sabins remote sensing & mineral exploration 1(8)

8/13/2019 Sabins Remote Sensing & Mineral Exploration 1(8)

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.Ore Geology Reviews 14 1999 157183

Remote sensing for mineral exploration

Floyd F. Sabins )

Remote Sensing Enterprises, 1724 Celeste Lane, Fullerton, CA 92833, USA

Received 13 November 1998; accepted 20 April 1999

Abstract

Remote sensing is the science of acquiring, processing, and interpreting images and related data, acquired from aircraft

and satellites, that record the interaction between matter and electromagnetic energy. Remote sensing images are used for . .mineral exploration in two applications: 1 map geology and the faults and fractures that localize ore deposits; 2 recognize

.hydrothermally altered rocks by their spectral signatures. Landsat thematic mapper TM satellite images are widely used to

interpret both structure and hydrothermal alteration. Digitally processed TM ratio images can identify two assemblages of

hydrothermal alteration minerals; iron minerals, and clays plus alunite. In northern Chile, TM ratio images defined the

prospects that are now major copper deposits at Collahuasi and Ujina. Hyperspectral imaging systems can identify individual

species of iron and clay minerals, which can provide details of hydrothermal zoning. Silicification, which is an important

indicator of hydrothermal alteration, is not recognizable on TM and hyperspectral images. Quartz has no diagnostic spectral

features in the visible and reflected IR wavelengths recorded by these systems. Variations in silica content are recognizable

in multispectral thermal IR images, which is a promising topic for research. q 1999 Elsevier Science B.V. All rights

reserved.

.Keywords: remote sensing; mineral exploration; thematic mapper TM ; Goldfield mining district

1. Introduction

Remote sensing is the science of acquiring, pro-

cessing, and interpreting images and related data,

acquired from aircraft and satellites, that record the

interaction between matter and electromagnetic en- .ergy Sabins, 1997, p. 1 . This report reviews the use

of remote sensing for mineral exploration. Section 2

describes the remote sensing systems that are em-

ployed in mineral exploration and introduces the

)

Tel.: q1-714-879-4367; e-mail: [email protected]

computer techniques used to process digital data

acquired by the systems. Section 4 describes how

multispectral data are digitally processed to recog-nize hydrothermal alteration minerals iron minerals,

.clays, and alunite , using the Goldfield, NV, mining

district as a training site. The methods developed atGoldfield were used in northern Chile to define

anomalies that are now world-class copper deposits.

Section 8 describes future remote sensing systems

and their potential applications to mineral explo-

ration. Most of this report is extracted from Sabins .1997 , to which the reader is referred for additional

information.

0169-1368r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. .P I I : S 0 1 6 9 - 1 3 6 8 9 9 0 0 0 0 7 - 4


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( )F.F. SabinsrOre Geology Reiews 14 1999 157183158

2. Remote sensing technology

Table 1 lists characteristics of the principal re-

mote sensing systems that are currently available for

mineral exploration. Some systems are deployed only .on satellites Landsat, SPOT . Other systems are

currently deployed only on aircraft hyperspectral.systems . Radar systems are deployed on both satel-

lites and aircraft. Images acquired by satellite sys- .tems have the following advantages: 1 archives of

.worldwide data are readily available; 2 images .cover large areas on the ground; 3 prices per square

kilometer are generally lower. Disadvantages of .satellite images are: 1 the latest hyperspectral tech-

.nology is currently available only from aircraft; 2

aircraft missions can be configured to match the

requirements of a project. The following sections

summarize the major systems.

2.1. Landsat images

NASA has launched two generations of un-

manned Landsat satellites that have acquired valu-

able remote sensing data for mineral exploration and

other applications. Both generations were placed in

sun-synchronous orbits that provide repetitive im-

ages of the entire earth, except for the extreme polar

.regions. The first generation Landsats 1, 2, and 3

operated from 1972 to 1985 and is essentially re-

placed by the second generation. Table 1 lists somecharacteristics of the second generation Landsats 4,

.5 and 7 , which began in 1982 and continues to the

present. Landsat 6 of the second generation was

launched in 1993, but failed to reach orbit. Images .are acquired by the thematic mapper TM which is

an optical-mechanical cross-track scanner Sabins,.1997, Fig. 1-12A . An oscillating scan mirror sweeps

the field of view of the optical system across the

terrain at a right angle to the satellite orbit path. A

spectrometer separates solar energy that is reflected

from the earths surface into narrow wavelength

intervals called spectral bands. Each band is recorded

as a separate image.

Fig. 1 shows reflectance spectra for vegetation

and three common sedimentary rocks. The vertical

axis shows the percentage of incident sunlight that isreflected by the materials. The horizontal axis shows

wavelengths of energy for the visible spectral region . 0.4 to 7.0 mm and the reflected portion 0.7 to 3.0

. .mm of the infrared IR region. Reflected IR energy

consists largely of solar energy reflected from the

earth at wavelengths longer than the sensitivity rangeof the eye. The thermal portion of the IR region 3.0

.to 1000 mm consists of radiant, or heat, energy and

Table 1

Remote sensing systems for mineral exploration

Characteristic Landsat 4, 5 Landsat 7 SPOT SPOT AVIRIS

thematic mapper enhanced multispectral panchromatic hyperspectral . . .TM TM scanner XS Pan scanner

Spectral region

Visible and reflected IR 0.45 to 2.35mm 0.45 to 2.35mm 0.50 to 0.89mm 0.40 to 2.50 mm

Panchromatic 0.52 to 0.90mm 0.51 to 0.73mm

Thermal IR 10.5 to 12.5mm

Spectral bands 7 8 3 1 224

Terrain coerageEast to west 185 km 185 km 60 km 60 km 10.5 km cross-track

North to south 170 km 170 km 60 km 60 km

Ground resolution cell

Visible and reflected IR 30 by 30 m 30 by 30 m 20 by 20 m 20 m

Panchromatic 15 by 15 m 10 by 10 m

Thermal IR 120 by 120 m 60 by 60 m


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( )F.F. SabinsrOre Geology Reiews 14 1999 157183 159

Fig. 1. Spectral bands recorded by remote sensing systems. Spec-tral reflectance curves are for vegetation and sedimentary rocks. .From Sabins 1997, Fig. 4-1 .

is not shown in Fig. 1. The TM system records three .wavelengths of visible energy blue, green, and red

and three bands of reflected IR energy, which are

indicated in Fig. 1. These visible and reflected IR

bands have a spatial resolution of 30 m. Band 6,

which is not shown on Fig. 1, records thermal IR .energy 10.5 to 12.5mm with a spatial resolution of

120 m. Each TM scene records 170 by 185 km of

terrain. The image data are telemetered to earth

receiving stations.

Fig. 2 shows images for the six visible and re-

flected IR bands for a small subarea that covers the

Goldfield, NV, mining district. Any three of the

bands can be combined in blue, green, and red to

produce color composite images. Fig. 3A shows

bands 123 combined in blue, green, and red re-

spectively to produce a color image similar to that

observed by the eye or recorded by a color photo-

graph. Several alternate color combinations of TMbands are commonly employed Sabins, 1997, Chap.

.3 . The second generation of Landsat continued with

Landsat 7, launched in April, 1999, with an en-hanced TM system. A panchromatic band 8 0.52 to

.0.90 mm with spatial resolution of 15 m is added.

Band 8 can be combined with the visible and re- .flected IR bands 30 m resolution to produce a color

image with an apparent resolution of 15 m. Spatial

resolution of the thermal IR band 6 is improved from

120 m to 60 m.

TM data of the world are available for sale from

two sources. TM image data acquired in the past

decade are available from:

Space Imaging EOSAT12076 Grant Street

Thornton, CO 80241

Phone: q1-303-254-2000

Fax: q1-303-254-2215

E-mail: - [email protected]) .

The Space Imaging-EOSAT archive of TM images

acquired during the past decade may be viewed .interactively and ordered on the Web at -

http:rrspaceimaging.com) .

TM image data acquired prior to the past decade

and Landsat 7 data are available from:U.S. Geological Survey EROS Data Center

Sioux Falls, SD 57198

Phone: q1-605-594-6511

Fax: q1-605-594-6589

E-mail: [email protected]) .

The EROS Data Center archive of TM images may .be viewed interactively and ordered on the Web at

-http:rredcwww.cr.usgs.gov) .

