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International Journal of Applied Earth Observation and Geoinformation 21 (2013) 184194

Contents lists available at SciVerse ScienceDirect

International Journal of Applied Earth Observation andGeoinformation

jo u rn al hom epage: www.elsev ier .com/ locate / jag

nalysis of ASTER data for mapping bauxite rich pockets within high altitudeateritic bauxite, Jharkhand, India

rindam Guhaa,, Vivek Kr. Singhb,1, Reshma Parveenb,1, K. Vinod Kumara,2, A.T. Jeyaseelanb,1,.N. Dhanamjaya Raoc,3

National Remote Sensing Centre, Indian Space Research Organization, Balanagar, Hyderabad 50037, IndiaJharkhand Space Application Centre, 2nd Floor, Engineers Hostel-I, Dhurwa, Ranchi 834004, IndiaDepartment of Geology, Andhra University, Vishakhapatnam, A.P. 530003, India

r t i c l e i n f o

rticle history:eceived 19 March 2012ccepted 7 August 2012

eywords:STERauxiteateritic bauxitendex based principal componentopography

a b s t r a c t

Bauxite deposits of Jharkhand in India are resulted from the lateritization process and therefore are oftenassociated with the laterites. In the present study, ASTER (Advanced Space borne Thermal Emission andReflection Radiometer) image is processed to delineate bauxite rich pockets within the laterites. In thisregard, spectral signatures of lateritic bauxite samples are analyzed in the laboratory with reference to thespectral features of gibbsite (main mineral constituent of bauxite) and goethite (main mineral constituentof laterite) in VNIRSWIR (visible-near infrared and short wave infrared) electromagnetic domain. Theanalysis of spectral signatures of lateritic bauxite samples helps in understanding the differences in thespectral features of bauxites and laterites. Based on these differences; ASTER data based relative banddepth and simple ratio images are derived for spatial mapping of the bauxites developed within thelateritic province. In order to integrate the complementary information of different index image, anindex based principal component (IPC) image is derived to incorporate the correlative information ofthese indices to delineate bauxite rich pockets. The occurrences of bauxite rich pockets derived fromdensity sliced IPC image are further delimited by the topographic controls as it has been observed that

the major bauxite occurrences of the area are controlled by slope and altitude. In addition to above,IPC image is draped over the digital elevation model (DEM) to illustrate how bauxite rich pockets aredistributed with reference to the topographic variability of the terrain. Bauxite rich pockets delineated inthe IPC image are also validated based on the known mine occurrences and existing geological map of thebauxite. It is also conceptually validated based on the spectral similarity of the bauxite pixels delineated

ASTEin the IPC image with the

. Introduction

India is known for its bauxite resources. These resources arerimarily restricted in the lateritic provinces; as it is the case for theauxite deposits at the eastern part of the country. Laterites andauxites are the end product of same tropical weathering process.he intensity of geochemical leaching process (governed by Ehnd pH of the solvent, which works as an driving force in leaching

f the host rock for residually enriching the deposit) of tropicaleathering controls whether bauxite will be formed or the lateritender similar geologic and geomorphic set up (Norton, 1973;

Corresponding author. Tel.: +91 40 23884276; fax: +91 40 23772470.E-mail addresses: [email protected] (A. Guha), [email protected]

V.Kr. Singh), [email protected] (R. Parveen), vinodkumar [email protected]. Kumar), [email protected] (A.T. Jeyaseelan).1 Tel.: +91 651 2401719.2 Tel.: +91 40 23884276.3 Tel.: +91 891 2844717.

303-2434/$ see front matter 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.jag.2012.08.003R convolved laboratory spectra of bauxite samples. 2012 Elsevier B.V. All rights reserved.

