Spectral quality assessment of Landsat 8 and Sentinel 2 bands for glacier identification in Upper Indus Basin Syed Najam ul Hassan (1,2,3), Mohd Nadzri Md. Reba (1,2), Dostdar Hussain (3), Aftab Ahmed (3) 1 Geoscience & Digital Earth Centre (INSTeG), Research Institute for Sustainability & Environment (RISE), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia. 2 Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia. 3 Department of Computer Science, Karakoram International University, Pakistan. Email:
[email protected];
[email protected];
[email protected];
[email protected] *corresponding;
[email protected] KEY WORDS: Glaciers, Upper Indus Basin, Sen2Core, Operational Land Imager, Multi Spectral Instrument. ABSTRACT: Glacier studies of Hindu Kush Karakoram Himalaya (HKKH) are inadequate where, the stability of glaciers in the Upper Indus Basin (UIB) of HKKH is known for anomaly studies. Despite of satellite based synoptic measuring schema, the quality of glacier anomaly estimate is always on debate. The advancement in Operational Land Imager (OLI) and Multi Spectral Instrument (MSI) offers the potential future of glacier measurement in UIB. Therefore, this study assesses the quality of OLI and MSI in mapping the glacier anomaly for glaciers of Hunzza in UIB. The methodology is based on acquisition of OLI Level 2 data, while for Sentinel MSI Level 2A data was derived using Level 1C, however, considering Landsat Enhanced Thematic Mapper Plus (ETM+) is motivation to support the sensors for their calibrated data with uncertainty of 3% as compare to 5% of the raw ETM+. Glacier outlines extracted from the Randolph Glacier Inventory and the snow line altitude (SLA) demarcated through contour generation from Global Digital Elevation Model (GDEM) to differentiate permanent snow and clear ice in the overall glacier polygon. Reflectance of each band was derived and Normalized Snow Differential Index (NDSI) calculated. Statistics applied in spectral quality assessment for glacier parameters. Overall glacier surface exhibited range of reflectance about 0.08 to 1.39, 0.07 to 1.39 and 0.04 to 1.44 at visible bands of OLI that was differed about 27%, 29% and 25% than that of MSI. Where, for SWIR band both sensors agreed by the mean reflectance of 0.10. Reflectance correlation between both sensors derived as 0.73 to 0.80 at visible band and 0.31 to 0.38 at SWIR which, allows clear discrimination between the clear ice and snow. But the overlap of reflectance within 0.20 to 0.35 in infrared bands of MSI may led to erroneous identification. To complement the results, NDSI of OLI with -0.01 to 0.95 becomes good indicator to distinguish different glacier features with disadvantage of inconsistent in MSI. These results clearly show that OLI and MSI have promising capability to map glacier anomaly and both variants can be synergized for better interpretation in climacterically intrinsic high-altitude zone of UIB. 1.
INTRODUCTION
Glacier is the best climate indicator that is responsive to even very small climate variation. Recent climate change issue is completely related to the ice melting on glaciers and as a result, the glacier extends and their morphology changes were interconnected to climate conditions (Paul et al., 2015). The Hindu Kush Karakoram Himalayas (HKKH) is a home to wide range of highest mountain glaciers and vulnerable to unprecedented climatic change (Najam ul Hassan, Md Reba, Hussain, & Ali, 2018). International and regional initiatives have put full attention on HKKH and glacier inventory is a major concern. Remote sensing plays important role in glacier studies in last two decades yet the inconsistent findings about glacier behaviors were evident in different ranges of this region. For the HKKH, glaciers in the Upper Indus Basin (UIB) known as Karakoram Pamir Anomaly is the most interesting site due to its stability behavior (A. Racoviteanu, 2011; Gardelle, Berthier, Arnaud, & Kääb, 2013). Despite of single mission optical remote sensing by Landsat has been exploited for continuous glacier monitoring, there are some technical limitation in spectral, spatial, temporal and radiometric resolution were reported in last few decades (Paul, Winsvold, Kääb, Nagler, & Schwaizer, 2016); (Zhu, Woodcock, Holden, & Yang, 2015). Recently, new generation optical sensors of Operational Land Imager (OLI) and Multispectral Instrument (MSI) with 12-bit quantization provide higher quality land surface monitoring application (Mandanici & Bitelli, 2016) and are recommended for glacier monitoring mission by exploiting the reflectance properties of these multispectral sensors for glacier surface extraction (Paul et al., 2016). The improved spectral and spatial resolution of OLI and MSI which are far better than the previous Landsat variant offer an opportunity to apply for UIB glaciers monitoring (Minora et al., 2013). The improved optical properties of OLI and MSI enhance the glacier extraction method such as Normalised Differential Index (NDSI) by offering multiple reflectance at different optical bands so that different glacier faces can be mapped at higher accuracy (A. E. Racoviteanu, Paul, Raup, Khalsa, & Armstrong, 2009; Minora et al., 2016). Currently, the spectral reflectance properties assessment of both sensors and potential bands combination for NDSI were understudied in line with the UIB climatic dependent mountainous glaciers research. Therefore, the study
focuses to determine the significant spectral band of OLI and MSI that can be used for snow and ice indexing. The objectives of this study are (1) to assess the spectral quality of both sensors by comparing the reflectance estimates at different band and (2) to develop the NDSI variant of which the best visible and short wave infrared (SWIR) bands used to distinguish different mountainous glacier features in UIB were defined. The relationship of selected bands is the major highlight towards the future research for data fusion in glacier mapping. 2.
STUDY AREA
For this study, one of the river basins in the Upper Indus Basin (UIB) named Hunzza sub basin which located in north east of HKKH is selected. The Hunzza sub-basin covers an area of 13715 km2 and about 25% of this area is the glacier of UIB that contributes water discharge to the Indus river. Almost 15% of the glaciated area is mainly distributed in the entire mountainous range of Karakoram and enclosed with heterogeneous glacier behavior (T. Bolch et al., 2012; Quincey & Luckman, 2014; Tobias Bolch, Pieczonka, Mukherjee, & Shea, 2017). The study focuses on mountainous glaciers layout shown in Figure 1.
Figure 1: Image of Sentinel-2 of MSI (purple outline) and Landsat 8 of OLI (green outline) show the glacier and snow cover with the corresponding GDEM presenting the various glacier altitudes of HKKH. Glacier outline in red line taken from RGI gives visual polygon of mountainous glacier used as the main reference in this study.
3.
MATERIAL AND METHODOLOGY
The methodology in this study is designed to assess OLI and MSI bands for their spectral quality to monitor the glacier surfaces in mountainous area of UIB. Selected glacier (depicted in Figure 2) of Hunzza sub basin with diversified elevation profile was used for intrinsic discussion for this study. 3.1 Data Acquisition Glacier outlines showing the latest extend in year 2005±3 (Bajracharya et al., 2015) were obtained from glacier inventory of Randolph Glacier Inventory (RGI) and Global Land Ice Measurements from Space (GLIMS) (RGI Consortium, 2015). While selected sample glaciers from the Hunzza Sub basin is debris cover glaciers (Khan, Naz, & Bowling, 2015) covering an area of 34.30 km2 however, the elevation range of overall glacier is 2700 ~ 7700 meters (Figure 1) with a heterogenous nature of terrain which consists debris cover, clear ice and snow and proved to be best sample to test and compare both sensors for mountainous glacier studies. The permanent snow and clear ice of overall glacier were differentiated by referring Snow Line Altitude (SLA) the delineated with the Global Digital Elevation Model (GDEM) at the accuracy of 17 meters ((A. Racoviteanu, 2011) GDEM Version 2, 2011).
Level 2 scientific data of OLI and Level 1C data of MSI acquired during the end of ablation period of 2017 are the primary data in this study of which radiometrically and geometrically were corrected and formed in Top of Atmosphere (TOA) reflectance product (USGS, 2018; ESA, 2015) and the snow cover was at minimum during the period of acquisition (Khan et al., 2015). To determine the spectral matching between MSI and OLI bands, the Relative Spectral Response Function (RSRF) was downloaded from European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA) web portals respectively. Table 1 provides the comprehensive description of the data acquired for this study. Table 1: Detail description of data for the study.
Sensor/Sou rce
Type of data
OLI
Optical imaging
MSI
Optical imaging
GDEM Glacier outlines
Microwa ve imaging Vector database
Band Blue Green Red NIR SWIR SWIR Blue Green Red NIR SWIR SWIR
Bands Band No B2 B3 B4 B5 B6 B7 B2 B3 B4 B8A B11 B12
Resolution
RSRF*
30 m 30 m 30 m 30 m 30 m 30 m 10 m 10 m 10 m 10 m 10 m 10 m
0.60 0.56 0.54 0.38 0.45 0.56 0.62 0.68 0.76 0.64 0.75 0.67
Acquisition Time
18 September 2017
20 September 2017
-
-
30 m
-
11 February 2000
-
-
-
-
2005±3
*RSRF as function taken as radiance response taken at pre-calibration stage. 3.2
Data Preparation
All satellite images were transformed into new geographical projection of Universal Transverse Mercator (UTM) to minimize the geometric distortion when comparing to the local DEM map (Paul et al., 2015). Comparison between MSI and OLI requires a resampling of MSI imageries by aggregating the pixels at raw spatial resolution of 10 m to a single 30 m pixel. The raw imageries of MSI and OLI are presented in the digital count and must be converted into the truth Top of Atmospheric (ToA) reflectance within the range of 1 to 10 by using the quantification number expressed in Eq.(1). The quantification number was used to reduce the processing capacity during the data retrieval via online and it is provided in the metadata of each image (European Space Agency, 2015); (USGS, 2018a). 𝑘 (𝜆𝑛 ) = 𝑄𝑘 /10∧ 4; ∀ 0 > 𝑄𝑘 < 10−4 𝜌𝑖,𝑗
(1)
𝑘 (𝜆𝑛 ) is the TOA reflectance of the respective wavelength, 𝜆𝑛 , the respective spectral band with the pixel where, 𝜌𝑖,𝑗 location of (i,j) and the correction of solar angle for each sensor (k), and 𝑄𝑘 is the digital count provided in each sensor data where ToA is computed and valid between 0 to 104.
The RSRF was used to redefine the quality of band similarity between both sensors and it is representing the “true” spectral response of each band estimated during the pre-calibration process (Mandanici & Bitelli, 2016). The similarity bands between both sensors was assessed by using histogram and the best band matching is later applied in ice and snow indexing. 3.3
The Normalized Differential Snow Index (NDSI)
The optimum reflectance (peak) is determined and visualized completely from RSRF and based on this result, the regression between band of OLI and MSI at the visible and the short-wave infrared (SWIR) wavelength is carried out. To examine more on the range of reflectance, the minimum and the maximum of the spectral range is determined so that the exact spectral overlapped is completed distinguished. From this band selection, list of similar spectral response of wavelength at visible to SWIR is extracted.
Band ratio is commonly used in satellite data processing for glacier mapping and in this study, the Normalised Differential Snow Index (NDSI) was applied. The NDSI is based on the ratio of band combination between visible and SWIR of which the visible bands give higher spectral response on snow and the SWIR reflectance remains higher even in infrared region (A. E. Racoviteanu, Williams, & Barry, 2008). It also has been proved as the promising tool in delineating glaciated regions (Khan et al., 2015). The NDSI was extracted within the glacier polygon based on the following expression. 𝑘 𝑁𝐷𝑆𝐼𝑖,𝑗 =
𝜌𝑉𝐼𝑆 −𝜌𝑆𝑊𝐼𝑅
(2)
𝜌𝑉𝐼𝑆 +𝜌𝑆𝑊𝐼𝑅
𝑘 where 𝑁𝐷𝑆𝐼𝑖,𝑗 is the Normalized Differential Snow Index (NDSI) of each pixel location, (i,j), for sensor, k (OLI or MSI), 𝜌𝑉𝐼𝑆 is the reflectance at visible band and 𝜌𝑆𝑊𝐼𝑅 is the reflectance at the short wave infrared band. All reflectance is presenting the total reflectance at ToA. From both sensors, there are several numbers of NDSI formation and this study examines the best VIS-SWIR combination using statistical evaluation.
