Application of remote sensing and GIS in mineral resource ...

83Application of remote sensing and GIS in mineral resource mappingJournal of Mineralogical and Petrological Sciences, Volume 99, page 83─103, 2004

H.M. Rajesh, [email protected] Corresponding author

REVIEW

Application of remote sensing and GIS in mineral resource mapping － An overview

H.M. RAJESH

Department of Geographical Sciences and Planning, Chamberlain Building,University of Queensland, St Lucia 4072, QLD, Australia

Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

Introduction

“Geologists seem to have rosy prospects in remote sensing for the next decade. This period is likely to be one of consolidation rather than innovation, giving the majority of geologists the time to get to grips with what has been happening over the last three decades in geolog-ical remote sensing research, to apply the new data to exciting new geological problems instead of repeatedly pawing over tiny test areas, and to catch up with their colleagues in other fields.” (Drury, 2001, p. 67)

Mineral resource mapping is an important type of geolo-gic mapping activity and usually covers a great part of varied studies, focused on spectral analysis (e.g. Longhi et al., 2001), geological mapping (e.g. Harris, 1991), structural mapping (e.g. Liu et al., 2000), identification of hydrothermal alteration zones (e.g. Podwysocki et al., 1983), mineral alteration mapping (e.g. Tangestani and Moore, 2002), ferric oxide and oxyhydroxide mineral mapping (e.g. Farrand, 1997), gold exploration (e.g. Spatz, 1997), hyperspectral imagery (e.g. Neville et al., 2003), integration with geographic information systems (GIS) (e.g. Akhavi et al., 2001) etc. Because most of the surface and near－surface mineral deposits in accessible

regions of the earth have been found, current emphasis is on the location of deposits far below the earth’s surface or in inaccessible regions. Geophysical methods that provide deep penetration into the earth are generally needed to locate potential deposits and drill holes are required to confirm their existence. However, much information about potential areas for mineral exploration can be provided by interpretation of surface features on aerial photographs and satellite images. For example, in Australia (where 70% of the continent is covered by sedi-ments), a comparison of all significant gold deposits currently known with the areas of weathered basement rocks (where gold and other mineral deposits are likely to be found) indicates the potential for remote sensing in discovering new deposits under the sedimentary cover (Fig. 1). While use of remotely sensed images cannot replace direct ground observation or data derived from field and laboratory studies, they can form valuable supplements to more traditional methods and provide information and a perspective not otherwise available.

It should be appreciated that there is one important limitation of remote sensing data in mineral exploration － the depth aspect. Remote sensing data have a depth pene-tration of approximately a few micrometers in the very near infrared region, to a few centimeters in the thermal infrared and some meters (in hyper arid regions) in the

84 H.M. Rajesh 85Application of remote sensing and GIS in mineral resource mapping

microwave region. Therefore, in most cases, a remote sensing data interpreter has to rely on indirect clues, such as general geologic setting, alteration zones, associated rocks, structure, lineaments, oxidation products, morphology, drainage, and vegetation anomaly, since only rarely is it possible to directly pinpoint the occurrence and mineralogy of a deposit based solely on remote sensing data. In this perspective, both multispectral and hyper-spectral sensors, which can define mineralogy, are expected to play a greater role in mineral exploration, by helping to delineate ore minerals or their pathfinders. The results are being more and more integrated into opera-tional exploration models based on geographic informa-tion systems (GIS) technology, which plays a relevant role in mineral exploration (e.g. Bonham－Carter, 1994; Memmi and Pride, 1997).

Locating mineral resources relies primarily on

knowledge of the general geological make up of an area. Therefore, a little basic geology about the assumptions of mineral deposits is relevant:

• A particular mineral deposit occurs in a particular rock type (e.g. diamond usually occurs in kimber-lite).

• Mineral deposits usually occur along or adjacent to geologic structures (e.g. Fig. 2).

• Mineral deposits usually show strong alteration on the surface.

• Mineral deposits are usually (spatially) associated with a high temperature rock (e.g. granite).

• Mineral deposits usually occur near the contact between favorable rock types (e.g. porphyry copper deposits have a direct spatial association with the contact of granitic to intermediate intrusive rocks; Guilbert and Park, 1996).A multiple combination of any of the above

mentioned hypotheses is used to locate mineral resources in this paper. For example, the Chalice gold deposit, Yilgarn Craton, Western Australia, occurs in a sequence of intercalated mafic and ultramafic amphibolites, is spatially and temporally related to granitic rocks, is controlled by localized asymmetric folds, and is charac-terized by high－temperature silicate and sulfide alteration assemblages (Bucci et al., 2002). Because mineral resources are associated most frequently with very small but highly anomalous areas where a great many processes have all acted together to concentrate the metals involved above their normal abundances, it is very easy to miss even very high value deposits in the field. However, the anomalous processes involved produce unusual rocks and minerals associated with mineral deposits. It is toward these that remote sensing is directed. Remote sensing becomes a powerful exploration tool in its own right when the primary and secondary processes of mineraliza-

Figure 1. a: Digital Elevation Model (DEM) of Australia. b: Map of Australia showing areas of weathered basement rocks (dark), areas where basement is covered by younger sediments (light), and all significant gold deposits currently known in Australia (circles). (Courtesy: CSIRO, Australia)

Figure 2. Proximity analysis (buffering) of anticlinal folds from an area, part of the Meguma terrane, Nova Scotia, Canada. The Meguma terrane consists of predominantly Cambrian and Ordo-vician sedimentary rocks intruded by Devonian granites. The fold axes were digitized from the geological map and buffered. This map has 24 distance buffers spaced at 250 m intervals. The reported gold occurrences occur close to the axial trace of the anticlines (Bonham－Carter, 1994).


tion result in the formation of spectral anomalies.To evaluate the different aspects of the application of

remote sensing in mineral resource mapping, this paper attempts a review approach, focusing on the application of remote sensing in lithologic, structural, and alteration mapping. In addition to these an appreciation of the use of hyperspectral remote sensing in mineral resource mapping is important as hyperspectral data can help iden-tify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, it is clear that the remote sensing data has to be integrated with other geoscience data like geochemical, geophysical data, etc. This demands a multithematic approach, and GIS forms the perfect tool as they allow more effective integration and analysis of large numbers of spatial data with different attributes and formats in selecting the best sites

of mineral deposits. All the discussions in this paper on satellite imagery refer to either electro－optical sensors [measuring reflectance in the visible and near－infrared (VNIR; 0.3－1.0 μm), short－wave infrared (SWIR; 1.0 －2.5 μm) and mid－infrared or thermal infrared (TIR; 3－5 μm; 8－14 μm) portions of the electromagnetic spectrum], the most common type carried aboard remote sensing satellites, or synthetic aperture radar (SAR) measuring reflectance in the microwave or radar portion [2 － 100 cm; typically at 2.5－3.8 cm (X band), 4.0－7.5 cm (C band), and 15－30 cm (L band)] of the spectrum. The common satellites in the former category, detecting reflected sun－source energy, include Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM), Système Probatoire d’Observation de la Terre (SPOT), Indian Remote Sensing Satellite (IRS), Advanced Spaceborne Thermal Emission

Figure 3. Spectra of common iron－bearing minerals (above dashed line) and minerals containing chemically bound water (below dashed line) (a), iron oxides and hydroxides (b), clay minerals and micas (above dashed line) and carbonate minerals (below dashed line) (c) (Hunt and Salisbury, 1970b; Hunt and Salisbury 1971; Hunt, 1979). Absorption features are shown by T－shaped symbols. Spectra of selected silicate (above dashed line) and non－silicates (below dashed line) in the mid－ or thermal－infrared part of the spectrum are given in (d) (Hunt and Salisbury, 1970a; Kahle et al., 1996). The progressive shift of the short wavelength peak (arrows) and the main trough towards longer wavelengths in (d) corresponds to a transition from felsic to increasingly mafic minerals. Thick horizontal ranges indicate the widths of spectral bands sensed by the Landsat TM. The spectra are offset for clarity.


and Reflection Radiometer (ASTER) and IKONOS, while those in the later category, which transmits and detects radiation, include RADARSAT, ERS, and the Japanese Earth Resource Satellite (JERS, now FUYO).

