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Ut il izat ion of Landsat ETM+data formineral-occurrences mapping overDalma and Dhanjori, Jharkhand,India: an Advanced Spect ral AnalysisapproachS. K. Pal

a, T. J. Maj umd ar

b, Amit K. Bhat t acharya

a& R.

Bhattacharyyab

aDepart ment of Geology and Geophysics, Indian Inst i t ut e of

Technology, Kharagpur, 721302, Indiab

Space Applications Centre (ISRO), Ahmedabad, 380015, India

Available onl ine: 30 Jun 2011

To cite this article: S. K. Pal, T. J. Majum dar, Amit K. Bhat t acharya & R. Bhat t acharyya (2011):Uti l izat ion of Landsat ETM+data for mineral-occurrences mapping over Dalma and Dhanjor i ,Jharkhand, India: an Advanced Spect ral Analysis approach, Int ernat ional Journal of Remot e

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Utilization of Landsat ETM data for mineral-occurrences mappingover Dalma and Dhanjori, Jharkhand, India: an Advanced Spectral

Analysis approach

S. K. PAL, T. J. MAJUMDAR*, AMIT K. BHATTACHARYA and

R. BHATTACHARYYA

Department of Geology and Geophysics, Indian Institute of Technology,

Kharagpur-721302, India

Space Applications Centre (ISRO), Ahmedabad-380 015, India

(Received 26 July 2007; in final form 27 March 2010)

The Advanced Spectral Analysis (ASA) technique, one of the most advanced

remote-sensing tools, has been used as a possible means of identifying mineral

occurrences over Dalma and Dhanjori. The ASA technique is a sixfold tool, which

includes the continuous processes of (1) the reflectance calibration of Landsat

Enhanced Thematic Mapper (ETM) images of the study area, (2) the generationof minimum noise fraction (MNF) transformation, (3) the calculation of the pixel

purity index (PPI), (4) the n-dimensional visualization and extraction of endmem-

ber spectra, (5) the identification of endmember spectra for mineral occurrences

and (6) the mapping of mineral occurrences. The identification of the extracted

endmember spectra is obtained by comparing it with available pre-defined library

spectra (United States Geological Survey (USGS), John Hopkins University

(JHU) and Jet Propulsion Laboratory (JPL) spectral libraries) using the Spectral

Analyst tool of ENVI 4.1 software (Research Systems Inc., Boulder, CO, US),

which provides scores of matching. Three techniques, namely Spectral Feature

Fitting (SFF), Spectral Angle Mapping (SAM) and Binary Encoding (BE), are

used for identification of the collected endmember spectra to produce a score

between 0 and 1, where the value of 1 equals a perfect match showing the exact

mineral type. A total of six endmember spectra are identified and extracted in the

study area. Mapping of mineral occurrences is carried out using the Mixture-

Tuned Matched Filtering (MTMF) technique over the study area on the basis of

collected and identified endmember spectra. Results of the present study using the

ASA technique ascertain that Landsat ETM data can be used to generatevaluable mineralogical information.

1. Introduction

The use of spectral reflectance measurements in the solar spectral range, 0.482.22 mm

of the electromagnetic spectrum, provides detailed information about many important

Earth-surface minerals (Clark et al. 1990). Previous works (Kruse 1988, Kruse et al.

1990, 1993a,b, Boardman and Kruse 1994, Staenz and Williams 1997, Kruse et al. 2003,

*Corresponding author. Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online# 2011 Taylor & Francis

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International Journal of Remote Sensing

Vol. 32, No. 14, 20 July 2011, 40234040


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Neville et al. 2003) have well established the efficiency of hyperspectral data for mineral

exploration using the Advanced Spectral Analysis (ASA) technique (Altinbas et al.

