference spectra. 

Although the Tetracorder system is described fully elsewhere 

(references in this paragraph), a brief explanation of the 

methodology is useful here. Tetracorder attempts to duplicate 

the quantitative and qualitative comparisons performed by 

a trained spectroscopist. Once supplied with a set of rules 

corresponding to absorption features in a reference spectral 

library, the algorithm applies complete band shape 

least-squares matching criteria to compare each spectrum in 

the image cube to each spectrum in the reference library.

Infrared Spectral Comparison

The AVIRIS sensor returns measurements of spectral 

intensity for the visible and near-infrared wavelength range 

extending from 0.35 to 2.5 μm. Representative spectra for 

the iron oxide minerals jarosite, goethite, and hematite are 

shown in figure 4; spectra of muscovite and the clay minerals 

kaolinite and halloysite are given in figure 5. The materials in 

each figure exhibit diagnostic spectral absorption features in 

different wavelength regions. In figure 4, a broad absorption 

band is evident near 1 μm in all three spectra. This diagnostic 

absorption arises from crystal field transitions of the Fe3+ atom 

in the molecular structures (Burns, 1993; Clark, 1999) of these 

three minerals. The left and right edges of this absorption 

feature relative to the surrounding continuum reflectance 

levels (fig. 4) differ in each spectrum. For example, the absorption 

feature is much broader in the jarosite spectrum. The position 

of the band minimum, the full width at half-maximum, and the 

relative strengths of the absorptions also vary. These characteristics 

of the diagnostic Fe3+ absorption can be exploited to differentiate 

between iron-bearing minerals. In similar fashion, the three 

spectra in figure 5 may be differentiated using their diagnostic 

absorption features centered around 2.2 μm. These arise from 

cation-OH vibrational transitions in the mineral matrix. Note 

that the spectra in figure 5 do not exhibit strong absorption 

features near 1 μm as with the iron-bearing minerals in figure 

4: this is because of differences in their chemical composition. 

Similarly, the goethite and hematite do not have diagnostic 

Classification Methodology: The USGS Tetracorder Algorithm 
SCALED REFLECTANCE

0.0

0.5

1.0

0.5 1.0 1.5 2.0 2.5

WAVELENGTH, IN MICROMETERS

Hematite FE2602

Goethite WS222

Jarosite GDS99

Figure 4. Visible to near-infrared spectra of the iron-bearing minerals jarosite, goethite, 

and hematite (from Clark, Swayze, Gallagher, and others, 1993). Spectra are offset vertically for clarity. Sample numbers noted after mineral name. 

0.0

0.5

1.0

0.5 1.0 1.5 2.0 2.5

WAVELENGTH, IN MICROMETERS

Kaolinite GDS11

Muscovite HS24.3

Halloysite KLH503

SCALED REFLECTANCE

Figure 5. Visible to near-infrared spectra of the alteration products kaolinite, muscovite, 

and halloysite (from Clark, Swayze, Gallagher, and others, 1993). Spectra are offset vertically for clarity. Sample numbers noted after mineral name. 

10 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
absorption features in the 2-μm range, although the jarosite, 

a weathering product, exhibits an Fe-OH absorption at 

2.27 μm (fig. 4). Differences occur again in the widths, 

center positions, and overall shapes of the 2.2-μm diagnostic 

absorption features in figure 5 which may be used to distinguish 

between these minerals. Even better, complete band shapes 

may be compared in a least-squares sense to provide a 

quantitative discrimination method.

The discrimination of vegetation types proceeds in a similar 

manner. The spectral behavior of vegetation is dominated by 

pigments such as chlorophyll and carotene, and the cellular 

structures composed of cellulose, lignin, and proteins. These 

common components always absorb at the same wavelengths, 

though with differing relative strengths and band shapes. The 

most obvious feature in vegetation spectra is the chlorophyll 

absorption just short of 0.7 μm in the grass and lichen 

spectra (arrows, fig. 6). The lichen in this example is a 

pale-green species (Xanthoparmelia cumberlandia), which is 

common in the San Juan Mountains. Note that the chlorophyll 

absorption feature is missing in the lower spectrum, of dry 

(senescent) grass. Absorptions arising from water in the cellular 

structure can be seen in the green grass spectrum at 1.0, 1.2, 

1.45, and 1.9 μm, but are subdued in the lichen and dry grass 

spectra. A local reflectance peak at 0.55 μm in the green grass 

highlights the green color of chlorophyll-bearing vegetation, 

and again it is missing from the dry grass and subdued in 

the lichen spectrum. The Tetracorder 3.6a2 library includes 

several vegetation types ranging from grasses, lichens, and 

ground cover to deciduous and coniferous tree species. A 

more complete description is given in Clark and others (2003). 

All of the minerals shown have weaker absorptions in 

addition to the diagnostic absorption features discussed. 

In practice, many of these absorptions may not be used 

for discrimination between mineral types arising from 

signal-to-noise issues or interference from other minerals 

or other materials. For example, the weak absorption feature 

at 0.65 μm in the goethite spectrum may be completely 

obscured by noise in an AVIRIS scene and so may not 

be useful for discrimination. An additional concern is highlighted by noting that each spectrum in figure 5 exhibits 

absorption features at 1.4 and 1.9 μm arising from water 

molecules present within the sample. Because the Earth’s 

atmosphere also contains water, AVIRIS spectra are typically 

overwhelmed by the atmospheric water signature at these 

wavelengths, even after atmospheric correction. Thus, 

these absorption features cannot be exploited for mineral 

identification. Another example is the 2.27-μm absorption 

in the jarosite spectrum: because of its proximity to the clay 

and mica absorption features, and because jarosite is an 

alteration product which often occurs together with several 

clays, this absorption feature is frequently unusable for 

identification of jarosite. 

The complex interplay of spectral behavior among 

materials that sometimes occur together in the same scenes is 

embodied in a set of decision rules. The USGS Tetracorder 3.6a2 

implementation is the result of several years’ experimentation 

to derive the best criteria for simultaneous, automated 

discrimination of multiple mineral types. The following sections discuss this approach in greater detail.

Continuum Removal and Band-Fitting 

Methodology

Whereas the full spectrum may not always be useful 

for spectral discrimination, diagnostic absorption features 

can usually be specified for each material, which can be 

applied to uniquely identify that material. The USGS 

Tetracorder algorithm, as an expert system, relies on user 

interaction to define the diagnostic absorption features to 

be used for each material under consideration. Because in 

practice the observed spectrum will usually be of a mixture 

of materials, rather than a pure material, the algorithm uses 

a continuum-removal scheme (Clark and Roush, 1984) to 

minimize the spectral contributions from other materials 

in the scene.

All materials in an AVIRIS pixel, whether mineral, 

vegetation, water, or other substances, contribute to the total 

reflectance spectrum measured by the instrument. In an idealized 

case, each material could be considered to exhibit spectral 

behavior only in the region of a strong absorption band. 

Hematite (fig. 4) displays little structure outside the primary 

absorption features at 0.46 and 0.85 μm other than a gradual 

slope. The kaolinite spectrum in figure 5 is nearly level 

to either side of the 1.4-μm water absorption feature. Other 

materials may depart from such idealized behavior, as with 

jarosite (fig. 4); still, each absorption feature can be seen as 

superimposed upon an overall reflectance level defined by 

the edges of the absorption feature. Each AVIRIS pixel can 

realistically be expected to contain several materials. Averaging 

over the spectral contributions of all these materials produces 

a continuum that can be modeled as a background reflectance 

level approximated by a straight line. Removing continuum 

effects in the vicinity of diagnostic absorptions allows individual 

absorption features to be compared to those of a reference 

material with a minimum of interference.

The method is illustrated in figure 7. For a given 

reference material having a diagnostic absorption feature 

spanning a set of wavelengths (λ), a library spectrum L(λ) 

is examined interactively by the operator (fig. 7A). Anchor 

points are defined as the wavelengths bounding the absorption 

feature; this sometimes requires a reasonable working 

knowledge of spectroscopy and the materials of interest. A 

straight line CL(λ) is fit to the anchor points and considered 

to approximate the continuum across the absorption feature 

in spectrum L(λ). During automated comparison, each 

observed spectrum O(λ) is compared to every spectrum in 

the reference library. For each diagnostic absorption feature, 

a continuum level CO(λ) is calculated for the observed 

spectrum using the anchor points of the library spectrum 

L(λ). The anchor points from the library spectrum, and not 

the observed spectrum, are used for three reasons. First, the 

Classification Methodology: The USGS Tetracorder Algorithm 11
library spectrum generally has better signal-to-noise and consequently produces improved wavelength estimates. Second, 

determination of anchor points is time consuming. Third, 

the observed spectrum can vary with composition, viewing 

geometry, scattered light, and other factors, which could result 

in different anchor points for each spectrum. This complicates 

the automated comparison. In the case where the reference 

material is not present in the observation, any mismatch of 

anchor points is of secondary concern because the spectra are 

not likely to match anyway, so the material will be rejected. 

The next step (fig. 7B) is to divide each spectrum by its 

corresponding straight line continuum to produce the continuum-removed spectra, LC(λ) and OC(λ), corresponding to 

the library and observed spectra, respectively. The apparent 

depth D of an absorption feature relative to the surrounding 

continuum in a reflectance spectrum (Clark and Roush, 1984) 

is given by

 (1)

where Rb is the reflectance at the absorption band center 

wavelength λ bc and Rc is the reflectance value of the continuum C(λ) at the band center wavelength. The band center 

wavelength λ bc is found by locating the band minimum in the 

continuum-removed feature, as in figure 7B. The reason for 

using the continuum-removed feature to choose the wavelength 

rather than the raw spectrum is to remove the influences of other 

materials contributing to the spectrum. Because the depths of 

absorption features change with the abundance of a material, 

the strength of each absorption feature must be scaled, without 

changing its spectral shape, in order to compare the strong 

spectral absorption features of pure reference materials with 

features in the observed spectra where abundances may be much 

lower. After continuum removal, the Tetracorder algorithm 

adjusts the library spectrum absorption feature depth to match 

the depth of the observed spectral absorption feature (fig. 7C). 

