Application of Remote Sensing and GIS in Mineral Resource Mapping − an Overview

Journal ofApplication Mineralogical of remote and Petrological sensing and GISSciences, in mineral Volume resource 99, page mapping 83─ 103, 2004 83

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 regions of the earth have been found, current emphasis is on the location of deposits far below the earth’s surface or “Geologists seem to have rosy prospects in remote in inaccessible regions. Geophysical methods that sensing for the next decade. This period is likely to be provide deep penetration into the earth are generally one of consolidation rather than innovation, giving the needed to locate potential deposits and drill holes are majority of geologists the time to get to grips with what has been happening over the last three decades in geolog- required to confirm their existence. However, much ical remote sensing research, to apply the new data to information about potential areas for mineral exploration exciting new geological problems instead of repeatedly can be provided by interpretation of surface features on pawing over tiny test areas, and to catch up with their aerial photographs and satellite images. For example, in . colleagues in other fields ” (Drury, 2001, p. 67) Australia (where 70% of the continent is covered by sedi- ments), a comparison of all significant gold deposits Mineral resource mapping is an important type of geolo- currently known with the areas of weathered basement gic mapping activity and usually covers a great part of rocks (where gold and other mineral deposits are likely to varied studies, focused on spectral analysis (e.g. Longhi be found) indicates the potential for remote sensing in et al., 2001), geological mapping (e.g. Harris, 1991), discovering new deposits under the sedimentary cover structural mapping (e.g. Liu et al., 2000), identification of (Fig. 1). While use of remotely sensed images cannot hydrothermal alteration zones (e.g. Podwysocki et al., replace direct ground observation or data derived from 1983), mineral alteration mapping (e.g. Tangestani and field and laboratory studies, they can form valuable Moore, 2002), ferric oxide and oxyhydroxide mineral supplements to more traditional methods and provide mapping (e.g. Farrand, 1997), gold exploration (e.g. information and a perspective not otherwise available. Spatz, 1997), hyperspectral imagery (e.g. Neville et al., It should be appreciated that there is one important 2003), integration with geographic information systems limitation of remote sensing data in mineral exploration － (GIS) (e.g. Akhavi et al., 2001) etc. Because most of the the depth aspect. Remote sensing data have a depth pene- surface and near －surface mineral deposits in accessible tration of approximately a few micrometers in the very near infrared region, to a few centimeters in the thermal H.M. Rajesh, [email protected] Corresponding author infrared and some meters (in hyper arid regions) in the 84 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 85

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)

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 Figure 2. Proximity analysis (buffering) of anticlinal folds from an with a high temperature rock (e.g. granite). area, part of the Meguma terrane, Nova Scotia, Canada. The Meguma terrane consists of predominantly Cambrian and Ordo- • Mineral deposits usually occur near the contact vician sedimentary rocks intruded by Devonian granites. The between favorable rock types (e.g. porphyry copper fold axes were digitized from the geological map and buffered. deposits have a direct spatial association with the This map has 24 distance buffers spaced at 250 m intervals. The contact of granitic to intermediate intrusive rocks; reported gold occurrences occur close to the axial trace of the Guilbert and Park, 1996). anticlines (Bonham－Carter, 1994). A multiple combination of any of the above mentioned hypotheses is used to locate mineral resources microwave region. Therefore, in most cases, a remote in this paper. For example, the Chalice gold deposit, sensing data interpreter has to rely on indirect clues, such Yilgarn Craton, Western Australia, occurs in a sequence as general geologic setting, alteration zones, associated of intercalated mafic and ultramafic amphibolites, is rocks, structure, lineaments, oxidation products, spatially and temporally related to granitic rocks, is morphology, drainage, and vegetation anomaly, since only controlled by localized asymmetric folds, and is charac- － rarely is it possible to directly pinpoint the occurrence and terized by high temperature silicate and sulfide alteration mineralogy of a deposit based solely on remote sensing assemblages (Bucci et al., 2002). Because mineral data. In this perspective, both multispectral and hyper- resources are associated most frequently with very small spectral sensors, which can define mineralogy, are but highly anomalous areas where a great many processes expected to play a greater role in mineral exploration, by have all acted together to concentrate the metals involved helping to delineate ore minerals or their pathfinders. The above their normal abundances, it is very easy to miss results are being more and more integrated into opera- even very high value deposits in the field. However, the tional exploration models based on geographic informa- anomalous processes involved produce unusual rocks and tion systems (GIS) technology, which plays a relevant minerals associated with mineral deposits. It is toward role in mineral exploration (e.g. Bonham －Carter, 1994; these that remote sensing is directed. Remote sensing Memmi and Pride, 1997). becomes a powerful exploration tool in its own right Locating mineral resources relies primarily on when the primary and secondary processes of mineraliza- 84 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 85