2005). The study area has been extensively explored geologically, as well as for mineral

occurrences (Dunn 1929, Naha 1965, Mukhopadhayay et al. 1975, Sarkar et al. 1979,

Sarkar and Chakraborty 1982, Saha 1994, Majumdar 1995, 1998, Acharyya 1999, Pal

et al. 2006a, 2006b, 2007a, 2007b). However, in the present study an attempt has beenmade for mineral mapping using Landsat Enhanced Thematic Mapper (ETM) multi-spectral data with the help of the ASA technique. The location map of the present study

area is shown in figure 1(a). The image data have been initially converted to radiance

and then to surface reflectance. Spectral endmembers are extracted automatically and

have been compared with available reference library spectra, namely the United States

Geological Survey (USGS) (Clark et al. 1993), Jet Propulsion Laboratory (JPL) (Grove

et al. 1992) and John Hopkins University (JHU) spectral libraries (Salisbury et al. 1991)

for mapping of various mineral occurrences. Landsat ETM data using the ASAtechnique provides basic mineralogical information within limited mapping of the

fine spectral detail due to the lower number of spectral bands available within therange 0.482.22 mm (Altinbas et al. 2005).

The endmember pixel spectra have been identified by comparing with the available

spectral libraries of minerals, which theoretically assumes that a surface of at least

30 m 30 m (pixel resolution of Landsat ETM image used) is completely (since thespectrum is considered as pure) covered by homogeneous rock, and the corresponding

spectrum is solely dominated by the spectral signature of a single mineral (as a

dominating mineral). With this assumption, some work has been carried out by

Kruse et al. (2003) and Neville et al. (2003) using the Airborne Visible/Infrared

Imaging Spectrometer (AVIRIS) (spatial resolution of 20 m 20 m) imagery and

EO-1 Hyperion (spatial resolution of 30 m 30 m) imagery.

2. Geology and mineralogical occurrences

The area has been studied extensively by various geologists (Dunn 1929, Naha 1965,

Mukhopadhayay et al. 1975, Sarkar et al. 1979, Sarkar and Chakraborty 1982, Saha

1994). The various mineral occurrences over the study area, as presented in the

published Mineral Map of India (Acharyya 1999), are as follows.

Apatite mineralization is found along the Singhbhum Shear Zone (SSZ), extending

over a length of 60 km, occurring as veins and lenses in biotite-chlorite rock. Asbestos

minerals are entirely confined to the basic and ultrabasic rocks of the iron-ore group

and Dalma lavas in Singhbhum district. Extensive deposits of copper occur over a

length of 160 km. An important ore of copper mineral, chalcopyrite, occurs as veins,

patches and dissemination, mainly in chlorite schist. Gold-bearing quartz veins are

reported from a number of locations in Singhbhum district. The iron-ore group consists

mainly of banded haematite. Manganese occurrences are found in the form of thin

beds, lenses and concentrations in the schist and quartzite of Dalma group.

The sulphide mineralization is considered to be associated mainly with the meta-

volcanics and meta-tuff of Singhbhum and Dhanjori groups. The predominant

sulphide minerals are chalcopyrite, pyrite and pyrhotite. The mode of occurrence

varies from massive to braided veins, stringers and disseminations, discordant to

sheet-like bodies and also as en-echelon veins. Sarkar et al. (1986) suggested thatthe sulphide mineralization in this belt is confined mainly within certain stratigraphic

horizons that are adjacent to the Dhanjori metavolcanics. The general trend of the ore

4024 S. K. Palet al.


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body is controlled by the local trend of slip planes. Wall-rock alterations, in the form

of chloritization, sericitization, biotitization, tourmalinization and albitization, are

common (Gangopadhyay and Samanta 1984). Clay minerals, consisting of serisite

and mica-feldspars, iron ores, consisting of magnetite and hematite, and copper ore,

consisting of chalcopyrite and chalcocite, are also reported by Saha (1984, 1994). The

reserves of important minerals available over the study area are presented in table 1.The geological set-up over the area of interest is shown in figure 1(b).

Study area

36

24

8

68 88 95

86 00E 86 30E 87 00E

22

00N

22

30N

2

3

00N

Road

River

District boundary

State boundary

Railway

0 5 10 20 km

(a)

Figure 1. (a) Location map of the present study area. (b) Geological map of the present study

area. 1: older metamorphic tonalite-gneiss; 2: iron-ore group shales, tuffs, phyllites; 3:Singhbhum granite phase III, Bonai granite, Chakradharpur granite; 4: Singhbhum grouppelites; 5: Singhbhum group quartzites; 6: quarzite-conglomerate-pelite of Dhanjori group; 7:Dhanjori-Simlipal-Jagannathpur-Malangtoli lavas; 8: Dalma lavas; 9: proterozoic gabbro-anorthosite-ultramafics; 10: Mayurbanj granite; 11: alluvium, tertiaries.