This is done through a modified least-squares approach, and the 

goodness-of-fit estimated for the best match in spectral contrast 

is the value used for the later decision-making routines. The 

modified least-squares technique, originally outlined in Clark 

and others (1990) and further developed in Clark, Swayze, and 

Gallagher (1993), Clark and Swayze (1995), and Clark and others 

SCALED REFLECTANCE

1.5

1.0

0.5

0.0

0.5 1.0 1.5 2.0 2.5

WAVELENGTH, IN MICROMETERS

Green grass

Lichen

Dry grass

Figure 6. Visible to near-infrared spectra of green (lawn) grass, lichen (Xanthoparmelia cumberlandia), 

and dry, senescent grass. The grass spectra are from Clark, Swayze, Gallagher, and others (1993), whereas 

the lichen was measured as part of this study. Arrows mark the location of principal chlorophyll absorption 

feature in the upper two spectra, which is not apparent in the dry grass spectrum.

D = 1– —, Rb

Rc

12  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
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Figure 7. Continuum-removal and band-fitting procedure as used by Tetracorder algorithm. A, Anchor points are defined utilizing an absorpreference spectrum of known material. A straight-line continuum is then fit to both the library and observed spectra. B, Each spectrum is div

at all wavelengths. The band center wavelength is determined from the continuum-removed library spectrum. C, The spectral contrast of the adjusted to match that of the observed spectrum to facilitate comparison by least-squares methods. The correlation coefficient calculated foabsorption features is used as input to other routines of the Tetracorder system. Modified from Clark and Roush (1984), Clark and others (1990FWHM is full width at half maximum. 
(2003), accomplishes this without iteration through a succinct and 

computationally efficient enhancement to the standard least-squares 

equations described in Bevington (1992). The method is summarized in Dalton (2000) and described fully in Clark and others 

(2003). Briefly, the continuum-removed reflectance values at all 

wavelengths within the anchor points are used to determine a scaling which minimizes the differences between the continuumremoved library and observed spectra. The resultant scaled, 

continuum-removed library spectrum L′C (λ) gives the closest 

possible match to the observed spectrum, and its correlation 

coefficient r estimates the quality of the match. 

Each observed spectrum in the image cube is compared 

to all components of the reference library. For each material 

under consideration, the Tetracorder algorithm produces three 

values. The goodness-of-fit, F, is just the correlation coefficient r, 

and measures how well a material matches the observed 

spectrum. The depth D of the absorption feature is indicative 

of the abundance of that material in the scene. Although D is 

also sensitive to grain size variations, in general the changes 

in composition are much more important (Hapke, 1993). At 

constant grain size, D roughly corresponds to relative abundance 

changes throughout a given scene (Lucey and Clark, 1985). 

Combining the fit and depth by multiplying [F•D] provides a 

more robust estimate of relative abundance (Clark and others, 

1990, 2003). Using this value helps to avoid confusion arising 

from noise, other materials having absorptions at similar 

wavelengths, and situations where a low abundance of one 

material is accompanied by a high abundance of another.

In the simplest case, a material will have a single absorption 

feature, which is compared to the observed spectrum. Spectra 

of some materials may have multiple spectral absorption 

features, which can be used for identification. The Tetracorder 

algorithm can be instructed to use the relative areas of individual 

absorption features in a single reference spectrum to compute 

a weighted fit Fw, weighted depth, Dw, and weighted fit times 

depth [F•D]w which is used in the decision process. If ci is the 

area of the ith absorption feature relative to the continuum, 

then the weighted fit, weighted depth, and weighted (fit times 

depth) are defined as:

 (2)

 and (3)

 (4)

where (5)

and the absorption feature depths and weights are computed from 

the fitted, continuum-removed library reference features to 

minimize effects of noise and other materials in the scene 

(Clark and others, 1990; Dalton, 2000; Clark and others, 2003). 

Once the weighted fit (Fw), depth (Dw), and their product 

[F•D]w values have been generated, the operator-specified 

rules are applied. At its most basic, Tetracorder declares the 

reference material having the highest Fw or [F•D]w value to 

match the observed spectrum. However, more advanced logic 

can be applied. For example, Tetracorder may be instructed to 

reject a mineral assignment if a strong chlorophyll absorption 

feature is found. This would mean that the observed spectrum 

is in fact caused by vegetation. These “NOT” features may be 

defined for any material, and are particularly helpful in cases 

where certain materials have similar or overlapping absorption 

features but one material has a characteristic absorption feature 

which can be used for discrimination. Another approach is to 

group certain materials and compete them against each other 

in subclasses. One example is the grouping of iron oxides, 

which are characterized by strong electronic transitions in the 

1-μm range (Group 1); another would be grouping of the 

clay minerals, which exhibit vibrational absorptions in the 

2.2-μm range (Group 2); a third grouping would be that of 

vegetation types, all of which would display chlorophyll-related 

absorption features near 0.7 μm. 

Materials within each group are competed against each 

other to select the best fit. For each material, three processed 

images are generated from the AVIRIS data. The intensity 

values of each pixel in these three images correspond to the 

weighted fit (Fw), depth (Dw), and fit times depth [F•D]w for 

the reference material class under consideration. A clustering 

algorithm prioritizes output images on the basis of the size of 

contiguous regions of high pixel values. The operator examines 

these images and assembles them into color-coded maps of 

material distributions. For this investigation, only materials 

having [F•D] values above a given threshold are displayed. 

Values above the threshold are all assigned the same intensity 

in the final images. The threshold was determined individually 

for each material by the operator, based not only on the quality 

of the fit and the spectral data, but also on results of field studies. 

The variable extent of operator involvement is one factor in the 

characterization of the Tetracorder as an expert system.

To discriminate goethite from hematite, the algorithm 

compares the fit values for the primary diagnostic absorption 

features centered near 1.0 μm (fig. 4). Though subtle to the 

eye, the different shapes produce substantially different 

correlation coefficients when least-squares calculations are 

performed for the full band shape. Similarly, the 2.2-μm 

absorption features of halloysite and kaolinite (fig. 5) are 

compared directly for discrimination. In the case of muscovite, 

this absorption feature is supplemented by a second feature 

near 2.3 μm for a more robust discrimination. Jarosite is 

treated somewhat differently: the 0.43-μm and 0.91-μm 

absorption features are competed against the 1-μm absorption 

features of the other iron oxides and Group 1 minerals, but the 

2.27-μm absorption is not used to supplement this. Rather, the 

2.27-μm absorption feature is utilized in a separate grouping, 

with the clays, carbonates, and other Group 2 minerals, to 

prevent misidentifications in that spectral range. The reason 

that the jarosite absorption features in both spectral ranges are 

not used together is that whereas the shorter-wavelength 

absorption features are quite strong, the 2.27-μm absorption is 

not as strong when compared to the clay and carbonate minerals 

which exhibit diagnostic absorption features in that portion 

F w

 = ¤ ciFi i =1

w

D w

 = ¤ ciDi i =1

w

[F • D] w

 = ¤ ciFi

Di i =1

w

¤ci i =1

w

=1.0

14  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
of the spectrum. This could cause jarosite to be ruled out 

in some cases where it is present, or where it is inaccurately 

assigned to pixels containing mixtures of clays, carbonates, 

and iron oxides. Such knowledge of the spectral characteristics 

of the materials expected in a scene is crucial to the correct 

implementation of Tetracorder identification strategies. 

Potential Sources of Error

The researcher must take some precautions when interpreting 

the processed AVIRIS images. First, the value [F•D] is not a 

strictly valid abundance estimator. A material having strong 

absorption features may dominate the spectrum despite the 

presence of other compounds. Certain minerals are virtually featureless in the near infrared: quartz has no strong 

absorption features in the AVIRIS spectral range, whereas 

pyrite is so strongly absorbing that its spectrum is merely a 

flat continuum. In mixtures, these two minerals cannot be 

discriminated. The identification of a given mineral in a certain pixel must not be taken to mean that it is the only material 

present. Conversely, the absence of a material in the final 

images does not mean that it is not present.

Variations in signal-to-noise ratios give rise to additional 

concerns. For example, where two materials have spectral 

absorption features, changes in noise levels can influence the 

spectral match so that one material is chosen over the other. In 

areas where shade cover or surface orientation changes rapidly, 

this can result in mineral identifications which alternate between 

spectrally similar materials. In some cases, a material may 

spectrally dominate a pixel despite the presence of other, more 

abundant components. Spurious identifications also may occur 

because of fluctuations in noise levels. Regions of low signal, 

such as shadowed areas, north-facing slopes, and dark or wet 

rocks in particular may produce such spurious identifications. 

Near the edges of the flightlines, increased path length results 

in reduced signal levels. Although all mineral identifications 

presented in this report have been spot-checked in the field to 

verify accuracy, only major and easily accessible target 

areas have been visited because of the large size and extreme 

ruggedness of the study area. In general, large areas of a given 

material identification should be considered more trustworthy, 

whereas a single-pixel identification with no neighbors must 

be treated with caution.

Similar Mineral Signatures: Calcite, Epidote, 

and Chlorite (CEC)

Special attention must be paid to minerals having highly 

similar spectra, including calcite, epidote, and chlorite. The 

distribution of calcite is important herein because of calcite’s ability 

to neutralize acidic waters resulting from pyrite weathering and 

acid mine drainage (Church and others, 2000). Chlorite also 

has some acid-neutralizing potential (Desborough and 

others, 1998), although it is an order of magnitude less than 

that of calcite. Limestone occurs throughout the sedimentary 

units in the lower Animas River area south of the Silverton 

caldera, and these units were readily identified using the 

Tetracorder 3.4a8 implementation (Dalton and others, 1998). 

Calcite-bearing propylitic alteration zones containing abundant 

epidote and chlorite also occur within the caldera region 

and may also produce significant neutralization of acidic water 

(Bove and others, 2000; Dalton and others, 2000). 

Progress in mapping the extent and composition of 

these zones was hampered by the spectral similarity of the 

diagnostic bands of calcite, epidote, and chlorite (fig. 