4.
RESULTS AND DISCUSSION
The RSRF of OLI and MSI dataset suggests that most of the bands in both sensors is not identical with mere radiometric difference (Mandanici & Bitelli, 2016). Though there are significant spectral match between corresponding bands at visible, near infrared and SWIR wavelength (Zhang et al., 2018) and these bands have potential to exploit further in glacier extraction through NDSI. Therefore, the histogram of all possible combination of visible (blue, green and red) and SWIR bands was designed and shown in Figure 4. By using the glacier outline of GLI, the reflectance of OLI in visible and SWIR spectra varies between 0.07 to 1.44 and 0.0 to 1.37 respectively while MSI is from 0.07 to 1.41 and 0.02 to 1.37 respectively. 800 800 600
Pixel Freq
Pixel Freq
600 MSI (B2) OLI (B2)
400
200
200 0 0.00
MIS (B3) OLI (B3)
400
0.20
0.40
0.60
0.80
1.00
1.20
0 0.00
1.40
0.20
0.40
Reflectance 800
600
600
MSI (B4) OLI (B4)
400
200
0 0.00
1.00
1.20
1.40
(b)
800
Pixel Freq
Pixel Freq
(a)
0.60 0.80 Reflectance
MSI (B11) OLI (B6)
400
200
0.20
0.40
0.60
0.80
Reflectance
1.00
1.20
1.40
0 0.00
0.20
0.40
0.60 0.80 Reflectance
1.00
1.20
1.40
(c) (d) Figure 2: Histogram of pixel reflectance at visible bands in (a) Blue, (b) Green, (c) Red wavelengths and at (d) SWIR band for OLI (in dashed red line) and MSI (in solid black line).
800
800
600
600
600
MSI (B2 & 11) OLI (B2 & 6)
400
MSI (B3 & 11) OLI (B3 & 6)
400 200
200
0 0.00
Pixel Freq
800
Pixel Freq
Pixel Freq
Later, the NDSI was computed for all combination of visible and SWIR bands with variation range of -0.01 to 0.95 and -0.05 to 0.97 for OLI and MSI sensor respectively. This result is clearly illustrated in Figure 5 for visible and first SWIR combination and Figure 6 is for visible and second SWIR combination.
200
0 0.20
0.40
0.60
0.80
0.00
1.00
MSI (B4 & 11) OLI (B4 & 6)
400
0.20
0.40
0.60
0.80
1.00
0 0.00
0.20
Reflectance
Reflectance
(a)
0.40
0.60
0.80
1.00
Reflectance
(b)
(c)
800
800
600
600
600
MSI (B2 & 12) OLI (B2 & 7)
400
200
0 0.00
Pixel Freq
800
Pixel Freq
Pixel Freq
Figure 3: NDSI Trend for possible combinations of VIS and SWIR-1 bands for OLI and MSI (a) Blue & SWIR-1 (b) Green & SWIR -1 (c) Red & SWIR-1.
MSI (B3 & 12) OLI (B3 & 7)
400
200
0.20
0.40
0.60
0.80
1.00
0 0.00
MSI (B4 & 12) OLI (B4 & 7)
400
200
0.20
Reflectance
0.40
0.60
Reflectance
(a)
(b)
0.80
1.00
0 0.00
0.20
0.40 0.60 Reflectance
0.80
1.00
(c)
Figure 4: NDSI Trend for possible combinations of VIS and SWIR-2 bands for OLI and MSI (a) Blue & SWIR-2 (b) Green & SWIR -2 (c) Red & SWIR-2. A comprehensive summary of reflectance range for individual band of OLI and MSI is tabulated in Table 2. The result suggests that OLI and MSI reflectance have difference of 27%, 29% and 25% in their respective visible bands of blue, green and red, however, the difference in infrared spectrum is considerably high with mean reflectance of 0.10 in SWIR. It also noted that MSI has explicit reflectance range of 0.02 to 1.35 and 0.00 to 0.35 in NIR and SWIR respectively which is an advantage to discriminate glacier extends while overlapping reflectance values in said range (0.02 to 0.35) may lead to an erroneous identification. Table 2: Reflectance range of OLI and MSI over the glaciated surface of Hunzza sub basin.
Bands (ID) Blue (B2) Green (B3) Red (B4) NIR (B5) SWIR-1 (B6) SWIR-2 (B7)
Landsat 8 (OLI) Reflectance 0.08 to 1.39 0.07 to 1.39 0.04 to 1.44 0.02 to 1.37 0.00 to 0.24 0.00 to 1.20
Sentinel-2 (MSI) Bands (ID) Reflectance Blue (B2) 0.07 to 1.38 Green (B3) 0.05 to 1.36 Red (B4) 0.05 to 1.41 NIR (B8A) 0.02 to 1.35 SWIR-1 (B11) 0.00 to 0.25 SWIR-2 (B12) 0.00 to 0.19
Table 3 summarises the NDSI range for all possible combinations in which the maximum pixel frequency in OLI and MSI image was determined (Dorothy K Hall & George A Riggs, 2011). Those NDSI ranges are explicit and thus they are promising advantage to discriminate the glacier extents particularly for glacier surface observed with pixel shift between both sensors.
Table 3: Normalized Differential Snow Index (NDSI) for the selected glacier in Hunzza Sub Basin. Landsat 8 (OLI) Sentinel-2 (MSI) Band Combination NDSI Band Combination NDSI B2 & B6 -0.10 to 0.95 B2 & B11 -0.13 to 0.96 B2 & B7 -0.02 to 0.95 B2 & B12 -0.05 to 0.97 B3 & B6 -0.09 to 0.94 B3 & B11 -0.13 to 0.96 B3 & B7 -0.01 to 0.95 B3 & B12 -0.06 to 0.97 B4 & B6 -0.08 to 0.94 B4 & B11 -0.15 to 0.96 B4 & B7 -0.01 to 0.95 B4 & B12 -0.07 to 0.97 To assess the relation between matching band of OLI and MSI, correlation of pixels representing the glacier was generated and shown in Figure 7(a). Here, the correlation between 0.72 to 0.80 is evident for visible band and this not a case for SWIR band with the lower correlation between 0.31 to 0.38. However, the correlation in NIR band is higher than SWIR and such correlation (0.860) is consistently existed with visible band and this suggests that the NIR and visible bands are responsive to the same snow reflectance. 800
1.00 0.86 0.80 0.73
0.72
600
OLI MSI
0.60 0.38
0.40
0.31
Pexil Freq
Correlation
0.80
400
200
0.20
0.00
0 Blue
Green
Red
(a)
NIR
SWIR-1 SWIR-2
Blue
Green
Red
NIR
SWIR-1 SWIR-2
(b)
Figure 5: Plot of (a) degree of correlation for each OLI-MSI band combination from visible to SWIR band; and histogram of pixels representing the selected glacier which later being used for correlation estimation in (a).
5.
Conclusion
This study was carried out to assess the spectral quality of OLI and MSI bands for mountain glacier extend mapping. Pixels of each band of both MSI and OLI located within the selected glacier of RGI and specific GDEM were taken as the primary sample. Prior to data processing, the pre-calibration data of reflectance namely RSRF dataset for both sensors were assessed to determine the degree of similarity between bands. Highly attention is put to visible and SWIR bands as they are going to be used in the snow indexing of NDSI. The ToA reflectance in visible bands has slight difference in spectral estimate but it is much higher in SWIR band. The correlation at visible band is higher than the SWIR and this gives prospective for accurate clear ice and snow discrimination. The results have shown both sensors provide promising spectral response for monitoring the mountainous glaciers.
6.
References
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USING DTMs TO DELINEATE ACTIVE FAULTS OF THE PROXIMAL PART OF THE GANGA PLAIN, UTTARAKHAND, INDIA
Pradeep K Goswami Centre of Advanced Study, Department of Geology, Kumaun University, Nainital – 263002, India *E-mail :
[email protected],
KEY WORDS: Geomorphology, Tectonics, DTM, IRS imagery ABSTRACT: The present study pertains to effective use of Digital Terrain Models (DTMs) in identifying and mapping active faults in a large, proximal part of the Ganga (also called Gangetic) plain, where field geological investigations are mostly refrained due to inaccessibility owing to dense, multistoried forest cover. The SRTM (Shuttle Radar Topography Mission) 90 m DEMs were used to make a reconnaissance of the study area, but for detailed investigations the DTMs were prepared from relief information given in toposheets. Several hydrologically correct, grid-based DEMs were prepared in a Geographic Information System (GIS) for different resolutions. 2-D profiles along a number of longitudinal and transverse sections were drawn. Several 3-D perspective views were generated by draping the enhanced IRS imagery over the DEMs, for different exaggeration factors of the z-value, Sun azimuth and Sun angles to emphasize subtle topographic variations. These DTMs were then visually analyzed in conjunction with the satellite imagery to delineate the morpho-tectonic features. Throughout the analysis of DTMs, special emphasis was placed on drainage characteristics. Subsequently, the maps were verified during extensive fieldwork, and required corrections were made by incorporating the field data. The investigations reveal that the area is traversed by a number of criss-crossing lineaments. Some of these lineaments are active faults. Most of the active faults are generally concealed below the alluvium, but they could be identified and demarcated on the basis of their geomorphic characteristics as discernible on the satellite image or various DTMs. In the north is the active Himalayan Frontal Thrust (HFT), which defines the northern structural limit of the Ganga basin against the Himalayan Mountains. Parallel to HFT in the south is the blind Najibabad Fault (NF); however, it is identifiable only in the western part of the area. The HFT is offset by a number of dip-slip, oblique-slip and strike-slip faults, some of which extend northward into Himalaya and are related to the basement structures of the basin. Ongoing activities along these faults/thrusts have pronounced control on the river dynamics and landscape of the Ganga foreland basin and adjoining Himalayan mountain-front. 1. INTRODUCTION The Ganga Plain represents the central part of the alluvium filled Indo-Gangetic foreland basin system (Fig.1a). Its western and eastern limits are defined by the NE-SW trending Delhi-Hardwar and Monghyr-Saharsa basement ridges respectively (Sastri et al. 1971; Karunakaran and Ranga Rao, 1979). There are a number of ridges, spurs, depressions and faults in the basement of the Ganga Basin (Sastri et al., 1971), some of which are extensions of the faults of the Peninsular India and extend further northwards into the Himalaya (Sastri et al., 1971; Raiverman et al., 1983). The northern limit of the Ganga Basin is sharply defined by the Himalayan Frontal thrust (HFT), whereas the southern limit is diffuse and marked by the highlands of the Peninsular Craton. Geomorphically, the Ganga Plain is subdivided into three broad zones extending apparently parallel to the axis of the basin. These are, the Piedmont Zone (PZ), Central Alluvial Plain (CAP) and Marginal Alluvial Plain (MAP) (Fig.1b) (Singh, 1996). The PZ is formed to the south of the Himalayan foothills as a result of the coalescence of several alluvial fans and talus deposits. This is generally a 10-50 km wide, southwards sloping zone, gravelly in the proximal part and silty-fine sandy in the distal part. The CAP is a gently south-eastwards sloping, generally sandy zone extending between PZ and axial river of the basin (Yamuna River in the western part and Ganga River in the eastern part). The sediments of the CAP are derived from the Himalaya. The MAP is a north to north-eastwards sloping zone extending between the Peninsular Craton and axial river of the basin. It is composed of sediments derived from the Peninsular Craton. Despite that a substantial amount of information has been generated over the past few years on various geological aspects of different parts of the Ganga Plain, the information on the active faults of the PZ has not yet been compiled. The field identification and demarcation of morpho-tectonic features in the PZ is impeded because of the thick, multi-storeyed forest cover, extensive anthropogenic modifications in the landscape and easily reworkable nature of the gravels. The dense forest cover also makes a large part of the area inaccessible. Therefore, this study is based on analysis and interpretation of satellite imagery and Digital Terrain Models (DTMs) together with detailed fieldwork.
