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Minerals identification and mapping using ASTER satellite image
Article in Journal of Applied Remote Sensing · October 2017
DOI: 10.1117/1.JRS.11.046006
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Minerals identification and mapping
using ASTER satellite image
Khunsa Fatima
Muhammad Umar Khan Khattak
Allah Bakhsh Kausar
Muhammad Toqeer
Naghma Haider
Asid Ur Rehman
Khunsa Fatima, Muhammad Umar Khan Khattak, Allah Bakhsh Kausar, Muhammad Toqeer,
Naghma Haider, Asid Ur Rehman, “Minerals identification and mapping using ASTER satellite image,” J.
Appl. Remote Sens. 11(4), 046006 (2017), doi: 10.1117/1.JRS.11.046006.
Minerals identification and mapping using ASTER
satellite image
Khunsa Fatima,a,* Muhammad Umar Khan Khattak,a
Allah Bakhsh Kausar,b Muhammad Toqeer,c Naghma Haider,b and
Asid Ur Rehmand
a
National University of Sciences and Technology, Institute of Geographical Information System,
Islamabad, Pakistan
b
Geological Survey of Pakistan, Islamabad, Pakistan
c
Quaid-e-Azam University, Islamabad, Pakistan
d
United Nations Human Settlements Programme, Islamabad, Pakistan
Abstract. Advanced spaceborn thermal emission and reflection radiometer (ASTER) has fine
spectral bands in short-wave infrared (SWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum. The purpose behind the study is to explore the potential of ASTER for lithological and minerals detection; in comparison with Landsat-ETM+ Khaira Murat range (KMR)
of Gali Jagir area, district Attock was selected as a test site; enriched with industrial minerals of
the Eocene age. Maximum likelihood classification was applied on Landsat-ETM+ and ASTER
images. Maximum likelihood classification on ASTER satellite image exhibits better discrimination among various lithologies as compared to Landsat-ETM+. Classified image of ASTER
showed a correlation coefficient of 0.6 with the geological survey of Pakistan’s map while a
classified image of Landsat-ETM+ exhibited a correlation of only 0.43. Landsat-ETM+ and
ASTER satellite images were further investigated for minerals detection. Landsat-ETM+
band ratio detected clay. ASTER SWIR band ratios detected various clay and carbonate minerals. X’PertPRO diffractometer and differential thermal analysis of field samples verified the
detected minerology. The results suggest that ASTER can be successfully used for lithological
and minerals mapping of less-examined areas. © 2017 Society of Photo-Optical Instrumentation
Engineers (SPIE) [DOI: 10.1117/1.JRS.11.046006]
Keywords: landsat-ETM+; advanced spaceborn thermal emission and reflection radiometer;
shortwave infrared; band rationing; X’PertPRO diffractometer; DTA.
Paper 170079 received Feb. 1, 2017; accepted for publication Sep. 21, 2017; published online
Oct. 17, 2017.
1 Introduction
Landsat-enhanced thematic mapper (ETM+) has eight spectral bands and has been used for mapping of hydrothermal alteration zones.1 Landsat-ETM+ bands 5 and 7 (Table 2) are potentially
useful in detecting a clay mineral zone.2
Advanced spaceborn thermal emission and reflection radiometer (ASTER) provides better
spectral resolution as compared to Landsat-ETM+ (Tables 1 and 2) for discriminating mineral
spectra.3 ASTER has six spectral bands in the short-wave infrared (SWIR) region as compared to
Landsat-ETM+, which has only two in this region (see Fig. 1). Likewise, Landsat-ETM+ has
only one thermal infrared (TIR) band, while ASTER has five TIR bands. The increased numbers
of SWIR and TIR bands of ASTER data enhance the capability for minerals discrimination that
is not readily recognizable from the Landsat-thematic mapper data.4,5 Because of this advantage,
ASTER data are increasingly used for minerals identification, including phyllosilicates (clay
minerals), sulphates, and carbonates.6–8 ASTER images provide preliminary mineralogical information and georeferenced alteration maps at low cost and with high accuracy. In short, ASTER
data have proven to be a powerful tool in the initial steps of ore deposit exploration.9 As shown in
*Address all correspondence to: Khunsa Fatima, E-mail: [email protected]
1931-3195/2017/$25.00 © 2017 SPIE
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Table 1 Spectral and spatial characteristics of ASTER, as mentioned in Ref. 10. ASTER has
three subsystems, i.e., visible near infrared (VNIR), SWIR, and TIR having spatial resolution
of 15, 30, and 90 m, respectively. Two spectral bands of VNIR system cover the same wavelength
range, i.e., 0.76 to 0.86 m, of which one is nadir-looking while other is tilted at an angle of 27.6 deg.