Petersen, 1971). Delineation of the bauxite rich pockets within thelaterites is the primary requirement to initiate survey for bauxiteexploration. It is indeed a challenging task to separate bauxitefrom laterite in spatial domain using conventional methods as thetwo rock types are closely associated to each other and these rocksare the result of same progressive process of chemical degradationof same source rock. Consequently, the similar minerals can befound in both the lithologies but with different amounts and acontinuous transition from one species to the other can also beexpected with the formation of different intermediate products inthe outcrop. In general; bauxite is rich in gibbsite, i.e., aluminumhydroxides whereas laterite is enriched with different variants ofiron hydroxides and oxides. The present study has attempted todelineate bauxite rich pockets within the lateritic province basedon the spectral character of bauxite in the visible near infrared and

shortwave infrared (VNIRSWIR) domain. In this effort, visible nearinfrared and shortwave infrared (VNIRSWIR) data of the AdvancedSpace borne Thermal Emission and Reflection Radiometer (ASTER)sensor is utilized for delineating such bauxite rich pockets within

dx.doi.org/10.1016/j.jag.2012.08.003http://www.sciencedirect.com/science/journal/03032434http://www.elsevier.com/locate/jagmailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]/10.1016/j.jag.2012.08.003

A. Guha et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 184194 185

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ig. 1. Study area shown over ASTER false color composite; where R = 3rd band of Ao color in this figure legend, the reader is referred to the web version of the article

he lateritic province. ASTER sensor onboard at Terra satellite plat-orm is one of the most advanced space borne multispectral sensorn terms of its coverage of spectral domain, spectral resolution,nd spatial resolution (Abrams, 2000).The main motivation for carrying out the research lies in the fact

hat the research account of ASTER based indices for delineatingauxite within the lateritic province is absent, although similarndices are available for other minerals and economic rocks. Aomprehensive list of ASTER based mineral indices for mineralnd rock mapping is provided and discussed in the ASTER Mineralndex Processing Manual compiled by Aleks Kalinowski and Simonliver (http://www.ga.gov.au/image cache/GA7833.pdf). Theseuthors have proposed detail range of band combinations andatios for mineral mapping (van der Meer et al., 2012). Therefore,here is a definite scope for further research on the analysis of theSTER data for mapping the bauxite deposit based on using newineral indices. Few attempts on targeting tropical bauxite usingSTER data are available in the literature (Das, 2007; Sanjeevi,008). But their attempts are restricted to ASTER data processingased on the statistically derived end members from image forineral mapping. Although identifying the end members from

mage has its own advantage but the analysis of multispectral dataike ASTER based on the image-derived end members also hasome practical limitation as this approach often can delineate falseositives especially in a terrain with complex geological and landover elements (van der Meer and de Jong, 2001). Moreover, theerivation of end members from the ASTER image has its own lim-tation as spectral profiles of ASTER image is often generalized (ast has only nine spectral channels in VNIRSWIR spectral domain)nd therefore limits the delineation of minerals which have similar

nd closely spaced spectral features in their respective spectralurves. Moreover, ASTER data is a multispectral data and capablef highlighting significant and broad spectral features of theerrain elements based on the derivation of indices or ratio images., G = 2nd band of ASTER, B = 1st band of ASTER. (For interpretation of the references

Therefore it is essential to focus on using the approaches which canseparate these broad yet diagnostic spectral features of differenttargets from each other. In this regard, in the present study, effortsare made to derive new ASTER based indices based on the analysisof the differences in the ASTER convolved laboratory spectral signa-tures of laterites (iron-rich-residue) and bauxites (Al-rich residue).The indices thus derived are useful in separating the bauxites fromthe laterites. Further, these indices are processed to derive a prin-ciple component for delineating the spatial distribution of bauxiticpockets. PC image derived from the different indices allows the con-vergence of the complementary and supplementary informationderived from different indices to a single product. Bauxite pockets,thus delineated are further delimited by the topographic controls ofbauxite formation (i.e., slope and altitude). The empirical relation-ship between the existing mines and the altitudes (with specificslope ranges) is used as reference for fixing topographic delimitingfactors of bauxite occurrence. These topographic parameters (slopeand altitude; suitable for bauxitization) are derived from the ASTERdigital elevation model. Further, the bauxite rich exposures delin-eated from ASTER data are draped over digital elevation model tojustify the bauxite exposures in light of its required geomorphicset up of occurrences. The ideal geomorphic set up of bauxite expo-sures is localized depression or undulating cross sections exposedat surface (where erosion may have removed the top lateritic coverand exposed the bauxite). These areas are ideal to remove overlyingthe lateritic cover to expose the bauxite at the surface by removingthe lateritic capping as it has been observed, in general, that thebauxites are concentrated in the lower part of a lateritic section.