Advanced Spectral Analysis approach 4025


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3. Data source and the area of interest

A Landsat ETM image, with path/row 139/44 (date of acquisition: 7 May 2003)covering the study area, was chosen for the present study. The area of interest lies

between latitudes 22230 N and 22530 N and longitudes 86150 E and 86450 E. The

image was chosen under optimum conditions prevailing during the summer season,

such as bright targets and well-exposed geology.The climate of the area is tropical with hot and dry summers during AprilMay and

pleasant dry winters during NovemberFebruary. The forests of the area are mainly

(b)

Itagarh-Khajurdari, apatite (Ap)

Pathargora-Kulmore, apatite (Ap)

Baharagora, copper (Cu)

Rakha, Cu, Ni, Co

Chendapathar, tungsten (W)

Surda-Mosabani, copper (Cu)

86250E

22550N

22500N

22450N

22400

N

22350N

22300N

22500N

22450N

22400

N

22350N

22300N

86300E 86350E 86400E 86450E

86250E

1

2

3

4

5

6

7

8

9 11 0 2.5 5 10 km10

86300E 86350E 86400E 86450E

Figure 1. (Continued.)



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on open ridges and in undulating valleys. On the hillside, in these areas, there are forests

present, but they have been much exploited for a pretty long time, and the jungles are in

a poor state. There has been much cutting and grazing. The soil of this area has been

classified mainly into three groups: rocky, red and black soils. Rocky soil remains

practically uncultivated. Red soil is spread throughout the area: it is sandy and loamy

and has poor fertility. The iron-rich laterites are distributed all over the area. Cultivated

fields surrounding isolated villages are located mostly near the roads and rail lines. Riceis the main crop during JuneNovember.

4. Methodology

The Landsat ETM imagery over the study area was corrected by converting theLandsat ETM digital numbers (DNs) to radiance and then to reflectance units (eachpixel is represented by a reflectance value). DN values were converted to radiance

values (Ll) using the calibration equation (1), and then reflectance values (rl) were

calculated using equation (2) (Vermote et al. 1997). The DNs were converted into

absolute radiance using the relation:

Ll Lmax Lmin =255 DN Lmin; . . . . . . . . . . . . (1)

where Ll is the spectral radiance at wavelength l, Lmax and Lmin (W m-2 sr-1 mm-1) are

the spectral radiances for each band at DN 0 and 255, respectively. The values ofLmaxand Lmin for bands 15 and bands 78 were taken from the Landsat 7 Science Data

Users Handbook (NASA 2006). Then, the reflectance value was calculated using the

relation:

rl pd2 Ll=E0l cos ; . . . . . . . . . . . . (2)

where d is the EarthSun distance correction (1.00901 astronomical units), is the

solar zenith angle (21.32), Ll is the radiance as a function of the bandwidth, E0l is thesolar spectral irradiance. The E0l values were taken from the Landsat 7 Science Data

Users Handbook(NASA 2006). The values ofdand were collected from the header

Table 1. Reserves of the important minerals available over the study area (http://seraikela.ni-c.in/mines/jharmine.htm).

Reserve as on 1 April 1995 (tons)*

Minerals Proved Probable Possible Total Location

Apatite 2110 960 3070 SinghbhumChina clay 4424 8830 32 676 45 930 Singhbhum, Dumka, Ranchi,

SahibganjCopper ore 46 584 41 852 20 245 108 690 Singhbhum, GiridhiIron ore 1825 528 304 2657 Singhbhum, PalamuManganese ore 542 143 1678 2363 SinghbhumQuartz (silica

sand)882 6064 129 483 136 429 Koderma, Singhbhum,

Deogarh, GiridhiFeldspar 123 191 438 606 4 589 709 5 151 506 Dumka, HazaribaghMica 13 554 13 554 Koderma, Giridhi, HazaribaghTalc/stealite/

soapstone

12 49 228 289 Singhbhum, Giridhi

Note: *1 ton 1.016 metric tonne, 1 metric tonne 1000 kg.



8/20

file of the corresponding image over the study area. Dark-object subtraction (DOS)

using the band minimum was applied for atmospheric scattering corrections consid-

ered in the calculation of reflectance image data. The DOS method of atmospheric

correction is a scene-based method to approximate the path radiance added by

scattering, based on the assumption that within an area of a full scene, there will be

a location that is in deep topographic shadowing, and any radiance recorded by thesatellite for that area arises from the path radiance component, assumed to be

constant across the scene (Moran et al. 1992, Chavez 1996).

