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Pour and HashimASTER captured data are processed to generate Level1A data product. It consists of unprocessed raw imagedata and coefficients for radiometric correction. TheLevel-1B (radiance-at-sensor) data product is a resampled image data generated from the Level-1A data byapplying the radiometric correction coefficients. Level-2Bdata products of physical parameters including surfaceradiance data with nominal atmospheric corrections(Level-2B01), surface reflectance data containsatmospherically corrected VNIR-SWIR data (Level-2B07or AST-07), surface emissivity data with MODTRANatmospheric correction and a temperature-emissivityseparation (TES) algorithm (Level-2B04) are generatedon the basis of user request. Level-4B data product isalso generated under the user request from the alongtrack stereo observation in the near infrared channel(band 3N and 3B) in order for the construction of digitalelevation models (DEMs). Level-3A is a geometricallywell-corrected orthorectified ASTER standard dataproduct with ASTER-driven DEM, which is radiometricallyequivalent to Level-1B radiance-at-sensor data (Gillespieet al., 1998; Yamaguchi et al., 1999; Abrams, 2000;Yamaguchi et al., 2001; Ninomiya, 2005, 2003a, b, c).Recently, two new crosstalk-corrected ASTER SWIRreflectance products had been released, which include:(1) AST-07XT SWIR reflectance data product available‘on-demand’ from ERSDAC and EDC (Iwasaki andTonooka, 2005) and (2) RefL1b SWIR reflectance dataproduct complied and introduced (Mars and Rowan,2010). The AST-07XT SWIR reflectance data product issimilar to AST-07 surface reflectance data that consist ofVNIR and SWIR bands, except that the crosstalkcorrection algorithm and atmospheric correction (nonconcurrently acquired MODIS water vapor data) havebeen applied (Iwasaki and Tonooka, 2005; Biggar et al.,2005; Mars and Rowan, 2010). RefL1b and AST-07XTreflectancedatasetshave crosstalkcorrection;differences between these datasets should be caused bythe addition of the radiometric correction factors and useof concurrently acquired water vapor data in theatmospheric correction of the RefL1b data (Mars andRowan, 2010). During scene acquisition of ASTER data,there is optical ‘crosstalk’ effect caused by stray of lightfrom band 4 detector into adjacent band 5 and 9detectors on SWIR subsystem. Such deviations fromcorrect reflectance result in false absorption features anddistortion of diagnostic signatures and consequentlymisidentification of spectroscopic results. Fortunately,ASTER cross-talk correction software is available inwww.gds.aster.ersdac.or.jp (Kanlinowski and Oliver,2004; Iwasaki and Tonooka, 2005; Hewson et al., 2005;Mars and Rowan, 2006, 2010).Since 2000, ASTER data have been widely andsuccessfully used in lithological mapping and mineralexploration, because of their characteristics of thespectral properties and the diversity of standard dataproducts (Hewson et al., 2001; Rowan and Mars, 2003;7659Rowan et al., 2003; Junek, 2004; Hellman and Ramsey,2004; Galva o et al., 2005; Vaughan et al., 2005;Hubbard and Crowly, 2005; Rowan et al., 2005; Mars andRowan, 2006; Rowan et al., 2006, 2010; Ducart et al.,2006; Qiu et al., 2006; Di Tommaso and Rubinstein,2007; Khan et al., 2007; Moghtaderi et al., 2007; Kruseand Perry, 2007; Sanjeevi, 2008; Moore et al., 2008;Tangestani et al., 2008; Khan and Mahmood, 2008; Aziziet al., 2010; Amer et al., 2010; Aboelkhair et al., 2010;Gabr et al., 2010; Kratt et al., 2010). ASTER is the firstmultispectral space-borne instrument that allowsdiscrimination and identification of hydrothermal alterationminerals in the SWIR region of electromagnetic spectrum(Abram and Hook, 1995). Moreover, the ASTER VNIRand TIR data can provide capability for the remoteidentification of vegetation and iron oxide minerals insurface soil and mapping carbonates and silicates,respectively (Bedell, 2001; Ninomiya, 2003a; Rockwelland Hofstra, 2008). Accordingly, this paper reviews theapplication of ASTER data for lithological mapping andthe identification of