2. Study area and geologyThe study area (Fig. 1) is situated at the central-westernportion of Jharkhand state, India and geologically is the part of thePre Cambrian shield of Indian Peninsula spread mainly in three

http://www.ga.gov.au/image cache/GA7833.pdf

186 A. Guha et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 184194

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Fig. 2. Geological map of the study area. (For interpretation of the referenource: Geological survey of India.

istricts of Jharkhand: namely Latehar, Lohardaga, and Gumla.t is located in the North West part of Jharkhand state betweenatitude 23172323N and longitude 84148428E and locatedt south-east of Lohardaga (Fig. 1). Bauxite deposits in Jharkhandre generally found between altitudes of 960 m and 1075 m aboveean sea level (MSL) and occur in form of an extensive blanket

ying below the laterite cover on the flat topped plateaus withently undulating topography (Roy chowdhury et al., 1965). Itccurs as segregated discontinuous boulders and also charac-erized by its blanket form over lateritic residuum. The bauxiteearing area of Jharkhand belongs to the Pre-Cambrian shield ofndian Peninsula. The area consists of mainly of Chhotonagpurranite Gneiss associated with intrusions of quartzite and olderasic rocks. The Pre Cambrian rocks of Singhbhum lie in the south.n the north-western side of the area, Deccan trap is exposed athe base of the laterite capping (Fig. 2).

Most of the laterites are of ferruginous nature and color of theock varies from red to various shades of pink with pisolitic texture.auxite formation process is an extreme case in the lateritizationrocess where silica and iron oxides are progressively leached outrom the host rock and enriching the host rock in alumina (Bourman

nd Ollier, 2002; Schellmann, 1994). The enrichment of aluminand selective leaching of iron from laterite is essential to localize theauxite within laterite and this is achievable by low Eh conditions both Al and Fe are soluble in the similar pH condition. Therefore, color in the text, the reader is referred to the web version of the article.)

micro-variability in terrain parameters; which can play vital role inmaintaining desired Eh could have played role in localizing bauxiterich pocket within laterite above the extensive planation surface(Petersen, 1971). Minor depression on the relatively flat terrain(where the presence of humus is significant and can provide lowEh condition) is suitable for bauxite segregation (Petersen, 1971).Moreover, it is also observed from the vertical profile of lateriticbauxite (Fig. 3) that the bauxites are concentrated below the lateriteand are capped by iron-oxide residue. Therefore surface exposuresof bauxite only can be expected in the deeply incised geologicalsection with undulating surface (where erosion has removed thetop lateritic cover and exposed the underlying bauxite).

3. Materials and methods

3.1. Data

3.1.1. ASTER dataASTER (Advanced Space borne Thermal Emission and Reflection

Radiometer) acquires moderate resolution data in 14 bands, from

the visible to the thermal infrared wavelengths; and also providesstereo viewing capabilities for digital elevation model creation(ASTER, 2011). In the present study, nine spectral channels of ASTERdata (within the spectral domain of 0.522.430 m) are used. These


Table 1ASTER data specification (ASTER, 2010).

Data product Spectral bands Spectral range (m) Spatial resolution (M) Radiometric resolution

VNIR 1 0.50.60 15 82 0.630.69 15 8

3N 0.780.86 15 83B 0.780.86 15 8

SWIR 4 1.601.70 30 85 2.1452.185 30 86 2.1852.225 30 87 2.2352.285 30 88 2.2952.365 30 89 2.3602.430

csfdci

for separating bauxite from laterite based on the analysis of ASTERconvolved spectral profiles of lateritic bauxites. The detailed spec-ification of ASTER data is given in Table 1.Fig. 3. Generalized cross section of bauxite occurrence.

hannels are known as ASTER visible near infrared (VNIR) andhort wave infrared (SWIR) channels. ASTER Level 1B data productor VNIRSWIR channels are used for the study. ASTER Level 1B

ata product contains radiance-at-sensor data with the geometricorrection applied to the data. ASTER SWIR channels are verymportant for detecting absorption signatures of minerals with the

Fig. 4. Laboratory spectra of lateritic bauxite samples.30 8

Al OH, Mg OH, Ca CO3 bonds whereas the ASTER VNIR channelcan highlight the absorption features of the iron-hydroxides.ASTER sensor is better suitable for the spectral discriminationstudies for mapping economic rock like bauxite than the otherexiting multispectral sensors such as Landsat, SPOT, and IndianRemote sensing (IRS) satellites (Zhang et al., 2007) since it has sixspectral channels each of which is suitable for mapping specificlithology. In addition to this, ASTER derived digital elevation datacan provide very good topographic information suitable for under-standing the terrain morphology (Abrams, 2000); which is alsoessential for understanding the topographic controls for bauxiteformation.