The corrected reflectance images were then processed using the advanced hyper-

spectral tool of ENVI 4.1 (Research Systems Inc. 2003) for mineral-occurrences

mapping over Dalma and Dhanjori. Figure 2 shows a flowchart for the ASA techni-

que, as used in this study. The ASA technique also includes: (1) generation of

minimum noise fraction (MNF) transformation to determine the inherent dimension-

ality of the image, to segregate noise in the image and to reduce the computational

requirements (reduce the number of channel) for subsequent processing (Boardman

and Kruse 1994), (2) calculation of the pixel purity index (PPI) image for delineationof spectrally pure pixels from the less spectrally pure/darker pixel and to reduce the

number of pixels in the input of n-dimensional (n-D) visualization (Boardman et al.

1995), (3) n-D visualization, for extraction of endmember spectra (Kruse et al. 2003),

(4) identification of endmember spectra for mineral occurrences and (5) mapping of

mineral occurrences (Research Systems Inc. 2003). The lower MNF bands, which are

coherent and contain most of the spectral information, were used to calculate the PPI

Apparent reflectance

MNF

transformation

PPI

generation

n-D

visualization

ID

Map distribution and

abundance

Calibration of Landsat ETM datato reflectance

Spectral data reduction and noise

segregation

Spatial data reduction and

extraction of purest pixel

Purest pixel clustering and

endmember extraction

Identification of endmembers

using BE, SAM and SFF scores

Mapping and abundance

calculation using unmixing, MF,

SAM, MTMF, etc.

Figure 2. Flowchart showing different steps of Advanced Spectral Analysis technique.



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image and finally to determine the most likely endmembers, using the PPI technique.

Purest pixels were located and clustered as corner points in the n-D (number of input

MNF bands) scatterplot of the n-D visualizer by using inputs of corresponding MNF

bands and PPI images. The scatterplots were rotated manually in real time on the

computer screen until the corner points or extremities were delineated on the scatter

diagram. These corner points were then painted using region-of-interest (ROI) tech-niques and then rotated again in a different dimension (three or more MNF bands) to

identify other available unique signatures or corner points corresponding to the

purest pixels. Once the set of unique corner points were identified in the n-D visualiza-

tion, each separate projection of the corner-points cloud was exported to an ROI in

the image. Then, the mean spectra were extracted for each ROI from the apparent

reflectance data. These spectra act as endmember spectra (Kruse et al. 2003, Research

Systems Inc. 2003).

All the spectra available in USGS, JPL and JHU spectral libraries were subsetted

and resampled to the collected six-band endmember spectra. The endmember spectra

were analysed through comparative assessment with different library spectra (USGS,JPL and JHU) to find out the best match using the three techniques: Spectral Feature

Fitting (SFF), Spectral Angle Mapping (SAM) and Binary Encoding (BE). Each SFF

(Clark and Swayze 1995), SAM (Kruse et al. 1993b) and BE (Mazer et al. 1988)

produces a score between 0 and 1, where the value of 1 equals a perfect match showing

the exact mineral type. Hence, the total score for a perfect match will be 3 (SAM 1,SFF 1 and BE 1), whereas the total score for the worst match will be 0. Theendmember spectra were discriminated by finding the best suitable match after

comparing with all the spectra of the different spectral libraries, using the spectral

analyst tool of ENVI 4.1, which provides scores of matching. The absorption feature

is the main diagnostic characteristic, and the spectral slope and pattern of reflectancemaxima also have diagnostic roles for identifying ores and mineral occurrences

(Singer 1981, Vincent 1997, Younis et al. 1997), which could be used over the

geologically complex area. Finally, the identified spectra were used for mapping

mineral occurrences. A number of spectral-mapping techniques are available:

SAM classification (Kruse et al. 1993b), Spectral Unmixing (Boardman 1989),

Matched Filtering (MF) (Boardman et al. 1995) and Mixture-Tuned Matched

Filtering (MTMF) (Stocker et al. 1990, Yu et al. 1993, Harsanyi and Chang 1994,

Boardman 1998). In this study, mapping of mineral occurrences over the study area

was carried out using MTMF on the basis of the collected and identified endmember

spectra. The SAM and other techniques were also checked, but could not provide

satisfactory results.