Spectral signature of minerals and rocks

Since Hunt (1979) and co－workers, a number of papers provided both exhaustive libraries of laboratory spectra of minerals and rocks, and accurate analyses of the absorption features of specific minerals in the visible－short wave infrared, mid－infrared and thermal infrared intervals, establishing the scientific background for the interpreta-tion of remotely sensed spectroscopic data (e.g. Gaffey, 1985; Clark et al., 1990; Salisbury et al., 1991; Christensen et al., 2000). Spectroscopic criteria are widely applied in hyperspectral image analysis for mineral and alteration identification and mapping (see later section). Comparing spectra of freshly cut rocks with those of exposed surfaces gives an insight into the relationship between original rock and superficial altera-tion products, allowing the development of reconnais-sance criteria that may also be applied in other areas with similar environmental conditions.

Mineral structures are such that numerous absorption bands exist due to electronic transitions and ion vibrations (Hunt, 1977). Although minerals are of widely varying types, electronic transitions are most often created by iron, while vibrational ones are often created by water, hydroxyl ions or carbonates. Figure 3a shows reflectance spectra of several iron－bearing minerals that display features resulting from electronic transitions in ferrous (Fe2+) ions. The typical spectra of iron oxides are in the range 0.35 to 1.5 μm (Hunt and Ashley, 1979). It has been observed that the occurrence of absorption anoma-lies at wavelengths less than 0.9 μm is a good indication that hematite is the predominant mineral, when the anom-alies are found at wavelengths close or larger than 0.9 μm, then jarosite or goethite are more abundant (Hunt and Ashley, 1979).

The most common charge－transfer is involved in the migration of electrons from iron to oxygen, and results in a broad absorption band at wavelengths shorter than about 0.55 μm. The most noticeable effect is with iron oxides and hydroxides (Fig. 3b), and is the reason why these minerals and the rocks containing them are colored yellow, orange, red and brown. Vibrational transitions produce reflectance anomalies in the near－infrared region of the spectrum, between 1.1 and 2.5 μm, and they provide more information about the mineralogical rock composition than the spectra features observed in the visible and near－infrared regions. In the SWIR part of

the spectrum the most important vibrational transitions in minerals are those associated with the presence of OH－ ions or water molecules. Absorption features at 1.9, 1.4, 1.14 and 0.94 μm indicate the presence of molecular water in minerals (Fig. 3a). The bending of Al－OH and

Figure 4. a: Bidirectional reflectance spectra of some sedimentary rocks (Salisbury and Hunt, 1974). b: Thermal－infrared transmis-sion spectra of some igneous rocks (Vickers and Lyon, 1967). c: Bidirectional reflectance spectra of metamorphic rocks (Hunt and Salisbury, 1976).


Mg－OH, producing distinctive absorption features in the reflectance spectra, are prominent in aluminous micas and clay minerals (Fig. 3c) and dominate signatures of hydroxylated minerals that contain magnesium, such as talc, chlorites, serpentines and magnesium－rich clays (saponites). Carbonates give rise to a number of absorp-tion features in the SWIR of which that around 2.3 μm is most prominent (Fig. 3c). The mid－infrared region contains high reflectance anomalies for most rocks (basalt, gabbro, etc.) and minerals (clays, micas, sulphates, carbonates) at around 1.65 μm and high absorption at approximately 2.2 μm (Hunt, 1979).

Quartz shows trough in the emittance curve between 8 and 9 μm as a result of Si－O bond－stretching vibra-tions. This and related spectral structures are best shown in transmission spectra, and they occur in both silicates (Fig. 3d) and non－silicates (Fig. 3d). Within the range 8 to 14 μm the emission spectra of silicate minerals contain a prominent, broad absorption trough and associated features caused by Si－O bond stretching (Fig. 3d). In this region of the spectrum, various vibrational transitions in non－silicates produce spectral features that are different from those of silicates (Fig. 3d). The most important are those associated with carbonate and iron oxides, which are so distinct that even small amounts of these non－sili-cates in dominantly silicate rocks drastically alter their spectra.

Rock spectra are mixtures of those for each of their constituents, proportional to their abundance. It has long been known that rocks can be distinguished from each other under ideal conditions by their spectral signatures in the thermal emission region of the spectrum (e.g. Lahren et al., 1988; Sabine et al., 1994). Representative spectra of sedimentary, igneous and metamorphic rocks are given in Figure 4. In general, the dominating features in sedi-mentary rocks are due to the additional presence of the carbonate radical, which produces absorption bands between 1.9 and 2.3 μm (Fig. 4a). All sedimentary rocks generally have water absorption bands at 1.4 μm and 1.9 μm. Clay－shales have an additional absorption feature at 2.1－2.3 μm. Limestones and calcareous rocks are char-acterized by absorption bands of carbonates (at 1.9 μm and 2.35 μm, the latter being more intense); the ferrous ion bands at 1.0 μm are more common in dolomites, due to the substitution of Mg2+ by Fe2+. Falling SiO2 content of igneous rocks (and metamorphic rocks of the same range of compositions) results in a progressive shift of the Si－O bond stretching absorption feature to longer wave-lengths in thermal emission spectra (Fig. 4b). The simi-larity of the spectra for a class of rocks, such as the gran-ites, allows a composite signature to be generated, which may be used as representative of all granites. The broad

absorption due to ferrous ion is prominent in rocks such as termolite schists (Fig. 4c). The features displayed by marbles are strong carbonate absorptions (1.9 μm and 2.35 μm) and high reflectivity in the near infrared. Water and hydroxyl bands are found in schists, marbles and quartz-ites (Hunt and Salisbury, 1976). Figure 4 based on labo-ratory experiments, confirms that rocks possess the poten-tial to be classified from airborne or satellite sensor data if sufficient spectral detail is generated.

Field spectroscopy is a tool to perform feasibility studies to help in understanding the nature of the spectral characteristics of surface materials and their spectral sepa-rability. Remote sensing has been used in combination with field spectroscopy as an aid in alteration mapping leading to mineral exploration (e.g. Van der Meer et al., 1997; Mazzarini et al., 2001; Ferreir et al., 2002). A weathered rock surface (with modified mineralogical composition) will mask some of the spectral properties of the original surface (fresh surface). In such cases, it is necessary to study the spectral differences between the exposed surface in the field and the fresh one. Younis et al. (1997) showed that the spectral regions where the fresh and weathered surfaces show minimum spectral differences can be used to better characterize and discrim-inate the lithological units. Van der Meer et al. (1997) used field spectra to study the spectral characteristics of unweathered rocks samples and the alteration minerals that formed due to low－grade metamorphism in the Troodos ophiolite complex, Cyprus. Further they investi-gated the spectra of soils developing on the different lithologies and showed that with reflectance spectroscopy it is theoretically possible to discriminate the different lithological units based on the soils that develop on them. They used the information to extract characteristic TM spectra from the Landsat TM image of the area. Thus field spectroscopy can eventually lead to the selection areas that are spectrally representative for the different lithologies that can be detected in the image data.

