geological setting and structural pattern of the

AL-AZHAR UNIVERISTY FACULTY OF SCIENCE GEOLOGY DEPARTMENT

GEOLOGICAL SETTING AND STRUCTURAL PATTERN OF THE BASEMENT ROCKS OF WADI UM GHEIG AREA, CENTRAL EASTERN DESERT IN LIGHT OF THE FIELD STUDIES AND REMOTE SENSING INTERPRETATIONS. A Thesis Submitted for Degree of Master in Geological Sciences BY

Mohamed Sami Mohamed El-Hebiry B.Sc. in Geology

SUPERVISORS

Prof.Dr. Ibrahim Abu El-Liel Ali Professor of Hard Rocks, Faculty of Science, Al-Azhar University

Dr. Mahmoud Hussien Bekiet Assistant Professor of Structural Geology Faculty of Science, Al-Azhar University

Dr. Nehal Mohamed A. Soliman Researcher at Geological department, National Authority for Remote Sensing and Space Sciences

To Department of Geology Faculty of Science Al-Azhar University Cairo, 2014

ACKNOWLEDGEMENTS Praise is to ALLAH, the lord of the worlds, who guided and aided me to bring-forth to light this work and by whom grace this work has been completed. I am heartily thankful to, Prof.Dr. Ibrahim Abu El-Liel Ali, Professor of Hard Rocks, Faculty of Science, Al-Azhar Univeristy, for suggesting the pointof the research, direct supervision either in the field or the office, guidance and encouragement in all the steps of the research, critical reading of manuscript several times without him this work cannot be done. Deep thanks are due to Dr. Mahmoud Husien Bekhiet, Assistant Professor of structural geology, Faculty of Science, Al-Azhar University, for his direct supervision, accompanying during the field trip and critical reading of the manuscript. My grateful thanks are to .Dr. Nehal Mohamed A. Soliman, researcher at Geological Department. Mineral Exploration section National Authority for Remote Sensing and Space Sciences, for her direct supervision, help and encouragement and critical comments of the manuscript, particularly in remote sensing interpretation. I wish to thank my parents. They bore me, raised me, supported me, taught me, and loved me. To them I dedicate this work. I wish to thank my wife. the main driving force for me to graduate. I am also thankful to all the staff members and colleagues in the Department of Geology, Faculty of Science, Al-Azhar University and to everyone who made a contribution to this Thesis. Mohamed S. El-Hebiry

CONTENTS___________________________________________________

CONTENTS Subject

Page

Contents List of Figures List of Tables Abstract CHAPTER I: INTRODUCTION AND PREVIOUS WORKS 1.1. Location 1.2. Population and Climate 1.3. Physiography 1.4. Pervious Work on the Study Area 1.5. Scope of the Study CHAPTER II: GEOLOGIC SETTING 2.1. Lower Crust 2.2. Upper Crust 2.2.1. Ophiolites 2.2.2. Island Arc 2.2.2.1. Metavolcanosedimentary rocks 2.2.2.2. Metavolcanics 2.2.2.3. Metagabbro 2.3. Late to Post Orogenic Magmatism 2.3.1. Granites 2.3.2. Fresh Gabbro 2.3.3. Dykes CHAPTER III: STRUCTURAL PATTERN 3.1. Early Compressional Phase (Arc-Accretion) (D1) 3.1.1. Early foliation (S1) 3.1.2. Thrusting 3.1.3. Early folding (F1) 3.1.3.1. Major folds 3.1.3.2. Minor folds 3.2. Early Extensional Phase (D2) 3.2.1. El-Shush shear zone 3.2.2. Um Laseifa fault 3.2.3. Kab Ahmed fault 3.2.4. El-Mirifiya fault 3.3. Late Extensional Phase (D3) i

i iii x xi 1 1 1 3 4 13 15 16 25 26 28 29 32 33 34 34 38 39 41 41 43 44 45 46 47 48 50 53 54 54 56

CONTENTS___________________________________________________ 3.4. Late Compressional Phase (D4) 3.5. Fractures and Joints CHAPTER IV: REMOTE SENSING INTERPRETATION 4.1. Introduction 4.1.1. The Electromagnetic Radiation Spectrum (ERS) 4.1.2. Satellites and Sensors 4.1.3. Methodology 4.2. Preprocessing Techniques 4.2.1. Calibrated Landsat Digital Number (DN) to Top of Atmosphere (TOA) Reflectance Conversion 4.2.2. Calibrated ASTER Digital Number (DN) to Top of Atmosphere (TOA) Reflectance Conversion 4.2.3. Geometric correction (image rectification) 4.3. Digital Image Processing's and Their Interpretations 4. 3.1. Color Combination Images and Band Selections 4.3.1.1. Correlation coefficient method 4.3.1.2. Optimum Index Factor (OIF) 4.3.2. Color Ratio Composite (CRC) 4.3.2.1. Landsat Color Ratio Composite 4.3.2.2 ASTER Color Ratio Composite 4.3.3. Principal Component Analysis (PCA) 4.3.3.1. Principal Component Analysis for Landsat ETM Bands 4.3.3.2. Principal Component Analysis on ASTER data 4.3.4. Classification 4.3.4.1. Unsupervised Classification 4. 3.4.2. Supervised Classification 3.4.2.1. Supervised Classification Using End Members 3.4.2.2. Supervised Classification using Spectral Signature 3.4.2.3. Supervised Classification using Region of Interests (ROIs) 4.4. Lineament Extraction 4.4.1. Data Used and Their Processing 4.4.2. Automated Lineament Extraction 4.4.3. Lineament Directional Analysis CHAPTER V: SUMMERY AND CONCLUSIONS REFERENCES

ii

58 59 63 63 66 67 71 73 74 75 76 78 81 82 84 91 94 96 103 104 107 110 110 113 114 117 118 125 126 127 127 131 141

LIST OF FIGURES______________________________________________

LIST OF FIGURES Figures Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 2.1 Fig. 2.2 Fig. 2.3a Fig. 2.3b Fig. 2.3c Fig. 2.4

Subject

Page

Location Map of the study area. Digital Elevation Model (DEM) of the study area. Drainage system for the study area. Geologic map of the study area (Wadi Um Gheig). Sharp intrusive contact between Abu Marakhat biotite granite (G) and syntectonic tonalite-granodiorite (Gd). Photomicrograph showing plagioclase crystal altered to serecite in the tonalite-granodiorite, (C.N.40 X). Photomicrograph showing highly deformed quartz in the tonalite-granodiorite, (C.N.40 X). Photomicrograph showing the biotite crystals altered to chlorite in tonalite-granodiorite, (PPL.40 X). Highly sheared and foliated tonalite along El-Shush shear

2 3 4 19 20 21 21 21 22

zone. Fig. 2.5 Fig. 2.6a Fig. 2.6b

Fig. 2.6c Fig. 2.7a Fig. 2.7b

Fig. 2.7c

Fig. 2.8 Fig. 2.9 Fig. 2.10

Vertical dyke (DY) and pegmatitic vein (PV) invading El-Shush gniessic tonalite. Photomicrograph showing corroded edges and sericitized cores of plagioclase in the gneissic tonalite, (C.N.40 X). Photomicrograph showing highly deformed quartz with wavy extinction surrounded by biotite laths in the gneissic tonalite, (C.N.40 X). Photomicrograph showing plagioclase crystals altered to serecite in the gneissic tonalite, (C.N.40 X). Migmatites with clear leuco and melano-bands. Photomicrograph showing highly deformed quartz with wavy extinction in the leuco-band of the migmatites, (C.N.40 X). Photomicrograph showing clinopyroxene crystal surrounded by fine plagioclase crystals, in the melanoband of the migmatites (C.N.40 X). Highly deformed and mylonatized sheared granite. Stretched mafic minerals and deformed quartz directed NW-SE in the sheared granite. Panorama showing serpentinites(S) thrusting over the metavolcanosedimentry rocks (MVS) at wadi Um Gheig. iii

22 23 23

23 24 24

24

25 25 27

LIST OF FIGURES______________________________________________ Highly altered and deformed talc carbonates forming the core of the open anticline at Wadi Um Gheig. Minor parasitic angular (chevron) folds associating the western limb of Um Laseifa anticline. Photomicrograph showing the mesh texture in the serpentinites, (PPL.40 X). Photomicrograph showing chromite mineral in the serpentinites, (PPL.40 X). Two minor inclined tight folds (marked by orange color) in the core of an open anticline (marked by black color) associating the thrust plane of the ophiolite over the metavolcanosedimentary rocks. Photomicrograph showing the elongated crystals of tremolite in the tremolite actinolite schist, (C.N.40 X). Photomicrograph showing well schistose texture in the schist, (PPL.40 X). Felsite dyke invading the foliated metavolcanosedimentary rocks along Wadi Um Gheig. BIF hosted in the metavolcanosedimentary rocks. Photomicrograph showing epidote in BIF, (C.N.40 X). Photomicrograph showing quartz bands alternated with magnetite bands in BIF, (PPL.40 X).