Subsystem
VNIR
SWIR
TIR
Spectral bands
Wavelength range (μm)
Spatial resolution (m)
1
0.52–0.6
15
2
0.63–0.69
15
3N
0.76–0.86
15
3NB
0.76–0.86
15
4
1.6–1.7
30
5
2.145–2.185
30
6
2.185–2.225
30
7
2.235–2.285
30
8
2.295–2.36
30
9
2.36–2.43
30
10
8.125–8.475
90
11
8.475–8.825
90
12
8.925–9.275
90
13
10.25–10-95
90
14
10-95–11.65
90
Table 2 Characteristics of Landsat-ETM+. It has three spectral bands in the visible region of the
EMS at a spatial resolution of 30 m. Bands of infrared region include one NIR band and two SWIR
bands of 30 m. It also consists of one TIR band of 60 m and one panchromatic band of 15-m spatial
resolution.
Spectral bands
Wavelength range (μm)
Spatial resolution (m)
1 (Blue)
0.45–0.515
30
2 (Green)
0.525–0.605
30
3 (Red)
0.63–0.69
30
4 (NIR)
0.775–0.90
30
5 (SWIR)
1.55–1.75
30
6 (TIR)
10.4–12.5
60
7 (SWIR)
2.08–2.35
30
8 (Pan)
0.52–0.90
15
Ref. 10, lithology of Gali Jagir area, district Attock, was successfully mapped by spectral bands
of ASTER satellite data, as compared to Landsat-ETM+ data. The current study further investigated the potential of spectral bands of Landsat-ETM+ and ASTER for mineral detection and
delineation.
To be able to analyze spectral responses of surface cover types using SWIR bands of ASTER
data, it is necessary to apply the log residual algorithm, which reduces noises from topography,
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Fig. 1 Comparison of Landsat-ETM+ (shown in green) and ASTER (shown in red) spectral bands
(a) Visible and NIR bands, (b) SWIR bands, and (c) TIR bands.
instrument, and sun illumination.11 The resultant image is assumed to be more representative of
the lithology of the exposed areas.
Rocks and minerals possess a crystalline lattice structure. Many interactions are possible
between the chemical bonds in the lattice and incident solar radiation. Such interactions produce
identifiable features in the reflectance spectrum of the material.12 Lithological features and/or
rocks may have high reflectance values in one spectral range and they may absorb in another
spectral region. This property may be exploited to emphasize or exaggerate the anomaly of the
target object.13,14 Band ratio images have spectral enhancement by dividing the digital number
values in one spectral band by the corresponding values in another band. Resultant images convey the spectral characteristics of the image features, regardless of variations in scene illumination condition. A band ratio image effectively compensates for the brightness variation caused
by the varying topography and emphasizes the color content of the image data.15 Band ratio
images are effective for mineral mapping as they enhance compositional variations.16 Band
rationing of Landsat-ETM+ and ASTER data minimizes the effects of environmental factors.17
Clay minerals are a part of a general but important group within the phyllosilicates that contain large percentages of water trapped between their silicate sheets. Clay minerals are divided
into four major groups: kaolinite, montmorillonite/smectite, illite, and chlorite. The kaolonite
group ½Al2 Si2 O5 ðOHÞ has three members, i.e., kaolinite, dickite, and nacrite, which are polymorphs. The montmorillonite group includes pyrophyllite, talc, vermiculite, sauconite, saponite,
nontronite, and montmorillonite. Illite is basically a hydrated microscopic muscovite, and is the
main component of shales.18 Silicate minerals are the most common of Earth’s minerals and
include quartz, feldspar, mica, amphibole, pyroxene, and olivine. Silica tetrahedra, made up
of silicon and oxygen, form chains, sheets, and frameworks, and bond with other cations to
form silicate minerals.19 Carbonate minerals are those minerals containing the carbonate ion:
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Fig. 2 Geographical map of KMR, Tehsil Fatehjang, district Attock. The study area is outlined as
red.
CO−2
3 . Limestone is a sedimentary rock and its main minerals are calcite and aragonite, which are
different crystal forms of calcium carbonate CaCO3 . Dolomite is calcium magnesium carbonate
CaMgðCO3 Þ2 . The limestone that contains some dolomite is called dolomitic limestone.
2 Study Area
The proposed study area for the current research work is Gali Jagir area, Tehsil Fatehjang, district
Attock. It ranges from 72°33′34′′E to 72°52′2′′E, and 33°24′38′′N to 33°30′48′′N. The accessibility to this area is from Rawalpindi via Tarnol to Fatehjang (17 km from study area). The
focus in this research study is on the Khaira Murat range (KMR) which contains marine deposits
of the eocene age, divided into Margalla hill formation (PMh), Chorgali formation (PCh), and
Kuldana formation. Major rock types are limestone, claystone, sandstone, siltstone, and shale.