2.2. SPOT

Beginning in 1986 a French company, called

SPOT Image, has launched a series of unmanned sun

synchronous satellites that acquire image data in two . .modes Table 1 . The multispectral XS mode ac-

quires three bands of data at green, red, and reflected .IR wavelengths Fig. 1 with a spatial resolution of

.20 m. The panchromatic pan mode acquires a


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single band of data, primarily at green and red

wavelengths, with a spatial resolution of 10 m. Both

image modes cover 60 by 60 km of terrain and may

be acquired in a stereoscopic format.

2.3. Hyperspectral imaging systems

Conventional multispectral scanning systems, such

as Landsat TM and SPOT XS, record up to 10

spectral bands with bandwidths on the order of 0.10

mm. Hyperspectral scanners are a special type of

multispectral scanner that record many tens of bandswith bandwidths on the order of 0.01 mm Sabins,

.1997, Chap. 1 . Many minerals have distinctive spec-

tral reflectance patterns at visible wavelengths and .especially at reflected IR wavelengths Hunt, 1980 .

Under favorable conditions, many minerals may be

identified on suitably processed hyperspectral data.

Fig. 1 shows the spectral region covered by the 224spectral bands recorded by the airborne visiblerin-

.frared imaging spectrometer AVIRIS which is a

hyperspectral system carried on high altitude aircraft

by NASA. AVIRIS image strips are 10.5 km wide

and several tens of kilometers long. The airborne

system is operated on an experimental basis, primar-ily in the U.S. A website http:rrmakalu.jpl.nasa.

.govraviris.html provides access to the archive of .AVIRIS images. Green et al. 1998 describe the

AVIRIS system and summarize a number of applica-

tion studies, including geology. Examples of AVIRISimages are shown in the section on the Goldfield

. mining district Section 4.3.1 . Sabins 1997, Tables.1 4 lists some airborne hyperspectral scanners that

are commercially available.

2.4. Radar systems

Radar is an active form of remote sensing that

provides its own source of electromagnetic energy to

illuminate the terrain. Radar energy is measured in

wavelengths of centimeters that penetrate rain and

clouds which is an advantage in tropical regions.

Another advantage is that radar images may be

acquired at a low depression angle that causes pro-

nounced highlights and shadows that enhance subtle

topographic features. These features are commonly

the expression of faults, fractures, and lithology.

Radar images of vegetated regions record the vegeta-

tion surface, rather than the underlying terrain. In .Indonesia, Sabins 1983 demonstrated that the forest

canopy conforms to the underlying terrain and that

geologic information can be interpreted from the

images. In Papua New Guinea, the Chevron Corpora-

tion relied on aircraft radar images to discover major

oil fields.

2.5. Digital image processing

Modern remote sensing systems record image data

in a digital raster format that is suitable for computer

processing using readily available software and per- .sonal computers. Sabins 1997, Chap. 8 groups

image-processing methods into three functional cate-

gories that are listed below, together with lists of

typical processing routines

1. Image restoration compensates for image errors,

noise, and geometric distortions introduced during

the scanning, recording, and playback operations.

The objective is to make the restored image re-

semble the scene on the terrain. Typical process-

ing routines include:

a. Restoring line dropoutsb. Restoring periodic line striping

c. Restoring line offsets

d. Filtering random noise

e. Correcting for atmospheric scattering

f. Correcting geometric distortions

2. Image enhancementalters the visual impact that

the image has on the interpreter. The objective is

to improve the information content of the image.

Typical processing routines include:

a. Contrast enhancement

b. Density slicingc. Edge enhancement

Fig. 2. Landsat TM visible and reflected IR images of Goldfield mining district, NV. Fig. 4 is a map of the area which covers 7 by 7 km. . . . . . . From Sabins 1997, Fig. 11-7 . A Band 1, blue 0.45 to 0.52 mm . B Band 2, green 0.52 to 0.60mm . C Band 3, red 0.63 to 0.69

. . . . . . .mm . D Band 4 reflected IR 0.76 to 0.90 mm . E Band 5, reflected IR 1.55 to 1.75mm . F Band 7, reflected IR 2.08 to 2.35mm .


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d. Making digital mosaics

e. Intensity, hue, and saturation transformations

f. Merging data sets

g. Synthetic stereo images

3. Information extraction utilizes the computer to

combine and interact between different aspects of

a data set. The objective is to display spectral and

other characteristics of the scene that are not

apparent on restored and enhanced images. Typi-

cal processing routines include:

a. Principal-component images

b. Ratio images

c. Multispectral classification

d. Change-detection images

The images in this report have been processed with

various combinations of these routines.

3. Mineral exploration overview

Table 2 lists representative recent mineral explo-

ration studies using remote sensing. These studies

describe two different approaches to mineral explo-

ration. .1 Mapping of geology and fracture patterns at

regional and local scales. Prospectors and mining

geologists have long recognized the importance of

regional and local fracture patterns as controls on ore .deposits. Rowan and Wetlaufer 1975 used a Land-

sat mosaic of Nevada to interpret regional linea-

ments. Comparing the lineament patterns with ore

occurrences showed that mining districts tend to

occur along lineaments and are concentrated at the .intersections of lineaments. Nicolais 1974 inter-

preted local fracture patterns from a Landsat image

in Colorado. The mines tend to occur in areas with a

high density of fractures and a concentration of .fracture intersections. Rowan and Bowers 1995

used TM and aircraft radar images to interpret linear

features in western Nevada. They concluded that the

linear features correlate with the geologic structures

that controlled mineralization. .2 Recognition of hydrothermally altered rocks

that may be associated with mineral deposits. The

spectral bands of Landsat TM are well-suited forrecognizing assemblages of alteration minerals iron

.oxides, clay, and alunite that occur in hydrother-

mally altered rocks. In my experience the best explo-

ration results are obtained by combining geologic

and fracture mapping with the recognition of hy-

drothermally altered rocks.

4. Mapping hydrothermal alteration at epither-

mal vein deposits Goldfield, Nevada

Most epithermal vein deposits are accompanied

by hydrothermal alteration of the adjacent country

rocks. Not all alteration is associated with ore bod-

ies, and not all ore bodies are accompanied by

alteration, but the presence of altered rocks is a

valuable indicator of possible deposits. Prospectors

have long been aware of the association between

hydrothermally altered rocks and ore deposits. Many

mines were discovered by recognizing outcrops of

altered rocks, followed by assays of rock samples.

Prior to remote sensing, altered rocks were recog-

nized by their appearance in the visible spectral

bands. Today remote sensing and digital image pro-

cessing enable us to use additional spectral bands for

mineral exploration. In regions where bedrock is

exposed, multispectral remote sensing can be used to

recognize altered rocks because their reflectance

spectra differ from those of the unaltered country

rock. The Goldfield Mining District in south-central

Nevada is the test site where remote sensing methods

Fig. 3. Recognizing hydrothermally altered rocks at Goldfield mining district, NV. Image F courtesy F.A. Kruse, Analytical Imaging and. . . .Geophysics, LLC, Boulder, CO. From Sabins 1997, Plate 21 . A TM 12 3 normal color image. B TM color ratio image. Ratio

. .5r7sred, 3r1sgreen, 3r5sblue. C TM ratio 5r7 image with density slice. High ratio values shown in red. D TM ratio 3r1 image . .with density slice. High ratio values shown in red. E TM unsupervised classification map. F Color composite image of AVIRIS

.endmember abundance images from Fig. 12 . Illitesred, alunitesgreen, kaolinitesblue.


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Table 2 .Representative mineral exploration investigations using remote sensing. From Sabins 1997, Table 11-3

Locality Reference Comments

.Western North and Spatz and Wilson 1994 Summarizes published remote sensing studies of

South America 12 major mining districts from British Columbia to Chile. .Altiplano, Bolivia Knepper and Simpson 1992 TM color ratio composite images used to recognize

hydrothermally altered rocks.