Figure 1 (a) Extent of the Indo-Gangetic Plain (IGP) between the Himalaya and the Indian craton (after Singh 1996). The extent of Ganga Plain is marked by two thick lines on the figure. (b) Simplified map of the Ganga Plain showing Piedmont Zone (PZ), Central Alluvial Plain (CAP) and Marginal Alluvial Plain (MAP) (simplified after Singh 1996). Rectangle marks the location of the study area. HFT: Himalayan Frontal Thrust; R: River.
Following our previous studies (i.e., Goswami et al., 2009; Goswami and Yhokha, 2010; Goswami, 2012, 2017; Goswami and Mishra, 2014a,b), the main purpose of this paper is to demonstrate the successful utilization of RS and GIS technologies in mapping of active faults in densely forested plain areas. For this, compiled here is the data of a part of the densely forested PZ of the western Ganga Plain. We hope that the present study will provide a model for carrying out similar investigations in densely forested plain areas of other parts of the world. 2. MATERIALS AND METHODS The active faults in the present study area have been demarcated on the basis of their geomorphic expressions, as enunciated by many workers (e.g. Keller and Pinter, 1996; Burbank and Anderson, 2001; Arrowsmith and Zielke, 2009). The flatness of the surface, thick forest cover and anthropogenic modifications of the landscape and the friable and easily reworkable nature of the unconsolidated sediment fill make difficult the delineation of subtle surface deformations through traditional field methods. Therefore, the investigations are based on analysis and interpretation of satellite imagery, stereopairs of black & white aerial photographs and Digital Terrain Models (DTMs) together with detailed fieldwork. The active faults of the study area have been delineated through visual interpretation of the enhanced images and DTMs on the basis of geomorphic features and terrain characteristics, such as linear stream courses, linear valleys, linear pattern of deflected stream courses, linear train of active landslides, linear zones of slope break, linear pattern of tonal variations in satellite images. Escarpments, terraces and topographic undulations are easily identifiable on aerial photo stereograms under a zoom stereoscope. A number of lineaments become clear in shaded relief maps at different sun illumination, whereas in slope and slope aspect maps the faults can be identified with fair confidence because in many sectors they are represented as linear zones of sharp break of slope of comparatively uniform slope aspect. In 2-D cross-sections the faults exhibit abrupt increase in the gradient. The interpreted maps were subsequently verified in the field, and required corrections were made by incorporating the field data. DTMs, like shaded relief, slope, slope aspect, two-dimensional (2-D) cross-sections and Digital Elevation Models (DEMs) formed the primary data for the present study. The SRTM (Shuttle Radar Topography Mission) 90 EMs (available from the United States Geological Survey (USGS) server at http://dds.cr.usgs.gov/srtm/ and http://seamless.usgs.gov/) have been used to make a reconnaissance of the study area, but for detailed investigations
the DTMs were prepared in ArcGIS Geographic Information System (GIS) from relief information provided in the Survey of India toposheets. Several hydrologically correct, grid-based DEMs were prepared for different resolutions (25 m, 50 m 100 m and 150 m grid sizes) using the TOPOGRID command in ArcGIS software. Several 3-D perspective views were generated by draping the enhanced satellite imagery over the DEMs, for different exaggeration factors (ranging from 1 to 25) of the z-value (elevation), Sun azimuth and Sun angles to identify subtle topographic variations. The DTMs were analysed in conjunction with LISS III digital imagery, having a spatial resolution of 23.5m, from the Indian Remote Sensing Satellites (IRS) using the ArcGIS software. The imagery were georeferenced and enhanced using ERDAS IMAGINE software. The linear contrast stretched NIR (Near Infrared) band grey-scale image, linear contrast stretched NIR/Red band-ratioed grey-scale image and a False Colour Composite (FCC) image generated by coding linear contrast stretched SWIR (Shortwave Infrared), NIR and red bands in red, green and blue colour planes respectively are found most suitable for the present investigations. Subsequently, the maps were verified by fieldwork, and required corrections were made by incorporating the field data. 3. RESULTS AND DISCUSSION 3.1 Active faults of the area The active faults of the study area are shown in figure 2, and a brief description of the geomorphic features expressing them, as discernible in sundry DTMs and verified in field, is given below: 3.1.1 Longitudinal faults Trending parallel to the axis of the basin are the HFT in the north and Najibabad Fault in the south. The HFT defines the northern structural limit of the PZ, but larger segments of it are concealed below alluvia. In the DTMs it is expressed by relief anomalies, upwarps and drainage deflections and anomalies, and terraces (Fig. 3–5). At places the HFT is expressed by scarps, which are 7–70 m high in different stretches; for example, a 7–18 m high scarp between Malin River and Kotwali Rao stream, 50–70 m high scarp to the west of Ramganga River, and 18 m
Figure Figure2. 2. Map Mapshowing showing active active faults faults of of the thePiedmonte Piedmonte Zone Zoneof of the the study study area. area. HFT, HFT,Himalayan HimalayanFrontal Frontal Thrust; Thrust; MBT, MBT, Main Main Boundary Boundary Thrust; Thrust; NF, NF, Najibabad Najibabad Fault; Fault; 1–12 1–12 faults faults transverse transverse to to Himalayan Himalayan strike; strike; a–p, a–p, rivers/streams: rivers/streams: 1, 1, Sukh Sukh Rao Rao Fault; Fault; 2, 2, Khoh Khoh River River Fault; Fault; 3, 3, Kalagarh Kalagarh Fault; Fault; 4, 4, Jhirna Jhirna Fault; Fault; 5, 5, Ramnagar Ramnagar Fault; Fault; 6, 6, Dabka Dabka Fault; Fault; 7, 7, Nihal Nihal Fault; Fault; 8, 8, Unchapul Unchapul Fault; Fault; 9, 9, Haldwani Haldwani Fault; Fault; 10, 10, Kathgodam Kathgodam Fault; Fault; 11, 11, Chorgallia Chorgallia Fault; Fault; 12, 12, Kalaunia Kalaunia Fault; Fault; 13, 13, Tanakpur Tanakpur Fault; Fault; a, a, Kotwali Kotwali Sot Sot stream; stream; b, b, Malin Malin River; River; c,c, Sukh Sukh Rao Raostream; stream; d, d, Khoh Khoh River; River; e,e, Ramganga RamgangaRiver; River;f,f, Dhara Dhara River; River; g, g, Banaili Banaili River; River; h, h, Phika Phika River; River; i,i, Dhela Dhela River; River; j,j, Kosi Kosi River, River, k, k, Nihal Nihal River; River; l,l, Bhakhra Bhakhra River; River; m, m, Gola Gola River; River; n, n, Sukhi Sukhi River; River; o, o, Nandhaur NandhaurRiver; River; p, p,Kalaunia Kalaunia River. River. xx’, xx’, yy’, yy’, zz’ zz’and and x’’y’’ x’’y’’are arelines linesacross acrosswhich which2-D 2-Dprofiles profiles shown shownin infigure figure33are are drawn drawn
high scarp to the west of the Sarda River. At many other places gravel ridges have developed along the HFT zone; for example a 70–80 m high ridge to the northwest of Dhela River, 5 m high ridge to the west of Nandhaur River, and 50 m high ridge to the west of Sarda River (Fig 4a). These ridges may be fault-bend/fault-propagation folds related to the blind segments of the HFT. In the western part of the study area, the drainages show marked deflections along the trace of the HFT; the Kotwali Sot flows towards SSW in the Siwalik terrain but on reaching the PZ it takes a sharp, knee-bend turn to flow towards W along the HFT and again turns sharply towards SW along a lineament (Fig 5b). Ground tilting associated with HFT has produced paired or unpaired terraces along the proximal parts of most of the rivers, and palaeochannels along the right bank of the Ramganga River. Most of the mountain-fed streams enter into the PZ through deeply cut V-shaped valleys, suggesting accelerated undercutting in response to faster ground uplift along the HFT. The Najibabad Fault traverses through the south of the western part of study area. It’s a blind fault with no surface exposure, but in DTMs it’s delineated on the basis of drainage, surface gradient and relief anomalies. In 2D profiles it is expressed by surface upwarping and sharp break in surface gradient, suggesting subsidence of the southern fault block (Fig. 3a,b). Terrain upwarping along the NF has also caused accelerated erosion and thus development of badland zone in the west of Ramganga River. The Ramganga River’s consistently rightward and leftward migratory trends on the northern and southern fault blocks respectively indicate left-lateral strike-slip component on the NF. The similar migratory trends of Kosi River further corroborate to a left-lateral strike-slip component on the NF. The NF as such is an oblique-slip fault, and seems to be what Yeats and Thakur (2008) call as the Piedmont Fault. 3.1.2 Transverse faults There are many faults that trend transverse to the axis of the basin; most of these faults offset the HFT (Fig. 2). These faults have been identified and the sense of movement on them ascertained on the basis of drainage and relief anomalies discernible in the DTMs. In the western part of the study area, the N-S trending, branching Sukh Rao Fault and Koh River Fault offset the HFT left-laterally and right laterally, respectively. Admittedly, the area between these two faults produces a marked indent into the adjoining mountain-front. Eastward, the NE-SW trending Kalagarh Fault and Jhirna Fault left-laterally offset the HFT. Analyses of DTMs reveal that the Jhirna Fault has oblique-slip movement with ~20 m up-throw of the left-laterally displaced western fault block. In the central part of the study area, the NNE–SSW trending Ramnagar Fault and Dabka Fault offset the HFT right-laterally and left-laterally, respectively. While the former of these controls the anomalously straight course of the Kosi River, the latter controls the course of the Dabka River, compelling its flow to take a knee-bend turn from E–W to N–S direction, and thus preventing it from joining the Kosi River that flows just 500 m ahead.
Figure 3. 2-D profiles drawn across the lines shown in figure 2. The faults/thrusts are drawn irrespective of dips, just to highlight their surface expressions.
Figure 4. (a) SRTM DEM showing gravel ridges marking surface expression of the HFT between Kalaunia and Sarda rivers (highlighted by red ellipse. (b) IRS LISS III FCC (bands 3,4,5 in BGR colour planes, respectively), showing gridiron drainage pattern marking the surface expression of the Kathgodam Fault (KF)
Further eastward, NNE-SSW trending Nihal Fault controls the trend of Nihal River in the Siwalik as well as the PZ. In DEMs, the NF is seen to right-laterally offset a linear ridge associated with HFT by ~800 m, indicating oblique slip along this fault with western block upthrown to north. East of Nihal Fault the NE-SW trending Unchapul Fault traverses the proximal PZ, which is expressed in DEM by a 6 km long and up to 15 high scarp with western fault block uplifted up to 15 m. Parallel to the Unchapul Fault is the blind Haldwani Fault. Trending NNE-SSW, it is delineated mainly on the basis of drainage deflections in PZ and adjoining Siwalik Hills. The presence of a NNE-SSW trending palaeochannel downstream of Haldwani, in line with the active channel upstream, suggests that movements along this fault have caused uplift of the western fault block so that the Gola River has shifted eastwards following an avulsion just downstream of Haldwani. A comparison of the toposheet (surveyed in 1964-65) with the satellite data of 1997 and 2004 reveals that, over the period of forty years, the width of the river channel in the northeast of Haldwani has increased from ~522 m to ~632 m as a result of lateral-cutting, and has shifted up to 360 m eastwards. This may also be due to uplift of the right bank of the river along the Haldwani Fault. The deflection in the course of Gola River near the Haldwani indicates that this fault also has a right-lateral strike-slip component of movement. As such, it is an oblique-slip fault with western block upthrown to northeast (Fig. 3c). East of the Haldwani Fault, the NW–SE trending Kathgodam Fault controls the straight course of the Sukhi River. In its upper reaches the Sukhi River descends southwestwardly from the adjoining Siwalik, but in the PZ it sharply turns towards SSE along the Kathgodam Fault, rather than joining the Gola River flowing just ~1.3 km away (Fig.2). All streams descending from the adjoining mountain between the northeast of Haldwani and west of Chorgallia join this fault controlled Sukhi River almost perpendicularly, giving rise to a gridiron drainage pattern (Fig. 4b). In the eastern part of the study area, the NNE-SSW trending Chorgallia Fault dextrally offsets the HFT as borne out in a DEM by the across-trend displacement of a gravel ridge related to a blind segment of the HFT. These geomorphic features indicate that the Chorgallia Fault is an oblique-slip fault with western block upthrown to the north. Eastward, the NNE–SSW trending Kalaunia Fault controls the course of Kalaunia River and its tributaires, givin rise to gridiron-type drainage pattern. The southeastwardly flowing Kalaunia River takes a sharp turn towards SSW along this fault, rather than joining the Sarda River flowing just ~2.5 km away. Along the eastern extremity of the study area, the NE–SW trending Tanakpur Fault is expressed in a DTM by steeper south-easterly gradient of the surface, indicating that the eastern fault block is downthrown (Fig. 3d, 5a). The displacement of gravel ridges in the PZ indicates that the transverse Tanakpur and Kalaunia faults have dextrally offset the longitudinal HFT.