27

Fig. 2.18

Sharp intrusive contact btween Kadabora granite (G) and metavolcanics (MV).

34

Fig. 2.19

Isolated islands of gabbro-diorite along Kab El-Rekab fault plane. Sharp intrusive contact between Um Shaddad granite (G) and the metavolcanics (MV). Abu Marakhat biotite granite (G) intruding the tonalitegranodiorite (Gd). Photomicrograph showing orthoclase perthites in Abu Mraakhat biotite granite, (C.N.40 X). Photomicrograph showing fine aggregates of biotite, (PPL.40 X). Photomicrograph showing Rabikivi texture, in the ElSibai granite (C.N.40 X).

34

Fig. 2.11 Fig. 2.12 Fig. 2.13a Fig. 2.13b Fig. 2.14

Fig. 2.15a Fig. 2.15b Fig. 2.16 Fig. 2.17a Fig. 2.17b Fig. 2.17c

Fig. 2.20 Fig. 2.21 Fig. 2.22a Fig. 2.22b Fig. 2.23a

iv

28 29 29 30

31 31 31 32 32 32

36 36 37 37 37

LIST OF FIGURES______________________________________________ Fig. 2.23b Fig. 2.24a Fig. 2.24b Fig. 2.25a Fig. 2.25b Fig. 2.25c Fig. 2.26 Fig. 2.27a Fig. 2.27b Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4

Fig. 3.5 Fig. 3.6

Fig. 3.7a Fig. 3.7b Fig. 3.7c &d Fig. 3.7e

Photomicrograph showing vein type perthites texture in El-Sibai granite, (C.N.40 X). Photomicrograph showing fine aggregates of quartz with wavy extinction in Um Shaddad granite, (C.N.40 X). Photomicrograph showing perthite texture in Um Shaddad granite, (C.N.40 X). Highly weathered younger olivine gabbro. Photomicrograph showing pyroxene crystal in fresh gabbro, (PPL.100 X) Photomicrograph showing olivine crystals in fresh gabbro, (PPL.40 X). Trachyte dyke swarms invading the metavolcanics. Photomicrograph showing trachytic texture in trachyte dykes, (C.N.40 X). Photomicrograph showing plagioclase phenocryst in the trachy-andesite dyke, (C.N.40 X). The (S1) foliation planes parallel to (F1). Serpentinites thrusted over the metavolcanosedimentary rocks. Tight folded structures associated with the NE verging antiformal structures along Wadi Um Gheig. Two minor inclined tight folds (marked by orange color) in the core of an open anticline (marked by black color) associating the thrust plane of the ophiolite over the metavolcanosedimentary rocks. Minor chevron-shaped folds in Wadi Um Laseifa. Distribution of metamorphic and magmatic core complexes (M, Meatiq; Si, Sibai; H, Hafafit) and structural basement in the Pan-African Orogen in the Eastern Desert of Egypt (after Fritz et al., 1996). P, Palestine; J, Jordan; NFS, Najd Fault System. Minor fault cutting the leuco band of the migmatites showing reverse movement. Alignment of the mafic minerals along El-Shush shear zone. Photomicrographs showing the mylonite and augen structure in the deformed granite along El-Shush shear zone. Highly sheared granites along El-Shush shear zone. v

37 38 38 39 39 39 40 40 40 43 45 45 46

47 49

51 51 51

51

LIST OF FIGURES______________________________________________ Fig. 3.8 Fig. 3.9a Fig. 3.9b Fig. 3.10 Fig. 3.11 Fig. 3.12 Fig. 3.13 Fig. 3.14 Fig. 3.15

Fig. 4.1 Fig. 4.2

Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7a Fig. 4.7b Fig. 4.7c

The brecciated rocks along Um Laseifa fault zone. Kab Ahmed fault plane with highly foliated shear zone. Slickensides in Kab Ahmed fault surface. El-Mirifiya fault surface showing sub horizontal slickensides of sinistral sense displacement. Um Shaddad granite pluton intruded along the reactivated NW-SE sinistral fault system. NW upright open folds affected the felsic dykes and the late orogenic granite. Density contour of the poles of the foliation planes indicating NW-SE folds. Orthogonal tensile joints system in the tonalite indicating extension forces along NW-SE direction. A cartoon displaying the tectonic history in the study area.1: the high grade of metamorphism M2 of migmatites and gneisses. 2: late to post orogenic granites. 3: highly folded metavolcanosedimentry. 4: other upper crustal rock unites. Stage (A), multiple arcs accretion. Stage (B), multiple arc accretion led to lithospheric thickening by thrusting and folding. Stage (C), proposed cross section during the first extension stage and the intrusion of alkali granites at mid crustal levels. Stage (D) cross section throw Wadi Um Gheig showing the continuing exhumation process and the forming of core complex. Simplified scheme showing the main remote-sensing elements. Diagrams showing the electromagnetic spectrum ranges, where: (a): Range of ultraviolet, (b): Range of visible spectrum, (c): Range of infrared and (d): Range of microwaves. Comparison of spectral bands between Landsat-7 ETM and ASTER. Methodology flow chart. Diagram showing the georeferencing. Diagram showing the nearest neighbor resampling Landsat ETM 751color composite image in (RGB). Landsat ETM 741color composite image in (RGB). Landsat ETM 543 color composite image in (RGB). vi

55 55 55 56 57 58 59 60 62

65 68

69 71 77 78 88 89 89

LIST OF FIGURES______________________________________________ Fig. 4.8a Fig. 4.8b Fig. 4.8c Fig. 4.9

Fig. 4.10

Fig. 4.11

Fig. 4.12 Fig. 4.13

Fig. 4.14

Fig. 4.15 Fig. 4.16 Fig. 4.17 Fig. 4.18 Fig. 4.19 Fig. 4.20

ASTER 964 color composite image in (RGB). ASTER 463 color composite image in (RGB). ASTER 984 color composite image in (RGB). Spectral reflectance of the serpentinites, granites and metavolcanics (andesite and amphipolite) for the Eastern Desert, Egypt (Frei and Jutz, 1989). Landsat RGB color ratio image (7/1, 3/1 and 5/7) (GS: El-Sibai ganite, GN: Nusla granite, GU: Um Shaddad ganite, GK: Kadabora granite, BG: biotite granite, TO: tonalite, MG: migmatites MV: metavolcanics, MVS: metavolcanosedimentary, Sch: schists, UM: ultramafic, DI: Meta gabbro and GB: Fresh Gabbro). Landsat RGB color ratio image (5/7, 5/1, 5/4 * 4/3) for the study area (Sultan & Arvidson, 1986). Symbols as in Fig.4.10. Landsat RGB color ratio image (3/5, 3/1, 5/7) for the study area (Sabins, 1999). Symbols as in Fig.4.10. Landsat RGB color ratio image (5/3, 5/1, 7/1) for the study area (Gad and Kusky, 2006). Symbols as in Fig.4.10. Landsat RGB color ratio image (7/5, 5/4, 3/1) for the study area (Gad and Kusky, 2006). Symbols as in Fig.4.10. ASTER RGB color ratio image (4/7, 6/4, 4/9) for the study area. Symbols as in Fig.4.10. ASTER RGB color ratio image (4/7, 3/4, 2/1) for the study area (Abdeen et al., 2001). Symbols as in Fig.4.10. ASTER RGB color ratio image (7/6, 6/5, 6/4) for the study area (Wolter et al., 2005). Symbols as in Fig.4.10. ASTER RGB color ratio image (4/7, 4/1, 2/3*4/3) for the study area (Abrams et al., 1983). Symbols as in Fig.4.10. PC Landsat ETM image (PC1 PC2 PC3 in RGB respectively) for the study area. Symbols as in Fig.4.10. PC Landsat ETM image (PC1 PC2 PC4 in RGB respectively) for the study area. Symbols as in Fig.4.10. vii