ASTER data is the most widely used sensor for geological explo-ration in last decade especially for the spectral information of itsSWIR channels. ASTER data has been utilized extensively for map-ping the alteration zones associated with hydrothermal deposit ormapping specific lithology for different part of the world (Aziziet al., 2010; Bedini, 2011; Crosta et al., 2003; Crosta and Filho, 2003;Gabr et al., 2010; Galvao et al., 2005; Haselwimmer et al., 2011;Hewson et al., 2005; Hosseinjani and Tangestani, 2011; Hubbardand Crowley, 2005; Pour and Hashim, 2011, 2012). Moreover,ASTER data also have been utilized for mapping lithological unitsbased on the thermal properties of rocks derived from the thermalchannels (Abrams, 2000; Chen et al., 2007; Ninomiya and Fu, 2005;Rowan and Mars, 2005). However, there are few published liter-atures available on ASTER data utility in bauxite mapping. In thepresent study, ASTER data is processed to derive spectral indicesFig. 5. ASTER convolved laboratory derived spectral profiles of lateritic bauxite sam-ples. Profiles are categorized into laterite-dominant and bauxite-dominant based onsimilarities of these spectral profiles with the ASTER convolved spectral profiles ofgoethite and gibbsite. These spectral profiles are derivatives of spectral profiles ofFig. 4. In this figure, 1, 4, 5, 7, 8 are the position of the ASTER channels of interestwithin VNIRSWIR domain. These bands are chosen to derive ASTER based indicesfor bauxite mapping.


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ig. 6. Density sliced ABI (a), ABRBD (b), and ALI (c) image delineating bauxite and onvention indicate increment in laterite content in for ALI image. (For interpretatiof the article.)

.1.2. ASTER Global Digital Elevation Data (ASTER-GDEM)ASTER Global Digital Elevation Data (ASTER-GDEM) is one of

he suites of ASTER standard data products (ASTER-GDEM). ASTERDEM standard data products are produced with 30 m postings,nd have Z accuracies generally between 10 m and 25 m root meanquare error (ASTER-GDEM). ASTER GDEM data is used to deriveltitude information and slope maps; important for understandinghe role of slope and altitude in the chemical weathering processesponsible for lateritization.

.1.3. Spectral profile datasetsSpectral profiles are collected with portable spectroradiometer

Fieldspec 3) developed by Analytical System Device IncorporationASD, 2011). The spectral signature of bauxite is used as the basis forelection of ASTER band ratios to delineate the bauxite rich zones.ieldspec 3 spectroradiometer; used for the purpose of the presenttudy, has two types of detectors; one 512 elements Si photo-iode detector operative in 3501000 nm and two separate, InGaAs

hotodiodes operative in 10002500 nm. The spectroradiometer isperative in the spectral rage of 3502500 nm domain and havepectral resolution of 3 nm@700 nm and 10 nm@1400/2100 nm.

able 2igenvector statistics of principal components different index images. PC2 containsegative value for both ABR and ABI. Therefore, PC2 contains correlative (similar)nformation for both the index image delineating bauxite but laterite index hasnverse relation with these two bauxite index as it has positive value.

Index vs PC PC1 PC2 PC3

ABRBD 0.186223 0.202581 0.961396ABI 0.637078 0.720027 0.275123ALI 0.747966 0.005026 0.663718ic. Bauxite content increase from blue to red for ABI, ABRBD images and same colorhe references to color in this figure legend, the reader is referred to the web version

3.1.4. Geological mapThe regional geological map (unpublished) of the study area

(1:50,000 scale) prepared by the Geological Survey of India (basedon the systematic field mapping) is used as reference to comparethe results derived from the satellite data. Geological map is alsoessential to understand the lithological variants and the source rockfor lateritization process.

