Ore Geology Geology Reviews Reviews 14 14 Ž1999. 157–183
Remote Remote sensing sensing for mineral mineral exploration exploration Floyd F. Sabins
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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 mineral explorati exploration on in two applicat applications: ions: Ž 1. map geology geology and the the faults and and fractures fractures that localiz localizee ore deposits; deposits; Ž 2. recognize recognize Ž . hydrotherm hydrothermally ally altered altered rocks by their spectral signature signatures. s. Landsat Landsat thematic mapper mapper TM satellite satellite images images are widely 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 multis multispec pectra trall therma thermall IR images images,, which which is a promis promising ing topic topic for resear research. ch. q 1999 Elsevier Elsevier Science Science B.V. All rights rights reserved. mineral exploration; exploration; thematic mapper ŽTM .; Goldfield mining district Keywords: remote sensing; mineral
1. Introduction
Remote sensing is the science of acquiring, processing cessing,, and interp interpret reting ing images images and relate related d data, data, acquired from aircraft and satellites, that record the interaction interaction between matter and electromagn electromagnetic etic energy ŽSabins, Sabins, 1997, p. 1.. This report reviews reviews the use of remote sensing for mineral exploration. Section 2 descri describes bes the remote remote sensing sensing systems systems that that are employed ployed in minera minerall explor explorati ation on and introd introduce ucess the
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comp comput uter er tech techni niqu ques es used used to proc proces esss digi digita tall data data acquir acquired ed by the systems. systems. Section Section 4 descri describes bes how multisp multispectr ectral al data data are digital digitally ly process processed ed to recogrecognize hydrotherma hydrothermall alteration alteration minerals Žiron minerals, minerals, clays, and alunite., using the Goldfield, NV, mining district as a training site. The methods developed at Gold Goldfi fiel eld d were were used used in nort northe hern rn Ch Chile ile to defi define ne anomalies that are now world-class copper deposits. Section Section 8 describ describes es future future remote remote sensing sensing systems systems and their their potent potential ial applica applicatio tions ns to minera minerall exploexploration. Most of this report is extracted from Sabins Ž1997., to which the reader is referred for additional information.
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F.F. Sabins r Ore Geology Re Õiews iews 14 (1999 1999) 157–183 157–183
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2. Remote sensing technology
Table Table 1 lists lists charac character teristi istics cs of the princi principal pal remote sensing systems that are currently available for mineral exploration. Some systems are deployed only on sate satelli llite tess ŽLand Landsa sat, t, SPOT SPOT.. Othe Otherr syste systems ms are are curren currently tly deploy deployed ed only only on aircra aircraft ft Žhypers hyperspec pectra trall systems. . Radar systems are deployed on both satellites lites and aircra aircraft. ft. Images Images acquir acquired ed by satellit satellitee sysŽ . tems tems have the the following following adva advanta ntages: ges: 1 archiv archives es of worl worldw dwid idee data data are are read readil ily y avai availa labl ble; e; Ž2. imag images es cover large areas on on the ground; ground; Ž3 . prices per square square kilo kilome mete terr are are gene genera rall lly y lowe lower. r. Disad Disadva vant ntag ages es of satellite satellite images images are: are: Ž1. the latest latest hypersp hyperspectral ectral technology nology is curren currently tly availabl availablee only only from from aircraf aircraft; t; Ž2 . airc aircra raft ft missi mission onss can can be conf config igur ured ed to match match the the requir requireme ements nts of a project project.. The follow following ing section sectionss summarize the major systems. 2.1. Landsat images
NASA NASA has has laun launch ched ed two two gene genera rati tion onss of ununmanned manned Landsat Landsat satelli satellites tes that that have have acquir acquired ed valuvaluable remote sensing data for mineral exploration and other applications. Both generations were placed in sun-sy sun-synch nchron ronous ous orbits orbits that that provid providee repeti repetitive tive images of the entire earth, except for the extreme polar