tion result in the formation of spectral anomalies. of mineral deposits. All the discussions in this paper on To evaluate the different aspects of the application of satellite imagery refer to either electro －optical sensors remote sensing in mineral resource mapping, this paper [measuring reflectance in the visible and near －infrared attempts a review approach, focusing on the application (VNIR; 0.3－1.0 μm), short－wave infrared (SWIR; 1.0 － of remote sensing in lithologic, structural, and alteration 2.5 μm) and mid－infrared or thermal infrared (TIR; 3－5 mapping. In addition to these an appreciation of the use μm; 8－14 μm) portions of the electromagnetic spectrum], of hyperspectral remote sensing in mineral resource the most common type carried aboard remote sensing mapping is important as hyperspectral data can help iden- satellites, or synthetic aperture radar (SAR) measuring tify and thematically map regions of exploration interest reflectance in the microwave or radar portion [2 － 100 by using the distinct absorption features of most minerals. cm; typically at 2.5 －3.8 cm (X band), 4.0 －7.5 cm (C Finally coming to the exploration stage, it is clear that the band), and 15 －30 cm (L band)] of the spectrum. The remote sensing data has to be integrated with other common satellites in the former category, detecting geoscience data like geochemical, geophysical data, etc. reflected sun－source energy, include Landsat Multispectral This demands a multithematic approach, and GIS forms Scanner (MSS), Landsat Thematic Mapper/Enhanced the perfect tool as they allow more effective integration Thematic Mapper (TM/ETM), Système Probatoire and analysis of large numbers of spatial data with d’Observation de la Terre (SPOT), Indian Remote Sensing different attributes and formats in selecting the best sites 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. 86 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 87 and Reflection Radiometer (ASTER) and IKONOS, while the spectrum the most important vibrational transitions in those in the later category, which transmits and detects minerals are those associated with the presence of OH － radiation, include RADARSAT, ERS, and the Japanese ions or water molecules. Absorption features at 1.9, 1.4, Earth Resource Satellite (JERS, now FUYO). 1.14 and 0.94 μ m indicate the presence of molecular water in minerals (Fig. 3a). The bending of Al －OH and 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 μ m, and they Figure 4. a: Bidirectional reflectance spectra of some sedimentary rocks (Salisbury and Hunt, 1974). b: Thermal－infrared transmis- provide more information about the mineralogical rock sion spectra of some igneous rocks (Vickers and Lyon, 1967). c: composition than the spectra features observed in the Bidirectional reflectance spectra of metamorphic rocks (Hunt and visible and near －infrared regions. In the SWIR part of Salisbury, 1976). 86 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 87

Mg－OH, producing distinctive absorption features in the absorption due to ferrous ion is prominent in rocks such reflectance spectra, are prominent in aluminous micas and as termolite schists (Fig. 4c). The features displayed by clay minerals (Fig. 3c) and dominate signatures of marbles are strong carbonate absorptions (1.9 μm and 2.35 hydroxylated minerals that contain magnesium, such as μm) and high reflectivity in the near infrared. Water and talc, chlorites, serpentines and magnesium －rich clays hydroxyl bands are found in schists, marbles and quartz- (saponites). Carbonates give rise to a number of absorp- ites (Hunt and Salisbury, 1976). Figure 4 based on labo- tion features in the SWIR of which that around 2.3 μm is ratory experiments, confirms that rocks possess the poten- most prominent (Fig. 3c). The mid －infrared region tial to be classified from airborne or satellite sensor data if contains high reflectance anomalies for most rocks (basalt, sufficient spectral detail is generated. gabbro, etc.) and minerals (clays, micas, sulphates, Field spectroscopy is a tool to perform feasibility carbonates) at around 1.65 μm and high absorption at studies to help in understanding the nature of the spectral approximately 2.2 μm (Hunt, 1979). characteristics of surface materials and their spectral sepa- Quartz shows trough in the emittance curve between rability. Remote sensing has been used in combination 8 and 9 μm as a result of Si －O bond －stretching vibra- with field spectroscopy as an aid in alteration mapping tions. This and related spectral structures are best shown leading to mineral exploration (e.g. Van der Meer et al., in transmission spectra, and they occur in both silicates 1997; Mazzarini et al., 2001; Ferreir et al., 2002). A (Fig. 3d) and non－silicates (Fig. 3d). Within the range 8 weathered rock surface (with modified mineralogical to 14 μm the emission spectra of silicate minerals contain composition) will mask some of the spectral properties of a prominent, broad absorption trough and associated the original surface (fresh surface). In such cases, it is features caused by Si－O bond stretching (Fig. 3d). In this necessary to study the spectral differences between the region of the spectrum, various vibrational transitions in exposed surface in the field and the fresh one. Younis non －silicates produce spectral features that are different et al. (1997) showed that the spectral regions where the from those of silicates (Fig. 3d). The most important are fresh and weathered surfaces show minimum spectral those associated with carbonate and iron oxides, which differences can be used to better characterize and discrim- are so distinct that even small amounts of these non－sili- inate the lithological units. Van der Meer et al. (1997) cates in dominantly silicate rocks drastically alter their used field spectra to study the spectral characteristics of spectra. unweathered rocks samples and the alteration minerals Rock spectra are mixtures of those for each of their that formed due to low －grade metamorphism in the constituents, proportional to their abundance. It has long Troodos ophiolite complex, Cyprus. Further they investi- been known that rocks can be distinguished from each gated the spectra of soils developing on the different other under ideal conditions by their spectral signatures in lithologies and showed that with reflectance spectroscopy the thermal emission region of the spectrum (e.g. Lahren it is theoretically possible to discriminate the different et al., 1988; Sabine et al., 1994). Representative spectra lithological units based on the soils that develop on them. of sedimentary, igneous and metamorphic rocks are given They used the information to extract characteristic TM in Figure 4. In general, the dominating features in sedi- spectra from the Landsat TM image of the area. Thus mentary rocks are due to the additional presence of the field spectroscopy can eventually lead to the selection carbonate radical, which produces absorption bands areas that are spectrally representative for the different between 1.9 and 2.3 μm (Fig. 4a). All sedimentary rocks lithologies that can be detected in the image data. generally have water absorption bands at 1.4 μm and 1.9 μm. Clay－shales have an additional absorption feature at Lithologic mapping 2.1－2.3 μm. Limestones and calcareous rocks are char- acterized by absorption bands of carbonates (at 1.9 μm Reconnaissance lithologic mapping is usually the first and 2.35 μm, the latter being more intense); the ferrous step of mineral resource mapping studies. Broad litho- ion bands at 1.0 μm are more common in dolomites, due logical information is deduced from a number of parame- 2+ 2+ to the substitution of Mg by Fe . Falling SiO2 content ters observed in remote sensing images, viz. general of igneous rocks (and metamorphic rocks of the same geologic setting, weathering and landform, drainage, range of compositions) results in a progressive shift of the structural features, soil, vegetation, and spectral character- Si－O bond stretching absorption feature to longer wave- istics. In the case of sedimentary rocks, especially those lengths in thermal emission spectra (Fig. 4b). The simi- that are exposed at hillsides or by folding or faulting, larity of the spectra for a class of rocks, such as the gran- bedding is one of the strongest clues to lithologic compo- ites, allows a composite signature to be generated, which sition in images. These linear features are long, even － may be used as representative of all granites. The broad spaced, and few in number (in comparison to those 88 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 89