hydrothermal alteration mineral zonesfor the exploration of porphyry copper and epithermalgold deposit.PORPHYRY COPPER DEPOSITPorphyry copper deposits are associated with volcanicplutonic arcs in subduction environments andpaleosubduction zones (Pirajno, 1992). There are severalanalogous of porphyry copper belts in subduction tectonicsetting of the world, such as the Andean Volcanic Belt inWestern South America, the Urumieh-Dokhtar Volcanicbelt in Iran, Northern Sulawesi in Indonesia, CarpathianBalkan in Central and South Europe, Yulong-Yunna(Himalaya) in China, Mulong in NSW Australia (Singer etal., 2005). Porphyry copper deposits are usually locatedwithin or near plutonic intrusions, formed typically belowdepths of one kilometer which mostly have rocks ofMesozoic to Cenozoic age (Titley, 1972; Jacobsen,1975). Porphyry copper deposits, in contrast to otherhydrothermal deposits, invariably are associatedgenetically with porphyry plutons localized along islandand continental-arc strike-slip fault system (Titley andBeane, 1981; Carranza and Hale, 2002a). The crustaldeformation present near subduction zones may play asignificant role in allowing plutons to passively rise toshallow crustal levels (Wolfe, 1988; Tosdal and Jeremy,2001). Felsic plutonic rocks ranging in composition fromquartz monzonite to granodiorite are common ore hosts.However, this type of mineralization can occur in dioriticto syenitic rocks (Rowins, 1999). Porphyry copperdeposits are generated by hydrothermal fluid processesthat alter the mineralogy and chemistry of thecomposition of country rocks (Hunt and Ashley, 1979;Ferrier and Wadge, 1996; Ferrier et al., 2002). Thisalteration can produce distinctive mineral assemblages

7660Int. J. Phys. Sci.Figure 1. Hydrothermal alteration zones associated with porphyry copperdeposit (Modified from Lowell and Guilbert, 1970; Mars and Rowan, 2006).(A) Schematic cross section of hydrothermal alteration mineral zones, whichconsist of propylitic, phyllic, argillic, and potassic alteration zones. (B)Schematic cross section of ores associated with each alteration zone.with diagnostic spectral absorption features in the visiblenear-infrared through the short-wave infrared (0.4 to 2.5µm) and/or the thermal-infrared (8.0 to 14.0 µm)wavelength regions (Abrams et al., 1983; Abrams andBrown, 1984; Spatz and Wilson, 1995). These absorptionfeatures are generated by vibrational processes due toover tones and combination tones of fundamentalhydroxyl groups (Hunt and Salisbury, 1974, 1975, 1976;Hunt, 1977). Porphyry copper deposits typically occur inassociation with hydrothermal alteration mineral zones,such as phyllic, argillic, potassic and propylitic (Lowelland Guilbert, 1970) (Figure 1). A core of quartz andpotassium-bearing minerals is surrounded by multiplezones which contain clay and other hydroxyl mineralswith diagnostic spectral absorption features in SWIRportion of electromagnetic spectrum. Furthermore, at thesame time an oxide zone with extensive iron oxideminerals is developing by virtue of supergene alterationprocesses over porphyry copper bodies. Iron oxides areone of the important mineral groups that are associatedwith hydrothermally altered rocks, and are rendered tocharacteristic yellowish or reddish color to the alteredrocks, termed gossan (Sabins, 1999; Abdelsalam andStern, 2000; Xu et al., 2004). Iron oxide minerals havelow reflectance in visible and higher reflectance in nearinfrared wavelength region (Hunt and Salisbury, 1974;Hunt, 1977). These hydrothermal alteration minerals withdiagnostic spectral absorption properties in visible nearinfrared through the short-wave infrared can be identifiedby multispectral remote sensing data as a guide in theinitial step of porphyry copper exploration. Remotesensing multispectral sensors with sufficient spectral andspatial resolution such as TM/ETM and ASTER arecapable of delineating these spectral absorption features,thus can be used to detect and remotely identify thesehydrothermal alteration mineral zones in well-exposedregions. Landsat TM/ETM data have been used