region regions. s. The first generat generation ion ŽLandsa Landsats ts 1, 2, and 3 . operate operated d from from 1972 1972 to 1985 1985 and is essenti essentially ally replaced by the second generation. Table 1 lists some characteristi characteristics cs of the second generation generation ŽLandsats Landsats 4, 5 and 7 ., which began in 1982 and continues to the prese present nt.. Land Landsat sat 6 of the the seco second nd gene genera ratio tion n was was launched in 1993, but failed to reach orbit. Images are acqui acquired red by by the themati thematicc mapper mapper ŽTM. which which is an optica optical-m l-mech echani anical cal cross-t cross-trac rack k scanner scanner ŽSabins Sabins,, . 1997, Fig. 1-12A . An oscillating scan mirror sweeps the the fiel field d of view view of the the opti optica call syste system m acro across ss the the terrain at a right angle to the satellite orbit path. A spectrometer separates solar energy that is reflected from from the the eart earth’ h’ss surf surfac acee into into narr narrow ow wavel wavelen engt gth h intervals intervals called spectral bands. Each band is recorded recorded as a separate image. Fig. Fig. 1 shows shows reflec reflectan tance ce spectra spectra for vegeta vegetation tion and three common sedimentary rocks. The vertical axis shows the percentage of incident sunlight that is reflected by the materials. The horizontal axis shows wavelengths of energy for the visible spectral region Ž0.4 to 7.0 m m. and the the refle reflecte cted d porti portion on Ž0.7 to to 3.0 . Ž . the infrar infrared ed IR region region.. Refle Reflected cted IR energ energy y m m of the consist consistss largel largely y of solar solar energy energy reflec reflected ted from from the earth at wavelengths longer than the sensitivity range of the eye. The thermal thermal portio portion n of the IR region region Ž3.0 to 1000 m m. consist consistss of radian radiant, t, or heat, energy energy and
Table 1 Remote sensing systems for mineral exploration Characteristic
Landsat 4, 5 thematic mapper ŽTM.
Landsat 7 enhanced TM
SPOT multispectral scanner ŽXS.
SPOT panchromatic ŽPan.
AVIRIS hyperspectral scanner
0.45 0.45 to 2.35 2.35 m m – 10.5 to 12.5 m m 7
0.45 to 2.35 m m 0.52 to 0.90 m m – 8
0.50 to 0.89 m m – – 3
– 0.51 to 0.73 m m – 1
0.40 to 2.50 m m
185 km 170 km
185 km 170 km
60 km 60 km
60 km 60 km
10.5 km cross-track
30 by 30 m – 120 by 120 m
30 by 30 m 15 by 15 m 60 by 60 m
20 by 20 m – –
– 10 by 10 m –
20 m
Spectral region
Visibl Visiblee and reflec reflected ted IR Panchromatic Thermal IR Spectral bands
224
Terrain coÕerage
East to west North to south Ground resolution cell
Visible and reflected IR Panchromatic Thermal IR
F.F. Sabins r Ore Geology Re Õiews 14 (1999) 157–183
Fig. 1. Spectral bands recorded by remote sensing systems. Spectral reflectance curves are for vegetation and sedimentary rocks. From Sabins Ž1997, Fig. 4-1..
is not shown in Fig. 1. The TM system records three wavelengths of visible energy Žblue, 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 Ž10.5 to 12.5 m m. 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 reflected 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
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produce color composite images. Fig. 3A shows bands 1–2–3 combined in blue, green, and red respectively to produce a color image similar to that observed by the eye or recorded by a color photograph. Several alternate color combinations of TM bands are commonly employed ŽSabins, 1997, Chap. 3.. The second generation of Landsat continued with Landsat 7, launched in April, 1999, with an enhanced TM system. A panchromatic band 8 Ž0.52 to 0.90 mm. with spatial resolution of 15 m is added. Band 8 can be combined with the visible and reflected IR bands Ž30 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 — EOSAT 12076 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 Žand 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 Žand 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 ŽTable 1.. The multispectral ŽXS. mode acquires three bands of data at green, red, and reflected IR wavelengths ŽFig. 1 . with a spatial resolution of 20 m. The panchromatic Žpan. 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 bands with bandwidths on the order of 0.01 mm ŽSabins, 1997, Chap. 1 .. Many minerals have distinctive spectral reflectance patterns at visible wavelengths and especially at reflected IR wavelengths ŽHunt, 1980.. Under favorable conditions, many minerals may be identified on suitably processed hyperspectral data. Fig. 1 shows the spectral region covered by the 224 spectral bands recorded by the airborne visible rinfrared imaging spectrometer ŽAVIRIS. 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, primarily in the U.S. A website Žhttp:rrmakalu.jpl.nasa. govraviris.html. provides access to the archive of AVIRIS images. Green et al. Ž1998. describe the AVIRIS system and summarize a number of application studies, including geology. Examples of AVIRIS images are shown in the section on the Goldfield mining district ŽSection 4.3.1.. Sabins Ž1997, 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.
