Journal of Asian Earth Sciences 42 (2011) 1309–1323

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Journal of Asian Earth Sciences

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Identification of hydrothermal alteration minerals for exploring of porphyrycopper deposit using ASTER data, SE Iran

Amin Beiranvnd Pour, Mazlan Hashim ⇑Institute of Geospatial Science & Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia

a r t i c l e i n f o

Article history:Received 1 June 2010Received in revised form 5 July 2011Accepted 13 July 2011Available online 6 August 2011

Keywords:ASTERCopper explorationAlteration zonesPrincipal Component AnalysisBand ratioMinimum Noise Fraction

1367-9120/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.jseaes.2011.07.017

⇑ Corresponding author. Tel.: +60 7 5530666; fax: +E-mail addresses: beiranvand.amin80@gmail.co

profmhashim@gmail.com (M. Hashim).

a b s t r a c t

The NW–SE trending Central Iranian Volcanic Belt hosts many well-known porphyry copper deposits inIran. It becomes an interesting area for remote sensing investigations to explore the new prospects of por-phyry copper and vein type epithermal gold mineralization. Two copper mining districts in southeasternsegment of the volcanic belt, including Meiduk and Sarcheshmeh have been selected in the present study.The performance of Principal Component Analysis, band ratio and Minimum Noise Fraction transforma-tion has been evaluated for the visible and near infrared (VNIR) and, shortwave infrared (SWIR) subsys-tems of ASTER data. The image processing techniques indicated the distribution of iron oxides andvegetation in the VNIR subsystem. Hydrothermal alteration mineral zones associated with porphyry cop-per mineralization identified and discriminated based on distinctive shortwave infrared (SWIR) proper-ties of the ASTER data in a regional scale. These techniques identified new prospects of porphyry coppermineralization in the study areas. The spatial distribution of hydrothermal alteration zones has been ver-ified by in situ inspection, X-ray diffraction (XRD) analysis, and spectral reflectance measurements.Results indicated that the integration of the image processing techniques has a great ability to obtain sig-nificant and comprehensive information for the reconnaissance stages of porphyry copper exploration ina regional scale. The results of this research can assist exploration geologists to find new prospects of por-phyry copper and gold deposits in the other virgin regions before costly detailed ground investigations.Consequently, the introduced image processing techniques can create an optimum idea about possiblelocation of the new prospects.

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1. Introduction

Mapping surface mineralogy using remote sensing sensors pro-vides an opportunity to improve initial steps of ore deposits explo-ration (Sabins, 1999; Rowan et al., 2003; Mars and Rowan, 2006; DiTommaso and Rubinstein, 2007; Zhang et al., 2007; Moghtaderiet al., 2007; Gabr et al., 2010). Hydrothermal fluid processes thatalter the mineralogy and chemical composition of the countryrocks generate porphyry ore deposits. The altered rocks havingdiagnostic spectral absorption features due to produced mineralassemblages (Hunt and Ashley, 1979). Exploration geologists haveused sophisticated remote sensors to detect hydrothermal alter-ation mineral assemblages for reconnaissance stages of porphyrycopper and gold exploration (Rowan et al., 2006; Gersman et al.,2008; Bedini et al., 2009; Gabr et al., 2010; Pour et al., 2011). TheAdvanced Spaceborne Thermal Emission and Reflection Radiome-ter (ASTER) sensor presents unprecedented opportunities for min-eral exploration. It was launched on NASA’s Earth Observing

ll rights reserved.

60 7 5531174.m, mazlanhashim@utm.my,

System AM-1 (EOS AM-1) polar orbiting spacecraft in December1999. The ASTER instrument is a cooperative effort between theJapanese Ministry of Economic Trade and Industry (METI) and Na-tional Aeronautics and Space Administration (NASA). ASTER is amultispectral imaging Sensor that measures reflected and emittedelectromagnetic radiation from Earth’s surface and atmosphere in14 bands (Yamaguchi et al., 1999; Abrams, 2000). There are threegroups of bands: (i) three recording visible and near infrared(VNIR) ranging between 0.52 and 0.86 lm at a spatial resolutionof 15 m; (ii) six recording portions of shortwave infrared (SWIR)from 1.6 to 2.43 lm at a spatial resolution of 30 m; and (iii) fiverecording thermal infrared (TIR) in the 8.125–11.65 lm wave-length region with resolution of 90 m. An additional backward-looking band in the VNIR makes it possible to construct digitalelevation models from bands 3 and 3b. ASTER swath width is60 km (each scene is 60 � 60 km) and useful for regional mapping(Fujisada, 1995). ASTER sensor allows the discrimination and iden-tification of hydrothermal alteration minerals in the SWIR region(Abrams and Hook, 1995). The ASTER bands in the SWIR were spe-cifically selected to highlight the presence of spectral absorptionfeatures present in minerals, such as clay, carbonates, sulfatesand other hydrous phases due to overtones and combination tones

Fig. 2. Laboratory spectra of muscovite, kaolinite, alunite, epidote, calcite, andchlorite re-sampled to ASTER bandpasses. Spectra include muscovite, typical inphyllic alteration zone, with a 2.20 lm absorption feature; kaolinite and alunite,which are common in argillic alteration zone, have 2.17 lm absorption features;and epidote, calcite, and chlorite, which are typically associated with propyliticalteration zone and display 2.35 lm absorption features (Clark et al., 1993b; Marsand Rowan, 2006).

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of fundamental absorptions of Al–O–H, Mg–O–H, Si–O–H, and CO3(Hunt, 1977; Abrams, 2000). Moreover, the ASTER visible and nearinfrared and thermal infrared data can provide adequate capabilityfor remote identification of vegetation and iron oxide minerals insurface soil, and mapping carbonates and silicates, respectively(Bedell, 2001; Ninomiya, 2003a,b; Rockwell and Hofstra, 2008).

Ideal porphyry copper deposits are typically characterized byhydrothermal alteration mineral zones. The core of quartz andpotassium-bearing minerals is surrounded by multiple alterationzones (Fig. 1; Lowell and Guilbert, 1970). The broad phyllic zoneis characterized by illite/muscovite (sericite) that indicates an in-tense Al-OH absorption feature centered at 2.20 lm, coincidingwith ASTER band 6. The narrower argillic zone including, kaoliniteand alunite displays a secondary Al-OH absorption feature at2.17 lm that corresponds with ASTER band 5. The mineral assem-blages of the outer propylitic zone are epidote, chlorite, andcalcite that exhibit absorption features situated in the 2.35 lm,which coincide with ASTER band 8 (Fig. 2) (Mars and Rowan,2006).

The objective of this paper is to utilize ASTER data for mappinghydrothermal alteration mineral zones associated with porphyrycopper mineralization in two large mining districts, includingSarcheshmeh and Meiduk in the southeastern segment of theNW–SE trending Central Iranian Volcanic Belt, southeast Iran. Itbecomes an interesting area for remote sensing investigations toexplore the new prospects of porphyry copper and vein typeepithermal gold mineralization. The major part of this belt haswell-exposed and sparse vegetated surface, which is ideal forremote sensing investigations. In this belt, the abundance ofknown porphyry copper deposits reflects the potential economicimportance and warrants the exploration of the new prospects.This paper emphasizes on the specific robust, fast, simple andhighly-effective spectral transform techniques for the identifica-tion of hydrothermal alteration minerals. The selected transformmethods are Principal Component Analysis (PCA), specialized bandratio (BR) and Minimum Noise Fraction (MNF). The following studyhypotheses are then set: (i) hydrothermal alteration mineral zonesassociated with porphyry copper mineralization can be detectedusing ASTER VNIR and SWIR bands; and (ii) three spectral trans-form techniques, namely PCA, BR and MNF could adequately detecthydrothermal alteration zones associated with porphyry copperdeposit using exclusively ASTER VNIR and SWIR data in a regionalscale.