Dominant minerals of the study area are calcite, quartz, and montmorillonite/bentonite.20 Gullies
emanate from the uplands of KMR, as shown in Fig. 2. On the northern slopes, the gullies flow
roughly northward, whereas on the southern slopes, they flow southward and southeastward
(Table 3).
Table 3 Stratigraphic sequence of the study area.21
Era
CENOZOIC
Period
Epoch
Age (mya)
Formation
Lithology
Quaternary
Holocene
Present to 0.01
Potowar clay
Clay
Alluvium
Sand, silt, clay
Tertiary
Pleistocene
1.8
Unconformity
Pliocene
5
Chinji
Mudstone, sandstone
Miocene
24
Kamlial
Sandstone, clay
Murree
Sandstone, siltstone,
conglomerate
Oligocene
73
Unconformity
Eocene
54
Chor gali
Shale, limestone,
marl
Margalla hill limestone
Limestone
Paleocene
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3 Materials and Methods
Materials used in this research study include Landsat ETM+ image, ASTER image, topographic
maps (at 1:50,000 scale) of survey of Pakistan (SoP), and geological map (at 1:50’000 scale) of
geological survey of Pakistan (GSP). Both primary and ancillary data are then georeferenced and
have been assigned to the World Geodetic System 1984. To improve the visual appearance of the
satellite images before further analysis, resolution merge and log residual algorithms were
applied. Supervised image classification and band rationing techniques were applied on the
resultant images. A field survey was conducted. Differential thermal analysis (DTA)/TG and
X’PertPRO diffractometer (XRD) analysis of samples collected from the field assisted in accurate assessment of the results of image classification and band ratio indices. Printed SoP sheets,
classified maps, the bal positioning system (GPS), and binoculars were used in the field survey.
X-ray diffraction is used for analyzing crystalline phase in solid materials to identify the
crystalline structure. X-rays of a known wavelength are passed through the sample to be identified. X-rays are diffracted by the lattice of the crystal. The diffracted beams from atoms in
successive planes cancel unless they are in phase, and the condition for this is given by the
BRAGG relationship:
nλ ¼ 2d sin θ;
EQ-TARGET;temp:intralink-;sec3;116;537
where n is a constant (usually 1), λ is wavelength of the x-rays, d is the interplanar spacing of the
crystalline material, and θ is angle of diffraction.
The x-ray detector moves around the sample and measures the intensity of these peaks and
the position of these peaks (diffraction angle 2θ). The highest peak is defined as the 100% peak
and the intensity of all the other peaks are measured as a percentage of the 100% peak.22
Differential gravimetric thermal analyses (DTA TG) were carried out on samples collected
from the study area. DTA measures the temperature difference between a substance and a reference material as a function of temperature while the substance and reference material (inert
material) are subjected to the same controlled temperature. DTA provides indirect analytical
information on a material and the quantification of a reaction is limited. TGA gives direct
and absolute values for thermal reactions making stoichiometric calculation possible and measures the mass of a sample as a function of temperature while the sample is subject to a controlled
temperature. Both DTA and TGA are undoubtedly the most widespread methods.23
4 GIS Analysis
The current research study focuses on the application of supervised classification and band
rationing techniques in analyzing the remotely sensed data for lithological and mineral mapping.
Six spectral bands of Landsat-ETM+ data were stacked while excluding the TIR band. The
Landsat image has a relatively low spatial resolution that was improved by fusing the multispectral bands at 30-m spatial resolution with the panchromatic band of 15-m resolution.24
The resultant image at an enhanced spatial resolution of 15 m was produced (see Fig. 3).
The decorrelation stretch is a process to enhance the color differences found in a color image.
This process includes the removal of interband correlations found in the input pixels. The resultant decorrelation stretched images have spectral variations large enough to be used for subsequent spectral analysis.11 Decorrelation stretched algorithm was applied on various band
Fig. 3 (a) Landsat-ETM+ pansharpened color composite image RGB 721 and (b) ASTER decorrelation stretched color composite image RGB 468.
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Fig. 4 Lithological map reproduced from Geological Survey of Pakistan’s geological map of KMR,
district Attock.21 Study area is outlined.
combinations to obtain an optimal result. ASTER band combination of 468 with enhanced color
variations was helpful for visual interpretation (see Fig. 3).
Maximum likelihood classification, the most common supervised image classification, was
performed both on Landsat ETM+ and ASTER satellite images. Training areas were defined
based upon knowledge of the published geological map of GSP. Geological age of the formations in the mapped area is Cenozoic, which is subdivided into quaternary, neogene, and paleogene periods. The study area contains five lithological formations. PMh and PCh are oldest and
lie in the paleogene period. Chinji formation, Kamlial formation, and Murree formation lie in the
neogene period. Alluvium and Potwar Clay lie in the recent quaternary period (see Fig. 4).