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Another advantage is that radar images may be acquired at a low depression angle that causes pronounced 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 vegetation surface, rather than the underlying terrain. In Indonesia, Sabins Ž1983. 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 Corporation 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 personal computers. Sabins Ž1997, Chap. 8 . groups image-processing methods into three functional categories 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 resemble the scene on the terrain. Typical processing routines include: a. Restoring line dropouts b. Restoring periodic line striping c. Restoring line offsets d. Filtering random noise e. Correcting for atmospheric scattering f. Correcting geometric distortions 2. Image enhancement alters 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 slicing c. 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 Ž1997, Fig. 11-7.. ŽA . Band 1, blue Ž0.45 to 0.52 m m. . ŽB. Band 2, green Ž0.52 to 0.60 m m. . Ž C. Band 3, red Ž0.63 to 0.69 m m.. ŽD. Band 4 reflected IR Ž0.76 to 0.90 m m.. ŽE. Band 5, reflected IR Ž1.55 to 1.75 m m. . ŽF. Band 7, reflected IR Ž2.08 to 2.35 m m. .
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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. Typical 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.
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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. Ž2. Recognition of hydrothermally altered rocks that may be associated with mineral deposits. The spectral bands of Landsat TM are well-suited for recognizing assemblages of alteration minerals Žiron oxides, clay, and alunite. that occur in hydrothermally altered rocks. In my experience the best exploration results are obtained by combining geologic and fracture mapping with the recognition of hydrothermally altered rocks.
4. Mapping hydrothermal alteration at epithermal vein deposits — Goldfield, Nevada 3. Mineral exploration overview
Table 2 lists representative recent mineral exploration studies using remote sensing. These studies describe two different approaches to mineral exploration. Ž1. 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 Ž1975. used a Landsat mosaic of Nevada to interpret regional lineaments. 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 Ž1974. interpreted 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 Ž1995.
Most epithermal vein deposits are accompanied by hydrothermal alteration of the adjacent country rocks. Not all alteration is associated with ore bodies, 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 recognized by their appearance in the visible spectral bands. Today remote sensing and digital image processing 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. ŽImage F courtesy F.A. Kruse, Analytical Imaging and Geophysics, LLC, Boulder, CO.. From Sabins Ž1997, Plate 21.. ŽA . TM 1–2– 3 normal color image. ŽB . TM color ratio image. Ratio 5r7 s red, 3r1 s green, 3r5 s blue. ŽC. TM ratio 5r7 image with density slice. High ratio values shown in red. ŽD . TM ratio 3 r1 image with density slice. High ratio values shown in red. ŽE . TM unsupervised classification map. ŽF . Color composite image of AVIRIS endmember abundance images Žfrom Fig. 12 .. Illite s red, alunite s green, kaolinite s blue.
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Table 2 Representative mineral exploration investigations using remote sensing. From Sabins Ž1997, Table 11-3 . Locality
Reference
Comments
Western North and South America Altiplano, Bolivia
Spatz and Wilson Ž1994.
Canada Chile, Peru, and Bolivia Jordan
Singhroy Ž1991 . Eiswerth and Rowan Ž1993 .
Jordan
Abdelhamid and Rabba Ž1994 .
Sonora, Mexico
Bennett et al. Ž1993 .
Nevada
Watson et al. Ž1990.
Spain
Goosens and Kroonenberg Ž1994 .
Sudan
Griffiths et al. Ž1987 .
Arizona
Abrams et al. Ž1983 .
Montana
Rowan et al. Ž1991.
Idaho and Montana Utah Zaire, Zambia, Angola
Segal and Rowan Ž1989 . Murphy Ž1995. Unrug Ž1988 .