Fig. 1. Hydrothermal alteration zones associated with porphyry copper deposit (modified from Lowell and Guilbert, 1970; Mars and Rowan, 2006). (A) Schematic crosssection of hydrothermal alteration mineral zones, which consist of propylitic, phyllic, argillic, and potassic alteration zones. (B) Schematic cross section of ores associated witheach alteration zone.

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2. Materials and methods

2.1. Geological setting

Iran is a semi-arid country located on the Tethyan Copper Belt.It has great potential for exploration porphyry copper and golddeposits using remote sensing instruments. Fig. 3 shows NASA’s2000 GeoCover global orthorectified Landsat 7 mosaics of Iran ascolor composite of band 7 as red, band 4 as green, and band 2 asblue (Merged MrSID files from http://zulu.ssc.nasa.gov/mrsid).Green color is as indicator of vegetated surface. Many of knownporphyry copper mineralization occurs in the Central Iranian Vol-canic Belt (Hassan-Nezhad and Moore, 2006). This Belt (trendsNW–SE) is the most important volcano-plutonic complex with tre-mendous economic potential for copper mineralization whichformed by subduction of the Arabian plate beneath central Iranduring the Alpine orogeny (Berberian and King, 1981; Shahabpour,2005, 2007). This study focuses on southeastern segment of theNW–SE trending Central Iranian Volcanic Belt. The Study area is lo-cated in black cubic in southeastern part of the Fig. 3. In this area,yearly precipitation average is 25 cm or less (Modarres and DaSilva, 2007), thus earth’s surface has well-exposed due to verysparse to nonexistent vegetation cover, which is quite suitablefor remote sensing studies. Simplified geologic map of the south-eastern segment of the Central Iranian Volcanic Belt illustratesthe location of study areas, rock units, and general structuralgeology (Fig. 4). The study areas include Sarcheshmeh and Meidukporphyry copper deposits located in the southern part of thevolcano-plutonic belt where Cu and Mo are currently mined. TheSarcheshmeh porphyry copper deposit (55�5202000E, 29�5804000N)is the most important copper ore deposit in Iran, 160 km south-west of Kerman city and is one of the largest porphyry copperdeposits in the world. Fig. 5 shows geological map of theSarcheshmeh area (Hubner, 1969a; Mars and Rowan, 2006). The

Fig. 3. NASA’s 2000 GeoCover global orthorectified Landsat 7 mosaics mergedimage of Iran as color composite of band 7 as red, band 4 as green, and band 2 asblue. The Study area is located in black cubic in southeastern part of image. (Forinterpretation of the references to color in this figure legend, the reader is referredto the web version of this article.)

deposit is within a belt of Eocene volcanic rocks and Oligo-Miocenesubvolcanic granitoid rocks. The oldest host rocks at theSarcheshmeh porphyry copper deposit belong to an Eocene volca-nogenic complex, also known as the Sarcheshmeh complex. Thecomplex consists of pyroxene trachybasalt, pyroxene trachyande-site of potassic and shoshonitic affinity (Aftabi and Atapour,1997), less abundant andesite and rare occurrences of agglomerate,tuff, and tuffaceous sandstone. These were intruded by a complexseries of Oligo-Miocene granitoid intrusive phases such as quartzdiorite, quartz monzonite and granodiorite. The granitoid rocksare cut by a series of intramineral hornblende porphyry, feldsparporphyry and biotite porphyry dykes (Waterman and Hamilton,1975). Hydrothermal alteration and mineralization at Sar-cheshmeh are centered on the stock and were broadly synchronouswith its emplacement. Early hydrothermal alteration was domi-nantly potassic and propylitic, and was followed later by phyllic,silicic and argillic alteration (Hezarkhani, 2006). The Meiduk por-phyry copper deposit (55�1000500E, 30�2501000N) is located 45 kmnortheast of Shahr-e-Babak, Kerman province. Fig. 6 shows geolog-ical map of the Meiduk area. Lower Eocene rocks are parts of a vol-canic complex consisting of rhyolite lavas, breccias, acidic tuffs,and pyroclastic rocks. This volcanic complex followed by anotherEocene to Oligocene volcanic complex, which consists of trachyba-salt, andesite and trachyandesite, andesite–basalt, and acidic tuff.The intrusive rocks were emplaced into the volcanic rocks as stockdykes at the Meiduk ore deposit. The volcanic complexes andintrusive rocks are partly covered by late Miocene–Pliocene volca-nic and subvolcanic rocks of the Masahim stratovolcano. The youn-gest volcanic rocks in the study area are Quaternary in age andrange from trachyte to dacite (Hassanzadeh, 1993). The Cu-miner-alization and associated hydrothermal alteration zones are focusedon the Miocene dioritic Meiduk porphyry and Eocene andesiticrocks (Boomeri et al., 2009). However, distribution of the variousalteration types is irregular. The concentric alteration zones fromthe center outward are potassic, phyllic, and propylitic. This pat-tern is similar to alteration envelopes that are associated withmany other porphyry Cu–Mo deposits. Amraie (1991) recognizedpotassic, phyllic, argillic, and propylitic alteration zones at Meidukarea.

2.2. Preprocessing of ASTER data

The ASTER data used in this study were obtained from the Earthand Remote Sensing Data Analysis Center (ERSDAC) Japan, andconsist of two cloud-free level 1B ASTER scenes of the study sitesin southeastern part of the Central Iranian Volcanic Belt. They wereacquired on July 15, 2007 for the Sarcheshmeh area, and June 20,2006 for the Meiduk area, respectively. The level 1B data productmeasures radiance at the sensor without atmospheric corrections,and were produced from the original level 1A format by ERSDAC(Abrams, 2000). The images were pre-georeferenced to UTM zone40 North projection with using the WGS-84 datum. The crosstalkcorrection performed to both data sets in this study, aimed atremoving the effects of energy overspill from band 4 into bands5 and 9 (Hewson et al., 2005). We have performed this correctionby Cross-Talk correction software that available fromwww.gds.aster.ersdac.or.jp. In addition, the ASTER Level 1B datawere converted to reflectance using the Internal Average RelativeReflection (IARR) method (Ben-Dor et al., 1995). Ben-Dor et al.(1995) recommended IARR reflectance technique for mineralogicalmapping as a preferred calibration technique; it does not requirethe prior knowledge of samples that collected from the field. The30 m resolution SWIR and of the ASTER data were re-sampled tocorrespond to the VNIR 15-m spatial dimensions. Pan-sharpeningtechnique applied to enhance the spatial resolution because it doesnot effect on the pixel digital numbers due to re-sampling affects.

Fig. 4. Simplified geology map of southeastern segment of the Central Iranian Volcanic Belt (modified from Shafiei, 2010). Study areas are located in ellipsoidal polygons.

Fig. 5. Geological map of Sarcheshmeh region (modified from Hubner, 1969b; Mars and Rowan, 2006).

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The ASTER surface reflectance scenes were processed and analyzedby ENVI (Environment for Visualizing Images) version 4.5 andERMapper version 6.4 software packages.

2.3. Image processing techniques

We have implemented robust, fast and reliable techniquesbased on spectral characteristics of alteration key minerals for asystematic selective extraction of the information of interest.

Principal Component Analysis (PCA), band ratio (BR), and Mini-mum Noise Fraction (MNF) performed over two full ASTER scenes.

2.3.1. Principal Component AnalysisPrincipal Component Analysis (PCA) is a multivariate statistical

technique that selects uncorrelated linear combinations (eigenvec-tor loadings) of variables in such a way that each component suc-cessively extracted linear combination and has a smaller variance(Singh and Harrison, 1985; Chang et al., 2006). PCA is a well-known

Fig. 6. Geological map of Meiduk region (modified from Hubner, 1969a; Mars and Rowan, 2006).

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method for lithological and alteration mapping in metalogenicprovinces (Crosta et al., 2003; Rowan and Mars, 2003; Ranjbaret al., 2004; Kargi, 2007; Massironi et al., 2008; Moore et al.,2008; Tangestani et al., 2008; Amer et al., 2010). Standard PCAtransformation has applied to the two full ASTER scenes of the Sar-cheshmeh and Meiduk regions. A total of nine new image compo-nents are generated from the original nine bands (VNIR + SWIR)ASTER data.

