Research ArticleIdentification of Rocks and Their Quartz Content inGua Musang Goldfield Using Advanced Spaceborne ThermalEmission and Reflection Radiometer Imagery

Kouame Yao Biswajeet Pradhan andMohammed Oludare Idrees

Department of Civil Engineering Faculty of Engineering Universiti Putra Malaysia (UPM) 43400 Serdang Selangor Malaysia

Correspondence should be addressed to Biswajeet Pradhan biswajeet24gmailcom

Received 17 January 2017 Accepted 20 February 2017 Published 7 March 2017

Academic Editor Hyung-Sup Jung

Copyright copy 2017 Kouame Yao et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Quartz is an important mineral element and the most abundant rock-forming mineral that controls the mineralogy of a reservoirAt the surface quartz is more stable than most other rock minerals because it is made up of interlocking silica that makes itquite resistant to mechanical weathering Quartz abundance is an indication of mineralization in many metal deposits thereforeidentification and mapping of quartz in rocks are of great value for exploration and resource potential assessments In this studythermal infrared (TIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery wereused to identify quartz contained rocks in Gua Musang First the image was corrected for atmospheric effect and the study areasubset for further processing Thereafter spectral transformation (principal component analysis (PCA)) was implemented onthe TIR bands and the resulting principal component (PC) images were analysed The three optimal PCs were selected usingthe strength of spectral interaction and the eigenvalues of each band To discriminate between quartz-rich and quartz-poorrocks RGB false colour composite and greyscale image of one of the PCs were analysed The result shows that volcanogenicigneous rock and carbonate sedimentary rocks of Permian formation are quartz-poor while Triassic sedimentary rock made upof organic particles and sandstone is quartz-rich On the contrary the quartz content in the metamorphic rock varies across thearea but is richer in quartz content than the igneous and carbonate rocks Classification of the composite image classified usingmaximum likelihood (ML) supervised classification method produced overall accuracy and Kappa coefficient of 9653 and 095respectively

1 Introduction

Malaysia is rich in mineral resources with a long history ingold exploration dated as far back as 1400s According to theMalaysia 2014 Annual Mining Report released by the Min-istry of Energy and Mines the country is currently rankedas one of the leading producers of gold in Asia exportingalmost $7 billion worth of gold in 2009 alone In an effortto boost the mining industry the Malaysian governmentinstitutionalized mining-friendly policies to attract investorsand promote gold mineral exploration However geologicmapping and mineral exploration in Malaysia still relieson traditional geological and geophysical site investigationsThese methods of mineral prospecting in tropical region likeMalaysia are usually faced with several challenges such as

limited transportation harsh climate and steep terrain withvegetation cover [1 2]

Several studies have been conducted to extract geologicinformation from satellite remote sensing imagery such asmultispectral and hyperspectral data using different pro-cessing techniques [3ndash5] Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER) data havebeen widely used for geologic mapping due to its sensitivityto rockmineral elements (alunite kaolinite calcite dolomitechlorite talc muscovite etc) and low-cost [6 7]The successof spectral transformation from recent investigations [7ndash10]motivates this research however under different topographyand vegetation cover

In this study ASTER data was used to map quartz-rich and quartz-poor rocks zones in Gua Musang goldfield

HindawiJournal of SensorsVolume 2017 Article ID 6794095 8 pageshttpsdoiorg10115520176794095

2 Journal of Sensors

Pahang

Johor

N

EW

S

TrengganuPerak

Selangor

Kedah

Kelan

tan

Melaka

NegeriSembilan

Pulau Pinang

Perlis

GuaMusang

Coordinate system WGS 84 UTM zone 47NProjection transverse mercatorDatum WGS 1984 - units meter

(km)

0 14 2135 7

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4∘50㰀0㰀㰀N

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4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

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∘40

㰀0㰀㰀E

102∘10

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∘50

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(a) (b)

100∘0㰀0㰀㰀E 101

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Figure 1 Location of the study area Map of Peninsular Malaysia (State of Kelantan in white) and Gua Musang district (a) false colourcomposite ASTER image of the site (b)

using spectral transformation technique principal compo-nent analysis (PCA) Gua Musang has a long history of goldexploration This mineralized zone typically consists of gold-bearing quartz veins and veinlet Despite active explorationprospecting is still based on geochemical and geophysicalsampling and analysis [11ndash14] which is considered timeconsuming and costly Again the tropical nature of theenvironment and steepness of the terrain limits accessibility

2 Geology of the Study Area

Gua Musang the largest district in the state of KelantanPeninsular Malaysia is known for its richness in goldmineralization Geographically the study area is locatedbetween longitudes 101∘391015840508610158401015840E and 102∘71015840130410158401015840E andlatitudes 4∘401015840456510158401015840N and 5∘61015840319210158401015840N (Figure 1) The siteis characterized by a mix of forest and sparsely vegetated landcover in predominant agricultural timber logging and goldmining activities therefore rock exposure is very minimalThe site is strategically selected to cover the southern gold andsilver mercury belts which span the central part of the state ina north-south trending [13 15]measuring about 45124 squarekilometres approximately 012 of Gua Musang territoryThe area is characterized with precious minerals and rockscomprising a good spread of hydrothermal quartz veins [13]

The geological evolution of the study area based onthe stratigraphy geophysical and volcanic activities hasoccasioned different geological pattern in the west centraland eastern belts [14 15]The surficial lithology of the state ofKelantan comprises igneous sedimentary and metamorphicrocks occurring in a north-south trending that influencesthe profile of mineralization zones To the west and easternborder lies igneous rock that forms the main range graniteand boundary range granite respectively [16]Themain range

granites belong to the middle Triassic age that comprisesintermediate intrusive igneous rock The central belt of thestate is composed of sedimentary andmetasedimentary rocksof both Triassic and Permian age The Triassic sediment is aconglomerate of interbedded sandstone siltstone and shalewhile the Permian counterpart contains phyllite slate andshale with subordinate sandstone and schist Also the centralbelt is intersected by intermediate to basic volcano mainlypyroclastic sediment [12] Specifically the study area GuaMusang falls south of the state within the central geologicalformation mostly of the Triassic and Permian sedimentaryrocks (Figure 2) Located in the north-central part is anoutcrop of igneous rock and also metamorphic rock in thesouth-western part

Looking at themineralizationmap of Kelantan (Figure 2)it can be observed that the central gold belt extends from theThai border with Kelantan and almost carpeting the entiresubsurface of the state of Pahang Most of the Malaysiannational gold production originates from the Paleo-Arc basinto which the central belt belongs [14 17] Geologically thebelt is composed of fragmented sediments and limestone aswell as igneous rock derived from volcanic and volcanoclasticactivities of the Permian to Triassic period [15] Figure 2(b) isthe (digital) geological map of Kelantan

3 Materials and Methods

31 Datasets Advanced Spaceborne Thermal Emission andReflection Radiometer (ASTER) imagery acquired on the19th of January 2011 was used in this study Detailed descrip-tion of ASTER sensors and properties of the data can befound inMatar and Bamousa [18] and Pour and Hashim [17]L1B (level-1B) data of the study area (16 cloud cover) wasmade available by NASA Land Processes Distributed

Journal of Sensors 3

Gold prospective areaZone boundaryCross section

1

(km)20100

(a)

Quaternary(clay silt sand peat and minor gravel)Cretaceous-Jurassic(sandstone shale and minor conglomerate)Triassic(shale siltstone sandstone and limestone)Permian(phyllite slate sandstone and limestone)

Carboniferous(phyllite slate shale and sandstone)Silurian-Ordovician(schist phyllite and slate)

Intrusive rockAcid intrusives(undifferentiated)

Extrusive rockAcid to intermediate volcanicIntermediate to basic volcanic

SymbolsFault

(b)

Figure 2 Distribution of mineralization and location of goldfields (modified from [13] p132) (a) General geology of Kelantan showing thedistribution of rocks source Nazaruddin (modified from [12]) (b)

4 Journal of Sensors

Active Archive Centre (NASA LP DAAC) upon applicationthrough the NASA Reverb website (httpreverbechonasagovreverb) The imagery obtained has been corrected forgeometric and radiometric effects and projected to universaltransverse Mercator (WGS 84) Due to inability of the VNIRand SWIR bands to detect the absorption features of quartzmineral [10] only the TIR bands were used in this studyWithin 8ndash14 120583m quartz minerals exhibit strong vibrationalabsorption feature spectrally [5] The emissivityabsorptionfeatures of quartz in the thermal bands are related tostructural imbalance in the chemical composition (silicaand oxide ndashSiO

2) stretching vibration Natural mineralogy

of hydrothermally altered rocks associated with porphyrycopper deposits is primarily comprised of quartz veins [19]In addition to the satellite data shape file of the digitalgeological map of the area was obtained from MalaysiarsquosMineral and Geoscience Department (Figure 2(b)) Thedigital map was used to visually validate the capability ofspectral transform method to accurately identify differentrock elements

32 Data Processing ASTER L-1B data comes preprocessedfor radiometric and geometric effect But like other applica-tions that utilise ASTERTIR data this present work dependson the reflectance value of the surface So it was necessary todistinguish vegetation area and outcrop areas since ASTERas an optical sensors cannot see through vegetation Thiswas achieved using NDVI (normalized difference vegetationindex) NDVI is a numerical indicator that uses the visibleand near-infrared bands of the electromagnetic spectrumto estimate the greenness of vegetation It has found wideapplications in vegetative studies and feature extractionNDVI values are represented as a ratio minus1 to 1 where extremenegative values represent water and values around zerorepresent bare soil while values over 06 represent densegreen vegetation Based on the NDVI values the result wasreclassified into vegetation cloud and outcrop using NDVIvalue of 05 and lt0 to identify vegetation area and cloud