Summarizes published remote sensing studies of 12 major mining districts from British Columbia to Chile. TM color ratio composite images used to recognize hydrothermally altered rocks. 10 papers on mineral exploration using Landsat and radar. TM color ratio composite images used to recognize hydrothermally altered rocks. Field studies evaluated results. Mapped hydrothermal alteration using digitally processed TM images. A variety of digitally processed TM images identified a historic Cu rMn deposit and located prospects. TM data were integrated with field and laboratory data to discover several prospects. TIMS data were processed to recognize silicified rocks associated with gold deposits. TM ratio images were used to identify altered rocks overlain by residual soil. Landsat MSS images and field work showed gold occurrences are concentrated along regional shear zones in mafic metavolcanics. Mapped hydrothermal alteration using digitally processed aircraft multispectral images. Compared the association of linear features with ore deposits in Butte region. Mapped hydrothermal alteration in the Dillon region. Used hyperspectral data to map jasperoid. Major lead–zinc vein deposits occur at intersections of Landsat lineaments with folds and thrust faults. Unexplored intersections are potential targets.
Knepper and Simpson Ž1992 .
Kaufmann Ž1988 .
were first developed to recognize hydrothermally altered rocks ŽRowan et al., 1974 .. 4.1. Geology, ore deposits, and hydrothermal alter ation
The Goldfield district ŽFig. 4 . was noted for the richness of its ore. Over 4 million troy ounces Ž130,000 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 and analyzed by the U.S. Geological Survey ŽAshley, 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 system. Hydrothermal alteration and ore deposition occurred during a second period of volcanism in the early Miocene epoch when the dacite and andesite flows that host the ore deposits were extruded. Heating 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 ringfracture system. Following ore deposition, the area was covered by younger volcanic flows. Later doming and erosion have exposed the older volcanic center with altered rocks and ore deposits. In the generalized map ŽFig. 4., the hydrothermally altered rocks are cross-hatched and the unaltered country rocks are blank. Approximately 40 km 2 of the area is underlain by altered rocks, but less than 2 km 2 of the altered area contains economic mineral
F.F. Sabins r Ore Geology Re Õiews 14 (1999) 157–183
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Fig. 4. Map showing geology and hydrothermal alteration of Goldfield mining district, NV. From Ashley Ž1979, Figs. 1 and 8 .
deposits, which are shown in black. The oval band of altered rocks was controlled by the circular ring-fracture 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 microcrystalline 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 montmorillonite plus alunite. This assemblage of alteration minerals is called the argillic zone ŽHarvey 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 assemblages.
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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 1–2–3 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 abandoned Columbia Mill, where gold was separated from the altered host rock. The tailings pond is a useful reference standard because it contains a concentration of altered rock material. The dark signatures 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 southeast portion are outcrops of volcanic tuff. Neither the normal color TM image nor alternate band color combinations are diagnostic for recognizing the hydrothermally altered rocks. Therefore, additional digital processing is required in order to map hydrothermal alteration from TM data. 4.2.1. Alunite and clay minerals on 5 r 7 ratio images
Fig. 5A shows reflectance spectra of alunite and the three common hydrothermal clay minerals illite, kaolinite, and montmorillonite. These minerals have distinctive absorption features Žreflectance 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 values within TM band 5. Ratio images can emphasize and quantify these spectral differences. A TM image consists of picture elements Žpixels. 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 ŽDNs. on an eight-bit scale from 0 to
Fig. 5. Recognition of hydrothermal clays and alunite from TM data, Goldfield mining district. From Sabins Ž1997, Fig. 11-8 .. ŽA . Laboratory reflectance spectra. TM bands 5 and 7 Žshaded. are used to calculate 5 r7 ratio image. ŽB. Ratio image of TM bands 5r7. ŽC. Histogram for 5 r7 image.
F.F. Sabins r Ore Geology Re Õiews 14 (1999) 157–183
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which have low blue reflectance ŽTM band 1 . and high red reflectance ŽTM band 3 .. Iron-stained hydrothermally altered rocks therefore have high values in a 3r1 ratio image. Fig. 6B is a 3 r1 ratio image with high DN values shown in bright tones. Fig. 3D is a color density slice version of the 3 r1 image, with color assignments shown in the histogram of Fig. 6C. Highest ratio values ŽDN ) 150. are shown in red, with the next highest values ŽDN 135 to 150. shown in yellow. The red and yellow colors therefore correlate with the altered rocks.