90 90 91 95

98

99

99 100

100

101 101 102 102 106 106

LIST OF FIGURES______________________________________________ Fig. 4.21 Fig. 4.22 Fig. 4.23 Fig. 4.24 Fig. 4.25 Fig. 4.26

Fig. 4.27

Fig. 4.28

Fig. 4.29

Fig. 4.30

Fig. 4.31

Fig. 4.32

Fig. 4.33

Fig. 4.34

PC ASTER image (PC4, PC1 and PC2 in RGB respectively) for the study area. Symbols as in Fig.4.10. PC ASTER image (PC4, PC1 and PC3 in RGB respectively) for the study area. Symbols as in Fig.4.10. Landsat unsupervised classification image for the study area. Symbols as in Fig.4.10. ASTER unsupervised classification image for the study area. Symbols as in Fig.4.10. The Spectral Hourglass Wizard Flow Chart. Spectral angle mapper classification for the Landsat bands of the study area using endmembers collection. Symbols as in Fig.4.10. Spectral Angle Mapper classification for the ASTER VNIR-SWIR bands of the study area using endmembers collection. Symbols as in Fig.4.10. Spectral Angular Mapper classification for Landsat bands of the study area using spectral signatures. Symbols as in Fig.4.10. Spectral Angular Mapper classification for ASTER 9 bands (VNIR-SWIR bands) of the study area using spectral signatures. Symbols as in Fig.4.10. Minimum Distance classification for Landsat bands of the study area using spectral signatures. For symbols referee to (Fig.4.10). Minimum Distance classification for ASTER 9 bands (VNIR-SWIR bands) of the study area using spectral signatures. Symbols as in Fig.4.10. Spectral Information Divergence classification for Landsat bands of the study area using spectral signatures. Symbols as in Fig.4.10. Spectral Information Divergence classification for ASTER 9 bands (VNIR-SWIR bands) of the study area using spectral signatures. Symbols as in Fig.4.10. Maximum Likelihood classification for Landsat bands of the study area using (ROIs). Symbols as in Fig.4.10.

viii

109 109 112 113 115 116

116

119

119

120

120

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LIST OF FIGURES______________________________________________ Fig. 4.35

Fig. 4.36 Fig. 4.37

Fig. 4.38

Fig. 4.39

Fig. 4.40

Fig. 4.41

Maximum Likelihood classification for ASTER 9 bands (VNIR-SWIR) of the study area using (ROIs). Symbols as in Fig.4.10. Support Vector Machine classification for Landsat bands of the study area using (ROIs). Symbols as in Fig.4.10. Support Vector Machine classification for ASTER 9 bands (VNIR-SWIR bands) of the study area using (ROIs). Symbols as in Fig.4.10. Eight shaded relief images derived from DEM with illumination directions (sun azimuth),0°,45°,90°,135°, 180°,225°, 270° ,and 315°. Shaded relief image created by combining eight shaded relief images with sun angle of 0°,45°,90°, 135o, 180°,225°,270° and 315°. Automatic lineament map of combining eight shaded relief images with sun angle of 0°,45°,90°, 135°, 180°, 225°, 270° and 315°. Rose diagram of automatic field map the first used the lineation frequency while the second used the lineation lengths.

ix

123

124 124

128

129

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LIST OF TABLES_______________________________________________

LIST OF TABLES Tables

Subject

Page

Table 3.1

Structural features, events and ages of the deformed phases subjected to the study area. The ages are given from some available radiometric dates (Bregar et al., 2002 and Johnson et al., 2011).

61

Table 4.1

Radiometric characteristics of ASTER and Landsat ETM data. ASTER Solar Spectral Irradiances for each band (after Smith, 2007). Correlation coefficient of Landsat ETM+ data in the study area. Results of the Correlation coefficient method for best band triplet selection of Landsat ETM+ data in the study area. Correlation coefficient of ASTER VNIR-SWIR data of the study area. Results of the Correlation coefficient method for best band triplet selection of ASTER VNIR-SWIR data in the study area. OIF ranking of Landsat ETM+ data in the study area. OIF ranking of ASTER VNIR-SWIR data in the study area. Results of visual inspection for best band-triplets selection of Landsat ETM+ and ASTER VNIR-SWIR data. Principal component analysis on Landsat ETM+ bands. Principal component analysis on ASTER VNIR-SWIR bands.

70

Table 4.2 Table 4.3 Table 4.4

Table 4.5 Table 4.6

Table 4.7 Table 4.8 Table 4.9

Table 4.10 Table 4.11

x

76 82 83

84 85

86 87 88

105 108

ABSTRACT___________________________________________________

ABSTRACT The present Thesis deals essentially with the geologic studies and structural geology in the light of the field studies and remote sensing interpretations in order to map the rock units and define the structural pattern of Wadi Um Gheig area. According to the field studies, Wadi Um Ghieg area comprises three different units: (1) Upper Crust that comprises the older rock units of ophiolites and island arc assemblage. (2) Lower Crust that consists mainly of high grade metamorphic crust with gneisses,

migmatites

and

syntectonic

tonalite-granodiorite,

gneissose tonalite of structurally contact with the highly sheared granite. (3) Late to post orogenic magmatism that comprises the different types of calc-alkaline and alkaline granites, younger fresh gabbro and post tectonic dykes. Structurally, the study area had been affected by four phases of deformations subjected during the East African Orogeny (EAO). 1-Early Compressional Phase (D1) that can be considered as compressional regime in the late Proterozoic due to arc accretion with a WNW-ENE to NW-SE compressional stress preserved well in the upper crustal rock units and represented by early foliation (S1), thrusting and early folding (F1).

xi

ABSTRACT___________________________________________________

2-Early Extensional Phase (D2) that comprises the early extension features of NW-SE sinistral strike-slip faults of Najd Fault System. 3- Late Extensional Phase (D3) that comprises the N-S and NWSE reactivated faults. 4- Late Compressional Phase (D4) that represents the later phase of deformation which caused the NW upright open regional folding. Remote sensing enhancement techniques are applied on the current study based on Landsat ETM+ data and ASTER data. New proposed Landsat color ratio composite (7/1, 3/1, 5/7) and ASTER color ratio composite (4/7, 4/6, 4/9) are used. Principal component analysis is also applied, as well as different classification techniques such as Spectral Angle Mapper, Maximum Likely Hood, Support Vector Machine, Minimum Distance and Spectral Information Divergence. The above mentioned remote sensing techniques are compiled and gave a good clues about the different rock unites which forming the study area. Hence, we can able to produce new detailed lithological and structural maps of the study area.

xii

CHAPTER I INTRODUCTION AND PREVIOUS WORKS________________________

CHAPTER I INTRODUCTION AND PREVIOUS WORKS 1.1. Location The study area is situated in the central part of the Eastern Desert of Egypt that represents the western part of the ArabianNubian Shield. It is bounded by Latitudes 25o 32' and 25o 42' N. and Longitudes 34o 13' and 34o 28', covering about 455 km2. It is easily accessible through the Red Sea coast asphaltic road and then along Wadi Um Gheig of about 50 km south of Qusier City (Fig. 1.1). 1.2. Population and Climate The area is unpopulated desert area. Only few Bedouin families live along Wadi Um Laseifa in the working of safari job. Most wells are dry with exception one wet well at Wadi Um Laseifa. The study area can be considered as other parts of the Eastern Desert as hot arid area in the summer, while it is warm and dry in the winter with rare sporadic flash floods. The vegetation is scarce, whereas some grass and few scattered trees are found.