Lithologic mapping

Reconnaissance lithologic mapping is usually the first step of mineral resource mapping studies. Broad litho-logical information is deduced from a number of parame-ters observed in remote sensing images, viz. general geologic setting, weathering and landform, drainage, structural features, soil, vegetation, and spectral character-istics. In the case of sedimentary rocks, especially those that are exposed at hillsides or by folding or faulting, bedding is one of the strongest clues to lithologic compo-sition in images. These linear features are long, even－spaced, and few in number (in comparison to those


Figure 5. a: Landsat image covering part of Pilbara region, Western Australia. The different features include lighter colored areas of Archaean granite batholiths (G), dark green colored metamorphosed basalt (B), Archaean sediments (AS), Archaean sediments and lavas (SL), Protero-zoic sediments (PS), limestone (L), alluvium (A), Tertiary sediments (TS), Quaternary/Tertiary sediments (QTS) and dykes (d). (Courtesy: ACRES, Geoscience Australia.) b: Landsat image covering part of the shield in the northeast corner of Sudan, close to the border of Egypt. The region, part of the Sahara desert, has little soil and vegetation cover; so the rock bodies are exceptionally well exposed. Igneous rock bodies form circular patterns ranging in size from almost 8 km across the body that is exposed in the large, white area, to small structures that appear as circles or dots. Tonal variations in the circular patterns reflect both composition and sand cover. Dark areas are probably rocks containing enough iron and magnesium to color the rock. Gold is commonly associated with such igneous rocks and has been mined in the area since the time of the ancient Egyptians. (Courtesy: NASA and Earth Satellite Corporation.) c: Satellite image of the Snake River plain, southern Idaho, USA vividly show how weathering modifies a rock body. Floods of older basalt form the smooth flat surface that extends diagonally across the area. The younger extrusions are fresh and black, and retain the original features of the flows. Older flows (irregular patches with tan hue, not black) have been subjected to longer periods of weathering and have developed a thin soil that supports sparse vegetation. The oldest flows appear as light reddish brown areas. (Courtesy: NASA and Earth Satellite Corporation.) d: Satellite image of Canadian Shield with dark tones indicating metamorphic rocks and light tones indicating areas of granitic rock. The complex folds and contortions in the rock units show the degree to which metamorphic rocks have been deformed. The long linear lakes, ridges, and depression are major fracture systems. Small lakes, shown on the image as black patches, occur in innumerable depressions. (Courtesy: NASA and Earth Satellite Corporation.)


produced by foliation in metamorphic rocks), and consti-tute rather continuous ridges and valleys. Examination of drainage density, drainage pattern and vegetation patterns may also provide clues to lithology even when beds are not directly exposed. The bareness of sandstone outcrops in arid areas, combined with their well－drained nature, usually permits them to show their true photographic tone or color. Silica－ and carbonate－cemented sandstones are usually pale, but those containing iron compounds can be any shade of yellow, orange, brown or red. In panchro-matic black and white photographs red sandstones appear dark. In humid climates, the main clue to the presence of sandstone is the wide spacing of drainage and the round-ness of topography. Gently dipping mudstones and silt-stones typically develop dendritic and closely spaced drainage patterns because of their poor internal drainage.

Intrusive igneous rocks are generally massive, isotropic and homogenous, which can be easily observed on the remote sensing images. Their shapes (batholiths, laccoliths, dykes, sills, etc.), dimensions, distinct image tones and topographic expression may also help in identi-fication (e.g. Fig. 5a, b). Extrusive igneous rocks can be delineated by distinctive landforms, such as cones, craters, and flows, especially if they are young, and differences in image tone or by drainage patterns and vegetation distri-butions (e.g. Fig. 5c). When they are closely integrated with surrounding sedimentary strata, intrusive igneous rocks are more subtly expressed than are extrusive rocks and can be interpreted with less confidence. Information concerning metamorphic rocks can also be interpreted from remotely sensed images, but typically only with great difficulty because of complex local structures induced by metamorphism (e.g. Fig. 5d). Metamorphism may also reduce the differences in resistance to erosion that are so important in inferring lithology. As in the case of igneous rocks, the response is controlled mainly by the relative proportion of quartz and other stable silicates, compared with the amounts of unstable, usually ferro-magnesian silicates.

Topographic patterns including drainage patterns often reflect geologic structure and lithology. Remote sensing systems operating in the microwave spectrum, such as SAR, is known to provide good topographic enhancements useful for geomorphic and structural inves-tigations while Landsat provides good mapping capabili-ties of cover types including vegetation. Although radar is used chiefly to map structure, it can be applied to litho-logic mapping due to variations in outcrop patterns, surfi-cial character of rocks, and the size and frequency of coarse rock detritus (Blom and Daily, 1982; Rebillard and Evans, 1983; e.g. Fig. 6). The advantage of radar was demonstrated by the fact that bedrock nature could be

qualified, and that, in some cases, the thickness of surfi-cial formations could be quantified. In addition, radar’s advantage was shown by the fact that the substratum under weathering material could be determined under certain conditions. Marble is characterized by relatively flat topography often occupied by farmland or low－lying vegetation. Landsat TM band 4 imagery (0.76－0.90 μm) typically shows dark homogenous gray tones for low－

lying vegetation growing in areas underlain by marble. The distribution of marble can also be mapped on radar imagery based on its relatively uniform flat topographic signature but this task is more difficult. Fairly massive gneissic complexes have a uniform vegetation cover and their radar signature usually reflects a mottled regional topography with substantial relief, and lack of regional ductile fabric. In some instances the radar imagery allows the precise delineation of subcircular intrusive rocks. For example, the plutons in Central Metasedimentary Belt, Quebec, Canada, offer a unique signature in radar imagery because they lack a regional fabric, show positive relief within the surrounding domain, and deflect the regional foliation (Rivard et al., 1999).