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.) 88 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 89

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- Figure 6. SIR－A image showing part of the Hammersley Range, ness of topography. Gently dipping mudstones and silt- western Australia. The area is underlain by lower Proterozoic stones typically develop dendritic and closely spaced volcanics, which are interbedded with some of the world’s largest iron －ore deposits, in the form of banded ironstone formations. drainage patterns because of their poor internal drainage. The two large structures, one in the top (Rocklea Dome) and one Intrusive igneous rocks are generally massive, in the right, are with a core of Archaean granite. Bright linear isotropic and homogenous, which can be easily observed features (including fine lines) cutting these granites are dykes. on the remote sensing images. Their shapes (batholiths, The dark unit with bright ridges surrounding the dome is weath- laccoliths, dykes, sills, etc.), dimensions, distinct image ered sandstone. Pillow basalts, the rough surface of which results in a bright signature, occurs between the large structures. tones and topographic expression may also help in identi- The synform near the dome contains a layered sequence of iron- fication (e.g. Fig. 5a, b). Extrusive igneous rocks can be stones, shales and carbonates. The rugged area in the top running delineated by distinctive landforms, such as cones, craters, to the right of the image is underlain by massive ironstone, and flows, especially if they are young, and differences in weathering of which has produced huge iron ore deposits. Bright image tone or by drainage patterns and vegetation distri- linear features, NW of the dome, are thick quartz veins, many of which follow minor faults. (Courtesy: NASA.) 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 qualified, and that, in some cases, the thickness of surfi- and can be interpreted with less confidence. Information cial formations could be quantified. In addition, radar’s concerning metamorphic rocks can also be interpreted advantage was shown by the fact that the substratum from remotely sensed images, but typically only with under weathering material could be determined under great difficulty because of complex local structures certain conditions. Marble is characterized by relatively induced by metamorphism (e.g. Fig. 5d). Metamorphism flat topography often occupied by farmland or low－lying may also reduce the differences in resistance to erosion vegetation. Landsat TM band 4 imagery (0.76－0.90 μm) that are so important in inferring lithology. As in the case typically shows dark homogenous gray tones for low － of igneous rocks, the response is controlled mainly by the lying vegetation growing in areas underlain by marble. relative proportion of quartz and other stable silicates, The distribution of marble can also be mapped on radar compared with the amounts of unstable, usually ferro- imagery based on its relatively uniform flat topographic magnesian silicates. signature but this task is more difficult. Fairly massive Topographic patterns including drainage patterns gneissic complexes have a uniform vegetation cover and often reflect geologic structure and lithology. Remote their radar signature usually reflects a mottled regional sensing systems operating in the microwave spectrum, topography with substantial relief, and lack of regional such as SAR, is known to provide good topographic ductile fabric. In some instances the radar imagery allows enhancements useful for geomorphic and structural inves- the precise delineation of subcircular intrusive rocks. For tigations while Landsat provides good mapping capabili- example, the plutons in Central Metasedimentary Belt, ties of cover types including vegetation. Although radar Quebec, Canada, offer a unique signature in radar imagery is used chiefly to map structure, it can be applied to litho- because they lack a regional fabric, show positive relief logic mapping due to variations in outcrop patterns, surfi- within the surrounding domain, and deflect the regional cial character of rocks, and the size and frequency of foliation (Rivard et al., 1999). coarse rock detritus (Blom and Daily, 1982; Rebillard and Combining Landsat data with a higher spatial resolu- Evans, 1983; e.g. Fig. 6). The advantage of radar was tion data, such as SPOT or IRS panchromatic data, will demonstrated by the fact that bedrock nature could be provide a better discrimination of lithological units than 90 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 91 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 Figure 7. Flowchart depicting the general processing steps to continuous adverse atmospheric conditions and terrain obtain a modified geologic map using remote sensing data. 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 of lineaments, and their networks. A flowchart depicting forest canopies reflect underlying topography, which in the general steps adopted to obtain a modified geologic turn relates to structure and lithology. Geophysical data map from remote sensing data is given in Figure 7. The can be of key importance for regional studies in places classified image is usually sufficiently geometrically where insufficient geologic knowledge is available, due to accurate to allow accurate location of sample sites for their ability to provide lithologic and structural informa- further studies on mineral resource mapping. tion. Integration and fusion of geophysical data with spaceborne SAR imagery is an effective method for Structural mapping enabling correlation of surface topographic expression with subsurface geological characteristics (e.g. Pedroso et As a corollary, most mineral deposits are related to some al., 2001). Stern et al. (2002) showed that the folded and type of deformation of the lithosphere, and most theories faulted Neoproterozoic sedimentary rocks in northern of ore formation and concentration embody tectonic or Ethiopia can be better characterized by images from the deformational concepts. As linear features (lineaments; MOMS－2P multispectral scanner (aboard the International O’Leary et al., 1976) shown on remote sensing imagery Space Station, orbiting at a ultra low orbit from the earth) of increasingly smaller scale (greater extent) reflect than the Landsat TM. The use of remote sensing data increasingly more fundamental structures, their study will clearly adds the textural component that is so important in provide insights not only to the location of the mineral detecting the subtle features related to lithologic changes deposits, but also to metallogenic theories as well. Many and is therefore important to confirm and strengthen the studies have emphasized the importance of lineament field data interpretation. interpretations and digital lineament analysis in localizing Maps produced from remotely sensed images can the major mineral deposits and notes that there is a strong serve as adequate reconnaissance geologic maps for correlation between mineral deposits and lineaments (e.g. mineral resource mapping. Further many studies have Kutina, 1969; Katz, 1982; Liu et al., 2000; Rein and shown that remotely sensed data could be employed to Kaufmann, 2003). improve the existing maps of an area (e.g. Rothery, 1987; Linear features, 10 km or less in length, are clearly Abrams et al., 1988). These studies have shown that discernible on aerial photographs, but are poorly seen in many of the discrepancies found between the published satellite imagery. They indicate the form and position of maps and those derived from the imagery were mostly individual folds, faults, joints, veins, lithologic contacts, due to omissions or errors in the field geological mapping and other geologic features that may lead to the location as proved through subsequent fieldwork. Krishnamurthy of individual mineral deposits. In general, they reflect (1997) used digitally enhanced IRS Linear Imaging Self only immediate surface and near－surface conditions, and Scanner (LISS)－I and II sensor data to revise/modify the are poor guides to concealed deposits. Linear features, 10 existing geological map of two areas in Karnataka, India, to 200 km or more in length, indicate the general geom- to considerable extent in terms of refined lithological etry of folds, faults and other structures of an area, boundaries, delineation of unmapped rock units, mapping providing a regional structural pattern. They are less 90 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 91