ASTER L-1B data comes preprocessed for radiometricand geometric effect Initially ASTERTIR was convertedto radiance using TIMS radiance tool [20] Thereafter theThermal Atmospheric Correction in ENVI was used tocorrect the atmospheric effectThe tool employs the In-SceneAtmospheric Compensation algorithm (ISAC) [21] whichassumes that the atmosphere is uniform over the scene andthat a near-blackbody surface exists within the sceneThefirstthing the process does is to determine the wavelength thatshows the maximum brightness temperature and uses that asthe reference wavelength Only spectra with their brightesttemperature at that wavelength are utilised to compute theatmospheric compensation

Subsequently the vegetation and cloud affected areaswere masked from the calibrated TIR bands to retain onlythe outcrop area Further the effect of the vegetation spec-tra present in the more exposed area was reduced usingforced invariance a technique that computes images thatare invariant relative to the vegetation index [22] In theresultant images the vegetation features were considerablysuppressed because they contribute insignificant variance

ASTERL1B data

Geometric and radiometric(correction applied to L1B by data provider)

ASTER-TIR bands(radiance at sensor level)

PCA(spectral transformation)

PC1 PC2 PC3 PC4 PC5

Eigenvectors and eigenvalues evaluation

Principal components analysis

(i) Greyscale amp composite PCs(ii) Derivation of rock quartz content

Figure 3 Methodological workflow describing the data processingsteps

Finally TIR image of only the outcrop area was subjected toprincipal component analysis (PCA) Figure 3 presents thedata processing and analysis steps

33 Method of Analysis

331 Spectral Transformation Theoccurrence of quartz rockunder different forms colours and in basically all mineralenvironments on earth makes distinguishing its richness adifficult task Particularly as rock-forming mineral quartzhas no spectral absorption features in the VNIR and SWIRbut possesses powerful molecular absorption features in TIR(8ndash10 120583m) bands Away from common lithological mappingusing VNIRSWIR bands [6 8] because of their failure toexhibit absorption features we used PCA transformation oftheASTERTIR bands to identify quartz richnessMeanwhileprior to implementing PCA the vegetation spectra in theimage were first suppressed using directed principal compo-nent analysis (DPCA) The widespread use of this methodfor mapping hydrothermal altered rocks lithology and othermineralogical applications [3 4 23ndash26] informed its selectionfor this study

PCA is a statistical multivariate method that employsuncorrelated linear combination of variables and transformsthem into new dataset containing new correlated variables(eigenvectors loadings) [7 23 27] PCA transform wasimplemented on the ASTER TIR bands (10 11 12 13 and14) to generate five different principal components (PC1PC2 PC3 PC4 and PC5) The strengths and weaknessesthe statistical output the eigenvectors (Table 1) and theirresponse to quartz content [25] were analysed to select thethree PCs components with the highest quality of informa-tion to identify quartz richness For qualitative evaluation ofthe result obtained from spectral transformation shapefile of

Journal of Sensors 3

Gold prospective areaZone boundaryCross section

1

(km)20100

(a)

Quaternary(clay silt sand peat and minor gravel)Cretaceous-Jurassic(sandstone shale and minor conglomerate)Triassic(shale siltstone sandstone and limestone)Permian(phyllite slate sandstone and limestone)

Carboniferous(phyllite slate shale and sandstone)Silurian-Ordovician(schist phyllite and slate)

Intrusive rockAcid intrusives(undifferentiated)

Extrusive rockAcid to intermediate volcanicIntermediate to basic volcanic

SymbolsFault

(b)

Figure 2 Distribution of mineralization and location of goldfields (modified from [13] p132) (a) General geology of Kelantan showing thedistribution of rocks source Nazaruddin (modified from [12]) (b)

4 Journal of Sensors

Active Archive Centre (NASA LP DAAC) upon applicationthrough the NASA Reverb website (httpreverbechonasagovreverb) The imagery obtained has been corrected forgeometric and radiometric effects and projected to universaltransverse Mercator (WGS 84) Due to inability of the VNIRand SWIR bands to detect the absorption features of quartzmineral [10] only the TIR bands were used in this studyWithin 8ndash14 120583m quartz minerals exhibit strong vibrationalabsorption feature spectrally [5] The emissivityabsorptionfeatures of quartz in the thermal bands are related tostructural imbalance in the chemical composition (silicaand oxide ndashSiO

2) stretching vibration Natural mineralogy

of hydrothermally altered rocks associated with porphyrycopper deposits is primarily comprised of quartz veins [19]In addition to the satellite data shape file of the digitalgeological map of the area was obtained from MalaysiarsquosMineral and Geoscience Department (Figure 2(b)) Thedigital map was used to visually validate the capability ofspectral transform method to accurately identify differentrock elements

32 Data Processing ASTER L-1B data comes preprocessedfor radiometric and geometric effect But like other applica-tions that utilise ASTERTIR data this present work dependson the reflectance value of the surface So it was necessary todistinguish vegetation area and outcrop areas since ASTERas an optical sensors cannot see through vegetation Thiswas achieved using NDVI (normalized difference vegetationindex) NDVI is a numerical indicator that uses the visibleand near-infrared bands of the electromagnetic spectrumto estimate the greenness of vegetation It has found wideapplications in vegetative studies and feature extractionNDVI values are represented as a ratio minus1 to 1 where extremenegative values represent water and values around zerorepresent bare soil while values over 06 represent densegreen vegetation Based on the NDVI values the result wasreclassified into vegetation cloud and outcrop using NDVIvalue of 05 and lt0 to identify vegetation area and cloud

ASTER L-1B data comes preprocessed for radiometricand geometric effect Initially ASTERTIR was convertedto radiance using TIMS radiance tool [20] Thereafter theThermal Atmospheric Correction in ENVI was used tocorrect the atmospheric effectThe tool employs the In-SceneAtmospheric Compensation algorithm (ISAC) [21] whichassumes that the atmosphere is uniform over the scene andthat a near-blackbody surface exists within the sceneThefirstthing the process does is to determine the wavelength thatshows the maximum brightness temperature and uses that asthe reference wavelength Only spectra with their brightesttemperature at that wavelength are utilised to compute theatmospheric compensation

Subsequently the vegetation and cloud affected areaswere masked from the calibrated TIR bands to retain onlythe outcrop area Further the effect of the vegetation spec-tra present in the more exposed area was reduced usingforced invariance a technique that computes images thatare invariant relative to the vegetation index [22] In theresultant images the vegetation features were considerablysuppressed because they contribute insignificant variance

ASTERL1B data

Geometric and radiometric(correction applied to L1B by data provider)

ASTER-TIR bands(radiance at sensor level)

PCA(spectral transformation)

PC1 PC2 PC3 PC4 PC5

Eigenvectors and eigenvalues evaluation

Principal components analysis

(i) Greyscale amp composite PCs(ii) Derivation of rock quartz content

Figure 3 Methodological workflow describing the data processingsteps

Finally TIR image of only the outcrop area was subjected toprincipal component analysis (PCA) Figure 3 presents thedata processing and analysis steps

33 Method of Analysis

331 Spectral Transformation Theoccurrence of quartz rockunder different forms colours and in basically all mineralenvironments on earth makes distinguishing its richness adifficult task Particularly as rock-forming mineral quartzhas no spectral absorption features in the VNIR and SWIRbut possesses powerful molecular absorption features in TIR(8ndash10 120583m) bands Away from common lithological mappingusing VNIRSWIR bands [6 8] because of their failure toexhibit absorption features we used PCA transformation oftheASTERTIR bands to identify quartz richnessMeanwhileprior to implementing PCA the vegetation spectra in theimage were first suppressed using directed principal compo-nent analysis (DPCA) The widespread use of this methodfor mapping hydrothermal altered rocks lithology and othermineralogical applications [3 4 23ndash26] informed its selectionfor this study

PCA is a statistical multivariate method that employsuncorrelated linear combination of variables and transformsthem into new dataset containing new correlated variables(eigenvectors loadings) [7 23 27] PCA transform wasimplemented on the ASTER TIR bands (10 11 12 13 and14) to generate five different principal components (PC1PC2 PC3 PC4 and PC5) The strengths and weaknessesthe statistical output the eigenvectors (Table 1) and theirresponse to quartz content [25] were analysed to select thethree PCs components with the highest quality of informa-tion to identify quartz richness For qualitative evaluation ofthe result obtained from spectral transformation shapefile of

4 Journal of Sensors

Active Archive Centre (NASA LP DAAC) upon applicationthrough the NASA Reverb website (httpreverbechonasagovreverb) The imagery obtained has been corrected forgeometric and radiometric effects and projected to universaltransverse Mercator (WGS 84) Due to inability of the VNIRand SWIR bands to detect the absorption features of quartzmineral [10] only the TIR bands were used in this studyWithin 8ndash14 120583m quartz minerals exhibit strong vibrationalabsorption feature spectrally [5] The emissivityabsorptionfeatures of quartz in the thermal bands are related tostructural imbalance in the chemical composition (silicaand oxide ndashSiO

2) stretching vibration Natural mineralogy

of hydrothermally altered rocks associated with porphyrycopper deposits is primarily comprised of quartz veins [19]In addition to the satellite data shape file of the digitalgeological map of the area was obtained from MalaysiarsquosMineral and Geoscience Department (Figure 2(b)) Thedigital map was used to visually validate the capability ofspectral transform method to accurately identify differentrock elements