MTMF images were generated from the estimated MNF images based on the

extracted endmember spectra. The MTMF results were presented as two sets of

images: (1) the MF score image, offered as grey-scale bands with values ranging

from 0 (score of no matching) to 1.0 (score of maximum matching) and (2) the

infeasibility image presented as bands with varying grey-scale values. The number

of bands in each set is the same as the number of endmember spectra used for the

MTMF technique; for example, if 11 endmember spectra are used, then 11 corre-

sponding MF score bands and 11 corresponding infeasible bands will be generated.

An MF score of 1.0 indicates a perfect match, whereas the high infeasible numbers

indicate mixing between the composite background and the target. The best mappingof the minerals could be obtained when the MF score is high (near 1) and the

infeasibility score is low (near 0). From the available bands list, MF score bands



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were loaded as grey-scale images. Scatterplots of MF score bands and infeasibility

score bands of corresponding endmember spectra were generated. In the scatterplots,

points having a maximum MF score (near unity) and minimum infeasibility score

(near zero) were marked using the ROI tool by creating polygons of ROIs. The

delineated regions of interest were exported to build ROIs showing individual miner-

als. Finally, the MTMF mineral map was obtained by ROI-based classification ofMNF images using all ROIs corresponding to individual minerals.

5. Results and discussion

The spectral bands, 1, 2, 3, 4, 5 and 7 of Landsat 7 ETM covering the 0.452.35 mmregion, were selected and linearly transformed using MNF transformation. Figure 3

shows plots of eigenvalues and MNF bands of the study area. It is clear that the

eigenvalues decrease with increasing MNF band; that is, the noise is segregated in the

higher number MNF bands. The spatial data coherency was calculated, and is shown

in figure 4. The spatial coherency plot exhibits that the dimensionality is 5 with athreshold level of 0.35. In the present study, thresholding is chosen with a spatial

coherence value of 0.35, as obtained from the ENVI software. The PPI image for

Dalma, Dhanjori and surroundings is shown in figure 5. A total number of iterations

of 100, with a threshold value of 3, was used for PPI calculation. Generally, for

Landsat ETM data, 100 iterations are used. However, 1000 iterations are used inhyperspectral data. The higher number of iterations in Landsat ETM data may leadto generating more extreme pixels, which are not yet extreme. Figure 6 shows the n-D

visualization plot for the present study. During pre-processing, the data dimension-

ality was changed accordingly through the n-D visualizer to demarcate more end-

member spectra. 14 are dimensional axes. The colour coding of clustered purestpixels which have been identified for different endmember spectra are same as

described in figure 7 (endmember spectra of water and vegetation are not shown).

8

10

6

Eigenvalue

4

2

1 2 3

Band no.

4 5 6

Figure 3. Plot of eigenvalues for the MNF bands over the study area.



11/20

The extracted and delineated endmember spectra corresponding to various mineral

occurrences are presented in figure 7. Details of delineated minerals, along with thescore of matching and most suitable library spectra, corresponding to various end-

members, are listed in table 2. A total of six endmember spectra were extracted and

Figure 5. Pixel purity index image over the study area. The total number of iterations is 100,and the threshold value is 3.

Spatialcoherencevalue

1.0

0.8

0.6

0.4

0.2

0.01 2 3 4 5 6

MNF band no.

Figure 4. Spatial coherence plot of MNF bands. The threshold level is 0.35 and the number ofbands above the threshold is 5.



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identified (figure 7/table 2) in the present study, namely magnetite, cuprite/chalcopyr-

ite, pyrite, kaolin, apatite and sodalite, corresponding to various mineral occurrences.

Figure 8 demonstrates the characteristic features (absorption features characteris-

tics, spectral slope and pattern of reflectance maxima) exhibited in the plot of relative

reflectance of various endmember spectra of mineral occurrences, together with the

corresponding library spectra. Younis et al. (1997) showed that fresh rocks (libraryspecimens) exhibit higher reflectances than those of the open weathered/fractured

rocks with rough surface.