Results of supervised image classification were compared with GSP’s geological map by
determining correlation coefficients R2 . 200 random points were selected in the study area
of KMR. Lithology mapped by supervised classification technique was recorded at these random
points. Correlation coefficients R2 of these random points were then determined between GSP
and supervised image classification mapped by ASTER and Landsat-ETM+ (Table 4). ASTER
classified image showed higher correlation with GSP as compared to Landsat-ETM+. The
increased numbers of SWIR and TIR bands of ASTER satellite data also enhanced the capability
for lithological discrimination as compared to Landsat-ETM+ data. A lithological map is then
constructed based upon the classified image of ASTER satellite data (see Fig. 5).
Spectral signatures for clay, silicate, and carbonate minerals were obtained from the United
States Geological Survey (USGS) spectral library. These spectra were resampled for LandsatETM+ and ASTER wavelength bands. In the process of resampling, the spectra lost some of the
detail depending upon the bandwidth. Broader wavelengths result in less detailed spectra.
Spectral comparisons of clay mineral groups, i.e., kaolinite, montmorillonite/smectite, illite,
and chlorite, are shown in Figs. 6–8.
Table 4 Correlation coefficient among lithologies mapped by geological survey of Pakistan and
maximum likelihood classification using Landsat-ETM+ and ASTER satellite images.
Correlation coefficient
GSP
GSP
1.00
Landsat-ETM+
0.43
ASTER
0.6
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Fig. 5 Lithological map produced from maximum likelihood classification of ASTER data. Study
area is outlined.
Fig. 6 Spectral signatures of kaolinite minerals: (a) from USGS spectral library, (b) resampled at
ASTER bands, and (c) resampled at Landsat-ETM+.
Kaolinite group minerals show absorption peaks at 1.4 and 2.2 μm due to OH−1 and Al-OH,
respectively [Fig. 6(a)]. Resampled spectra for ASTER shows absorption in bands 6, 8, and 9 and
reflectance in bands 4 and 7 [Fig. 6(b)]. Thus, a band ratio of ðB4 þ B7Þ∕B6 can identify kaolinite minerals.25–27 Landsat-ETM+ resampled spectra show a lower reflectance at band 7, as
compared to band 5. Thus, an SWIR band ratio of ðB5∕B7Þcan help to identify kaolinite minerals [Fig. 6(c)].
Landsat-ETM+ montmorillonite spectra show absorption in band 7 as compared to band 5,
which resembles kaolinite minerals. Therefore, Landsat-ETM+ SWIR band ratio of B5∕B7
detects kaolinite and montmorillonite minerals, but their further segregation is not attainable
(see Fig. 7).
As depicted in Fig. 8, chlorite shows absorption peaks in bands 7 and 8 and reflection in
bands 6 and 9 of ASTER, as shown in Fig. 8. As a result, ðB6 þ B9Þ∕ðB7 þ B8Þ can detect
chlorite minerals.28 Muscovite can be identified by band ratio ðB5 þ B7Þ∕B6 as its spectra has
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Fig. 7 Spectral signatures of montmorillonite minerals (a) from USGS spectral library,
(b) resampled at ASTER bands, and (c) resampled at Landsat-ETM+.
Fig. 8 Spectral signatures of illite and chlorite minerals (a) from USGS spectral library,
(b) resampled at ASTER bands, and (c) resampled at Landsat-ETM+.
strong absorption in band 6 while having reflection peaks in bands 5 and 7.29,30 Illite demonstrates absorption in band 5 but reflection in band 7 and can be identified by the band ratio
ðB7∕B5Þ. Chlorite has almost similar reflectance in both SWIR bands 5 and 7 of LandsatETM+. Illite and muscovite spectra have absorption peaks in band 7 as compared to band
5. Thus, Landsat-ETM+ band ratio of ðB5∕B7Þ picks up kaolinite, montmorillonite, and muscovite minerals, but is unable to identify chlorite minerals.
According to Fig. 9, ASTER band ratio ðB6∕B8Þ ðB9∕B8Þ aids in detecting calcite because
its spectra has absorption in band 8 and reflectance in bands 6 and 9. ASTER band ratio ðB6 þ
B8Þ∕B7 can identify dolomite CaMgðCO3 Þ2 having an absorption in band 7. Magnesite MgCO3
can be identified using band ratio ðB6 þ B8Þ∕ðB7 þ B9Þ. Landsat-ETM+ carbonate spectra are
identical to clay minerals’ absorption in band 7 and reflectance in band 5. Hence, the (B5∕B7)
band ratio assists in identifying clay and carbonate minerals.
The output of the Landsat-ETM+ band ratio ðB5∕B7Þ along its histogram is shown in Fig. 10.
