aster data analysis for mineral potential mapping around sawar-malpura area, central rajasthan by b....

RESEARCH ARTICLE

ASTER Data Analysis for Mineral Potential MappingAround Sawar-Malpura Area, Central Rajasthan

B. K. Bhadra & Suparn Pathak & G. Karunakar &

J. R. Sharma

Received: 24 February 2012 /Accepted: 27 September 2012 /Published online: 1 November 2012# Indian Society of Remote Sensing 2012

Abstract Advanced Spaceborne Thermal Emissionand Reflection Radiometer (ASTER) SWIR bandsare used in identification of alteration zones whichhave developed during hydrothermal activity. Amongthe available methods of hyperspectral data analysis,PCA and RBD techniques are found to be useful indelineation of clay alteration and iron oxide zones.ASTER data analysis by PCA and RBD of (B5+B7)/B6 shows delineation of two distinct alteration zoneswith characteristic mineral assemblages viz. propyliticzone (chlorite, epidote, montmorillonite and calcite)and phyllic zone (illite, kaolinite, white mica andquartz). Iron oxide rich zones (gossans) have beendelineated using ASTER band ratio technique (B2/B1). Geochemical dispersion of soil samples showsthat Pb and Zn concentration is higher in phyllic andpropylitic zones around Sawar and Malpura area re-spectively. Thus, ASTER data shows the potential indiscrimination of metasedimentary rocks and delinea-tion of alteration zones for targeting base metalsaround Sawar-Malpura area in central Rajasthan.

Keywords ASTER . Principal Component Analysis(PCA) . Relative absorption-Band Depth (RBD)Ratio . Clay Alteration zones . Geochemical Analysis

Introduction

Multispectral as well as hyperspectral satellite data areused to identify a variety of rocks and minerals viz.OH-bearing minerals, carbonates, sulphates, olivines,pyroxenes, iron oxides and hydroxides (Gupta 2003;Rowan and Mars 2003; Zhang and Pazner 2007; Liu etal. 2011). However, Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER) datawith specific bands are suitable for detecting ironoxides in VNIR band (0.4–1.0 μm), hydroxide-bearing clay and carbonate minerals in SWIR band(0.9–2.5 μm) and silica in TIR band (8–14 μm).Hydrothermal alteration zones due to Pb-Zn-Cu sul-phide mineral deposits have distinct alteration zones(Plumlee et al. 1995; Azizi et al. 2007) with a set ofmineralogical assemblage viz. Propylitic Zone andPhyllic Zone. In a typical model of epithermal sul-phide mineral deposit (Plumlee et al. 1995), a centralargillic zone is flanked by distal phyllic and propyliticzones with decreased Cu and As abundances andincreased Zn and Pb abundances. Spectral patternof different OH-bearing clay minerals shows grad-ual decrease in reflectance (%) from kaolinite,montmorillonite, illite to alunite (Ranjbar et al.2003). Thus, identification of alteration zones (iron

J Indian Soc Remote Sens (June 2013) 41(2):391–404DOI 10.1007/s12524-012-0237-0

B. K. Bhadra (*) : S. Pathak : J. R. SharmaRegional Remote Sensing Centre (West), NRSC/ISRO,CAZRI Campus,Jodhpur 342003, Indiae-mail: [email protected]

G. KarunakarHindustan Zinc Ltd,Udaipur 313004, India

oxides and clay minerals) is important in targetingany sulphide mineral deposit.