.Canada Singhroy 1991 10 papers on mineral exploration using Landsat and radar. .Chile, Peru, Eiswerth and Rowan 1993 TM color ratio composite images used to recognize

and Bolivia hydrothermally altered rocks. Field studies evaluated results. .Jordan Kaufmann 1988 Mapped hydrothermal alteration using digitally

processed TM images. .Jordan Abdelhamid and Rabba 1994 A variety of digitally processed TM images identified

a historic CurMn deposit and located prospects. .Sonora, Mexico Bennett et al. 1993 TM data were integrated with field and laboratory data

to discover several prospects. .Nevada Watson et al. 1990 TIMS data were processed to recognize silicified

rocks associated with gold deposits. .Spain Goosens and Kroonenberg 1994 TM ratio images were used to identify altered rocks

overlain by residual soil. .Sudan Griffiths et al. 1987 Landsat MSS images and field work showed gold

occurrences are concentrated along regional shear zonesin mafic metavolcanics.

.Arizona Abrams et al. 1983 Mapped hydrothermal alteration using digitally processed

aircraft multispectral images. .Montana Rowan et al. 1991 Compared the association of linear features with ore

deposits in Butte region. .Idaho and Montana Segal and Rowan 1989 Mapped hydrothermal alteration in the Dillon region.

.Utah Murphy 1995 Used hyperspectral data to map jasperoid. .Zaire, Zambia, Angola Unrug 1988 Major leadzinc vein deposits occur at intersections of

Landsat lineaments with folds and thrust faults.

Unexplored intersections are potential targets.

were first developed to recognize hydrothermally .altered rocks Rowan et al., 1974 .

4.1. Geology, ore deposits, and hydrothermal alter-

ation

.The Goldfield district Fig. 4 was noted for the

richness of its ore. Over 4 million troy ounces .130,000 kg of gold with silver and copper were

produced, largely in the boom period between 1903

and 1910. The geology and hydrothermal alteration

of the district have been thoroughly mapped andanalyzed by the U.S. Geological Survey Ashley,

.1974, 1979 , which makes Goldfield an excellent

locality to develop and test remote sensing methods

for mineral exploration.

Volcanism began in the Oligocene epoch with

eruption of rhyolite and quartz latite flows and the

formation of a small caldera and ring-fracture sys-

tem. Hydrothermal alteration and ore deposition oc-

curred during a second period of volcanism in the

early Miocene epoch when the dacite and andesite

flows that host the ore deposits were extruded. Heat-

ing associated with volcanic activity at depth caused

convective circulation of hot, acidic, hydrothermal

solutions through the rocks. Fluid movement was

concentrated in the fractures and faults of the ring-

fracture system. Following ore deposition, the area

was covered by younger volcanic flows. Later dom-

ing and erosion have exposed the older volcanic

center with altered rocks and ore deposits. .In the generalized map Fig. 4 , the hydrother-

mally altered rocks are cross-hatched and the unal-

tered country rocks are blank. Approximately 40 km2

of the area is underlain by altered rocks, but less than

2 km2 of the altered area contains economic mineral


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.Fig. 4. Map showing geology and hydrothermal alteration of Goldfield mining district, NV. From Ashley 1979, Figs. 1 and 8 .

deposits, which are shown in black. The oval band of

altered rocks was controlled by the circular ring-frac-

ture system, with a linear extension toward the east.

The central patch of alteration shown in Fig. 4 was

controlled by closely spaced faults and fractures. The

most highly altered rocks are the veins of microcrys-

talline quartz with some alunite. The ore occurs in

the veins, but the majority of veins are barren.

Adjacent to the veins, the country rock is altered to

the clay minerals illite, kaolinite, and montmoril-

lonite plus alunite. This assemblage of alterationminerals is called the argillic zone Harvey and

.Vitaliano, 1964 . The hydrothermal solutions also

deposited jarosite and pyrite in the veins and argillic

rocks. The pyrite weathers to iron oxides which

impart pink and red hues to the altered rocks. The

hydrothermally altered rocks at Goldfield, and other

epithermal vein deposits, are characterized by two

mineral assemblages:

1. Alunite and clay minerals

2. Iron minerals

The following sections describe how Landsat images

are digitally processed to recognize these assem-

blages.


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4.2. Recognizing hydrothermal alteration on Landsat

images

Fig. 2 shows the visible and reflected IR bands of

a TM subscene of the Goldfield district. Fig. 3A is

an enhanced normal color image of TM bands 123

shown in blue, green, and red, respectively. A yellow

patch directly northeast of the town of Goldfield is

caused by the mine dumps and disturbed ground of

the main mineralized area. A white patch 3 km north

of Goldfield is the dry tailings pond of the aban-

doned Columbia Mill, where gold was separated

from the altered host rock. The tailings pond is a

useful reference standard because it contains a con-

centration of altered rock material. The dark signa-

tures in the margins of the image are volcanic rocks

that are younger than the ore deposits and altered

rocks. Distinctive light blue signatures in the south-

east portion are outcrops of volcanic tuff. Neither thenormal color TM image nor alternate band color

combinations are diagnostic for recognizing the hy-

drothermally altered rocks. Therefore, additional dig-

ital processing is required in order to map hydrother-

mal alteration from TM data.

4.2.1. Alunite and clay minerals on 5r7 ratio im-

ages

Fig. 5A shows reflectance spectra of alunite and

the three common hydrothermal clay minerals illite,

kaolinite, and montmorillonite. These minerals have .distinctive absorption features reflectance minima

at wavelengths within the bandpass of TM band 7

which is shown with a stippled pattern in Fig. 5A.

The alteration minerals have higher reflectance val-

ues within TM band 5. Ratio images can emphasize

and quantify these spectral differences. A TM image .consists of picture elements pixels that represent a

ground resolution cell of 30 by 30 m. For each pixel

the reflectance values for all bands are recorded as .digital numbers DNs on an eight-bit scale from 0 to

Fig. 5. Recognition of hydrothermal clays and alunite from TM . .data, Goldfield mining district. From Sabins 1997, Fig. 11-8 . A

.Laboratory reflectance spectra. TM bands 5 and 7 shaded are .used to calculate 5r7 ratio image. B Ratio image of TM bands

.5r7. C Histogram for 5r7 image.


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255. Ratio images are prepared by dividing the value

for one band by that of another band, after atmo-spheric corrections have been made Sabins, 1997,

.Chap. 8 . Table 3 explains how TM ratio 5r7 distin-

guishes altered rocks containing clays and alunite

from unaltered rocks. Both rocks have similar values

in band 5. The reflectance of unaltered rocks in band

7 is similar to that in band 5. Therefore, the 5r7 .ratio for unaltered rocks is unity 1.00 . Altered

rocks, however, have lower reflectance in band 7

because of the absorption caused by the minerals

shown in Fig. 5A. Therefore, the 5r7 ratio for .altered rocks is much greater than unity 1.45 . The

numbers in Table 2 are typical and will differ for

other examples. The decimal ratio values are con- .verted to 8-bit digital numbers DNs and displayed

as images.

Fig. 5B is a 5r7 ratio image of Goldfield with

higher ratio values shown in brighter tones. Compar- .ing the image with the map Fig. 4 shows that the

high ratio values correlate with hydrothermally al-

tered rocks. Fig. 5C is a histogram of the 5r7 ratio .image that shows the higher ratio values DNs)145

of the altered rocks. Low ratio values represent

unaltered rocks.

Fig. 3C is a color density slice version of the 5r7

image in which the gray scale is replaced by the .colors shown in the histogram Fig. 5C . Highest

.ratio values DN)145 are shown in red, with the

.next highest values DN 125 to 145 shown inyellow. The red and yellow colors on the ratio image .Fig. 3C therefore correlate with the altered rocks.

4.2.2. Iron minerals on 3r1 ratio images

Iron oxides and sulfates are the second group of

minerals associated with hydrothermally altered

rocks. Fig. 6A shows spectra of the iron minerals

.which have low blue reflectance TM band 1 and .high red reflectance TM band 3 . Iron-stained hy-

drothermally altered rocks therefore have high values

in a 3r1 ratio image. Fig. 6B is a 3r1 ratio image

with high DN values shown in bright tones. Fig. 3D

is a color density slice version of the 3r1 image,

with color assignments shown in the histogram of .Fig. 6C. Highest ratio values DN)150 are shown

.in red, with the next highest values DN 135 to 150

shown in yellow. The red and yellow colors there-

fore correlate with the altered rocks.

4.2.3. Color composite ratio images

Color composite ratio images are produced by

combining three ratio images in blue, green, and red.