The ASTER band ratio results for various clay and carbonate minerals are shown in Fig. 11. The
output of band ratio images is in floating points, which are then converted into unsigned 8 bit (0
to 255) images. Depending upon the histograms, the threshold value of a band ratio image can be
defined using any of the following equations:
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Fig. 9 Spectral signatures of carbonate minerals (a) from USGS spectral library, (b) resampled at
ASTER bands, and (c) resampled at Landsat-ETM+.
Fig. 10 (a) Landsat-ETM+ band ratio B5∕B7 image and (b) histogram.
TH = mean + 3 * standard deviation (confidence 98%),
TH = mean + 2 * standard deviation (confidence 95%),
TH = mean + standard deviation (confidence 92%).
According to Table 5, the threshold value for Landsat-ETM+ band ratio ðB5∕B7Þ is 245,
which means that clay and carbonate minerals lie in the 245 to 255 range.
Figure 12 shows the extent of clay and carbonate minerals derived from Landsat-ETM+
ðB5∕B7Þ.
Threshold values for various band ratios of ASTER were derived (see Table 6), and the resultant band ratio extents of various clay and carbonate minerals are shown in Fig. 13.
A field survey of the study area was conducted to verify results of band ratio indices. SoP’s
topographic sheets assisted in path finding, while the structure of rocks was observed by binoculars. Results of the indices were verified throughout the study area. Twelve field samples
were collected from different locations (focus was KMR) of the study area (see Figs. 14
and 15) to further verify results of the band ratio indices. GPS was used to mark field locations
of the collected samples.
Laboratory XRD analysis was carried out by American Standard Test Method. The samples
were first crushed into powder and then sieved using Panalytical XRD at 45 Kv and 40 mA with
CuK α radiations with a scanning speed of 0.05 deg∕s. Pressed powder samples were scanned
by XRD and compared by x-ray diffractograms to the International Center for Diffraction Data
products PDF-4+ 2009; database of over 600,000 known compounds, to identify each mineral
phase including the weight percent of all rock-forming minerals.
XRD spectra of rock samples verified the presence of various clay and carbonate minerals
detected by band ratio of ASTER satellite image. Calcite is present in Margalla hill limestone
[Fig. 16(a)], dolomite in Kamlial formation [Fig. 16(b)], magnesite in PMh [Fig. 16(c)],
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Fig. 11 ASTER Band ratio images: (a) kaolinite ðB7∕B5Þ, (b) montmorillonite ðB4 þ B6Þ∕B7,
(c) muscovite/smectite/sericite/illite (phyllic alteration) ðB5 þ B7Þ∕B6, (d) chlorite/epidote
ðB6 þ B9Þ∕ðB7 þ B8Þ, (e) phengitic ðB5∕B6Þ, (f) calcite ðB7 þ B9Þ∕B8, (g) dolomite
ðB6 þ B8Þ∕B7, and (h) magnesite ðB6 þ B8Þ∕ðB7 þ B9Þ.
Table 5 Statistics derived from histogram of Landsat-ETM+ band ratio B5∕B7.
Band ratio
Min
Max
Mean
SD
Threshold
Confidence
B5∕B7
0
255
110
50
245
98%
Fig. 12 Landsat-ETM+ band ratio B5∕B7 result in red color overlaid on color composite RGB 752.
montmorillonite is present in Chorgali shale [Fig. 16(d)], muscovite in Kamlial formation
[Fig. 16(e)], and calcite and nontronite in sandstone [Fig. 16(f)].
Differential gravimetric thermal analyses (DTA TG) were carried out to analyze dehydration
and dehydroxylation of five samples, including dolomite, limestone, sandstone, and clay using
SHIMADZU Analysis Work Station TA-60WS at 20°C from 35°C to 1200°C using a sample
weight of ∼20 mg. TG/DTA spectra are shown in Figs. 17–21. The Y-axis on the left represents
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Table 6 Statistics derived from histogram of ASTER band ratios.
Clay minerals
Carbonate
minerals
Band ratio
Description
Min Max Mean SD Threshold Confidence
ðB7∕B5Þ
Kaolinite
0
255
158
55
213
92%
ðB4 þ B6Þ∕B7
Montmorillonite
0
255
129
65
194
92%
ðB5 þ B7Þ∕B6
Muscovite/smectite/
sericite/illite
0
255
149
58
207
92%
ðB6 þ B9Þ∕ðB7 þ B8Þ
Chlorite/epidote
0
255
94
51
247
98%
ðB5∕B6Þ
Phengitic
0
255
121
53
227
95%
ðB7 þ B9Þ∕B8
Calcite
0
255
78
54
240
98%
ðB6 þ B8Þ∕B7
Dolomite
0
255
154
59
213
92%
ðB6 þ B8Þ∕ðB7 þ B9Þ
Magnesite
0
255
196
36
232
92%
the mass loss in milligrams (mg) with respect to the increase in temperature and the Y-axis on
the right represents the variation in current microvolts (μV) with respect to increasing
temperature.
