Structural geology mapping using PALSAR data in the Bau gold mining district, Sarawak, Malaysia

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Advances in Space Research xxx (2014) xxx–xxx

Structural geology mapping using PALSAR data in the Bau goldmining district, Sarawak, Malaysia

Amin Beiranvand Pour ⇑, Mazlan Hashim

Institute of Geospatial Science & Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia

Received 11 January 2013; received in revised form 9 February 2014; accepted 10 February 2014

Abstract

The application of optical remote sensing data for geological mapping is difficult in the tropical environment. The persistent cloudcoverage, dominated vegetation in the landscape and limited bedrock exposures are constraints imposed by the tropical climate. Struc-tural geology investigations that are searching for epithermal or polymetallic vein-type ore deposits can be developed using SyntheticAperture Radar (SAR) remote sensing data in tropical/sub-tropical regions. The Bau gold mining district in the State of Sarawak, EastMalaysia, on the island of Borneo has been selected for this study. The Bau is a gold field similar to Carlin style gold deposits, but goldmineralization at Bau is much more structurally controlled. Geological analyses coupled with the Phased Array type L-band SyntheticAperture Radar (PALSAR) remote sensing data were used to detect structural elements associated with gold mineralization. The PAL-SAR data were used to perform lithological-structural mapping of mineralized zones in the study area and surrounding terrain. Struc-tural elements were detected along the SSW to NNE trend of the Tuban fault zone and Tai Parit fault that corresponds to the areas ofoccurrence of the gold mineralization in the Bau Limestone. Most of quartz-gold bearing veins occur in high-angle faults, fractures andjoints within massive units of the Bau Limestone. The results show that four deformation events (D1–D4) in the structures of the Baudistrict and structurally controlled gold mineralization indicators, including faults, joints and fractures are detectable using PALSARdata at both regional and district scales. The approach used in this study can be more broadly applicable to provide preliminary infor-mation for exploration potentially interesting areas of epithermal or polymetallic vein-type mineralization using the PALSAR data in thetropical/sub-tropical regions.� 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: PALSAR; Bau gold mining district; Structural mapping; Malaysia

1. Introduction

Synthetic Aperture Radar (SAR) image data provideinformation different from that of optical sensors operatingin the visible near-infrared through the shortwave infrared(0.4–2.5 lm) and/or the thermal infrared (8.0–14.0 lm)wavelength regions of the electromagnetic spectrum. SARis an active microwave remote sensing system which can

http://dx.doi.org/10.1016/j.asr.2014.02.012

0273-1177/� 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +60 7 5530666; fax: +60 7 5531174.E-mail addresses: [email protected], beiranvand.amin80@gmail.

com (A.B. Pour), [email protected], [email protected](M. Hashim).

Please cite this article in press as: Pour, A.B., Hashim, M. Structural geoloawak, Malaysia. J. Adv. Space Res. (2014), http://dx.doi.org/10.1016/j.as

acquire data with high resolution regardless of day or nighttime, cloud, haze or smoke over a region. Clouds are rea-sonably transparent to microwave providing measurementswith almost any weather conditions. Radar transmits anddetects radiation between 2.0 and 100 cm, typically at2.5–3.8 cm (X band), 4.0–7.5 cm (C band), and 15.0–30.0 cm (L band) (Spatz, 1997; Woodhouse, 2006; Camp-bell, 2007).

SAR sensors transmit electromagnetic radiation at spec-ified wavelengths and measure the reflected energy. Resolu-tion is a function of the antenna size and the wavelength.However, the shorter the wavelength, the higher the spatial

gy mapping using PALSAR data in the Bau gold mining district, Sar-r.2014.02.012


mailto:[email protected]







2 A.B. Pour, M. Hashim / Advances in Space Research xxx (2014) xxx–xxx

resolution possible and greater the surface detail recorded(Spatz, 1997). Several studies have shown that SAR imag-ery is useful for geological mapping. SAR images havebeen used for geological mapping in glaciated and vege-tated terrain, structural geology investigations that aresearching for mineral deposits and hydrocarbon traps,and studies of geologic hazards (Singhroy, 1992; Abdelsa-lam and Stern, 2000; Kusky and Ramadan, 2002; Jinet al., 2007, 2013; Zandbergen, 2008; Amer et al., 2012;Pour et al., 2013a,b; Chang et al., 2014). SAR data are usedmostly to map structural elements, but they can be appliedfor lithological mapping due to variations in outcrop pat-terns and the size and frequency of coarse rock detritus.