Fig. 3B shows ratios 3r5, 3r1, and 5r7 in red,

green, and blue, respectively. The orange and yellow

hues delineate the outer and inner areas of alteredrocks in a pattern similar to that of the density sliced

ratio images. An advantage of the color ratio image

is that it combines the distribution patterns of both

iron minerals and hydrothermal clays. A disadvan-

tage is that the color patterns are not as distinct as in

the individual density-sliced images.

4.2.4. Classification images

Multispectral classification is a computer routine

for information extraction that assigns pixels into

classes based on similar spectral properties. In asupervised multispectral classification, the operator

specifies the classes that will be used. In an unsuper-

vised multispectral classification, the computer spec-ifies the classes that will be used Sabins, 1997,

.Chap. 8 . An unsupervised multispectral classifica-

tion was applied to the TM bands in Fig. 2 and

resulted in 12 classes. These classes were aggregated

Table 3

.Calculation of TM 5r7 ratio values. From Sabins 1997, Table 11-1

Band 5 reflectance Band 7 reflectance Ratio 5r7 DNs for . . .typical typical typical ratio 5r7

Unaltered rocks 160 160 1.00 100 .without clays and alunite

Altered rocks 160 110 1.45 145 .with clays and alunite


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into the six classes shown in Fig. 3E with colors that

are explained in Table 4. Two types of altered rocks

were classified. The class shown in red Altered

rocks, A is confined to altered rocks, but does not

indicate the full extent of alteration. The class shown

in orange Altered rocks, B includes all of the

remaining altered rocks, as well as some rocks out- .side the alteration zone. Basalt blue , volcanic tuff

. .purple , and unaltered rocks green are reasonably .classified. Alluvium yellow is considerably more

.extensive in the classification image Fig. 3E than in .the geologic map Fig. 4 . Field checking and com-

.parison with the normal color image Fig. 3A shows

that much of the bedrock is thinly covered with

detritus and is correctly classed as alluvium by the

computer. The map, however, shows the lithology of

the underlying bedrock that was interpreted by the

field geologist.

4.3. Recognizing hydrothermal alteration on hyper-

spectral images

Because of their broad spectral band passes, TM

images cannot identify specific alteration minerals,

such as jarosite, alunite, and the individual clay

minerals. Such identifications could be valuable for

mapping details of hydrothermal zoning; these de-

tails can be mapped, however, from data acquired by

hyperspectral scanners. Fig. 7 shows laboratory spec-tra of common alteration minerals in the atmospheric

window from 2.0 to 2.5 mm and the 50 spectral

bands recorded by the AVIRIS hyperspectral scanner

for this wavelength interval. The bandpass of TM

band 7 is also shown for comparison. Van der Meer . .1994 , Kruse 1996 and others have shown that

AVIRIS has the spectral resolution to identify indi-

vidual alteration minerals.

The following sections describe AVIRIS images

that show the abundance and distribution of individ-

Fig. 6. Recognition of hydrothermal iron minerals from TM data, . .Goldfield mining district. From Sabins 1997, Fig. 11-9 . A

.Laboratory reflectance spectra. TM bands 1 and 3 shaded are .used to calculate 3r1 ratio image. B Ratio image of TM bands

.3r1. C Histogram for 3r1 image.


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Table 4

Explanation of colors in classification image of Goldfield mining . .district Fig. 3E . From Sabins 1997, Table 11-2

Color Classification Percent of image

Yellow Alluvium 39.2

Blue Basalt 14.0

Purple Tuff 6.6

Red Altered rocks, A 5.3Orange Altered rocks, B 18.3

Green Unaltered rocks 16.6

ual alteration minerals. There are, however, two

major technical challenges to producing such images. .1 Some alteration minerals, especially the clays,

.have similar spectra Fig. 7 . The major absorption

feature near 2.2 mm occurs at slightly different

wavelengths for the different clays and for alunite.

There are minor additional absorption features thatalso help distinguish the spectra. Image processing

programs can identify the spectrum recorded for a

single AVIRIS pixel by comparing it with a library

of reference spectra for known minerals. This proce-

dure is a form of supervised classification. The

procedure is effective, however, only for the rare

ground resolution cells in which only a single min-

eral occurs. .2 Each ground resolution cell of AVIRIS typi-

cally measures 20 by 20 m. In areas of complex

geology, such as Goldfield, the 400 m2

of a cellincludes a number of different minerals. The result-

ing pixel is called a mixed pixel because its spectrum

is a mixture of the spectra for the different minerals

that occupy the ground resolution cell. These indi-

vidual mineral species are called spectral endmem-

bers. Digital unmixing programs are used to derive

the spectra of the endmembers for each mixed pixel.

For each mineral, an endmember abundance image is

derived that shows the relative abundance of the

mineral.

4.3.1. AVIRIS images of Goldfield

AVIRIS hyperspectral images of the Goldfield

mining district were digitally processed at Analytical

Imaging and Geophysics LLC. Images showing spec-

tral endmember abundances of alteration minerals

were produced, using a spectral unmixing program .of Boardman 1993 . Fig. 3F is a color composite

image made by combining the endmember abun-

dance image of illite in blue, alunite in green, and

kaolinite in red. The black-and-white base is AVIRIS .band 30 visible red . The primary colors show areas

with high concentrations of the assigned mineral.

Other colors indicate co-occurrence of endmember

minerals. Yellow, for example, indicates a mixture .of kaolinite and alunite. Kaolinite red and illite

Fig. 7. Laboratory spectra of alteration minerals in the 2.0 to 2.5

mm band. Spectra are offset vertically. Note positions and band-

widths of the spectral bands recorded by AVIRIS and TM band 7. .From Sabins 1997, Fig. 11-16 .


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.green are the most abundant alteration minerals;their patterns coincide with the alteration map Fig.

.4 that was prepared earlier by field-mapping.

The AVIRIS color image covers the western two-

thirds of the TM 5r7 ratio image shown in Fig. 3C.

It is instructive to compare these images. The red

and yellow colors of the TM 5r7 image show the

aggregate distribution of clays and alunite. The col-

ors of the AVIRIS image show the distribution of

individual alteration minerals. In summary, TM im-

ages show the broad pattern of hydrothermal alter-

ation; AVIRIS images show the distribution of the

individual alteration minerals.

4.3.2. Other AVIRIS examples

The Cuprite district, 25 km south of Goldfield,

consists of volcanic rocks that are intensely altered

to silica, opal, and clay. No significant mineral de-posits occur, but the district has long been used as a

.remote sensing test site. Goetz and Srivastava 1985

analyzed hyperspectral images from a precursor sys-

tem to AVIRIS. They recognized spectra of various

clay minerals, plus buddingtonite which is an ammo-

nium feldspar that had not previously been reported

at Cuprite. Fig. 7 shows the distinctive spectrum of

buddingtonite. Buddingtonite is associated with hy-

drothermally altered rocks in several localities in the . .western U.S. Krohn et al., 1993 . Hook et al. 1991

recognized the alteration minerals on images ofCuprite acquired by AVIRIS and GEOSCAN, a

commercial hyperspectral system. .Crosta et al. 1998 analyzed AVIRIS images of

the Bodie mining district in eastern California, which

was an important goldsilver district in the second

half of the 19th century. Host rocks are intermediate

to mafic volcanic rocks. Gold occurs in quartz veins

and stockworks associated with hydrothermally al-

tered rocks. Silicification in the center is surrounded

by zones of potassic, argillic and sericitic alteration

and an outer zone of propylitic alteration. The

AVIRIS data were processed with algorithms that

classified the image spectra and compared them to

reference spectra. The resulting maps show the dis-tribution of three separate iron minerals hematite,

.g oe th ite , a nd ja ro site , fo ur c la y m in era ls .montmorillonite, kaolinite, halloysite, and illite ,

plus muscovite.

4.4. Summary

The spectra of alteration minerals Fig. 5A, 6A.and 7 were recorded in the laboratory using pure

minerals. Remote sensing images record data from

weathered outcrops of mixtures of rocks and miner-

als together with soil and vegetation. Despite thesecomplications, the digitally processed images give an

accurate picture of the alteration pattern at Goldfield.