1


Fig. 1.1: Location Map of the study area.

2


1.3. Physiography The area under investigation is characterized by wide variation in relief varying from 46 m to 1490 m above sea level as shown in the Digital Elevation Model (DEM) map (Fig. 1.2). Generally, the granitic plutons form the

highest relief, such as

Gabal El-Sibai (~1490 meters above sea level) and Gabal Umm Shaddad (776 meters above sea level).

Fig. 1.2: Digital Elevation Model map (DEM) of the study area.

The area is traversed by number of wadis, known as Wadi Um Ghieg, Wadi Um Laseifa, Wadi ElShush, Wadi Kab El-Rekab and Wadi Kab Ahmed (Fig. 1.3). Drainage pattern analysis 3


indicates that these trends are structurally controlled. Accordingly, Wadi Um Gheig runs in NE-SW, Wadi El-Shush and Wadi Kab Ahmed run in NW-SE, while Kab El-Rekab runs in N-S direction. The above mentioned wadis are usually covered by sands and gravels derived from the adjacent mountains. Some of them such as Wadi Kab Moussa area are filled by gravels and boulders directly derived from the younger gabbro.

Fig. 1.3: Drainage system for the study area.

4


1.4. Pervious Works The area under investigation had been studied by several authors since Hume (1934), who considered that the gneisses form the central portion of Gabal El-Sibai area with three vast outcrops of granitic and syenititc rocks occupying the southeastern, southwestern and northeastern sectors with older schists in between. These early studies dealing with the study area as other parts of the Eastern Desert, had been subjected in the light of the geosynclinal theory (e.g. Akaad and El Ramly, 1960; Sabet, 1961; El Shazly, 1964). Sabet (1961) stated that the granits of Gabal El-Sibai, G. Abu Tiyur, G. El-Shush and G. Um Shaddad are younger red granites with a pronounced cataclastic structure. These cataclastic granites are definitely younger than the surrounding ancient metasediments. However, at the end of the last century many authors discussed the geologic setting of the area under study in the light of the plate tectonic theory (e.g. Stern and Hedge, 1985; El-Gaby et al., 1988 and 1990; Hassan and Hashad 1990, Ragab and El-Alfy, 1996). These authors considered the area under study, as a part of the Eastern Desert and the Arabian Nubian Shield, was subjected also to the Pan African Orogeny.

5


Structural lineaments analysis of the aerial photograph by many authors for the Eastern Desert (e.g. El-Etr et al., 1979; El-Etr and Mohamed 1980; Mussa and Abu El-Leil, 1983; Abu El-Leil and Mussa, 1984) considered WNW, NNE and ENE trends are the preferred structure orientations. El-Rakaiby (1988) studied the tectonic lineaments of the basement belt of the Eastern Desert of Egypt and concluded that the tectonic events created a regiment pattern of deep seated faults and blocks striking N25o-35o W and N 55o-65o E, as well as other structural lineaments striking in different directions and possessing various densities and frequencies. Accordingly, he divided the Eastern Desert into three structural domains namely; Northern (NED), Central (CED) and Southern (SED). These domains are separated by two major fault zones trending N 65 o E. Also he mentioned that the CED seems to form a low-laying area affected by NNW and WNW structural lineaments, as well as by plutonic, volcanic and metallogenic activities, mainly controlled by the NWSE and ENE-WSWtrends. Sultan et al., (1988) showed that the Gabal El-Sibai area as many other parts of the Central Eastern Desert is affected by Najd strike-slip

deformation.

They

suggested

that

penetrative

deformation in the granitic body of G. El-Sibai is largely related to the extension of the Waja (Y) system. Also they interpreted the 6


NW-SE trending sub-horizontal mineral lineations, folds, leftlateral faults as a result of the Najd deformation or re-orientation of the previous structures to align with the NW-SE trend characteristics of the Najd system in Arabian Shield. Rabie and Ammar (1990) studied the main tectonic trends of the Central Eastern Desert on the basis of aero-magnetic and aeroradiometric data and considered that the N-S, NNW, NW, WNW, NNE, NE and ENE trends represent the structural trends of the area under consideration. Khudier et al., (1995) showed that the Gabal El-Sibai area represents a highly structural area in which variably deformed granites and gneisses were exposed underneath the overthrusted Pan African ophiolitic melange and intruded by the younger undeformed Pan African granites. The deformed granites constitute a complete granite cycle starting with autochthonous to be paraautochthonous, calc-alkaline I-type granites and ending with posttectonic within-plate alkaline granites. Also they indicate that the Sibai infrastructure represents a pre-Pan African continental crust incorporated in the Pan African Orogenic belt. Fritz and Puhl, (1997) favored a model of the enhanced heat flow along the Najd fault system which would have enabled the formation of syn-extensional plutonism and triggered the exhumation of the metamorphic core complexes. Lateral buoyancy 7


forces were concentrated within the Najd wrench corridor and enabled orogen-parallel extension. Blasband et al., (2000) interpreted that the gneissic domes throughout the Arabian-Nubian Shield as extensional metamorphic core complexes and the late Proterozoic ended with widespread NW-SE extension. They believe that there is a great similarity of the tectonic evolution of the Arabian-Nubian Shield with the Mesozoic and early Cenozoic evolution of western North America that lead them to consider that gravitational instability at the final stages of the arc accretion phase had been caused by the collapse and resulted in the latest extension stages of the Pan African orogeny in the Arabian-Nubian Shield. Ibrahim and Cosgrove (2001) divided the rocks of Wadi Um Gheig and El-Shush area into three units: (i) low grade metamorphosed rocks of the metavolcanic rocks interleaved with slices of ophiolitic melange (ii) high-grade metamorphic rocks of syn-tectonic granitoids (iii) post-tectonic granites intruding both the low- and high-grade metamorphic rocks. They deduced also four distinct tectonic stages and two magmatic events. (i) the formation of the major D1 sinistral strike slipped El-Shush shear zone, which occurs within the granitoid rocks (six individual granitoid bodies, all now intensely sheared and thought to be intruded into the active El-Shush Shear Zone); (ii) the emplacement of the metavolcanic 8


rocks over the granitoid rocks by major D2 thrusting along a lowangle dip-slip shear zone; (iii) upright open folding of the rocks during D3; and (iv) the intrusion of late-stage undeformed granites. Moreover, they argued that the major strike-slip shear zone of the region is possibly related to the island-arc accretion and the various granitoid rocks intruding along this active strike-slip zone. During the collision associated with island-arc accretion, the ophiolite sheets were interleaved with the volcanic rocks that together thrusted over the granitoid rocks. Abdeen (2003) showed that Wadis Um Luseifa, Um Gheig, El-Shush and El Sibai Swell represents a part of the fold and thrust belt of the Neoproterozoic Pan African Orogeny. It is dominated by three distinct Pan African rocks assemblages, including: (i) Pan African infrastructural unit formed of amphibolite, migmatite and granitic

gneiss

and

characterized

by

amphibolite

facies

metamorphic grade and syn-orogenic calc-alkaline island-arc affinity, (ii) suprastructural Pan African nappe complex formed of ophiolitic serpentinites and mafic volcanics characterized by green schist metamorphism and (iii) unmeta-morphosed late to postorogenic granitic rocks, molasse-type sedimentary rocks and felsic extrusions. He reported that the Sibai - Abu Tiyur - Um Shaddad composite folded often trends NW-SE direction and bounded from NE and SW by high - angle oblique-slip dextral faults. Also he considered that the earlier Pan African thrusting was followed by 9