Magnetite

Pyrite

Kaolin

Cuprite/Chalcopyrite

Sodalite

Apatite

0.5

0.1

0.2

0.3

0.4

0.5

0.6

1.0 1.5

Wavelength (m)

Reflectance(%)

2.0

Figure 7. Extracted and identified endmember spectra corresponding to various mineraloccurrences over Dalma and Dhanjori.

Figure 6. n-dimensional visualization plot for the present study. 14 are dimensional axes.The colour coding of clustered purest pixels which have been identified for different endmemberspectra are same as described in figure 7.



13/20

Table2.Detailsofthefeaturesidentifiedfromthep

resentstudyoverDalmaandDhanjhoriandsurroundings.

Serial

no.

Endmember

Descriptionofendmembe

rs

SAM

score

SFF

score

BE

score

Totalscore

SAM

SFF

BE

Sp

ectrallibrary

(b

estmatched)

1

Kaolin4

Al2Si2O5(OH)4

Kaolin

ofvariety4asperUSGS.

Groupofclay

minerals.

Thesearegenerallyderived

fromalteration

ofalkalifeldsparsandmicas

0.873

1.0

0.672

2.545

USGS

2

Apatite

Ca5(PO4)3(F,

Cl,

OH)

Amineraloccurringinigneousrocks,

especiallypegmatite,andinmetamorphosedlimestone

0.707

0.598

1.305

JPL

3

Chalcopyrite(CuFeS2)

/cuprite(Cu2O)

Widely

occurringmineralfoundmainlyinhydrothermal

andm

etasomaticveins/importantcop

perorethat

occursinweatheringzoneofcopperveins

0.472

0.651

0.6210.513

0.865

0.687

1.985

1.851

USGS

4

Pyrite1

(FeS2)

Pyriteo

fvariety1asperUSGS.

Most

widespread

sulph

idemineral.

Itoccursasanaccessorymineralin

igneo

usrocks,inhydrothermaloreveins,contact

metamorphicdepositsandanaerobic

sediments

0.523

0.961

0.544

2.028

USGS

5

Magnetite2

(Fe3O4)

Iron-ric

hmineral

0.844

0.48

1.000

2.324

USGS

6

Sodalite

Na2Al3Si3O12Cl

Referstoawhite,greyorgreenmineral

tectosilicateof

feldspathoidgroup

0.654

0.765

0.456

1.875

JPL



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The endmember spectra (figure 8(a)) demarcated as magnetite bears a good rela-

tionship (total score 2.324) with the USGS spectral library. It demonstrates that there

are three absorption minima near 0.45, 0.80 and 1.65 mm, with two peak reflectance

maxima near 0.65 and 2.22 mm. The spectral-reflectance distribution (figure 8(b)) of

pyrite (total score 2.028), as obtained from the present study and the USGS spectral

library, with which it has very good correlation, reveals that within the wavelength

region of 0.482.22 mm, there is a peak reflectance maxima near 0.60 mm, with a sharp

decrease up to almost 1.6 mm, and then a gentle decrease up to 2.22 mm. The end-

member spectra (figure 8(c)) demarcated as cuprite (total score 1.851) bears a good

relationship (gentle increase in reflectance) with library spectra for the higher wave-

length region (after 0.90 mm). However, it could also be correlated very well with

chalcopyrite (total score 1.985) in the short-wavelength region. One sharp absorptionminima and two shallow absorption minima are present near 0.48, 0.65 and 2.22 mm,

respectively. Two peak reflectance maxima are exhibited near 0.55 and 1.65 mm.

(a)

Wavelength (m)

Reflectance(%)

0.052

Magnetite(endmember)

Magnetite(library)

0.050

0.048

0.046

0.5 1.0 1.5 2.0

(b)

Wavelength (m)

Re

flectance(%)

0.105

0.100

0.095

0.090

0.085

0.080

0.5

Pyrite(endmember)

Pyrite(library)

1.0 1.5 2.0

Chalcopyrite/Cuprite(endmember)

Chalcopyrite(library)

Chlorite(library)

Cuprite(library)

Reflectance(%)

0.5

0.4

0.3

0.2

0.1

(c)

Wavelength (m)

0.5 1.0 1.5 2.0

Reflectance(%)

(e)

Wavelength (m)

0.5

0.7

0.6

0.5

0.4

1.0 1.5 2.0

Apatite(endmember)

Apatite(library)

Chlorite(library)

Biotite(library)

Ref

lectance(%)

0.8

0.6

0.4

0.2

(f)

Wavelength (m)

0.5 1.0 1.5 2.0

Kaolin(endmember)Ref

lectance(%)

Kaolin(library)

0.60

0.55

0.50

0.45

0.40

0.30

(d)

Wavelength (m)

0.5 1.0 1.5 2.0

Sodalite(endmember)Sodalite(library)

Figure 8. (a)(f). Plots of relative reflectance of various endmember spectra of mineraloccurrences, together with the corresponding library spectra.