32 Data Processing ASTER L-1B data comes preprocessedfor radiometric and geometric effect But like other applica-tions that utilise ASTERTIR data this present work dependson the reflectance value of the surface So it was necessary todistinguish vegetation area and outcrop areas since ASTERas an optical sensors cannot see through vegetation Thiswas achieved using NDVI (normalized difference vegetationindex) NDVI is a numerical indicator that uses the visibleand near-infrared bands of the electromagnetic spectrumto estimate the greenness of vegetation It has found wideapplications in vegetative studies and feature extractionNDVI values are represented as a ratio minus1 to 1 where extremenegative values represent water and values around zerorepresent bare soil while values over 06 represent densegreen vegetation Based on the NDVI values the result wasreclassified into vegetation cloud and outcrop using NDVIvalue of 05 and lt0 to identify vegetation area and cloud

ASTER L-1B data comes preprocessed for radiometricand geometric effect Initially ASTERTIR was convertedto radiance using TIMS radiance tool [20] Thereafter theThermal Atmospheric Correction in ENVI was used tocorrect the atmospheric effectThe tool employs the In-SceneAtmospheric Compensation algorithm (ISAC) [21] whichassumes that the atmosphere is uniform over the scene andthat a near-blackbody surface exists within the sceneThefirstthing the process does is to determine the wavelength thatshows the maximum brightness temperature and uses that asthe reference wavelength Only spectra with their brightesttemperature at that wavelength are utilised to compute theatmospheric compensation

Subsequently the vegetation and cloud affected areaswere masked from the calibrated TIR bands to retain onlythe outcrop area Further the effect of the vegetation spec-tra present in the more exposed area was reduced usingforced invariance a technique that computes images thatare invariant relative to the vegetation index [22] In theresultant images the vegetation features were considerablysuppressed because they contribute insignificant variance

ASTERL1B data

Geometric and radiometric(correction applied to L1B by data provider)

ASTER-TIR bands(radiance at sensor level)

PCA(spectral transformation)

PC1 PC2 PC3 PC4 PC5

Eigenvectors and eigenvalues evaluation

Principal components analysis

(i) Greyscale amp composite PCs(ii) Derivation of rock quartz content

Figure 3 Methodological workflow describing the data processingsteps

Finally TIR image of only the outcrop area was subjected toprincipal component analysis (PCA) Figure 3 presents thedata processing and analysis steps

33 Method of Analysis

331 Spectral Transformation Theoccurrence of quartz rockunder different forms colours and in basically all mineralenvironments on earth makes distinguishing its richness adifficult task Particularly as rock-forming mineral quartzhas no spectral absorption features in the VNIR and SWIRbut possesses powerful molecular absorption features in TIR(8ndash10 120583m) bands Away from common lithological mappingusing VNIRSWIR bands [6 8] because of their failure toexhibit absorption features we used PCA transformation oftheASTERTIR bands to identify quartz richnessMeanwhileprior to implementing PCA the vegetation spectra in theimage were first suppressed using directed principal compo-nent analysis (DPCA) The widespread use of this methodfor mapping hydrothermal altered rocks lithology and othermineralogical applications [3 4 23ndash26] informed its selectionfor this study

PCA is a statistical multivariate method that employsuncorrelated linear combination of variables and transformsthem into new dataset containing new correlated variables(eigenvectors loadings) [7 23 27] PCA transform wasimplemented on the ASTER TIR bands (10 11 12 13 and14) to generate five different principal components (PC1PC2 PC3 PC4 and PC5) The strengths and weaknessesthe statistical output the eigenvectors (Table 1) and theirresponse to quartz content [25] were analysed to select thethree PCs components with the highest quality of informa-tion to identify quartz richness For qualitative evaluation ofthe result obtained from spectral transformation shapefile of

Journal of Sensors 5

Table 1 Eigenvectors and eigenvalues for ASTER TIR transformation

Band 10 Band 11 Band 12 Band 13 Band 14 eigenvaluePCA1 0405888 0449008 0476808 0462932 0438171 9866PCA2 minus0504374 minus0292062 0322006 minus0265937 0697063 063PCA3 minus0296201 0323920 0686945 minus0284514 minus0504480 036PCA4 minus0626402 0615558 minus0386135 0268850 0055609 019PCA5 0317408 0478806 minus0219028 minus0749495 0245520 016

the digital geologicalmapwas overlaid on the greyscale imageof PC1

332 Image Classification The three selected PCs (PC1PC3 and PC4) were used to produce RGB false colourcomposite of the study area which highlights the respectiverock elements in different colours After visual examinationof the false colour composite relative to the existing mapthe PCs composite image was classified into four differentrock types based on quartz richness using supervised classifi-cation method and maximum likelihood (ML) classificationscheme ML classifier have been widely used and provensatisfactory to assign pixels in the digital satellite imagesto particular land cover classes or themes to produce landcover map [28] Training polygons were digitized on-screento represent each class based on the spectral representationof the different rocks in the RGB colour composite imageand the knowledge from the existing geological map Someof the training pixels were used for classification and theremaining were used as independent training sets for accu-racy assessment to eliminate bias Once the training sitesand classes were assigned the image was classified into fourclasses quartz concentration (quartz-rich and quartz-poor)quartz-rich sedimentary rock quartz-poor igneous rock andquartz-poor limestone Finally the classification accuracywas evaluated using error matrix

4 Results and Discussion

41 PCA Result The output of a PCA process is the compo-nent image accompanied with statistical values of the eigen-vector which could carry a positive or negative sign Thesesigns along with absorptionreflective capacity of specificbands are valuable information required in choosing bestcomponents bands for RGB false colour composite Table 1presents the eigenvalues of each image band for the respectivecomponent analysis

As mentioned earlier these TIR bands are useful foridentifying silicate and carbonate rocks while bands 10 and12 are particularly effective for detecting quartz absorptionfeatures and band 14 records high emissivity [10 29] Fromthe eigenvector positive sign indicates that the band plays amajor contribution in the variation of a particular componentwhile negative eigenvalues describe a lesser contribution tothe variation of the derived component The two guidingprinciples adopted in this study are based on the premise (1)that quartz content can be best determined in bands 10 and 12where absorption is maximum and band 14 where emissivityis highest and (2) on the condition that the eigenvalues of

bands 10 and 12 have positive signs while band 14 is negative[8]

In PC1 for example the weight matrix is positive andhas eigenvalue of 9866 When the eigenvalue is positive itindicates high correlation of spectral information among theinput image bands and the nearer the value of the resultingPC component derived is to 100 the higher the degree ofsimilarity in information contents of images is According to[27 30] in a situation like this it means that the varianceof the variables is at the maximum amount of the varianceof the variables which can be accounted for with a linearmodel by a single underlying factor On the contrary if allthe values carry negative sign it shows that they poorlycontribute to feature contents in the samemagnitude in all theimages Hence interpreting composite images with similarcharacteristics like this will not give useful information todiscern between different rock materials

So PC selection is interplay of the strength of the PCand the signs of eigenvalues of bands 10 12 and 14 InPC1 bands 10 and 12 meet the positive sign condition butband 14 does not because of dissimilarity in the informationcontent compared to bands 10 and 12 In PC2 only band12 satisfied the required positive contribution condition thatinformation in the input bands must be correlated So PC2is not informative enough to distinguish content richness ofquartz rocks In the case of PC3 it can be observed thatbands 12 and 14 have high contribution while band 10 appearsweak However the negative value (minus0296201) in band 10in PC3 can significantly contribute to identifying quartzcontent richness better than that of band 10 in PC2 Thistherefore makes PC3 a valuable input for this study Contraryto the first three PCs none of the bands in PC4 meets thepositivenegative sign condition so they cannot contributeto identifying quartz content Despite this it is very useful inthis process because it can distinctively highlight other rockelements in dark pixels and therefore increases backgroundvisibility [27] The last one PC5 does not have substantialinformation value to contribute to identifying quartz rockrichness because of the noise content On the final analysisPC1 PC3 and PC4 satisfied the enabling conditions toidentify quartz-rich and quartz-poor rocks These optimalPCs were used for R(PC3) G(PC1) and B(PC4) false colourcomposite to reveal variation in quartz content in the rocksThe composite image is presented in Figure 4

PCs false colour composite image deciphers quartz-richrocks from quartz-low rocks For instance deep blue colourdepicts rocks that are highly rich in quartz content Thiscorroborates [9] who observed that CP composite reveals

6 Journal of Sensors

Coordinate System WGS 84 UTM zone 47NProjection transverse mercatorDatum WGS 1984 - units meter

0 18 2745 (km)

Rock quartz content

9

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

N

EW

S

Figure 4 RGB false colour composite of PC3 PC1 and PC4 Deepblue colour indicates quartz mineral The deeper the blue colour thehigher the concentration of quartz content and vice versa

highly rich quartz content in deep blue In Figure 4 deepblue colour indicates quartz-rich rocks while yellow colourrepresents quartz-poor rocks The deeper the colour thehigher the concentration of quartz mineral however as thecolour becomes lighter quartz concentration decreases Inthe figure Triassic sedimentary rock has high concentrationof quartz mineral as opposed to volcanic igneous rock andlimestone which are apparently poor in quartz content Inthe west of the image quartz content varies across the areaas evidenced in the mixture of light-blue and yellow coloursrepresenting both classes of quartz richness content