Potential areas of hydrothermal alteration mineralscan be assessed through image processing techniquessuch as Band Ratioing and PCA. PCA is a powerfulstatistical technique that can be used for suppressingbackground reflectance to enhance spectral reflectancefeatures of geological materials (Crosta et al. 2003).This technique is widely used by Crosta and Moore(1989), Loughlin (1991), Hubbard and Crowley (2001),Crosta et al. (2003), Rowan and Mars (2003), Ranjbar etal. (2003), Rouskov et al. (2005), Azizi et al. (2007),Baloki and Poormirzaee (2009) and others. Crosta andMoore (1989) developed a technique based on PCA formapping iron oxide/hydroxides related sulphide ore bod-ies. The technique, called ‘Feature-oriented PrincipalComponent Selection (FPCS)’, is based on the relation-ship between the spectral responses of target materials(ferric-oxide-rich soils) and numeric values extractedfrom the eigenvector matrix, used to calculate the princi-pal component (PC) images. Using this relationship, theywere able to discriminate which PCs contained the spec-tral information due to iron minerals and whether theDNs of pixels containing the target materials had high(bright) or low (dark) values. Loughlin (1991) modifiedFPCS technique by selecting specific Landsat TM bandsets and applying PCA separately to them, to ensure thatcertain materials (e.g. vegetation) would not be mappedand that spectral information due to target materials (al-teration minerals) would be mapped into a single PC. Liuet al. (2011) observed that band combination withLandsat TM-1, 3, 4 and 5 are suitable for ferric oxides/hydroxides and Landsat TM-1, 4, 5 and 7 for hydroxyl-bearing minerals and carbonates, as shown by PC3 orPC4 due to highest eigenvector loading. PCA techniqueis also used for preparing mineral abundance images forkaolinite, illite and alunite using ASTER band combina-tions. Hubbard and Crowley (2001) shows the potentialof AVIRIS data with Hyperion, ALI and ASTER data formapping alteration minerals in the Central Andes using acombination of spectral shape-fitting and partial spectralunmixing algorithms. Crosta et al. (2003) used the PCAtechnique for targeting key alteration minerals in epither-mal deposits in Patagonia, Argentina. Rowan and Mars(2003) used different RBD ratio images to distinguishlimestone (Ca-CO3 by (B7+B9)/B8), dolomite (Ca, Mg-CO3 by (B6+B8)/B7), muscovite (Al-O-H by (B5+B7)/B6) and hematite/goethite (Fe+3 by B2/B1). Ranjbar et al.(2003) have compared ASTER and ETM data over

porphyry copper mineralization in Sar Cheshmesh areasof Iran and found that ASTER data has better capabilityfor recognition of hydrothermal alteration and delineationof iron oxide and hydroxyl minerals through PC transfor-mation technique. Simon et al. (2004) used Landsat TM,ASTER and Hyperion data to identify hydrothermal al-teration zones around Bull Creek area, Mount Isa Block.Band ratios and spectral mixing techniques are the prom-ising techniques to discriminate between different lithol-ogies and mineralogies. Band ratio technique has beenused by Rouskov et al. (2005) to identify iron oxides andclay alteration minerals in the area of Assarel and Medetporphyry Cu ore deposits. ASTER VNIR-SWIRreflectance data and spectral matched-filter pro-cessing were used to map several lithologicsequences characterized by distinct suites of min-erals that exhibit diagnostic spectral features (e.g.chlorite, epidote, amphibole and other ferrous-iron bear-ing minerals); other sequences were distinguished bytheir weathering characteristics and associated hydroxyland ferric-iron minerals, such as illite, smectite, andhematite (Hubbard et al. 2007).

The main objective of the present study is to delin-eate mineral potential zones for base metal depositsusing remote sensing and GIS techniques. Further,satellite derived alteration zone maps are integratedwith the field data such as litho-tectonic structures,field spectral characteristics, geochemical analysis ofsoil samples, clay-mineralogy through XRD etc.

Study Area

The study area lies around Sawar-Malpura area in partsof Ajmer and Tonk districts of Rajasthan (Fig. 1).Lithological map of Geological Survey of India (GSI2001) shows that Sawar-Malpura area is comprised ofoldest basement rocks of Magalwar Complex (quartzite/schist, granite/migmatite gneiss, dolomite/calc-silicaterocks etc.), belonging to Bhilwara Supergroup ofArchaean age (Table 1).

Sawar metasedimentary rocks (marble, in micaschist, quartz reef and silicified quartzite) form theProterozoic cover over the basement gneissic rocks(Fig. 2). A vast stretch of the older rocks are overlaidby alluvium of Quaternary Formation. The central partis covered by a huge water body (Bisalpur reservoir)which is formed by dam construction across the flowingwater of Banas River.

392 J Indian Soc Remote Sens (June 2013) 41(2):391–404

Structural disposition of Sawar area shows large-scale complex fold geometry as revealed by upper andlower marble bands (Ray 1988). An antiformal closurenear Sawar and synformal closure near Bajta are thedominant fold features of the area. The Sawar meta-sedimentary sequence forms a large basin structure,developed by superposed folding. The Sawar areacomprised of marble and schist rock exposures withknown deposits of Pb-Zn-Cu minerals (Fig. 3).Genesis of this deposit is believed to be sedimen-tary exhalative (SEDEX) type.