TG/DTA curves of limestone samples (see Figs. 17 and 18) exhibit calcite decomposition
by endothermic peaks at 842°C and 814°C. Traces of chlorite exhibit their presence due to a
minor exothermic peak at about 400°C. Well crystallized calcite decomposes between 780°C
and 990°C.31 Dihydroxylation takes place at 585°C, which corresponds to the removal of
the OH−1 group of Kaolinite. There is more than 45% weight loss in each sample of limestone.
Fig. 13 ASTER band ratio results in red color overlaid on color composite RGB: (a) kaolinite
ðB7∕B5Þ, (b) montmorillonite ðB4 þ B6Þ∕B7, (c) muscovite/smectite/sericite/illite (phyllic alteration)
ðB5 þ B7Þ∕B6, (d) chlorite/epidote: ðB6 þ B9Þ∕ðB7 þ B8Þ, (e) phengitic B5∕B6, (f) calcite
ðB7 þ B9Þ∕B8, (g) dolomite ðB6 þ B8Þ∕B7, and (h) magnesite ðB6 þ B8Þ∕ðB7 þ B9Þ.
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Fig. 14 (a) Exposed Margalla hill limestone in the study area, (b) boulders of limestone in the
water channel of Alam wali gali, (c) limestone exposed along bank of a water body,
(d) Chorgali limestone sample, and (e) Margalla hill limestone sample.
Fig. 15 (a) Alternate bands of silica and clay, (b) reddish clay of Murree formation and steeply
dipping sandstone, (c) Chorgali shale, (d) Murree formation sandstone sample, and (e) Murree
formation claystone sample.
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Fig. 16 XRD spectra of field samples.
Fig. 17 TG/DTA spectra of Margalla hill limestone.
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Fig. 18 TG/DTA spectra of Chorgali limestone.
The TG/DTA curve of dolomite exhibits endothermic peaks at 585°C, 838°C, and 1100°C
(see Fig. 19), where the endothermic peak of 838°C shows Al-OH minerals. Muscovite also
show its presence as it has dihydroxylation process at the 820°C to 920°C range. Traces of magnesite are depicted by minor endothermic process between 625°C and 640°C.
The TG/DTA curve of Murree formation clay (see Fig. 20) shows a significant loss of water
by endothermic process at 64°C. There are two minor endothermic processes at 565°C and
945°C.
Murree formation sandstone appeared to be most stable as only 21% weight was lost during
the entire episode of temperature increase from 38°C to 1170°C (see Fig. 21). Major weight loss
occurs between 700°C and 850°C. There is one minor endothermic peak at 577°C and another
major peak at 796°C.
Fig. 19 TG/DTA spectra of Chorgali dolomite.
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Fig. 20 TG/DTA spectra of Murree formation clay.
Fig. 21 TG/DTA spectra of Murree formation sandstone.
5 Conclusion and Recommendations
Supervised image classification techniques applied on ASTER data image succeeded in better
discriminating lithologies, as compared to Landsat-ETM+. ASTER data, due to its finer spectral
resolution than Landsat-ETM+, provided better results for lithological mapping than LandsatETM+.
Band ratio indices of ASTER data successfully mapped various clay minerals like kaolinite,
montmorillonite, muscovite, and chlorite. ASTER band ratios were also successful in identifying
carbonate minerals, including calcite, dolomite, and magnesite. Landsat-ETM+ SWIR bands
were only able to detect clay and carbonate minerals but their further identification was not
achieved. Log residual algorithm increased the spectral range of the image, thus the slight spectral characteristics of certain minerals were greatly enhanced. This led to the successful application of band ratio indices for mineral exploration.
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The remote sensing techniques that were explored may be applied in remote and inaccessible
areas to help delineate lithologies and mineral deposits at the surface. A sketch about the geology
and minerals of an area under investigation may be drawn to limit the area for surveying and to
target specific minerals. New generation satellites like Sentinel may be investigated for their
potential for geological and mineral mapping.
Acknowledgments
The funding for this research study was provided by National University of Sciences and
Technology, Islamabad, Pakistan. The XRD and DTA analyses of the field samples were performed in the Geoscience Lab, Islamabad, Pakistan. The geological maps were provided by GSP,
Islamabad and Lahore Offices. Landsat-ETM+ and ASTER satellite images were downloaded
from United States Geological Survey (USGS) website: https://earthexplorer.usgs.gov/.
References
1. A. Moghtaderi, F. Moore, and A. Mohammadzadeh, “The application of advanced spaceborne thermal emission and reflection (ASTER) radiometer data in the detection of alteration in the Chadormalu paleocrater, Bafq region, Central Iran,” J. Asian Earth Sci. 30(2),
238–252 (2006).