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).

abundant than the shorter linear features by probably an through the occurrence of ridges in the stereo model and order of magnitude and are useful in defining target areas differences in tonal response where beds differ in their － local settings in which mineral deposits may be concen- mineral constituents. The dip slopes of rocks often can be trated, and which merit more detailed study in the field. recognized more reliably on a stereo model than on the Linear features, more than 200 km long (some being ground because of the synoptic view of an area of dipping poorly identifiable for short stretches along their length), sediments obtained from the air. Accurate measurements are most effectively studied on mosaics of Landsat of dip can be made by photogrammetric measurements on imagery. These linear features seem to be globally ubiq- stereo pairs. Several studies have determined the attitude uitous, and usually display a nearly orthogonal pattern. of faults and lithological units of fold structures from Some lineament patterns have been defined to be the stereoscopic SPOT images (Berger et al., 1992; Bilotti most favorable structural conditions in control of various et al., 2000). A fold can be delineated by tracing the mineral deposits, such as: the traces of major regional bedding/marker horizon along the swinging strike, and lineaments, the intersection of major lineaments or both the recognition of the dips of the beds. Broad, open, major (regional) and local lineaments, lineaments of longitudinal folds are easy to locate on satellite images tensional nature, local highest concentration (or density) (e.g. Fig. 9). On the other hand, tight, overturned, of lineament, between en echelon lineaments, and linea- isoclinal folds are difficult to identify on satellite images, ments associated with circular features. For example, Liu owing to small areal extent of hinge areas (which provide et al. (2000) utilized lineament analysis from satellite the only clues to their presence); therefore, such folds imagery to delineate the following structural features of need to be studied on appropriately larger scales, such as considerable interest in search for mineral deposits in aerial photographs. Fu et al. (2004) used Landsat northeast Brazil: the warping (or dragging) part of the TM/ETM stereoscopic images in combination with high－ minor shear zones, which splay out (or branch off) from resolution IRS－IC PAN (5.8 m) satellite images to delin- the major wrench belt, is of extensional nature favorable eate Quaternary deformational structures, including for hydrothermal emplacement; the swollen parts along spatial distribution and arrangement of fold structures and the extending lineament (shear) zones are also favorable fault scarps, along the Tian Shan orogenic belt, northwest for magmatic fluid intrusion; the intersections of short and China. regional lineaments; the periphery, and the margin of One of the greatest advantages of remote sensing circular or ring structures, the internal and external data from aerial and space platforms lies in delineating peripheral parts of small rings in a large circular structure, vertical to high－angle faults or suspected faults. Where and the en echelon diagonal fractures crosscutting the mineralization has taken place along a fault line, however, circular features (see Fig. 8). there may be positive rather than negative surface feature. Aerial photographs provide evidence of bedding In most cases the most reliable evidence of faulting is 92 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 93