formineral exploration, because of the two SWIR bands(bands 5 and 7) which were used to detect hydrothermalalteration mineral assemblages associated withepithermal gold and hydrothermal porphyry coppermineralization (Rowan et al., 1977; Podwysocki et al.,1984; Crosta and Moore, 1989; Okada et al., 1993;Sabins, 1996, 1997; Van der Meer et al., 1997; RuizArmenta and Prol-Ledesma, 1998; Abdelsalam andStern, 2000; Tangestani and Moore, 2002; Carranza andHale, 2002b; Kusky and Ramadan, 2002; Inzana et al.,2003; Perry, 2004; Yujun et al., 2007). The broadconfiguration of bands 5 and 7 of TM/ETM in the shortwave infrared portion just allows the recognition ofhydrothermal alteration sites, without providing theessential spectral resolution to discriminate specificalteration zones and minerals, a very important task insearching for high potential prosperous mineralizedalteration zone (Gabr et al., 2010).The six spectral bands of the ASTER SWIRsubsystems were designed to measure reflected solarradiation in order to distinguish Al-OH, Fe, Mg-OH, Si-OH and CO3 absorption features (Fujisada, 1995; Abramand Hook, 1995). Therefore, the ASTER SWIR reflectivebands are capable to identify hydrothermal alterationmineral assemblages that include: (1) mineralogygenerated by the passage of low PH fluids (alunite andpyrophylite); (2) Al-Si-(OH) and Mg-Si-(OH)-bearingminerals including kaolinite and mica and chlorite groups;(3) Ca-Al-Si-(OH) bearing minerals including epidotegroup and also carbonate (calcite and dolomite) as agroup (Huntington, 1996). Previous studies havedemonstrated the identification of specific hydrothermalalteration minerals, such as alunite, kaolinite, calcite,dolomite, chlorite, talc and muscovite, as well as mineralgroups, through analysis of ASTER SWIR data which have

Pour and Hashim7661Figure 2. (A) ASTER spectral bands in the wavelength of the electromagnetic spectrum, (B)the comparison of ASTER spectral bands with Landsat-7 TM/ETM (Pieri and Abrams, 2004).have been proven using in situ field spectralmeasurements (Hewson et al., 2001; Rowan and Mars,2003; Rowan et al., 2003; Crosta and Filho, 2003;Hubbard et al., 2003; Junek, 2004; Hellman and Ramsey,2004; Wickert and Budkewistsch, 2004; Galvao et al.,2005; Mars and Rowan, 2006, 2010; Rowan et al., 2006;Ducart et al., 2006; Di Tommaso and Rubinstein, 2007;Sanjeevi, 2008; Azizi et al., 2010; Gabr et al., 2010; Krattet al., 2010). Figure 2 shows the location of ASTERspectral bands in the wavelength of the electromagneticspectrum and the comparison of spectral bands between Landsat-7 TM/ETM and ASTER. ASTER SWIR spectralproperties are unprecedented multispectral data for thediscrimination of hydrothermal alteration mineral zonesassociated with porphyry copper mineralization.Accordingly, the broad phyllic zone is characterized byillite/muscovite (sericite) that indicate an intense Al-OHabsorption feature centered at 2.20 µm coinciding withASTER band 6, and the narrower argillic zone including,kaolinite and alunite that display a secondary Al-OHabsorption feature at 2.17 µm corresponding with ASTERband 5. The mineral assemblage of the outer propyliticzone is epidote, chlorite and calcite that exhibitabsorption features situated in the 2.35 µm that coincidewith ASTER band 8 (Figure 3) (Hunt, 1977; Hunt andAshley, 1979; Crowley and Vergo, 1988; Clark et al.,1990; Spatz and Wilson, 1995; Dalton et al., 2004; Marsand Rowan, 2006; Rowan et al., 2006). The differentiation between alteration zones and the identification ofphyllic zone are important in the exploration of porphyrycopper mineralization, because phyllic zone can be anindicator of high potential prosperous area witheconomical mineralization of copper ore shell (Figure 1)(Lowell and Guilbert, 1970).LITHOLOGICALANDALTERATIONMAPPING BY ASTER DATAMINERALThe use of ASTER multispectral data in mineralexploration and lithological mapping has increased inrecent years due to spectral characteristics of uniqueintegral. ASTER bands is highly sensitive to hydrothermalalteration minerals, especially in SWIR region and thepossibility of applying the diversity of image processingtechniques, ‘on-demand’ data availability with low costand broad coverage for regional scale mapping. Here, wereview image processing techniques applicable on ASTER