Drilling and geochemical analysis of core samplesand field exposures show significant concentration ofsulphide mineral deposit around Sawar-Bajta area(Fig. 4a to f). However, Malpura area is mostly cov-ered with soil with numerous tiny exposures of granite

gneiss which is present as basement rock. This area isnot known for any significant sulphide mineral deposits,so far. Sulphidemineral deposits such as porphyry Cu orPb-Zn sulphides have very complex alteration systemscontaining several types and combinations of alterationsuits. Each alteration type (Azizi et al. 2007) has fairlydistinctive mineralogy viz. Propylitic Zone (Chlorite,Epidote, Zeolite, Montnorillonite, Illite, Carbonate),Potassic Zone (Feldspar, Biotite, Phlogopite, Chlorite,Vermiculite, Anhydrite, Gypsum), Phyllic Zone (Illite,Muscovite, Kaolinite, Quartz), Argillic Zone (Kaolinite,Smectite/Montmorillonite, Zunite, Diaspore, Topaz),Advanced Argillic (Pyrophyllic, Dickite, Alunite,Zunite, Diaspore, Topaz). An attempt has been madeto identify these alteration zones around Sawar-Malpuraarea with similar litho-tectonic set-up.

RAJASTHAN

Fig. 1 Location map of the study area around Sawar-Malpura area in central Rajasthan

J Indian Soc Remote Sens (June 2013) 41(2):391–404 393

Methodology

Data Used

In the present study, satellite data of IRS-P6 LISS-III(23.5 m) & LISS-IV (5.8 m), Landsat ETM (30 m) and

ASTER (30 m) have been used. GIS layers have beencreated for lithology, structures, geomorphology, soiland base map information by image interpretationfrom LISS-III, LISS-IV and ASTER images.Multispectral satellite data from IRS LISS-III andLISS-IV have proved to be useful in lithological

Table 1 Stratigrahpy of Sawar-Malpura Area (Source: Ray 1988 and GSI 2001)

Litho code Lithology Formation Stratigrahpy

Al Alluvium and aeolian sands Alluvium Formation Quaternary

Q Quartz Reef and silicifiedquartzite

Vein quartz andsilicified zone

Intrusive

P Pegmatite

Pt1bsm2 Quartz-biotite schist,Amphibolite, Quartzite

Upper Schist Member Morhi Formation Sawar Group Bhilwara Supergroup(Lower Proterozoic)

Pt1bsm1 Marble, Bandedcalc-silicate marble

Upper Marble Member Morhi Formation Sawar Group Bhilwara Supergroup(Lower Proterozoic)

Pt1bsrg2 Quartz Biotite Magnititeschist (Calcareous)

Lower Schist Member Ghantiyali Formation Sawar Group Bhilwara Supergroup(Lower Proterozoic)

Pt1bsrg1 Marble, Calc-Silicate marbleand thin quartzite

Lower Marble Member Ghantiyali Formation Sawar Group Bhilwara Supergroup(Lower Proterozoic)

Abm Granitoid gneiss, Biotite gneisswith Pegmatite, Amphiboliteand Calc-gneiss

Magalwar Complex Kekri Formation MangalwarComplex

Bhilwara Supergroup(Lower Proterozoic)

Sawar

ASTER Image (May, 2006-

07)

Malpura

Geological Map

Fig. 2 ASTER image and geological map of Sawar-Malpura area (GSI 2001), showing different lithological units


mapping in parts of Rajasthan (Bhadra et al. 2007 and2008). However, ASTER data have been analyzed(Bhadra et al. 2009) exclusively for lithological dis-crimination, identification of mineral alteration andgossan zones related to base metal deposits aroundSawar-Malpura area. In the study area, eight scenes ofASTER Level-1B product of May, 2006 and 2007 havebeen used. Detailed specifications of these ASTER

datasets are given in Table 2. The techniques used inASTER data processing are described below.

Principal Component Analysis (PCA) Technique

The Principal Component Analysis (PCA) is a multi-variate statistical technique that selects uncorrelated lin-ear combinations (eigenvector loadings) of variables in

(b) (a)

Sawar

Bajta Bajta

Sawar

ASTER Image (May, 2006-

07)

Location of base metals (Pb, Zn, Cu, Ni) occurrence

NS - Not Significant conc.