SAR imagery can be applied to tropical regions becauseit penetrates clouds and because the morphologies of uppersurface of forest canopies reflect underlying topograghywhich in turn relates to structure and lithology. X and Cband radar are reflected strongly by vegetation and for thisreason may indirectly highlight structure and lithology. Xand C band radar does not physically penetrate vegetation.These radar frequencies may, however, penetrate betweenplants. C band radar has a wavelength similar to the sizeof small-scale vegetation characteristics such as crop struc-ture, foliage, and tree canopy structure. SAR images at Cband are dependent on the variations of these features. Incontrast, longer wavelength L band radar has a wavelengthon the scale of tree trunk and branch structures, may scatterwithin the canopy and reradiate. Fine textural variation ofoutcrop and surface roughness are detectable features formicrowave X band. Intermediate and coarse textural varia-tion of outcrop and surface roughness can be detected usingmicrowave interval of C and L band radar, respectively(Spatz, 1997; Paillou et al., 2007; Zandbergen, 2008;Pettinato et al., 2013; Pour et al., 2013b). Therefore, delin-eate foliation, joint patterns, shears, lithologies, and alter-ation based on float size, outcrop pattern, and vegetationdifferences can be mapped using microwave X band radardata at a district scale. Faults, folds, topographic breaks,bedding, depressions, lithologies and intrusive contactsand vegetation species and distribution can be detectedusing C and L band radar images at a regional scale.

Phased Array type L-band Synthetic Aperture Radar(PALSAR) was developed by Japanese Ministry ofEconomy, Trade and Industry (METI) and Japan Aero-space Exploration Agency (JAXA) for acquisition of databeneficial to resource exploration and environmental protec-tion. It was launched on January 24, 2006 onboardAdvanced Land Observing Satellite (ALOS) (ERSDAC,2006). PALSAR data can be used in specific fields, include(i) land area basin mapping (geological structural analysisof target areas; collect database of potential natural resourcedeposit areas); (ii) coastal area basin mapping (extraction ofoil exudating areas; monitoring of contamination accompa-nied by development activities); (iii) monitoring ofenvironments and natural disasters (monitoring of disastersuch as landslide, volcanic activities, floods and other;environmental monitoring such as forests; international


cooperation); and (iv) research and development for the pro-cessing and application of multi polarimetric SAR data (geo-logical structural analysis on the first stage of resourceexploration) (ERSDAC, 2006). Consequently, PALSARdata contain great contribution for the geological structuralanalysis especially in tropical regions, where optical sensorsoften failed due to bad weather conditions.

In this investigation, the detection of structural elementssuch as faults, folds bedding, depressions, lithologies, intru-sive contacts at a regional scale and fractures associatedwith vein-type gold occurrences at a district scale in theBau gold mining district, Sarawak province, easternMalaysia, on the island of Borneo, Southeast Asia(Fig. 1) is evaluated using PALSAR data. Adaptive Leeand Co-occurrence texture filters are used to detect struc-tural elements and fractures associated with vein-type goldoccurrences.

2. Geology of the study area

The area under investigation is the Bau mining district,which is located 1� 250 1000 N and 110� 100 3700 E in 25 kmsouthwest of Kuching city, Sarawak. The climate of Bau ishumid tropical and characterized by heavy but seasonalrainfall, uniform temperature, and high humidity. Some40% of the land is primary rain forest restricted to theinfertile limestone hills and the higher mountains (Andri-esse, 1972). Bau gold field is located on a mineralized trendbelt, and a variety of styles of mineralization was recog-nized, ranging from mesothermal to low-temperaturedeposits. Previous geological studies indicated that goldmineralization exhibits significant structural (faults) andstratigraphic (composition/permeability) controls (Percivalet al., 1990; Schuh, 1993; Dill and Horn, 1996).