2.3.2. Band ratioBand ratio (BR) is a technique where the DN value of one band is

divided by the DN value of another band. BRs are very useful forhighlighting certain features or materials that cannot be seen inthe raw bands (Inzana et al., 2003). ASTER BR technique has alsoreported wide acceptance in geological mapping in the recentyears (Gad and Kusky, 2007; Khan and Mahmood, 2008; Massironiet al., 2008; Amer et al., 2010; Aboelkhair et al., 2010). Selected AS-TER VNIR and SWIR bands have used for BR in this study. Three BRshave performed (i) stabilized vegetation index (StVI) = (band 3/band 2) � (band 1/band 2) for detecting vegetation features(Ninomiya, 2003a); (ii) ratio of band 4/2 for identifying iron oxides(Abdelsalam and Stern, 2000), (iii) ratio of band 7/6 for identifyingmuscovite (Hewson et al., 2005). The strategy of the above ratios isto identify vegetation, iron oxides, and OH-bearing mineral zones,thus lead to an overall large area mapping of the hydrothermalalteration areas associated with porphyry copper deposits. RelativeAbsorption Band Depth (RBD) is a useful three-point ratio formula-tion for displaying Al–O–H, Fe, Mg–O–H, and CO3 absorptionintensities. For each absorption feature, the numerator is the sumof the bands representing the shoulders, and the denominator isthe band located nearest the absorption feature minimum (Crow-ley et al., 1989). Three RBD ratios have adopted in this study;RBD5, RBD6, and RBD8 were assigned for RGB (red, green, and blue)color combination to delineate argillic, phyllic and propylitichydrothermal alteration zones. The RBD ratios have been derivedbased on Crowley et al. (1989) as follows:

RBD5 ¼ðb4þ b6Þ

b5ð1Þ

RBD6 ¼ðb5þ b7Þ

b6ð2Þ

RBD8 ¼ðb7þ b9Þ

b8ð3Þ

where b4, b5, b6, b7, b8 and b9 designated for ASTER bands 5, 6, 7, 8and 9, respectively.

2.3.3. Minimum Noise FractionThe MNF transformation is used to determine the inherent

dimensionality of image data, segregate noise in the data, and re-duce the computational requirements for subsequent processing(Green et al., 1988; Boardman et al., 1995). The Minimum NoiseFraction (MNF) transformation involves two steps; first, which alsocalled noise-whitening, principal components for noise covariancematrix are calculated; this step decorrelates and rescales the noisein the data. In the second step, the principal components are de-rived from the noise whitened data. The data can then be dividedinto two parts: one part associated with large eigenvalues andthe other part with near unity eigenvalues and noise dominatedimages. Using data with large eigenvalues separates the noise fromthe data, and improves spectral results (Green et al., 1988). MNFanalysis can identify the locations of spectral signature anomalies.This process is of interest to exploration geologist because spectralanomalies are often indicative of alterations due to hydrothermalmineralization. MNF has applied on ASTER SWIR bands to discrim-inate hydrothermally altered rocks from surrounding igneousbackground in the study areas.

3. Results, analysis and discussion

3.1. Principal component analysis

PCA outputs are presented as tables of statistic factors and se-lected PC images from these transformations are reproduced in fig-ures to support the discussion. The image eigenvectors andeigenvalues obtained from PCA, using covariance matrix on all ninereflective bands of ASTER of the Meiduk and Sarcheshmeh scenesare indicated in Tables 1 and 2.

PC1 is composed of a positive weighting of all nine (VNIR + S-WIR) total bands. As indicated by the eigenvalues, PC1 accountsfor 95.71% and 94.06% of the total variance for the data for Meidukand Sarcheshmeh scene, respectively. Overall scene brightness, oralbedo, is responsible for the strong correlation between multi-spectral image bands (Loughlin, 1991). PCA has effectively mappedalbedo into PC1 of the transformation. All of eigenvector loadingsfor PC2 are negative (Tables 1 and 2). It is evident that PC2 is noisywithout any information. Eigenvector loadings for PC3 indicatethat PC3 probably describes the difference between the visible

Table 1Eigenvector matrix of principal components analysis on VNIR + SWIR bands of ASTER data for Meiduk scene.

Input bands Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Band 8 Band 9 Eigen values (%)

PC1 0.320 0.322 0.320 0.325 0.325 0.325 0.322 0.323 0.323 95.71PC2 �0.004 �0.012 �0.003 �0.120 �0.091 �0.067 �0.153 �0.142 �0.122 2.72PC3 �0.430 �0.422 �0.414 0.164 0.024 0.123 0.436 0.360 0.273 0.89PC4 �0.100 �0.124 0.417 0.421 �0.224 �0.456 �0.430 0.088 0.414 0.31PC5 �0.289 �0.249 0.573 0.245 0.037 0.034 0.439 �0.276 �0.437 0.14PC6 0.270 0.269 �0.386 0.519 0.117 �0.486 0.204 �0.331 �0.180 0.12PC7 �0.044 0.111 �0.087 0.316 �0.170 0.087 �0.254 0.638 �0.607 0.07PC8 0.019 �0.126 0.189 �0.299 0.721 �0.484 �0.072 0.247 �0.190 0.03PC9 �0.698 0.698 0.022 �0.069 0.088 �0.071 �0.001 �0.037 0.064 0.01

Table 2Eigenvector matrix of principal components analysis on VNIR + SWIR bands of ASTER data for Sarcheshmeh scene.

Input bands Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Band 8 Band 9 Eigen values (%)

PC1 0.320 0.320 0.320 0.329 0.328 0.328 0.324 0.326 0.326 94.06PC2 �0.068 �0.061 �0.058 �0.140 �0.111 �0.087 �0.175 �0.168 �0.149 3.56PC3 �0.474 �0.472 �0.434 0.186 0.108 0.022 0.307 0.285 0.243 1.53PC4 �0.135 �0.121 0.391 0.588 �0.144 �0.524 �0.292 �0.050 0.289 0.45PC5 �0.296 �0.225 0.538 0.124 �0.047 0.144 0.469 �0.174 �0.531 0.20PC6 0.248 �0.219 �0.454 �0.436 �0.246 0.358 �0.306 0.314 0.334 0.10PC7 0.001 �0.193 0.232 �0.433 0.710 �0.411 0.080 �0.131 0.154 0.07PC8 0.071 �0.065 �0.014 �0.070 �0.249 0.159 0.330 �0.701 0.544 0.02PC9 �0.688 0.703 �0.009 �0.074 0.078 �0.056 0.031 �0.083 0.091 0.01

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channels, including bands 1, 2, and 3 (negative eigenvector load-ings) and the infrared channels, including bands 4, 5, 6, 7, 8 and9 (positive eigenvector loadings). The remaining six PCs can be ex-pected to contain information due to the varying spectral responseof iron oxides and hydroxyl-bearing minerals (Loughlin, 1991) andvegetation.