42 Visualization for Base-Knowledge Analysis Qualitativeassessment of how precisely different rocks can be identifiedwas done visually by displaying the greyscale image of PC1and then by overlaying shapefile of the geological mapof the study area on the greyscale image (Figure 5) Theresult reveals distinction in spectral absorption and emis-sivity different rock types in varying shades of greyscale aspresented in Figure 5 For example the volcanogenic igneousrock metamorphic and sedimentary limestone have highability to emit thermal radiation energy hence producingbrighter greyscale On the other hand the composition oforganic minerals in sedimentary rock absorbs most of thethermal energy resulting in low emissivity and thereforedarker shades of grey Analysis of the PCs colour compositeand the greyscale images were used in identifying rock typesand their quartz concentration Result of this qualitativeassessment was validated by overlaying shapefile of theexisting geological map on the greyscale image (Figure 5)that shows correct match between the spatial distributionof rock types and their spectral delineation in the remote

D

C

C

BA

Metamorphic Igneous

Sedimentary (Triassic)Sedimentary (Permian)Sedimentary (Permian)

Coordinate System WGS 84 UTM zone 47NProjection transverse mercatorDatum WGS 1984 - units meter

0 18 2745 (km)9

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

N

EW

S

E

Figure 5 Existing geological map superimposed on PCI1 image forrock type identification

sensing product Information from the qualitative validationwas helpful in selecting training pixels for classification

Different types of rocks including igneous sedimentaryand metamorphic rocks carpet the surface and of coursesubsurface of Kelantan [11 14 31] The distribution of quartzcontent richness obtained through spectral transformationand rock type identification were presented in Figures 4 and5 It was observed that quartz mineral is unevenly distributedbetween the different rock types in the study area Whilesome rocks have high concentration of quartz some arenot Visualizing image of PC1 in greyscale highlights thebackground rock with clear visual perception of differentrocks that reflects the strength of absorptionemissivity ofASTER thermal bands [20] Shades of grey from dark (black)to bright (white) colours provide visual assessment of spatialdistribution of rocks with closely related mineral elements(Figure 5) High correspondence of the distribution of rocksin space and extent in the base map (existing geological map)and the spectral transform results shows that the method isnot only capable of revealing quartz richness it can equallydiscriminate between different rocks

In Figure 5 the western side which trends north-south(letter A) comprises metamorphic rock According to studiesconducted by Oliver et al and Zaw et al [32 33] the meta-morphic rock in this area consists of schist slate phylliteand other associated mineral elements that resulted fromintense pressure between the igneous and sedimentary rocksover time to the current texture structure and compositionNormally what controls the quartz content in metamorphicrock is the depositional composition during the conversionof sediments to sedimentary rock [16] So the presence ofsandstone in themetamorphic rock accounts for the presence

Journal of Sensors 7

of quartzmineral althoughnot as high as found in the quartz-rich sedimentary rock

Also it can be seen that the rock around the locationslabelled B and C appear bright in the image B is mainlyigneous rock formed through consolidated molten magmafrom volcanic activities before the late Triassic or in the lateto early Permian [22] which contains coarse-grained granitetextured with tough and rough surface For C the rockis purely Triassic sedimentary limestone formed mostly ofpyroclastic particles and calcite deposited in layers [13] withrough surface coarser and therefore brighter than the igneousrock They both contain low organic particles and much ofoxidized rock elements that have high thermal emissivityThePermian sedimentary rock around D appears a little brighterbecause of low organic material compared to its Triassiccounterpart that is darker due to active absorption action ofthe large organic component in the rock

The dominant quartz-rich sedimentary rocks are com-posed of limestone of the Triassic geologic period enrichedwith laminating shale sandstone and dolomite bonded bycementation rather than pressure [22] A combination ofdepositional elements and the diagenetic processes deter-mine the mineralogy of a reservoir The depositional envi-ronment of the Triassic sedimentary rock in the study area isdominated by sandstone which is controlled by large percent-age of quartz element starting from south of the central areaand trends northeast From the foregoing analysis ASTERdata specifically the TIR bands demonstrates high potentialto distinguish different rock types and their quartz contentusing spectral transformation It is interesting to note thatthe procedure has capability to highlight grain level detailsof quartz element in rocks However it is important tounderstand that when using optical remote sensing imagerythe result may vary according to season of the year Forinstance physical and chemical properties of the rocks maybe disbanded and change from under heavy precipitationand heat depending on their crystal structure Because ofthe relative stability seasonal change is not that a challengefor mapping quartz mineral using remote sensing imageryNevertheless when theminerals come in any indirect contactwith weak acid water which is the case in tropical regionquartz mineral is more prone to weathering

5 Conclusion

As mentioned earlier the lithospheric characteristic of thestudy area includes the igneous sedimentary and metamor-phic rocks Exploration of the ASTER thermal bands revealsthese different rocks and their quartz content richness Thegeneral observation is that the distribution of quartz amongthe different rocks is quite uneven While the pyroclasticvolcanic igneous rock and the carbonate dominated Permiansedimentary rocks are quartz-poor almost void the Triassicsedimentary rock largely composed of sandstone is quartz-rich On the other hand metamorphic rock shows variationacross the spatial extent having some grains of quartz contentwith some quartz-rich patches within Also the PC greyscaleimage enhanced surficial background highlighted agrees wellwith the known geologic information of the area Obviously

sandstone depositional sedimentary rock of the Triassicformation is more associated with high concentration ofquartz than other rocks

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This research was supported by the Ministry of Higher Edu-cation Malaysia under Exploratory Research Grant SchemeERGS12013STWNO6UPM022 and the University PutraMalaysia (Vote no 5527147) The authors would like toexpress their gratitude to the Department of Minerals andGeoscience Malaysia (JMG) and the Department of SurveyandMappingMalaysia (JUPEM) for providing various essen-tial datasets used in this study and also to Land ProcessesDistributed Active Archive Center (LP DAAC) for providingASTER L1B imagery used in this study

References

[1] T I R Almeida C R S Filho C Juliani and F CBranco ldquoApplication of remote sensing to geobotany to detecthydrothermal alteration facies in epithermal high-sulfidationgold deposits in the Amazon regionrdquo in SEG Reviews inEconomic Geology R Bedell A P Crosta and E Grunsky Edsvol 16 pp 135ndash142 Society of Exploration Geophysicists TulsaOkla USA 2009

[2] A B Pour M Hashim and M Marghany ldquoExploration of goldmineralization in a tropical region using Earth Observing-1(EO1) and JERS-1 SAR data a case study from Bau gold fieldSarawak Malaysiardquo Arabian Journal of Geosciences vol 7 no 6pp 2393ndash2406 2014

[3] F D van der Meer H M A van der Werff F J A van Ruiten-beek et al ldquoMulti-and hyperspectral geologic remote sensing areviewrdquo International Journal of Applied Earth Observation andGeoinformation vol 14 no 1 pp 112ndash128 2012

[4] E E Mshiu C Glaszliger and G Borg ldquoIdentification ofhydrothermal paleofluid pathways the pathfinders in the explo-ration of mineral deposits a case study from the SukumalandGreenstone Belt Lake Victoria Gold Field Tanzaniardquo Advancesin Space Research vol 55 no 4 pp 1117ndash1133 2015

[5] A B Pour and M Hashim ldquoThe application of ASTER remotesensing data to porphyry copper and epithermal gold depositsrdquoOre Geology Reviews vol 44 pp 1ndash9 2012

[6] L L Tornabene J E Moersch G R Osinski P Lee andS P Wright ldquoSpaceborne visible and thermal infrared litho-logic mapping of impact-exposed subsurface lithologies at theHaughton impact structure Devon Island Canadian HighArctic applications to MarsrdquoMeteoritics and Planetary Sciencevol 40 no 12 pp 1835ndash1858 2005

[7] S Gad and T Kusky ldquoASTER spectral ratioing for lithologicalmapping in the Arabian-Nubian shield the NeoproterozoicWadi Kid area Sinai Egyptrdquo Gondwana Research vol 11 no3 pp 326ndash335 2007

[8] A B Pour and M Hashim ldquoIdentification of hydrothermalalteration minerals for exploring of porphyry copper depositusing ASTER data SE Iranrdquo Journal of Asian Earth Sciences vol42 no 6 pp 1309ndash1323 2011

8 Journal of Sensors

[9] M Pournamdari M Hashim and A B Pour ldquoSpectral trans-formation of ASTER and Landsat TM bands for lithologicalmapping of Soghan ophiolite complex south Iranrdquo Advances inSpace Research vol 54 no 4 pp 694ndash709 2014

[10] A B Pour andMHashim ldquoIdentifying areas of high economic-potential copper mineralization using ASTER data in theUrumieh-Dokhtar Volcanic Belt Iranrdquo Advances in SpaceResearch vol 49 no 4 pp 753ndash769 2012

[11] D A-F Batchelor ldquoGeological characteristics of the Pulaialluvial gold deposit South Kelantan Malaysiardquo Journal ofSoutheast Asian Earth Sciences vol 10 no 1-2 pp 101ndash108 1994

[12] D A Nazaruddin N S Fadilah and Z Zulkarnain ldquoGeologicalstudies to support the tourism site a case study in the RafflesiaTrail near Kampung Jedip Lojing Highlands Kelantanrdquo Inter-national Journal of Geosciences vol 5 pp 835ndash851 2014

[13] G S Heng T G Hoe and W F W Hassan ldquoGold mineral-ization and zonation in the state of Kelantanrdquo Bulletin of theGeological Society of Malaysia vol 52 pp 129ndash135 2006

[14] B Li S-Y Jiang H-Y Zou M Yang and J-Q Lai ldquoGeologyand fluid characteristics of the Ulu Sokor gold deposit Kelan-tan Malaysia implications for ore genesis and classification ofthe depositrdquo Ore Geology Reviews vol 64 no 1 pp 400ndash4242015