Lithological Units

Reef quartz and silicified quartzite

Lower Marble Member

Lower Schist Member

Mangalwar Complex (Gneiss)

Upper Marble Member

Upper Schist Member

Fig. 3 a ASTER image showing different rock exposures around Sawar-Bajta area. b Geological map of Sawar-Bajta area withlocation of base metal concentration (>500 ppm). (Source: Ray 1988)

Fig. 4 (a) Sawar Hill asviewed from Deoli-Sawarroad. (b) Marble with veinsof toumaline and specksof pyrite/sphalerite/pyrrho-tite, S of Sawar (c) Pb, Zn,Cu mineralization alongveins in marble quarry,south of Sawar (d) Stainsof malachite on epidotisedschist surface, old workingpit, east of Sawar (e)Jerosite/calcite in quartzreef, NE of Tikhi Hill (f)Mineralized zone (gossan?)in quartz reef, NE of TikhiHill


such a way that each successively extracted linear com-bination, or principal component, has a smaller variance(Smith 2002; Ranjbar et al. 2003). Principal Component(PC) is used to produce uncorrelated output bands, tosegregate noise components and to reduce the dimen-sionality of data sets. Because multi-spectral data bandsare often highly correlated, the principal componentstransformation is used to produce uncorrelated outputbands. This is done by finding a new set of orthogonalaxes that have their origin at the data mean and that arerotated so the data variance is maximized. PC bands arelinear combinations of the original spectral bands andare uncorrelated. One can calculate the same number ofoutput PC bands as input spectral bands. The firstPC band contains the largest percentage of datavariance and the second PC band contains the sec-ond largest data variance, and so on. The last PCbands appear noisy because they contain very littlevariance, much of which is due to noise in theoriginal spectral data.

PC generated from the ASTER band combinationsis used to generate eigenvector matrix (including + veand –ve values) for each subset. The PC having high-est eigenvector loadings coincide with the target’smost diagnostic features which are highlighted afterappropriate thresholding. Standard spectral curves ofNASA/JPL (resampled from ASTER) for the constit-uent minerals of phyllic and propylitic alteration zoneshave been referred as a reference (Fig. 5) for selectionof a PC (after Azizi et al. 2007). The spectral patternshows good absorption in B7 and B8 and high reflectionin B5 and B6 for propylitic minerals (Chlorite, Epidote,Montmorillonite and Carbonate). On the contrary, phyl-lic minerals (Muscovite, Kaolinite, Alunite, Illite andQuartz) show good absorption in B6 and high reflectionin B5 and B7. Thus, B6 is a unique band for discrimi-nation of phyllic and propylitic zones.

Crosta et al. (2003) used ASTER SWIR data forspectral characterization of surface targets such asphyllosilicates (clay minerals), sulphates and

Table 2 Specification of ASTER Satellite Data, used in the study area

Satellite/Sensor Spectral Bands withWavelength Range (μm)

Resolution (m) Swath (Sq. km) Product/Scene ID Path/Row Date of pass

Terra ASTER 14 Bands ASTER level 1B

VNIR Bands (ASTER-1 to 3)(0.4–1.0 μm)

15 m 60×60 1. ID: 2043052189 147/41 12.5.2007

2. ID: 2043052194 147/41 12.5.2007

3. ID: 2043052198 147/42 12.5.2007

SWIR Bands (ASTER-4 to 9)(0.9–2.5 μm)

30 m 60×60 4. ID: 2043052199 147/42 12.5.2007

5. ID: 2034410229 147/41 25.5.2006

6. ID: 2034410232 147/41 25.5.2006

TIR Bands (ASTER-10 to 14)(8.1–11.0 μm)

90 m 60×60 7. ID: 2034410233 147/42 25.5.2006

8. ID: 2034410236 147/42 25.5.2006

Fig. 5 NASA/JPL stand-ards spectral curves(resampled from ASTER)for constituent minerals of(a) phyllic and (b) propyliticalteration zones (Source:Azizi et al. 2007)


carbonates. PCA is one of the important image pro-cessing techniques for the extraction of spectral infor-mation related to the alteration zones. However,contextual information should be corroborated to se-lect a suitable PC image output, depicting the spatialdistribution of a particular alteration mineral.

Relative absorption-Band Depth (RBD) RatioTechnique

Band ratio and Relative absorption-Band Depth (RBD)ratio techniques involve band arithmetic with the given

knowledge of the absorption features of a target mineral.Band rationing is a simple operation with division oftwo bands of highest reflectance and highest absorptionof the same material, which is given by

Band Ratio ¼ Band with high reflectance feature

Band with high absorption feature

Ratio images are suitable to display the spectralcontrast of specific absorption features which havebeen used extensively in geological remote sensing.The combination of three band ratio images as Red-

Fig . 6 a VNIR-SWIRbands (ASTER-5,3,2) ableto discriminate betweenlower (whitish yellow) andupper (sea green) marblebands. b SWIR (ASTER-6,4,8) bands discriminatemarble bands (yellow greentone) and quartz reefs (seagreen)