The 1:25,000 Bau sheet centered on the town of Bau hasbeen selected for this study (Fig. 2). The oldest units knowin the Bau are the Late Triassic andesitic Serian Volcanics.The Early Jurassic orogeny brought the emplacement ofthe Jagoi granodiorite complex. Deposition of the thickshallow marine limestone of the Bau limestone formationoccurred in Upper Jurassic. Massive medium to light gray,relatively pure limestone make up the bulk of the Bau lime-stone member. The Cretaceous Pedwan formation consistsof extensive Bouma-bedded turbidites; it was obducted in alate Cretaceous accretion event. Geographically, thePedwan is in an intermediate position between the formerEarly Jurassic to Early Cretaceous. An extensiveNNE-trending right-lateral shear zone opened during theMiocene. A string of shallow micrograodiorite intrusionswas emplaced along intersects of shear zone withENE-trending stress field. The ENE zones represent reacti-vated Early Jurassic fault systems. Igneous activity resultedin a string of E–W trending stocks. Regional uplift contin-ued through the late Tertiary and Quaternary, resulting inwidespread erosion to expose the Miocene stocks. Pleisto-cene sea level fluctuations caused a peneplanation surfaceat level of 60–90 m (Schuh, 1993).



Fig. 1. Location of the study area in Southeast Asia. The Bau gold mining district is on .westernmost Borneo. It is located in the State of Sarawak which ispart of Malaysia (Morley, 2012).

A.B. Pour, M. Hashim / Advances in Space Research xxx (2014) xxx–xxx 3

2.1. Structural geology of the Bau area

The strong structural control on essentially ore depositsin the Bau district is consonant with the important implica-tions for the search for them (Percival et al., 1990; Schuhand Guilbert, 1990; Dill and Horn, 1996). Four deforma-tion events (D1 to D4) are manifested in the structures ofthe Bau district.

2.1.1. Deformation event l

The ENE trend is the oldest structural trend in Bau.This deep-seated set of fractures must have been activebefore the Early Jurassic, when it was the site of the grano-diorite intrusions of Jagoi. Structures of the first deforma-tion event can be recognized in several places where theyare cut by the regional ENE Bau trend structures. TheENE trend consists of numerous parallel fracture zonesin the Bau such as Traan–Staat fault, Tegora fault, Joagoifault and Tuban fault (Fig. 2).

2.1.2. Deformation event 2

The compression direction of this event was SW–NEoriented; resulting in relative upright and tight folds (Mar-joribanks, 1989; Wolfenden, 1965).



The D3 event is compressive and varies from a W–E to aNW–SE direction, with less tight folds than D2. This eventhas gently folded the Tertiary molasse basins bordering thestudy area to the S and NW, respectively. Kojok fault wascreated with this deformation event. It is also responsiblefor the Bau anticline, regional folds of the Bau Limestone.Bau Anticline in the south is the most prominentNE-trending features of the NW–SE compressive D3 event(Fig. 2). Intersections of anticlines with anticlines of the D2and D3 are thought to have produced structural and topo-graphic highs, while additions of two synclines producedlows-superposition of orthogonal sets of folds (Wolfenden,1965). Transcurrent faulting along the Tai Parit and TiaTon faults and Tuban Fault Zone has apparently ‘bent’the axial trends of the D3 folds (Schuh, 1993).


The D4 event is characterized by NNE trending linea-ments. The most important one is the Bau Trend, alongwhich the Mid-Miocene microgranodiorites of the Bau dis-trict have been emplaced. Displacement of limestone unitsand apparent ‘bends’ of the strike axes of D3 fold axes areevidence for a right-lateral transcurrent to transtensional



Fig. 2. Geologic map of the Bau mining district and location of gold occurrences (modified from North Borneo Gold SDN BHD, 2012).


faulting along the Bau trend. The NNE trend which hasdeveloped during the D4 event is characterized by the TaiParit fault system (Fig. 2) (Schuh, 1993).

3. Materials

3.1. PALSAR data

PALSAR sensor is a L-band synthetic aperture radar,with multi mode observation function (Fine mode, Direct


downlink, ScanSar mode, and Polarimetric mode) of multipolarization configuration (HH, HV, VH, and VV),variable off-nadir angle (9.9–50.8�), and switching spatialresolution (10 m, 30 m, 100 m for Fine, Polarimetric, andScanSar modes, respectively) and swath width observation(30 km, 70 km, and 250–350 km for Polarimetric, Fine andScanSar modes, respectively) (Igarashi, 2001; Rosenqvistet al., 2004; ERSDAC, 2006). Full polarimetry (multi-polarization), off nadir pointing function and otherfunctions of PALSAR improved the accuracy of analyzing



Fig. 3. Geo-reference and Geo-coded Fine mode Level 1.5 PALSAR full scene of the west of Borneo.


geological structure, distribution of rocks, and expected tobe used for the first stage of ore deposits and hydrocarbonexploration, and environmental protection (ERSDAC,2006).