In order to bridge the gap between laboratory and .outcrop, Rowan et al. 1979, Fig. 2A used a portable

spectrometer in the field to record spectra of several

hundred representative outcrops of altered and unal-

tered rocks at Goldfield. Fig. 8 summarizes their

results as average reflectance curves for altered and

unaltered outcrops. The average curves lack the fine

spectral detail of the laboratory curves, but the dif-

ferences between altered and unaltered rocks areclearly shown. The altered rocks have distinctly lower

reflectance in band 7 than in band 5. Unaltered rocks

have similar values in those bands. In the visible

portion of the spectrum altered rocks have higher red

reflectance because of the hydrothermal iron miner-

als. These field spectra support the use of TM ratios

5r7 and 3r1 for recognizing alteration minerals.

Remote sensing studies of the Goldfield test site

developed techniques for recognizing hydrothermal

alteration from TM and hyperspectral data. Table 2

summarizes a number of projects that used these

.Fig. 8. Field spectra averaged of altered and unaltered rocks at .Goldfield mining district. From Rowan et al. 1979, Fig. 2A .


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techniques. The following section describes a suc-

cessful commercial application of digitally processed

TM images.

5. Mapping hydrothermal alteration at porphyry

copper deposits Collahuasi, Chile

Most of the worlds copper is mined from por-

phyry deposits, which occur in a different geologic

environment from vein deposits of the Goldfield

type. Hydrothermal alteration is also common at

porphyry deposits and may be recognized by the

same methods that were developed at Goldfield.

5.1. Alteration model

Fig. 9 is a model of hydrothermal alteration of

porphyry copper deposits that was developed by .Lowell and Guilbert 1970 . The most intense alter-

ation occurs in the core of the porphyry body and

diminishes radially outward in a series of concentric

zones described below.

.Fig. 9. Model of hydrothermal alteration zones associated with porphyry copper deposits. From Lowell and Guilbert 1970, Fig. 3


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Potassic zone. Most intensely altered rocks in the

core of the stock. Characteristic minerals are quartz,

sericite, biotite, and potassium feldspar. The re- .flectance spectra not shown of biotite and sericite

have absorption minima in TM band 7, similar to the

spectra of clays. The TM ratio 5r7 is effective in

recognizing these micas, which have reflectance

spectra similar to those of clays.

Phyllic zone. Quartz, sericite, and pyrite are com-

mon.

Ore zone. Disseminated grains of chalcopyrite,

molybdenite, pyrite, and other metal sulfides. Much

of the ore occurs in a cylindrical shell near the

boundary between the potassic and phyllic zones.

Copper typically constitutes 1%, or less, of the rock,

but the large volume of ore is suitable for open pit

mining. Where the ore zone is exposed by erosion,

pyrite oxidizes to form a red to brown iron-stained

crust called a gossan, or leached capping. Gossans

can be useful indicators of underlying mineral de-

posits, although not all gossans are associated with

ore deposits.

Argillic zone. Quartz, kaolinite, and montmoril-

lonite are characteristic minerals of the argillic zone

in porphyry deposits, just as they are associated with

the argillic zone at Goldfield and elsewhere.

Propylitic zone. Epidote, calcite, and chlorite oc-

cur in these weakly altered rocks. Propylitic alter-

ation may be of broad extent and have little signifi-

cance for ore exploration.

Few porphyry deposits have the symmetry and

completeness of the model in Fig. 9. Structural de-

formation, erosion, and deposition commonly con-

ceal large portions of the system. Nevertheless,

Fig. 10. Geologic map of Collahuasi mining district, Chile. Hydrothermal alteration anomalies are edited from Landsat TM ratio images. .Geology generalized from Vergara 1978A, B .


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recognition of small patches of altered rock on re-

mote sensing images can be a valuable exploration

clue.

In the early 1980s, NASA and the Geosat Com-

mittee evaluated satellite and airborne multispectral

images of porphyry copper deposits in southern Ari-

zona. At the Silver Bell mining district, Abrams and .Brown 1985 used color ratio images to separate the

phyllic and potassic alteration zones from the argillic

and propylitic zones. A supervised classification map

defined the outcrops of altered rocks.

5.2. Geologic and exploration background

The Collahuasi Mining District is located in

northern Chile, 180 km southeast of the city of

Iquique. The district lies within a north-trending belt

of porphyry copper deposits that includes the major

mines at El Teniente, Disputada, El Salvador, Escon-

dida, and Chuquicamata. The Collahuasi District isbounded on the west by a major regional fault

system that also passes through the open pit at the

Chuquicamata mine. Fig. 10 is a geologic map show-

ing distribution of the Macata, Capella, and Col-

lahuasi formations of Jurassic and Cretaceous age.

These country rocks are intruded by granitic stocks

of late Cretaceous to early Tertiary age that are hosts

for the porphyry copper deposits.

Mineral production in the Collahuasi District be-

gan in the late 1800s when copper was mined from .veins at Rosario Fig. 10 now known to be related

to the porphyry system. During the 1930s, these

veins were Chiles third largest producer of copper.

Modern exploration began in 1976 when a joint

venture of Superior Oil and Falconbridge acquired

the Collahuasi properties. The joint venture discov-

ered a porphyry deposit at Rosario. In 1985, owner-

ship of the district changed to a three-way joint

venture of Falconbridge, Shell Oil, and Chevron.

From 1985 to early 1991, exploration efforts were

concentrated on evaluating the Rosario deposit.Rosario, however, occupies only a small portion of

the 28,000 ha of the Collahuasi District. There were

Fig. 11. Collahuasi mining district, Chile. Landsat TM bands 247 shown in red, green, and blue merged with SPOT pan image. From .Sabins 1997, Plate 22 .


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indications of other mineralized centers within the

district, but geologic information was incomplete and

additional exploration data were required.

5.3. Remote Sensing

The Remote Sensing Research Group of Chevron

processed satellite images of the Collahuasi District

and adjacent areas. Northern Chile is ideally suited

for such studies, because vegetation, soils, and clouds

are virtually absent in this arid environment of the

high Andes Mountains. Landsat TM bands 24 7

were combined in blue, green, and red to produce a

color image that is optimum for geologic interpreta-

tion in this arid terrain. A SPOT panchromatic image .10 m spatial resolution was merged with the TM

image to produce the version shown in Fig. 11.

TM 3r1 and 5r7 ratio images were produced

using the methods developed at the Goldfield Mining

District. The ratio images were interpreted to identify

areas with high concentrations of iron oxide miner-

als, clays, and alunite. These areas, called anomalies,

were plotted on a preliminary map. The TM anoma-

lies were evaluated to eliminate false anomalies.

Three major types of false anomalies are:

1. Sedimentary rocks, such as shale, that are rich in

clay

2. Rocks with an original red color, such as iron-rich

volcanic rocks and sedimentary red beds

3. Detritus eroded from outcrops of altered rocks;

these recent deposits in alluvial fans and channels

may indicate the proximity of altered rocks.

The edited anomalies are shown in black on the .geologic map Fig. 10 . A circular cluster of anoma-

lies, over 6 km in diameter, occurs south and west of

Collahuasi and Rosario and is now called the Col-

lahuasi Hydrothermal System. The Rosario deposit,

with a diameter of 1.5 km, occupies only a small

Fig. 12. Contour map of resistivity values, Collahuasi mining district. H high values. L low values. Hydrothermal alteration .anomalies are edited from Landsat TM ratio images. From Sabins 1997, Fig. 11-13 .


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portion of the north margin of the system. The

remainder of the Collahuasi hydrothermal system

was largely unexplored.

A second cluster of anomalies, 3 km wide, occurs .southwest of Ujina Fig. 10 and is called the Ujina

Hydrothermal System. Minor alteration had been

recognized earlier at Ujina, but the area has received

very limited exploration attention in the past. The

alteration shown on the ratio images is much more

extensive than previously recognized at Ujina.

5.4. Geophysical sureys

Geophysical surveys were made to evaluate the .Landsat TM anomalies. Dick et al. 1993 provide

details of the configuration and results of the geo-

physical surveys. The entire district was covered by .a helicopter-borne aeromagnetic survey not shown

that mapped subsurface geologic structures and the

distribution of magnetic minerals. The aeromagnetic

map shows that the Collahuasi and Ujina hydrother-

.Fig. 13. Landsat TM band 4 image of Salar de Uyuni and vicinity, southwest Bolivia. From Sabins and Miller 1994, Fig. 2 .