255. Ratio images are prepared by dividing the value for one band by that of another band, after atmospheric corrections have been made ŽSabins, 1997, Chap. 8.. Table 3 explains how TM ratio 5 r7 distinguishes 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 5 r7 ratio for unaltered rocks is unity Ž1.00.. 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 Ž1.45 .. The numbers in Table 2 are typical and will differ for other examples. The decimal ratio values are converted to 8-bit digital numbers ŽDNs. and displayed as images. Fig. 5B is a 5 r7 ratio image of Goldfield with higher ratio values shown in brighter tones. Comparing the image with the map ŽFig. 4 . shows that the high ratio values correlate with hydrothermally altered rocks. Fig. 5C is a histogram of the 5 r7 ratio image that shows the higher ratio values ŽDNs ) 145. of the altered rocks. Low ratio values represent unaltered rocks. Fig. 3C is a color density slice version of the 5 r7 image in which the gray scale is replaced by the colors shown in the histogram ŽFig. 5C.. Highest ratio values ŽDN ) 145. are shown in red, with the next highest values ŽDN 125 to 145. shown in yellow. The red and yellow colors on the ratio image ŽFig. 3C. therefore 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 altered rocks 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 disadvantage 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 a supervised multispectral classification, the operator specifies the classes that will be used. In an unsupervised multispectral classification, the computer specifies the classes that will be used ŽSabins, 1997, Chap. 8 .. An unsupervised multispectral classification was applied to the TM bands in Fig. 2 and resulted in 12 classes. These classes were aggregated
4.2.2. Iron minerals on 3 r 1 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
Table 3 Calculation of TM 5 r7 ratio values. From Sabins Ž1997, Table 11-1 .
Unaltered rocks Žwithout clays and alunite . Altered rocks Žwith clays and alunite .
Band 5 reflectance Žtypical.
Band 7 reflectance Žtypical.
Ratio 5 r7 Žtypical.
DNs for ratio 5 r7
160
160
1.00
100
160
110
1.45
145
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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 outside the alteration zone. Basalt Žblue., volcanic tuff Žpurple., and unaltered rocks Žgreen. are reasonably classified. Alluvium Žyellow. is considerably more extensive in the classification image ŽFig. 3E. than in the geologic map ŽFig. 4.. Field checking and comparison with the normal color image ŽFig. 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 details can be mapped, however, from data acquired by hyperspectral scanners. Fig. 7 shows laboratory spectra 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 Ž1994., Kruse Ž1996. and others have shown that AVIRIS has the spectral resolution to identify individual 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 Ž1997, Fig. 11-9 .. ŽA . Laboratory reflectance spectra. TM bands 1 and 3 Žshaded. are used to calculate 3 r1 ratio image. ŽB. Ratio image of TM bands 3r1. ŽC. Histogram for 3 r1 image.
F.F. Sabins r Ore Geology Re Õiews 14 (1999) 157–183
Table 4 Explanation of colors in classification image of Goldfield mining district ŽFig. 3E. . From Sabins Ž1997, Table 11-2 . Color
Classification
Percent of image
Yellow Blue Purple Red Orange Green
Alluvium Basalt Tuff Altered rocks, A Altered rocks, B Unaltered rocks
39.2 14.0 6.6 5.3 18.3 16.6
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image made by combining the endmember abundance image of illite in blue, alunite in green, and kaolinite in red. The black-and-white base is AVIRIS band 30 Žvisible 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 Žred. and illite
ual alteration minerals. There are, however, two major technical challenges to producing such images. Ž1. Some alteration minerals, especially the clays, have similar spectra ŽFig. 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 that also 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 procedure is a form of supervised classification. The procedure is effective, however, only for the rare ground resolution cells in which only a single mineral occurs. Ž2. Each ground resolution cell of AVIRIS typically measures 20 by 20 m. In areas of complex geology, such as Goldfield, the 400 m 2 of a cell includes a number of different minerals. The resulting 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 individual mineral species are called spectral endmembers. 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 spectral endmember abundances of alteration minerals were produced, using a spectral unmixing program of Boardman Ž1993.. Fig. 3F is a color composite
Fig. 7. Laboratory spectra of alteration minerals in the 2.0 to 2.5 m m band. Spectra are offset vertically. Note positions and bandwidths of the spectral bands recorded by AVIRIS and TM band 7. From Sabins Ž1997, Fig. 11-16 ..