3.2. Methods

3.2.1. Spectroscopic studies of laterite and bauxiteUnderstanding the spectral signatures of bauxite is the impor-

tant aspect of the study. Bauxite (lateritic bauxites) samples arecollected in the field from different parts of the study area. Thesefield locations are well spread within the study area (Fig. 1). Thedistribution of the field points is limited due to the inaccessibilityby road; only few major parts are connected. This geographical setup necessitates the satellite based investigation for bauxites.

Rock samples collected in the field are made into rectangularblocks (5 in. 6 in.) for spectral profile collection in laboratoryunder controlled environment. Sample reflectance is measured bypointing vertically the measurement gun toward the sample. Themeasurement gun contains the fiber optics of the sensor. The lightsource illuminates the sample at about 45 angle (with respect tothe vertical drawn over sample) and the measurement is taken bykeeping the fore optics of the spectroradiometer vertically abovethe sample for making the phase angle is about 45 (phase angle isthe angle between the illumination source and the measurement

optics) to reduce the specular component of reflected energy in themeasured signal. The reflected energy resulted by volume scatter-ing of the rock is true representative of the internal chemistry asvolume scattering process allows incident radiation to get reflected


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ig. 7. Slope map showing topographic extent of planation surface above which baus meter and the slope is degree.

rom the multiple grain boundaries of different constituent min-ral. On the other hand, specular component of reflected energy isesulted from the surface roughness and restricted to few mineralrain boundaries. The reflectance of the rock surface is measuredased on the ratio of the radiant flux actually reflected by aample surface to the radiant-energy that have been reflected intohe same reflected-beam geometry by an ideal (lossless) perfectlyiffuse (Lambertian) standard optical surface; which is irradiatedn exactly the same way as the sample (Nicodemus et al., 1977).n present surface, Spectralon is used as perfectly diffuse standardptical surface for reflection calibration. Field of view of fore opticssed for recording the spectral profiles is 25. The measurementun is adjusted vertically in such a position so that it can create aSD (ground sampling diameter) to cover all the variability of theample. Twenty observations per sample are recorded and aver-ged to get the characteristic spectral curve for the rock sample.or each rock sample, two to three observations; each for specificample spots are recorded and averaged to incorporate everyetails of a sample. Spectral profiles are also post-processed usingiew spec software available with Fieldspec 3 spectroradiometer.he spectral profiles of laterite bauxite samples are illustratedn Fig. 4. Spectral profiles of lateritic bauxites are analyzed witheference to the spectral profiles of gibbsite, goethite, known ashe main constituent minerals in bauxite and laterite respectively.

hese spectral profiles of lateritic bauxite rock samples and spectralrofiles of the main constituent minerals of laterite and bauxitere further convolved to electromagnetic domain of ASTER datao understand the waveform of these spectral profiles in ASTER are concentrated; known bauxite occurrences are also shown. The unit for altitude

bandwidth. These ASTER convolved generalized spectral profileshave few conspicuous dips in the spectral curve (Fig. 5). Thesespectral features of the lateritic bauxite samples are comparedwith the spectral features of gibbsite and goethite. The comparativeanalysis is used as the basis for categorizing the lateritic bauxitesamples as bauxite-dominant or laterite-dominant (Fig. 5).

3.2.2. ASTER data preprocessingPreprocessing steps are aimed for calibrating the ASTER

Level-1B image (georectified radiance data) to derive apparentreflectance image. ASTER Level-1B data is georeferenced, radio-metrically corrected at-senor-radiance data. Short wave infraredbands (SWIR) of ASTER data are calibrated to apparent reflectanceusing log residual method. Log residual (LR) method corrects forinstrument gain, topography and albedo effects from radiance data.The log residual correction is considered as effective calibrationmethod to correct multiplicative atmospheric effects such as trans-mittance etc. (Azizi et al., 2010) and this correction method issuitable for mineral mapping. But the visible near infrared (VNIR)domain is affected by the additive atmospheric noise. Thereforeinternal average relative correction method is applied to VNIR(Azizi et al., 2010) channels of the data. After calibrating the ASTERdata using aforesaid methods, the apparent reflectance image isrescaled based on a scalar factor derived from the ratio of image

spectra and the laboratory spectra of spectrally conspicuous andspatially well distributed terrain element (in this case, vegetation).The quality of the final apparent reflectance product is validated bycomparing the image spectra of vegetation with that of the ASTER


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ig. 8. Density sliced PC 2 derived from index images (ALI, ABI, ABRBD) is draped auxitic). Known occurrences are in congruence with the bauxitic zone delineatedeader is referred to the web version of the article.)

onvolved laboratory spectra of vegetation, water body, etc. Afteralidating the apparent reflectance image, the reflectance image issed for further processing.