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mal systems are localized at intersections of major

northeast- and northwest-trending faults. The Ujina

System has a circular rim of high magnetic values

that is interpreted as an ore shell within the porphyry

deposit, similar to that shown in the porphyry model .Fig. 9 .

A ground-based survey measured resistivity of the

rocks. Unmineralized rocks typically have high resis-

tivity values. Metallic minerals, such as copper sul-

fides, have low resistivity values; therefore, mineral-

ized rocks have low resistivity values. Fig. 12 is a

contour map of the resistivity survey at the same . .scale as the image Fig. 11 and map Fig. 10 . High

resistivity values are shown by H; the very important

low values are shown by L.

Results of the resistivity survey are outstanding.

Circular patterns of low resistivity contours occur at

both the Collahuasi and Ujina hydrothermal systems

.Fig. 12 . These patterns are analogous to those of

classic porphyry copper deposits. At Collahuasi the

resistivity pattern is 5 km in diameter. The lowest

values form a marginal rim that may represent the

ore shell of the porphyry model. The very low

overall resistivity of the Collahuasi system is inter-

preted as an extensive development of veinlet miner-

alization.

The Ujina Hydrothermal System has a circular

pattern of low resistivity contours 3 km in diameter.

The eastern portion of the resistivity feature is cov-

ered by the Ujina tuff that post-dates the hydrother- .mal activity Fig. 10 . The Landsat anomalies coin-

cide with the exposed western portion of the system.

5.5. Ore discoeries

Core holes were drilled to evaluate the hydrother-

mal systems outlined by the remote sensing and

Fig. 14. Map of Salar de Uyuni. Triangles show high values for TM ratio 4r7 that correlate with high concentrations of ulexite. Contours y1 . .show boron concentration mg l in near-surface brine. From Risacher 1989, Fig. 34 .


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geophysical investigations. The first holes tested the

low resistivity values at Rosario, on the north rim of

the Collahuasi system, where the drills found zones

of structurally controlled copper mineralization.

These results led to the discovery of two major ore

bodies within the Collahuasi system that are shown

by stippled patterns in Fig. 12.

At Ujina, drilling of the resistivity feature discov-

ered a major new porphyry copper deposit shown by

the stippled pattern in Fig. 12. The primary ore

deposit is overlain by secondary enriched ore. By

early 1993, drilling had outlined over 150 million

tons of enriched ore with a grade of 1.8% copper.

In late 1992, Chevron decided to sell its mineral

properties in order to concentrate on its energy busi-

ness. Chevron sold its one-third interest in the unde-

veloped Collahuasi District to Minorco for US$190

million cash. Chevrons total investment in the prop-

erty is estimated at US$23 million. The remotesensing work that contributed so much to the in-

creased value of the property cost less than US$50

thousand. In 1995, Minorco and Falconbridge pur-

chased Shells one-third interest for US$195 million.

Minorco and Falconbridge will spend US$1.3 billion

to develop Collahuasi into a world-class copper mine.

Production started in late 1998 and will last for 45

years. Total mineable reserves are 14 million tons of

copper with a value of US$36.4 billion at 1994

copper prices. Remote sensing played a key role in

defining this valuable property.

6. Borate minerals Salar de Uyuni, Bolivia

Boron and its compounds occur as borate miner-

als in the crust and brine of certain evaporite de-

posits and in modern dry salt lakes, called salars in

Spanish. Fig. 13 is a TM image of the Salar de

Uyuni in southwest Bolivia, which is the worlds

largest salar with an area of 10,000 km 2. The Salar is

known to contain borate minerals, but the ore re-

serves and economic potential were incompletely .evaluated. Risacher 1989 analyzed brine samples

from 68 shallow drill holes and prepared a map of

boron concentration shown in Fig. 14. Had the holes

been uniformly distributed over the Salar, each hole

would represent an area of 147 km2, which is very

sparse sampling. Landsat TM, however, covers the

Salar with more than 1 million ground resolution

cells that represent 9=10y4 km2 each. The Boli-

vian government contracted with Intercontinental

Resources, to conduct a Landsat evaluation of the .Salar Sabins and Miller, 1994 .

A major question in the evaluation was whether

borate minerals in the crust of the Salar have spectral

features that can be recognized in TM data. Fig. 15shows the reflectance spectrum of ulexite NaCaB O5

.P8H O which is the principal borate mineral in the2Salar. Fig. 15 also shows the spectrum of halite .NaCl , or rock salt, which constitutes more than

90% of the crust. TM ratio 4r7 should have high

values for ulexite and low values for halite. A 4r7

ratio image was generated and density sliced to

highlight the highest ratio values which are shown as .triangles in the map Fig. 14 . The highest ratio

values coincide with the contours of maximum boron

concentration in an embayment at the south marginof the Salar. Additional triangles elsewhere around

the margin of the Salar indicate potential borate

.Fig. 15. Reflectance spectra for halite NaCl and ulexite .NaCaB O 8H O . TM bands 4 and 7 are used to calculate 4r75 2ratio image.


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reserves that were not detected by the sample pro-

gram.

This ratio method should be useful for borate

exploration in other dry lakes.

7. Mineral exploration in covered terrain

The Collahuasi and Goldfield districts are in arid

terrain with extensive exposures of bedrock and little

soil or vegetation. Much of the world has temperate

to humid climates, however, and mineral deposits are

obscured or concealed by soil and vegetation. As a

rule of thumb, remote sensing cannot reliably recog-

nize hydrothermally altered rocks where vegetation

and soil cover exceeds 50%. Remote sensing, espe-

cially radar, can map lithology and structure in cov-

ered terrain.

Explorationists have long recognized the relation-

ship between vegetation, soils, and underlying min-eral deposits that is shown diagrammatically in Fig.

16. Geochemical exploration techniques analyze the

metal content of samples of vegetation, soil, or wa-

ter. Areas with high metal concentrations are targets

for follow-up investigations. High concentrations of

metals in soils can cause changes in the vegetation

cover that include the following:

.1 Lack of egetation. This may be caused by

concentrations of metals in the soil that are toxic to

plants. These areas are sometimes called copper

barrens where they are caused by high concentra-

tions of that metal. Areas that lack vegetation may be

seen on remote sensing images. These barren areas

may result from causes other than mineralization,

however. .2 Indicator plants. These are species that grow

preferentially on outcrops and soils enriched in cer- .tain elements. Cannon 1971 prepared an extensive

list of indicator plants. For example, in the Katanga

region of southern Zaire, a small blue-flowered mint,

Acrocephalus robertii, is restricted entirely to cop-

per-bearing rock outcrops. .3 Physiological changes. High metal concentra-

tions in the soil may cause abnormal size, shape, and

spectral reflectance characteristics of vegetation. A

relationship between spectral reflectance propertiesof plants and the metal content of their soils could

form the basis for remote sensing of mineral deposits

in vegetated terrain.

It is reasonable to expect that vegetation growing

over mineral deposits should have different spectral

reflectance patterns from vegetation growing in non-

mineralized areas. The remote sensing of such spec-

.Fig. 16. Copper enrichment of vegetation and soil overlying a concealed copper deposit. From Sabins 1997, Fig. 11-19 .


23/27


tral differences could be an exploration method in

covered terrains. This concept was evaluated by

several research projects in the 1970s and 1980s.

Plants were grown hydroponically with metal salts

added to the nutrient solution. A control group was

grown with normal nutrients. Reflectance spectra of

the two groups were compared throughout the growth

cycle, but the results were inconclusive. .Yost and Wenderoth 1971 used the large, low-

grade, copper-molybdenum deposit at Catheart

Mountain, Maine, as a remote sensing test site. Field

spectrometers measured reflectance of trees growing

in normal soil and in mineralized soil overlying the .deposit Fig. 17 . Red spruce and balsam fir growing

in the mineralized soil both had higher metal concen-

trations than trees in unmineralized soil. In the re-

flected IR spectral region, the mineralized balsam

firs have a higher reflectance than the normal trees,

Fig. 17. Reflectance spectra of balsam fir and red spruce growing

in normal soil and in soil enriched in copper and molybdenum. .From Yost and Wenderoth 1971, Figs. 5 and 6 .

whereas mineralized red spruce have a lower re- .flectance than the normal trees Fig. 17 . In the green

spectral region, the mineralized trees of both specieshave a higher reflectance. Labovitz et al. 1983, Fig.