[15] K Shah ldquoMesothermal lode gold deposit Central Belt Peninsu-lar Malaysiardquo in Earth Sciences D Imran Ahmad Ed pp 314ndash342 InTech 2012

[16] S Yusoff B Pradhan M A Manap and H Z M ShafrildquoRegional gold potential mapping in Kelantan (Malaysia) usingprobabilistic based models and GISrdquo Open Geosciences vol 7no 1 pp 149ndash161 2015

[17] A B Pour and M Hashim ldquoStructural geology mappingusing PALSAR data in the Bau gold mining district SarawakMalaysiardquo Advances in Space Research vol 54 no 4 pp 644ndash654 2014

[18] S S Matar and A O Bamousa ldquoIntegration of the ASTERthermal infra-red bands imageries with geological map of JabalAl Hasir area Asir Terrane the Arabian Shieldrdquo Journal ofTaibah University for Science vol 7 no 1 pp 1ndash7 2013

[19] TheQuartzPage ldquoThe Quartz Page Quartz as a Rock-FormingMineralrdquo The Quartz Page 2014 httpwwwquartzpagedegen rockhtml

[20] Y Ninomiya and B Fu ldquoRegional scale Lithologic mappingin western Tibet using ASTER thermal infrared multispectraldatardquo in Proceedings of the Technical Commission VIII Sympo-sium on Networking the World with Remote Sensing (ISPRS rsquo10)vol 38 pp 454ndash458 August 2010

[21] H Tonooka S J Hook T Matsunaga S Kato E Abbott andH Tan ldquoASTERTIR vicarious calibration activities in the last 11yearsrdquo in Proceedings of the IEEE International Geoscience andRemote Sensing Symposium (IGARSS rsquo11) pp 3653ndash3656 July2011

[22] E A Bahiru Inter-relationship between lithology and structureand its control on gold mineralization in Buhweju area SW ofUganda [thesis] University of Twente 2011

[23] A Koc Remote Sensing Study of Surgu Fault Zone (MalatyaTurkey) Middle East Technical University 2005

[24] Q Cheng G Bonham-Carter W Wang S Zhang W Li andX Qinglin ldquoA spatially weighted principal component analysisfor multi-element geochemical data for mapping locations offelsic intrusions in the Gejiumineral district of Yunnan ChinardquoComputers and Geosciences vol 37 no 5 pp 662ndash669 2011

[25] A B Pour and M Hashim ldquoApplication of advanced space-borne thermal emission and reflection radiometer (ASTER)data in geological mappingrdquo International Journal of PhysicalSciences vol 6 no 33 pp 7657ndash7668 2011

[26] C Kwang E Osei Jnr and A Duker ldquoApplication of remotesensing and geographic information systems for gold potentialmapping in birim North District of Eastern Region of Ghana-gold potential mapping using GIS and remote sensingrdquo Inter-national Journal of Remote Sensing Applications vol 4 no 1 p48 2014

[27] A Ezzati RMehrnia and K Ajayebi ldquoDetection of Hydrother-mal potential zones using remote sensing satellite data inRamand region Qazvin Province Iranrdquo Journal of Tethys vol2 no 2 pp 93ndash100 2014

[28] L Bertoldi ldquoRemote Sensing of granitoid rocks image elabora-tion and spectral signatures (Morocco calc-alkaline Corse andHimalayan peraluminose granitoid case studies)rdquo Interpret AJ Bible Theol 2010

[29] N Surip A H Hamzah M R Zakaria A Napiah and JA Talib ldquoMapping of gold in densely vegetated area usingremote sensing and GIS techniques in Pahang MalaysiardquoOpenGeosciences vol 7 pp 149ndash161 2015

[30] R Amer T Kusky and A El Mezayen ldquoRemote sensingdetection of gold related alteration zones in Um Rus areacentral eastern desert of EgyptrdquoAdvances in Space Research vol49 no 1 pp 121ndash134 2012

[31] BHe C Chen andY Liu ldquoGold resources potential assessmentin eastern Kunlun Mountains of China combining weights-of-evidence model with GIS spatial analysis techniquerdquo ChineseGeographical Science vol 20 no 5 pp 461ndash470 2010

[32] G Oliver K Zaw M Hotson S Meffre and T Manka ldquoU-Pbzircon geochronology of Early Permian to Late Triassic rocksfrom Singapore and Johor a plate tectonic reinterpretationrdquoGondwana Research vol 26 no 1 pp 132ndash143 2014

[33] K Zaw S Meffre C-K Lai et al ldquoTectonics and metallogenyofmainland Southeast Asiamdasha review and contributionrdquoGond-wana Research vol 26 no 1 pp 5ndash30 2014

6 Journal of Sensors

Coordinate System WGS 84 UTM zone 47NProjection transverse mercatorDatum WGS 1984 - units meter

0 18 2745 (km)

Rock quartz content

9

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

N

EW

S

Figure 4 RGB false colour composite of PC3 PC1 and PC4 Deepblue colour indicates quartz mineral The deeper the blue colour thehigher the concentration of quartz content and vice versa

highly rich quartz content in deep blue In Figure 4 deepblue colour indicates quartz-rich rocks while yellow colourrepresents quartz-poor rocks The deeper the colour thehigher the concentration of quartz mineral however as thecolour becomes lighter quartz concentration decreases Inthe figure Triassic sedimentary rock has high concentrationof quartz mineral as opposed to volcanic igneous rock andlimestone which are apparently poor in quartz content Inthe west of the image quartz content varies across the areaas evidenced in the mixture of light-blue and yellow coloursrepresenting both classes of quartz richness content

42 Visualization for Base-Knowledge Analysis Qualitativeassessment of how precisely different rocks can be identifiedwas done visually by displaying the greyscale image of PC1and then by overlaying shapefile of the geological mapof the study area on the greyscale image (Figure 5) Theresult reveals distinction in spectral absorption and emis-sivity different rock types in varying shades of greyscale aspresented in Figure 5 For example the volcanogenic igneousrock metamorphic and sedimentary limestone have highability to emit thermal radiation energy hence producingbrighter greyscale On the other hand the composition oforganic minerals in sedimentary rock absorbs most of thethermal energy resulting in low emissivity and thereforedarker shades of grey Analysis of the PCs colour compositeand the greyscale images were used in identifying rock typesand their quartz concentration Result of this qualitativeassessment was validated by overlaying shapefile of theexisting geological map on the greyscale image (Figure 5)that shows correct match between the spatial distributionof rock types and their spectral delineation in the remote

D

C

C

BA

Metamorphic Igneous

Sedimentary (Triassic)Sedimentary (Permian)Sedimentary (Permian)

Coordinate System WGS 84 UTM zone 47NProjection transverse mercatorDatum WGS 1984 - units meter

0 18 2745 (km)9

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

4∘40㰀0㰀㰀N

4∘50㰀0㰀㰀N

5∘0㰀0㰀㰀N

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

102∘10

㰀0㰀㰀E102

∘0㰀0㰀㰀E101

∘50

㰀0㰀㰀E101

∘40

㰀0㰀㰀E

N

EW

S

E

Figure 5 Existing geological map superimposed on PCI1 image forrock type identification

sensing product Information from the qualitative validationwas helpful in selecting training pixels for classification

Different types of rocks including igneous sedimentaryand metamorphic rocks carpet the surface and of coursesubsurface of Kelantan [11 14 31] The distribution of quartzcontent richness obtained through spectral transformationand rock type identification were presented in Figures 4 and5 It was observed that quartz mineral is unevenly distributedbetween the different rock types in the study area Whilesome rocks have high concentration of quartz some arenot Visualizing image of PC1 in greyscale highlights thebackground rock with clear visual perception of differentrocks that reflects the strength of absorptionemissivity ofASTER thermal bands [20] Shades of grey from dark (black)to bright (white) colours provide visual assessment of spatialdistribution of rocks with closely related mineral elements(Figure 5) High correspondence of the distribution of rocksin space and extent in the base map (existing geological map)and the spectral transform results shows that the method isnot only capable of revealing quartz richness it can equallydiscriminate between different rocks

In Figure 5 the western side which trends north-south(letter A) comprises metamorphic rock According to studiesconducted by Oliver et al and Zaw et al [32 33] the meta-morphic rock in this area consists of schist slate phylliteand other associated mineral elements that resulted fromintense pressure between the igneous and sedimentary rocksover time to the current texture structure and compositionNormally what controls the quartz content in metamorphicrock is the depositional composition during the conversionof sediments to sedimentary rock [16] So the presence ofsandstone in themetamorphic rock accounts for the presence

Journal of Sensors 7

of quartzmineral althoughnot as high as found in the quartz-rich sedimentary rock

Also it can be seen that the rock around the locationslabelled B and C appear bright in the image B is mainlyigneous rock formed through consolidated molten magmafrom volcanic activities before the late Triassic or in the lateto early Permian [22] which contains coarse-grained granitetextured with tough and rough surface For C the rockis purely Triassic sedimentary limestone formed mostly ofpyroclastic particles and calcite deposited in layers [13] withrough surface coarser and therefore brighter than the igneousrock They both contain low organic particles and much ofoxidized rock elements that have high thermal emissivityThePermian sedimentary rock around D appears a little brighterbecause of low organic material compared to its Triassiccounterpart that is darker due to active absorption action ofthe large organic component in the rock