.2.3. ASTER data processingProcessing of apparent reflectance product is carried out based

n the analysis of the ASTER convolved laboratory spectra. It isbserved from the analysis of the spectral profiles that the lateriticauxites can be broadly subdivided in two categories: laterite-ominant and bauxite-dominant. Goethite and gibbsite are theajor constituent minerals of laterite and bauxite respectively andre used to categorize the lateritic bauxite samples either as lateritend bauxite based on the relative matching of diagnostic spectraleatures of these minerals with the spectral features of the each lat-ritic bauxite samples. It is also observed that each lateritic bauxitepectra matches either with the goethite or gibbsite spectra. Oneet of samples has sharp fall in reflectance from fourth channel ofSTER data centered around 1.65 m with a dip at around 0.55 mfirst channel of ASTER) (Fig. 5). This spectral behavior is in congru-nce with the absorption feature of the goethite. For another set ofamples, reflectance falls toward higher wavelength bearing SWIRhannels from the fourth channel of ASTER data (Fig. 5). This par-icular set has strong absorption at 2.26 m (7th channel of ASTER)ith its shoulder at 2.16 m (5th ASTER channel) and 2.33 m

8th ASTER Channel). This spectral behavior is similar to the typi-

al spectral behavior of gibbsite. Based on these differences in thepectral signatures of laterite and bauxites; band ratio, relativeand-depth images are derived to delineate bauxite rich pocketsithin the lateritic counterpart. Index based principal componentSTER FCC image for selected areas to illustrate for bauxite pockets (red zones areis product. (For interpretation of the references to color in this figure legend, the

(IPC) of ratio images are used to delineate bauxite pockets withinlaterite province. The eigen-vector matrix of the PC is the guidingfactor to understand the contribution of each index to each prin-cipal component (Rowan and Mars, 2006). This eigen matrix is thebasis for the selection of suitable PC for delineating the bauxite.

3.2.4. Derivation of indices and index based principle componentanalysis

Processing of ASTER data is carried out based on the analysis ofASTER-convolved spectral profiles of lateritic bauxite samples. Inorder to delineate bauxites within the laterite province, the ratioof band 5 and band 7 of ASTER data is derived. This ratio imageis derived based on the observation that the reflectance of ASTERconvolved spectral profiles of bauxite dominant laterite bauxitesamples sharply falls at band 7 (central wavelength 2.26 m) incomparison to the reflectance of same samples in the band 5 (cen-tral wavelength 2.16 m) (Fig. 5). A relative band depth (RBD)image is also prepared to highlight this characteristic absorptionfeature of bauxite rich samples using 5, 7, and 8th bands of theASTER data by using the absorption feature of bauxite at band 7with the shoulders of absorption features placed within the spec-tral range of band 5 and band 8 of the ASTER data. Relative banddepth image is a three point ratio method; characterizes shapeof the absorption feature better than conventional ratio methodand therefore can delineate spatial distribution of bauxite more

accurately than the conventional ratio method. In addition to afore-said ratio images, a complementary laterite delineation index isalso derived to highlight the laterite dominant samples of lateriticbauxite samples using band 4 and band 1 of ASTER data as it has


Fig. 9. The part of the bauxitic province (shown as square) in the ASTER FCC image (where R = 3rd band, G = 2nd band, B = 1st band) (a) is draped over enhanced ASTER digitalelevation data (b) to show topographic set up of bauxite exposures (marked as red color). The bauxites are exposed at the edge of the flat undulatory terrain where deep gullye erite. To n of to

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rosion has removed the overlying laterites; which otherwise are covered with latver the bauxite map draped enhanced digital elevation model (b). (For interpretatiof the article.)

een observed that lateritic or iron rich (goethite or iron hydroxideich) lateritic bauxite samples have very low reflectance in the firstand of ASTER (0.556 m) in comparison to the 4th band (1.65 m)Fig. 5). The ratio images and RBD image thus derived are listedelow:

ASTER based bauxite Index (ABI) = Band 5/Band 7;ASTER based bauxite relative band depth (ABRBD)