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 Figure 9. Landsat image covering part of the Anti－Atlas Moun- conjunction with the regional field work (e.g. Fig. 10). tains of southern Morocco. By virtue of the climate, degree of Topographic features observable in RADARSAT images erosion, and contrast in rock types (both in terms of erodibility and spectral response), it is one of the most spectacularly reflect either changes in slope, such as erosional escarp- exposed fold belts in the world. Large －scale basement cored ments due to variation of resistant and recessive litholog- domes with irregular shapes form a continuous area of positive ical units (Evans et al., 1986). Since SAR sensors provide structural relief. The rocks are complexly folded and faulted. their own illumination source, the look direction can Rock sequences are repeated, anticlines encroach upon anti- influence the information content of the imagery. In low－ clines, and synclines encroach upon synclines. Numerous zones of structural discontinuity or disharmonic folding are obvious in relief environments, the look direction can be used to the image. In general, the older rocks to the northwest appear to provide a greater enhancement of lineaments. With infor- be more highly deformed than the younger rocks to the south and mation from previously collected field measurements, southeast. The lighter colored yellowish brown Precambrian complex structural patterns like details of the regional rocks in the northwest are tightly folded, highly fractured, trends of the foliation, shear zones, etc., can be mapped intruded, and metamorphosed. The white sinuous band against a fold ridge is a dry stream. Recently, Helg et al. (2004) showed and traced over large areas using radar imagery (e.g. Fig. that the Anti－Atlas of Morocco is a special type of foreland fold 10). Simple photogeologic principles like image texture belt lacking any evidence for thrust faults other than the occa- can be used to outline foliation patterns, folds, brittle and sional steep reverse fault found near basement inliers. (Courtesy: ductile shear zones and lithology, while image tone can be NASA and Earth Satellite Corporation.) traced to cover type and could be traced and related to

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). 92 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 93

lithology. In the presence of a near complete forest field efforts. There are difficulties in integrating linea- canopy, radar imagery largely conveys topographic infor- ment maps with mineral exploration models, as some of mation without the distraction offered by tonal variations the features mapped as lineaments may not be of struc- associated with varying ground cover in Landsat imagery. tural－geologic nature, and it may not be possible to distin- In such areas, field observations are usually limited by guish between post －mineralization and pre －mineraliza- outcrop availability and often consist of densely popu- tion structures. Many studies integrated lineament lated observations separated by substantial distance structures derived from satellite (Landsat TM, SAR, etc.) (hundreds of meters to kilometers). The imagery allows data with a database of known occurrences in GIS for a separation of isolated structures from more common ones, more fruitful interpretation of lineaments for mineral and provides a regional framework for the regional inter- exploration (e.g. Akhavi et al., 2001). Other studies pretation of structures documented in the field. The correlated lineament intersection density to alteration and important feature of radar imagery is manifested in the observed that lineament intersection density was nearly capacity of images to provide stereoscopic view, which twice as dense in altered zones as compared to unaltered further facilitates the work of lineament identification (e.g. zones (e.g. Zakir et al., 1999). Use of lineament intersec- Sharma et al., 1999). tion relationships for mineral exploration gains validity Recently different algorithms have been published when defined by multi －technique approaches, such as describing the extraction of lineaments directly from combinations of remote sensing, geophysical, and geolog- digital images and aerial photos (e.g. Budkewitsch et al., ical methods (e.g. Chernicoff et al., 2002). In addition, 1994; Raghavan et al., 1995; Koike et al., 2001; Costa applicability can be tested by inclusion of known metallo- and Starkey, 2001). These techniques can reduce to a genic information, which may help to identify favorable minimum the bias in the manual interpretation. The auto- structural settings in a given regional context. mated methods in extracting lineaments provide a flow of data, which requires in turn elaborate and exact methods Alteration mapping for the analysis and presentation. Principal component analysis (PCA) is a classical statistical method that Mineral deposits are commonly associated with hydro- produces images (components) that are a linear combina- thermal alteration of the surrounding rocks (e.g. Fig. 11), tion of multiband images. When applying PCA, the rela- the style and extent of the alteration reflecting the type of tive image variance is a measure of the amount of infor- mineral deposit. The host rocks of hydrothermal mineral mation observable in each image. PCA of SAR from deposits invariably show the results of their chemical Seasat －SAR and the shuttle imagery radar (SIR－B) has interactions with the hydrothermal fluids that caused been used successfully to enhance topographic informa- mineral deposition (Pirajno, 1992). Such alteration tion for structural and lineament mapping (e.g. Masouka commonly forms a halo around the mineralization, et al., 1988). Paganelli et al. (2003) recognized four providing an exploration target considerably larger than lineament trends (N－NE, NW, NE, and E－NE) in each of the deposit itself. The delineation and characterization of the RADARSAT－1 principal component images from the hydrothermal alteration can therefore be of great value in Buffalo Head Hills area, Alberta, Canada. The intersec- mineral exploration and assessment of new targets. The tion and offset relationships between the various linea- spatial distribution of hydrothermally altered rocks is a ment groups in the images enabled definition of a relative key to locating the main outflow zones of hydrothermal succession of events in which the N－NE lineaments was systems, which may lead to the recognition of mineral recognized as the oldest, followed by NW and NE linea- deposits. ments, which define a conjugate set, and the E －NE － Several airborne and orbital imagery studies have trending lineaments interpreted as the latest as it show shown the feasibility of remote sensing techniques to crosscutting relationships with all the previous linea- detect hydrothermal altered areas. These studies are ments. The lineaments interpreted by Paganelli et al. based on the fact that certain diagnostic minerals associ- (2003) and their tectonic－geologic implications provided ated with hydrothermal processes, such as iron －bearing a basis for kimberlite exploration in the Buffalo Head minerals (e.g. hematite, goethite, jarosite), hydroxyl － Hills area. bearing minerals (e.g. clays, micas), and hydrated At the initial stage of image analysis, focus must be sulphates (e.g. gypsum, alunite), show diagnostic spectral given to well －mapped areas where detailed maps had features that permit their remote identification (e.g. Hunt been completed, and predictive capabilities should be and Ashley, 1979; Prost, 1980; Podwysocki et al., 1983; developed for image －based reconnaissance mapping of Gladwell et al., 1983; Townsend, 1987; Clark et al., 1990; various structural features, thereby optimizing planning of Fraser, 1991). Weathering processes produce the same 94 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 95