the later transgression thickened of the crust and brought dense rocks over the light rocks. Therefore, buoyancy most likely caused gravitative uplift of the granites as well as gravity sliding of the overlying dense materials. Moreover, the compression associated with the transpressional regime had contributed to the uplift of the core of the Sibai antiform. Subsequent erosion and exhumation exposed the deeper levels of the crust. Abd El-Wahed (2006) showed that the Sibai core complex and Pan African nappe complex were developed through four successive deformational events (D1-D4) accompanied by three metamorphic events (M1-M3). The first deformational event (D1) is only recognized in the amphibolite representing an old oceanic crust (900-740 Ma). The first metamorphic episode (M1) is a high temperature metamorphism led to the formation of amphibolite. Between 740 and 660 Ma, the El-Shush gneissic tonalite that intruded at about 680 Ma and had been affected by thrusting and folding during (D2). In Sibai core complex, the seconed metamorphic event (M2) occurred in the range of green schist facies under PT conditions 480-5250C at 2-4.5 Kbar D3 (660-560 Ma) is represented by two stages of transpression and transtension, accompanied by emplacement of three groups of granitoids, development along NNW- trending major asymmetrical anticline and NE trending open folds as well as minor asymmetric, overturned and partly recumbent shear-folds in Pan African nappe 11


complex (PNC). Mylonitic fabrics within strike-slip shear zones were developed under retrograde green schist facies metamorphism in the range 0f 222-3210C in PNC and 202-2300C in the gneissic tonalite. Presence of retrograde metamorphism supports the slow exhumation of Sibai core complex under brittle-ductile low temperature conditions. El Taky et al., (2007) studied the petrology and the petrochemistry of the basic rocks of Wadi Um Gheig and considered that the metagabbro-diorite complex of composition varying from metagabbro and diorite suggests fractional crystallization and crystalized from calc-alkaline to subductionrelated high alumina basaltic (or basaltic andesitic) magma in a magmatic arc environment. They considered the Kab El-Rekab gabbro as an intrusive unmetamorphosed younger gabbro and mapped the outcrop rocks of Kab Moussa as syenite. Fowler et al., (2007) showed that El-Sibai gneissic association rocks cannot be considered as infrastructural but they form a unit within the ophiolitic association nappes. Moreover, they belive that the El Sibai structure is not represented by domal shape or antiformal structure, while according to them the main gneissic association rocks are tabular intrusions roughly concordant with the shears and dividing the ophiolitic association into nappes and synkinematic stacking event (700–650 Ma). The gneissic granite, 11


tabular intrusions and the ophiolitic host were later folded to upright NW-SE trending open folds during NE–SW directed shortening event (625–590 Ma). Subsequently, NW–SE regional extension effects became evident, including low angle normal ductile shear zones and mylonites. The latest gneissic red granites are synkinematic, probably continuing from the low-angle shearing event, steep normal faults, sinistral WNW and N–S trending transcurrent faults (590–570 Ma). The normal faults mark the southeastern and northwestern limits of the El Sibai gneissic association rocks. Johnson et al., (2011) proposed that during the late Cryogenian–Ediacaran (650–542 Ma), the Arabian–Nubian Shield (ANS) underwent final assembly and accretion to the Saharan Metacraton concurrent with the assembly of eastern and western Gondwana. Following a 680-640 Ma orogenic event reflecting amalgamation of a core grouping of island-arc terrains (the protoArabian–Nubian Shield; pANS), the region underwent extensive exhumation, erosion, and subsidence. Many basins were filled by terrestrial, molasse-type sediments interfingering with subordinate to predominant amounts of volcanic rocks. Magmatism was extensive throughout the period, initially characterized by tonalitetrondhjemite-granodiorite (TTG) and granite (monzogranite, syenogranite), but also characterized (610 Ma) amounts of alkali-feldspar granite and alkali granite.

12

by increasing


These authors considered that the magma sources of the late Cryogenian–Ediacaran granitoids were dominated by juvenile crust and (or) depleted mantle magmas mostly originated in an orogenic, post-collisional, commonly of extensional settings. By 630 Ma, the region was affected by oblique (transpressional) convergence of continental blocks that formed eastern and western Gondwana. In

the

northwestern

ANS,

convergence

and

Najd

transpression buckled the crust causing structural highs with domes of gneissic infracrust overlain by supracrust composed of ophiolitic and volcanosedimentary assemblages dating from the Tonian– Middle Cryogenian period of island-arc activity. The supracrust was extensively translated to the northwest above a high-strain zone. Extension and tectonic escape augmented exhumation of the gneissic infra-crust particularly between 620 and 580 Ma. In the northeastern ANS, linear belts of gneiss formed from reworked older intrusive bodies or syntectonic intrusions that were emplaced along Najd faults. Granite magmatism continued until 565-560 Ma and orogeny ceased by 550 Ma. 1.5. Scope of the Study The present study deals essentially with the geologic studies and structural pattern in the light of the field studies and remote sensing interpretations.

13


From the field studies the relationship of the different rock units are shown and discussed as the trend of foliation, lineation. The structural features and elements are carefully measured and examined such as folding, faulting and shearing. To complete these field studies, some represented collected samples of the different rock units are examined petrographically and documented by the remote sensing investigation to fulfill all the given results. Remote sensing studies are shown by using many tools of remote-sensing techniques, such as; band ratio, principal component analysis and different types of supervised classification of available remotesensing data Landsat7 ETM+ (Enhanced Thematic Mapper plus, 2000) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The given data are used in order to produce new lithological and structural map for the study area. The present Thesis comprises four chapters. The first one deals mainly with the introduction and the previous works. The second shows the geologic setting. The third discusses the structural pattern and the fourth demonstrates the remote sensing interpretation. From these field and office works a new geologicalstructural map has been prepared as well as new contribution has been achieved.

14

CHAPTER II GEOLOGIC SETTING AND PETROGRAPHY_______________________

CHAPTER II GEOLOGIC SETTING Wadi Um Gheig area lies in the Central Eastern Desert, south west of Qusier City to form a part of the Neoporeterozoic evolution of the Nubian Shield in NE Africa as a result of accretion of intraoceanic island arc, continental microplates and oceanic plateaus in the course of consolidation of Gondwana (Gass, 1982, Stern 1994, Kroner et al., 1994, Abdelsalam and Stern, 1996). A generalized picture shows three major orogens that shaped the final configuration of greater Gondwana, whereas the East African Orogen (EAO) resulted from the collision of amalgamated arc terrains of the Arabian-Nubian Shield (ANS) with the Sahara and Congo-Tanzania Cratones to the west and the Azania and Afif terrains to the east (Collines and Pisarevisky, 2005) constituting one or more continental blocks between the Indian Shield and CongoTanzania- Bangweula Craton. The Wadi Um Gheig area represents a part of the northern ANS formed during a second growth phase of EAO between ~760 and 730Ma when the Midyan-Eastern Desert terrain was formed. This terrain subsequently collided and amalgamated with the earlier formed older terrain along the Yanbu-Onib-Sol Hamed-GerfAllaqi- Heaini sutures (Johnson et al., 2011), The resulting geologic entity is commonly referred to as the "western arc or oceanic 51


terrains" of ANS (Stoester and Forst, 2006, Ali et al., 2009, Johnson et al., 2011). The western arc or oceanic terrains collided and amalgamated between 680 and 640 Ma with Afif and Tathlith terrains creating a neocontinental crustal block referred to as the proto-Arabian-Nubian Shield( pANS) (Johnson et al., 2011). The area under study of Wadi Um Gheig represents a part of crustal structure of the Eastern Desert terrain comprising the structural lower crustal rock units (Lower crust) of higher grade metamorphic crust (M2) dominant with gneissic rocks (structural basement), structurally covered by nappes of lower grade metamorphic rocks (M1) of ophiolites and metavolcanosedimentry, metavolcanics and metagabbro sequences forming upper crustal units (Upper crust)(Fritz et al., 1996). The juxtaposition of the low grade metamorphic rocks of ophiolite and island arc sequences against the high grade gneisses along extensional shears suggest crustal-scale thinning by NW-SE extension accompanied by intense late to post magmatic activity (Blasband et al., 2000, Fowler and El Kalioubi, 2004, Fowler and Osman, 2009, Anderson et al., 2010). 2.1. Lower Crust The sequence of the geological features reveals a complex tectonic history of Wadi Um Gheig area. The area consists mainly of the lower-crustal high grade metamorphic crust with gneisses, 51


