See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/320457413 Minerals identification and mapping using ASTER satellite image Article in Journal of Applied Remote Sensing · October 2017 DOI: 10.1117/1.JRS.11.046006 CITATIONS READS 31 22,462 6 authors, including: Khunsa Fatima Asid Ur Rehman Newcastle University Newcastle University 15 PUBLICATIONS 127 CITATIONS 13 PUBLICATIONS 104 CITATIONS SEE PROFILE All content following this page was uploaded by Khunsa Fatima on 17 October 2017. The user has requested enhancement of the downloaded file. SEE PROFILE 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: khunsafatima@gmail.com 1931-3195/2017/$25.00 © 2017 SPIE Journal of Applied Remote Sensing 046006-1 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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, Journal of Applied Remote Sensing 046006-2 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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: Journal of Applied Remote Sensing 046006-3 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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 Journal of Applied Remote Sensing 65 046006-4 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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. Journal of Applied Remote Sensing 046006-5 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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 Journal of Applied Remote Sensing 046006-6 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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 Journal of Applied Remote Sensing 046006-7 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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: Journal of Applied Remote Sensing 046006-8 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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)], Journal of Applied Remote Sensing 046006-9 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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 Journal of Applied Remote Sensing 046006-10 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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. Journal of Applied Remote Sensing 046006-11 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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. Journal of Applied Remote Sensing 046006-12 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image Fig. 16 XRD spectra of field samples. Fig. 17 TG/DTA spectra of Margalla hill limestone. Journal of Applied Remote Sensing 046006-13 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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. Journal of Applied Remote Sensing 046006-14 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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. Journal of Applied Remote Sensing 046006-15 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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. 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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 Journal of Applied Remote Sensing 046006-17 Oct–Dec 2017 • Vol. 11(4) Fatima et al.: Minerals identification and mapping using ASTER satellite image 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. Journal of Applied Remote Sensing View publication stats 046006-18 Oct–Dec 2017 • Vol. 11(4)