2. E. J. M. Carranza and M. Hale, “Mineral imaging with landsat thematic mapper data for
hydrothermal alteration mapping in heavily vegetated terrain,” Int. J. Remote Sens. 23,
4827–4852 (2002).
3. F. A. Kruse, S. L. Perry, and A. Caballero, “Integrated multispectral and hyperspectral mineral mapping, Los Menucos, Rio Negro, Argentina, Part II: EO-1 Hyperion/AVIRIS comparisons and landsat TM/ASTER extensions,” in Proc. 11th JPL Airborne Geoscience
Workshop, Jet Propulsion Laboratory (2002).
4. S. Gad and T. Kusky, “ASTER spectral rationing for lithological mapping in the
Arabian-Nubian shield, the neoproterozoic Wadi Kid area, Sinai, Egypt,” Gondwana
Res. 11, 326–335 (2007).
5. O. Corumluoglu, A. Vural, and I. Asri, “Determination of Kula Basalts (geosite) in Turkey
using remote sensing techniques,” J. Arabian Geosci. 8, 10105–10117 (2015).
6. M. M. Abdeen et al., “Application of ASTER band-ratio images for geological mapping in
arid regions: the neopaterozoic allaqi suture, Egypt,” Geol. Soc. Am. 3(3), 289 (2001).
7. Y. Ninomiya, B. Fu, and T. J. Cudahy, “Detecting lithology with advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral thermal infrared radiance-atsensor data,” Remote Sens. Environ. 99, 127–139 (2005).
8. I. D. Tommaso and N. Rubinstein, “Hydrothermal alteration mapping using ASTER data in
the infiernillo porphyry deposit, Argentina,” Ore Geol. Rev. 32, 275–290 (2006).
9. M. Abrams, S. Hook, and B. Ramachandran, ASTER User Handbook, Version 2, Jet
Propulsion Laboratory, California (2002).
10. K. Fatima, U. K. Khattak, and A. B. Kausar, “Selection of appropriate classification technique for lithological mapping of Gali Jagir area, Pakistan,” Int. J. Earth Sci. Eng. 7(12),
964–971 (2013).
11. A. A. Green and M. D. Craig, “Analysis of aircraft spectrometer data with logarithmic
residuals,” in Proc. of the Airborne Imaging Spectrometer Data Analysis Workshop,
pp. 111–119, JPL Publication No. 85–41 (1985).
12. “Spectral reflectance of minerals and rocks,” http://www.nln.geos.ed.ac.uk/courses/english/
frs/f2710/f2710003.htm (14 August 2017).
13. J. Inzana, T. Kusky, and R. Tucker, “Comparison of TM band ratio images, supervised classifications, and merged TM and radar imagery for structural geology interpretations of the
central Madagascar highlands,” in Abstracts with Programs of American Society of
Photogrammetry and Remote Sensing Annual Meeting, Vol. 34 (2001).
14. A. Vural, O. Corumluoglu, and I. Asri, “Exploring Gördes Zeolite sites by feature oriented
principle component analysis of LANDSAT images,” Caspian J. Environ. Sci. 14(4),
285–298 (2016).
Journal of Applied Remote Sensing
046006-16
Oct–Dec 2017 • Vol. 11(4)
Fatima et al.: Minerals identification and mapping using ASTER satellite image
15. M. T. Lillesand, R. W. Kiefer, and J. W. Chipman, Remote Sensing and Image
Interpretation, Wiley, New York (2004).
16. J. F. Mustard and M. J. Sunshine, “Spectral analysis for earth science: investigations using
remote sensing data,” in Remote Sensing for Earth Sciences: Manual for Remote Sensing, A.
Rencz, Ed., Vol. 3, 3rd ed., pp. 251–306, Wiley, New York (1998).
17. J. C. Volesky, R. J. Stern, and P. R. Johnson, “Geological control of massive sulfide
mineralization in the neoproterozoic Wadi Bidah shear zone, southwestern Saudi
Arabia, inferences from orbital remote sensing and field studies,” Precambrian Res.
123, 235–247 (2003).
18. “The clay mineral group,” http://www.galleries.com/Clays_Group (12 August 2017).
19. “The silicate minerals-earth science: vision learning,” http://www.visionlearning.com/en/
library/Earth-Science/6/The-Silicate-Minerals/140 (12 August 2017).
20. M. Latif and H. Hussain, “Limestone quarry sites around Islamabad and Kohat,” Geol. Surv.
Pak. 721–722 (2002).
21. M. Akhtar, M. S. Bajwa, and A. B. Kausar, “Geology of Gali Jagir area Attock district,
Punjab, Pakistan,” Geol. Surv. Pak. 172 (1983).