8; Dalton and others, 1998, 2000, 2002, 2004). Although 

the pure end members are readily distinguished, the identical 

band center positions make it difficult to correctly identify 

mixtures of these three minerals. Most perplexing is that 

mixtures of chlorite and epidote containing no calcite mimic 

the calcite spectral signature quite closely. The bottom 

spectrum in figure 8 is of a rock collected in the field. This 

sample (SJ98-74D) is a fine-grained, propylitically altered 

igneous rock containing abundant epidote (approximately 

45 percent) intimately mixed with about 7 percent calcite 

and 11 percent chlorite, as determined by point counts. As it 

2.0 2.1 2.2 2.3 2.4 2.5

WAVELENGTH, IN MICROMETERS

0.0

0.5

1.0

SCALED REFLECTANCE

SJ98-74D

Epidote

Chlorite

Calcite

Figure 8. Diagnostic spectral absorptions of calcite, chlorite, 

and epidote in the 2-μm region. Field sample SJ98-74D is a finegrained mixture of all three (Dalton and others, 2002).

Classification Methodology: The USGS Tetracorder Algorithm 15
is an intimate mixture, its spectrum is not merely a linear 

weighted average of the spectra of its components. This 

made theoretical modeling of mixture spectra virtually 

intractable, and as the relative proportions of these three 

minerals change throughout the propylitic alteration zones, 

mapping the distribution of these particular minerals was 

not straightforward.

As described in Dalton and others (1998, 2002, 2004), the 

identification of mixtures containing these three minerals was 

accomplished by collecting samples of the pure end members, 

grinding and sieving them in the laboratory, and mixing 

them in various proportions. The spectra of these laboratory 

mixtures were measured under controlled conditions and then 

added to the Tetracorder reference library. Once the continuum 

anchor points were defined, the Tetracorder system was able to 

categorize the three end members (including calcite as limestone) 

from mixtures of two or three components. The method also 

separated the results into classes dominated by one or another 

component. Computation of more precise, quantitative unmixing 

and abundance estimates were, however, deemed prohibitively 

expensive at the time. 

A further complication arose concerning the presence 

of sericite within the propylitic zones of the study area. The 

petrographic name sericite is a general name used by field 

geologists to describe highly birefringent, fine-grained micas 

found in the vicinity of hydrothermal mineral deposits (Meyer 

and Hemley, 1967). This term has been loosely applied over 

time, and can include muscovite (KAl2(Si2Al)O10(OH,F)2), 

paragonite (NaAl2(Si3Al)O10(OH)2), and potassiumbearing, poorly crystalline, disordered micas such as illite 

[(K,H3O)(Al,Mg,Fe)2(Si,Al)4O10((OH)2•H2O)] (Fleischer 

and Mandarino, 1995). These are all compositionally and 

structurally quite similar (Klein and Hurlbut, 1993), particularly 

with regard to the position of the hydroxyl components, and 

their spectra as a result are also similar, especially in the 2-μm 

range, which is most sensitive to hydroxyl vibrational transitions 

(Hunt and others, 1973; Hunt and Ashley, 1979). For example, 

muscovite (fig. 5) exhibits spectral behavior arising from 

the presence of hydroxyl at 2.2 and 2.3 μm. The positions 

and relative strengths of these absorptions are very close to 

those in paragonite and illite (Hunt and Ashley, 1979; 

Clark, Swayze, Gallagher, and others, 1993). Whereas virtually 

all of the sericite encountered in the field during this study was 

determined by X-ray diffraction analysis to be illite (Bove and 

others, 2000), the spectral similarities within the class and the 

level of discrimination achieved by the Tetracorder 3.6a2 system, 

coupled with limited field coverage, precluded confident 

assignment of all sericite identified by the Tetracorder analysis 

as illite. Hence, in this report the term sericite will be used in its 

general sense, with the understanding that illite is the most 

abundant form in the region (see, for example, Eberl and others, 

1987) and the most likely to be encountered on the ground.

Where calcite, epidote, and chlorite occur together with 

sericite (illite), the 2.3-μm hydroxyl absorption feature in the 

sericite spectra (see, for example, muscovite in fig. 5) interferes with the comparisons of the 2.3-μm absorption features 

of the calcite, chlorite, and epidote. Fortunately, the 2.2-μm 

absorption band is readily distinguished, and so pixels containing sericite in combination with calcite, epidote, and (or) 

chlorite have been identified. More detailed interpretation 

of these spectra is not yet possible without a comprehensive 

laboratory investigation of the same type reported in Dalton 

and others (2004), but for the six minerals (muscovite, illite, 

paragonite, calcite, epidote, and chlorite) and combinations 

thereof, rather than three (calcite, epidote, and chlorite). 

Such a study would be valuable in extending the capabilities of 

remote sensing for characterization of hydrothermal alteration 

systems. Nevertheless, useful results have been obtained 

by grouping mixtures of these materials into categories 

establishing a primary mineral phase where possible. Breakdown of the various classifications into color-coded images is 

described further in the next section, with the specific detail of 

the calcite-epidote-chlorite distributions in the subsection on 

acid-generating and acid-neutralizing mineral assemblages.

Tetracorder Results for the Animas 

River Watershed

The spatial and spectral analysis of the Animas River 

watershed AVIRIS data concentrated on five general regions 

of the watershed and separated the spectral information into 

four broad classes. Note that as more than one material may 

be present in a given pixel, it is not possible to show all of the 

materials in a single image. Additionally, there are far more 

“colors” in the infrared than can be discerned by the human 

eye using the visible range, and so it makes more sense to 

present the information in a series of separate images, which 

also enables multiple coexisting materials to be depicted. The 

spatial regions within the caldera and farther downstream have 

markedly different characters, and thus are described separately. 

In the spectral domain, the first two classes (iron oxides and 

alteration products) are defined by broad mineral groups having 

their infrared absorptions in different wavelength ranges: Group 

1 absorbing in the 1-μm range, and Group 2 in the 2-μm range. 

The last three spectral classes are defined in terms of end-user 

interests (vegetation types and water-quality issues). Group 

3 includes vegetation and snow; Groups 4 and 5 are acidgenerating and acid-neutralizing minerals. The Tetracorder 

algorithm is capable of separating hundreds of materials. The 

results presented in this report are confined to those relevant to 

the scope of this study.

Group 1: Iron Oxide Family

The Fe3+ and Fe2+ crystal field transitions near 1 μm 

provide a convenient method of discriminating iron-bearing 

minerals through infrared spectroscopy. Because the exact 

wavelength positions and spectral shapes of the absorptions 

are strongly affected by the crystal structures hosting the iron 

16 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
atoms, many minerals have distinctive spectra in this range 

that are easily discriminated by the least-squares shape fitting 

approach of the Tetracorder system. In some cases, the 1-μm 

spectral comparisons were supplemented with information 

from the 2-μm range for a more robust identification.

Of greatest importance to this study are the pyrite (FeS2) 

oxidation assemblage products. Goethite (FeO(OH)), 

hematite (α-Fe2O3), maghemite (γ-Fe2O3) and schwertmannite 

(Fe8O8(OH)6SO4) are easily discriminated by spectral 

comparisons, and they tend to occur in concentric zones 

associated with pyrite weathering (Swayze, Smith, and Clark, 

2000; Swayze, Smith, Clark, Sutley, and others, 2000). Very little 

maghemite and schwertmannite were identified spectrally in 

the Animas River watershed, although schwertmannite is a 

fairly common secondary mineral along stream reaches 

(Desborough and others, 1998). Pyrite itself, as mentioned 

earlier, is difficult to detect spectrally, but the iron sulfate 

jarosite (K2Fe3+

6

(SO4)4(OH)12) is easily identified and 

stands as a proxy for some of the most highly concentrated 

exposures of pyrite (Swayze, Smith, and Clark, 2000; Swayze, 

Smith, Clark, Sutley, and others, 2000). Because the hematite 

spectrum is strongly affected by grain size, it was possible to 

separate fine-grained nanocrystalline hematite from the more 

common coarser grained variety. Coarser grained specular hematite 

occurs in the Sultan Mountain stock (Yager and Bove, 2002).

Several other Fe3+-bearing minerals found within the study 

area have absorption features in the 1-μm range, but their 

spectra are too similar to fully separate one from another. An 

additional suite of Fe2+-bearing minerals were found to be 

widely distributed in the region. Although these Fe2+-bearing 

minerals fall into roughly four classes, further discrimination 

is not yet possible. One class is spectrally similar to hematites; 

others have amorphous, poorly crystalline structures. The 

spectral differences among these materials are detailed (Clark 

and others, 2003) and yet of limited interest; thus they will be 

omitted here. The Fe2+ minerals as discerned from the 1-μm 

spectral region are quite useful in that they tend to differentiate 

weakly altered rock from intensely altered and pyritic rock. 

Field mapping within the study area demonstrates that propylitic 

to weak sericite-pyrite altered rocks typically map as Fe2+-

bearing minerals in the 1-μm region (characterized by an 

illite-dominated spectral signature). This generally reflects 

oxidation of primary Fe-Ti oxide minerals such as ilmenite 

and magnetite. In contrast, more intensely altered rock of 

the quartz-sericite-pyrite assemblage, which may also map as 

illite-dominant, tends to correlate well with Fe3+-bearing 

minerals produced by weathering of pyrite, such as goethite 

and jarosite. Also of interest, these materials, along with 

amorphous iron oxides, ferrihydrites (such as, 5Fe2O3•9H2O), 

and amorphous iron hydroxides, have been identified along 

stream reaches of the Animas River watershed and are 

believed to have precipitated from metal-rich acidic stream 

waters (Dalton and others, 2000; Church and others, 2000; 

Schemel and others, 2000). However, because the affected 

zones along streams tend to be small fractions of the AVIRIS 

pixel size, spectral identification of these precipitates is impeded 

by their low relative contribution to the collected radiation field.

Although plagioclase is difficult to detect spectrally 

because of its lack of strong VNIR (very near infrared) 

absorption features, it is a primary rock-forming mineral and 

common in volcanic rocks. Where found, it has been included 

in the AVIRIS images. It is much more prevalent than the images 

indicate, and so its absence in the images should not be 

interpreted to mean that it is not present on the ground. 