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Žgreen. are the most abundant alteration minerals; their patterns coincide with the alteration map ŽFig. 4. that was prepared earlier by field-mapping. The AVIRIS color image covers the western twothirds of the TM 5 r7 ratio image shown in Fig. 3C. It is instructive to compare these images. The red and yellow colors of the TM 5 r7 image show the aggregate distribution of clays and alunite. The colors of the AVIRIS image show the distribution of individual alteration minerals. In summary, TM images show the broad pattern of hydrothermal alteration; 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 deposits occur, but the district has long been used as a remote sensing test site. Goetz and Srivastava Ž1985. analyzed hyperspectral images from a precursor system to AVIRIS. They recognized spectra of various clay minerals, plus buddingtonite which is an ammonium feldspar that had not previously been reported at Cuprite. Fig. 7 shows the distinctive spectrum of buddingtonite. Buddingtonite is associated with hydrothermally altered rocks in several localities in the western U.S. ŽKrohn et al., 1993.. Hook et al. Ž1991 . recognized the alteration minerals on images of Cuprite acquired by AVIRIS and GEOSCAN, a commercial hyperspectral system. Crosta et al. Ž1998. analyzed AVIRIS images of the Bodie mining district in eastern California, which was an important gold–silver 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 altered 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 distribution of three separate iron minerals Žhematite, g oe th ite , a nd ja ro site ., fo ur c la y m in era ls Žmontmorillonite, kaolinite, halloysite, and illite., plus muscovite.
4.4. Summary
The spectra of alteration minerals ŽFig. 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 minerals together with soil and vegetation. Despite these complications, 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. Ž1979, Fig. 2A. used a portable spectrometer in the field to record spectra of several hundred representative outcrops of altered and unaltered 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 differences between altered and unaltered rocks are clearly 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 minerals. These field spectra support the use of TM ratios 5r7 and 3 r1 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 Žaveraged. of altered and unaltered rocks at Goldfield mining district. From Rowan et al. Ž1979, Fig. 2A ..
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techniques. The following section describes a successful commercial application of digitally processed TM images.
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porphyry deposits and may be recognized by the same methods that were developed at Goldfield. 5.1. Alteration model
5. Mapping hydrothermal alteration at porphyry copper deposits — Collahuasi, Chile
Most of the world’s copper is mined from porphyry deposits, which occur in a different geologic environment from vein deposits of the Goldfield type. Hydrothermal alteration is also common at
Fig. 9 is a model of hydrothermal alteration of porphyry copper deposits that was developed by Lowell and Guilbert Ž1970.. The most intense alteration 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 Ž1970, 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 reflectance spectra Žnot shown. of biotite and sericite have absorption minima in TM band 7, similar to the spectra of clays. The TM ratio 5 r7 is effective in recognizing these micas, which have reflectance spectra similar to those of clays. Phyllic zone. Quartz, sericite, and pyrite are common. 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 deposits, although not all gossans are associated with ore deposits. Argillic zone. Quartz, kaolinite, and montmorillonite 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 occur in these weakly altered rocks. Propylitic alteration may be of broad extent and have little significance for ore exploration. Few porphyry deposits have the symmetry and completeness of the model in Fig. 9. Structural deformation, erosion, and deposition commonly conceal 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 Ž1978A, B ..