Combining Landsat data with a higher spatial resolu-tion data, such as SPOT or IRS panchromatic data, will provide a better discrimination of lithological units than

Figure 6. SIR－A image showing part of the Hammersley Range, western Australia. The area is underlain by lower Proterozoic volcanics, which are interbedded with some of the world’s largest iron－ore deposits, in the form of banded ironstone formations. The two large structures, one in the top (Rocklea Dome) and one in the right, are with a core of Archaean granite. Bright linear features (including fine lines) cutting these granites are dykes. The dark unit with bright ridges surrounding the dome is weath-ered sandstone. Pillow basalts, the rough surface of which results in a bright signature, occurs between the large structures. The synform near the dome contains a layered sequence of iron-stones, shales and carbonates. The rugged area in the top running to the right of the image is underlain by massive ironstone, weathering of which has produced huge iron ore deposits. Bright linear features, NW of the dome, are thick quartz veins, many of which follow minor faults. (Courtesy: NASA.)


either dataset alone (e.g. Fraser et al., 1997; Mickus and Johnson, 2001; Chatterjee et al., 2003). Wiart et al. (2000) mapped the extent of pumice deposits from a previous (1861) eruption of the Dubbi volcano, located in the northeastern part of the Afar triangle, Eritrea, using JERS－1 SAR and Landsat TM imagery. SAR imagery revealed old lava flows buried below tephra deposits, emphasizing the ground penetrating property of the L－

band. The merged SAR and TM imagery provided insights into the differences between weathered lava flows and the extent of the lava flow boundaries, and in the process helped in revising the geological map of Dubbi. The use of multispectral optical sensors (like Landsat) is particularly troublesome in tropical environments, due to continuous adverse atmospheric conditions and terrain characteristics. SAR imagery is applied to tropical regions both because it is able to sense through clouds and because the morphologies of the upper surface of forest canopies reflect underlying topography, which in turn relates to structure and lithology. Geophysical data can be of key importance for regional studies in places where insufficient geologic knowledge is available, due to their ability to provide lithologic and structural informa-tion. Integration and fusion of geophysical data with spaceborne SAR imagery is an effective method for enabling correlation of surface topographic expression with subsurface geological characteristics (e.g. Pedroso et al., 2001). Stern et al. (2002) showed that the folded and faulted Neoproterozoic sedimentary rocks in northern Ethiopia can be better characterized by images from the MOMS－2P multispectral scanner (aboard the International Space Station, orbiting at a ultra low orbit from the earth) than the Landsat TM. The use of remote sensing data clearly adds the textural component that is so important in detecting the subtle features related to lithologic changes and is therefore important to confirm and strengthen the field data interpretation.

Maps produced from remotely sensed images can serve as adequate reconnaissance geologic maps for mineral resource mapping. Further many studies have shown that remotely sensed data could be employed to improve the existing maps of an area (e.g. Rothery, 1987; Abrams et al., 1988). These studies have shown that many of the discrepancies found between the published maps and those derived from the imagery were mostly due to omissions or errors in the field geological mapping as proved through subsequent fieldwork. Krishnamurthy (1997) used digitally enhanced IRS Linear Imaging Self Scanner (LISS)－I and II sensor data to revise/modify the existing geological map of two areas in Karnataka, India, to considerable extent in terms of refined lithological boundaries, delineation of unmapped rock units, mapping

of lineaments, and their networks. A flowchart depicting the general steps adopted to obtain a modified geologic map from remote sensing data is given in Figure 7. The classified image is usually sufficiently geometrically accurate to allow accurate location of sample sites for further studies on mineral resource mapping.

Structural mapping

As a corollary, most mineral deposits are related to some type of deformation of the lithosphere, and most theories of ore formation and concentration embody tectonic or deformational concepts. As linear features (lineaments; O’Leary et al., 1976) shown on remote sensing imagery of increasingly smaller scale (greater extent) reflect increasingly more fundamental structures, their study will provide insights not only to the location of the mineral deposits, but also to metallogenic theories as well. Many studies have emphasized the importance of lineament interpretations and digital lineament analysis in localizing the major mineral deposits and notes that there is a strong correlation between mineral deposits and lineaments (e.g. Kutina, 1969; Katz, 1982; Liu et al., 2000; Rein and Kaufmann, 2003).

Linear features, 10 km or less in length, are clearly discernible on aerial photographs, but are poorly seen in satellite imagery. They indicate the form and position of individual folds, faults, joints, veins, lithologic contacts, and other geologic features that may lead to the location of individual mineral deposits. In general, they reflect only immediate surface and near－surface conditions, and are poor guides to concealed deposits. Linear features, 10 to 200 km or more in length, indicate the general geom-etry of folds, faults and other structures of an area, providing a regional structural pattern. They are less

Figure 7. Flowchart depicting the general processing steps to obtain a modified geologic map using remote sensing data.


abundant than the shorter linear features by probably an order of magnitude and are useful in defining target areas － local settings in which mineral deposits may be concen-trated, and which merit more detailed study in the field. Linear features, more than 200 km long (some being poorly identifiable for short stretches along their length), are most effectively studied on mosaics of Landsat imagery. These linear features seem to be globally ubiq-uitous, and usually display a nearly orthogonal pattern.

Some lineament patterns have been defined to be the most favorable structural conditions in control of various mineral deposits, such as: the traces of major regional lineaments, the intersection of major lineaments or both major (regional) and local lineaments, lineaments of tensional nature, local highest concentration (or density) of lineament, between en echelon lineaments, and linea-ments associated with circular features. For example, Liu et al. (2000) utilized lineament analysis from satellite imagery to delineate the following structural features of considerable interest in search for mineral deposits in northeast Brazil: the warping (or dragging) part of the minor shear zones, which splay out (or branch off) from the major wrench belt, is of extensional nature favorable for hydrothermal emplacement; the swollen parts along the extending lineament (shear) zones are also favorable for magmatic fluid intrusion; the intersections of short and regional lineaments; the periphery, and the margin of circular or ring structures, the internal and external peripheral parts of small rings in a large circular structure, and the en echelon diagonal fractures crosscutting the circular features (see Fig. 8).

Aerial photographs provide evidence of bedding

through the occurrence of ridges in the stereo model and differences in tonal response where beds differ in their mineral constituents. The dip slopes of rocks often can be recognized more reliably on a stereo model than on the ground because of the synoptic view of an area of dipping sediments obtained from the air. Accurate measurements of dip can be made by photogrammetric measurements on stereo pairs. Several studies have determined the attitude of faults and lithological units of fold structures from stereoscopic SPOT images (Berger et al., 1992; Bilotti et al., 2000). A fold can be delineated by tracing the bedding/marker horizon along the swinging strike, and the recognition of the dips of the beds. Broad, open, longitudinal folds are easy to locate on satellite images (e.g. Fig. 9). On the other hand, tight, overturned, isoclinal folds are difficult to identify on satellite images, owing to small areal extent of hinge areas (which provide the only clues to their presence); therefore, such folds need to be studied on appropriately larger scales, such as aerial photographs. Fu et al. (2004) used Landsat TM/ETM stereoscopic images in combination with high－resolution IRS－IC PAN (5.8 m) satellite images to delin-eate Quaternary deformational structures, including spatial distribution and arrangement of fold structures and fault scarps, along the Tian Shan orogenic belt, northwest China.

One of the greatest advantages of remote sensing data from aerial and space platforms lies in delineating vertical to high－angle faults or suspected faults. Where mineralization has taken place along a fault line, however, there may be positive rather than negative surface feature. In most cases the most reliable evidence of faulting is

Figure 8. Lineament and fracture map of Paraiba State, northeast Brazil, extracted and classified by the analysis of the Landsat TM image (Liu et al., 2000).


displacement of bedding along negative linear surface features. Low－angle faults are difficult to interpret, since the images provide planar views from above. Such faults have strongly curving or irregular outcrop and can be inferred on the basis of discordance between rock groups. Joints form patterns in rocks, which are very similar in photographic appearance to faults, i.e. they often provide fairly straight negative features. If relative movement can be seen then the feature can be classified as a fault, and conversely if no movement can be detected it is better to record the feature as a joint. Shear zones involve changes in angular relationships, so that linear features outside the shear system are seen to swing into it. If marker units are present then their displacement is immediately apparent.