7662Int. J. Phys. Sci.Figure 3. Laboratory spectra of muscovite, kaolinite, alunite,epidote, calcite and chlorite resampled to ASTER bandpasses. Spectra include muscovite, typical in phyllic alterationzone, with a 2.20 µm absorption feature; kaolinite and alunite,which are common in argillic alteration zone, have 2.17 µmsecondary absorption features and epidote, calcite andchlorite, which are typically associated with propyliticalteration zone and display 2.35 µm absorption features(Mars and Rowan, 2006).data for lithological and hydrothermal alteration mineralmapping as follows.Hewson et al. (2001) simulated the ability of theASTER multispectral data for geologic and alterationmineral mapping in Mountain Fitton, South Australia. Thistest site has been previously surveyed by visibleshortwave hyperspectral automatic modal selection(AMS) (HyMap) and thermal infrared multispectralscanner (TIMS) data sets and several field campaignscollecting relevant spectral measurements. Hewson et al.(2001) applied decorrelation stretch on simulated ASTERbands 3-2-1 so as to the delineation of the drainage andvegetation and band 13-12-10 for the identification ofquartz rich areas, respectively. They also implementedmixture tuned matched filtering (MTMF) technique on thesimulated ASTER SWIR bands to obtain spectrallyunmixed end members related to the rich areas ofhydrothermal alteration mineral assemblages. Derivedresults showed good accuracy in comparison with in situfield spectral measurements and HyMap and thermalinfrared multispectral scanner’s outputs collectedpreviously.Rowan et al. (2003) evaluated the capability of theASTER data for mapping the hydrothermally altered rocksand the country rocks in the Cuprite mining district inNevada, USA. They used matched filtering (MF)technique for the identification of the surface distributionof hydrothermal alteration minerals. Results indicated thatspectral reflectance differences in the nine bands invisible near-infrared through the shortwave infrared (0.52to 2.43 µm) can provide subtle spectral information formapping and the discrimination of main hydrothermalalteration mineral zones. They identified silicified zone,opalized zone, argillized zone and the distribution ofunaltered country rock units which in comparison withairborne visible/infrared imaging spectrometer (AVIRIS)results demonstrate good agreement. Rowan and Mars(2003) used ASTER data for lithological mapping inMountain Pass, California, USA. They applied relativeabsorption-band depth (RBD), matched filtering (MF) andspectral angle mapping (SAM) techniques for thedifferentiation of calcitic, granodioritic, gneissic, graniticand quartzose rocks. The output results showed similaritybetween the patterns of the mapped rock units withgeologic map of the study area. Yamaguchi and Natio(2003) proposed the spectral indices for lithologicaldiscrimination and mapping of surface rock types byusing ASTER short wave infrared (SWIR) bands. Theseconsisted of alunite index, kaolinite index, calcite indexand montmorillonite index that can be calculated by linearcombination of reflectance values of the five SWIRbands.ASTER has a total of 14 spectral channels; but withspectral ratioing among these selective bands, morelithologic and mineralogic indices and more accurateresults can be derived from ASTER image. Ninomiya(2003 a, b) defined vegetation index and mineralogicindices for ASTER VNIR and SWIR bands and lithologicindices for ASTER TIR bands with considering spectralabsorption features of vegetation and different mineralsand rocks in ASTER spectral channels. These indices arelisted as follows: band 3 band1 Stabilized Vegetation Index (StVI) band 2 band 2 band 7 band 4 OH bearing altered minerals Index (OHI) band 6 band 6 (1)(2) band 4 band 8 Kaolinite Index (KLI) band 5 band 6 (3) band 7 band 7 Alunite Index (ALI) band 5 band 8 (4) band 6 band 9 Calcite Index (CLI) band 8 band 8 (5)(band11 * band11)band10 * band12(6)Quartz Index (QI)