.1 summarized other investigations of vegetation

spectra. With some exceptions, vegetation re-

flectance in the green and red bands generally in-

creased with increasing metal concentration in the

soil. In the reflected IR region, however, there is less

agreement; some studies show increased vegetation

reflectance and others show decreased reflectance. .Labovitz et al. 1983, p. 759 also noted that the

geobotanical model of Fig. 16 is not universally

correct. In Virginia, they found that the leaves of oak

trees growing in metal-rich soil may have a lower

metal content than leaves from trees in normal soil.

Geophysical Environmental Research used a non-

imaging airborne system that acquires detailed re-

flectance spectra. The spectra in Fig. 18 were ac-quired for conifers growing in a mineralized area and

in an adjacent nonmineralized area. In the green .band 0.5 to 0.6 mm reflectance is higher for trees

in the mineralized area, which is consistent with

other studies. Beginning at a wavelength of about 0.7

mm, vegetation spectra have a steep upward slope to

the high reflectance values in the IR region. In Fig.

18, this steep slope is shifted slightly toward shorter

wavelengths for the conifers growing in the mineral-

ized area. This shift, called the blue shift, has been

noted in vegetation over several mineralized areas .Collins et al., 1983 and may have exploration

potential.

There is little research today on remote sensing of

vegetation spectra for mineral exploration, to my

knowledge. The original researchers are retired or

are working on environmental projects. The avail-

ability of hyperspectral data may encourage new

investigations.

8. Future technology

Secondary silica in the form of quartz is an

important component of hydrothermal alteration sys-

tems, but has no diagnostic spectral features in the .visible or reflected IR spectral regions Fig. 7 . This


24/27


Fig. 18. Airborne reflectance spectra of conifers in Cotter Basin, MT. Note the blue shift for conifers growing in a mineralized area. .From Collins et al. 1983, Fig. 4B .

inability to detect quartz is a handicap for remote

sensing systems, regardless of their spectral resolu-

tion. A possible solution lies in the thermal IR region .8 to 14 mm where silica content is indicated by the

wavelength where the greatest energy absorption oc-

curs. Fig. 19 shows emissivity spectra of igneous

rocks in the thermal region from 8 to 14 mm. All the

spectra contain broad emissivity minima, called ab-

sorption bands, that are caused by the silica content

of the rocks. Arrows indicate the center of each

absorption band. Note that the arrows shift to longer

wavelengths as the silica content of the rocks de-

creases. .The thermal IR multispectral scanner TIMS is a

NASArJPL experimental aircraft system that ac-

quires six bands of imagery in the thermal IR region.

Fig. 19 shows the TIMS bands which are positioned .to record the absorption minima. Hook et al. 1992

processed TIMS data of the Cuprite, Nevada district

and recognized the high concentrations of silica that

occur in the hydrothermally altered rocks.

NASA plans to deploy the advanced spaceborne .thermal emission and radiation radiometer ASTER

.on the first Earth Observation Satellite EOS-A that

may be launched in the future. Fig. 19 shows the five

thermal IR bands recorded by ASTER, which should

enable us to interpret variations in silica content.

TIMS and ASTER data can recognize high concen-

trations of silica, but cannot distinguish hydrothermal

silica from other forms such as igneous or sedimen-

tary silica. Hydrothermal silica can be recognized by

interpreting TIMS and ASTER images in conjunc-

tion with images showing geology and other alter- .ation mineral iron minerals, clays, and alunite .

Australia is organizing support for a satellite that

will include a hyperspectral scanner in the instru-


25/27


Fig. 19. Thermal emissivity spectra of igneous rocks with differ-

ent silica and quartz contents. Arrows show centers of absorption

bands. Note positions of spectral bands recorded by ASTER and .TIMS. From Sabine et al. 1994, Fig. 3 .

ment package. The worldwide availability of hyper-

spectral images could be a major advance in mineral

exploration.

9. Summary

Remote sensing has proven a valuable aid in

exploring for mineral resources. Many ore deposits

are localized along regional and local fracture pat-

terns that provided conduits along which ore-forming

solutions penetrated host rocks. Landsat and radar

images are used to map these fracture patterns. Hy-

drothermally altered rocks associated with many ore

deposits have distinctive spectral features that are

recognizable on digitally processed TM images. In

the future, hyperspectral scanners may identify spe-

cific alteration minerals. Multispectral thermal IR

systems have the potential to map hydrothermal sili-

cification.

Detection of hydrothermally altered rocks is not

possible in vegetated areas, so this environment re-

quires other remote sensing methods. Reflectance

spectra of foliage growing over mineralized areas

may differ from spectra of foliage in adjacent non-

mineralized areas. The spectral differences, however,

are variable for different plant species. Additional

research and development is needed for remote de-

tection of mineral deposits in vegetated terrain.

Some explorationists object to the use of remote

sensing because Remote sensing is no substitute for

field mapping. We do not advocate remote sensing

as a substitute for field mapping. Our points are:

1. On a digitally processed TM image, a geologistcan interpret the rock types, structure, and hy-

drothermal alteration for a region of 31,000 km 2.

2. Occurrences of important hydrothermal minerals .clays and alunite are expressed, using wave-

lengths that are undetectable by the eye.

3. The image interpretation will produce a map of

localities, or prospects, with favorable conditions

for mineral deposits. The image can also be used

to plan the best ground access to the prospects.

4. The field geologist can now efficiently locate,

evaluate, and sample the prospects. Some of theimage-derived prospects will not merit additional

investigation. Some potential deposits will not be

recognized on the image. Nevertheless, field work

can be concentrated in areas with higher mineral

potential.

In summary, remote sensing when properly em-

ployed is a valuable technical resource for mineral

exploration.

Acknowledgements

Much of my research on this topic was done

during my career with the Chevron. Many colleagues

in the remote sensing community allowed me to use

illustrations from their work and are acknowledged

in the figure captions.


26/27


References

Abdelhamid, G., Rabba, I., 1994. An investigation of mineralized

zones revealed during geological mapping, Jabal Hamra Fad-

dan-Wadi Araba, Jordan, using Landsat TM data. Int. J.

Remote Sensing 15, 14951506.

Abrams, M.J., Brown, D., 1985. Silver Bell, Arizona, porphyry

copper test site: The Joint NASArGeosat test case study,

Section 4, American Association of Petroleum Geologists,Tulsa, OK, 73 pp.

Abrams, M.J., Brown, D., Lepley, L., Sadowski, R., 1983. Re-

mote sensing for porphyry copper deposits in southern Ari-

zona. Economic Geology 78, 591604.

Ashley, R.P., 1974. Goldfield mining district. Nevada Bureau of

Mines and Geology Report 19, 4966.

Ashley, R.P., 1979. Relation between volcanism and ore deposi-

tion at Goldfield, Nevada. Nevada Bureau of Mines and

Geology Report 33, 7786.

Bennett, S.A., Atkinson, W.W., Kruse, F.A., 1993. Use of the-

matic mapper imagery to identify mineralization in the Santa

Teresa District, Sonora, Mexico. Int. Geol. Rev. 35, 1009

1029.

Boardman, J.W., 1993. Automated spectral unmixing of AVIRIS

data using convex geometry concepts: Summaries of Fourth

Annual JPL Airborne Geoscience Workshop, Vol. 1, JPL

Publication 93-26, Pasadena, CA, pp. 1114.

Cannon, H.L., 1971. The use of plant indicators in ground water

surveys, geologic mapping, and mineral prospecting. Taxon

20, 227256.

Collins, W., Chang, S.H., Raines, G., Canney, F., Ashley, R.,

1983. Airborne biogeophysical mapping of hidden mineral

deposits. Econ. Geol. 78, 737749.

Crosta, A.P., Sabine, C., Taranik, J.V., 1998. Hydrothermal alter-

ation mapping at Bodie, California, using AVIRIS Hyperspec-

tral data. Remote Sensing of The Environment 65, 309319.

Dick, L.A., Ossandon, G., Fitch, R.G., Swift, C.M., Watts, A., .1993. In: Romberger, S.B., Fletcher, D.I. Eds. , Discovery of

Blind Copper Mineralization at Collahuasi, Chile, Integrated

Methods in Exploration and Discovery, Society of Economic

Geologists, Abstracts.