All the faults and thrusts identified on the DTMs have also been validated by fieldwork at a number of places.
Figure 5. (a) 3-D perspective view obtained by draping IRS LIII FCC (band 2,3,4 in BGR colour planes, respectively) depicts grounding tilting caused by the Tanakpur Fault (TF). (b) Drainage deflections marking surface trace of the Himalayan Frontal Thrust (HFT) in the western part of the study area.
4. CONCLUSION The present study demonstrates successful use of DTMs in mapping active tectonic features in flat, densely forested and cultivated terrains. The best method to delineate active tectonic features in such terrains is to first identify the lineaments on imagery and DTMs, following the geomorphic criterion, and then verify them in the field. The 2-D sections and perspective views at grater vertical exaggerations are in particular very useful in delineating subtle ground undulations associated with the active faults. Many lineaments become clear in shaded relief maps at different sun illumination. Similarly, many faults are identifiable in slope maps (angle and aspect) due to their characteristic linear zones of steeper surface gradient with a comparatively uniform aspect, or in acrossstrike profiles due to abrupt increase in the gradient.
REFERENCES Arrowsmith, J.R. and Zielke, O., 2009. Tectonic geomorphology of the San Andreas fault zone from high resolution topography: An example from the Cholame segment. Geomorphology, 113, pp. 70–81. Burbank, D. W. and Anderson, R. S., 2001. Tectonic Geomorphology. Blackwell Science, Massachusetts. Goswami P.K., 2012. Geomorphic evidences of active faulting in the northwestern Ganga Plain, India: Implications for the impact of basement structures. Geosciences Journal, 16, pp. 289-299. Goswami, P.K., 2017. Controls of basin margin tectonics on the morphology of alluvial fans in the western Ganga foreland basin’s piedmont zone, India. Geological Journal, DOI: 10.1002/gj.3010. Goswami, P.K. and Yokha, A., 2010. Geomorphic evolution of the Piedmont Zone of the Ganga Plain, India: a study based on remote sensing, GIS and field investigation. International Journal of Remote Sensing, 31, pp. 5349-5364 Goswami, P.K. and Mishra, J.K., 2014a. Tectonic and climatic controls on the Quaternary landscape evolution of the Piedmont Zone of the Ganga Plain, India. Zeitschrift für Geomorphologie 58, pp. 367-384. Goswami, P.K. and Mishra, J.K., 2014b. Morphotectonic evolution of the Piedmont Zone of the west Ganga Plain, India. Zeitschrift für Geomorphologie 58, pp. 117-131. Goswami, P.K., Pant, C.C. and Pandey, S., 2009. Tectonic controls on the geomorphic evolution of alluvial fans in the Piedmont Zone of the Ganga Plain, Uttarakhand, India. Journal Earth System Science, 118, pp. 245-259. Karunakaran, C. and Ranga Rao, A., 1979. Status of exploration for hydrocarbons in the Himalayan region – contribution to stratigraphy and structure. Geological Survey of India Publication, 41, pp. 1-66. Keller, E.A. and Pinter, N., 1996. Active tectonics: Earthquakes, uplift and landscape. Prentice Hall, New Jersey. Nakata, T., 1972. Geomorphic History and Crustal Movements of the Foothills of the Himalaya. Tohoku University Science Reports, 7th Series, Japan 22, pp. 39-177.
Raiverman, V, Kunte, S.V. and Mukherjea, A., 1983. Basin geometry, Cenozoic sedimentation and hydrocarbon in north western Himalaya and Indo-Gangetic plains. Petroleum Asia Journal, 6, pp. 67-92. Sastri, V.V., Bhandari, L.L., Raju, A.T.R. and Dutta, A.K., 1971. Tectonic framework and subsurface stratigraphy of the Ganga Basin. Journal of the Geological Society of India, 12,pp. 222-233. Singh, I.B., 1996. Geological evolution of Ganga Plain- an overview. Journal of the Palaeontological Society of India 41, pp. 99-137. Yeats, R.S., and Thakur, V.C., 2008. Active faulting south of the Himalayan Front: establishing a new plate boundary. Tectonophysics, 453, pp. 63–73.
DETECTION OF HYDROTHERMAL ALTERATION ZONES AND LINEAMENTS ASSOCITED WITH OROGENIC GOLD MINERALIZATION USING ASTER REMOTE SENSING DATA IN SANANDAJ-SIRJAN ZONE, EAST IRAN Abdollah Sheikhrahimi1, Amin Beiranvand Pour2, Mazlan Hashim*3 and Danboyi Joseph Amusuk3 1
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Department of Geography and Urban Planning, Tabriz University, Tabriz, Iran Korea Polar Research Institute (KOPRI) Songdomirae-ro,Yeonsu-gu, Incheon 21990, Republic of Korea
3
Geoscience and Digital Earth Centre (INSTeG), Universiti Teknologi Malaysia (UTM), 81310 UTM Skudai, Johor Bahru, Malaysia Email:
[email protected];
[email protected];
[email protected]
KEY WORDS: Saqqez; ASTER; PCA; SAM; SID; Alteration zones; Lineaments; Gold exploration
ABSTRACT: The Sanandaj-Sirjan Zone (SSZ) is considered as an important region for exploration of orogenic gold mineralization in the eastern sector of Iran. Mountainous topography and relatively lack of accessible route are challenging for researchers and costly for mining companies for gold exploration in the SSZ. Gold mineralization mainly occurs as irregular to lenticular sulfide veins along shear zones in extremely altered and deformed mafic to intermediate metavolcanic and metasedimentary rocks. In this investigation, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for mapping indicator hydrothermal alteration minerals and geological structural features associated with orogenic gold mineralization in the Saqqez plot of the SSZ. Image transformation techniques such as specialized band ratioing and Principal Component Analysis were used to delineate lithological units and alteration minerals. Supervised classification, namely Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification methods were used to detect subtle differences between indicator alteration minerals associated with gold mineralization in the study area. Directional filtering was implemented to trace structural features. Results demonstrate that the integration of image transformation techniques and supervised classification derived from ASTER remote sensing analysis with fieldwork and previous stream geochemical study has a great ability for targeting new prospects of gold mineralization in the Saqqez plot of the SSZ.
1.
INTRODUCTION
Hydrothermal alteration mineral detection and lithological and structural geology mapping is one of the most prominent applications of remote sensing satellite data for regional ore exploration programs during last decade (Pour et al.,2017a,b). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a high spatial, spectral and radiometric resolution multispectral remote sensing sensor. The ASTER data consist of three separate subsystems with a total of 14 spectral bands: (a) the visible–near infrared (VNIR) subsystem contains three bands (0.52–0.86 μm) with 15 m spatial resolution; (b) the shortwave infrared (SWIR) subsystem has six bands (1.60–2.43 μm) with 30 m spatial resolution; and (c) the thermal infrared (TIR) obtains five bands (8.12– 11.65 μm) with 90 m spatial resolution. ASTER swath width is 60 km that each individual scene is cut to a 60 × 60 km2 area (Abrams, 2000). Iron oxide/hydroxide minerals such as limonite, goethite, jarosite and hematite tend to have diagnostic absorption features due to charge transfer and crystal-field processes in the VNIR region (0.4 to 1.1 μm) of the electromagnetic spectrum (Hunt and Ashley 1979). Thus, these spectral characteristics can be used to map iron oxide/ hydroxide minerals at the Earth’s surface using the VNIR bands of ASTER remote sensing data (Noda and Yamaguchi, 2017). Hydroxyl-bearing minerals including clay and sulfate groups as well as carbonate minerals present diagnostic spectral absorption features due to vibrational processes of fundamental absorptions of Al–O–H, Mg–O–H, Si–O–H, and CO3 groups in the SWIR region (Hunt and Ashley 1979). Therefore, spectral bands of ASTER in the SWIR region (1.60 μm to 2.5 μm) have great ability to map hydrothermal alteration mineral zones associated with ore mineralization and alteration of the rocks surface (Pour et al., 2013; Safari et al., 2017). The ASTER TIR bands (10–14) are useful for detecting silicate and carbonate rocks (Ninomiya et al., 2005). The area of Saqqez plot (46˚ to 46˚.30' east longitude and 36˚ to 36˚.30' north latitude), North West of Kurdistan is
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located in the SSZ, east Iran (Fig. 1). It is a high potential zone for orogenic gold mineralization due to infiltration of granitoid masses related to Precambrian and Mesozoic into Archaic metamorphosed stones and Paleozoic carbonate–debris coverage (Fig. 2). A comprehensive remote sensing investigation has not been reported for gold mineral exploration and prospecting in the SSZ especially for Saqqez plot, yet. Accordingly, the objectives of this investigation are: (1) to map indicator hydrothermal alteration minerals and geological structural features such faults and fracture (lineaments) associated with orogenic gold mineralization in the Saqqez plot of the SSZ using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data.
Figure 1. The location of the Sanandaj-Sirjan Zone (SSZ) and the Saqqez plot in the Kurdistan province, east Iran.
2. 2.1
MATERIALS AND METHODS Remote sensing data
In this research, a cloud-free ASTER level 1T (AST_L1T_00309062002075134_20150424203448_10828) covering Saqqez plot was obtained from the USGS EROS (https://earthexplorer.usgs.gov) in 2002/09/06 (Path-168 and ROW-35 and. The ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B), that has been geometrically corrected, and rotated to a north up UTM projection (https://lpdaac.usgs.gov/dataset_discovery/aster/aster_products_table/ast_l1t). The image has been pre-georeferenced to UTM zone 38 North projection with the WGS-84 datum.