By looking for moderate or large eigenvector loadings for bands1, 2 and 4 in PCs where these loadings are also in opposite sign, wecan predict that iron oxides can be distinguished by bright pixels inPC4 (Tables 1 and 2). Iron oxide minerals have low reflectance invisible and higher reflectance in near infrared coincide with bands1, 2 and band 4 of ASTER data, respectively (Abdelsalam and Stern,2000; Velosky et al., 2003). Electronic processes produce absorp-tion features in the visible and near infrared radiation (VNIR)(0.4–1.1 lm) due to the presence of transition elements such asFe2+, Fe3+, Mn, Cr, and Ni in the crystal structure of minerals (Hunt,1977; Hunt and Ashley, 1979). Iron oxides can be mapped as brightpixels in PC4 because of the positive contribution from band 4(0.421) and (0.588); while negative contributions from band 1(�0.100) and (�0.135), band 2 (�0.124) and (�0.121) for Meidukand Sarcheshmeh scenes, respectively (Tables 1 and 2). PC4 imageshows iron oxide minerals that manifested as bright pixels withcircular and semi-circular shapes around known porphyry copperdeposits and new prospects in the study areas (Fig. 7A and B). Con-sidering the PC4 (iron oxide pixels), it has positive great loading ofband 3 (0.417) and (0.391) (Tables 1 and 2, respectively), that re-veals the interruption of vegetation associated with iron oxidesin the PC image as bright pixels. Iron oxides are one of the impor-tant mineral groups that associated with hydrothermally alteredrocks (Sabins, 1999). Iron oxides are create during supergene alter-ation and render to characteristic yellowish or reddish color to thealtered rocks that termed gossan (Abdelsalam and Stern, 2000; Xuet al., 2004). Eigenvector loadings for PC5 indicate that PC5 is dom-inated by vegetation, because vegetation has highly reflectance inband 3 and very low in band 2 of ASTER data (Ninomiya, 2003a;Xu et al., 2004). The positive loading of band 3 in PC5 (0.573)and (0.538) and negative loading of band 2 (�0.249) and(�0.225) are shown in Tables 1 and 2 for Meiduk and Sarcheshmehscenes, respectively. So, Vegetation pixels appear as bright pixels in

the PC5 image. Vegetation pixels follow the drainage systems, andare as field form in the plain (Fig. 8A and B). The percentage of var-iance of this ‘vegetation’ PC is only 0.14 and 0.20, showing thesparse vegetated surface in the study area. Iron oxide mineralshave spectral absorption features in the visible to middle infraredfrom 0.4 to 1.1 lm of the electromagnetic spectrum (Hunt andAshley, 1979). Vegetation shows absorption features from 0.45 to0.68 lm, and high reflectance in near infrared. It is observed thatiron oxide minerals have high reflectance in the range of 0.63–0.69 lm, while the range of 0.76–0.90 lm covers higher range ofthe vegetation red-edge reflectance feature in near infrared, thischaracteristic can be used to differentiate iron oxide mineralsfrom vegetation (Crosta and Moore, 1989; Ruiz-Armenta andProl-Ledesma, 1998). Hence, bands 2 (0.63–0.69 lm) and 3(0.78–0.86 lm) of ASTER data include typical features that can beused to separate iron oxide minerals from vegetation. Accordingly,vegetation pixels have bright signature in PC5 while iron oxide pix-els have dark signature (Fig. 8A and B). Al(OH)-bearing mineralssuch as kaolinite, alunite, muscovite and illite show major absorp-tion in bands 5, 6 and 7 (2.14–2.28 lm). Fe, Mg(OH)-bearing min-erals such as chlorite, as well as carbonates such as calcite anddolomite have distinctive absorption in bands 8 and 9 (2.29–2.43 lm) of ASTER data (Fig. 2) (Hunt and Ashley, 1979; Marsand Rowan, 2006). After analyzing the magnitude and sign of theeigenvector loadings and the percentage of variance, it seems thatPC6 and PC7 contain the desired information. PC8 and PC9 arenoisy and uninformative.

Eigenvector loadings of bands 4, 5, 6 and 7 in PC6 are (0.519),(0.117), (�0.486), and (0.204), respectively (Table 1). Accordingto Crosta and Moore (1989) and Loughlin (1991) a PC image withmoderate to high eigenvector loading for diagnostic reflectiveand absorptive bands of mineral or mineral group with oppositesigns enhances that mineral. If the loading are positive in reflectiveband of a mineral the image tone will be bright, and if they are neg-ative, the image tone will be dark for the enhanced target mineral.Thus, eigenvector statistic in each PCA would identify the PC imagein which the spectral information of mineral under exam is loaded.This information usually represents, in quantitative terms, a verysmall fraction of the total information content of the original

Fig. 7. PC4 image shows iron oxide minerals as bright pixels with circular and semi-circular shapes that delimited by ellipsoidal polygons in the Meiduk scene (A) andSarcheshmeh scene (B).

Fig. 8. PC5 image shows vegetation as bright pixels that follow the drainagesystem. (A) Meiduk scene. (B) Sarcheshmeh scene.

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bands, but it is expected that the loaded information indicates inthe spectral signature of the desired mineral. Considering the mag-nitude and sign of the eigenvector loadings of band 4 and alsobands 5, 6, 7 in PC6, this is evident that PC image manifests desiredinformation including Al(OH)-bearing minerals as bright pixels

(Fig. 9A). PC6 can be indicator of argilic and phylic alteration zonesbecause of the magnitude and sign of the eigenvectors loadings ofbands 5, 6, 7 in PC6. Analyzing of eigenvector loadings in PC7 indi-cate similar results with small discrepancies in magnitude and signof the eigenvector loadings especially in bands 8 (0.638) and 9

Fig. 9. (A) PC6 image for the Meiduk scene; ellipsoidal polygons delimited thelocations of Al(OH)-bearing minerals as bright pixel. (B) PC7 image for the Meidukscene, ellipsoidal polygons delimited the locations of Fe, Mg(OH)-bearing mineralsas bright pixel.

Fig. 10. (A) RGB color composite of PC5, PC6, and PC7 images, showing hydrother-mal alteration halos around known copper deposits in Meiduk scene. Hydrothermalalteration halos are delimited by ellipsoidal polygons around the known copperdeposits (highlighted by their names) and identified prospects. Vegetation appearsas red color in the drainage patterns and filed form in the plain. (B) RGB colorcomposites of PC5, PC6, and PC7 images for Sarcheshmeh scene. Vegetation appearsas light yellow color in the drainage patterns and filed form in the plain. (Forinterpretation of the references to color in this figure legend, the reader is referredto the web version of this article.)

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(�0.607) (Fe, Mg(OH)-bearing minerals), respectively (Table 1).Therefore, PC7 can be indicator of propylitic alteration zones(Fig. 9B). The results of PC images for two ASTER scenes are similar,but the OH-bearing minerals in PC6 and PC7 manifest in dark pix-els in the Sarcheshmeh scene because the band 4 (reflective band)

has eigenvector loading with negative sign (�0.436) (Table 2).According to Yamaguchi and Naito (2003) the reason for these dis-crepancies can be due to a PCA result which is scene dependent, i.e.transform coefficient change from scene to scene. Fig. 10A and Bshow RGB color composites of PC5 (Vegetation pixels), PC6(Al(OH)-bearing minerals’ pixels), and PC7 (Fe, Mg(OH)-bearingminerals’ pixels) images of the Meiduk and Sarcheshmeh scenes.The RGB color composites were generated to show vegetation

Fig. 11. ASTER StVI = (band 3/band 2) � (band 1/band 2) image shows vegetation asbright pixel in Meiduk scene (A), and Sarcheshmeh scene (B).

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covers and hydrothermal alteration zones in study areas. Alter-ation halos in the Meiduk region are depicted as white to blue color(Phylic and Argilic zones), and green color (propylitic zone) thatsurrounds phyllic and argillic zones, which are easily recognizablefrom surrounding rocks. Vegetation covers are appeared as purplecolor in the drainage systems and as field form in the plain(Fig. 10A). The RGB appearances for alteration halos inSarcheshmeh scene are manifested in red and yellow colors (Phylicand Argilic zones) and light blue color (propylitic zone) thatsurrounds phyllic and argillic zones, while vegetation covers areappeared as whitish yellow color (Fig. 10B). In this way, hydrother-mal alteration zones around known copper deposits and vegeta-tion are identifiable in the scenes. Some new alteration halos arealso distinguished in Fig. 10A and B, using geology maps as refer-ence; it seems that a few of them are associated with sedimentaryrocks. Sedimentary rocks (mudstone, shale, claystone, etc.) can beas erroneous materials in mapping hydrothermal alteration miner-als due to large amounts of detrital clay minerals in their composi-tions (Mars and Rowan, 2006). We have delimited the knowncopper deposits and new identified alteration halos by ellipsoidalpolygons in the igneous background in Fig. 10A and B.