Pour and HashimCarbonate Index (CI) Mafic Index (MI) band 13band 14band 12band 13(7)(8)These defined indices are applied on ASTER Level-1Bradiance at the sensor data in Cuprite district in Nevada,USA; Yarlung Zangbo ophiolite zone, Tibet; Beishanmountains area, China; North-western Gansu province,China and ophiolite zone in Oman (Ninomiya, 2002,2003c, 2004). These indices provided accurateinformation for vegetation, mineral and lithologicalmapping, and showed the distribution of selectedminerals and rocks, satisfactorily. Produced map resultsindicated excellent spatial coherency and correlate wellwith the published geology map of the study areas.Crosta et al. (2003) employed principal componentanalysis (PCA) over ASTER VNIR and SWIR bands fortargeting of key alteration minerals associated withepithermal gold deposits in Los Menucos, Patagonia,Argentina. PCA was applied to selected subsets of fourASTER bands according to the position of characteristicspectral absorption features of key hydrothermalalteration mineral end members, such as alunite, illite,smectite and kaolinite in the VNIR and SWIR regions ofelectromagnetic spectrum. Obtained results revealed thatPCA technique can extract detailed mineralogicalspectral information from ASTER data for producingabundance images of selected minerals. Thus, thistechnique can be employed for the identification ofhydrothermal alteration minerals in the exploration ofprecious and base-metal deposits. Volesky et al. (2003)distinguished the propylitic alteration zone and gossanassociated with massive sulfide mineralization from thehost rock by ASTER (4/2, 4/5 and 5/6) band ratio imagein the Neoproterozoic Wadi Bidah shear zone, Southwestern Saudi Arabia.Xu et al. (2004) identifiedhydrothermal alteration mineral zones around golddeposits using ASTER data in the North-eastern part ofLaizhou region, China. They used PCA technique andband ratio of 3/2, 4/1 and 4/6 for the delimitation ofvegetation, iron oxide and clay minerals, respectively.Hewson et al. (2005) generated maps of Al-OH andMg-OH/carbonate minerals and ferrous iron content fromASTER SWIR data as well as a map of quartz contentfrom ASTER TIR data for the Broken Hill-Curnamonaprovince of Australia. Rowan et al. (2005) evaluatedASTER band ratio and relative absorption band depth(RBD), matched filtering (MF) and spectral anglemapping (SAM) techniques for lithological mapping ofultramafic complex in the Mordor Pound, NT, Australia.They identified subtle information for the classification offelsic and mafic-ultramafic rocks, alluvial-colluvialdeposits and quartzose to intermediate compositionrocks. This classification was based on spectral absorption7663features of Al-OH and ferric-iron mineralogical groups forfelsic rock, ferrous-iron and Fe, Mg-OH mineralogicalabsorption features in mafic-ultramafic rock in the ASTERVNIR SWIR data and Si-O spectral features for diversityof ultramafic complex and adjacent rocks, such as maficultramafic, quartzose to intermediate composition rocks,alluvial-colluvial deposits and quartzite unite in ASTERTIR data, respectively. Ninomiya et al. (2005) appliedquartz index (QI), carbonate index (CI) and mafic index(MI) on ASTER-TIR ‘radiance-at-sensor’ data fordetecting mineralogic or chemical composition ofquartzose, carbonate and silicate rocks in MountainYushishan, China and Mountain Fitton, Australia. Theselithologic indices could discriminate quartz, carbonateand mafic-ultramafic rocks which were compatible withpublished geologic map and field observation. They also,suggested that these lithologic indices can be one unifiedapproach for lithological mapping of the earth, especiallyin arid and semi-arid regions.Galvao et al. (2005) implemented the spectral anglemapping (SAM) technique on the ASTER SWIR bands toinvestigate the spectral discrimination of hydrothermallyaltered minerals in a tropical savannah environment inthe Northern portion of Gious state, central Brazil.Results showed the