2.2
Data analysis
The main purpose of the methodology is to apply image processing techniques that are capable detecting subtle hydrothermal alteration minerals and mapping structure elements associated with orogenic gold mineralization in the study area using VNIR+SWIR spectral bands of ASTER. Image transforms are commonly used to reduce the dimensionality of the input dataset and processing time, focus processing on the information of interest within the input file and removing noise. Each output band of a transformed image is a linear combination of every input image, thus helping to identify those spectral bands that are most important for finding targets of interest, or which bands contribute the most noise (Research Systems, Inc. 2008). To reduce the effects of topography and enhancing the spectral differences between bands, band ratioing technique was selected in this analysis. It is a technique where the digital number value of one band is divided by the digital number value of another band. Band ratios are very useful
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for highlighting hydrothermal alteration minerals (Abrams et al., 1983). Dividing one spectral band by another produces an image that provides relative band intensities, this is able to minimize the illumination differences due to topography. Therefore, this technique is particularly applicable for highly exposed areas and rugged terrains in arid and semi-arid environments. Moreover, Red-Green-Blue (RGB) color composites technique could be easily applied on band ratios to produce image map of lithological units of the study area. Multispectral data bands are often highly correlated; the principal components (PC) transformation could be used to produce uncorrelated output bands, segregate noise components and reduce the dimensionality of data sets. This is done by finding a new set of orthogonal axes that have their origin at the data mean and that is rotated so the data variance is maximized. PC bands are linear combinations of the original spectral bands and are uncorrelated. The principal component analysis is a well-known method for alteration mapping in metallogenic provinces for mineral exploration objectives (Safari et al., 2017). In this study, the forward PC rotation was applied to VNIR+SWIR and TIR bands of ASTER covering the study area. It uses a linear transform to maximize the variance of the data. Table 1 show the eigenvector values for VNIR+SWIR bands, respectively, which obtained using a covariance matrix. Convolution filters produce output images in which the brightness value at a given pixel is a function of some weighted average of the brightness of the surrounding pixels (Research Systems, Inc. 2008). The directional filter is a first derivative edge enhancement filter (convolution filter) that selectively enhances image features having specific direction components (gradients). The sum of the directional filter kernel elements is zero. The result is that areas with uniform pixel values are zeroed in the output image, while those that are variable are presented as bright edges. 5*5 kernel matrix was selected in this study to enhance semi-smooth and smooth/rough features. Four principal Directional filters N–S, E–W, NE–SW, and NW–SE with a 5 × 5 kernel size were applied to band 6 of ASTER. Directional filter angles were adjusted as N–S: 0°, E–W: 90°, NE–SW: 45° and NW–SE: 135°. North (up) is 0° and the other angles are measured in the counterclockwise direction. Image Add Back value was entered 70%. The Image Add Back value is the percentage of the original image that is included in the final output image. Adding back part of the original image to the convolution filter results helps preserve the spatial context and is typically done to sharpen an image.
3. RESULTS AND DISCUSSION The combination of band ratios is a robust method for information extraction of specific hydrothermal alteration zones and reducing the effects of topography. Therefore, a specialized band ratio image map derived from image spectra were developed by assigning 6/8, 4/6 and 4/5 in RGB for mapping and discriminating the argillic, phyllic and propylitic zones in the study area (Fig. 2). Band ratio of 6/8 was employed for identifying Fe, Mg-OH rich area (propylitic zone). Band ratio of 4/6 was used for identifying muscovite/illite (phyllic zone) by virtue of their reflectance in band 4 and absorption features in band 6 of ASTER data. Band ratio of 4/5 was added to the RGB composites for identifying kaolinite and alunite (argillic zone) and reducing the effect of unaltered/silicate rocks of the background. Figure 7 shows the resultant image map. Propylitic alteration zone appears in magenta to pink color due to strong absorption of Mg-OH minerals in band 8 of ASTER and argillic and phyllic alteration zones appear in green and whitish yellow because of strong absorption of Al-OH minerals in bands 5 and 6 of ASTER. Blue tone is attributed to unaltered/silicate lithological units. Argillic and phyllic alteration are mostly recognizable in the southwestern and northeastern part of the study area, while propylitic alteration distributed in the background of the scene (Fig. 2). For detail mapping of alteration minerals and lithological units, PCA transformation was applied to VNIR+SWIR and TIR bands of ASTER. The statistic results of PCA transformation for VNIR+SWIR bands of ASTER (Table 1) show that the PC1 is composed of a negative weighting of all total bands, which corresponds with overall scene brightness (albedo) and the strong correlation between image bands (Loughlin, 1991). According to Loughlin, 1991, the PC that contains loadings of similar signs on both input band, explains the variance due to similarities in the spectral responses of the interfering component and the component of interest. The other PC, whose loadings are of different signs on either of the input band images, highlights the contributions unique to each of the components. The sign of the loadings dictates whether the component of interest is represented as bright or dark pixels in the PC image. Considering the eigenvector loadings in PC2 for enhancing alteration minerals in VNIR+SWIR bands of ASTER in the scene, this PC only shows the difference between the visible and infrared bands (bands 1, 2, and 3 as negative eigenvector loadings) and shortwave infrared bands (band 4, 5, 6, 7, 8 and 9 as positive eigenvector loadings) (Table 1). Therefore, the remaining seven PCs could be considered to have information related to the spectral response of iron oxides and hydroxyl-bearing minerals components.
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Figure 2. RGB color composite image map of the Saqqez plot derived from band ratios 6/8, 4/6 and 4/5.
Table 1. Eigenvector matrix of VNIR+SWIR bands provides to the principal component analysis for the study area. Eigenvector
Band 1
Band 2
Band 3
Band 4
Band 5
-0.339137
-0.319744
PCA1
-0.320855
-0.387854
-0.301641
PCA2
-0.412968
-0.438544
-0.526861 0.076469
PCA3
-0.412042
PCA4
-0.172872
PCA5
Band 6
Band 7
Band 8
-0.338363
-0.326673
-0.346945
-0.311140
0.265347
0.226342
0.274314
-0.451051 0.710046 0.327660
0.036797
0.052432
0.001750
-0.063192
-0.084551
0.286868
-0.326148 0.683599
0.130490
0.135934
-0.116134
-0.351494
-0.376300
-0.612885 0.506871
0.052764 0.001191
-0.269497
-0.123264
-0.186849
0.059803 0.488218
PCA6
0.344701 -0.307718
-0.076463 0.258405
-0.112259
0.149391
-0.435074
-0.341308 0.610877
PCA7
-0.175322 0.131155 0.100178
0.297927
0.682738
-0.150000
-0.364049
PCA8
0.040450 -0.049919 -0.047171 0.082836
-0.760040
0.553595
0.050323
0.257065
PCA9
-0.005055 0.014379 0.001155 -0.022089
-0.239416
-0.054230
-0.479944
0.745193
0.293055
Band 9
0.266339
-0.041894 -0.184501
-0.589512 0.190213
A PC image contains strong eigenvector loading for diagnostic reflectance and absorption bands of mineral with opposite signs enhance that mineral. If the loading is positive in reflectance band of a mineral the image tone will be bright, while if negative the image tone will be dark for target mineral (Loughlin, 1991). Considering the eigenvector loadings for bands 1, 2 and 3 in PC3 where these loadings are also in opposite sign, iron oxides could be mapped due to strong positive contribution of band 3 (0.710046) and strong negative loadings of bands 1 (-0.412042) and 2 (-0.451051) as bright pixels in PC3 (Table 1). Iron oxide minerals have low reflectance in visible and higher reflectance in near-infrared corresponding bands 1, 2 and band 3 of ASTER data. Eigenvector loadings for PC4 indicate that PC4 has high potential to enhance Fe-Mg-OH minerals (propylitic alteration zone: chlorite, biotite and epidote). The strong positive contribution of band 4 (0.683599) and negative strong loadings of band 8 (-0.351494) and band 9 (-0.376300) in the PC4 emphasize for enhancing the Fe-Mg-OH minerals as bright pixels in the PC4 image (Table 1). Fe, Mg(OH)-bearing minerals such as chlorite, epidote and biotite contain high reflectance in band 4 (1.60-1.70 μm) and distinctive absorption in bands 8 and 9 (2.29–2.43 μm) of ASTER data. After analyzing the eigenvector loadings for PC5 and PC6, it seems that they do not contain desired information
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related to Al-OH alteration minerals. Therefore, these PCs are uninformative for alteration mineral mapping. Eigenvector loadings for PC7 indicate high potential of this PC for mapping Al-OH alteration minerals (argilic and phylic alteration zones: kaolinite, alunite, muscovite and illite) due to the strong negative loading of band 4 (-0.479944) and strong positive weighting of band 6 (0.682738) (Table 1). Hence, they appear as dark pixels in the PC7. Al(OH)-bearing minerals such as kaolinite, alunite, muscovite and illite show major absorption in band 6 (2.185-2.228 μm) of ASTER. Eigenvector loadings of PC8 and PC9 do not show appropriate contributions of reflectance and absorption bands for enhancing alteration minerals. Accordingly, RGB color composite was assigned to PC3, PC4 and PC7 image map to represent the surface distribution of iron oxide, Fe-Mg-OH and Al-OH mineral groups in the study area. It must be noted that before applying the RGB color composite, dark pixels in PC7 were inverted to bright pixels by multiplication to -1. Figure 9 shows the resultant image map. Surface distribution of iron oxide minerals appears in magenta color, which mostly observable in the northeastern, eastern and southwestern parts of the study area associated with carbonate rocks. Fe-Mg-OH minerals depict in green color in the northwestern, northeastern and central southern parts of the study area, where the outcrops of different sedimentary rocks, metamorphic rocks and andesitic volcanic rocks are observable. Surface distribution of blue pixels (Al-OH minerals) is less in abundance. Yellow pixels might show the surface distribution of Al-OH mineral groups that they mixed with other mineral groups in the northeastern and southern part of the study area (Fig. 3).
Figure 3. RGB color composite image map of the Saqqez plot derived from PC3, PC4 and PC7 of VNIR+SWIR bands. After analyzing four principal directional filters to band 6 of ASTER, the most pronounced trends and lineaments were mapped in the study area. Figure 15 shows the resultant lineament map. Two dominant trends, including NE–SW and W–E sets of lineaments are identified in the study area. Several curvilinear structures indicate open-upright fold systems with N–S axial plane in central and southern parts of the image map (Fig. 4). Intersections of lineament and curvilinear elements can be seen in central, southern and southwestern parts of the scene. The main trend of lineaments in the anomaly zones is NE–SW. However, intersections of structural elements are favorable sites for intrusions and mineralization in the study area, which are also mapped in the vicinity of the anomaly zones. Shear zone, mylonite, cataclasite and igneous intrusive coincident with hydrothermal alteration zones are especially important for gold exploration. Therefore, in terms of structural analysis, the southern and northeastern parts of the study are more suitable for potential gold mineralization.
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Figure 4. Lineament map of the Saqqez region.
4. CONCLUSIONS The new information extracted from specialized band ratioing, PCA, SAM, SID and directional filtering (DF) defines several potential zones for gold exploration in the Saqqez region. The results overlap with published stream geochemical surveys and show good coincidence. Statistical assessment and fieldwork data also verified the consistency of the results. Accordingly, it is concluded that the structural traps (valley intersections) south of the Saqqez town is a highly prospective area for future gold exploration program. This investigation emphasizes that a remote sensing approach using ASTER data should be considered as a strong reconnaissance tool for targeting high potential gold mineralization zones before costly field-data-required techniques in the SSZ.
Acknowledgements This study was conducted as a part of KOPRI research grant PE17160. KOPRI grants PE17050 was also acknowledged for supporting the research. We are thankful to Korea Polar Research Institute (KOPRI) for providing all the facilities for this investigation. University Technology Malaysia (UTM) also appriciated.