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recognition of small patches of altered rock on remote sensing images can be a valuable exploration clue. In the early 1980s, NASA and the Geosat Committee evaluated satellite and airborne multispectral images of porphyry copper deposits in southern Arizona. At the Silver Bell mining district, Abrams and Brown Ž1985. 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, Escondida, and Chuquicamata. The Collahuasi District is bounded on the west by a major regional fault system that also passes through the open pit at the
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Chuquicamata mine. Fig. 10 is a geologic map showing distribution of the Macata, Capella, and Collahuasi 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 began in the late 1800s when copper was mined from veins at Rosario ŽFig. 10 . now known to be related to the porphyry system. During the 1930s, these veins were Chile’s 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 discovered a porphyry deposit at Rosario. In 1985, ownership 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 2–4–7 shown in red, green, and blue merged with SPOT pan image. From Sabins Ž1997, 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 2–4 –7 were combined in blue, green, and red to produce a color image that is optimum for geologic interpretation in this arid terrain. A SPOT panchromatic image Ž10 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 minerals, clays, and alunite. These areas, called anomalies, were plotted on a preliminary map. The TM anomalies 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 ŽFig. 10.. A circular cluster of anomalies, over 6 km in diameter, occurs south and west of Collahuasi and Rosario and is now called the Collahuasi 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 Ž1997, 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 ŽFig. 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.
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5.4. Geophysical sur Õeys
Geophysical surveys were made to evaluate the Landsat TM anomalies. Dick et al. Ž1993. provide details of the configuration and results of the geophysical surveys. The entire district was covered by a helicopter-borne aeromagnetic survey Žnot 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 Ž1994, 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 ŽFig. 9.. A ground-based survey measured resistivity of the rocks. Unmineralized rocks typically have high resistivity values. Metallic minerals, such as copper sulfides, have low resistivity values; therefore, mineralized rocks have low resistivity values. Fig. 12 is a contour map of the resistivity survey at the same scale as the image ŽFig. 11. and map ŽFig. 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
ŽFig. 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 interpreted as an extensive development of veinlet mineralization. The Ujina Hydrothermal System has a circular pattern of low resistivity contours 3 km in diameter. The eastern portion of the resistivity feature is covered by the Ujina tuff that post-dates the hydrothermal activity ŽFig. 10.. The Landsat anomalies coincide with the exposed western portion of the system. 5.5. Ore discoÕeries
Core holes were drilled to evaluate the hydrothermal systems outlined by the remote sensing and
Fig. 14. Map of Salar de Uyuni. Triangles show high values for TM ratio 4 r7 that correlate with high concentrations of ulexite. Contours show boron concentration Žmg l y1 . in near-surface brine. From Risacher Ž1989, 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 discovered 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 business. Chevron sold its one-third interest in the undeveloped Collahuasi District to Minorco for US$190 million cash. Chevron’s total investment in the property is estimated at US$23 million. The remote sensing work that contributed so much to the increased value of the property cost less than US$50 thousand. In 1995, Minorco and Falconbridge purchased Shell’s 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.
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Salar with more than 1 million ground resolution cells that represent 9 = 10y4 km2 each. The Bolivian government contracted with Intercontinental Resources, to conduct a Landsat evaluation of the Salar ŽSabins 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. 15 shows the reflectance spectrum of ulexite ŽNaCaB5 O P 8H 2 O. which is the principal borate mineral in the Salar. Fig. 15 also shows the spectrum of halite ŽNaCl., or rock salt, which constitutes more than 90% of the crust. TM ratio 4 r7 should have high values for ulexite and low values for halite. A 4 r7 ratio image was generated and density sliced to highlight the highest ratio values which are shown as triangles in the map ŽFig. 14.. The highest ratio values coincide with the contours of maximum boron concentration in an embayment at the south margin of the Salar. Additional triangles elsewhere around the margin of the Salar indicate potential borate
6. Borate minerals — Salar de Uyuni, Bolivia
Boron and its compounds occur as borate minerals in the crust and brine of certain evaporite deposits 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 world’s largest salar with an area of 10,000 km 2 . The Salar is known to contain borate minerals, but the ore reserves and economic potential were incompletely evaluated. Risacher Ž1989. 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 km 2 , which is very sparse sampling. Landsat TM, however, covers the
Fig. 15. Reflectance spectra for halite ŽNaCl. and ulexite ŽNaCaB5 O 8H 2 O.. TM bands 4 and 7 are used to calculate 4 r7 ratio image.