The dominant quartz-rich sedimentary rocks are com-posed of limestone of the Triassic geologic period enrichedwith laminating shale sandstone and dolomite bonded bycementation rather than pressure [22] A combination ofdepositional elements and the diagenetic processes deter-mine the mineralogy of a reservoir The depositional envi-ronment of the Triassic sedimentary rock in the study area isdominated by sandstone which is controlled by large percent-age of quartz element starting from south of the central areaand trends northeast From the foregoing analysis ASTERdata specifically the TIR bands demonstrates high potentialto distinguish different rock types and their quartz contentusing spectral transformation It is interesting to note thatthe procedure has capability to highlight grain level detailsof quartz element in rocks However it is important tounderstand that when using optical remote sensing imagerythe result may vary according to season of the year Forinstance physical and chemical properties of the rocks maybe disbanded and change from under heavy precipitationand heat depending on their crystal structure Because ofthe relative stability seasonal change is not that a challengefor mapping quartz mineral using remote sensing imageryNevertheless when theminerals come in any indirect contactwith weak acid water which is the case in tropical regionquartz mineral is more prone to weathering

5 Conclusion

As mentioned earlier the lithospheric characteristic of thestudy area includes the igneous sedimentary and metamor-phic rocks Exploration of the ASTER thermal bands revealsthese different rocks and their quartz content richness Thegeneral observation is that the distribution of quartz amongthe different rocks is quite uneven While the pyroclasticvolcanic igneous rock and the carbonate dominated Permiansedimentary rocks are quartz-poor almost void the Triassicsedimentary rock largely composed of sandstone is quartz-rich On the other hand metamorphic rock shows variationacross the spatial extent having some grains of quartz contentwith some quartz-rich patches within Also the PC greyscaleimage enhanced surficial background highlighted agrees wellwith the known geologic information of the area Obviously

sandstone depositional sedimentary rock of the Triassicformation is more associated with high concentration ofquartz than other rocks

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This research was supported by the Ministry of Higher Edu-cation Malaysia under Exploratory Research Grant SchemeERGS12013STWNO6UPM022 and the University PutraMalaysia (Vote no 5527147) The authors would like toexpress their gratitude to the Department of Minerals andGeoscience Malaysia (JMG) and the Department of SurveyandMappingMalaysia (JUPEM) for providing various essen-tial datasets used in this study and also to Land ProcessesDistributed Active Archive Center (LP DAAC) for providingASTER L1B imagery used in this study

References

[1] T I R Almeida C R S Filho C Juliani and F CBranco ldquoApplication of remote sensing to geobotany to detecthydrothermal alteration facies in epithermal high-sulfidationgold deposits in the Amazon regionrdquo in SEG Reviews inEconomic Geology R Bedell A P Crosta and E Grunsky Edsvol 16 pp 135ndash142 Society of Exploration Geophysicists TulsaOkla USA 2009

[2] A B Pour M Hashim and M Marghany ldquoExploration of goldmineralization in a tropical region using Earth Observing-1(EO1) and JERS-1 SAR data a case study from Bau gold fieldSarawak Malaysiardquo Arabian Journal of Geosciences vol 7 no 6pp 2393ndash2406 2014

[3] F D van der Meer H M A van der Werff F J A van Ruiten-beek et al ldquoMulti-and hyperspectral geologic remote sensing areviewrdquo International Journal of Applied Earth Observation andGeoinformation vol 14 no 1 pp 112ndash128 2012

[4] E E Mshiu C Glaszliger and G Borg ldquoIdentification ofhydrothermal paleofluid pathways the pathfinders in the explo-ration of mineral deposits a case study from the SukumalandGreenstone Belt Lake Victoria Gold Field Tanzaniardquo Advancesin Space Research vol 55 no 4 pp 1117ndash1133 2015

[5] A B Pour and M Hashim ldquoThe application of ASTER remotesensing data to porphyry copper and epithermal gold depositsrdquoOre Geology Reviews vol 44 pp 1ndash9 2012

[6] L L Tornabene J E Moersch G R Osinski P Lee andS P Wright ldquoSpaceborne visible and thermal infrared litho-logic mapping of impact-exposed subsurface lithologies at theHaughton impact structure Devon Island Canadian HighArctic applications to MarsrdquoMeteoritics and Planetary Sciencevol 40 no 12 pp 1835ndash1858 2005

[7] S Gad and T Kusky ldquoASTER spectral ratioing for lithologicalmapping in the Arabian-Nubian shield the NeoproterozoicWadi Kid area Sinai Egyptrdquo Gondwana Research vol 11 no3 pp 326ndash335 2007

[8] A B Pour and M Hashim ldquoIdentification of hydrothermalalteration minerals for exploring of porphyry copper depositusing ASTER data SE Iranrdquo Journal of Asian Earth Sciences vol42 no 6 pp 1309ndash1323 2011

8 Journal of Sensors

[9] M Pournamdari M Hashim and A B Pour ldquoSpectral trans-formation of ASTER and Landsat TM bands for lithologicalmapping of Soghan ophiolite complex south Iranrdquo Advances inSpace Research vol 54 no 4 pp 694ndash709 2014

[10] A B Pour andMHashim ldquoIdentifying areas of high economic-potential copper mineralization using ASTER data in theUrumieh-Dokhtar Volcanic Belt Iranrdquo Advances in SpaceResearch vol 49 no 4 pp 753ndash769 2012

[11] D A-F Batchelor ldquoGeological characteristics of the Pulaialluvial gold deposit South Kelantan Malaysiardquo Journal ofSoutheast Asian Earth Sciences vol 10 no 1-2 pp 101ndash108 1994

[12] D A Nazaruddin N S Fadilah and Z Zulkarnain ldquoGeologicalstudies to support the tourism site a case study in the RafflesiaTrail near Kampung Jedip Lojing Highlands Kelantanrdquo Inter-national Journal of Geosciences vol 5 pp 835ndash851 2014

[13] G S Heng T G Hoe and W F W Hassan ldquoGold mineral-ization and zonation in the state of Kelantanrdquo Bulletin of theGeological Society of Malaysia vol 52 pp 129ndash135 2006

[14] B Li S-Y Jiang H-Y Zou M Yang and J-Q Lai ldquoGeologyand fluid characteristics of the Ulu Sokor gold deposit Kelan-tan Malaysia implications for ore genesis and classification ofthe depositrdquo Ore Geology Reviews vol 64 no 1 pp 400ndash4242015

[15] K Shah ldquoMesothermal lode gold deposit Central Belt Peninsu-lar Malaysiardquo in Earth Sciences D Imran Ahmad Ed pp 314ndash342 InTech 2012

[16] S Yusoff B Pradhan M A Manap and H Z M ShafrildquoRegional gold potential mapping in Kelantan (Malaysia) usingprobabilistic based models and GISrdquo Open Geosciences vol 7no 1 pp 149ndash161 2015

[17] A B Pour and M Hashim ldquoStructural geology mappingusing PALSAR data in the Bau gold mining district SarawakMalaysiardquo Advances in Space Research vol 54 no 4 pp 644ndash654 2014

[18] S S Matar and A O Bamousa ldquoIntegration of the ASTERthermal infra-red bands imageries with geological map of JabalAl Hasir area Asir Terrane the Arabian Shieldrdquo Journal ofTaibah University for Science vol 7 no 1 pp 1ndash7 2013

[19] TheQuartzPage ldquoThe Quartz Page Quartz as a Rock-FormingMineralrdquo The Quartz Page 2014 httpwwwquartzpagedegen rockhtml

[20] Y Ninomiya and B Fu ldquoRegional scale Lithologic mappingin western Tibet using ASTER thermal infrared multispectraldatardquo in Proceedings of the Technical Commission VIII Sympo-sium on Networking the World with Remote Sensing (ISPRS rsquo10)vol 38 pp 454ndash458 August 2010

[21] H Tonooka S J Hook T Matsunaga S Kato E Abbott andH Tan ldquoASTERTIR vicarious calibration activities in the last 11yearsrdquo in Proceedings of the IEEE International Geoscience andRemote Sensing Symposium (IGARSS rsquo11) pp 3653ndash3656 July2011

[22] E A Bahiru Inter-relationship between lithology and structureand its control on gold mineralization in Buhweju area SW ofUganda [thesis] University of Twente 2011

[23] A Koc Remote Sensing Study of Surgu Fault Zone (MalatyaTurkey) Middle East Technical University 2005

[24] Q Cheng G Bonham-Carter W Wang S Zhang W Li andX Qinglin ldquoA spatially weighted principal component analysisfor multi-element geochemical data for mapping locations offelsic intrusions in the Gejiumineral district of Yunnan ChinardquoComputers and Geosciences vol 37 no 5 pp 662ndash669 2011

[25] A B Pour and M Hashim ldquoApplication of advanced space-borne thermal emission and reflection radiometer (ASTER)data in geological mappingrdquo International Journal of PhysicalSciences vol 6 no 33 pp 7657ndash7668 2011

[26] C Kwang E Osei Jnr and A Duker ldquoApplication of remotesensing and geographic information systems for gold potentialmapping in birim North District of Eastern Region of Ghana-gold potential mapping using GIS and remote sensingrdquo Inter-national Journal of Remote Sensing Applications vol 4 no 1 p48 2014

[27] A Ezzati RMehrnia and K Ajayebi ldquoDetection of Hydrother-mal potential zones using remote sensing satellite data inRamand region Qazvin Province Iranrdquo Journal of Tethys vol2 no 2 pp 93ndash100 2014

[28] L Bertoldi ldquoRemote Sensing of granitoid rocks image elabora-tion and spectral signatures (Morocco calc-alkaline Corse andHimalayan peraluminose granitoid case studies)rdquo Interpret AJ Bible Theol 2010