REFERENCES Abrams, M.J., Brown, D., Lepley, L. and Sadowski, R., 1983. Remote sensing of porphyry copper deposits in Southern Arizona. Economic Geology, 78, 591-604. Abrams, M., 2000. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): data products for the high spatial resolution imager on NASA‘s Terra platform. International Journal of Remote Sensing, 21, 847-859. Hunt, G.R., and Ashley, P., 1979. Spectra of altered rocks in the visible and near infrared. Economic Geology, 74, 1613-1629. Loughlin, W.P., 1991. Principal components analysis for alteration mapping. Photogrammetric Engineering and Remote Sensing 57, 1163–1169. Ninomiya, Y., Fu, B. and Cudahy, T.J. (2005). Detecting lithology with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral thermal infrared radiance-at-sensor‖data. Remote Sensing of
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Environment, 99 (1-2), 127-139. Noda, S., Yamaguchi, Y., 2017. Estimation of surface iron oxide abundance with suppression of grain size and topography effects. Ore Geology Reviews 83, 312–320. Pour, A.B., Park, Y., Park, T.S., Hong, J.K. Hashim, M., Woo, J., Ayoobi, I. 2018a. Evaluation of ICA and CEM algorithms with Landsat-8/ASTER data for geological mapping in inaccessible regions. Geocarto International, doi.org/10.1080/10106049.2018.1434684 Pour, A.B., Park, Y., Park, T.S., Hong, J.K. Hashim, M., Woo, J., Ayoobi, I. 2018b. Regional geology mapping using satellite-based remote sensing approach in Northern Victoria Land, Antarctica. Polar Science, 16, 23-46. Research Systems, Inc., 2008. ENVI Tutorials. Research Systems, Inc., Boulder, CO. Safari, M., Maghsodi, A., Pour, A.B., 2017. Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran. Geocarto International http://dx.doi.org/10.1080/10106049.2017.1334834.
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SPACE-BORNE SATELLITE SENSORS FOR MINERAL EXPLORATION IN HIGH ARCTIC REGIONS Amin Beiranvand Pour1, Tae-Yoon S. Park1, Mazlan Hashim*2, Yongcheol Park1, Jong Kuk Hong1 1
Korea Polar Research Institute (KOPRI) Songdomirae-ro,Yeonsu-gu, Incheon 21990, Republic of Korea
2
Geoscience and Digital Earth Centre (INSTeG), Universiti Teknologi Malaysia (UTM), 81310 UTM Skudai, Johor Bahru, Malaysia Email:
[email protected];
[email protected];
[email protected];
[email protected];
[email protected]
KEY WORDS: Arctic regions; Landsat-8; ASTER; PALSAR; The Franklinian Basin; Zinc exploration; North Greenland ABSTRACT: The Franklinian Basin in North Greenland has a distinctive potential for exploration of world-class zinc deposits. In this study, image processing algorithms are implemented on satellite remote sensing datasets define hydrothermal alteration halos associated with Zn-Pb±Ag sulfide mineralization in the trough sequences and shelf-platform carbonate of the Franklinian Basin. Directed Principal Component Analysis (DPCA) is applied to selected Landsat-8 mineral indices to map carbonate and clay alteration. Major lineaments, intersections, curvilinear structures and sedimentary formations are traced by the application of Feature-oriented Principal Components Selection (FPCS) to cross-polarized backscatter PALSAR ratio images. PC image with strong textural variations was selected as input band for directional filtering and consequently mapping geological structures. Mixture Tuned Matched Filtering (MTMF) algorithm is applied to ASTER VNIR/SWIR bands for subpixel detection and classification of hematite, goethite, jarosite, alunite, gypsum, chalcedony, kaolinite, muscovite, chlorite, epidote, and carbonate. The resultant MF score images were subsequently used for virtual verification. We identified several high potential zones with distinct alteration mineral assemblages and structural fabrics that could represent undiscovered Zn-Pb sulfide deposits in the region.
1.
INTRODUCTION
Geological investigations and mineral exploration in the Arctic have been naturally hampered by its remoteness and climatic conditions. Greenland has a variety of mineral resources (Fig. 1) and mineral exploration tradition since 18th century, but mineral exploration activities have been only focused on certain regions with variable intensity and density of data collection, leaving most of the parts of Greenland largely underexplored compared to other areas with similar geology elsewhere in the Arctic (Kolb et al., 2016). The Franklinian Basin as a highly prospective part of Greenland for zinc exploration extends for more than 2,500 km E-W through the Canadian Arctic Islands and northern Greenland (Fig. 1). Greenland's coastline is cut by numerous deep fjords; the topography and lack of roads require helicopter support for accessibility of inland areas and plateaus for mineral exploration. Moreover, the time frame for fieldwork varies largely with the geography, which is July to August in the northern parts (High Arctic environment) of Greenland. Sea ice in the north breaks up during May to June and results in a wide pack ice girdle along the east coast, which may hinder access to land (Kolb et al., 2016). Subsequently, the remote nature and environmental challenges posed by the Arctic environment reduces the capacity to economically explore and locate mineral resources by using traditional techniques. Satellite remote sensing data are capable of providing key information for mineral exploration community to explore larger areas, reduce exploration costs and focus on key hydrothermal alteration mineral assemblages, lithological units and structural features that are associated with different types of ore mineralization (Safari et al., 2017; Pour et al., 2018 a,b). Since no report on comprehensive remote sensing investigation is available for Zn-Pb exploration purposes in the Franklinian Basin. However, key geological criteria for zinc exploration using satellite remote sensing data could be considered as specific alteration mineral zones and lineament trends. Consequently, the main objectives of a satellite-based remote sensing investigation for zinc exploration in the Franklinian Basin are then set: (i) to apply robust image processing algorithms for detecting pixels/sub-pixels contain spectral features related to key alteration minerals and assemblages (gossan, hydrated sulfate, clay and carbonates) that may represent potential undiscovered
1
Zn-Pb mineralization zones in the Franklinian Basin using spectral bands of Landsat-8 and ASTER remote sensing satellite data; and (ii) to map and highlight the major lineaments (faults and fractures), intersections, curvilinear structures and sedimentary formations in the prospective target regions using PALSAR remote sensing satellite data.
Figure 2. Simplified geological map of the Greenlandic part of the Franklinian Basin showing the distribution of known Zn-Pb occurrences. Inset: location of the Franklinian Basin within the wider context of Greenland and Arctic Canada. Abbreviations to zinc occurrences: BE-Børglum Elv; CF-Citronen Fj; C-Cass Fj; HB-Hand Bugt; KB-Kayser Bjerg; KL-Kronprins Christian Land; KS-Kap Schuchert; KW-Kap Wohlgemuth; LE-Løgum Elv; NF-Navarana Fj; P-Petermann; RH-Repulse Havn; TE-Tvillum Elv (modified from Kolb et al.,2016).
2. 2.1
MATERIALS AND METHODS Remote sensing data
In this investigation, Landsat-8, ASTER and PALSAR datasets were used to map lithological-mineralogical-structural features hosting CD and MVT Zn-Pb mineralization in the trough sequences and carbonate shelf-platform of the Franklinian Basin at both regional and district scales. Two Landsat-8 level 1T (terrain corrected) images covering the trough sequences and carbonate shelf-platform of the eastern part of the Franklinian Basin were obtained through the U.S. Geological Survey Earth Resources Observation and Science Center (EROS) (http://earthexplorer.usgs.gov). The images (LC80402442016199LGN00; Path/Row 040/244) and (LC80402452016199LGN00; Path/Row 040/245) covering Peary Land, Amundsen Land, Johannes V. Jensen land, Nansen land and Freuchen Land were acquired on July 17, 2016. Scene cloud cover was 17.55 % and 9.15 % for the Landsat-8 images, respectively. During acquisition time (19:44:49 to 19:45:44) of the images, sun elevation recorded as 21.380 and 22.680 and sun azimuth were -91.670 and -103.265, respectively. For district scale lithological-mineralogical-structural mapping in the eastern part of the Franklinian Basin, several available scenes of ASTER surface reflectance VNIR-SWIR data (Level-2B07 or AST-07) and PALSAR Fine mode Level 1.5 dual polarization (HH + HV polarization) were used in this investigation. ASTER (AST-07) scenes contain atmospherically corrected data were obtained on-demand from USGS Earth Resources Observation and Science (EROS) center (https://earthdata.nasa.gov/). They acquired under favourable conditions of minimal could- and snow-cover. PALSAR Fine mode Level 1.5 scenes were obtained from the Earth and Remote Sensing Data Analysis Center (ERSDAC) Japan (http://gds.palsar.ersdac.jspacesystems.or.jp/e/). The Level 1.5 product used in this study has a high-resolution mode with 6.25 m pixel spacing and dual polarization (HH + HV), which is geo-reference and geo-coded. Nominal incident angle is 7.9-60.0.
2
2.2
Data analysis
To accomplish the objectives, directed principal components analysis (DPCA) and directed independent component analysis (DICA) (Hyvarinen, 2013) were selected and implemented for mapping the target minerals at the pixel level. Correspondingly in this analysis, Mixture Tuned Matched Filtering (MTMF) algorithm (Boardman, 1998) was selected for applying to VNIR/SWIR spectral bands of ASTER surface reflectance data for subpixel detection and classification of the alteration minerals using the reference spectra of selected end-member minerals extracted from the USGS spectral library version 7.0 (Kokaly et al., 2017). Therefore, several mineralogic band ratio indices derived from spectral bands of Landsat-8 and ASTER were selected on the basis that one band ratio index contains information related to the component of interest (e.g. target alteration minerals). The indices were considered as input image datasets for implementing DPCA and DICA in this analysis. Normalised Difference Snow Index (NDSI) (band ratio of 3-6/3+6), ferric iron oxide index (band ratio of 4/2), ferrous iron oxide index (band ratio of 6/5) clay minerals index (band ratio of 6/7) and thermal radiance lithology index (TRLI) (band ratio of 10+11×11) were used for Landsat-8. NDSI attends as a significant snow/ice indicator whereas band ratios of 4/2, 6/5, 6/7 and 10+11×11 are utilized to enhance ferric and ferrous iron oxide/hydroxide, clay/carbonate minerals and lithological units using Landsat-8 spectral bands, respectively. For implementing MTMF to VNIR+SWIR ASTER data, Minimum Noise Fraction (MNF) statistics is required. Forward MNF to spectra was applied to transform endmember spectra into MNF space for use in MTMF. New covariance statistics were computed. Subspace background was enabled for removing anomalous pixels before calculating background statistics. Background threshold was adjusted 0.800 in this analysis for the fraction of the background in the anomalous image. The MTMF output is a set of rule images equivalent to both the MF score and the infeasibility score for each pixel matched to each endmember spectrum. For extracting radar information to map the major lineaments (faults and fractures), intersections, curvilinear structures and sedimentary formations in the Franklinian Basin using PALSAR data a developed image processing technique using the combination of HH (co-polarized) and HV (cross-polarized) polarizations is required. As different polarizations are sensitive to ground surface features of different dimensions, they collectively bring out greater geological-geomorphological-structural detail.