Rather, the weak spectral signature of plagioclase is easily 

overshadowed by other minerals. The images detailing iron 

oxide distributions also include epidote and some chlorite 

identified on the basis of 1-μm spectral comparisons. The 

distributions of epidote and chlorite should be considered as 

supplemental data to the more complete Group 5 distributions 

derived from 2-μm spectral comparisons.

Group 2: Alteration Products

Many of the minerals associated with hydrothermal 

alteration exhibit diagnostic absorption features in the 2-μm 

range of the infrared spectrum. As with the iron oxides, 

these minerals tend to occur in roughly concentric zones 

about a central area in which alteration was most intense. 

At progressively greater distances from the center zone, the 

intensity of alteration generally decreases (Rose and Burt, 

1979; Sabins, 1987; Bove and others, 2000; Yager and others, 2000) resulting in different mineral products. Because 

of structural and geologic complexities, the eventual surface 

expression of such alteration does not generally present 

itself as neat, perfect “bullseyes,” but the overall trend is 

commonly observed in the field (Swayze, Smith, and Clark, 

2000; Swayze, Smith, Clark, Sutley, and others, 2000). In 

areas of sparse vegetation this is more evident.

Sulfide minerals in acid-sulfate systems are usually 

deposited in a breccia-hosted silicic zone dominated by substantial quartz and smaller amounts of alunite and kaolinite 

(Luedke and Burbank, 1966; Fisher and Leedy, 1973; Ashley, 

1974). Sodium and potassium alunite may be discriminated 

spectrally, and they are given separate colors in the AVIRIS 

mineral maps. Beyond the silicic zone commonly is a zone 

of argillic alteration which hosts many clays, including 

kaolinite, halloysite, dickite, and pyrophyllite, along with 

minor alunite (Meyer and Hemley, 1967). Smectitic clays 

are commonly found along the periphery of this zone and 

grade outward into propylitic-altered zones, in which chlorite, epidote, and calcite are abundant (Meyer and Hemley, 

1967; Sabins, 1987). All of these materials have spectral 

absorption features in the 2-μm range and are important to 

interpretation of the AVIRIS mineral maps. Sericite is also 

found in argillic, quartz-sericite-pyrite (QSP), and propylitic 

zones, often as illite, muscovite, or paragonite. Most of the 

sericite encountered in the Animas River watershed during 

this study has been in the form of illite (Eberl and others, 

1987; Bove and others, 2000). The spectral behavior of 

Tetracorder Results for the Animas River Watershed 17
sericite (see, for example, muscovite in fig. 5) in the 2-μm 

range includes some overlap with absorption features diagnostic of alunites, kaolinite, pyrophyllite, calcite, epidote, 

and chlorite (fig. 8). In some areas where all these occur 

together, the Tetracorder system merely identifies the presence of a mixture (such as sericite with alunite and kaolinite) 

rather than attempt a computationally expensive and likely 

error-prone quantitative unmixing. Some of these identifications rely upon empirical laboratory mixtures created under 

controlled conditions and included in the reference library, as was 

done in the case of calcite, epidote, and chlorite.

Group 3: Snow and Vegetation Distributions

Exposed rock makes up a very small percentage of the 

study area. Because of the high annual precipitation, the San 

Juan Mountains are widely vegetated. Additionally, at the time 

of the AVIRIS overflight, significant snow cover remained. 

The Tetracorder 3.6a2 reference library contains spectra 

representative of snow in a range of conditions. Fine-grained 

frozen snow, medium-grained snow, and partially melted 

snow are included along with snow-water mixtures and 

slush. Vegetation spectra representative of lichen (fig. 6), 

conifers (such as fir, spruce, and pine), deciduous species 

(aspen, willow, maple), grasses, and crops (alfalfa, wheat) are 

all included in the library. As the primary focus of this study 

was to document the effects of historical mining, only broad 

classes of vegetation and snow cover appear in this report. 

Whereas these general vegetation maps should be useful to 

many, their inclusion in this report also demonstrates that 

minerals were not identified in large portions of the study area 

because these were covered by vegetation. Field analysis and 

geologic interpretation are necessary in order to interpolate 

over these heavily vegetated sites. (See Bove and others, 2001, 

for more information.) 

Most areas of the Animas River watershed contributing 

significantly to acidic mine drainage are so heavily mineralized 

that little or no vegetation occurs on them. The presence or 

absence of vegetation serves as an indicator of soil and water 

quality. Dry or senescent vegetation is readily identified. 

Vegetation with dew or rainfall on it has a unique spectral 

signature—a combination of the water signal and a vegetation 

spectrum; similarly, snow and vegetation combine to give 

a unique signal that was identified in certain regions of 

the study area. Further discrimination of vegetation species 

in the watershed could prove interesting as well as useful for 

certain land-management applications.

Groups 4 and 5: Acid-Generating and 

Acid-Neutralizing Mineral Assemblages

The specific iron oxides associated with pyrite oxidation 

have been assembled into a separate image on the basis of 

their importance as indicators of potential sources of acidic 

drainage. Similarly, the calcite-epidote-chlorite distributions 

have been assembled into their own image for future assessment 

of acid-neutralizing potential. The end members calcite, 

epidote, chlorite, and sericite are identified wherever they 

dominate the spectrum. In neighboring areas where the end 

members have decreased in abundance to the extent that they 

no longer dominate, mixtures of two and three mineral 

components are identified; in many cases, the primary 

component is separable, but where all three are present the 

images do not distinguish the abundances of the lesser two 

phases. Although the Tetracorder output from which the 

images in this report were assembled is much more detailed, 

space and cost limitations prevent publication of the intermediate 

results. Where sericite is dominant, relative abundances of 

accessory calcite, epidote, and chlorite could not be ascertained 

with sufficient confidence to warrant inclusion in the report; 

interpretations utilizing the images included here should take 

this uncertainty into account.

Imaging Spectroscopy Tour of 

Study Area

The digital images provided with this report can best be 

utilized if first examined in the context of a brief tour. The 

five broad material classes just described each correspond to a 

fully processed, orthorectified image mosaic included with this 

report. Five separate regions of the watershed (fig. 2, areas A–E) 

have been selected for this tour. They range from the most 

highly altered portions of the Silverton caldera, through 

surrounding igneous and sedimentary formations, to the heavily 

vegetated region near Hermosa. Examination of each of these 

five regions provides an opportunity to discuss the significant 

features of each, and provides an introduction to the application 

of remote sensing in this context. In this fashion, a general 

familiarity with the data set may be developed from which 

to expand into other related studies. As the distributions of 

materials in each of the five classes change from region to 

region, inferences can be drawn about the underlying mineralogy, 

ecology, water quality, and other aspects of the study area, 

from a watershed perspective.

Ironton and Red Mountain Scenes

The Ironton and Red Mountain area (inset A, fig. 2) 

exhibits most of the salient characteristics of the study area 

and will be examined in detail. The scenes are dominated by 

Red Mountains #1–3, which are near the northwest margin of 

the Silverton caldera. Heavily mineralized, the Red Mountains 

were actively mined for nearly a century. It is helpful to 

compare photographs to the AVIRIS mineral maps in order 

to get a sense of scale and placement. Figures 9, 10, and 11 

are photographs taken from the peak of Red Mountain #3 in 

1998. Colorado Highway 550 (fig. 12) is discernible in both 

the photographs and the AVIRIS mineral maps (figs. 13–17). 

18 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Figure 9. Photograph from Red Mountain #3 facing northwest toward Imogene Basin. Colorado Highway 550 

is in foreground, with Governor Gulch just right of center and Galena Lion Gulch to the right. These two exposures 

stand out clearly in figures 11-14. Hayden Mountain is rightmost peak at top of photograph.

Figure 10. Photograph from peak of Red Mountain #3 facing north toward Crystal Lake and Ironton Park. Colorado Highway 550 passes through valley below Hayden Mountain on left. Ridge on lower right is Red Mountain 

#2. The brightly colored, weathered rocks of Red Mountain #2 (and #3 in the foreground) contain abundant 

goethite and jarosite.

Imaging Spectroscopy Tour of Study Area 19
In the AVIRIS mineral maps, Crystal Lake is in the upper right 

corner. The mountains beyond Crystal Lake in figure 10 are 

outside the range of figures 13–17 but are apparent in figure 

1 and in the full image mosaics. Hayden Mountain is to the 

west of Highway 550 and Red Mountain Creek. Exposed rock 

along Monument Gulch near the center of figure 10 is dominated 

by goethite (green) in figure 13, and by kaolinite and sericite 

(yellow-green, bright rose) in figure 14. 

The slopes of Red Mountain are colored with various 

shades of red, orange, and yellow associated with goethite, 

hematite, and jarosite on the slopes. Examination of figure 

13 reveals a very close correspondence between the AVIRIS 

mineral map and the visible colors in the photographs (figs. 

9–11). Tree patterns on the slopes of Red Mountain in figures 10 and 11 can be used to orient oneself along the mountain. Although the iron oxides have absorption bands extending into the visible, which allow them to be distinguished by 

eye, the minerals in figure 14 do not show an obvious color 

correlation in all cases. This is because the clays, sericites, 

and other alteration products shown in figure 14 have 

infrared signatures in the 2.0-μm range, but no distinct color 

in the visible portion of the spectrum. Pure samples are 

predominantly gray and white. Two large exposures, along 

Governor and Galena Lion Gulches (fig. 9, lower left; see 

also fig. 12), are mostly gray and brown rocks, which appear 

similar, but the mineral maps in figures 13 and 14 clearly distinguish iron oxides, clays, and micas. This should further 

clarify the reason for separate maps to illustrate the distributions of different mineral groups.