[29] N Surip A H Hamzah M R Zakaria A Napiah and JA Talib ldquoMapping of gold in densely vegetated area usingremote sensing and GIS techniques in Pahang MalaysiardquoOpenGeosciences vol 7 pp 149ndash161 2015

[30] R Amer T Kusky and A El Mezayen ldquoRemote sensingdetection of gold related alteration zones in Um Rus areacentral eastern desert of EgyptrdquoAdvances in Space Research vol49 no 1 pp 121ndash134 2012

[31] BHe C Chen andY Liu ldquoGold resources potential assessmentin eastern Kunlun Mountains of China combining weights-of-evidence model with GIS spatial analysis techniquerdquo ChineseGeographical Science vol 20 no 5 pp 461ndash470 2010

[32] G Oliver K Zaw M Hotson S Meffre and T Manka ldquoU-Pbzircon geochronology of Early Permian to Late Triassic rocksfrom Singapore and Johor a plate tectonic reinterpretationrdquoGondwana Research vol 26 no 1 pp 132ndash143 2014

[33] K Zaw S Meffre C-K Lai et al ldquoTectonics and metallogenyofmainland Southeast Asiamdasha review and contributionrdquoGond-wana Research vol 26 no 1 pp 5ndash30 2014

Journal of Sensors 7

of quartzmineral althoughnot as high as found in the quartz-rich sedimentary rock

Also it can be seen that the rock around the locationslabelled B and C appear bright in the image B is mainlyigneous rock formed through consolidated molten magmafrom volcanic activities before the late Triassic or in the lateto early Permian [22] which contains coarse-grained granitetextured with tough and rough surface For C the rockis purely Triassic sedimentary limestone formed mostly ofpyroclastic particles and calcite deposited in layers [13] withrough surface coarser and therefore brighter than the igneousrock They both contain low organic particles and much ofoxidized rock elements that have high thermal emissivityThePermian sedimentary rock around D appears a little brighterbecause of low organic material compared to its Triassiccounterpart that is darker due to active absorption action ofthe large organic component in the rock

The dominant quartz-rich sedimentary rocks are com-posed of limestone of the Triassic geologic period enrichedwith laminating shale sandstone and dolomite bonded bycementation rather than pressure [22] A combination ofdepositional elements and the diagenetic processes deter-mine the mineralogy of a reservoir The depositional envi-ronment of the Triassic sedimentary rock in the study area isdominated by sandstone which is controlled by large percent-age of quartz element starting from south of the central areaand trends northeast From the foregoing analysis ASTERdata specifically the TIR bands demonstrates high potentialto distinguish different rock types and their quartz contentusing spectral transformation It is interesting to note thatthe procedure has capability to highlight grain level detailsof quartz element in rocks However it is important tounderstand that when using optical remote sensing imagerythe result may vary according to season of the year Forinstance physical and chemical properties of the rocks maybe disbanded and change from under heavy precipitationand heat depending on their crystal structure Because ofthe relative stability seasonal change is not that a challengefor mapping quartz mineral using remote sensing imageryNevertheless when theminerals come in any indirect contactwith weak acid water which is the case in tropical regionquartz mineral is more prone to weathering

5 Conclusion

As mentioned earlier the lithospheric characteristic of thestudy area includes the igneous sedimentary and metamor-phic rocks Exploration of the ASTER thermal bands revealsthese different rocks and their quartz content richness Thegeneral observation is that the distribution of quartz amongthe different rocks is quite uneven While the pyroclasticvolcanic igneous rock and the carbonate dominated Permiansedimentary rocks are quartz-poor almost void the Triassicsedimentary rock largely composed of sandstone is quartz-rich On the other hand metamorphic rock shows variationacross the spatial extent having some grains of quartz contentwith some quartz-rich patches within Also the PC greyscaleimage enhanced surficial background highlighted agrees wellwith the known geologic information of the area Obviously

sandstone depositional sedimentary rock of the Triassicformation is more associated with high concentration ofquartz than other rocks

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This research was supported by the Ministry of Higher Edu-cation Malaysia under Exploratory Research Grant SchemeERGS12013STWNO6UPM022 and the University PutraMalaysia (Vote no 5527147) The authors would like toexpress their gratitude to the Department of Minerals andGeoscience Malaysia (JMG) and the Department of SurveyandMappingMalaysia (JUPEM) for providing various essen-tial datasets used in this study and also to Land ProcessesDistributed Active Archive Center (LP DAAC) for providingASTER L1B imagery used in this study

References

[1] T I R Almeida C R S Filho C Juliani and F CBranco ldquoApplication of remote sensing to geobotany to detecthydrothermal alteration facies in epithermal high-sulfidationgold deposits in the Amazon regionrdquo in SEG Reviews inEconomic Geology R Bedell A P Crosta and E Grunsky Edsvol 16 pp 135ndash142 Society of Exploration Geophysicists TulsaOkla USA 2009

[2] A B Pour M Hashim and M Marghany ldquoExploration of goldmineralization in a tropical region using Earth Observing-1(EO1) and JERS-1 SAR data a case study from Bau gold fieldSarawak Malaysiardquo Arabian Journal of Geosciences vol 7 no 6pp 2393ndash2406 2014

[3] F D van der Meer H M A van der Werff F J A van Ruiten-beek et al ldquoMulti-and hyperspectral geologic remote sensing areviewrdquo International Journal of Applied Earth Observation andGeoinformation vol 14 no 1 pp 112ndash128 2012

[4] E E Mshiu C Glaszliger and G Borg ldquoIdentification ofhydrothermal paleofluid pathways the pathfinders in the explo-ration of mineral deposits a case study from the SukumalandGreenstone Belt Lake Victoria Gold Field Tanzaniardquo Advancesin Space Research vol 55 no 4 pp 1117ndash1133 2015

[5] A B Pour and M Hashim ldquoThe application of ASTER remotesensing data to porphyry copper and epithermal gold depositsrdquoOre Geology Reviews vol 44 pp 1ndash9 2012

[6] L L Tornabene J E Moersch G R Osinski P Lee andS P Wright ldquoSpaceborne visible and thermal infrared litho-logic mapping of impact-exposed subsurface lithologies at theHaughton impact structure Devon Island Canadian HighArctic applications to MarsrdquoMeteoritics and Planetary Sciencevol 40 no 12 pp 1835ndash1858 2005

[7] S Gad and T Kusky ldquoASTER spectral ratioing for lithologicalmapping in the Arabian-Nubian shield the NeoproterozoicWadi Kid area Sinai Egyptrdquo Gondwana Research vol 11 no3 pp 326ndash335 2007

[8] A B Pour and M Hashim ldquoIdentification of hydrothermalalteration minerals for exploring of porphyry copper depositusing ASTER data SE Iranrdquo Journal of Asian Earth Sciences vol42 no 6 pp 1309ndash1323 2011

8 Journal of Sensors

[9] M Pournamdari M Hashim and A B Pour ldquoSpectral trans-formation of ASTER and Landsat TM bands for lithologicalmapping of Soghan ophiolite complex south Iranrdquo Advances inSpace Research vol 54 no 4 pp 694ndash709 2014

[10] A B Pour andMHashim ldquoIdentifying areas of high economic-potential copper mineralization using ASTER data in theUrumieh-Dokhtar Volcanic Belt Iranrdquo Advances in SpaceResearch vol 49 no 4 pp 753ndash769 2012

[11] D A-F Batchelor ldquoGeological characteristics of the Pulaialluvial gold deposit South Kelantan Malaysiardquo Journal ofSoutheast Asian Earth Sciences vol 10 no 1-2 pp 101ndash108 1994

[12] D A Nazaruddin N S Fadilah and Z Zulkarnain ldquoGeologicalstudies to support the tourism site a case study in the RafflesiaTrail near Kampung Jedip Lojing Highlands Kelantanrdquo Inter-national Journal of Geosciences vol 5 pp 835ndash851 2014

[13] G S Heng T G Hoe and W F W Hassan ldquoGold mineral-ization and zonation in the state of Kelantanrdquo Bulletin of theGeological Society of Malaysia vol 52 pp 129ndash135 2006

[14] B Li S-Y Jiang H-Y Zou M Yang and J-Q Lai ldquoGeologyand fluid characteristics of the Ulu Sokor gold deposit Kelan-tan Malaysia implications for ore genesis and classification ofthe depositrdquo Ore Geology Reviews vol 64 no 1 pp 400ndash4242015

[15] K Shah ldquoMesothermal lode gold deposit Central Belt Peninsu-lar Malaysiardquo in Earth Sciences D Imran Ahmad Ed pp 314ndash342 InTech 2012

[16] S Yusoff B Pradhan M A Manap and H Z M ShafrildquoRegional gold potential mapping in Kelantan (Malaysia) usingprobabilistic based models and GISrdquo Open Geosciences vol 7no 1 pp 149ndash161 2015

[17] A B Pour and M Hashim ldquoStructural geology mappingusing PALSAR data in the Bau gold mining district SarawakMalaysiardquo Advances in Space Research vol 54 no 4 pp 644ndash654 2014

[18] S S Matar and A O Bamousa ldquoIntegration of the ASTERthermal infra-red bands imageries with geological map of JabalAl Hasir area Asir Terrane the Arabian Shieldrdquo Journal ofTaibah University for Science vol 7 no 1 pp 1ndash7 2013

[19] TheQuartzPage ldquoThe Quartz Page Quartz as a Rock-FormingMineralrdquo The Quartz Page 2014 httpwwwquartzpagedegen rockhtml