22. “X-ray diffraction analysis,” http://www.plasma-biotal.com/xraydif1.html (31 January
2010).
23. M. Földvári, “Handbook of thermogravimetric system of minerals and its use in geological
practice,” Occasional Papers of the Geological Institute of Hungary, Vol. 213, https://www.
mfgi.hu/sites/default/files/files/K%C3%B6nyvtar/Alkalmi_teljes/Fodvari_egyben.pdf (11
August 2017).
24. L. Bruzzone, Image and Signal Processing for Remote Sensing IX, pp. 9–12, SPIE,
Bellingham, Washington (2003).
25. Y. Yamaguchi and C. Naito, “Spectral indices for lithological discrimination and mapping
using the ASTER SWIR bands,” Int. J. Remote Sens. 24(22), 4311–4323 (2003).
26. C. R. Lawrence, G. S. Robert, and C. M. John, “Distribution of hydrothermally altered rocks
in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data,”
Remote Sens. Environ. 104(1), 74–87 (2006).
27. Y. Ninomiya, “A stabilized vegetation index and several mineralogic indices defined for
ASTER VNIR and SWIR data,” in IEEE Proc. Int. Geoscience and Remote Sensing
Symp., Toulouse, pp. 1552–1554 (2003).
28. W. R. Barnaby, Description and Validation of an Automated Methodology for Mapping
Mineralogy, Vegetation, and Hydrothermal Alteration Type from ASTER Satellite
Imagery with Examples from the San Juan Mountains, Colorado, Scientific
Investigation Map 3190, USGS, Reston (2012).
29. C. R. Lawrence and C. M. John, “Lithological mapping in the Mountain Pass, California
area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data,”
Remote Sens. Environ. 84, 350–366 (2003).
30. B. P. Amin and H. Mazlan, “ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration,” SpringerPlus 3, 130 (2014).
31. S. Ali, H. Teruo, and H. Tamao, “Mineralogical and spectroscopic characterization, and
potential environmental use of limestone from the Abiod formation, Tunisia,” Environ.
Earth Sci. 61, 1275–1287 (2010).
Khunsa Fatima received her MSc space science degree from the University of the Punjab,
Lahore, Pakistan, in 2005, and her MS remote sensing and GIS degree from NUST,
Pakistan, in 2010. Currently, she is working as a faculty member in the Institute of
Geographical Information System, National University of Sciences and Technology (NUST),
Islamabad, Pakistan. Her research interests include satellite image analysis, GIS analysis,
and air pollutants mapping. In past, she worked for UN-OCHA, ICRC, and NESPAK.
Muhammad Umar Khan Khattak received his PhD in earth sciences from the University of
South Carolina in 1995 and his master’s degree in earth sciences from the University of
Washington. He served Institute of Geographical Information System of National University
of Sciences and Technology, Islamabad, Pakistan, as principal and head of department. He
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worked as a hydrogeologist in the South Carolina Department of Health and Environmental
Control. He has many publications in international journals of repute to his credit.
Allah Bakhsh Kausar completed his MS geology degree from the Department of Geosciences,
Oregon State University, Corvallis, USA, and his doctoral degree from the Institute Dolomieu,
Joseph Fourier University, Grenoble, France. He served Geological Survey of Pakistan for 33
years. He is author of the book Gems and Gemology in Pakistan. He was a chief editor of
Geologica an International Research Bulletin of the Geological Survey of Pakistan, and
Proceedings of Geoscience Colloquium. He has more than 30 publications.
Muhammad Toqeer received his PhD in geophysics from the université de Pau et des pays de
l’Adour (UPPA), France. He is currently working as a faculty member in the Department of
Earth Science, Quaid-I-Azam University, Islamabad, Pakistan. His research interests include
the numerical aspects of geophysics and geology, seismic data processing, and seismic rock
physics modeling. He extensively uses remote sensing techniques for geophysical prospection.
Naghma Haider is a master’s in geology, working in geological survey of Pakistan. She has
working experience on WD-XRF, ED-XRD, ICPMS, AAS, XRD, DTA-TG, SEM with
EDS&CL, petrography and gems identification, also working on ARCGIS and ERDAS imagine
software for GIS based maps. She got training on remote sensing and GIS from Turkish,
Japanese, and Germans experts. She has over 33 national and international publications to
her credit.
Asid Ur Rehman received his MSc degree from the University of the Punjab, Lahore, Pakistan,
in 2008, and his MS degree from the Institute of Space Technology, Islamabad, Pakistan, in
2017. Currently, he is working with UN-Habitat Pakistan. He has applied remote sensing
and GIS on various thematic areas, such as natural resource management, environment protection, wetlands conservation, forestry, sustainable urbanization, and disaster risk reduction. His
research interests include optical and radar remote sensing, and satellite climatology.
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