As described in the previous sections, the Tetracorder 

method does not provide quantitative abundance estimates 

for mixtures of minerals. However, it can determine when 

one material is spectrally dominant compared to others. In 

the explanation to figure 14, for example, “Kaolinite and

alunite” is denoted by a light orange, whereas a darker 

orange is used for “Alunite with pyrophyllite.” In the former 

case, the kaolinite and alunite occur in roughly equivalent 

proportions, so the color is assigned to kaolinite and alunite, 

more or less equally. The term “Alunite with pyrophyllite” 

on the other hand is meant to imply that the minerals denoted 

by the dark-orange pixels are predominantly alunite, with

some pyrophyllite. Similar logic applies to mixtures of three 

or more minerals. “Alunite with sericite and (or) pyrophyllite” 

means that alunite is dominant, but there may be either sericite or pyrophyllite, or both, present as well. Sorting out the 

relative abundances of the minor mineral components may 

prove intractable in some of these cases.

The general shape of the Silverton caldera can be discerned from the concentration of mineralized zones about the 

primary hydrothermal alteration zones. The reds and oranges 

Figure 11. Photograph from Red Mountain #3 facing northeast. Brown Mountain dominates background, with 

Red Mountain #1 directly in front of it. The ridge of Red Mountain #2 runs from middle left to middle right (partially obscured at center by Red Mountain #3 in immediate foreground).

Figure 12 (following page). Major geographic features of Ironton Park and Red Mountain region (inset A, fig. 2) as described in 

text.

20 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Gladstone

Ironton

Tailings

HIGHWAY 550

IDARADO

MINE

GOLD

KING

MINE

Cemen

t

Creek

Red Mountain

Creek

Crystal

Lake

IRONTON PARK

Red Mountain

Pass

Ohio

Peak

Red

Mtn #3

Red

Mtn #1

Red

Mtn #2

Brown

Mtn

IMOGENE

BASIN

Storm

Peak

Prospect Gulch

Galena Lion

Gulch

Governor

Gulch

Commodore

Gulch

Monument Gulch

Ohio Gulch

 

Imaging Spectroscopy Tour of Study Area  21
Goethite

Jarosite

Chlorite 

Nanocrystalline Hematite

Hematite

Epidote

Maghemite

Schwertmannite

Fe -bearing Minerals 3+

Plagioclase Feldspar

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals

with Hematite

2+

Amorphous and Other Iron 

Oxides and Hydroxides 

Group 1 Materials

Iron Oxides and 

Associated Minerals

1 km

N

Figure 13. AVIRIS 1-μm (Group 1) mineral map of Ironton Park and Red Mountain (#1, #2, and #3, at center) region (fig. 2, inset A) 

showing distributions of minerals associated with the iron oxide family. The ridge in upper left corner displays different mineralogy because it is outside the Silverton caldera.

22  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Figure 14. AVIRIS 2-μm (Group 2) mineral map of Ironton Park and Red Mountain (#1, #2, and #3, at center) region (fig. 2, inset 

A) showing distributions of alteration products and associated minerals, including some mixtures.

Group 2 Materials

Alteration Products

and Associated Minerals

Na-Alunite

Intermediate Alunite

K-Alunite

Kaolinite and Alunite

Alunite with Pyrophyllite

Alunite with Kaolinite 

and/or Sericite

Alunite with Sericite

and/or Pyrophyllite

Sericite (Illite)

Sericite and Jarosite

Sericite and Pyrophyllite

Sericite with Chlorite,

Epidote, and/or Calcite

Calcite

Epidote

Na-Montmorillonite

Ca-Montmorillonite

Dickite

Kaolinite

Kaolinite and Sericite

Kaolinite with Smectite

Kaolinite with Pyrophyllite

Pyrophyllite

1 km

N

Imaging Spectroscopy Tour of Study Area  23
Vegetation

Partially Melted Snow

Wet Vegetation

Dry Vegetation

Frozen Snow

Rock, Soil, or Water

Snow and Slush

Snow, Slush, Vegetation

Melting Snow, Vegetation

Melting Snow

Group 3 Materials

Snow and Vegetation

1 km

N

Figure 15. AVIRIS Group 3 map of Ironton Park and Red Mountain region (fig. 2, inset A) showing distribution of snow, soil, and 

vegetation.

24  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
denoting hematite in the Group 1 (iron oxide) explanation of 

figure 13 are distributed widely along the ridge connecting 

Telluride Peak and Hayden Mountain (figs. 9 and 10). These 

ridges are separated from Red Mountain by Ironton Park 

and Red Mountain Creek. This creek does not flow into the 

Animas River; rather it flows northward through Ouray and 

into the Uncompahgre River. Red Mountain in figure 13 is 

dominated by the shade of green indicative of goethite in the 

Group 1 explanation, as well as some jarosite (dark blue) and 

hematite. These minerals are the products of pyrite weathering 

that generally define the enormous Red Mountain acid-sulfate 

alteration; the facing peaks of Telluride and Hayden Mountains 

are outside the zone of strongest alteration and hence display 

different mineralogy. Kaolinite and alunite (fig. 14) delineate 

the main centers of the acid-sulfate alteration zones. Almost 

no vegetation grows within the most intensely mineralized 

zones (compare figs. 14 and 15). Calcite, sericite, and chlorite 

indicative of weaker propylitic alteration (fig. 14) are virtually 

absent on the peaks of the Red Mountains, but are more 

abundant farther from the central acid-sulfate alteration zone. 

Exposures of calcite, epidote, and chlorite begin to predominate 

near Cement Creek, in the lower right corner of figure 14. As 

described previously, the ridge to the west also contains abundant 

propylitic alteration products. 

Although the photographs in figures 9, 10, and 11 were 

taken in midsummer, at the end of July 1997, remnant snow 

pack from the previous winter abounds. The AVIRIS image 

cubes were acquired in June 1996, and it is therefore not surprising 

that they exhibit even more extensive snow cover. This obscures 

some of the mineralogy, particularly near the mountain peaks. 

Because snow undergoes changes in grain size as it melts 

(and refreezes), and because the spectrum of water is different 

from that of snow, the Tetracorder system finds it simple to 

differentiate various qualities of the snow pack. Because much 

of the Animas River watershed is perennially covered by 

snow and (or) vegetation, it is instructive to view the 

Tetracorder images for snow and vegetation to see how 

the mineralogy is obscured.

Figure 15 is a snow and vegetation map for the Ironton 

and Red Mountain scene. The most heavily mineralized zones, 

such as the Red Mountains, are typically free of vegetation. 

Only relatively small areas are completely free of either snow 

or vegetation. The “wet vegetation” class is widely distributed: 

the images were acquired in the morning, so dew may account 

for much of the observed signal. Rainwater is not expected 

to be a factor, as the sky was clear at the time; however, some 

standing water may still be on the ground beneath the canopy 

from a previous rain. Most of the mountain peaks in the 

image are at least partially covered by frozen snow. Melting 

snow occurs at the edges of the snow pack, sometimes in 

combination with vegetation. Snow and slush are readily 

identified by the spectral combination of liquid water and 

snow. An interesting phenomenon is apparent in figure 15 to 

the east of Cement Creek in the lower right of the image. On 

the lower reaches of this forested, northwest-facing slope, large 

areas of heavy snow pack have melted and refrozen several 

times over the course of the summer, yet resisted melting 

completely. This large-grained snow and slush mixture is 

found in many locations in the AVIRIS data, and almost 

always on steep, forested, north-facing slopes.

Given the extensive vegetation cover, one might expect 

the AVIRIS mineral maps to be more sparse. However, in that 

the Tetracorder system computes correlation coefficients for 

all spectra in the reference library, then makes decisions on 

the basis of these coefficients, it is capable of assigning high 

confidence levels to more than one material in a given pixel. 

Hence, small mineralized zones surrounded by vegetation 

are still evident in the processed images. Cement Creek, for 

example, is less than a pixel wide in most places along its 

length. Precipitated minerals along its banks are generally 

about a meter to either side of the creek. The creek, and the 

various mineralized areas such as Ohio Gulch which feed into 

it, are gray in figure 15 but are colored to reveal the presence 

of alteration minerals in figures 13 and 14. The precipitates 

along the banks of Cement and other creeks reveal the extent 

of acidification of the stream waters. Amorphous iron oxides 

and various Fe2+- and Fe3+-bearing minerals outline the stream 

reaches (fig. 13), and their strong VNIR absorption features 

allow them to be identified even where their spatial extent 

is limited.

Of particular importance are the distributions of minerals 

associated with acid-generating and acid-neutralizing chemical 

reactions. The minerals associated with acidic runoff are combined 

into Group 4, which is a subset of the Group 1 minerals. Group 

4 minerals are just those which have been found to be most 

closely associated with pyrite weathering and acid generation, 

and include jarosite, goethite, hematite, and amorphous iron 

oxides and hydroxides (Swayze, Smith, and Clark, 2000; 

Swayze, Smith, Clark, Sutley, and others, 2000). The extent and 

distribution of acid-generating potential are most conveniently 

viewed in the Group 4 mineral maps (fig. 16), because they contain fewer minerals to distract the eye. Furthermore, the image 

overlays have been constructed with a higher abundance threshold, 

so that areas containing small amounts of these minerals are not 

shown. The distribution of goethite, for example, is somewhat 

less extensive in figure 16 than in figure 13, because only areas 

of high concentrations are shown. The widespread outcrops of 

jarosite on the Red Mountains, and additional jarosite in mill 

tailing piles on the west side of the mountains (fig. 16, center), 

indicate the presence of large quantities of pyrite in the area. 

Runoff from these areas contributes to the acidification of Red 

Mountain, Cement, and Mineral Creeks. The AVIRIS mineral 

maps reveal that unmined and intensely altered areas contribute 

significantly to the acidic runoff. Furthermore, the AVIRIS 

images indicate very little calcite present in this area to neutralize 

the acidic runoff, resulting in extremely low pH levels (2–4.5), 

and high concentrations of dissolved metals in these streams 

(Schemel and others, 2000).

Complementary to the Group 4 AVIRIS mineral maps 

showing areas of high acid-generation potential are the Group 5 

AVIRIS mineral maps showing areas of acid-neutralizing potential (fig. 17). This is generally associated with the presence of 

Imaging Spectroscopy Tour of Study Area  25
1 km

N

Group 4 Materials

Minerals Associated with

Acid-Generating Potential

Goethite

Jarosite

Nanocrystalline Hematite

Hematite

Amorphous and Other Iron 

Oxides and Hydroxides 

Figure 16. AVIRIS Group 4 mineral map of Ironton Park and Red Mountain region (fig. 2, inset A) showing distribution of minerals associated with weathering of pyrite. Jarosite (blue) has been found to be an excellent proxy for pyrite, which has no strong 

VNIR absorption features of its own.