Fig. 7 a FCC of ASTERPC-2,3,4 with histogramequalization stretchinghighlights the hinges of theupper marble band. b BandDepth Ratio for ASTER(B5+B7)/B6 with linearStretching (2 %) discrimi-nate marble bands (darkblue) and silicified quartzreefs (yellowish red)


Table 3 Eigenvector Statistics for PC of ASTER SWIR Bands for Alteration Zones

Bands (wavelength, μm) PC1 PC2 PC3 PC4 PC5 PC6

Band 4 (1.600–1.700) 0.873 0.275 0.259 0.241 0.162 0.101

Band 5 (2.145–2.185) 0.460 -0.373 -0.362 -0.369 -0.458 -0.414

Band 6 (2.185–2.225) 0.148 -0.272 -0.217 -0.460 0.212 0.775

Band 7 (2.235–2.285) 0.046 -0.814 0.129 0.430 0.351 -0.100

Band 8 (2.295–2.365) 0.037 0.169 -0.851 0.472 0.140 0.057

Band 9 (2.360–2.430) -0.022 -0.137 0.117 0.432 -0.759 0.452

Covariance Eigenvalue 218.536 0.099 0.011 0.004 0.003 0.001

Variance % 45.65 14.39 13.57 12.58 8.51 5.3

Eigenvector Loadingfor Propylitic Minerals

Highest -ve (Reflectionin B5 or B6)

B5 (None) B5 (-0.373) B5 (-0.362) B6 (-0.460) B6 (-0.458) B5 (-0.414)

Highest +ve (Absorptionin B7 or B8)

B7 (0.046) B8 (0.169) B7 (0.129) B8 (0.472) B7 (0.351) B8 (0.057)

Loading Range Nil 0.542 0.491 0.932 0.809 0.471

Highest Loadingfor Phyllic Minerals

Highest +ve (Reflectionin B5 or B7)

B5 (0.460) B5 or B7(None)

B7 (0.129) B7 (0.430) B7 (0.351) B5 or B7(None)

Highest-ve (Absorptionin B6 or B9)

B9 (0.022) B6 (-0.272) B6 (-0.217) B6 (-0.460) B9 (-0.759) None

Loading Range 0.482 Nil 0.346 0.890 1.110 Nil

Fig. 8 Phyllic alterationzones are identified fromPCA technique with SWIRbands (ASTER-4,5,6,7,8,9).PC-5 image with 2 % linearstretching, smooth medianfiltering (3×3) and ENVIColor Table (BGRY) showspossible Phyllic alterationzones in Sawar-Malpuraarea


Green-Blue (RGB) is very useful for the interpretationof the results.

RBD is the representation of three-point ratio for-mulation for displaying Al-O-H, Mg-O-H and CO3

absorption intensities (Crowley et al. 1989; Rowan andMars 2003). RBD images have the numerator as the sumof the bands representing the shoulders (Bands 1 and 3)and the denominator as the band located nearest theabsorption feature minimum (Band 2).

RBD ¼ Band 1þ Band 3

Band 2

RBD is a good method for removing albedo andtopographic effects and is known as ‘continuum-re-moval’ procedure. The continuum consists of thebackground reflectance which extrapolates the base-line of the general curve i.e. it fits a smoothed curve tothe general trend so as to extend across the base ofabsorption bands. This local reduction specifies the

continuum and is determined by mathematic opera-tion. Several RBD images may be created to highlightcertain minerals like Al-O-H, Mg-O-H and CO3 bear-ing minerals. The RBD with (B7+B9)/B8 is a suitablecombination to highlight CaCO3 and Mg-O-H bearingminerals by the sharp absorption feature at B8 com-pared to B7 and B9. Similarly, RBD images with (B5+B7)/B6 highlight Al-O-H bearing clay minerals dueto strong absorption feature in band 6.

Results and Discussion

ASTER Data Analysis for Mineral Potential Zones

In the present study, ASTER data have been analysedfor discrimination of rocks, altered clay minerals andgossans with identification of base metals aroundSawar-Malpura area in central Rajasthan. Amongstthe available methods of hyperspectral data analysis,

Fig. 9 PCA technique withSWIR bands (ASTER-4,5,6,7,8,9) discriminate twopossible hydrothermal alter-ation zones (Propylitic andPhyllic) in Sawar-Malpuraarea in RGB image (PC-5,PC-4 and PC-3 combina-tion) with histogram equal-ization and smooth medianfiltering (3×3)


PCA and RBD techniques are found to be useful fordelineation of clay alteration zones and iron oxidezones.