Radar imagery (e.g. RADARSAT) and Landsat data can be used to generate valuable structural information during the study of structurally complex terrains, particu-larly when the interpretation of imagery is conducted in conjunction with the regional field work (e.g. Fig. 10). Topographic features observable in RADARSAT images reflect either changes in slope, such as erosional escarp-ments due to variation of resistant and recessive litholog-ical units (Evans et al., 1986). Since SAR sensors provide their own illumination source, the look direction can influence the information content of the imagery. In low－

relief environments, the look direction can be used to provide a greater enhancement of lineaments. With infor-mation from previously collected field measurements, complex structural patterns like details of the regional trends of the foliation, shear zones, etc., can be mapped and traced over large areas using radar imagery (e.g. Fig. 10). Simple photogeologic principles like image texture can be used to outline foliation patterns, folds, brittle and ductile shear zones and lithology, while image tone can be traced to cover type and could be traced and related to

Figure 9. Landsat image covering part of the Anti－Atlas Moun-tains of southern Morocco. By virtue of the climate, degree of erosion, and contrast in rock types (both in terms of erodibility and spectral response), it is one of the most spectacularly exposed fold belts in the world. Large－scale basement cored domes with irregular shapes form a continuous area of positive structural relief. The rocks are complexly folded and faulted. Rock sequences are repeated, anticlines encroach upon anti-clines, and synclines encroach upon synclines. Numerous zones of structural discontinuity or disharmonic folding are obvious in the image. In general, the older rocks to the northwest appear to be more highly deformed than the younger rocks to the south and southeast. The lighter colored yellowish brown Precambrian rocks in the northwest are tightly folded, highly fractured, intruded, and metamorphosed. The white sinuous band against a fold ridge is a dry stream. Recently, Helg et al. (2004) showed that the Anti－Atlas of Morocco is a special type of foreland fold belt lacking any evidence for thrust faults other than the occa-sional steep reverse fault found near basement inliers. (Courtesy: NASA and Earth Satellite Corporation.)

Figure 10. Structural interpretation of an area from Mont－Laurier in the Central Metasedimentary Belt, Quebec, Canada: A) RADARSAT imagery; B) folia-tion trends interpreted from the imagery and selected field measurements of gneissosity; C) foliation trends (light line, as shown in B) and distribution of shear zones with shear indicators (dark line) interpreted from the radar imagery with lineaments and gneissosity (intermediate colored line) measured in the field. Interpretation of the satellite imagery, combined with field traverses, shows that NNE－striking dextral shear zones dominate in the northern part of the mapped area whilst SSE－striking sinistral ductile shear zones occur mainly in the south. Here the sense of shear was inferred from the rotation of the foliation trends seen in imagery and then confirmed in the field from rotation of gneissosity into the shear zones and S/C relation-ships (Rivard et al., 1999).


lithology. In the presence of a near complete forest canopy, radar imagery largely conveys topographic infor-mation without the distraction offered by tonal variations associated with varying ground cover in Landsat imagery. In such areas, field observations are usually limited by outcrop availability and often consist of densely popu-lated observations separated by substantial distance (hundreds of meters to kilometers). The imagery allows separation of isolated structures from more common ones, and provides a regional framework for the regional inter-pretation of structures documented in the field. The important feature of radar imagery is manifested in the capacity of images to provide stereoscopic view, which further facilitates the work of lineament identification (e.g. Sharma et al., 1999).

Recently different algorithms have been published describing the extraction of lineaments directly from digital images and aerial photos (e.g. Budkewitsch et al., 1994; Raghavan et al., 1995; Koike et al., 2001; Costa and Starkey, 2001). These techniques can reduce to a minimum the bias in the manual interpretation. The auto-mated methods in extracting lineaments provide a flow of data, which requires in turn elaborate and exact methods for the analysis and presentation. Principal component analysis (PCA) is a classical statistical method that produces images (components) that are a linear combina-tion of multiband images. When applying PCA, the rela-tive image variance is a measure of the amount of infor-mation observable in each image. PCA of SAR from Seasat－SAR and the shuttle imagery radar (SIR－B) has been used successfully to enhance topographic informa-tion for structural and lineament mapping (e.g. Masouka et al., 1988). Paganelli et al. (2003) recognized four lineament trends (N－NE, NW, NE, and E－NE) in each of the RADARSAT－1 principal component images from the Buffalo Head Hills area, Alberta, Canada. The intersec-tion and offset relationships between the various linea-ment groups in the images enabled definition of a relative succession of events in which the N－NE lineaments was recognized as the oldest, followed by NW and NE linea-ments, which define a conjugate set, and the E－NE－

trending lineaments interpreted as the latest as it show crosscutting relationships with all the previous linea-ments. The lineaments interpreted by Paganelli et al. (2003) and their tectonic－geologic implications provided a basis for kimberlite exploration in the Buffalo Head Hills area.

At the initial stage of image analysis, focus must be given to well－mapped areas where detailed maps had been completed, and predictive capabilities should be developed for image－based reconnaissance mapping of various structural features, thereby optimizing planning of

field efforts. There are difficulties in integrating linea-ment maps with mineral exploration models, as some of the features mapped as lineaments may not be of struc-tural－geologic nature, and it may not be possible to distin-guish between post－mineralization and pre－mineraliza-tion structures. Many studies integrated lineament structures derived from satellite (Landsat TM, SAR, etc.) data with a database of known occurrences in GIS for a more fruitful interpretation of lineaments for mineral exploration (e.g. Akhavi et al., 2001). Other studies correlated lineament intersection density to alteration and observed that lineament intersection density was nearly twice as dense in altered zones as compared to unaltered zones (e.g. Zakir et al., 1999). Use of lineament intersec-tion relationships for mineral exploration gains validity when defined by multi－technique approaches, such as combinations of remote sensing, geophysical, and geolog-ical methods (e.g. Chernicoff et al., 2002). In addition, applicability can be tested by inclusion of known metallo-genic information, which may help to identify favorable structural settings in a given regional context.

Alteration mapping

Mineral deposits are commonly associated with hydro-thermal alteration of the surrounding rocks (e.g. Fig. 11), the style and extent of the alteration reflecting the type of mineral deposit. The host rocks of hydrothermal mineral deposits invariably show the results of their chemical interactions with the hydrothermal fluids that caused mineral deposition (Pirajno, 1992). Such alteration commonly forms a halo around the mineralization, providing an exploration target considerably larger than the deposit itself. The delineation and characterization of hydrothermal alteration can therefore be of great value in mineral exploration and assessment of new targets. The spatial distribution of hydrothermally altered rocks is a key to locating the main outflow zones of hydrothermal systems, which may lead to the recognition of mineral deposits.

Several airborne and orbital imagery studies have shown the feasibility of remote sensing techniques to detect hydrothermal altered areas. These studies are based on the fact that certain diagnostic minerals associ-ated with hydrothermal processes, such as iron－bearing minerals (e.g. hematite, goethite, jarosite), hydroxyl－bearing minerals (e.g. clays, micas), and hydrated sulphates (e.g. gypsum, alunite), show diagnostic spectral features that permit their remote identification (e.g. Hunt and Ashley, 1979; Prost, 1980; Podwysocki et al., 1983; Gladwell et al., 1983; Townsend, 1987; Clark et al., 1990; Fraser, 1991). Weathering processes produce the same


minerals as hydrothermal alteration processes and mask the spectral response of underlying rocks with coatings and internal mineralogical transformations (Buckingham and Sober, 1983). Hence it is important that careful field verification should be performed in the areas marked as hydrothermally altered by satellite image processing. The color of the rock is a good key to the identification of these minerals: when iron oxides are present, the rock color is red, brown, orange or yellow; and the presence of clay minerals usually gives pale colors (yellow, violet, green, beige).