3. RESULTS AND DISCUSSION A regional view of the eastern part of the Franklinian Basin was constructed using mosaic of the Landsat-8 ratio images assigned to NDSI (b3-b6/b3+b6), ferric iron oxide index (b4/b2) and clay minerals index (b6/b7) as Red-Green-Blue (RGB) color composites, respectively (Fig. 2). The image map provides a color-based classification of pixels with intense H2O, Fe3+, Al-OH and CO3 absorption features. It highlights snow/ice in dark orange to gold colors, surface distribution of ferric iron oxide/hydroxide minerals in light green to green tones, clay and carbonate minerals in blue color. The areas with admixture of iron oxide/hydroxide, clay and carbonate minerals represent in cyan color (Fig. 5). NDSI allows discriminating snow/ice from exposed lithologies due to the fact that snow reflects visible radiation (in 0.544 -0.565 μm) more strongly than it reflects radiation in the middle-infrared region (in 1.628-1.652 μm). Rock exposure produces very low or negative NDSI values as rocks are generally less reflective in the visible and near-infrared portion. For mineral constituents, combinations and overtones of H2O or OH fundamentals and CO3 can produce absorption features in the 2.1 μm to 2.5 μm (e.g., Hunt 1977; Hunt and Ashley 1979), which coincide with band 7 (2.11-2.29 µm) of Landsat-8. Hydroxo-bridged Fe3+ results in absorption bands in the 0.43 to 0.9 μm regions coinciding with bands 2 and 4 of Landsat-8. Therefore, the combination of NDSI, ferric iron oxide and clay minerals indices as RGB color composites enhances the target geologic materials contain distinctive spectral characteristics in the study area. The trough clastic sediments of Amundsen Land group (Lower Ordovician to Lower Silurian) host CD Zn-Pb mineralization of Citronen Fjord deposit. The CD deposits tend to occur in clusters within their host stratigraphy and second order basins focusing hydrothermal fluids. Thus, the Amundsen Land group and time-equivalent horizon associated with synsedimentary faults could be considered as highly prospective strata in the trough sequences. Moreover, the CD mineralization is pyrite rich and yields iron oxide/hydroxide and clay alteration minerals due to the oxidation and acid weathering. Tureso Formation (Upper Ordovician to Lower Silurian – Morris Bugt Group) of the carbonate platform was documented as a favorable stratigraphic horizon for MVT Zn-Pb mineralization (Rosa et al., 2014). It is characterized by pale and dark-weathering dolostones with 150-180 m thick that often distinctly burrow-mottled. Accordingly, some spatial extents of the Landsat-8 images covering potential tracts for CD and MVT Zn-Pb deposits in the Peary Land, Amundsen Land and Nansen Land were selected for detail mapping of target alteration minerals and related lithologies. Several significant stream sediment anomalies have been reported in the selected regions by Rosa et al. (2014). In this analysis, spatial subsets covering the Citronen Fjord Zn-Pb deposit (northeastern part of the Peary Land and southeastern part of Johannes V. Jensen Land), the southern part of the Amundsen Land and the central part of the
3
Nansen Land were considered as potential tracts for CD mineralization. Three spatial subsets of the southern part of Peary Land, namely the Erlandsen Land (SW Peary Land), the Melville Land (SE Peary Land) and the Melville Land (2) (SE Peary Land) contain MVT Zn-Pb mineralization (Rosa et al., 2014) were also selected. DPCA/ICA analysis was implemented to the NDSI, ferric iron oxide, ferrous iron oxide, clay minerals and TRLI indices of the selected spatial subset scenes of Landsat-8. Statistical analysis has been limited to the selected regions to evaluate the resultant images (Fig. 3).
Figure 2. Mosaic of the Landsat-8 ratio images as RGB color composites assigned to NDSI, ferric iron oxide and clay minerals indices, showing a regional view of the eastern part of the Franklinian Basin.
Structurally, CD Zn-Pb mineralization is the product of local development in a sub-basin controlled by syn-genetic faults and metal-bearing fluids derived from underlying fractures in the sea floor (Leach et al., 2010). In the Franklinian basin, sulfide mineralization in the trough is associated with faults and fault splays with specific trends for instance NW-SE TLFZ mineralization trend in Citronen Fjord deposit and E-W fractures in the Navarana Fjord Zn occurrence. MVT Zn-Pb mineralization produce from basinal brine (mineralizing fluids) could have ascended and precipitated metals in extensional fault systems within the carbonate-platform (Leach et al., 2010). Important lineaments for MVT Zn-Pb mineralization in the Franklinian basin are long lasting structures such as Central Peary Land Fault Zone (CPLFZ) striking N70 and other parallel structures. Moreover, structures related to strike slip movements and extensional domains such as synclines with negative flower structure and lineaments strike N110 can be considered amongst important structural groups (Rosa et al., 2014). For mapping the significant structural trends associated with CD and MVT Zn-Pb mineralization, FPCS technique was applied to available scenes of PALSAR data for the study area. The Amundsen Land and the Nansen Land of the trough sequence and Erlandsen Land and the Melville Land (2) of the carbonate shelf succession were selected as spatial subsets (almost similar size with the Landsat-8 subsets) for FPCS image analysis. Statistical factors were calculated for the cross-polarized backscatter ratio images, namely HH/HV, HH+HV, HH-HV, HH+(HH+HV)/HV and HH+(HH-HV)/HV (Fig. 4).
4
Figure 3. ICA image maps of Landsat-8 spatial subset scenes considered for CD Zn-Pb mineralization. (A) ICA2 image map showing ferric iron oxide/hydroxide minerals (gossan) for the subset of Citronen Fjord Zn-Pb deposit; (B) ICA5 image map showing clay minerals for the subset of Citronen Fjord Zn-Pb deposit; (C) ICA2 image map showing ferric iron oxide/hydroxide minerals (gossan) for the subset of the Amundsen Land; (D) ICA5 image map showing clay minerals for the subset of the Amundsen Land; (E) ICA5 image map showing ferric iron oxide/hydroxide minerals (gossan) for the subset of the Nansen Land; (F) ICA3 image map showing clay minerals for the subset of the Nansen Land.
Figure 5 (A-C) shows resultant alteration mineral maps derived from the ASTER MF score rule images for the SE Citronen Fjord deposit, SW Amundsen Land and SW Nansen Land zones that contain a high potential for CD Zn-Pb mineralization. Large areas of the alteration zones in the selected regions exhibit a high sub-pixel abundance of chlorite and/or epidote and hematite and/or goethite, while muscovite and alunite and/or kaolinite have low surface distribution. Gypsum shows very low surface abundance among the detected minerals in the selected subsets, which only detected in some small zones in Citronen Fjord deposit subset (Fig 5 (A-C)). It is discernable that the association of iron oxide minerals (hematite/goethite) and clay mineral assemblages (chlorite/epidote, muscovite and alunite/kaolinite) possibly point to numerous target zones for CD Zn-Pb mineralization in the selected regions. For example, several alteration zones in the vicinity of Trolle Land Fault Zone (TLFZ) in the Citronen Fjord deposit subset (Fig 5 (A)), a strip between the Odin Fjord and Heimedal Icecap, some alteration zones in the northwestern and central parts of the Amundsen Land subset (Fig. 5 (B)) and the northeastern corner and southern part (adjacent to the J.P.Koch Fjord) of the Nansen Land subset (Fig. 5 (C)) could be considered as target zones for CD Zn-Pb mineralization.
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Figure 4. (A) FCC image map of PCA2, PCA3 and PCA5 for the PALSAR subset of the Amundsen Land; (B) NW–SE (135°) directional filter image map derived from PCA2 for the subset of the Amundsen Land; (C) FCC image map of PCA2, PCA3 and PCA4 for the subset of the Nansen Land; (D) NW–SE (135°) directional filter image map derived from PCA2 for the subset of the Amundsen Land. Lithostratigraphic units are abbreviated as Pm= Polkorridoren Group, VG=Volvedal Group, ME=Merqujoq Formation; AG=Amundsen Land Group, Qa= Quaternary alluvium, and PS= Paradisfjeld Group.
4. CONCLUSIONS The results of this investigation demonstrate that analysis of combined remote sensing mapping techniques has great capability as an exploration tool for mapping potential occurrences of Zn-Pb deposits in the Franklinian Basin, North Greenland. Numerous potential zones for Zn-Pb deposits were mapped using the combined remote sensing techniques. Their identification was based on small-scale (~100 - 200 m) mineral zoning patterns between hydrous silica, jarosite, kaolinite, and smectite, which graded into background rocks dominated by ferric-iron, chlorite and sericite. This investigation indicate that combined remote sensing mapping techniques can aid in identifying unknown Zn-Pb sulfide deposits in the High Arctic Franklinian Basin by improving how and where permissive and/or favorable mineral occurrence zones are located.
Acknowledgements This study was conducted as a part of KOPRI research grant PE17160. KOPRI grants PE17050 was also acknowledged for supporting the research. We are thankful to Korea Polar Research Institute (KOPRI) for providing all the facilities for this investigation. University Technology Malaysia (UTM) also appriciated.
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Figure 5. Alteration mineral maps derived from the ASTER MF score rule images for selected ASTER spatial subsets in the trough sequence. (A) SE Citronen Fjord deposit; (B) SW Amundsen Land; and (C) SW Nansen Land.
REFERENCES Boardman, J. W. 1998. Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering, in: Summaries of the Seventh Annual JPL Airborne Geoscience Workshop, Pasadena, CA, p. 55. Hyvärinen, A. 2013. Independent component analysis: recent advances. Phil Trans R Soc A 371: 20110534. http://dx.doi.org/10.1098/rsta.2011.0534. Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., Driscoll, R.L., and Klein, A.J. 2017. USGS Spectral Library Version 7: U.S. Geological Survey Data Series 1035, 61 p., https://doi.org/10.3133/ds1035. Kolb, J., Keiding, J.K., Steenfelt, A., Secher, K., Keulen, N., Rosa, D., Stengaard, B.M. 2016. Metallogeny of Greenland. Ore Geology Reviews 78, 493-555. Leach, D. L., Bradley, D. C., Houston, D., Pisarevsky, S. A., Taylor, R. D., Gardoll, S. J. 2010. Sediment-hosted lead-zinc deposits in earth history. Economic Geology, 105, 593–625. Pour, A.B., Park, Y., Park, T.S., Hong, J.K. Hashim, M., Woo, J., Ayoobi, I. 2018a. Evaluation of ICA and CEM
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algorithms with Landsat-8/ASTER data for geological mapping in inaccessible regions. Geocarto International, doi.org/10.1080/10106049.2018.1434684 Pour, A.B., Park, Y., Park, T.S., Hong, J.K. Hashim, M., Woo, J., Ayoobi, I. 2018b. Regional geology mapping using satellite-based remote sensing approach in Northern Victoria Land, Antarctica. Polar Science, 16, 23-46.
Rosa, D., Rasmussen, J.A., Sørensen, E.V. and Kalvig, P., 2014. Reconnaissance for Mississippi Valley-type and SEDEX Zn-Pb deposits in the Franklinian Basin, Eastern North Greenland – Results of the 2013 Season, 2014/6. Geological Survey of Denmark and Greenland Report, Copenhagen, 41 pp.
Safari, M., Maghsodi, A., Pour, A.B., 2017. Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran. Geocarto International http://dx.doi.org/10.1080/10106049.2017.1334834.