3.2. Band ratio

Band ratio (BR) transformation is useful for qualitative detec-tion of hydrothermal alteration minerals. High digital number val-ues in the scene indicate spectral signatures similar to those of theparticular materials they were designed to map. Ninomiya (2003a)defined vegetation index for study of distribution vegetation byASTER VNIR data. According this definition, the most widelyacknowledged vegetation index is NDVI (Normalized DifferenceVegetation Index), defined as (NIR–red)/(NIR + red), where NIR,the datum in near infrared, corresponds to ASTER band 3, andred corresponds to ASTER band 2. It is stable and sensitive enoughfor studying vegetation if it is applied to atmospherically correctedreflectance data. Stabilized vegetation index (StVI) is defined as:StVI = (band 3/band 2) � (band 1/band 2) (Ninomiya, 2003a). Thisindex used for distinguishing vegetation in two full ASTER scenesof the study areas. Fig. 11A and B shows the result of stabilizedvegetation index (StVI) as bright pixel, vegetation highlight indrainage as linear pattern and in plain as field form. These resultsare similar to PC5 of PCA transformation. Velosky et al. (2003) dis-tinguished the gossan (iron oxide minerals) associated with mas-sive sulfide mineralization from the host rock by ASTER 4/2 BRimage in the Neoproterozoic Wadi Bidah shear zone, southwesternSaudi Arabia. Applying ratio of band 4/2 on two subscenes was dis-tinguished the gossan associated with Mieduk, Sara, Sarcheshmeh,and Seridune mines as bright pixels (Fig. 12A and B), and againthese identified areas are fully match with PC4 of the PCA transfor-mation. Previous studies have documented the identification ofspecific hydrothermal alteration minerals through the analysis ofASTER BR in the SWIR region. Hewson et al. (2005) suggested BRof 7/6 for identification of muscovite. We used the BR to highlightmuscovite in the study area because this mineral is a useful tool inmapping the effects of hydrothermal alteration processes (Van Rui-tenbeek et al., 2006). Fig. 13A and B shows the output of the BR forMeiduk and Sarschesmeh scenes. Hydrothermally altered zonesare shown as bright pixels in the BR 7/6 because muscovite hashigh reflectance in band 7 and low reflectance in band 6. RelativeAbsorption Band Depths (RBD) consist of RBD5, RBD6, and RBD8images have used in this study to delineate argillic and phyllicand propylitic mineral assemblages using ASTER SWIR bands. Arg-illic alteration zone is consisted of Kaolinite and alunite that dis-plays absorption features at 2.17 lm (coincide with ASTER band5). Phyllic alteration spectral characteristics include muscoviteand illite reflectance spectra that exhibit an intense Al-OH

absorption feature, which is typically centered at 2.20 lm (coin-cide with ASTER band 6). Propylitic mineral-assemblage reflec-tance spectra are characterized by Fe, Mg-OH absorption featuresand CO3 features caused by molecular vibrations in epidote, chlo-rite and carbonate minerals (Spatz and Wilson, 1995). Theseabsorption features are situated in the 2.35 lm region (coincidewith ASTER band 8) (Mars and Rowan, 2006). The RBDs have ap-plied over two ASTER scenes. RGB color composites were assignedto present the output. In this regard, alteration mineral assem-blages are demonstrated with different colors, narrow argillic areasas bluish green and broad phyllic as yellow color that occupiedmajor parts of the hydrothermal alteration mineral haloes, and

Fig. 12. ASTER band ratio image of 4/2 shows Iron oxide minerals (Gossan) as brightpixel in Meiduk and Sara mines (A), and Sarcheshmeh and Seridune mines (B).

Fig. 13. ASTER band ratio image of 7/6 shows hydrothermal altered zones(muscovite), which are located inside ellipsoidal polygons in Meiduk scene (A),and Sarcheshmeh scene (B).

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propylitic zone as pinkish purple that surrounded outside of thesehydrothermal alteration mineral zones (Fig. 14A and B). The loca-tion of the alteration haloes are corresponded with highlightedellipsoidal polygons in the PCA images. However, identified hydro-thermal alteration zones are more recognizable in comparisonwith PCA results.

3.3. MNF transformation

MNF transformation was performed on SWIR bands of ASTERdata to detect hydrothermally altered rocks. We focused on thepercentage of eginvalues greater than 1 for all of MNF eigenimages,and then RGB color composite applied over results. The percentageof eigenvalues for MNF bands is shown in Table 3 for SWIR bandsof ASTER data. According to Boardman and Green (2000) the eigen-value of each MNF transformed band provides a measure of its

Fig. 14. (A) ASTER RBD-ratio image of RBD6, RBD5, RBD8 in RGB. Argillic alterationzones manifest as bluish green and phyllic alteration zones as yellow color andpropylitic alteration zones as pinkish purple in Meiduk region. (B) Sarcheshmehregion. Alteration halos associated with known copper deposits and new prospectsare highlighted by their names.

Table 3The percentage of eigenvalues for all of MNF bands extracted from AST

MNF band (Meiduk) Eigenvalues Percent% M

1 482.6525 85.66 12 43.5428 7.72 23 15.8743 2.82 34 9.6814 1.72 45 6.2923 1.11 56 5.4405 0.97 6

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information content, with progressively noisier bands approachingeigenvalues near zero. These authors also noted that bands havinglow eigenvalues generally have very limited (or no) spatial coher-ency, again reflecting the dominance of incoherent noise. MNFeigenimages with values close to 1 contain mostly noise (Jensen,2005). MNF component images show steadily decreasing imagequality with increasing component number (Chen, 2000). MNFtransformation results derived from SWIR ASTER data indicatedthat MNF eigenimages of bands 4, 5 and 6 have eigenvalues per-cent near to 1 (Table 3). Thus, we excluded the 4th, 5th and 6thMNF components. We used the remaining eigenimages for produc-ing RGB color composite. MNF bands 1, 2 and 3 were assigned toRGB color composites. Fig. 15A and B depicts the output of RGB col-or composites for two ASTER scenes. In Meiduk scene, hydrother-mally altered rocks manifest as brownish yellow color. Most ofthe rock units’ contacts are recognizable in comparison with thecorresponding geological map (Figs. 15A, and 6). Fig. 15B showshydrothermally altered rocks as blue to darkish-blue, and mostof rock units’ contacts are observable in comparison with geologi-cal map of Sarcheshmeh region (Fig. 5). Because of the erroneouseffect of sedimentary rocks, the known copper deposits and newidentified alteration halos are delimited in the igneous backgroundby ellipsoidal polygons in the two figures. Identified altered rockshave different colors in the two study areas probably due to differ-ent statistical factors for MNF transformed bands, which were as-signed for generating RGB color composites images (Table 3).Therefore, altered rocks demonstrated as different colors in Sar-cheshmeh and Meiduk scenes. The results extracted from MNFtransformation technique for detecting the spatial distribution ofaltered rocks were similar to PCA and BR results. This is discerniblethat PCA, MNF, and specialized BR transformations have good func-tion in identifying hydrothermally altered mineral areas and vege-tation. Some organic materials such as lignin–cellulose havespectral absorption features centered near 2.10 and 2.30 lm,which are near the distinctive absorption features of hydrothermalalteration minerals. The presence of organic materials has affectedthe remote detection of hydroxyl-bearing minerals (VanRuitenbeek et al., 2006; Mars and Rowan, 2006). Thus, the delinea-tion of vegetation is paramount in discriminating of hydrother-mally altered rocks from surrounding area.

3.4. Comparison with previous remote sensing studies and groundtruth

As the literature admitted, a few remote sensing studies werecarried out in the study areas. Tangestani and Moore (2002) usedThematic Mapper (TM) data for enhancing the alteration patternsaround porphyry intrusive bodies in the Meiduk area. They appliedthe Crosta technique, principal component transformation on sixand four TM bands for hydroxyl mapping. Ranjbar et al. (2004) uti-lized Enhanced Thematic Mapper Plus (ETM+) data for porphyrycopper alteration mapping in the southern part of the Central Ira-nian Volcanic Belt. Crosta technique performed on selected fourand six ETM+ bands for enhancing the areas in which the regolithcontains a high proportion of hydroxyl and iron oxide minerals.