migmatites, directly concordant with the syntectonic tonalitegranodiorite, gneissose tonalite and structurally contact with the highly sheared granite forming up the upper layer. The high grade metamorphic and sheared rocks are structurally overthrust by the metavolcanosedimentry-metavolcanic-metagabbro sequence of island arc and ultramafic rocks (serpentinites) of ophiolite assemblage. The high grade lower crust with gneisses, migmatites and syntectonic tonalite-granodiorite (metamorphic core complexes) are situated along Wadi El-Shush to form the southern margin of the known Sibai Dome, (Fig. 2.1). These gneisses related to the known Sibai Dome were referred to as "infrastructure ", by El-Gaby et al., (1990). On the other hand, many authors believe that the core complex formation within the Central Eastern Desert of Egypt is loosely linked with one of the most spectacular feature of NW-SE trending Najd Fault System (Sturchio et al, 1983, Fritz et al., 1996, Blasband et al., 2000, Fowler and Osman, 2001). Exhumation of internal core complexes accompanied NW-SE sinistral strike slip faulting and resulted in NW-SE directed extension provide dipocenters for the accumulation of intermontance molasses basins (Fritz and Messner, 1999) and intrusion of granitoid rocks. Single zircon evaporation ages from the El-Shush gneiss, interpreted as magmatic age (Bregar et al., 1996) indicate that the accretion of Pan African Orogeny occurred at ~ 680 Ma and NW-SE directed 51


structural features had been initiated at ~ 660 Ma together with emplacement of gneisses. Moderate exhumation and delivery of molasses sediments to basins had been initiated at ~ 645 Ma and accompanied by extensive magmatic activity. The major rapid exhumation phase and extension within a dominant strike-slip setting suggests a tectonically induced exhumation, occurred after ~645 Ma and continued until~ 580 Ma. Three main layers are subsequently distinguished for the lower crust. The deeper layer has the tonalite-granodiorite and gneissose tonalite gradually concordant with the migmatite and gneisses forming the middle layer. The upper layer comprises the highly sheared granites structurally contact with them. Generally these rocks constitute a NW-SE belt, configurated with the main structural trends of the area, (Fig. 2.1).

51


Fig. 2.1: Geologic map of the study area (Wadi Um Gheig). 51


The more deeper tonalite -granodiorite rocks cropout forming scattered elongated low lands south east of Gebel El-Sibai, (Fig. 2.2). They are directly cut by Abu Marakhat biotite granite and El-Sibai late tectonic granites. The syntectonic tonalite and granodiorite consist mainly of quartz, plagioclase, biotite and hornblende. Plagioclase occurs as subhedral crystals altered to sericite and epidote (Fig. 2.3a). Quartz crystals occur as highly deformed crystals, often showing wavy extinction (Fig. 2.3b). Biotite and hornblede crystals occur in tabular form, partly altered to chlorite (Fig. 2.3c).

Fig. 2.2: Sharp intrusive contact between Abu Marakhat biotite granite (G) and syntectonic tonalite-granodiorite (Gd).

02


Fig. 2.3a: Photomicrograph showing plagioclase crystal altered to sassurite in the tonalite-granodiorite, (C.N.40 X). Fig. 2.3b: Photomicrograph showing highly deformed quartz in the tonalitegranodiorite, (C.N.40 X). Fig. 2.3c: Photomicrograph showing the biotite crystals altered to chlorite in tonalite-granodiorite, (PPL.40 X).

The gneissic tonalite rocks form an elongated major NW-SE belt. They show well developed NW-SE gneissosity. These rocks are highly sheared, mylonitized and brecciated along NW-SE ElShush shear zone (Fig. 2.4). They are intruded and cut by the late orogenic granites as well as many felsic dykes (Fig. 2.5). These gneissic tonalite rocks are gradually changed to the upper layer forming the gneisses and migmatites. Petrographically they display a gniessosity texture, deformation and mylonitization are shown by augen quartz crystals and stretched mafic minerals. Plagioclase shows often corroded edges and sericitized cores (Fig. 2.6 a, b &c). Epidote due to the alteration of the mafic minerals is a common feature.

05


Fig.2.4: Highly sheared and foliated tonalite along El-Shush shear zone.

DY

PV

Fig. 2.5: Vertical dyke (DY) and pegmatitic vein (PV) invading El-Shush gniessic tonalite.

00


Fig. 2.6a: Photomicrograph showing corroded edges and sericitized cores of plagioclase in the gneissic tonalite, (C.N.40 X). Fig. 2.6b: Photomicrograph showing weakly deformed quartz with wavy extinction surrounded by biotite laths in the gneissic tonalite, (C.N.40 X). Fig. 2.6c: Photomicrograph showing plagioclase crystals altered to serecite in the gneissic tonalite, (C.N.40 X).

The gneisses and migmatites of the middle layer to east of Wadi El-Shush are formed by alternating leuco- and melano-bands with thickness varying from 3cm to 15 cm and striking often NWSE, with horizontal to sub horizontal dip, (Fig. 2.7a). The metavolcanics are partly migmatized with contact zone up to 50 m at the north of El-Shush migmatites. This suggests detachment between deeply buried island arc roots (migmatized metavolcanics) and the metavolcanosedimentary cover nappes that remained at shallow crustal levels, (Fritz et al., 2013). These migmatized metavolcanic rocks occur sometimes as minor NE plunging folds. Deformation is defined by wavy extinction and augen texture. Plagioclase and perthite occur as porphyroblast crystals forming up the main constituents of the leuco-bands, (Fig. 2.7b). In the melanobands, amphibol and pyroxene minerals highly altered to epidote are the main constituents, (Fig. 2.7c). 02


Fig.2.7a: Migmatites with clear leuco and melano-bands. Fig.2.7b: Photomicrograph showing deformed quartz with wavy extinction in the leuco-band of the migmatites, (C.N.40 X). Fig.2.7c: Photomicrograph showing clinopyroxene crystal surrounded by fine plagioclase crystals, in the melano-band of the migmatites (C.N.40 X).

The uppermost layer of highly sheared granites is situated along NW-SE sinistral El-Shush shear zone. Occasionally, they show a structural contact with the metavolcanic rocks of island arc origin. These granites are highly deformed and mylonitized, (Fig. 2.8). Stretched mafic mineral, and deformed quartz directed NWSE represent the common feature (Fig. 2.9).

02


Fig. 2.8: Highly deformed and mylonatized sheared granite.

Fig. 2.9: Stretched mafic minerals and deformed quartz directed NW-SE in the sheared granite.

2.2. Upper Crust The upper crust of Wadi Um Gheig comprises the older rock units of ophiolite and island arc assemblage. Overall the ophiolites of ANS are thought to represent the oceanic crust of Mozambique Ocean that were formed upon the rifting of Rodinia (Abdelsalam and Stern, 1996). These ophiolites occur often as deformed linear 01


belts, decorated the shear zone patterns. The metavolcanics and the metavolcanosedimentary sequences form the cover nappes evolved in thin-skinned tectonic style and maximum metamorphic conditions of ~500oC and 0.8 GPa (Neumayr et al., 1998, Loizenbauer et al., 2001, Abd El-Naby et al., 2008; Abu-Alam and Stuwe, 2009). They typically have crystallization age of pre-650 Ma arc- magmatism of Tonian-Middle Cryogenian island arc terrains, (Stoeser and Camp, 1985; Genna et al., 2002; Johnson and Woldehaimanot, 2003; Stoeser and Frost, 2006) and constitute juvenile additions to the crust (Stern, 2002). The terrains converged and amalgamated as a result of intra-oceanic subduction driven arcarc and ultimately arc- continent collisions. 2.2.1. Ophiolites Along Wadi Um Gheig, the ophiolites are represented by serpentenites and talc carbonate bodies overthrusted the metavolcanosedimentary rocks, (Fig. 2.10), during compression, shortening accretion and amalgamation phase, accompanied with members of antiformal and synformal structures, (Fig. 2.11). At the mouth of Wadi Um Laseifa, the serpentinite bodies overthrusted the western limb of the configurated anticline forming the metavolcanosedimentary rocks. The core of this anticline contains some serpentinite bodies forming up two tight minor anticlines, in addition to some minor parasitic angular (chevron) folds at the 01


western limb, (Fig. 2.12). to suggest the complexity of the tectonic setting. The thrust plane strikes NE-SW. It is characterized by brecciated rocks, alteration of srpentinites to talc-carbonate and actinolite- tremolite- talc- carbonate association.