(a) Lithology—It has been observed that images withVNIR (ASTER-3, 2 and 1) as well as VNIR-SWIR (ASTER-5, 3 and 2) band combinationsare able to discriminate between Lower Marbleand Upper Marble bands (Fig. 6a). Similarly,SWIR bands (ASTER-6, 4 and 8) are found tobe suitable in discriminating marble and quartzreef (Fig. 6b). Similar lithological discriminationis also observed using PC and BDR images(Fig. 7a and b).

(b) Alteration zones—Out of the 14 bands ofASTER data, SWIR bands show absorption fea-tures by the OH-bearing minerals which are in-dicative of hydrothermal alteration zones. Hence,

in discriminating different alteration zones, 6 PCimages with eigenvector statistics have been gen-erated using SWIR Bands (ASTER-4, 5, 6, 7, 8,9). The eigenvector matrix (Table 3) was used tocalculate PCA to identify PC containing themineral information. The first PC does not con-tain relevant spectral features, as it is a combina-tion of all bands having variance of 45.65 %.PC1 gives information on albedo and topogra-phy. However, the PC that has the highest eigen-vector (either + ve or –ve eigenvalue) isimportant for discrimination of alteration zonessuch as phyllic and propylitic. Eigenvector load-ing (highest +ve, highest –ve and their maxi-mum range) have been calculated for phyllicand propylitic minerals respectively.

Since spectral patterns of propylitic minerals(Fig. 5) show reflection in B5 andB6 in comparison

Fig. 10 ASTER Band Ratio(B2/B1) with histogramstretching and median filter-ing showing brighter areas(yellow-red) as Fe-richzones (Gossans?) in (a)Sawar-Malpura area (b)Sawar area and (c) Thresh-olded Fe zones aroundSawar


to absorption in B7 and B8, highest –ve andhighest +ve eigenvalues of the correspondingbands have been selected while choosing thebest suitable PC for the propylitic alterationzone. Similarly, highest +ve and highest -veeigenvalues with corresponding high absorptionin B6 or B9 and high reflection in B5 and B7have been selected for phyllic alteration zone.

At the same time, highest loading ranges ofthe respective propylitic and phyllic zones havebeen calculated. Thus, characteristic features ofthese two alteration zones are illustrated byeigenvalues with opposite sign. From the givenmatrix, it has been observed that highest loadingrange for propylitic and phyllic zones corre-sponds to PC4 (0.932) and PC5 (1.110) respec-tively. For example, PC5 image around Sawararea highlighted the phyllic alteration zone,marked by quartz reef and silicified quartzite(Fig. 8). It has been observed that colour com-posite in RGB mode (R0PC5, G0PC4 and B0PC3) shows two distinct alteration zones viz.Propyllitic zone in bluish green colour and phyl-lic zone in magenta colour (Fig. 9).

Similarly, Band Depth Ratio (BDR) imageswith (B5+B7)/B6 are found to be useful indiscriminating Phyllic and Propylitic alterationzones. Because of high reflection in B5 andB7 and high absorption in B6, Phyllic zoneappears as bright white. In contrary, low reflec-tion in B7 and B8 and high reflection in B5and B6, Propylitic alteration zone appearsdarker than Phyllic zone. The demarcated alter-ation zones on BDR image (B5+B7)/B6 (Fig. 7b)is well matched with the RGB image of PC-5, 4and 3 (Fig. 9).

In the study area, phyllic alteration zones havebeen identified along the patches of silicifiedquartzite and quartz reef in Sawar-Tikhi rangeof metasedimentary rocks as well as a fewexposures around Tordi village. The propyliticalteration zones are identified mainly in thenorth in Malpura area, having basement rocksof granite-migmatite gneiss.

(c) Gossan Zones—Gossan zones are the leached andoxidized near-surface part of a concealed deposit,containing base metal sulphides. ASTER bandratio images have been used in delineating Fe-rich zones which may be a part of the gossan of

Pb-Zn-Cu mineralization. In the present studyarea, B2/B1 ratio images are produced to showthe distribution of Fe+3 absorption (Fig. 10a).Histogram stretching with appropriate threshold-ing of the B2/B1 image product shows isolatedpatches of possible gossan zone in the basementgneissic rocks around Sawar (Fig. 10b, c).

Field verification at two places near Tikhi Hill andBajta village in Sawar area indicate the presence offerruginous substance along with suphide bearing

Fig. 11 Location of soil sample overlaid on ASTER image ofSawar-Malpura area


quartzose rocks (possible gossan zones). As these twolocalities also confirm the presence of base metaldeposits, it is expected that similar spectral signaturein the northern part belongs to gossan-like features. Afew patches of iron oxide rich zones are also foundaround the exposed quartzite/schist in the north at fewlocalities in Malpura area.