ER SWIR data, Meiduk and Sarcheshmeh scenes.

NF band (Sarcheshmeh) Eigenvalues Percent%

457.3547 83.7550.3946 9.2315.3305 2.8110.9961 2.016.5126 1.195.4901 1.01

Fig. 15. RGB color composite of MNF eigenimages 1, 2, and 3 extracted from ASTERSWIR bands. (A) Yellow to brownish yellow color areas show hydrothermal alteredrocks in Meiduk scene, most of the rock units’ contacts are also observable. (B)Darkish-blue color areas show hydrothermal altered rocks in Sarcheshmeh scene,most of the rock units’ contacts are also observable. Known copper deposits(highlighted by their names) and identified prospects are delimited by ellipsoidalpolygons. (For interpretation of the references to color in this figure legend, thereader is referred to the web version of this article.)

Fig. 16. Field photographs of the study area. (A) Regional view of the open-pitquarry of Sarcheshmeh porphyry copper mine; (B) view of the phyllic zone; (C)view of the argillic zone; (D) close-up of the gossan (iron oxide minerals).

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Mars and Rowan (2006) performed logical operator algorithmsbased on the Advanced Spaceborne Thermal Emission and Reflec-tion Radiometer (ASTER) defined band ratios for regional mappingof phyllic and argilic altered rocks in the southern part of the Cen-tral Iranian Volcanic Belt. Tangestani et al. (2008) evaluated ASTERdata for alteration zone enhancement associated with porphyrycopper mineralization in the Meiduk area. Directed Principal Com-ponent Analysis (DPCA) implemented on a selected subset that

covering the Meiduk porphyry copper mine as well as Sara and Ab-dar copper occurrences. DPCA technique applied on three spectralbands (4, 5 and 7), PC3 was detected montmorilonite/illite, chlo-rite, and muscovite minerals. However, the previous investigationshave not studied many parts of the Central Iranian Volcanic Belt indetail by ASTER remote sensing data and the integration of imageprocessing techniques. Therefore, the present study tried to use anapproach in the image processing techniques to evaluate the AS-TER VNIR and SWIR data for identifying the new prospects ofhigh-potential alteration zone associated with porphyry coppermineralization in a regional scale. In this study, the performanceof Principal Component Analysis, band ratioing and Minimum

A.B. Pour, M. Hashim / Journal of Asian Earth Sciences 42 (2011) 1309–1323 1321

Noise Fraction transformation has been evaluated for the VNIR andSWIR subsystems of ASTER data in a regional scale. The image pro-cessing techniques indicated the distribution of iron oxides andvegetation in the VNIR subsystem and hydrothermal alteration ha-los in SWIR subsystem. According to previous remote sensing andgeology studies in the study area (Geological Survey of Iran, 1973;Tangestani and Moore, 2002; Ranjbar et al., 2004; Mars and Rowan,2006; Tangestani et al., 2008), the applied techniques in the pres-ent study efficiently revealed the alteration halos around knowncopper deposits and identified new prospects. Detected anomalouspixels by these techniques are coincided with hydrothermal alter-ation haloes. Results indicate that the integration of the techniqueshas a great ability to obtain comprehensive and significant infor-mation for the reconnaissance stages of porphyry copper explora-tion in a regional scale. This approach in the image processingtechniques can be extrapolated to virgin regions for exploring ofthe new prospect of high-potential copper mineralization zonesof the Central Iranian Volcanic Belt and other arid and semi-arid re-gions of the Earth. The spatial distribution of identified alterationzones by the image processing techniques were verified throughin situ inspection. A field reconnaissance was carried out between10 and 15 December 2010. Geological locations were measured byGPS survey with an average accuracy 7 m. Samples for laboratorystudies were collected through a systematic rock sampling ofhydrothermal alteration zones. The field photographs of the hydro-thermal alteration zones are shown in Fig. 16A–D. The mineralogyof fine grained samples was studied using the X-ray diffraction(XRD) technique for bulk mineralogy of the hydrothermally alteredrocks. The XRD analyses were implemented on bulk powder usinga X-ray diffractometer D8ADVANCE model. The minerals predom-inantly detected in the hydrothermal alteration zones includedmuscovite, illite and quartz in phyllic zone, kaolinite, montmoril-lonite and quartz in argillic zone, and epidote, chlorite, and quartzin propylitic zone. Spectral reflectance measurements were madeusing an Analytical Spectral Devices (ASD) filed-portable spec-trometer Fieldspac� HandHeld (HH) model, which records a reflec-tance spectrum across an overall spectral range of 325–2500 nm(nm) with a 10 nm individual band width. The measurements wereperformed at the Remote Sensing laboratory in Technology Univer-sity of Malaysia using an artificial light source and contact prob. Al-tered rock samples were measured multiple times to get averagespectrum. Fig. 17 indicates the average spectra of collected rocksamples from hydrothermal alteration zones. The spectra of phyllicrock samples exhibit absorption features in 2200 nm, Argillic rock

Fig. 17. Laboratory reflectance spectra of altered rock samples, arrows pointed themaximum absorption. (A) The average spectra of phyllic rock samples hasmaximum absorption in 2200 nm; (B) argillic rock samples in 2170 and 2200 nm;(C) propylitic rock samples in 2350 nm.

samples in 2170 and 2200 nm and propylitic rock samples in2350 nm.

4. Conclusions

In this study, ASTER data have been used to identify the hydro-thermal alteration zones associated with porphyry copper mineral-ization, test the data, and image processing techniques for using asan exploration tool in the Central Iranian Volcanic Belt with greatpotential economic importance and many known porphyry copperdeposits, where warranting the exploration of new prospects. Prin-cipal Component Analysis (PCA), Band ratio, and Minimum NoiseFraction (MNF) transformation techniques carried out for detailedhydrothermal alteration mapping, resulting in the identification ofhigh economic-potential zones for copper mineralization. Analysisof ASTER level 1 B data after applying Cross-Talk and atmosphericcorrections, and using the Internal Average Relative Reflection(IARR) method for converting radiance to reflectance, providedstandard data for hydrothermal alteration mineral mapping. Theimage processing techniques have been evaluated over theSarcheshmeh and Mieduk mining districts to mapping the priorknown hydrothermal alteration zones. Results of the PCA transfor-mation yielded PC images, which are useful for identifying the spa-tial distribution of specific materials based on their spectralproperties in the VNIR + SWIR bands. PC4, PC5 revealed the distri-bution of iron oxides and vegetation in the VNIR subsystem,respectively. All anomalous pixels for (OH)-bearing minerals weredetected in PC6 and PC7 images of the SWIR subsystem. It shouldbe noted that the PCA is statistics-based and results may differ inthe same area with different geologic sizes. Stabilized vegetationindex (StVI) and band ratio of 4/2 depicted vegetation and iron oxi-des, respectively. The results of band ratio of (7/6) showed the ef-fects of hydrothermal alteration processes that can be utilized as auseful tool in mapping high-potential alteration zones. RGB colorcomposites of RBD5, RBD6, and RBD8 images were useful indistinguishing of the argillic and phyllic and propylitic mineralassemblages using ASTER SWIR bands. The results of MNF transfor-mation in SWIR were detected hydrothermally altered rocks andalso rock units’ contacts. Results are proven to be effective, andin accordance with results of field and laboratory studies. It isshown that the integration of the image processing techniqueshas great ability to assist economic geologists for the reconnais-sance stages of mineral exploration, and can be extrapolated to vir-gin regions for exploring high-potential copper mineralizationzones.

Acknowledgements

This study was conducted as part of Fundamental ResearchGrant Scheme, Ministry of Higher Education Malaysia. We aregrateful to the Universiti Teknologi Malaysia for providing the facil-ities for this investigation. We also thank reviewers for their com-ments, which were especially helpful for clarifying certain pointsin the manuscript.