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, Figure 11. Landsat image covering part of the Transvaal craton, an image incorporating ratios of bands 5 and 7 (TM5/7; South Africa. Important features in the image include the Bush- clay mineral index), bands 3 and 1 (TM3/1; iron oxide veld layered basic －ultrabasic intrusion in the north －east, the circular Pilansberg intrusion (syenite and foyaite) at the top index), and bands 5 and 4 (TM5/4; ferrous index) will center, strongly folded (dipping northward) Proterozoic Trans- highlight areas where concentrations of these minerals vaal Supergroup rocks in the central part, and the northern edge occur, thereby discriminating altered from unaltered [east －west bands of green －grey (darker portions in the ground. In arid and semi －arid regions, outcropping southwestern part of the image)] of the Witwatersrand basin, hydrothermal alteration zones are mineralogically South Africa’s most productive area of gold and uranium, near the bottom. A variety of important mineral deposits －such as conspicuous enough to be detected successfully from chromium, vanadium, nickel, copper and tin－occur in and around Landsat TM data (e.g. Amos and Greenbaum, 1989; the Bushveld complex. (Courtesy: NASA and Earth Satellite Fraser, 1991; Spatz, 1997). In tropical regions, however, Corporation.) high vegetation density can critically limit the successful application of Landsat TM data to the detection and

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). 94 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 95

mapping of hydrothermally altered rocks (e.g. Siegal and altered minerals in the visible, NIR and MIR regions of Goetz, 1977). In this case, techniques for digital enhance- the spectra. Tangestani and Moore (2002) showed that ment of Landsat TM data to map hydrothermally altered the application of Crósta technique (Crósta and Rabelo, rocks commonly aim at the identification of clay and iron 1993), another variant of PCA, on TM bands 1, 4, 5 and 7 oxide alteration zones (Fraser and Green, 1987). The enhances the hydroxyl －rich altered haloes around the remote detection of iron oxide and clay zones in the pres- porphyry copper deposits of the Meiduk area, Iran. The ence of vegetation, however, is difficult due to similarities Crósta technique (using four TM bands) involves the in the reflectance spectra of the materials. If TM data are analysis of the eigenvector values allowing identification to provide information regarding the distribution of ferric of principal components that contain spectral information oxide minerals (hematite, goethite), the effects of vegeta- about specific minerals, as well as the contribution of each tion need to be minimized. Different techniques for of the original bands to the components in relation with image processing of Landsat TM to detect and map the spectral response of the mineral of interest. Remote hydrothermally altered rocks are hence aimed at sepa- sensing of limonitic and clay alteration by the different rating or reducing substantially the spectral effects of PCA techniques proves inadequate where the hydro- vegetation from the spectral effects of the underlying thermal alteration mineral assemblages are not iron oxides substrate (Fraser and Green, 1987). Spectral unmixing and clays, as pointed by Carranza and Hale (2002) for the (Smith et al., 1985) is one such technique and endeavors Baguio district, Phillippines. They developed a mineral at searching the abundances or fractions of pure spectral imaging methodology using Landsat TM data. It includes components, so－called end－members, which best explain four steps: a) first to use the selective PCA technique to the observed mixed pixel spectra. PCA of images has enhance the spectral response of each alteration mineral been shown to be a successful tool to minimize the vege- into a separate mineral image based on published reflec- tation effect in the resulting images (Abrams et al., 1983; tance spectra of minerals, b) second to extract training Kaufman, 1988; Loughlin, 1991; Fraser, 1991; Bennett et areas for known hydrothermal alteration zones, c) third to al., 1993; Ruiz －Armenta and Prol －Ledesma, 1998; carry out a supervised classification of the mineral images Tangestani and Moore, 2001). The principal components to map hydrothermal alteration zones, and d) fourth to can be analyzed using the standard or selective method. incorporate a DEM for improving the results of the classi- In the standard analysis all available bands of an image fication. In the Baguio district, the accuracy of the classi- are used as input for the principal components calculation, fied hydrothermal alteration map based on the mineral while in the selective analysis only certain bands are images reached 69%, while inclusion of a DEM in the chosen. Fraser (1991) used the selective method classification enhances the accuracy to 82%. involving directing a principal component analysis at two In order to improve the definition (both spatial and selected input bands TM3/TM1 and TM4/TM1 to spectral) of the target areas, Landsat images can be discriminate between ferric oxide (hematite and goethite) merged with a digitized aerial photograph through inten- and vegetation from the Newman area, western Australia. sity, hue and saturation (IHS; here intensity represents Here band ratios are chosen because they are more useful brightness, hue represents color and saturation represents than the TM bands as they compensate for the variations the purity of the color) transform. Although multi－dimen- caused by topographic features and illumination condi- sional images are typically portrayed in RGB (red, green, tions in the scene. A pixel containing hematite and blue), IHS transformed images incorporate more informa- goethite would plot on the hematite －goethite line (Fig. tion because they include more easily defined and identifi- 12). If vegetation were added to that pixel, its position in able color attributes, greater control over the chromatic 3/1 versus 4/1 space would tend to move away from the and achromatic components of the image, and the ability hematite－goethite line in the direction of vegetation (Fig. to create images utilizing information from more than 12). Similarly, for an area in central Mexico, Ruiz － three input data channels when the image is returned to Armenta and Prol －Ledesma (1998) used two selected RGB color space (Harris et al., 1990). IHS incorporating input ratio pairs (TM3/TM1 and TM4/TM3 ratios, and the concept of high pass filter (HPF; Chavez et al., 1991) TM4/TM5 and TM5/TM7 ratios), each chosen because of technique is used for merging high spectral resolution its effectiveness at highlighting and/or separating hema- multi－spectral satellite remote sensor data (e.g. Landsat) tite, goethite, hydroxyl minerals, and vegetation, in with higher spatial resolution panchromatic (e.g. IRS) n－dimensional ratio－versus－ratio space. data, to increase the spectral and spatial frequency distri- The enhancement of the iron oxide and hydroxyl － bution. With its high spatial resolution, and bands bearing areas around intrusive bodies in an area relies covering a wide part of the electromagnetic spectrum, mainly on the spectral characteristics of the dominant ASTER data is known to provide accurate alteration maps 96 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 97