Fig. 2.10: Panorama showing serpentinites(S) thrusting over the metavolcanosedimentry rocks (MVS) at Wadi Um Gheig.

Fig. 2.11: Highly altered and deformed talc carbonates forming the core of the open anticline at Wadi Um Gheig.

01


Fig. 2.12: Minor parasitic angular (chevron) folds associating the western limb of Um Laseifa anticline.

The serpentinites consist essentially of lazardite, chrysotile and antigorite with chromite and magnetite accessory minerals and characteristic mesh texture, (Fig. 2.13a). The talc-carbonate rocks are composed of talc and antigorite fibrous crystals. Carbonate minerals are represented by subhedral crystals. Chromite and magnetite occur as lensoidal aggregates with blood red and dark brown color, (Fig. 2.13b). 2.2.2. Island Arc The island arc assemblage comprises the cover nappe of lowgrade metavolcanosedimentary, metavolcanic and metagabbro sequences covering the northern and the southern parts of the mapped area, (Fig. 2.1). 01


Fig. 2.13a: Photomicrograph showing the mesh texture in the serpentinites, (PPL.40 X). Fig. 2.13b: Photomicrograph showing chromite mineral in the serpentinites, (PPL.40 X).

2.2.2.1. Metavolcanosedimentary rocks The metavolcanosedimentary rocks costitute the upper horizon of the island arc assemblage. In the present study, these rocks are differentiated into three verities. (i) The first comprises the highly folded metavolcanosedimentary rocks, forming the upper folded rock units (Fig. 2.14), overthrusted by serpentinite rock units. (ii) The second comprises the schists of high intercalated schistose rocks and talc carbonate rocks. (iii) The third comprises the metavolcanosedimentary rocks associated with the banded iron formation (BIF).

01


Fig. 2.14: Two minor inclined tight folds (marked by orange color) in the core

of an open anticline (marked by black color) associating the thrust plane of the ophiolite over the metavolcanosedimentary rocks.

The highly folded metavolcanosedimentary rocks are formed mainly by succession of lappli metatuff and ash metatuff with moderate relief outcrops showing well foliated rocks. The schists occur as an elongated NW-SE successive belts, cross-cut by N-S and NE-SW left lateral strike slip faults. They are formed by succession of graphite-schiste, tremolite-schist, biotitemuscovite schist and hornblende–biotite schist (Fig. 2.15a &b). They are directly invaded by the late orogenic granites as well as some felsic and trachytic dykes (Fig. 2.16). Banded Iron Formation (BIF) is found in the metavolcanosedimentary rocks east of G.Um Shaddad. BIF bands are varying from few centimeters to half meter in thickness characterized by high ridges, massive and dark color (Fig. 2.17a), of volcanogenic Algoma type origin (Hassan et al., 1992). Microscopically they 22


have well alternated bands of magnetite and quartz. Epidote represents the main secondary mineral (Fig. 2.17b & c).

Fig. 2.15a: Photomicrograph showing the elongated crystals of tremolite in the tremolite actinolite schist, (C.N.40 X). Fig. 2.15b: Photomicrograph showing well schistose texture in the schist, (PPL.40 X).

Fig. 2.16: Felsite dyke invading the foliated metavolcanosedimentary rocks along Wadi Um Gheig. 25


Fig. 2.17a: BIF hosted the metavolcanosedimentary rocks. Fig.2.17 b: Photomicrograph showing epidote in BIF, (C.N.40 X). Fig. 2.17c: Photomicrograph showing quartz bands alternated with magnetite bands in BIF, (PPL.40 X).

2.2.2.2. Metavolcanics Metavolcanics are situated at the southern part of the mapped area to form NE-SW belt cross cut by Wadi Um Gheig and Wadi Kab El-Rekab, (Fig.2.1). They are massive and form low and moderate hills. They show variable fractured and mylonitized belts along the shear zones, sometime, hornblende green crystals and chlorite occur as alignmented forms in N50oW direction. These rocks are partly migmatized along their contact with migmatites and gneisses to suggest that may represent the more deeply island arc roots, and the metavolcanosedimentary rocks represent the cover nappe remained at shallow crustal level (Fritz et al., 2013). The metavolcanics are represented mainly by intermediate

20


metavolcanics (metaandesite) and basic metavolcanic (metabasalt) while the acidic metavolcanic (metarhyolite) are relatively rare. They are directly invaded by the late to post orogenic granites of Kadabora granite and Um Shaddad granite as well as the metagabbroic rocks (Fig. 2.18). The intermediate metavolcanics (meta-andesite) consist mainly of highly altered plagioclase, amphiboles altered to epidote and chlorite. Magnetite is the main accessory mineral. In the metabasalt the amphibole minerals and plagioclase represent the main constitues, while in the metarhyolite tuff, quartz and plagioclase are predominant. 2.2.2.3. Metagabbro The metagabbro occurs as small elongated NW-SE belt at the northern side of Wadi Kab El-Rekab, (Fig. 2.1). It is massive, slightly deformed and shows moderate relief (Fig. 2.19). It varies from medium to coarse grain sizes with mesocratic to melanocratic color. Along Kab El-Rekab fault plane, it occurs as scattered small islands, invaded by the late orogenic granite of Kadabora pluton, as well as N-S dykes. The metagabbro represents the younger phase of island arc assemblage, however it directly cuts the metavolcanics. On the other hand, it is cut by the syntectonic gneissose tonalite as well as the younger olivine gabbro.

22


Fig. 2.18: Sharp intrusive contact btween Kadabora granite (G) and metavolcanics (MV).

Fig. 2.19: Isolated islands of gabbro-diorite along Kab El-Rekab fault plane.

2.3. Late to Post Orogenic Magmatism The late to post orogenic magmatism comprises the different types of calc-alkaline and alkaline granites, younger fresh gabbro and post tectonic dykes. 2.3.1. Granites A commonly accepted view of Cryogenian-Ediacaran ANS magmatism involved from arc-related tholeiite and calc-alkaline tonalite, trondhjemite and granodiorite (TTG) and granite 22


assemblage, to collisional –related calc-alkaline TTG and granite assemblage, to post-collisional within-plate A-type granitoids and formed in extensional regimes during orogenic collapse (Stern and Hedge, 1985, Beyth et al., 1994, Moghazi et al., 1998, Garfunkel, 1999, Mushkin et al., 2003, Moussa et al., 2008). In the mapped area, the late to post orogenic granites are represented by four plutons known as Um Shaddad, Abu Marakhat, El-Sibai and Kadabora plutons, however the last two plutons (ElSibai and Kadabora) cover a huge area outside of the investigated area. Generally, these plutons have circular and oval like shapes except Abu Marakhat with NW-SE striking pluton. These granites have intrusive nature (Fig. 2.20). Both Um Shaddad and Kadabora plutons invade directly the surrounding metavolcanics and metagabbros. On the other hand, El-Sibai pluton intrudes the metavolcanosedimentary rocks with sharp obvious contact and the Abu Marakhat granite directly cross-cuts the gneissic tonalite and granodiorite, (Fig. 2.21). Nearly these granites have the same mineral composition with some little diffrences in mafics, Kfeldspars and quartz content. In Abu Marakhat granite, biotite is relatively abundant, (Fig. 2.22a & b), K- feldspars (mainly perthite) are relatively high in El-Sibai (Fig. 2.23a & b). In Um Shaddad and Kadadbora granite they rise them up to the alkali feldspare granites. Shearing and deformation are common, defined by wavy extinction in quartz and deformation of other minerals (Fig. 2.24a & b). Most 21


often, these granites emplaced at time span ~650 and 640Ma. This age is concerned for El-Sibai and Abu Marakhat granites (Fritz et al., 2002) to suggest that they were placed prior the fresh gabbro emplacement of crystallization age ~545-540Ma (Augland et al., 2011)

Fig. 2.20: Sharp intrusive contact between Um Shaddad granite (G) and the metavolcanics (MV).