References

Abdelsalam, M., Stern, R., 2000. Mapping gossans in arid regions with landsat TMand SIR-C images, the Beddaho Alteration Zone in northern Eritrea. Journal ofAfrican Earth Sciences 30 (4), 903–916.

Aboelkhair, H., Yoshiki, N., Yasushi, W., Isao, S., 2010. Processing and interpretationof ASTER TIR data for mapping of rare-metal-enriched albite granitoids in theCentral Eastern Desert of Egypt. Journal of African Earth Sciences 58 (1), 141–151.

Abrams, M., 2000. The Advanced Spaceborne Thermal Emission and ReflectionRadiometer (ASTER): data products for the high spatial resolution imager onNASA’s Terra platform. International Journal of Remote Sensing 21, 847–859.

1322 A.B. Pour, M. Hashim / Journal of Asian Earth Sciences 42 (2011) 1309–1323

Abrams, M., Hook, S.J., 1995. Simulated ASTER data for geologic studies. IEEETransactions on Geoscience and remote sensing 33 (3).

Aftabi, A., Atapour, H., 1997. Geochemical and petrological characteristics ofshoshonitic and potassic calcalkaline magmatism at Sarcheshmeh andDehsiahan porphyry copper deposits, Kerman, Iran. Research Bulletin ofIsfahan University 9, 127–156 (in Persian).

Amer, R., Kusky, T., Ghulam, A., 2010. Lithological mapping in the CentralEastern Desert of Egypt using ASTER data. Journal of African Earth Sciences56, 75–82.

Amraie, A., 1991. The Study of Mineralization and Hydrothermal Alteration atMeiduk Porphyry Copper Deposit, M.Sc. Thesis. Shiraz University, p. 125.

Bedell, R.L., 2001. Geological mapping with ASTER satellite: new global satellitedata that is a significant leap in remote sensing geologic and alterationmapping. Special Publication Geology Society of Nevada 33, 329–334.

Bedini, E., Van Der Meer, F., Van Ruitenbeek, F., 2009. Use of HyMap imagingspectrometer data to map mineralogy in the Rodalquilar caldera, southeastSpain. International Journal of Remote Sensing 30 (2), 327–348.

Ben-Dor, E., Kruse, F.A., lefkoff, A.B., Banin, A., 1995. Comparison of three calibrationtechniques for utilization of GER 63-channel aircraft scanner data of MakhteshRamon, Nega, Israel. International Journal of Rock Mechanics and Miningscience and Geomechanics Abstract 32, 164A.

Berberian, M., King, G.C., 1981. Towards a paleogeography and tectonic evolution ofIran. Canadian Journal of Earth Sciences 18, 210–265.

Boardman, J.W., Green, R.O., 2000. Exploring the Spectral Variability of the Earth asMeasured by AVIRIS in 1999. Summaries of the Ninth Annual JPL AirborneGeosciences Workshop. Jet Propulsion Laboratory Special Publication 18, p. 10.

Boardman, J.W., Kruse, F.A., Green, R.O., 1995. Mapping Target Signatures via PartialUnmixing of AVIRIS data. Summaries of the 5nd Annual JPL Airborne GeoscienceWorkshop, JPL Publication 95-1, vol. 1, pp. 23–26.

Boomeri, M., Kazuo, N., David, R.L., 2009. The Meiduk porphyry Cu deposit, Kerman,Iran: a geochemical analysis of the potassic zone including halogen elementsystematics related to Cu mineralization processes. Journal of GeochemicalExploration 103, 17–29.

Chang, Q., Jing, L., Panahi, A., 2006. Principal component analysis with optimumorder sample correlation coefficient for image enhancement. InternationalJournal of Remote Sensing 27 (16), 3387–3401.

Chen, C.M., 2000. Comparison of principal component analysis and Minimum NoiseFraction transformation for reducing the dimensionality of hyperspectralimagery. Geographical Research 33, 163–178.

Clark, R.N., Swayze, G.A., Gallagher, A., King, T.V.V., Calvin, W.M., 1993b. The USGeological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 microns: USGeological Survey Open File Report 93-592, 1340 p. <http://speclab.cr.usgs.gov>(August 1999).

Crosta, A., Moore, J. McM., 1989. Enhancement of Landsat Thematic Mapper imageryfor residual soil mapping in SW Minais Gerais State, Brazil: a prospecting casehistory in Greenstone belt terrain. In: Proceedings of the 7th ERIM ThematicConference: Remote Sensing for Exploration Geology, pp. 1173–1187.

Crosta, A.P., Souza Filho, C.R., Azevedo, F., Brodie, C., 2003. Targeting key alterationminerals in epithermal deposits in Patagonia, Argentina, using ASTER imageryand principal component analysis. International Journal of Remote sensing 24,4233–4240.

Crowley, J.K., Brickey, D.W., Rowan, L.C., 1989. Airborne imaging spectrometer dataof the Ruby Mountains, Montana: mineral discrimination using relativeabsorption band-depth images. Remote Sensing of Environment 29 (2), 121–134.

Di Tommaso, I.D., Rubinstein, N., 2007. Hydrothermal alteration mapping usingASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews32, 275–290.

Fujisada, H., 1995. Design and performance of ASTER instrument. In: Breckinridge,J.B. (Ed.), Proceedings of International Society of Optical Engineering, vol. 2583,pp. 16–25.

Gabr, S., Ghulam, A., Kusky, T., 2010. Detecting areas of high-potential goldmineralization using ASTER data. Ore Geology Reviews 38, 59–69.

Gad, S., Kusky, T., 2007. ASTER spectral ratioing for lithological mapping in theArabian–Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt.Gondwana Research 11, 326–335.

Geological Survey of Iran, 1973. Exploration for ore deposits in Kerman region.Report No. Yu/53.

Gersman, R., Ben-Dor, E., Beyth, M., Avigad, D., Abraha, M., Kibreba, A., 2008.Mapping of hydrothermally altered rocks by the EO-1 Hyperrion sensor,northern Danakil, Eritrea. International Journal of Remote Sensing 29 (13),3911–3936.

Green, A.A., Berman, M., Switzer, P., Craig, M.D., 1988. A transformation forordering multispectral data in terms of image quality with implications fornoise removal. IEEE Transactions on Geoscience and Remote Sensing 26 (1),65–74.

Hassan-Nezhad, A.A., Moore, F., 2006. A stable isotope and fluid inclusion study ofthe Qaleh-Zari Cu–Au–Ag deposit, Khorasan Province, Iran. Journal of AsianEarth Sciences 27, 805–818.

Hassanzadeh, J., 1993. Metallogenic and tectonomagmatic events in the SE sector ofthe Cenozoic active continental margin of central Iran (Shahr e Babak area,Keman Province). Ph.D. thesis: University of California, Los Angeles, Los Angeles,p. 204.

Hewson, R.D., Cudahy, T.J., Mizuhiko, S., Ueda, K., Mauger, A.J., 2005. Seamlessgeological map generation using ASTER in the Broken Hill–Curnamona provinceof Australia. Remote Sensing of Environment 99, 159–172.

Hezarkhani, A., 2006. Hydrothermal evolution of the Sar-Cheshmeh porphyry Cu–Mo deposit, Iran: evidence from fluid inclusions. Journal of Asian Earth Sciences28, 409–422.

Hubner, H., 1969a. Geological Map of Iran Sheet No. 5, Southcentral Iran: Tehran,National Iranian Oil Company, Scale 1:1000,000.

Hubner, H., 1969b. Geological map of Iran sheet no. 6, southeast Iran: Tehran,National Iranian Oil Company, scale 1:1,000,000.

Hunt, G., 1977. Spectral signatures of particulate minerals in the visible and nearinfrared. Geophysics 42, 501–513.

Hunt, G.R., Ashley, P., 1979. Spectra of altered rocks in the visible and near infrared.Economic Geology 74, 1613–1629.