Eiswerth, B.A., Rowan, L.C., 1993. Analyses of Landsat thematic

mapper images of study areas located in western Bolivia,

northern Chile, and southern Peru: Investigation de Metales

Preciosos en El Complejo Volcanico Neogeno-Cuaternario de

Los Andes Centrales, U.S. Geol. Surv. Projecto BIDrTC-88-

02-32-5, pp. 1944.

Goetz, A.F.H., Srivastava, V., 1985. Mineralogic mapping in the

Cuprite mining district: AIS Data Analysis Workshop Pro-

ceedings, Jet Propulsion Laboratory Publication 85-41,

Pasadena, CA, pp. 2231.

Goosens, M.A., Kroonenberg, S.B., 1994. Spectral discrimination

of contact metamorphic zones and its potential for mineral

exploration, province of Salamanca, Spain. Remote Sensing of

The Environment 47, 331344.

Green, R.O. et al., 1998. Imaging spectroscopy and the airborne .visiblerinfrared imaging spectrometer AVIRIS . Remote

Sensing of The Environment 65, 227248.

Griffiths, P.S., Curtis, P.A.S., Fadul, S.E.A., Scholes, P.D., 1987.

Reconnaissance geological mapping and mineral exploration

in northern Sudan using satellite remote sensing. Geol. J. 22,

225249.

Harvey, R.D., Vitaliano, C.J., 1964. Wall-rock alteration in the

Goldfield District, Nevada. Journal of Geology 72, 564579.

Hook, S.J., Elvidge, C.D., Rast, M., Watanabe, H., 1991. An .evaluation of short-wave-infrared SWIR data from the

AVIRIS and GEOSCAN instruments for mineralogical map-ping at Cuprite, Nevada. Geophysics 56, 14321440.

Hook, S.J., Gabell, A.R., Green, A.A., Kealy, P.S., 1992. A

comparison of techniques for extracting emissivity information

from thermal infrared data for geologic studies. Remote Sens-

ing of the Environment 42, 123 135.

Hunt, G.R., 1980. Electromagnetic radiation the communica-

tion link in remote sensing. In: Siegal, B.S., Gillespie, A.R. .Eds. , Remote Sensing in Geology. Wiley, New York, NY.

Kaufmann, H., 1988. Mineral exploration along the AqabaLevant

structure by use of TM data; concepts, processing, and results.

Int. J. Remote Sensing 9, 16391658.

Knepper, D.H., Simpson, S.L., 1992. Remote sensing in Geology

and mineral resources of the Altiplano and Cordillera Occiden-

tal, Bolivia, U.S. Geol. Surv. Bull. 1975, pp. 4755.

Krohn, M.D., Kendall, C., Evans, J.R., Fries, T.L., 1993. Rela-

tions of ammonium mineral in several hydrothermal systems

in the western U.S. J. Volcanol. Geotherm. Res. 56, 401413.

Kruse, F.A., 1996. Cuprite, Nevada supplemental field trip .information. In: Shaulis, L. Ed. , Remote Sensing Field Trip

Guidebook: Environmental Research Institute of Michigan

Eleventh Thematic Conference on Geologic Remote Sensing,

Guidebook, Ann Arbor, MI, pp. I92I103.

Labovitz, M.L., Masuoka, E.J., Bell, R., Segrist, A.W., Nelson,

R.F., 1983. The application of remote sensing to geobotanical

exploration for metal sulfides results from the 1980 field

season at Mineral, Virginia. Econ. Geol. 78, 750760.

Lowell, J.D., Guilbert, J.M., 1970. Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Econ. Geol.

65, 373408.

Murphy, R.J., 1995. Mapping of jasperoid in the Cedar Moun-

tains, Utah, U.S.A., using imaging spectrometer data. Int. J.

Remote Sensing 16, 10211041.

Nicolais, S.M., 1974. Mineral exploration with ERTS imagery:

Third ERTS-1 Symposium, NASA SP-351, Vol. 1, pp. 785

796.

Risacher, F., 1989. Economic study of the Salar de Uyuni:

Institute Francis de Recherche Scientifique Pour le Developp-ment en Cooperation, Informe no, 32, Translated by E. Jack-

son-Reardon, 67 p.

Rowan, L.C., Bowers, T.L., 1995. Analysis of linear features

mapped in Landsat thematic mapper and side-looking airborne

radar images of the Reno, Nevada 18 by 28 quadrangle,

Nevada and California implications for mineral resource

studies. Photogramm. Eng. Remote Sensing 61, 749759.

Rowan, L.C., Wetlaufer, P.H., 1975. Iron-absorption band analy-

sis for the discrimination of iron-rich zones. U.S. Geol. Surv.

Type III Final Report, Contract S-70243-AG.

Rowan, L.C., Wetlaufer, P.H., Goetz, A.F.H., Billingsley, F.C.,


27/27


Stewart, J.H., 1974. Discrimination of rock types and detec-

tion of hydrothermally altered areas in south central Nevada

by the use of computer-enhanced ERTS images. U.S. Geol.

Surv. Prof. Pap. 883, 35.

Rowan, L.C., Goetz, A.F.H., Ashley, R.P., 1979. Discrimination

of hydrothermally altered and unaltered rocks in the visible

and near infrared. Geophysics 42, 533535.

Rowan, L.C., Trautwein, C.A., Purdy, T.L., 1991. Maps showing

association of linear features and metallic mines and prospectsin the Butte 18 by 28 quadrangle, Montana. U.S. Geol. Surv.

Misc. Invest. Ser. Map I-2050-B.

Sabins, F.F., 1983. Geologic interpretation of Space Shuttle im-

ages of Indonesia. Am. Assoc. Pet. Geol. Bull. 67, 20762099.

Sabins, F.F., 1997. Remote Sensing Principles and Interpreta-

tion, 3rd edn., W.H. Freeman, New York, NY., 494 pp.

Sabins, F.F., Miller, R.M., 1994. Resource assessment Salar de

Uyuni and vicinity. Proceedings of Tenth Thematic Confer-

ence on Geologic Remote Sensing. Environmental Research

Institute of Michigan, Ann Arbor, MI, pp. I92I103.

Sabine, C., Realmuto, V.J., Taranik, J.V., 1994. Quantitative

estimation of granitoid composition from thermal infrared .multispectral scanner TIMS data, Desolation Wilderness.

northern Sierra Nevada, California. J. Geophys. Res. 99,

42614271.

Segal, D.B., Rowan, L.C., 1989. Map showing exposures of

limonitic rocks and hydrothermally altered rocks in the Dillon

1 by 2 quadrangle, Idaho and Montana. U.S. Geol. Surv. Misc.

Invest. Ser. Map I-1803-A.

.Singhroy, V.H. Ed. , 1991. Geological remote sensing in Canada.

Can. J. Remote Sensing, 17, 71200.

Spatz, D.M., Wilson, R.T., 1994. Exploration remote sensing for

porphyry copper deposits, western America Cordillera. Pro-

ceedings Tenth Thematic Conference on Geologic Remote

Sensing. Environmental Research Institute of Michigan, Ann

Arbor, MI, pp. 12271240.

Unrug, R., 1988. Mineralization controls and source metals in the

.Lufilian fold belt, Shaba Zaire , Zambia and Angola. Econ.Geol. 83, 12471258.

Van der Meer, 1994. Extraction of mineral absorption features

from high-spectral resolution data using non-parametric geo-

statistical techniques. Int. J. Remote Sensing 15, 21932214.

Vergara, H., 1978A. Cuadrangulo Ujina: Carta Geologica de

Chile, No. 33, Escala 1:50,000, Instituto de Investigaciones

Geologicas, Santiago, Chile.

Vergara, H., 1978B. Cuadrangulo Quehuita y sector occidental del

cuadrangulo Volcan Mino: Carta Geologica de Chile, No. 32,

Escala 1:50,000, Instituto de Investigaciones Geologicas, San-

tiago, Chile.

Watson, K., Kruse, F.A., Hummer-Miller, S., 1990. Thermal

infrared exploration in the Carlin trend, northern Nevada.

Geophysics 55, 7079.

Yost, E., Wenderoth, S., 1971. The reflectance spectra of mineral-

ized trees. Proceedings of Seventh International Symposium

on Remote Sensing of Environment, Vol. 1, University of

Michigan, Ann Arbor, MI, pp. 269284.

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