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reserves that were not detected by the sample program. 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 recognize hydrothermally altered rocks where vegetation and soil cover exceeds 50%. Remote sensing, especially radar, can map lithology and structure in covered terrain. Explorationists have long recognized the relationship between vegetation, soils, and underlying mineral deposits that is shown diagrammatically in Fig. 16. Geochemical exploration techniques analyze the metal content of samples of vegetation, soil, or water. 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:
Ž1. Lack of Õegetation. 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 concentrations 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. Ž2. Indicator plants. These are species that grow preferentially on outcrops and soils enriched in certain elements. Cannon Ž1971. 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 copper-bearing rock outcrops. Ž3. Physiological changes. High metal concentrations in the soil may cause abnormal size, shape, and spectral reflectance characteristics of vegetation. A relationship between spectral reflectance properties of 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 nonmineralized areas. The remote sensing of such spec-
Fig. 16. Copper enrichment of vegetation and soil overlying a concealed copper deposit. From Sabins Ž1997, Fig. 11-19 .
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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 Ž1971. used the large, lowgrade, 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 ŽFig. 17.. Red spruce and balsam fir growing in the mineralized soil both had higher metal concentrations than trees in unmineralized soil. In the reflected IR spectral region, the mineralized balsam firs have a higher reflectance than the normal trees,
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whereas mineralized red spruce have a lower reflectance than the normal trees ŽFig. 17.. In the green spectral region, the mineralized trees of both species have a higher reflectance. Labovitz et al. Ž1983, Fig. 1. summarized other investigations of vegetation spectra. With some exceptions, vegetation reflectance in the green and red bands generally increased 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. Ž1983, 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 nonimaging airborne system that acquires detailed reflectance spectra. The spectra in Fig. 18 were acquired for conifers growing in a mineralized area and in an adjacent nonmineralized area. In the green band Ž0.5 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 mineralized area. This shift, called the blue shift , has been noted in vegetation over several mineralized areas ŽCollins 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 availability of hyperspectral data may encourage new investigations.
8. Future technology
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 Ž1971, Figs. 5 and 6 ..
Secondary silica in the form of quartz is an important component of hydrothermal alteration systems, but has no diagnostic spectral features in the visible or reflected IR spectral regions ŽFig. 7.. This
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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. Ž1983, Fig. 4B ..
inability to detect quartz is a handicap for remote sensing systems, regardless of their spectral resolution. A possible solution lies in the thermal IR region Ž8 to 14 mm. where silica content is indicated by the wavelength where the greatest energy absorption occurs. Fig. 19 shows emissivity spectra of igneous rocks in the thermal region from 8 to 14 m m. All the spectra contain broad emissivity minima, called absorption 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 decreases. The thermal IR multispectral scanner ŽTIMS. is a NASArJPL experimental aircraft system that acquires 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. Ž1992 .
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 ŽASTER. on the first Earth Observation Satellite ŽEOS-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 concentrations of silica, but cannot distinguish hydrothermal silica from other forms such as igneous or sedimentary silica. Hydrothermal silica can be recognized by interpreting TIMS and ASTER images in conjunction with images showing geology and other alteration mineral Žiron minerals, clays, and alunite.. Australia is organizing support for a satellite that will include a hyperspectral scanner in the instru-
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Fig. 19. Thermal emissivity spectra of igneous rocks with different silica and quartz contents. Arrows show centers of absorption bands. Note positions of spectral bands recorded by ASTER and TIMS. From Sabine et al. Ž1994, Fig. 3 ..
ment package. The worldwide availability of hyperspectral images could be a major advance in mineral exploration.
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recognizable on digitally processed TM images. In the future, hyperspectral scanners may identify specific alteration minerals. Multispectral thermal IR systems have the potential to map hydrothermal silicification. Detection of hydrothermally altered rocks is not possible in vegetated areas, so this environment requires other remote sensing methods. Reflectance spectra of foliage growing over mineralized areas may differ from spectra of foliage in adjacent nonmineralized areas. The spectral differences, however, are variable for different plant species. Additional research and development is needed for remote detection 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 geologist can interpret the rock types, structure, and hydrothermal alteration for a region of 31,000 km 2 . 2. Occurrences of important hydrothermal minerals Žclays and alunite. are expressed, using wavelengths 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 the image-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 employed is a valuable technical resource for 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 patterns that provided conduits along which ore-forming solutions penetrated host rocks. Landsat and radar images are used to map these fracture patterns. Hydrothermally altered rocks associated with many ore deposits have distinctive spectral features that are
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.
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