Geochemical Data Analysis

Hydrothermal alteration zones such as propylitic andphyllic zones have been identified from PC analysis ofASTER SWIR bands. Around 30 soil samples werecollected in a grid pattern from Sawar-Malpura area(Fig. 11) and their elemental concentration in each

Fig. 12 Contour pattern of elemental distribution of base metals (a) Pb (b) Zn and (c) Cu in soil samples, overlaid on geological maparound Sawar-Malpura area. Enlarge view of Pb, Zn, Cu distribution patterns in Sawar area are also shown


samples were analysed. The dispersion pattern of themajor base metals (Pb, Zn and Cu) along with theoccurrence of clay minerals is studied for any possiblecorrelation with the alteration zones. Illite and kaolin-ite are commonly found around Sawar and Deoli area.Montmorillonite is found around Malpura and TodaRai Singh area and a few places around Sawar. Mica,chlorite and calcite are the common minerals foundthroughout the area.

The delineated alteration zones are also verifiedwith the major metal concentration of soil samples.Contour pattern has been generated for 3 base metals(Pb, Zn and Cu) which are the target minerals of thestudy area (Fig. 12a, b and c). It has been observedthat Pb and Zn concentration is higher in phyllic andpropylitic zones around Sawar area where significantPb (~156 ppm) and Zn (~98 ppm) concentration isfound in the soil samples. Geological set-up indicatesthat Tikhi Hill area comprised of silicified quartz withreef but the southern Malpura area has smaller plutonsof granitic gneisses. Occurrence of Pb concentrationaround Tikhi Hill is quite known (Ray 1988). But thePb (~59 ppm) and Zn (~77 ppm) concentration ofsouthern Malpura area is quite new which might hassome affinity with the granitic plutons. Hence, the areahas to be re-looked for any occurrence of altered/gossan zone. Apart from some old workings area, Cuconcentration is found to be higher in and aroundTikhi Hill.

Conclusions

ASTER data is very much useful in lithological dis-crimination such as lower and upper marble bands,marble and quartz reef etc. PCA and RBD techniquesare found to be useful for delineation of alterationzones and iron oxide zones. Hydrothermal alterationzones such as Propylitic zones and Phyllic zones canbe identified from PC analysis of ASTER SWIRbands. The same is also found true in BDR techniquewith ASTER bands i.e. (B5+B7)/B6. The delineatedzones are verified with the metal concentration of soilsamples. It has been observed that Pb and Zn concen-tration is higher in phyllic and propylitic zones aroundSawar and Malpura area respectively. Apart fromknown Sawar deposits, Malpura is the new prom-ising area where detailed exploration work can betaken up.

Acknowledgements Authors are thankful to Dr. V. K.Dadhwal, Director and Dr. Y.V.N Krishnamurthy, Dy. Director(RS-AA), NRSC, Hyderabad for their constant inspiration tocarry out hyperspectral data analysis. Fruitful discussion withDr. Vinod Kumar, NRSC, Hyderabad and Dr. A. K. Joshi,RRSC, Nagpur was very much helpful in this study. Activeassociation with Shri L.K. Gurjar and Shri N. Kavadia, HZLOfficials, Udaipur is highly acknowledged. Dr. A. K. Gupta, Ex.ISRO Scientist, RRSC-W helped during conceptualization ofthe project.

References

Azizi, H., Rsaouli, A. A., & Babaei, K. (2007). Using SWIRBands from ASTER for discrimination of HydrothermalAltered Minerals in the Northwest of Iran (Se-SanandajCity): A Key for Exploration of Copper and Gold Minerali-sation. Research Jour. of Applied Sciences, 2(6), 763–768.

Baloki, M., & Poormirzaee, R. (2009). Using ASTER ImageProcessing for Hydrothermal Alteration and Key AlterationMinerals Mapping in Siyahrud. Iran. Int. Jour. of Geology,3(2), 38–43.

Bhadra, B. K., Gupta, A. K., Sharma, J. R., & Chaudhary, B. R.(2007). Mining activity and its impact on the environment:Study from Makrana marble and Jodhpur sandstone miningareas of Rajasthan. Jour. Geol. Soc. of India, 70, 557–570.

Bhadra BK, Pathak S and Sharma JR (2008) High ResolutionSatellite data for large-scale geological mapping, DEMgeneration and mine planning around Beawar LimestoneBelt, Ajmer district, Rajasthan. In: Proceedings of ISRSSymposium, Ahmedabad, Paper No.3-3-075, pp.1-12.