Landsat TM images are useful for hydrothermal mineral identification because of the availability of mid－infrared bands in which the characteristic spectral features of most hydrothermal minerals are present. The tradi-tional processing of an image with an aim of identifying alteration minerals includes application of band combina-tions, band ratios, and/or PCA. Using Landsat TM data, an image incorporating ratios of bands 5 and 7 (TM5/7; clay mineral index), bands 3 and 1 (TM3/1; iron oxide index), and bands 5 and 4 (TM5/4; ferrous index) will highlight areas where concentrations of these minerals occur, thereby discriminating altered from unaltered ground. In arid and semi－arid regions, outcropping hydrothermal alteration zones are mineralogically conspicuous enough to be detected successfully from Landsat TM data (e.g. Amos and Greenbaum, 1989; Fraser, 1991; Spatz, 1997). In tropical regions, however, high vegetation density can critically limit the successful application of Landsat TM data to the detection and

Figure 11. Landsat image covering part of the Transvaal craton, South Africa. Important features in the image include the Bush-veld layered basic－ultrabasic intrusion in the north－east, the circular Pilansberg intrusion (syenite and foyaite) at the top center, strongly folded (dipping northward) Proterozoic Trans-vaal Supergroup rocks in the central part, and the northern edge [east－west bands of green －grey (darker portions in the southwestern part of the image)] of the Witwatersrand basin, South Africa’s most productive area of gold and uranium, near the bottom. A variety of important mineral deposits－such as chromium, vanadium, nickel, copper and tin－occur in and around the Bushveld complex. (Courtesy: NASA and Earth Satellite Corporation.)

Figure 12. Scatter plot in the TM3/TM1 vs. TM4/TM1 space of hematite, goethite, and vegetation training sets for the Newman area, Australia. The inset is a similar plot with the eigen vectors superimposed (Fraser, 1991).


mapping of hydrothermally altered rocks (e.g. Siegal and Goetz, 1977). In this case, techniques for digital enhance-ment of Landsat TM data to map hydrothermally altered rocks commonly aim at the identification of clay and iron oxide alteration zones (Fraser and Green, 1987). The remote detection of iron oxide and clay zones in the pres-ence of vegetation, however, is difficult due to similarities in the reflectance spectra of the materials. If TM data are to provide information regarding the distribution of ferric oxide minerals (hematite, goethite), the effects of vegeta-tion need to be minimized. Different techniques for image processing of Landsat TM to detect and map hydrothermally altered rocks are hence aimed at sepa-rating or reducing substantially the spectral effects of vegetation from the spectral effects of the underlying substrate (Fraser and Green, 1987). Spectral unmixing (Smith et al., 1985) is one such technique and endeavors at searching the abundances or fractions of pure spectral components, so－called end－members, which best explain the observed mixed pixel spectra. PCA of images has been shown to be a successful tool to minimize the vege-tation effect in the resulting images (Abrams et al., 1983; Kaufman, 1988; Loughlin, 1991; Fraser, 1991; Bennett et al., 1993; Ruiz－Armenta and Prol－Ledesma, 1998; Tangestani and Moore, 2001). The principal components can be analyzed using the standard or selective method. In the standard analysis all available bands of an image are used as input for the principal components calculation, while in the selective analysis only certain bands are chosen. Fraser (1991) used the selective method involving directing a principal component analysis at two selected input bands TM3/TM1 and TM4/TM1 to discriminate between ferric oxide (hematite and goethite) and vegetation from the Newman area, western Australia. Here band ratios are chosen because they are more useful than the TM bands as they compensate for the variations caused by topographic features and illumination condi-tions in the scene. A pixel containing hematite and goethite would plot on the hematite－goethite line (Fig. 12). If vegetation were added to that pixel, its position in 3/1 versus 4/1 space would tend to move away from the hematite－goethite line in the direction of vegetation (Fig. 12). Similarly, for an area in central Mexico, Ruiz－Armenta and Prol－Ledesma (1998) used two selected input ratio pairs (TM3/TM1 and TM4/TM3 ratios, and TM4/TM5 and TM5/TM7 ratios), each chosen because of its effectiveness at highlighting and/or separating hema-tite, goethite, hydroxyl minerals, and vegetation, in n－dimensional ratio－versus－ratio space.

The enhancement of the iron oxide and hydroxyl－bearing areas around intrusive bodies in an area relies mainly on the spectral characteristics of the dominant

altered minerals in the visible, NIR and MIR regions of the spectra. Tangestani and Moore (2002) showed that the application of Crósta technique (Crósta and Rabelo, 1993), another variant of PCA, on TM bands 1, 4, 5 and 7 enhances the hydroxyl－rich altered haloes around the porphyry copper deposits of the Meiduk area, Iran. The Crósta technique (using four TM bands) involves the analysis of the eigenvector values allowing identification of principal components that contain spectral information about specific minerals, as well as the contribution of each of the original bands to the components in relation with the spectral response of the mineral of interest. Remote sensing of limonitic and clay alteration by the different PCA techniques proves inadequate where the hydro-thermal alteration mineral assemblages are not iron oxides and clays, as pointed by Carranza and Hale (2002) for the Baguio district, Phillippines. They developed a mineral imaging methodology using Landsat TM data. It includes four steps: a) first to use the selective PCA technique to enhance the spectral response of each alteration mineral into a separate mineral image based on published reflec-tance spectra of minerals, b) second to extract training areas for known hydrothermal alteration zones, c) third to carry out a supervised classification of the mineral images to map hydrothermal alteration zones, and d) fourth to incorporate a DEM for improving the results of the classi-fication. In the Baguio district, the accuracy of the classi-fied hydrothermal alteration map based on the mineral images reached 69%, while inclusion of a DEM in the classification enhances the accuracy to 82%.

In order to improve the definition (both spatial and spectral) of the target areas, Landsat images can be merged with a digitized aerial photograph through inten-sity, hue and saturation (IHS; here intensity represents brightness, hue represents color and saturation represents the purity of the color) transform. Although multi－dimen-sional images are typically portrayed in RGB (red, green, blue), IHS transformed images incorporate more informa-tion because they include more easily defined and identifi-able color attributes, greater control over the chromatic and achromatic components of the image, and the ability to create images utilizing information from more than three input data channels when the image is returned to RGB color space (Harris et al., 1990). IHS incorporating the concept of high pass filter (HPF; Chavez et al., 1991) technique is used for merging high spectral resolution multi－spectral satellite remote sensor data (e.g. Landsat) with higher spatial resolution panchromatic (e.g. IRS) data, to increase the spectral and spatial frequency distri-bution. With its high spatial resolution, and bands covering a wide part of the electromagnetic spectrum, ASTER data is known to provide accurate alteration maps


(e.g. Fig. 13). Aster 4 operates in the spectral range 1.6－1.7 μm, which is a general high－reflectance band, and is coded in red. Aster 6 (2.225－2.245 μm) is absorbed by the Al－OH minerals, whereas Aster 8 (2.295－2.365 μm) corresponds to the absorption by Mg－OH minerals (and carbonates, if present). Therefore in this color coding scheme, Al－OH－bearing minerals appear in shades of blue－purple, Mg－OH－bearing minerals appear in shades of green－yellow, and Al－Mg－OH－bearing minerals appear in shades of red (e.g. Fig. 13).