26 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
 Group 5 Materials

Minerals Associated with

Acid-Neutralizing Potential

Sericite

Chlorite with Sericite

Chlorite

Sericite with Chlorite, 

Epidote and/or Calcite

Calcite

Epidote

Calcite with Epidote

Epidote with Calcite

Epidote with Calcite 

and Chlorite

Epidote with Chlorite

1 km

N

Figure 17. AVIRIS Group 5 mineral map of Ironton Park and Red Mountain region (fig. 2, inset A) showing distribution of calcite, 

epidote, chlorite, sericite, and mixtures thereof. These distributions have direct relevance to assessments of acid-neutralizing 

potential.

Imaging Spectroscopy Tour of Study Area  27
calcite, and to a lesser extent, chlorite, in propylitic alteration zones. The Red Mountains (fig. 16) do not exhibit very 

much of this mineralogy, but the surrounding areas, particularly 

to the south and east (fig. 17) have appreciable quantities of 

calcite, epidote, chlorite, and sericite. These minerals tend to 

occur together, and so the mixture classes in figure 17 tend to 

dominate. Several exposures are characterized by variations 

in relative abundance among these minerals, such as east of 

Cement Creek and northeast of Brown Mountain (fig. 17). 

Animas Forks and the Animas River 

Upstream from Silverton

The upper Animas River headwaters are located outside 

of the major areas of most intense hydrothermal alteration. 

This area (inset B, fig. 2; see also fig. 18) is characterized by a 

relatively dense network of mineralized veins that are typically 

accompanied by narrow envelopes of hydrothermally altered 

rock. However, several large jarosite zones are found, most 

notably in the vicinity of Houghton, California, Tuttle, 

Cinnamon, and Eureka Mountains (fig. 19, Group 1). Jarosite 

zones around the latter three reflect the presence of closely 

spaced veins and overlapping envelopes of altered rock. In 

contrast, large zones of jarositic-altered rock in the Houghton 

and California Mountain areas are indicative of pervasive 

quartz-sericite-pyrite alteration associated with the intrusion 

of volatile-rich, high-silica rhyolite plugs. The extent of 

these exposures appears much larger in the Group 2 mineral 

map (fig. 20). This is because in the two different images, 

the jarosite is competing against different mineral spectra 

for dominance. As discussed previously, the jarosite does 

not cease to exist at the edges of the dark-blue areas in the 

image: it is most likely less abundant than the mineral which 

supersedes it. In the Group 1 mineral map, this is commonly 

an iron-bearing mineral such as goethite, but in the Group 2 

mineral map it is typically sericite. All three minerals may 

actually coexist. This is a good illustration of how all of the 

mineral identifications and distributions presented in this study 

should be interpreted flexibly.

In figure 19, jarosite is associated with the extensive 

mill tailings along the Animas River near Middleton and 

Howardsville. In figure 20, however, jarosite is overwhelmed 

by the spectral signature of sericite at Middleton, and of 

montmorillonite, an argillic alteration product, at the 

Howardsville Mill site. This is the only major surface exposure 

of montmorillonite found in the AVIRIS study and was 

verified by field-checking and laboratory spectroscopy of 

hand-collected samples.

The presence of jarosite in the upper Animas River 

region suggests the potential for significant acidic drainage. 

Geochemical analyses (Mast and others, 2000), however, 

indicate that with few exceptions, non-mining related waters 

are generally of good quality. This is largely because of the 

absence of large expanses of intensely altered, pyrite-rich 

rock such as is found in the Red Mountains, Ohio Peak–Anvil 

Mountain, and peak 3,792 m areas (Bove and others, 2000; 

Mast and others, 2000). However, the preponderance of calcitebearing, propylitic-altered rock in these basins undoubtedly has 

an acid-neutralizing effect on low-pH runoff where it does occur 

(Bove and others, 2000; Mast and others, 2000). Appreciable 

quantities of calcite are evident along the Animas River 

(fig. 20), most notably around Cataract Gulch, Eureka Mountain, 

and Burns Gulch, but also elsewhere. This calcite is generally 

associated with epidote and chlorite. In figure 21, the regions 

of epidote and calcite from figure 20 are broken down into 

mixtures of calcite, epidote, chlorite, and sericite, with colors 

denoting the dominant material(s) in each pixel. The variations 

in concentration of calcite may be estimated by following the 

color changes across each area. These variations in composition 

have been checked in the field by traverses across accessible 

deposits, such as the ≈1/4 km exposure paralleling the Animas 

River just below Cinnamon Mountain. In that pixels containing moderate amounts of calcite are enumerated in addition to 

those dominated by calcite, the Group 5 mineral assemblage 

images should prove useful for estimating the potential for 

acid neutralization occurring along the stream reaches.

Recent laboratory studies (Desborough and others, 1998) 

indicate that chlorite provides some acid neutralization as well, 

though on a scale an order of magnitude less than that presented 

by calcite. Very little chlorite was identified in the Group 1 

AVIRIS mineral maps, a notable exception being an area in 

Hematite Gulch (fig. 19). Although chlorite is rather abundant 

in the San Juan Mountains, it is spectrally overshadowed in the 

1-μm range by other iron-bearing minerals, and in the 2-μm 

range by sericite. Most of the chlorite detected by this study 

is in the form of ripidolite (clinochlore group), and its distribution is best approximated using the Group 5 images (for 

example, fig. 21).

Another lesson to be learned from figure 21 is in regards 

to single-pixel identifications. Large areas of sericite, such 

as those on the slopes overlooking the South Fork Animas 

River south of Eureka Gulch (figs. 18 and 21, center) are often 

surrounded by pixels identified as being dominated by sericite 

but containing other minerals (orange in fig. 21). Farther 

out, many scattered small areas meet the criteria for inclusion 

in this class. In many cases, the spectra of these pixels are 

contaminated by vegetation, snow cover, and other materials, 

so that the confidence levels are very close to the minimum 

threshold values. Such single-pixel units should be interpreted 

with caution, as noise levels and interference may result in 

some incorrect identifications.

Silverton and Molas Lake Scenes 

As the Animas River flows south past Silverton 

(inset C, fig. 2; see also fig. 22), pH decreases and significant 

quantities of metals are added from Cement and Mineral 

Figure 18 (following page). Geographic features of the upper 

Animas River region near Animas Forks (fig. 2, inset B).

28 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Eureka

Animas

Forks

Middleton

Howardsville

Eureka Gulch

Maggie

Gulch

Cataract Gulch

Hematite Gulch

Burns

Gulch

SUNNYSIDE

MINE

Houghton

Mtn

California

Mtn

Tuttle

Mtn

Eureka

Mtn

Cinnamon

Mtn

B

 

Imaging Spectroscopy Tour of Study Area  29
Goethite

Jarosite

Chlorite 

Nanocrystalline Hematite

Hematite

Epidote

Maghemite

Schwertmannite

Fe -bearing Minerals 3+

Plagioclase Feldspar

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals

with Hematite

2+

Amorphous and Other Iron 

Oxides and Hydroxides 

Group 1 Materials

Iron Oxides and 

Associated Minerals

1 km

N

Figure 19. AVIRIS 1-μm (Group 1) mineral map of upper Animas River near Animas Forks (fig. 2, inset B) showing distributions 

of iron oxides and associated minerals.

30 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Group 2 Materials

Alteration Products

and Associated Minerals

Na-Alunite

Intermediate Alunite

K-Alunite

Kaolinite and Alunite

Alunite with Pyrophyllite

Alunite with Kaolinite 

and/or Sericite

Alunite with Sericite

and/or Pyrophyllite

Sericite (Illite)

Sericite and Jarosite

Sericite and Pyrophyllite

Sericite with Chlorite,

Epidote, and/or Calcite

Calcite

Epidote

Na-Montmorillonite

Ca-Montmorillonite

Dickite

Kaolinite

Kaolinite and Sericite

Kaolinite with Smectite

Kaolinite with Pyrophyllite

Pyrophyllite

1 km

N

Figure 20. AVIRIS 2-μm (Group 2) mineral map of upper Animas River near Animas Forks (fig. 2, inset B) showing distributions of 

alteration products and associated minerals.

Imaging Spectroscopy Tour of Study Area  31
 Group 5 Materials

Minerals Associated with

Acid-Neutralizing Potential

Sericite

Chlorite with Sericite

Chlorite

Sericite with Chlorite, 

Epidote, and/or Calcite

Calcite

Epidote

Calcite with Epidote

Epidote with Calcite

Epidote with Calcite 

and Chlorite

Epidote with Chlorite

1 km

N

Figure 21. AVIRIS Group 5 mineral map of upper Animas River near Animas Forks (fig. 2, inset B) showing distributions of calcite, 

epidote, chlorite, sericite, and mixtures thereof.

32  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Creeks (Church and others, 1997; Schemel and others, 2000). 

The town of Silverton is located near the confluences of 

these two creeks with the Animas River. The streets of the 

town can be distinguished in the Group 1 AVIRIS mineral 

map (fig. 23) as a rectangular grid of goethite and other 

iron-bearing minerals in the roadbeds and gravel, and as 

sericite in the Group 2 AVIRIS mineral map (fig. 24). The 

stream beds of Mineral and Cement Creeks, as they cut 

across the town, are also dominated by goethite and ironbearing minerals, including amorphous iron oxides and 

hydroxides precipitated from the streams. As the relatively 

acidic and metal-laden streams mix with the higher pH 

waters of the Animas River, aluminum oxy-sulfates and iron 

oxyhydroxides precipitate, coating the rocks on the banks 

and in the riverbed (Church and others, 1997). Several 

areas of oxidized and highly pyritic rock are exposed on 

the mountainsides overlooking Mineral Creek. As shown 

in figure 24, these exposures contain alunite and kaolinite. 

These assignments were field-checked in 1996.