Main distortions suffered by a SAR image due to the side-looking architecture are foreshortening, layover and shad-owing (Gelautz et al., 1998; Franceschetti and Lanari, 1999).

Foreshortening is a dominant effect in SAR images ofmountainous areas. It is due to the side-looking geometryof Synthetic Aperture Radar. Especially in the case ofsteep-looking spaceborne sensors, the across-track slant-range differences between two points located on foreslopesof mountains are smaller than they would be in flat areas.Foreshortening is obvious in mountainous areas, where themountains seem to “lean” towards the sensor. This effectresults in an across-track compression of the radiometricinformation backscattered from foreslope areas whichmay be compensated during the geocoding process if a


terrain model is available (Ouchi, 1988; Franceschetti andLanari, 1999).

Layover occurs in those cases where the top of themountain is closer to the sensor than the bottom, i.e.the terrain is sufficiently steep. Layover areas appear inthe image as bright regions with the original geometricorder being disturbed. Similar to optical images, thoseareas which are not illuminated by the radar beam arecalled shadows. Shadow areas appear in the image as darkregions corrupted by thermal noise (Gelautz et al., 1998).Shadowing areas (i.e. slopes facing away from the sensor)are darker since the energy is spread over a larger area orthey are not visible at all.

In this investigation, a PALSAR Fine mode Level 1.5scene was obtained from the Earth and Remote SensingData Analysis Center (ERSDAC) Japan (http://gds.pal-sar.ersdac.jspacesystems.or.jp/e/) for the Bau mining dis-trict and surrounding area. It was acquired on 14 March


http://www.gds.palsar.ersdac.jspacesystems.or.jp/e/

http://www.gds.palsar.ersdac.jspacesystems.or.jp/e/


Fig. 4. The adaptive Lee filtered PALSAR image of the Bau mining district and surrounding area.

Fig. 5. The Co-occurrence texture filtered PALSAR image of the Bau mining district and surrounding area.


2011. Level 1.5 products are such data that are performedthe following processing to Level 1.0 (raw) data of high res-olution mode. The Level 1.5 product used in this study hashigh resolution mode with 6.25 m pixel spacing and single


polarization (HH or VV), which is geo-reference and geo-coded. Nominal incident angle is 7.9–60.0�. The PALSARdata were processed using the ENVI (Environment forVisualizing Images) version 4.8 software package.




3.2. Field reconnaissance and verification of image

processing results

A field reconnaissance was carried out to verify theimage processing results during 7–10 November 2012.Geological locations were measured by a Garmin� eTrexLegend�H GPS. Ground photos were taken of thegeomorphology, rock units and structure elements in theBau and surrounding area. Image processing results com-pared with geology map (1:25,000) (Fig. 2), Quickbirdimage map, and geophysical map of 450 HZ resistivity(1:25,000) of the Bau area. These data sources were pro-vided by Olympus Pacific Minerals INC. Company in2012 (North Borneo Gold SDN BHD).

4. Methods

The main purpose of the methodology is to apply imageprocessing techniques that can detect the structural ele-ments at both regional and district scales in the study areausing Level 1.5 PALSAR data. Radar images are inher-ently corrupted by speckle. The presence of speckle in animage reduces the detectability of ground targets, obscuresthe spatial patterns of surface features, and decreases the

Fig. 6. The adaptive Lee filtered PAL


accuracy of automated image classification. Therefore, itis necessary to treat the speckle by filtering the data beforeit can be used in various applications. A speckle suppres-sion filter is expected to filter the homogeneous areas withreasonable speckle reduction capability, retain edges, pre-serve features (linear features and point features), and havereasonable theoretical assumptions.

In this study, we applied the adaptive Lee and Co-occur-rence texture filters to extract structural elements fromLevel 1.5 PALSAR data. An adaptive filter uses a standarddeviation of those pixels within a box surrounding eachpixel to calculate a new pixel value. Usually the originalvalue is replaced to the one calculates (those that satisfythe standard deviation criteria) (Shi and Fung, 1994).Adaptive filters such as Lee, Kuan, Frost, Gamma, LocalSigma, and Bit Errors preserve image sharpness and detailwhile suppressing noise (speckle) in the SAR image. Theseadaptive filters are implemented and documented withinthe ENVI software system.