Inzana, J., Kusky, T., Higgs, G., Tucker, R., 2003. Supervised classifications of LandsatTM band ratio images and Landsat TM band ratio image with radar forgeological interpretations of central Madagascar. Journal of African EarthSciences 37, 59–72.

Jensen, J.R., 2005. Introductory Digital Image Processing. Person Prentice Hall, UpperSaddle River, pp. 307 and 444.

Kargi, H., 2007. Principal components analysis for borate mapping. InternationalJournal of Remote Sensing 28 (8), 1805–1817.

Khan, S.D., Mahmood, K., 2008. The application of remote sensing techniques to thestudy of ophiolites. Earth-Science Reviews 89, 135–143.

Loughlin, W.P., 1991. Principal components analysis for alteration mapping.Photogrammetric Engineering and Remote Sensing 57, 1163–1169.

Lowell, J.D., Guilbert, J.M., 1970. Lateral and vertical alteration-mineralizationzoning in porphyry ore deposits. Economic Geology and the Bulletin of theSociety of Economic Geologists 65 (4), 373–408.

Mars, J.C., Rowan, L.C., 2006. Regional mapping of phyllic- and argillic-altered rocksin the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emissionand Reflection Radiometer (ASTER) data and logical operator algorithms.Geosphere 2 (3), 161–186.

Massironi, M.L., Bertoldi, P., Calafa, D., Visona, A., Bistacchi, C., Giardino, A., Schiavo,B., 2008. Interpretation and processing of ASTER data for geological mappingand granitoids detection in the Saghro massif (eastern Anti-Atlas, Morocco).Geosphere 4 (4), 736–759.

Modarres, R., Da Silva, V.P.R., 2007. Rainfall trends in arid and semi-arid regions ofIran. Journal of Arid Environments 70, 344–355.

Moghtaderi, A., Moore, F., Mohammadzadeh, A., 2007. The application of advancedspace-borne thermal emission and reflection (ASTER) radiometer data in thedetection of alteration in the Chadormalu paleocrater, Bafq region, Central Iran.Journal of Asian Earth Sciences 30, 238–252.

Moore, F., Rastmanesh, F., Asady, H., Modabberi, S., 2008. Mapping mineralogicalalteration using principal component analysis and matched filter processing inTakab area, north-west Iran, from ASTER data. International Journal of RemoteSensing 29 (10), 2851–2867.

Ninomiya, Y., 2003a. A stabilized vegetation index and several mineralogic indicesdefined for ASTER VNIR and SWIR data. In: Proceedings of the IEEE 2003International Geoscience and Remote Sensing Symposium (IGARSS’03), vol. 3,Toulouse, France, 21–25 July 2003, pp. 1552–1554.

Ninomiya, Y., 2003b. Advanced remote lithologic mapping in ophiolite zone withASTER multispectral thermal infrared data. In: Proceedings of the IEEE 2003International Geoscience and Remote Sensing Symposium (IGARSS’03), vol. 3,Toulouse, France, 21–25 July 2003, pp. 1561–1563.

Pour, B.A., Hashim, M., Marghany, M., 2011. Using spectral mapping techniques onshort wave infrared bands of ASTER remote sensing data for alteration mineralmapping in SE Iran. International Journal of the Physical Sciences 6 (4), 917–929.

Ranjbar, H., Honarmand, M., Mozafari, Z., 2004. Application of the Crosta techniquefor porphyry copper alteration mapping, using ETM+ data in the southern partof the Iranian volcanic sedimentary belt. Journal of Asian Earth Sciences 24,237–243.

Rockwell, B.W., Hofstra, A.H., 2008. Identification of quartz and carbonate mineralsacross northern Nevada using ASTER thermal infrared emissivity dataImplications for geologic mapping and mineral resource investigations inwell-studied and frontier areas. Geosphere 4 (1), 218–246.

Rowan, L.C., Mars, J.C., 2003. Lithologic mapping in the Mountain Pass, Californiaarea using Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER) data. Remote Sensing of Environment 84, 350–366.

Rowan, L.C., Hook, S.J., Abrams, M.J., Mars, J.C., 2003. Mapping hydrothermallyaltered rocks at Cuprite, Nevada, using the Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER), a new satellite-imaging system.Economic Geology 98 (5), 1019–1027.

Rowan, L.C., Robert, G.S., John, C., 2006. Distribution of hydrothermally altered rocksin the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTERdata. Remote Sensing of Environment 104, 74–87.

Ruiz-Armenta, J.R., Prol-Ledesma, R.M., 1998. Techniques for enhancing the spectralresponse of hydrothermal alteration minerals in Thematic Mapper images ofCentral Mexico. International Journal of Remote Sensing 19, 1981–2000.

Sabins, F.F., 1999. Remote sensing for mineral exploration. Ore Geology Reviews 14,157–183.

Shafiei, B., 2010. Lead isotope signatures of the igneous rocks and porphyry copperdeposits from the Kerman Cenozoic magmatic arc (SE Iran), and theirmagmatic-metallogenetic implications. Ore Geology Reviews 37 (1–2), 27–36.

Shahabpour, J., 2005. Tectonic evolution of the orogenic belt in the region locatedbetween Kerman and Neyriz. Journal of Asian Earth Sciences 24, 405–417.

Shahabpour, J., 2007. Island-arc affinity of the Central Iranian Volcanic Belt. Journalof Asian Earth Sciences 30, 652–665.

A.B. Pour, M. Hashim / Journal of Asian Earth Sciences 42 (2011) 1309–1323 1323

Singh, A., Harrison, A., 1985. Standardized principal components. InternationalJournal of Remote Sensing 6, 883–896.

Spatz, D.M., Wilson, R.T., 1995, Remote sensing characteristics of porphyry coppersystems, western America Cordillera. In: Pierce, F.W., Bolm, J.G. (Eds.), ArizonaGeological Society Digest, vol. 20, pp. 94–108.

Tangestani, M.H., Moore, F., 2002. Porphyry copper alteration mapping at theMeiduk area, Iran. International Journal of Remote Sensing 23 (22), 4815–4825.

Tangestani, M.H., Mazhari, N., Ager, B., Moore, F., 2008. Evaluating advancespaceborne thermal emission and reflection radiometer (ASTER) data foralteration zone enhancement in a semi-arid area, northern shahr-e-Babak, SEIran. International Journal of Remote Sensing 29 (10), 2833–2850.

Van Ruitenbeek, F.J.A., Pravesh, D., van der Meer, F.D., Cudahy, T., van der Meijde, M.,Hale, M., 2006. Mapping white micas and their absorption wavelengths usinghyperspectral band ratios. Remote Sensing of Environment 102, 211–222.

Velosky, J.C., Stern, R.J., Johnson, P.R., 2003. Geological control of massive sulfidemineralization in the Neoproterozoic Wadi Bidah shear zone, southwestern

Saudi Arabia, inferences from orbital remote sensing and field studies.Precambrian Research 123 (2–4), 235–247.

Waterman, G.C., Hamilton, R.L., 1975. The Sarcheshmeh porphyry copper deposit.Economic Geology 70, 568–576.

Xu, Y., Qizhong, L., Yun, S., Lu, W., 2004. Extraction Mechanism of Alteration Zonesusing ASTER Imagery. 0-7803-8742-2/04 (c) 2004 IEEE.

Yamaguchi, Y., Naito, C., 2003. Spectral indices for lithologic discrimination andmapping by using the ASTER SWIR bands. International Journal of RemoteSensing 24 (22), 4311–4323.

Yamaguchi, Y.I., Fujisada, H., Kudoh, M., Kawakami, T., Tsu, H., Kahle, A.B., Pniel, M.,1999. ASTER instrument characterization and operation scnorio. AdvancedSpace Research 23 (8), 1415–1424.

Zhang, X., Micha, P., Norman, D., 2007. Lithologic and mineral informationextraction for gold exploration using ASTER data in the south ChocolateMountains (California). Journal of Photogrammetry and Remote Sensing 62,271–282.