The border between the two AVIRIS flightlines crosses 

Sultan Mountain southwest of Silverton. This is most 

obvious as a change in contrast levels in the mosaicked 

AVIRIS mineral maps (figs. 23, 24). Less apparent effects 

of the orientation of the AVIRIS platform are illustrated 

by changes in mineral identifications across the flightline 

boundary. As viewing angle is different in each flightline, 

the signal level can differ greatly, especially on steep slopes 

such as those near the peak of Sultan Mountain. Because 

many minerals are typically present, different materials may 

produce a better spectral match on each side of the boundary. 

This gives rise to such artifacts as the abrupt border of the 

epidote-dominated region where the flightline crosses Sultan 

Mountain: in the east part of the image, the epidote signal 

was below the detection threshold.

Highly altered mineralized areas northwest of Silverton 

(top center, fig. 24) containing abundant alunite, kaolinite, 

and pyrophyllite contrast markedly with the less altered 

mineralogy on either side of the Animas River south of 

Silverton. Kendall Mountain, Sultan Mountain, and 

Whitehead Gulch all exhibit the distinct characteristics of 

propylitic alteration, indicating that they lie outside the 

central alteration zones of the caldera. The oxidized rocks 

south of Sultan Mountain are dominated by hematite, which 

is quite noticeable in figure 23, but they have very little in 

the way of alteration products with spectral signatures in the 

2-μm range (fig. 24).

South of Silverton, the Animas River passes exposures 

of abundant calcite and epidote on the slopes of Kendall 

Mountain (fig. 24) and along the riverbed. Eventually 

it departs the caldera and flows through a sequence of 

sedimentary rocks containing abundant limestones. The 

detailed breakdown of acid-neutralizing minerals in 

figure 25 shows the variation of calcite, epidote, and chlorite 

concentrations in the propylitic zones on the periphery of the 

Silverton caldera. The utility of building a separate image 

for Group 5 minerals (calcite-epidote-chlorite) is demonstrated 

by comparison of figures 24 and 25: the acid-neutralizing 

potential of the rocks just downstream of Silverton in the 

Animas River canyon is obvious in figure 25 but takes 

more effort to observe in the much busier Group 2 AVIRIS 

mineral map (fig. 24). Figure 24 includes a large component 

of alteration products, north of Mineral Creek, and there are 

just not enough colors available to visually segregate these 

materials from the acid-neutralizing assemblages. 

Looking farther downstream, sets of parallel yellow 

bands in the lower left corner of figures 24 and 25 delineate

sedimentary limestone beds of the Pennsylvanian-age 

Hermosa Formation (Yager and Bove, 2002) west of the 

Animas River near Molas and Andrews Lakes (fig. 22). This 

much more concentrated form of calcite contributes significant 

acid-neutralizing potential to the watershed (Church and 

others, 1997; Desborough and others, 1998). Field investigation 

of these limestone beds revealed that whereas the yellow 

banded areas corresponding to calcite identification are 

confined to a small area, the entire region exhibiting banded 

structure is also heavily interbedded with limestone. The 

reason that not all of these bands were recognized by the 

Tetracorder system is that abundant grasses growing along 

the rocks obscure the spectral signature.

Engineer Mountain and Purgatory Scenes

Downstream of Silverton the mineralogy of the 

Animas River watershed becomes much more subdued. 

South of Molas Lake are some oxidized outcrops containing 

jarosite and goethite on Snowdon and Twilight Peaks 

(below C and right of D, fig. 2; see also fig. 1), but there is 

little hydrothermal alteration evident elsewhere. The 

Engineer Mountain area (fig. 2, inset D; see also fig. 26) 

contains abundant vegetation and little in the way of 

intensely altered rocks. Significant acid-neutralizing potential 

is contributed to the Animas River by drainage from Lime 

Creek (fig. 26 and also very visible on east side of area of 

fig. 27) (Desborough and others, 1998; Mast and 

others, 2000). Most of this portion of the study area is 

heavily vegetated, which reduces the amount of surface 

mineralogy visible to the AVIRIS sensor.

Hematite was identified on Engineer Mountain (fig. 27) 

from a very faint absorption feature in the 1-μm range. 

The lightly hematitic talus slopes on the southeast face of 

Engineer Mountain include the calibration site used for 

ground-truthing the AVIRIS cubes. The absorption profile of 

the nanocrystalline hematite on the talus slopes of Engineer 

Imaging Spectroscopy Tour of Study Area  33

Figure 22 (following page). Silverton and Molas Lake region 

of study area (fig. 2, inset C) detailing major geographic features 

discussed in text.
Silverton

C

Whitehead Gulch

Sultan

Mtn

Elk Creek East

Lime

Creek

Andrews

Lake

Little

Molas

Lake

Molas

Lake

A

nim

as

Rive

r

Mineral Creek

Bear Creek

 

34  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Peak is quite subtle, but the color-coded images produced 

for this study do not reflect this: the same intensity of color 

(orange in this case) is used wherever a mineral (hematite in 

this case) has been identified. Some hematite is evident also 

at Potato Hill on the right side of the figure, and in the roads 

and ski trails of the Purgatory Resort (lower left). A number 

of other iron-bearing minerals were identified in the tributaries 

of Lime Creek (far right).

Interestingly, the snow and vegetation image (fig. 28) 

reveals some large-grained slushy snow at lower elevations, 

such as at Potato Hill. These patches are almost exclusively 

on steep, north-facing wooded hillsides. This is the same 

spectral signature of snow beneath a forest canopy identified 

in figure 15. Frozen and partially melted snow is prevalent 

only at the peak of Engineer Mountain but can also be 

discerned in ridged areas near the top (fig. 28). As the elevation 

has decreased from the headwaters regions, the abundances 

of snow and various vegetation species have changed 

significantly. Less snow remains even near the peaks, although 

wet vegetation occupies a zone about the margin of the peak 

regions. Wet vegetation is also visible along Cascade and 

Lime Creeks.

Hermosa and Haviland Lake Scenes

As with the previous scene, most of the lower reaches 

of the study area (inset E, fig. 2; see also fig. 29) are heavily 

vegetated, with only limited amounts of exposed rock and 

soil. Some mineralogy is evident in the Group 1 (fig. 30) 

image, along the Hermosa Cliffs and on Spud Hill (fig. 

29), and in the flats along the Animas River near Stratton 

Lake and in the southern part of the image area. A few 

locations along the shores of Haviland and Shalona Lakes 

also exhibit evidence of iron oxide mineralogy. A selection 

effect is at work here, as the minerals of greatest interest to 

this study are abundant in the Silverton caldera and other 

parts of the northern portion of the study area; and these 

are the minerals the Tetracorder system was programmed to 

identify. Hematite is the most widely visible mineral in this 

image, although there are several others, including calcite, in 

the mosaics. Hematitic soils, such as in the diamond-shaped 

field at lower right and nearby residential areas, are classed 

as hematite in these images. Along the Animas River, a few 

small areas containing plagioclase feldspar were identified 

by the mapping algorithm. Subsequent investigation found 

that these pixels corresponded to rock piles in a commercial 

gravel operation. Permission was obtained to sample the 

rock piles, which turned out to be feldspathic river cobbles. 

Although plagioclase was only identified in small patches 

throughout the rest of the study region, it was kept in the 

final images for future reference. 

The snow and vegetation image (fig. 31) is typical of 

the southern reaches of the study area, with only sporadic 

areas having sufficient exposed mineralogy to be mapped 

by the Tetracorder system. No significant contributions to 

acidic runoff are to be expected in this area. Again as with 

the previous scene (fig. 28), some patches of snow and slush 

are identified on north-facing steep slopes. Some of these, 

being single pixels, are suspect and may be there because of 

subpixel combinations of liquid water and vegetation.

It is worth noting that much of the exposed mineral area 

apparent in the Hermosa scene (fig. 30) occurs in the original 

flood plain of the Animas River. Deposition of materials 

from the upper Animas River is to be expected in this area. 

High erosion rates and significant human presence, however, 

have likely obscured any evidence for premining sediment 

baselines in the area.

Overview and Discussion

The merged AVIRIS flightline mosaics contain a great 

deal of useful information about the mineralogy of the Animas 

River watershed study area of this report (area of data 

acquisition in fig. 2, particularly areas A–E). The hydrothermal 

alteration zones of the Silverton caldera are easily distinguished 

on the basis of the primary mineral groups shown in the 

AVIRIS mineral maps. Minerals associated with acid generation 

and neutralization of acidic waters can be traced and mapped 

as they occur throughout the watershed. The general character 

of the region as viewed by AVIRIS and presented in the mosaics 

and figure 2 should be more easily interpreted utilizing the 

descriptions presented in the preceding sections, which provide 

fundamental background for use of the image mosaics. 

Detailed investigation of specific areas will be facilitated by 

an understanding of the limitations inherent to the data and 

processing methods.

Large, unmined exposures of highly pyritic rock are 

indicated by the presence of abundant jarosite as observed at 

peak 3,792 m, Bear Mountain, Animas Forks, Eureka, and 

the Red Mountains areas. Smaller outcrops are abundant 

throughout the headwaters areas of the upper Animas River, 

Cement Creek, and Mineral Creek. The results of this study 

are useful for identification of areas where both natural and 

anthropogenic low-pH and metal-bearing water may be present. 

The lack of large pervasively altered and pyritized zones, and 

the abundance of acid-neutralizing minerals (calcite and chlorite) 

within broad expanses of propylitically altered rock along the 

upper Animas River near Animas Forks, Eureka, and Silverton, 

likely account for the improved water quality in these areas. 

Calcite and chlorite are far less prevalent in the Mineral Creek 

and Cement Creek areas, where stream acidity levels are the 

highest in the watershed (Church and others, 1997). The 

interactions of these mineral assemblages explain why the 

upper Animas River, which drains a basin containing 

abundant propylitic-altered rocks, has a near-neutral pH (>6.5), 

while Mineral and Cement Creeks have pH values as low as 4.5. 