The adaptive Lee filter was applied to the PALSARimage to accomplish speckle reduction and preserving bothedges and features. Lee filter is based on the multiplicativemodel of speckle noise, which is at first approximated by alinear model. Then the minimum mean square error

SAR image of the Bau limestone.




criterion is applied to this linear model (Lee, 1980; Shi andFung, 1994; Sheng and Xia, 1996; Sveinsson and Bene-diktsson, 1996).

The second moment Co-occurrence texture filter isbased on the Co-occurrence matrix, including mean, vari-ance, homogeneity, contrast, dissimilarity, entropy, secondmoment and correlation. The Co-occurrence texture filteruses gray-tone spatial dependence matrix to calculate tex-ture values. This is a matrix of relative frequencies withwhich pixel values occurs in two neighboring processingwindows separated by a specified distance and direction.It shows the number of occurrences of the relationshipbetween a pixel and its specified neighbor (Anys et al.,1994; Park and Chen, 2001).

In this study, the second moment Co-occurrence texturefilter was applied to the Lee-filter resultant PALSAR imagefor more detail lineament feature extraction. We used thepixels in the 3 * 3 base window and the pixels in a 3 * 3 win-dow that was shifted by 1 pixel for implementing the Co-occurrence matrix. High gray scale 64 was setting to reducethe shade and highlight the dark pixels of fracture zones.We organized the procedure for extracting the structuralelements at both regional and district scales in the studyarea as a few plates, including (1) the original Geo-codedPALSAR image filtered using the adaptive Lee filter atregional scale; (2) PALSAR image filtered using the adap-tive Lee filter at district scale; (3) PALSAR image filteredusing the Co-occurrence texture at district scale; and (4)structures extracted using the filtered PALSAR image.

Fig. 7. (A) A panoramic view of structural elements covered by vegetation; (B)D4 faults; (D) a view of the intersections of lineaments with folds.


5. Results and discussion

Analysis and interpretation of the PALSAR image wasdone based on the above mentioned organization. How-ever, during this section, our field work and literaturereview are used to interpret the lineaments found in thePALSAR image. Fig. 3 shows Geo-reference and Geo-coded Fine mode Level 1.5 PALSAR full scene of the westof Borneo, which is filtered using the adaptive Lee filter.The image shows regional structural elements in the westof Borneo, which manifests the ENE to NE D1 trend.The earliest directly recognizable tectonic event in Bau ismanifested in the ENE to NE D1 trend of the 190 ma Jagoigranodiorite intrusions, and in the Traan–Staat fault, Joa-goi fault and Tegora fault zones (Schuh, 1993). Hereafter,the study focuses on north western part of the scene whereBau limestone dissected by structural features.

Fig. 4 shows PALSAR image filtered using the adaptiveLee filter, geological structures, including Bau anticline,Bau anticline axis, Tegora fault, Traan–Staat Fault, Tubanfault zone, Tai Parit fault, Kojok fault and several linearand curvature features in the Bau and surrounding areaare detected at a regional scale. Bau anticline (NE–SWtrending) and its axis are located in the south and south-western part of the PALSAR image (Fig. 4). Tai Parit faultand Tuban fault zone (NNE-trending) and several linearand curvature features (WNW-to NW-trending) dissectedthe Bau limestone, which are identifiable in the north wes-tern part of the scene (Fig. 4). D3 event produced gentle

a view of the intersections of folds; (c) a view of the intersection of Dl with




SW to WSW trending folds, represented by broad synclinesof Paleocene molasse sediments. Corresponding antiformsare the Bau and Ropih anticlines (Kirk, 1968; Kim, 1994;Metcalfe, 2006).

The Co-occurrence texture filter identified the edges ofthe surrounding pixels, highlighting the fault and lineamentstructures in the PALSAR image. Hence, geological struc-tures can be more obviously detected in the PALSARimage of the study area at a regional scale. Fig. 5 showsresultant mean image derived from the Co-occurrence tex-ture filter. Structural features such as fault and fracturesappear clearly in Fig. 5, ENE to NE D1 trend, NE–SWand WNW to NW trending D3 event and NNE trend ofD4 event are well exposed in the PALSAR image filteredusing the Co-occurrence texture. Structural elementsshown in the image reflect a verity of geological and topo-graphical features in the study area. Some faults andfractures such as Joagoi fault, Tai Ton fault, Tuban faultzone have also been detected using the Co-occurrencetexture filtered image (Fig. 5). These structural elementshave not been clearly identified by just implementing theadaptive Lee filter on Level 1.5 PALSAR image.