Imaging Spectrometry

Typically, the number of spectral bands of remote sensors determines the amount of spectral information that remote sensors can acquire. Early remote sensors only have several spectral bands (e.g. Landsat 7 has 7 spectral bands), and thus limited spectral information can be obtained from such remote sensors. Hyperspectral sensors (also referred to as Imaging Spectrometers) can acquire images in many, very narrow, contiguous spectral bands throughout the visible, near－IR, mid－IR, and

thermal－IR portions of the spectrum. These systems typi-cally collect 200 or more bands of data, which enables the construction of an effectively continuous reflectance (emittance in the case of thermal－IR energy) spectrum for every pixel in the scene. Thus hyperspectral sensors can produce data of sufficient spectral resolution for direct identification of minerals, whereas the broader band TM cannot resolve these diagnostic spectral differences.

Most hyperspectral remote sensing is still performed using airborne systems such as the Compact Airborne Spectrographic Imager (CASI), the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), the SWIR Full Spectrum Imager (SFSI), HYperspectral Digital Imagery Collection Experiment (HYDICE), Probe－1 and HyMap. Examples of satellite sensors include Hyperion, FTHSI and Australian Resource Information and Environment Satellite (ARIES－1). Reflectance spectra of minerals measured by different spectroradiometers with different spectral resolution are stored in spectral libraries that are available in digital format (e.g. Grove et al., 1992; Clark et al., 1993) against which the data can be correlated and classified.

Several approaches to extract information from high－spectral resolution image data have been reported, processing spectral information on a pixel－by－pixel basis, including binary encoding (Goetz et al., 1982), spectral unmixing (Adams et al., 1986), relative absorption band－depth mapping (Crowley et al., 1989), waveform charac-terization (Okada and Iwashita, 1992), classification (Cetin et al., 1993), spectral angle mapping (SAM; Kruse et al., 1993), decorrelation stretching (Abrams and Hook, 1995), probability density function (Nedeljkovic and Pendock, 1996), constrained energy minimization (CEM; Farrand and Harsanyi, 1997), cross correlogram spectral matching (Van der Meer and Bakker, 1997), back－propagation neural network (BNN; Yang et al., 1999), and geophysical inversion (Van der Meer, 2000). These methods all use imaging spectrometer data corrected to reflectance and quantitatively test the similarity of unknown imaged spectra with known spectra measured in the field or labo-ratory or extracted from the image data at locations of known ground truth. This often results in mineral maps, which portray the probability that a pixel is composed of a certain mineral.

Most of the image analysis algorithms developed specifically to exploit the extensive information contained in hyperspectral imagery also provide accurate, although more limited, analysis of multispectral data. The different algorithms can be grouped as either whole－pixel or sub－pixel analysis methods. Whole－pixel analysis methods calculates the spectral similarity between a test (or pixel) reflectance spectrum and a reference (or laboratory)

Figure 13. ASTER image of the Escondida copper, gold, and silver open－pit mine, Chile. The hydrothermal alteration mineral zones include porpylitic, phyllic and potassic zones. A high－

grade supergene cap overlies primary sulfide ore. The top image is a conventional 3－2－1 (near infrared, red, green) RGB composite. The bottom image displays short－wave infrared bands 4－6－8 (1.65 μm, 2.205 μm, 2.33 μm) in RGB, and high-lights the different rock types present on the surface, as well as the changes caused by mining. (Courtesy: NASA GSFC, MITI, ERSDAC, JAROS, and U.S./Japan ASTER Science Team.)


reflectance spectrum and include standard supervised classifiers such as minimum distance or maximum likeli-hood, as well as tools developed specifically for hyper-spectral imagery such as SAM and spectral feature fitting. The SAM computes a spectral angle between each pixel spectrum and each reference spectrum. The outcome of SAM－algorithm gives a qualitative estimate of the pres-ence of absorption features, which can be related to mineralogy. Van der Meer et al. (1997) demonstrated the potential of SAM technique for a first assessment of mineral potential in ultramafic terrains. Another approach to matching reference and pixel spectrum is to examine specific absorption features in the spectra, as used in spec-

tral feature fitting. Back－Propagation neural Network (BPN) is a method, which examine all the pixels in the image in parallel. For BPN method, first each classifica-tion resulting from a hidden－layered neural network containing hidden units is trained using a back－propaga-tion algorithm. The result is an image classified by the acquired network, which responds to each new unseen vector with the knowledge gained from the training stage (e.g. Yang et al., 1999). Sub－pixel analysis methods calculate the quantity of reference materials in each pixel of an image and include tools such as linear spectral unmixing and matched filtering. Linear spectral unmixing exploits the theory that the reflectance spectrum of any

Figure 14. a and b: Mineral maps derived from AVIRIS data obtained over Cuprite in 1995. The image in a is derived from analyzing the vibrational absorption features in minerals (typically in the 2－2.5 μm spectral region) common to OH－, CO3－, and SO4－bearing minerals. The image in b is derived from analyzing the electronic absorption features in minerals (typically in the 0.4－1.2 μm spectral region) common of Fe2+ and Fe3+ bearing minerals (Clark et al., 2003).

c. SAM classification map over Cuprite derived from SFSI－2 image. Collected end－member spectra with corresponding JPL library spectra are shown on the right (Borstad et al., 2000).


pixel is the result of linear combinations of the spectra of all end members inside that pixel.

Beraton et al. (1997) showed that AVIRIS could be successfully used for mapping the occurrence of potas-sium metasomatism (usually the alteration marked by pervasive replacement of diverse rock types by adularia + hematite ± quartz ± illite－montmorillonite; Roddy et al., 1988) in well－exposed areas. The AVIRIS derived mineral distribution maps for the Cuprite mining district, Nevada, well known in the remote sensing community as a test site for many years because of its exposed altered bedrock, alluvial deposits and minimal vegetation cover (e.g. Abrams et al., 1977; Kruse et al., 1990; Rast et al., 1991), is given in Figure 14 (a, b). Figure 14 (a, b) utilizes the tricorder algorithm (Clark et al., 2003), that uses a digital spectral library of known reference minerals and a fast, modified least－squares method of determining if a diagnostic spectral feature for a given mineral is present in the image. SAM classification map (2.0－2.4 μm) over Cuprite derived from the modified and updated SFSI (SFSI－2) is shown in Figure 14c. Resmini et al. (1997) illustrated the application of CEM, which requires only the spectrum of the mineral to be mapped and no prior knowledge of background constituents, as a rapid technique for mineral mapping from the Cuprite area. Yang et al. (1999) illustrated the high classification accu-racy of the BPN over SAM for the Curpite area, and is attributed to its ability to deal with complex relationships and the nature of the data set. Neville et al. (2003) showed the ability of constrained linear unmixing proce-dure to create mineral fraction maps, using SFSI and AVIRIS data sets collected over Cuprite. The resulting mineral abundance maps are usually merged with other exploration data such as the digital elevation models to get a better perspective.