[20] Y Ninomiya and B Fu ldquoRegional scale Lithologic mappingin western Tibet using ASTER thermal infrared multispectraldatardquo in Proceedings of the Technical Commission VIII Sympo-sium on Networking the World with Remote Sensing (ISPRS rsquo10)vol 38 pp 454ndash458 August 2010

[21] H Tonooka S J Hook T Matsunaga S Kato E Abbott andH Tan ldquoASTERTIR vicarious calibration activities in the last 11yearsrdquo in Proceedings of the IEEE International Geoscience andRemote Sensing Symposium (IGARSS rsquo11) pp 3653ndash3656 July2011

[22] E A Bahiru Inter-relationship between lithology and structureand its control on gold mineralization in Buhweju area SW ofUganda [thesis] University of Twente 2011

[23] A Koc Remote Sensing Study of Surgu Fault Zone (MalatyaTurkey) Middle East Technical University 2005

[24] Q Cheng G Bonham-Carter W Wang S Zhang W Li andX Qinglin ldquoA spatially weighted principal component analysisfor multi-element geochemical data for mapping locations offelsic intrusions in the Gejiumineral district of Yunnan ChinardquoComputers and Geosciences vol 37 no 5 pp 662ndash669 2011

[25] A B Pour and M Hashim ldquoApplication of advanced space-borne thermal emission and reflection radiometer (ASTER)data in geological mappingrdquo International Journal of PhysicalSciences vol 6 no 33 pp 7657ndash7668 2011

[26] C Kwang E Osei Jnr and A Duker ldquoApplication of remotesensing and geographic information systems for gold potentialmapping in birim North District of Eastern Region of Ghana-gold potential mapping using GIS and remote sensingrdquo Inter-national Journal of Remote Sensing Applications vol 4 no 1 p48 2014

[27] A Ezzati RMehrnia and K Ajayebi ldquoDetection of Hydrother-mal potential zones using remote sensing satellite data inRamand region Qazvin Province Iranrdquo Journal of Tethys vol2 no 2 pp 93ndash100 2014

[28] L Bertoldi ldquoRemote Sensing of granitoid rocks image elabora-tion and spectral signatures (Morocco calc-alkaline Corse andHimalayan peraluminose granitoid case studies)rdquo Interpret AJ Bible Theol 2010

[29] N Surip A H Hamzah M R Zakaria A Napiah and JA Talib ldquoMapping of gold in densely vegetated area usingremote sensing and GIS techniques in Pahang MalaysiardquoOpenGeosciences vol 7 pp 149ndash161 2015

[30] R Amer T Kusky and A El Mezayen ldquoRemote sensingdetection of gold related alteration zones in Um Rus areacentral eastern desert of EgyptrdquoAdvances in Space Research vol49 no 1 pp 121ndash134 2012

[31] BHe C Chen andY Liu ldquoGold resources potential assessmentin eastern Kunlun Mountains of China combining weights-of-evidence model with GIS spatial analysis techniquerdquo ChineseGeographical Science vol 20 no 5 pp 461ndash470 2010

[32] G Oliver K Zaw M Hotson S Meffre and T Manka ldquoU-Pbzircon geochronology of Early Permian to Late Triassic rocksfrom Singapore and Johor a plate tectonic reinterpretationrdquoGondwana Research vol 26 no 1 pp 132ndash143 2014

[33] K Zaw S Meffre C-K Lai et al ldquoTectonics and metallogenyofmainland Southeast Asiamdasha review and contributionrdquoGond-wana Research vol 26 no 1 pp 5ndash30 2014

8 Journal of Sensors

[9] M Pournamdari M Hashim and A B Pour ldquoSpectral trans-formation of ASTER and Landsat TM bands for lithologicalmapping of Soghan ophiolite complex south Iranrdquo Advances inSpace Research vol 54 no 4 pp 694ndash709 2014

[10] A B Pour andMHashim ldquoIdentifying areas of high economic-potential copper mineralization using ASTER data in theUrumieh-Dokhtar Volcanic Belt Iranrdquo Advances in SpaceResearch vol 49 no 4 pp 753ndash769 2012

[11] D A-F Batchelor ldquoGeological characteristics of the Pulaialluvial gold deposit South Kelantan Malaysiardquo Journal ofSoutheast Asian Earth Sciences vol 10 no 1-2 pp 101ndash108 1994

[12] D A Nazaruddin N S Fadilah and Z Zulkarnain ldquoGeologicalstudies to support the tourism site a case study in the RafflesiaTrail near Kampung Jedip Lojing Highlands Kelantanrdquo Inter-national Journal of Geosciences vol 5 pp 835ndash851 2014

[13] G S Heng T G Hoe and W F W Hassan ldquoGold mineral-ization and zonation in the state of Kelantanrdquo Bulletin of theGeological Society of Malaysia vol 52 pp 129ndash135 2006

[14] B Li S-Y Jiang H-Y Zou M Yang and J-Q Lai ldquoGeologyand fluid characteristics of the Ulu Sokor gold deposit Kelan-tan Malaysia implications for ore genesis and classification ofthe depositrdquo Ore Geology Reviews vol 64 no 1 pp 400ndash4242015

[15] K Shah ldquoMesothermal lode gold deposit Central Belt Peninsu-lar Malaysiardquo in Earth Sciences D Imran Ahmad Ed pp 314ndash342 InTech 2012

[16] S Yusoff B Pradhan M A Manap and H Z M ShafrildquoRegional gold potential mapping in Kelantan (Malaysia) usingprobabilistic based models and GISrdquo Open Geosciences vol 7no 1 pp 149ndash161 2015

[17] A B Pour and M Hashim ldquoStructural geology mappingusing PALSAR data in the Bau gold mining district SarawakMalaysiardquo Advances in Space Research vol 54 no 4 pp 644ndash654 2014

[18] S S Matar and A O Bamousa ldquoIntegration of the ASTERthermal infra-red bands imageries with geological map of JabalAl Hasir area Asir Terrane the Arabian Shieldrdquo Journal ofTaibah University for Science vol 7 no 1 pp 1ndash7 2013

[19] TheQuartzPage ldquoThe Quartz Page Quartz as a Rock-FormingMineralrdquo The Quartz Page 2014 httpwwwquartzpagedegen rockhtml

[20] Y Ninomiya and B Fu ldquoRegional scale Lithologic mappingin western Tibet using ASTER thermal infrared multispectraldatardquo in Proceedings of the Technical Commission VIII Sympo-sium on Networking the World with Remote Sensing (ISPRS rsquo10)vol 38 pp 454ndash458 August 2010

[21] H Tonooka S J Hook T Matsunaga S Kato E Abbott andH Tan ldquoASTERTIR vicarious calibration activities in the last 11yearsrdquo in Proceedings of the IEEE International Geoscience andRemote Sensing Symposium (IGARSS rsquo11) pp 3653ndash3656 July2011

[22] E A Bahiru Inter-relationship between lithology and structureand its control on gold mineralization in Buhweju area SW ofUganda [thesis] University of Twente 2011

[23] A Koc Remote Sensing Study of Surgu Fault Zone (MalatyaTurkey) Middle East Technical University 2005

[24] Q Cheng G Bonham-Carter W Wang S Zhang W Li andX Qinglin ldquoA spatially weighted principal component analysisfor multi-element geochemical data for mapping locations offelsic intrusions in the Gejiumineral district of Yunnan ChinardquoComputers and Geosciences vol 37 no 5 pp 662ndash669 2011

[25] A B Pour and M Hashim ldquoApplication of advanced space-borne thermal emission and reflection radiometer (ASTER)data in geological mappingrdquo International Journal of PhysicalSciences vol 6 no 33 pp 7657ndash7668 2011

[26] C Kwang E Osei Jnr and A Duker ldquoApplication of remotesensing and geographic information systems for gold potentialmapping in birim North District of Eastern Region of Ghana-gold potential mapping using GIS and remote sensingrdquo Inter-national Journal of Remote Sensing Applications vol 4 no 1 p48 2014

[27] A Ezzati RMehrnia and K Ajayebi ldquoDetection of Hydrother-mal potential zones using remote sensing satellite data inRamand region Qazvin Province Iranrdquo Journal of Tethys vol2 no 2 pp 93ndash100 2014

[28] L Bertoldi ldquoRemote Sensing of granitoid rocks image elabora-tion and spectral signatures (Morocco calc-alkaline Corse andHimalayan peraluminose granitoid case studies)rdquo Interpret AJ Bible Theol 2010

[29] N Surip A H Hamzah M R Zakaria A Napiah and JA Talib ldquoMapping of gold in densely vegetated area usingremote sensing and GIS techniques in Pahang MalaysiardquoOpenGeosciences vol 7 pp 149ndash161 2015

[30] R Amer T Kusky and A El Mezayen ldquoRemote sensingdetection of gold related alteration zones in Um Rus areacentral eastern desert of EgyptrdquoAdvances in Space Research vol49 no 1 pp 121ndash134 2012

[31] BHe C Chen andY Liu ldquoGold resources potential assessmentin eastern Kunlun Mountains of China combining weights-of-evidence model with GIS spatial analysis techniquerdquo ChineseGeographical Science vol 20 no 5 pp 461ndash470 2010

[32] G Oliver K Zaw M Hotson S Meffre and T Manka ldquoU-Pbzircon geochronology of Early Permian to Late Triassic rocksfrom Singapore and Johor a plate tectonic reinterpretationrdquoGondwana Research vol 26 no 1 pp 132ndash143 2014

[33] K Zaw S Meffre C-K Lai et al ldquoTectonics and metallogenyofmainland Southeast Asiamdasha review and contributionrdquoGond-wana Research vol 26 no 1 pp 5ndash30 2014

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