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Copper mineralization is reported within breciated sericite-quartz schist and quartz-

chlorite schist. Furthermore, copper mineralization is also reported in biotite-chlorite

schist and silicified schist. Hence, there is a great chance for mixing with sericite/

chlorite, which could produce a spectral signature similar to copper mineral (chalco-

pyrite/cuprite) at Landsat ETM resolution. This was examined cautiously with the

available library spectra, and it was found that there are noticeable differences inabsorption minima and maxima (figure 8(c)). Consequently, the endmember spec-

trum is demarcated as chalcopyrite/cuprite. The sericite spectrum is not available in

the library spectra (USGS, JPL and JHU). The kaolin endmember (figure 8(d)) has a

good correlation with the USGS library spectra (total score 2.545) and exhibits a

wedge-shape reflectance curve within 0.482.22 mm, with two peak reflectances near

1.70 and 1.65 mm. The endmember spectra (figure 8(e)), demarcated as sodalite, could

be correlated (total score 1.875) with the JPL library spectra. The spectra exhibit flat

absorption minima centred at 0.58 mm with a sharp peak reflectance. Sodalite,

identified as endmember spectra, shows comparatively less reflectance than that of

the JPL library spectra. The apatite endmember spectra (figure 8(f)) has a goodcorrelation (total score 1.305) with the JPL library spectra. The spectra exhibit a

low absorption minima centred at 0.60 mm and then show a gradual increase up to the

1.70 mm wavelength region. Apatite, identified as endmember spectra, shows com-

paratively less reflectance than that of the JPL library spectra. As the apatite occurs

within biotite/chlorite, which could produce a spectral signature similar to apatite,

there is a chance of mixing. This was checked carefully with the available library

spectra and it was found that there are noticeable differences in positions of absorp-

tion minima and maxima (figure 8(f)). Hence, the endmember spectrum is accredited

to apatite mineralization.

The MTMF-based inferred mineral-occurrence map of Dalma volcanic, Dhanjorigroup and surroundings is shown in figure 9. An attempt was made to validate thefindings

obtained from the present MTMF-based inferred mineral occurrences by comparing with

the mineral map of the Geological Survey of India (GSI) (Acharyya 1999) (table 3).

Over Dalma volcanic, Dhanjori group and their surroundings, three copper occur-

rences, namely, Baharagora, Surda-Mosaboni, Rakha, and two apatite occurrences,

namely, Itagarh-Khajurdari and Pathargora-Kulmore, were mapped correctly.

However, the tungsten (Chendapathar), cobalt, nickel (Rakha) occurrences could

not be identified in the present study. It is observed in the MTMF-derived mineral

map (figure 9) that kaolin/clay minerals are exposed over most of the area; a group of

clay minerals that are generally derived from alteration of alkali feldspar and mica.

Cuprite/chalcopyrite minerals were mapped at a number of places (figure 9), in the

fractured/weathering zone of copper veins/hydrothermal and metasomatic zones. The

identified occurrences of cuprite/chalcopyrite are very interesting and require further

detailed study. Magnetite, pyrite, sodalite and apatite are mapped at very few places

(Sarkar et al. 1979, Sarkar and Chakraborty 1982, Saha 1984, 1994, Sarkar et al.

1986).Water bodies over the Dalma Lake and in some parts of the Subarnarekha

River have also been mapped correctly.

6. Conclusions

The endmember spectra collected from Landsat ETM have only six bands, and theextracted relative reflectance curve will have less spectral resolution. Accordingly,

some spectral information is lost during resampling of library spectra (of large



16/20

sampling rate/higher spectral resolution) by Landsat ETM endmember spectra (of

low sampling rate/lower spectral resolution). However, as Landsat ETM is a multi-channel sensor with good coverage (six bands) of various important wavelength

regions, which exhibit diagnostic spectra for imperative materials, it could be very

useful for mapping of mineral occurrences using the ASA technique.