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 Figure 13. ASTER image of the Escondida copper, gold, and available in digital format (e.g. Grove et al., 1992; Clark silver open－pit mine, Chile. The hydrothermal alteration mineral et al., 1993) against which the data can be correlated and zones include porpylitic, phyllic and potassic zones. A high － classified. grade supergene cap overlies primary sulfide ore. The top image Several approaches to extract information from － － is a conventional 3 2 1 (near infrared, red, green) RGB high－spectral resolution image data have been reported, 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- processing spectral information on a pixel by pixel basis, lights the different rock types present on the surface, as well as including binary encoding (Goetz et al., 1982), spectral the changes caused by mining. (Courtesy: NASA GSFC, MITI, unmixing (Adams et al., 1986), relative absorption band－ ERSDAC, JAROS, and U.S./Japan ASTER Science Team.) 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., (e.g. Fig. 13). Aster 4 operates in the spectral range 1.6－ 1993), decorrelation stretching (Abrams and Hook, 1995), 1.7 μm, which is a general high－reflectance band, and is probability density function (Nedeljkovic and Pendock, coded in red. Aster 6 (2.225－2.245 μm) is absorbed by 1996), constrained energy minimization (CEM; Farrand the Al－OH minerals, whereas Aster 8 (2.295－2.365 μm) and Harsanyi, 1997), cross correlogram spectral matching corresponds to the absorption by Mg －OH minerals (and (Van der Meer and Bakker, 1997), back －propagation carbonates, if present). Therefore in this color coding neural network (BNN; Yang et al., 1999), and geophysical scheme, Al －OH －bearing minerals appear in shades of inversion (Van der Meer, 2000). These methods all use blue－purple, Mg－OH－bearing minerals appear in shades imaging spectrometer data corrected to reflectance and of green －yellow, and Al －Mg －OH －bearing minerals quantitatively test the similarity of unknown imaged appear in shades of red (e.g. Fig. 13). spectra with known spectra measured in the field or labo- ratory or extracted from the image data at locations of Imaging Spectrometry known ground truth. This often results in mineral maps, which portray the probability that a pixel is composed of Typically, the number of spectral bands of remote sensors a certain mineral. determines the amount of spectral information that remote Most of the image analysis algorithms developed sensors can acquire. Early remote sensors only have specifically to exploit the extensive information contained several spectral bands (e.g. Landsat 7 has 7 spectral in hyperspectral imagery also provide accurate, although bands), and thus limited spectral information can be more limited, analysis of multispectral data. The different obtained from such remote sensors. Hyperspectral algorithms can be grouped as either whole－pixel or sub－ sensors (also referred to as Imaging Spectrometers) can pixel analysis methods. Whole －pixel analysis methods acquire images in many, very narrow, contiguous spectral calculates the spectral similarity between a test (or pixel) bands throughout the visible, near －IR, mid －IR, and reflectance spectrum and a reference (or laboratory) 96 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 97

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 μ m 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 μ m 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).