Bhadra BK, Pathak S and Sharma JR (2009) ASTER dataanalysis for mineral potential mapping around Sawar.Ajmer district, Rajasthan. In: Abstract Volume of ISRSSymposium, Nagpur, 17-19th Sep., 2009, pp.129–130.

Crosta AP and Moore J McM (1989) Enhancement of LandsatThemartic Mapper imagery for residual soil mapping inSW Minas Gerais State Brazil: A prospecting case historyin greenstone belt terrain. In: Proceedings of the 9th The-matic Conference on Remote Sensing for Exploration Ge-ology, Calgary, pp.1173-1187.

Crowley, J. K., Brockely, D. W., & Rowan, L. C. (1989).Airborne imaging spectrometer data of the Ruby Moun-tains, Montana: Mineral discrimination using relative ab-sorption band-depth images. Remote Sensing ofEnvironment, 29, 121–134.

Crosta, A. P., De Souza Filho, C. R., Azevedo, F., &Brodie, C. (2003). Targeting key alteration mineralsin epithermal deposits in Patagonia, Argentina, usingASTER imagery and PCI. Int. Jour. Remote Sensing,24(21), 4233–4240.

GSI Map (2001) District Resource Maps of Ajmer, Tonk, Jaipurand Bhilwara districts. Publ. Geological Survey of India.

Gupta, RP (2003) Remote Sensing Geology, 2nd Edition.Springer-Verlag: berlin, Heidelberg, New York, 656p.

Hubbard, BE and Crowley JK (2001) Alteration mineral map-ping in the Central Andes using HYPERIAN, ALI andASTER. Paper No. 134–0.


Hubbard, B. E., Rowan, L. C., Dusel-Bacon, C., & Eppinger, R.G. (2007). Geologic mapping and mineral resource assess-ment of the Healy and Talkeetna Mountains Quadrangles,Alaska using minimal cloud- and snow-cover ASTER data.USGS Open-File Report, 2007–1046, 18p.

Liu, L., Zhuang, D., Zhou, J., & Qiu, D. (2011). Alterationmineral mapping using masking and Crosta technique formineral exploration in mid-vegetated areas: A case study inAreletuobie, Xinjiang (China). Int. Jour. of Remote Sens-ing, 32(7), 1931–1944.

Loughlin, W. (1991). Principal component analysis for alter-ation mapping. Photogrammetric Engineering and RemoteSensing, 57, 1163–1169.

Plumlee GS, Gray JE, Roeber MM, Coolbaugh M, Flohr M andWhitney G (1995) The importance of geology in under-standing and remediating environmental problems at Sum-mitville. In: Posey HH, Pendleton JA and Van Zyl D (Eds):Proceedings, Summitville Forum '95, Colorado, and Geol.Surv. Spl. Publ. No. 38, pp. 13–22.

Ranjbar, H., Shahriari, H., & Honarmand, M. (2003).Comparisonof ASTER and ETM data for exploration of porphyry coppermineralization: A case study of Sar Cheshmesh areas. Ker-man, Iran: Map Asia Conference in 2003.

Ray SK (1988) Report on Prospecting and Exploration ofLead-Zinc and Copper-Gold Mineralised Zones in

Sawar-Bajta area, Ajmer District, Rajasthan. Geol. Surv.of India Unpublished Report for F.S. 1982–83 to 1986–87, pp.1–61.

Rouskov K, Popov K, Stoykov S and Yamaguchi Y (2005) SomeApplications of Remote Sensing in Geology by using AS-TER Images. In: Proceedings of Scientific Conference‘Space, Ecology, Safety’, Varna, Bulgaria, pp.167-173.

Rowan, L. C., & Mars, J. C. (2003). Lithologic mapping in theMountain Pass, California area using Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER) data. Remote Sensing and Environment, 84,350–366.

Smith (2002) A Tutorial on Principal Component Analysis.Source: www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

Simon van der Wielen, Simon Oliver and Aleks Kalinowski(2004) Remote sensing and spectral investigations in theWestern Succession, Mount Isa Inlier: Implications forexploration. CRC Conference, Barossa Valley, 1–3 June2004, pp.205-208.

Zhang, X., & Pazner, M. (2007). Comparison of lithologicmapping with ASTER, Hyperion, and ETM Data inthe Southeastern Chocolate Mountains, USA. Photo-grammetric Engineering & Remote Sensing, 73(5),555–561.


http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

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