A total of six mineral spectra, magnetite, cuprite/chalcopyrite, pyrite, kaolin, apatite

and sodalite, were extracted from the processed Landsat ETM image of Dalmavolcanic, Dhanjori group and surroundings, and were validated very well by comparing

with the available library spectra. The spectrum of sodalite could be also considered as

a mixing of clay and iron oxides, considering the presence of lateritic soils. It can thus be

concluded that most of the minerals occurring in the host rock have been identified

using the spectral-analysis technique. However, comparison based only on the locationof mineral-occurrence sites is not meaningful without any information about the actual

expression of those mineral occurrences at the ground surface (e.g. presence of

8615E 8630E 8645E

8615E

0 3 6 9 12 15km

8630E

Kaolin Magnetite

Apatite

Pyrite

Cuprite

Sodalite

8645E

2245E

2230E

2230E

2245E

N

S

EW

1 1km

S

Figure 9. Inferred mineral map obtained using the MTMF method. White areas and spots areunclassified.



17/20

outcrops, their extension, geo-mineralogical characteristics), and details of the map

obtained are needed to demonstrate the correlation. Further detailed ground surveys

by professional teams from the GSI and the National Mineral Development

Corporation (NMDC) are required for further confirmation of such occurrences.

The results of the present study ascertain that the Landsat ETM data can be usedto generate valuable geological/mineralogical information. It further establishes that

the spectroscopy using Landsat ETM images brings a new conception in remotesensing that enables the identification and mapping of major scene components. It can

have great potential to aid numerous other fields of study, for example soil, carto-

graphy, land use/land cover, vegetation-cover mapping and so on. The success of thisstudy is very much dependent on the quality and correctness of the data, analysis

techniques used and spectral library/collected ground-truth data for spectral

reflectance.

Acknowledgements

The authors wish to thank the anonymous reviewers for their valuable suggestions

and comments for improving this manuscript. The authors are also thankful to Dr. R.

R. Navalgund, Director, Space Applications Centre (SAC), and Dr. B. K. Rastogi,

Director General of Institute of Seismological Research (ISR), for their keen interest

in this study. Thanks are also due to Dr. P. K. Srivastava, Department of Geology and

Geophysics, Indian Institute of Technology (IIT), Kharagpur, for his help. Dr. T. J.

Table 3. Details of comparative study of mineral occurrences, between the inferred MTMFmineral map and the mineral map (economical mineral deposits) of GSI over Dalma volcanics,

Dhanjori group and surroundings.

Serialno.

Economical mineral

deposits reported asper GSI over Dalma/

Dhanjori andsurroundings

Inferred mineral

occurrence as perpresent study over

Dalma/Dhanjori andsurroundings

Location of mineraloccurrences over

Dalma/Dhanjoriand

surroundings as perGSI map

Remarks onaccuracy

assessment overDalma/Dhanjori

andsurroundings

1. Baharagora: copper(Cu)

Chalcopyite (CuFeS2)/cuprite (Cu2O)

22 280 39.8600 N,86 320 24.0500 E

Copper has beenmappedcorrectly

2. Chendapathar:tungsten (W)

22 500 21.4100 N,86 400 18.6900 E

Tungsten couldnot be mapped

3. Surda-Mosabani:copper (Cu)


22 310 25.1300 N,86 240 24.6300 E

Copper has beenmapped

correctly4. Itagarh-Khajurdari:apatite (Ap)Ca5(PO4)3(F,Cl, OH)

ApatiteCa5(PO4)3(F,Cl,OH)

22 340 48.7300 N,86 230 2.9200 E

Apatite has beenmappedcorrectly

5. Pathargora-Kulmore: apatite(Ap) Ca5(PO4)3(F,Cl, OH)

ApatiteCa5(PO4)3(F,Cl,OH)

22 260 48.5200 N,86 220 35.1300 E

Apatite has beenmappedcorrectly

6. Rakha: copper (Cu),nickel (Ni), cobalt(Co)


22 420 4.0200 N,86 210 40.0600 E

Copper has beenmappedcorrectly



18/20

Majumdar would wish to thank Council of Scientific and Industrial Research (CSIR),

New Delhi, for the Emeritus Scientist Fellowship since January 2011.

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