Numerous calc-alkaline, I-type intrusions (Mioceneintrusive) were emplaced in transtensional portions alongcrustal weakness in the southern part of the Bau area

Fig. 8. The Co-occurrence texture filtered


(Fig. 5). Intersections of Dl and D4 became favorable sitesfor intrusions and mineralization. Differential movementalong several fault strands of the D4 Bau trend with anoverall right-lateral sense produced locally NE–SW-direc-ted extension. The resulting pathways were occupied byhydrothermal mineralization and by numerous dikes (Mar-joribanks,1989; Schuh, 1993). A major regional lineamentNNE-striking Bau Trend was the locus of Mid-Mioceneintrusions. All major ore deposits of the Bau district havebeen found along this trend (Hon, 1981; Schuh, 1993; Dilland Horn, 1996). The intrusions, associated dikes, and theore deposits are spatially associated with sites of pro-nounced structural weakness along the NNE Bau Trend.These sites are: (i) intersection of D1 with D4 lineaments;(ii) intersections of lineaments with folds; (iii) intersectionsof folds; (iv) a combination of a lineament intersection withtwo folds.

Fig. 6 illustrates the selected spatial subset scene ofPALSAR image filtered using the adaptive Lee filter, show-ing fault and lineament structures in the Bau at districtscale. Tuban fault zone, Tai Parit fault, Kojok fault, Joagoifault and Traan–Staat Fault are identified in the image at adistrict scale (Fig. 6). The length of these faults may reachup to 7 km. Several prominent faults are exposed for3–4 km within and parallel to the trend of the Bau

PALSAR image of the Bau limestone.




limestone (e.g. Tai Parit Fault, Tuban fault zone) and rep-resent exposures of the deep-seated structural zone. Mostgold deposits in the Bau district lie within the area wherethis northeast-trending structural zone intersects the axialzone of the east-to northeast-trending Bau anticline (Perci-val et al., 1990). Numerous linear and curvature featuresare detected, which are oriented W–E to NW–SE andNNE (Fig. 6). These lineaments are parallel to the majorlineaments trend in the study area.

Some surface photographs of topographic expression ofthe structural elements in the study area have been shownin Fig. 7(A)–(D). A panoramic view of structural elementscovered by vegetation, the intersections of folds, the inter-section of Dl with D4 faults and the intersections of linea-ments with folds have been shown in Fig. 7(A)–(D),respectively. Fig. 8 shows detailed local structure elementsin the Bau mining district, which is derived from theCo-occurrence texture filter. More clear appearances ofthe lineament features in the Bau gold mining district wereproduced using Co-occurrence texture filter.

6. Conclusions

We attempted to extract structural element associatedwith gold mineralization using PALSAR data in a tropicalregion. Bau gold mining district in Sarawak located onBorneo Island in Southeast Asia has been selected for thisstudy. The results demonstrate the importance and advan-tages of the use of PALSAR remote sensing data in detect-ing faults, joints, and fractures associated with vein-typegold mineralization in tropical/sub-tropical regions. Fourdeformation events (D1–D4) in the structures of the Baudistrict and structurally controlled gold mineralizationindicators, including faults, joints and fractures have beendetected using Level 1.5 PALSAR data and the approachused in this investigation. L band radar images are usefulto detect geological structural elements at regional scale,however, high resolution microwave X band radar datasuch as TerraSAR-X and COSMO-SkyMed can be morebroadly applicable to provide fine textural variation of out-crop and surface roughness for structural mapping in thefuture study.

Acknowledgements

This study was conducted as a part of Potential Aca-demic Staff (PAS) scheme granted by Universiti Teknologi

Malaysia (UTM). We acknowledge the assistance of theOlympus Pacific Minerals INC. Company (North BorneoGold SDN BHD) for their logistic support during the fieldinvestigations and ground truth data collection, as well asappreciate their assistance in various other ways during thisresearch. We also would like to express our great appreci-ation to the anonymous reviewers for their very useful andconstructive comments and suggestions for improvementof this manuscript.


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Structural geology mapping using PALSAR data in the Bau gold mining district, Sarawak, Malaysia