Application of GIS

Mineral exploration has traditionally been based on the application of a variety of prospecting methods, namely geochemistry, geophysics, geological mapping, aerial photo interpretation and ground surveys, operatively inte-grated within exploration phases. The main criterion of the prospector is to identify anomalies associated with target mineral areas, gradually reducing the original extent area to a small set of anomalies. This process is complex and it needs both analysis and integration of the above multithematic exploration information, from which decisions must be made over time and at different stages. In the applied context remote sensing, and more recently, GIS have shown their usefulness as support tools of great interest. GIS is considered because they allow for more

effective integration and analysis of large numbers of geo-referenced spatial data with different attributes and formats.

As far as the mineral potential mapping using GIS is concerned, it generally involves four main steps: building a spatial digital database, extracting predictive evidence for a particular mineral deposit type, calculating weights for each predictive evidence map, and combining the maps to predict mineral potential. The probability that a mineral deposit exists increases where the predictor themes areally overlap. Here the initial database building step is the most time consuming step and can include among others, remote sensing data, geophysical data (magnetic, gravity), geochemical data (for each element of interest), geological data (structural, lithological), topo-graphical data (DEM), and mineral occurrence data.

A general scheme of the methodology followed for GIS implementation is given in Figure 15 and consists of two main parts: the relational database and spatial anal-ysis leading to a decision support system (geognostic variable selection and weighting of variables). The rela-

Figure 15. Flow chart illustrating the initial database building, decision support system, and final modeling used for mineral potential mapping in a GIS. The possible layers included within the GIS database are given in the dashed－line box. Depending on the availability of data, different steps illustrated in the flow-chart will be modified.


tional database contains and integrates the georeferenced information coming from remote sensing imagery, existing maps and exploration/prospecting data, expressed as tables and maps. A major function of a GIS is the ability to analyze the spatial relationships between data sources. Here spatial analysis is based on the criterion on combining (overlaying) multi－class maps (Bonham－

Carter, 1994) leading to the mineral potential map or mineral favorability map. It can also be carried out with reference to a practical concept named Mineral Potential Index (MPI) or Mineral Potential Score (MPS). This index is numerically a linear expression in which such a calculation requires us to assign score to each class－map and weights to each input－map according to some criteria, leading to the final MPI or MPS map (see Fig. 15).

Data integration methods in GIS may be divided into two main groups: knowledge－driven models, such as weighted index overlay, fuzzy logic and multi－criteria evaluation, estimate the parameters of a function for combining datasets on the basis of expert opinion; and data－driven models, such as regression, weights of evidence modeling, Dempster－Shafer belief functions, certainty factors, Bayesian statistics and artificial neural networks (ANN), estimate model parameters from measured data (Bornham－Carter, 1994). The data－driven approach, unlike the knowledge－driven approach, requires a number of known deposits to exist in the area of interest, and the parameters identified for a particular deposit type in an area can only be applied to other regions with similar geology. It is now known that both knowledge－driven data integration and data－driven models involve assumptions that are difficult to satisfy when dealing with geological variables such as linear relationships. ANNs, however, are an exception and seem to offer advantages over other methods because they make no assumptions about the data. Although ANNs are used frequently as remote sensing data classifiers, their potential as spatial modeling tools in mineral potential mapping is illustrated in Figure 16.

A growing number of studies evaluated the potential for extracting geologic information (e.g. lithologic mapping, structural mapping, etc.) by integrating tradi-tional remote sensing, geophysical data and ancillary data (e.g. DEM) in a GIS environment. Structural analysis of satellite imagery and aerial photograph can generate large population of interpreted linear features. This population can be reduced through lineament interpretations, based on the identification of features which are geologically significant, within a GIS; thus allowing the lineament vectors to be properly registered immediately. The linea-ments interpreted from the satellite imagery can be converted to a raster grid and, using a local block summa-

tion function, a lineament density can be calculated for the concerned area. Lineament density, in similar fashion to fracture density, but on a larger scale, provides an approximate location of mineral concentration and measure of how broken is the rock mass (McGregor et al., 1999). This is a very good example for the application of GIS in extracting accurate geologic information including linear features (lineaments) for mineral resource mapping.

Concluding remarks

The important question that a geologist can ask is “Why should one use remotely sensed imagery for mineral resource mapping?” The answer is, because it can provide information that is not available any other way. Geologic maps (assuming that they are even available) may be inaccurate and, at best, are usually generalizations. The geologist doing the mapping may have painstakingly mapped the boundaries of a particular formation and missed an obvious alteration zone within it. Figures drawn on the ground by ancient peoples are overlooked by those walking on them; however, they stand out clearly when viewed from the air. Similarly, geologic structures and mineral alteration patterns can be quite vivid on satel-lite imagery. In a hilly terrain, where poor accessibility and dense vegetation cover hinder fieldwork, it is not always possible to map or collect data from field throughout any structural feature. The data are collected from accessible places and geologists then interpolate those data to obtain continuity. In this context, image interpretation of structural features is more reliable than their interpolation from field data. Subtle color variations that would go unnoticed on the ground can be made quite bold in false－color renditions, begging to be assayed, as it were. Known mineral resource areas can sometimes be extended by tracing lineaments outside the areas. Remote sensing data, by virtue of its synoptic overview, multi-spectral and multi－temporal coverage, can help to rapidly delineate metallogenic provinces/belts/sites over a larger terrain, based on known models of commercial ore occur-rences. This can help to isolate potential areas from non－interesting areas for further exploration.

Figure 16. Example of the structure of a network model used for gold potential mapping.


The main considerations for remote sensing applica-tions in mineral resource mapping are:

• Reconnaissance lithologic mapping is usually the first step of mineral resource mapping projects. Better results can be achieved when remote sensing and potential field data are used for the extraction of lithologic information.

• Structural mapping to supplement the lithologic information. Deduction of information regarding localization of mineral deposits by certain types of geologic structures (including lineaments) is vital in planning detailed mineral resource exploration.

• Alteration mapping to deduce the information about the possible mineral resource. Mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks, the style and extent of the alteration reflecting the type of mineral deposit.

• Hyperspectral data can help identify and thematically map regions of mineral resource interest by using distinct absorption features of most minerals. With little doubt, the advent of hyperspectral imagery is the most exciting thing to happen to geology in a decade. The detection of subtle differences in reflec-tance (or, more correctly, radiance), measured across a large number of narrow bandwidths, allows classi-fication of rock types and analysis of mineral contents with far greater accuracy than ever before.

• Finally coming to the exploration stage, it is clear that the remote sensing data gathered has to be inte-grated with other geoscience data like geochemical, geophysical, etc. This demands a multithematic approach, and GIS form the perfect tool to integrate the data sets and do spatial analysis leading to mineral potential maps.

Acknowledgments

Stuart Phinn, David Pullar and Pramod Sharma are thanked for comments and discussions. University of Queensland is thanked for facilities. Natsumi Takao is thanked for support and discussions. Steve Drury is thanked for the clarification regarding his quote on the unending search for the hitherto undiscovered potato field and the interest in the work. Bonham－Carter GF (and Anna Ypma from Elsevier), Benoit Rivard, Gary Borstad, Gregg Swayze, Manoel Araújo Sousa (Jr.), and Roger Clark are thanked for the permission to reproduce figures from their book/article. The two journal reviewers provided constructive and encouraging comments. Koichiro Fujimoto is thanked for editorial efforts. This is a contribution to the Gondwana Institute for Geology and Environment (GIGE) and IGCP 453.

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(Manuscript received; 16 February, 2004)(Manuscript accepted; 28 June, 2004)

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