= (Band 5 + Band 8)/2 Band 7ASTER based Laterite Index (ALI) = Band 4/Band 1

Color shaded ALI, ABI, ABRBD images are illustrated in Fig. 6.elective principal component (PC) analysis using different bandsf multispectral data are attempted by many worker (Moore et al.,008). In the present study, an index based principal (IPC) images derived from the aforementioned ratio images. This is aimed tonhance the bauxite rich zones based on the correlative informa-ion of ratio and relative band depth images for bauxite (ABI andBRBD) and complementary information of the ALI index (laterite

ndex) image. It is observed from the eigen matrix analysis of IPCmage (an integrated image of three PCs derived from the threendex/ratio images) that the second principal component (PC2) hasegative but correlative value for both the ABRBD and ABI (Table 2)he location of Guradari mines (field photograph (c) is also enclosed) is illustratedhe references to color in this figure legend, the reader is referred to the web version

indices and therefore this PC highlights bauxite rich zone based oncorrelative information of two index images delineating bauxites.As the loading of these two indices in the PC-2 image is negativetherefore bauxite rich zone would be darker in this PC image. On theother hand, eigen vector loading of laterite index or ratio image ispositive in the PC-2 image. Therefore this PC-2 image is multipliedwith 1 to illustrate the bauxite rich zones with brighter shades;which is further density sliced with red color in color graded imageto delineate bauxite rich zones in red color. This classified image ispresented in Fig. 8.

3.2.5. Analysis of processed output in conjunction with thetopography

Topography plays an important role in lateritic-bauxite for-mation. In the study area, it is also observed that the knownbauxite mines occur above the 1000 m altitude (Fig. 7). It is alsoobserved that the slope condition changes around this contour(Fig. 7) and the surface above this altitude is essentially a planation

surface. Therefore 1000 m contour is used as limiting boundaryof bauxitization. Bauxite exposure distribution derived from dif-ferent indices is therefore delimited by the topographic boundarycharacterized by the 1000 m contour. The validity of the bauxite


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ig. 10. (a) Location of the image spectra collected for validation. (b) Comparative anf bauxites.

xposures delineated by spectral mapping methods is enhancednce it is correlated and delimited by geomorphic or topographicnformation (Kruse, 2012).

. Results

The two indices derived for bauxite (ABI and ABRBD) and oneor laterite (ALI) by processing the ASTER data. ALI image provideshe complementary information of the ABI, ABRBD images. Theomplementary nature of laterite index image with the other twoauxite index image justifies the fact that the bauxite rich pocketsnd lateritic pockets are mutually exclusive in the lateritic provinceFig. 6). On the other hand, relative band depth image (ABRBD) andimple ratio image (ABI) both are attempted for demarcating theisposition of the bauxite and similar result on disposition of baux-te is also an indication of the efficiency of these indices in bauxite

apping based on the enhancement of the spectral feature of baux-

te as analyzed in the ASTER convolved bauxite spectra. Moreover,he complementary nature of bauxite indices and supplementaryature of laterite index to the bauxite indices provide the self of image spectra of bauxite locations with the ASTER-convolved laboratory spectra

validation of the different indices as a tool for separating bauxitefrom the laterite. In order to utilize the complementary informa-tion of these ratio images, all the ratio images are integrated toderive the principal components (PC) to transfer the correlativeinformation of these ratio images to a composite Image. Based onthe analysis of eigen matrix of PC; it has been observed that thePC-2 of the ratio images has same signs of eigen vector loading forboth the bauxite ratio (ABI) and bauxite band depth image (ABRBD)and the signs of eigen-vector loading of laterite index image (ALI)is reversed in this PC-2. Therefore PC-2 is density sliced to separatebauxite from laterite (Fig. 8). The outline of the bauxite distributionderived from the geological map of the area has been draped overthe graded bauxite map (Fig. 8).