efficacy of ASTER SWIR data indiscriminating areas of altered minerals from thesurrounding vegetation environment. Gomez et al. (2005)estimated the 9 ASTER bands for geological mapping inthe Western margin of the Kalahari Desert in Namibia.They realized principal component analysis (PCA) andsupervised classification on visible near infrared andshort wave infrared ASTER bands. The processing ofASTER data demonstrated validation of the lithologicalboundaries defined on previous geological map and alsoprovided the information for characterizing new lithological units, which were previously unrecognized.Rowan et al. (2006) identified distribution ofhydrothermally altered rocks consisting of phyllically,argillically and propylitically altered zones based onspectral analysis of VNIR SWIR ASTER bands, as wellas hydrothermally silicified rocks were mapped by TIRASTER bands in the Reko Diq, Pakistan Cu-Aumineralized area. Qui et al. (2006) employed spectralangle mapping (SAM), spectral feature fitting (SFF) andlinear spectral unmixing (LSU) techniques on 14 ASTERbands for geological mapping in the NeoproterozoicAllaqi-Heiani suture, Southern Egypt. These techniqueshave proved to be useful tools in geological mapping.Mars and Rowan (2006) developed logical operatoralgorithms based on ASTER defined band ratios forregional mapping of phyllic and argillic altered rocks inZargros magmatic arc, Iran. The logical operatoralgorithms have illustrated distinctive patterns of argillicand phyllic alteration zones associated with Eocene toMiocene intrusive igneous rocks, as well as knownporphyry copper deposits.Ducart et al. (2006) applied mixture tuned matched

7664Int. J. Phys. Sci.filtering (MTMF) technique on ASTER SWIR data toprovide regional and local information of the spatialdistribution of hydrothermal alteration zones at the CerroLa Mina epithermal gold prospect, Patagonia, Argentina.The main identified alteration zones, such as advancedargillic, argillic and silicic, showed a satisfactorycorrelation of the spatial distribution with achieved fieldspectroscopy data. Zhang and Panzer (2007) employedthe matched filtering (MF) technique on EO-1 Hyperionand ASTER data to extract abundance images for goldassociated lithological mapping in South-easternChocolate Mountain, California, USA. The assessment ofmatched filtering score index indicated ASTER has goodcapability in discrimination and classification of rocktypes. Although, the Hyperion data can produce betteraccuracy than ASTER, but the lithologic informationextracted from ASTER image data is mostly similar withHyperion results and the better availability and vastspatial coverage make it more suitable for regional scalelithological mapping.Di Tommaso and Rubinstein (2007) used band ratioand band combination and spectral angle mapping (SAM)techniques for mapping hydrothermal alteration mineralsassociated with Infiernillo porphyry copper deposit byASTER data in the San Rafale Massif, SouthernMendoza Province, Argentina. They detected illite,kaolinite, sericite and jarosite through analysis of SWIRbands and surface silica and potassic alteration zone byTIR bands, respectively. Yujun et al. (2007) delineatedthe hydrothermal alteration anomalies for predicting CuAu mineral resources by ASTER data in Oyu Tolgoi,Mongolia. The spectral angle mapping (SAM) andprincipal component analysis (PCA) techniques are usedfor alteration anomaly extraction. Gad and Kusky (2007)resolved geological mapping problems by using newASTER band ratio image 4/7, 4/6 and 4/10 for lithologicalmapping in the Arabian-Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. These ASTER bandratios mapped the main rock units including gneiss andmigmatite, amphibolite, volcanogenic sediments with

setting of the world, such as the Andean Volcanic Belt in Western South America, the Urumieh-Dokhtar Volcanic belt in Iran, Northern Sulawesi in Indonesia, Carpathian-Balkan in Central and South Europe, Yulong-Yunna (Himalaya) in China, Mulong in NSW Australia (Singer et al., 2005). Porphyry copper deposits are usually located