reflectance spectrum and include standard supervised tral feature fitting. Back －Propagation neural Network classifiers such as minimum distance or maximum likeli- (BPN) is a method, which examine all the pixels in the hood, as well as tools developed specifically for hyper- image in parallel. For BPN method, first each classifica- spectral imagery such as SAM and spectral feature fitting. tion resulting from a hidden －layered neural network The SAM computes a spectral angle between each pixel containing hidden units is trained using a back－propaga- spectrum and each reference spectrum. The outcome of tion algorithm. The result is an image classified by the SAM－algorithm gives a qualitative estimate of the pres- acquired network, which responds to each new unseen ence of absorption features, which can be related to vector with the knowledge gained from the training stage mineralogy. Van der Meer et al. (1997) demonstrated the (e.g. Yang et al., 1999). Sub －pixel analysis methods potential of SAM technique for a first assessment of calculate the quantity of reference materials in each pixel mineral potential in ultramafic terrains. Another approach of an image and include tools such as linear spectral to matching reference and pixel spectrum is to examine unmixing and matched filtering. Linear spectral unmixing specific absorption features in the spectra, as used in spec- exploits the theory that the reflectance spectrum of any 98 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 99 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- Figure 15. Flow chart illustrating the initial database building, racy of the BPN over SAM for the Curpite area, and is decision support system, and final modeling used for mineral potential mapping in a GIS. The possible layers included within attributed to its ability to deal with complex relationships the GIS database are given in the dashed－line box. Depending and the nature of the data set. Neville et al. (2003) on the availability of data, different steps illustrated in the flow- showed the ability of constrained linear unmixing proce- chart will be modified. 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 effective integration and analysis of large numbers of geo- exploration data such as the digital elevation models to referenced spatial data with different attributes and formats. get a better perspective. As far as the mineral potential mapping using GIS is concerned, it generally involves four main steps: building Application of GIS a spatial digital database, extracting predictive evidence for a particular mineral deposit type, calculating weights Mineral exploration has traditionally been based on the for each predictive evidence map, and combining the application of a variety of prospecting methods, namely maps to predict mineral potential. The probability that a geochemistry, geophysics, geological mapping, aerial mineral deposit exists increases where the predictor photo interpretation and ground surveys, operatively inte- themes areally overlap. Here the initial database building grated within exploration phases. The main criterion of step is the most time consuming step and can include the prospector is to identify anomalies associated with among others, remote sensing data, geophysical data target mineral areas, gradually reducing the original (magnetic, gravity), geochemical data (for each element extent area to a small set of anomalies. This process is of interest), geological data (structural, lithological), topo- complex and it needs both analysis and integration of the graphical data (DEM), and mineral occurrence data. above multithematic exploration information, from which A general scheme of the methodology followed for decisions must be made over time and at different stages. GIS implementation is given in Figure 15 and consists of In the applied context remote sensing, and more recently, two main parts: the relational database and spatial anal- GIS have shown their usefulness as support tools of great ysis leading to a decision support system (geognostic interest. GIS is considered because they allow for more variable selection and weighting of variables). The rela- 98 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 99

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 － Figure 16. Example of the structure of a network model used for Carter, 1994) leading to the mineral potential map or gold potential mapping. 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 tion function, a lineament density can be calculated for index is numerically a linear expression in which such a the concerned area. Lineament density, in similar fashion calculation requires us to assign score to each class－map to fracture density, but on a larger scale, provides an and weights to each input－map according to some criteria, approximate location of mineral concentration and leading to the final MPI or MPS map (see Fig. 15). measure of how broken is the rock mass (McGregor et al., Data integration methods in GIS may be divided into 1999). This is a very good example for the application of two main groups: knowledge －driven models, such as GIS in extracting accurate geologic information including weighted index overlay, fuzzy logic and multi －criteria linear features (lineaments) for mineral resource mapping. evaluation, estimate the parameters of a function for combining datasets on the basis of expert opinion; and Concluding remarks data －driven models, such as regression, weights of evidence modeling, Dempster －Shafer belief functions, The important question that a geologist can ask is “Why certainty factors, Bayesian statistics and artificial neural should one use remotely sensed imagery for mineral networks (ANN), estimate model parameters from resource mapping?” The answer is, because it can provide measured data (Bornham－Carter, 1994). The data－driven information that is not available any other way. Geologic approach, unlike the knowledge －driven approach, maps (assuming that they are even available) may be requires a number of known deposits to exist in the area inaccurate and, at best, are usually generalizations. The of interest, and the parameters identified for a particular geologist doing the mapping may have painstakingly deposit type in an area can only be applied to other mapped the boundaries of a particular formation and regions with similar geology. It is now known that both missed an obvious alteration zone within it. Figures knowledge －driven data integration and data －driven drawn on the ground by ancient peoples are overlooked models involve assumptions that are difficult to satisfy by those walking on them; however, they stand out clearly when dealing with geological variables such as linear when viewed from the air. Similarly, geologic structures relationships. ANNs, however, are an exception and seem and mineral alteration patterns can be quite vivid on satel- to offer advantages over other methods because they lite imagery. In a hilly terrain, where poor accessibility make no assumptions about the data. Although ANNs are and dense vegetation cover hinder fieldwork, it is not used frequently as remote sensing data classifiers, their always possible to map or collect data from field potential as spatial modeling tools in mineral potential throughout any structural feature. The data are collected mapping is illustrated in Figure 16. from accessible places and geologists then interpolate A growing number of studies evaluated the potential those data to obtain continuity. In this context, image for extracting geologic information (e.g. lithologic interpretation of structural features is more reliable than mapping, structural mapping, etc.) by integrating tradi- their interpolation from field data. Subtle color variations tional remote sensing, geophysical data and ancillary data that would go unnoticed on the ground can be made quite (e.g. DEM) in a GIS environment. Structural analysis of bold in false－color renditions, begging to be assayed, as it satellite imagery and aerial photograph can generate large were. Known mineral resource areas can sometimes be population of interpreted linear features. This population extended by tracing lineaments outside the areas. Remote can be reduced through lineament interpretations, based sensing data, by virtue of its synoptic overview, multi- on the identification of features which are geologically spectral and multi－temporal coverage, can help to rapidly significant, within a GIS; thus allowing the lineament delineate metallogenic provinces/belts/sites over a larger vectors to be properly registered immediately. The linea- terrain, based on known models of commercial ore occur- ments interpreted from the satellite imagery can be rences. This can help to isolate potential areas from non－ converted to a raster grid and, using a local block summa- interesting areas for further exploration. 100 H.M. Rajesh Application of remote sensing and GIS in mineral resource mapping 101

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