5. DiscussionIt is observed that the density sliced index derived PC-2image (which encompasses the correlative information two bauxiteindices, i.e., ABI and ABRBD) is suitable for delineating the baux-ite pockets within the lateritic province. The bauxite rich surface

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xposures; thus delineated are contained well within the knownxtent of bauxite (Fig. 8). As bauxites are often capped by laterite;herefore lateritic surface covers are also present in associationith these bauxite exposures (Fig. 8). Moreover, bauxite map (in

his case density sliced principal component image) is drapedver digital elevation model of the area to understand the topo-raphic disposition of the bauxite exposures in the area (Fig. 9).eomorphologically flat (low dipping) and geologically stable (noterturbed by geological deformation for longer geological time)urface; which remains exposed to the tropical weathering for con-iderable geological time is suitable for residual enrichment forateritization or bauxitization. In the area, known bauxite mines areocated above a height of 1000 m (Fig. 7) and localized over a lowipping surface (essentially a planation surface) suitable for exten-ive leaching for laterite formation. It has been also observed thathe bauxites are formed at the base of the geological section andhese bauxites are often capped by the laterite (Fig. 3). Therefore,opographic set up like the dipping slope or the incised depres-ion (developed by gully erosion) or the geological section at thedge of the flat plateau top only can expose the bauxites; which aretherwise covered under the laterite or the lateritic soil. Similar dis-osition of the bauxite pockets (Fig. 9) are observed when we drapeensity sliced index derived PC-2 image over the digital elevationodel after delimiting bauxite occurrences based on the altitude

derived from ASTER GDEM). This helps in clarifying mineral occur-ences in light of its topographic set up of the field occurrenceZhang et al., 2007). Moreover; image spectra are collected fromhe bauxite rich zones delineated from the index derived PC-imageFig. 10a) and compared with the ASTER convolved laboratory spec-ral profile of bauxite (Fig. 10b). The matching of spectral profileserived from the image locations of the bauxite exposures derivedrom the PC-image with the laboratory spectra of bauxite speciallyor the diagnostic absorption signature at 2.26 m helps in clarify-ng the bauxite enrichment map.

. Conclusion

The approach and results of the study bring out the utility of theroad spectral information of ASTER data in visible-near infrarednd short wave infrared (VNIRSWIR) domain for demarcatingauxite from laterite within the vast lateritic province. The spec-ral contrast between laterite and bauxite is notable in VNIRSWIRomain and these contrasts are utilized effectively in generatingSTER based relative band depth and ratio images for their sepa-ation. Moreover, index derived principal component image (IPC)s used as an combined input of the ASTER based ratio imagesABI, ALI) and ASTER based relative band depth (ABRBD) image forelineating bauxite exposures within the laterite and therefore itrovides the convergence of both the correlative and complemen-ary information of the ASTER based indices used for delineatinghe bauxite pockets within the laterite.

But mineral deposits like lateritic bauxites, which are the resultf tropical weathering process, are topographically controlled.herefore, the bauxite distribution pockets delineated based onpectral information is also clarified with its topographic controlsf bauxitisation for conceptual validation. The congruence ofpectral information with the geomorphic information is essentialor delineating deposits like bauxites (which occurs within lateriticountry). The extent of bauxite distribution derived from existingeological map is also used as an another basis for the validation.he spectral validation of bauxite rich pockets delineated from thendex derived from the PC image is also attempted by comparing

he image spectra of bauxite locations of the IPC image with theaboratory spectra of bauxite. This also ensures the effectivenessf IPC image in delineating the bauxitic pockets within the vastateritic province.servation and Geoinformation 21 (2013) 184194 193

Acknowledgments

Authors are grateful to Dr. V.K. Dadhwal, Director, NRSC for hissupport and encouragement. Authors are also thankful to Depart-ment of Geology and Mines; Govt. of Jharkhand for sponsoring thisproject and extending all kind of help during the fieldwork.

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Analysis of ASTER data for mapping bauxite rich pockets within high altitude lateritic bauxite, Jharkhand, India1 Introduction2 Study area and geology3 Materials and methods3.1 Data3.1.1 ASTER data3.1.2 ASTER Global Digital Elevation Data (ASTER-GDEM)3.1.3 Spectral profile datasets3.1.4 Geological map

3.2 Methods3.2.1 Spectroscopic studies of laterite and bauxite3.2.2 ASTER data preprocessing3.2.3 ASTER data processing3.2.4 Derivation of indices and index based principle component analysis3.2.5 Analysis of processed output in conjunction with the topography

4 Results5 Discussion6 ConclusionAcknowledgmentsReferences

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bauxite resources

mapping bauxite rich

high altitudeateritic

principal component

laterites andauxites

aster advanced space

differences aster data

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