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CHANGE MONITORING OF BHAGIRATHI & ALAKHNANDA BASIN GLACIER USING SATELLITE IMAGE DarshitSavani*(1), R. D. Shah(1), I. Bahuguna (2), B.P. Rathor(2) 1
Department of Geology, M. G. Science Institute, Gujarat University, Ahmedabad, India. 2
Space Applications Centre, ISRO, Ahmedabad, India E-mail:
[email protected]
Abstract: The history of glacier length fluctuations serves as a reliable indicator of the past climate. In this paper, a numerical flow line model has been used to study the relationship between length variations of the Himalayan glacier and local climate since 1876. The front positions of Alakhnanda & Bhagirathi area drained by a river huge mass of ice are in agreement with those followed. After a successful test run that appears to the real thing of the past retreat, the model was also used to describe a possible future event over time of the huge mass of ice for the next 100 years under different climatic situations. This work puts into numbers huge mass of ice different versions in the Alakhnanda & Bhagirathi basin area of the northern Himalaya by integrating huge mass of ice existing in satellite data from IRS LISS and Landsat series of different years, which are 2001 and 2016. Glacier variations were mapped and analysed; discrepancies between images could be detected and removed from the integrated data using remap tables in Arc/Info grid both graphically and numerically. Our results show that glaciers in the region both retreated and advanced during the last 15 years; difference between the year 2001 and 2016, average Alakhnanda & Bhagirathi basin glacier area decreased from 1.71m2& 1.11m2. On average, during the period 2001 and 2016 respectively, suggesting that glacier retreat has an expedition. Keywords: Himalayan glacier, Alakhnanda & Bhagirathi basin area, retreated and advanced,
Introduction The Himalaya comprise one of the largest collections of glaciers outside the polar regions, with a total glacier cover of 33 000km2 [1] and around 9600 glaciers exists in the Indian Himalaya [2]. Himalayan glaciers are the important source of fresh water for the innumerable rivers that flow across the Indo-Gangetic plains. The rivers flow trans-boundary and meet the potable water, irrigation, hydropower, fishery, inland navigation and other needs of more than 1.3 billion people living downstream. With about 9,575 small and large glaciers in the Himalayas [3], they hold the largest reserves of water in the form of ice and snow outside the
Polar Regions [4]. The Himalayas are thus also referred to as the ‘water towers’ of Asia and a ‘third pole’ of the earth. Monitoring of glaciers actuates scientific interest for two main reasons. First, Glaciers change monitoring has been used for climatic change investigation. The surface area and volume of individual glaciers are monitored to estimate future water availability. Second, glaciers in Indian Himalayas, have been recognized as important water storage systems for municipal, industrial and hydroelectric power generation purposes. GLACIERS occur in the high-altitude regions of the moun-tains and in the polar regions of the earth. They are vitalto mankind as they control the global hydrological cycle, maintain the global sea levels and perennially supply freshwater to the rivers. In the wake of climatic variations arising due to increasing concentration of greenhouse gases in the atmosphere resulting in global warming and its implications on various resources, glaciers are increas-ingly being monitored worldwide. The Himalayan moun-tain system to the north of the Indian land mass with arcuate strike of NW–SE for about 2400 km holds one of the largest concentration of glaciers outside the polar regions in its high-altitude regions. Perennial snow and ice-melt from these frozen reservoirs is used in catchments and alluvial plains of the three major Himalayan river systems, i.e. Indus, Ganga and Brahmaputra for irri-gation, hydropower generation, production of bio-resources and fulfilling the domestic water demand. Also, variations in the extent of these glaciers are understood to be a sensitive indicator of climatic variations of the earth system and might have implications on the availability of water resources in the river systems. Therefore, mapping and monitoring of these natural, frozen freshwater re-sources is required for the planning of water resources and understanding the impact of climatic variations. However, ground-based studies on monitoring of the Himalayan glaciers require enormous effort in terms of time and logistics due to lack of atmospheric oxygen in high altitudes, trekking in rough terrain and cold climatic regimes. Despite these difficulties, the efforts made by many expedition teams have led to the generation of vital information on the fluctuations of Himalayan glaciers in terms of mass balance or simply snout monitoring 1–9 . Remote sensing having the capability of providing synoptic view, multi-temporal coverage and multispectral char-acterization of earth surface features has demonstrated its utility for glacier monitoring in different mountain regions of the world, including the Himalaya 10–20 . The satellite data available in the public domain such as Landsat TM 21 , topographic maps prepared in the past, ae-rial photographs and recently released CORONA photo-graphs along with data from other earth observation satellites such as IRS series, ASTER, etc. have been the main sources for generating this information. However, it is seen that very few studies compare the changes in gla-ciers from data of similar sources. The present study uses mainly data from LISS III sensor of IRS satellites for an interval of about one decade between 2001 and
2016 for monitoring of 513 glaciers taken from dif-ferent parts of the Alakhnanda and Bhagirathi basin area.
Study Area Gangotri Glacier originates in the Chaukhamba massif (6853–7138 m a.s.l.) and flows northwest towards Gaumukh. The equilibrium-line altitude (ELA) of Gangotri Glacier is 4875 m a.s.l. [11].The Gangotri glacier, one of the largest ice bodies in the Garhwal Himalayas, is located in the Uttarkashi district of the state of Uttarakhand in India (See Fig 1). It is one of the most sacred shrines in India, with immense religious significance. Being the main source of the river Ganga, it attracts thousands of pilgrims every year. The Gangotri glacier is a vital source of freshwater storage and water supply, especially during the summer season for a large human population living downstream. The discharge from the glacier flows as the river Bhagirathi initially before meeting the Alaknanda River at Devprayag to form the river Ganga. Snow and glaciers contribute about 29% to the annual flows of the Ganga (up to Devprayag) and hence any impacts on these glaciers are likely to affect this large river system [3].Numerous smaller glaciers join the main stream of the main glacier to form the Gangotri group of glaciers. The study area is Gangotri glacier including it tributary glaciers such as Maidani Glacier Swachand glacier, Sumeru Glacier, Ghanohim Glacier and Kriti Glacier system. For ease in writing we abbreviated these all tributary glacier as Gangotri Glacier System in this investigation. This system covers an area of 156.587 sq km (ETM+2010).The area of the main trunk of the glacier 62.412 sq km [12], Average width of the glacier is 1.847 km and glacier, lies between 79o4’ 46.13” E-79o16’ 9.45” E and 30o43’ 47.00” N-30o55’ 51.05” N (ETM+2000). It has varying elevation of 4082–6351 meters above sea level (SRTM Data Analysis)
Fig. 1. False Color Composite (FCC) of RED (4) Green (3) Blue (2) in the Gangotri Glacier, Subset of Landsat-7 ETM+ Image(G=Glacier, AB= Ablation zone and AC= Accumulation Zone).
Data Sources
The multi-spectral satellite data of Landsat 8 for the year 2001 and 2016, Landsat TM5 data for 2001 and 2016, Landsat ETM+ data for the years 2001 and 2016 have been procured in the present study (see table 1). The Landsat data used in current investigation system was downloaded for free from the USGS Global Visualization Viewer (GLOVIS). Also used the advanced wide field sensor (AWIFS) these data used from the sac
Satellite Data Landsat MSS Landsat MSS Landsat TM 5 Landsat TM 5 Landsat ETM+ Landsat ETM+ AWIFS AWIFS
Date of acquisition 26/10/2001 19/11/2016 21/10/2001 13/11/2016 25/05/2001 13/11/2016 25/05/2001 13/11/2016
Spatial resolution (m) 79 79 30 30 30 30 56 56
Glacier mapping was undertaken employing digital elevation models (DEMs) i.e. ASTER and SRTM DEM freely downloaded from ASTER GDEM and US Earth Explorer. In this we investigation we find out that elevation values from ASTER DEM are higher than SRTM DEM. However SRTM DEM looking smoother but problematic at moraines.
Methodology The spatial-based detection of variations in glacial extent requires co-registration of multi-temporal images with one another, a task easily achievable in GIS. GIS is an efficient tool for analyzing current state and changes in glaciers (Li et al., 1998). Other analyses such as classification and detection can also be carried out in GIS, as can measure the glacier area and change in glacier termini. A database within a GIS may be manipulated to yield information on changes in glacier size. Glaciers can be mapped by supervised and unsuperevised classification method tested. Pre-processing data / post processing data such as georeferencing and Orthorectification.
For Landsat MSS data In this study, a Landsat MSS imagery of Oct 1972 and Nov 1976 covering the Gangotri glacier system was downloaded for free from the USGS Global Visualization Viewer (GLOVIS). The main glacier body is almost cloud free. The terrain of Himalayan glaciers has undulating surface and steep slopes, so the radiance reaching the sensor greatly depends on the orientation (slope and aspect) of the target. The incoming radiance is highly depend on the orientation of the object Therefore, for better recognition of the classes for effective mapping, the DN numbers have to be converted into topographically correctedreflectance images. AAR Estimation requires mapping of glacier extent and accumulation area, Therefore to get accumulation area classification was required so in a second step, both supervised and unsupervised classification are performed to extract four classes rock, snow, ice and debris ice. In third step accumulation area and total glacier area has been measured by visual demarcation and GIS techniques using ArcGIS 9.3.
For Landsat TM and ETM+ data Earlier studies have shown that normalized-difference snow index (NDSI) and band ratio methods could not differentiate debris covered glacier ice from surrounding rock surface due to similar spectral signatures[14], However, when compared with manual delineation, thresholding of NDSI and band ratio methods are better approaches for mapping clean glacier ice [14]. It has been reported that for shaded areas with thin debris cover the band ratio near-infrared/ shortwave infrared (NIR/SWIR) performs better than red/ SWIR and NDSI [16]. In our study, several image band ratios (1/3; 3/ 4), NDSI (1–4/1+4) and classifications were tested using Landsat imagery. Band ratio NIR/SWIR was most suitable for mapping clean glacier ice. Ratio images have been successfully used for the delineation of glaciers for the Swiss Glacier Inventory (SGI) and a study in the Inner Tien Shan [16]. In a first step, TM4/TM5 ratio images were calculated and segmented using a threshold value of 1 using raster math tool in ArcGIS 9.3. Using Image enhancement techniques snow and snow free area classified easily. Ratio Images was associated with ASTER Digital Elevation Model for mapping glacial extent and accumulation area using visual demarcation. One major problem in mapping glacier is related to the exact definition of glacier, whether ‘inactive’ bodies of ice above a bergschrund connected to a glacier should be considered as part of the glacier. Currently, there is no consensus within the glaciological community on these
issues. For example, some previous studies (e.g. 15) excluded the inactive parts at the heads of glaciers, so concerning that problem we generated surface slope image from ASTER DEM using Terrain and TIN surface tool. Now we overlay delineated area of glacier extent derived from ratio image and surface slope image. Accumulation area, ablation area and total area of glacier was measured much accurately and misclassified pixels of peaks and rocky surface are eliminated. ESRI ArcScene was used for visualization of glacial extent image with digital elevation model.
Fig. 2.all are images are the change detection inventory on the image of 2001 and 2016
Fig. 3.all are images are the change ditection inventory of the 2001 and 2016 changes
Results and Discussions The Gangotri glacier is one of the largest glaciers in the Himalayas. Numerous small sized glaciers also join the main Gangotri glacier from all sides and form the Gangotri group of glaciers. The main glaciers as well as its tributaries are valley glaciers .The total ice cover is approximately 156.587 km 2 and estimated volume of ice is 36.17 km 3. The area and length of the main trunk of the glacier is 62.412 sq km and 29.38 km respectively [12]. The average width of the glacier is 1.85 km. The glacier, lies between 79o4’ 46.17” E-79o16’ 10” E and 30o43’ 46.98” N-30o55’ 50.96” N (ETM+2000). It has elevation range from 4,017–6,146 meters above sea level (SRTM data analysis). Table 2-Measured and estimated characteristics of Gangotri Glacier Year 2001 2016
Total Area(Sq. km) 1766.744 1763.929
Table 3-Measured and estimated characteristics of Gangotri Glacier Year 2001 2016 2001 2016
Basin alakhnanda alakhnanda Bhagirathi bhagirathi
Total Area(Sq. km) 867.92 866.21 898.92 897.71
Table 4- Elevation and topographic analysis of Gangotri Glacier Characteristics of Gangotri Glacier Min altitude Max altitude ELA Relief Aspect Slope
ASTER DEM Analysis 4101 6389 4684 2288 NW 5.15 0
SRTM DEM analysis 4082 6351 4649 2269 NW 5.61 0
In this we investigation we find out that elevation values from ASTER DEM are higher than SRTM DEM, However SRTM DEM looking smoother but problematic at moraines. Glacier mapping using a TM4/TM5-ratio image in combination with a DEM was successfully performed and the accumulation area, ablation area and total area of Gangotri along with TG’s (Maidani Glacier, Swachand glacier, Sumeru Glacier, Ghanohim Glacier and Kriti Glacier). Also Our results show that glaciers in the region both retreated and advanced during the last 15 years; difference between the year 2001 and 2016, average Alakhnanda & Bhagirathi basin glacier area decreased from 1.71m2& 1.11m2. On average, during the period 2001 and 2016 respectively, suggesting that glacier retreat has an expedition.
Conclusion The recent advent of Geographic Information Systems (GIS) and Remote sensing techniques have created an effective means by which the acquired data are analyzed for the effective monitoring and mapping of temporal dynamics of glaciers. Longitudinal variations in glacial extent have been detected from multi-temporal images in GIS. A large number of researchers have taken advantage of remote sensing, GIS and GPS in their studies of glaciers. In this study, accumulation area, ablation area and total glacial extent of Gangotri Glacier with TG’s (Maidani
Glacier, Swachand glacier, Sumeru Glacier, Ghanohim Glacier and Kriti Glacier) mapped using ratio images with the association of surface slope. This study shows that the glaciers in the region both retreated and advanced during the last 15 years; The retreated and advanced study using remote sensing is good for monitoring a larger area and the glacier those are difficult to access.
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