Overview and Discussion  35
Goethite

Jarosite

Chlorite 

Nanocrystalline Hematite

Hematite

Epidote

Maghemite

Schwertmannite

Fe -bearing Minerals 3+

Plagioclase Feldspar

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals

with Hematite

2+

Amorphous and Other Iron 

Oxides and Hydroxides 

Group 1 Materials

Iron Oxides and 

Associated Minerals

1 km

N

Figure 23. AVIRIS 1-μm (Group 1) mineral map of Silverton area (fig. 2, inset C) including Molas Lake. Streets of Silverton stand 

out in upper right of this image.

36 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Figure 24. AVIRIS 2-μm (Group 2) mineral map of Silverton area (fig. 2, inset C) including Molas Lake. Streets of Silverton in 

upper right appear to be dominated by sericite, whereas beds of limestone map as calcite at lower left.

Group 2 Materials

Alteration Products

and Associated Minerals

Na-Alunite

Intermediate Alunite

K-Alunite

Kaolinite and Alunite

Alunite with Pyrophyllite

Alunite with Kaolinite 

and/or Sericite

Alunite with Sericite

and/or Pyrophyllite

Sericite (Illite)

Sericite and Jarosite

Sericite and Pyrophyllite

Sericite with Chlorite,

Epidote, and/or Calcite

Calcite

Epidote

Na-Montmorillonite

Ca-Montmorillonite

Dickite

Kaolinite

Kaolinite and Sericite

Kaolinite with Smectite

Kaolinite with Pyrophyllite

Pyrophyllite

1 km

N

Overview and Discussion  37
 Group 5 Materials

Minerals Associated with

Acid-Neutralizing Potential

Sericite

Chlorite with Sericite

Chlorite

Sericite with Chlorite, 

Epidote, and/or Calcite

Calcite

Epidote

Calcite with Epidote

Epidote with Calcite

Epidote with Calcite 

and Chlorite

Epidote with Chlorite

1 km

N

Figure 25. AVIRIS Group 5 mineral map of Silverton and Molas Lake area (fig. 2, inset C) showing distributions of calcite, 

epidote, chlorite, and sericite. The sedimentary limestones at lower left bear much higher concentrations of calcite than 

are found in zones of propylitic alteration such as those at Sultan Mountain (southwest of Silverton) and Kendall Mountain 

(upper right, southeast of Silverton).

38 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Determination of the importance and extent of acid-neutralizing 

minerals should be assisted by the availability of the 

AVIRIS calcite-epidote-chlorite-sericite (Group 5) mineral 

maps in this report.

On three separate occasions, fieldwork was performed to 

verify the accuracy of the Tetracorder results. Although many 

areas could not be field-verified, spot-checks were made at 

several locations for every material presented in this report 

and in the AVIRIS mineral maps. The Tetracorder algorithm 

was trained and reapplied to the data set until the results 

matched the field determinations. Additional fieldwork and 

further refinements to the algorithm and spectral library will 

most likely result in modification of the specific details of 

the image mosaics, particularly along the periphery of some 

mineral zones, and within the calcite-epidote-chlorite-sericite 

deposits; but the general assessments and distributions are to 

first order more than sufficient for present purposes.

A great deal of information contained in the AVIRIS data 

set has not made its way into this report. Specific details of 

vegetation species, for example, may be extracted using the 

Tetracorder approach, but this is not of immediate relevance 

to the present investigation. Certain minerals have been found 

only in limited regions of the study area, for example 

schwertmannite, which was found only at the periphery of 

snow banks or water bodies in regions of strong pyrite oxidation. Meaningful subdivisions of known mineral classes 

(such as high versus low aluminum-content muscovites, 

or alunites of varying formation temperatures) have not 

been included. The full Tetracorder results, which involve 

more than 400 minerals and material classes, could not be 

included here because of practical space limitations.

This report does attempt to include some information that 

may be of use to future investigators having other applications. 

The Group 1 and Group 2 AVIRIS mineral maps, in particular, 

contain information on the distributions of more than 37 

minerals and mixtures. Every effort was made to assure that 

this information was of the highest possible quality. This 

knowledge can be applied not only to studies of water quality, 

but also to studies of riparian habitats, mineral distributions, 

environmental impact, structural geology, and hydrothermal 

alteration processes. The details of snow cover and vegetation 

distribution, and many of the mineral results, may have other 

uses unforeseen in the preparation of this report. The spectral 

library, which is an enhancement of that published in Clark, 

Swayze, Gallagher, and others (1993), is intended for public 

release. The techniques of imaging spectroscopy utilized by 

the Tetracorder 3.6a2 system are still under development, and 

future improvements are expected (Clark and others, 2003).

Conclusions

The AVIRIS mineral maps presented with this report are 

intended for general use in studies that require knowledge of 

the distribution of minerals in the Animas River watershed, 

and in particular how they relate to water quality issues. The 

mineral maps are composed of processed image mosaics 

which have been orthorectified, field checked, and formatted 

for maximum applicability. The Group 1 AVIRIS mineral 

maps detail the distributions of iron oxides and associated 

minerals throughout the watershed, from the Silverton caldera 

and into the lower reaches of the Animas River. The Group 

2 AVIRIS mineral maps of alteration products reveal further 

details about the hydrothermal and other geologic processes 

that gave rise to the present landscape. The Group 3 AVIRIS 

maps illustrate the highly vegetated nature of the watershed 

and provide a rough estimate of vegetation distributions. In 

addition, they demonstrate a new approach for discrimination 

of snow in alpine environments from AVIRIS imaging 

spectroscopy. This has a number of applications beyond the 

Animas River watershed (Nolin and Dozier, 1993, 2000; 

Painter and others, 1998). The Group 4 AVIRIS mineral maps 

were assembled from a subset of the Iron Oxide and Alteration 

Products groups. Their usefulness is specific to those concerned 

with acid-generating potential; the omission of other minerals 

from these images should make it easier to focus upon the 

materials of interest. The Group 5 AVIRIS mineral maps 

represent a further separation of the calcite, epidote, chlorite, 

and sericite distributions, including mixtures of the four. 

Though quantitative abundances cannot be precisely determined 

at present, knowledge of the presence, dominance, or absence 

of a given material at particular locations is integral to 

characterization of acid-neutralizing potential.

In the hands of a capable user, the techniques presented 

here can be used to map surface distributions at a fraction 

of the cost of traditional field studies. In the Animas River 

watershed study area, AVIRIS imaging spectroscopy makes 

it possible to discern the relative abundance and distribution of acid-generating and acid-neutralizing minerals 

across a large and extremely rugged area. The primary 

acid-generating mineral assemblages of the Silverton 

caldera, and their acid-neutralizing counterparts, can 

now be quantified. While not without its caveats, the 

AVIRIS study should significantly enhance understanding 

of water quality issues in the Animas River watershed.

This project demonstrates that imaging spectroscopy data 

sets over rugged terrain can be accurately calibrated, interpreted, 

registered, and orthorectified to a standard map base. Difficulties 

Conclusions  39

Figure 26 (following page). Engineer Mountain portion of study 

area (fig. 2, inset D) showing geographic features discussed in text. 
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40 Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Goethite

Jarosite

Chlorite 

Nanocrystalline Hematite

Hematite

Epidote

Maghemite

Schwertmannite

Fe -bearing Minerals 3+

Plagioclase Feldspar

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals

with Hematite

2+

Amorphous and Other Iron 

Oxides and Hydroxides 

Group 1 Materials

Iron Oxides and 

Associated Minerals

1 km

N

Figure 27. AVIRIS Group 1 mineral map of Engineer Mountain region (fig. 2, inset D) showing distribution of iron oxides, 

predominantly hematite. Heavy vegetation in this portion of the study area obscures much of the mineralogy.

Conclusions  41
Vegetation

Partially Melted Snow

Wet Vegetation

Dry Vegetation

Frozen Snow

Rock, Soil, or Water

Snow and Slush

Snow, Slush, Vegetation

Melting Snow, Vegetation

Melting Snow

Group 3 Materials

Snow and Vegetation

1 km

N

Figure 28. AVIRIS Group 3 map of Engineer Mountain region (fig. 2, inset D) demonstrating the prevalence of thick vegetation 

throughout the area. Some snow is also found at high altitudes and on protected north-facing slopes.

Figure 29 (following page). Hermosa and Haviland Lake region 

of study area (fig. 2, inset E) showing geographic features discussed in text.

42  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Spud Hill

H

e

mr o

s

a

Cliffs

Haviland

Lake

Shalona

Lake

Stratton

Lake

Hermosa

Creek

A

n

mi a

s

River

E

 

Conclusions  43
Goethite

Jarosite

Chlorite 

Nanocrystalline Hematite

Hematite

Epidote

Maghemite

Schwertmannite

Fe -bearing Minerals 3+

Plagioclase Feldspar

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals 2+

Fe -bearing Minerals

with Hematite

2+

Amorphous and Other Iron 

Oxides and Hydroxides 

Group 1 Materials

Iron Oxides and 

Associated Minerals

1 km

N

Figure 30. AVIRIS Group 1 mineral map of Hermosa region (fig. 2, inset E) showing distributions of exposed iron oxides and associated 

minerals.

44  Remote Sensing Characterization of the Animas River Watershed by AVIRIS
Vegetation

Partially Melted Snow

Wet Vegetation

Dry Vegetation

Frozen Snow

Rock, Soil, or Water

Snow and Slush

Snow, Slush, Vegetation

Melting Snow, Vegetation

Melting Snow

Group 3 Materials

Snow and Vegetation

1 km

N

Figure 31. AVIRIS Group 3 map of Hermosa and Haviland Lake region (fig. 2, inset E), detailing snow and vegetation cover.

Conclusions  45
specific to the airborne AVIRIS high-altitude ER-2 platform 

have been overcome, and those caused by the extreme elevation changes in the San Juan Mountains have been shown to 

be surmountable. In addition, even materials having their 

diagnostic spectral absorptions at the same wavelength may 

be successfully discriminated by the Tetracorder approach. 

Future improvements to the AVIRIS sensor and Tetracorder 

expert system should provide even more powerful techniques 

for remote sensing. The expected advent of orbiting civilian 

imaging spectrometers will undoubtedly create new directions 

for geologic investigations.

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