Fig.2.21: Abu Marakhat biotite granite (G) intruding the tonalite-granodiorite (Gd).

21


Fig. 2.22a: Photomicrograph showing orthoclase perthites in Abu Mraakhat biotite granite, (C.N.40 X). Fig. 2.22b: Photomicrograph showing fine aggregates of biotite, (PPL.40 X).

Fig. 2.23a: Photomicrograph showing vein type perthites texture in El-Sibai granite, (C.N.40 X). Fig. 2.23b: Photomicrograph showing Rabikivi texture, in the El-Sibai granite (C.N.40 X).

21


Fig. 2.24a: Photomicrograph showing fine aggregates of quartz with wavy extinction in Um Shaddad granite, (C.N.40 X). Fig. 2.24b: Photomicrograph showing perthite texture in Um Shaddad granite, (C.N.40 X).

2.3.2. Fresh Gabbro The fresh gabbro is represented by single moderate outcrop situated at the north of Wadi Kab El-Rekab, known as Kab Mousa younger gabbro (Fig. 2.25a). As other younger gabbro rocks in ANS it is emplaced during Cryognian-Ediacaran conforming geochronologic age ~ 640-540 Ma (Augland et al., 2011), implying that the younger gabbro and granite magmatism overlapped in the ANS during late Cryognian-Ediacaran (Coleman et al., 1972, Sadek, 1994, Greiling et al., 1994). The investigated fresh gabbro represents some of the youngest undeformed post tectonic plutonic rocks. It is represented by isometric unlayered small mass intruding the metavolcanic and the metavolcanosedimentary rocks. The investigated gabbro is highly weathered characterized by abundant content of pyroxene, 21


calcic plagioclase and olivine minerals, (Fig. 2.25b &c). It is intruded by some trachyte dykes dated at ~ 540 Ma. It may be unrelated to slightly older layered gabbro cropping out in other places of the Eastern Desert, with crystallization age ~545-540 Ma (Augland et al., 2011). Occasionally it was emplaced after El-Sibai and Abu Marakhat granite of determined age ~ 650 and 640 Ma (Fritz et al., 2002).

Fig. 2.25a: Highly weathered younger olivine gabbro. Fig. 2.25b: Photomicrograph showing pyroxene crystal in fresh gabbro, (PPL.100 X) Fig. 2.25c: Photomicrograph showing olivine crystals in fresh gabbro, (PPL.40 X).

2.3.3. Dykes The dykes represent the post tectonic intrusions of the youngest Neoproterozoic (Ediacaran) rocks in the study area. They cross-cut most of the investigated rocks with predominant linear feature in 21


N-S and less common in E-W and NW-SE direction (Fig. 2.26). Occasionally, they are concentrated in the granites. In Kadabora pluton they occur as linear N-S latite, trachyte and felsite dyke swarms (Fig. 2.27a & b). Generally, the N-S trends can be considered as a common theme of Ediacaran upper crustal extension parallel to the orogeny, whereas the less E-W directed dykes traverse to the orogeny, (Johnson et al., 2011).

Fig. 2.26: Trachyte dyke swarms invading the metavolcanics.

Fig. 2.27a: Photomicrograph showing trachytic texture in trachyte dykes, (C.N.40 X). Fig. 2.27b: Photomicrograph showing plagioclase phenocryst in the latite dyke, (C.N.40 X). 22

CHAPTER III STRUCTURAL PATTERN_______________________________________

CHAPTER III STRUCTURAL PATTERN This chapter deals mainly with the structural pattern of Wadi Um Gheig area interpreted as the result of the field work study and analysis of the Landsat Thematic Mapper (ETM) and ASTER images as well as aerial photographs at a scale 1:40,000 (Fig. 2.1). Generally, the study of the structure features demonstrates four main phases of deformations subjected in the study area during the development of the Eastern Desert as a part of the ArabianNubian Shield and the East African Orogeny (EAO). These phases are: 1- The early compressional phase (D1). 2- The early extensional phase (D2). 3- The late extensional phase (D3). 4- The late compressional phase (D4). 3.1. Early Compressional Phase (Arc-Accretion) (D1) Throughout the Arabian-Nubian Shield a number of deformed linear belts of ophiolitic rocks have been observed and interpreted as sutures (e.g. Ries et al., 1983; Vail, 1985; Abdelsalam and stern, 1996). The activity on the sutures took place at 750-650

14


Ma, while the strike-slip movement is related to the later stages of arc accretion event (Abdelsalam and Stern, 1996). As that has been shown in the previous Chapter of the Geologic Setting, the study area is tectonically constructed by two successive crustal rock units. The lower crustal rock units (younger) and the upper crustal rock units (older). However, the former comprising the high grade metamorphic rocks had been formed during the second stage of metamorphism (M2). The later comprising the highly folded and highly schistose rocks of low to moderate degree of metamorphism had been subjected at the first stage of metamorphism (M1). In the view of the tectonic development of the ArabianNubian Shield the compressional phase is attributed to the compressional regime in the late Proterozoic due to arc accretion with a WNW-ESE to NW-SE compressional stress. In the study area of Wadi Um Ghieg, the early compressional phase features are preserved in the upper crustal rock units. These features are well represented by 1) the early foliation (S1), 2) thrusting and 3) early folding.

14


3.1.1. Early foliation (S1) The upper crustal units are represented by the older foliation (S1) that was formed in the metavolcanoclastic rocks and associated schists developed essentially during the early phase of compressional regime (D1). Generally, the (S1) foliation strikes parallel to the main trend of the thrust fault zone and their associated fold axial planes in NNE and NW dips. Occasionally, the main trend of the (D1) deformation, responsible for the formation of (S1) indicates development of NE verging (F1) folding (Fig. 3.1).

Fig. 3.1: The (S1) foliation planes parallel to (F1).

14


3.1.2. Thrusting One thrust fault is recorded along Wadi Um Gheig at the mouth of Wadi Um Laseifa (Fig.2.1). It strikes NNE-SSW with moderately dips toward the WNW to suggest WNW-ESE displacement of the serpentinites over the metavolcanosedimentry rock units due to arc accretion and thickening of the upper crustal units. The thrust zone is characterized by brecciated rocks and highly altered serpentinite bodies to talc carbonate in one hand (Fig.3.2), as well as tightly folded structures associated with the NE verging antiformal and synformal structures in the other hand (Fig.3.3). 3.1.3. Early folding (F1) The early folding stage (F1) can be considered as the most predominant structural features in the early compressional phase (D1) of arc accretion regime, comprising the major and minor folds. The major ones are the large-scale mappable folds, whereas the minor ones are unmappable small-scale folds.

11


Fig. 3.2: Serpentinites thrusted over the metavolcanosedimentary rocks.

Fig. 3.3: Tight folded structures associated with the NE verging antiformal structures along Wadi Um Gheig. 14


3.1.3.1. Major folds The major folds of the early folding stage (F1) are recorded along Wadi Um Gheig at the mouth of Wadi Um Laseifa. They are represented by antiformal and synformal structures with parallel axial planes trending NNE-SSW direction to indicate WNW-ESE compressional stress during the thrusting of the ophiolite rock units (serpentinites) over the metavolcanosedimentary rock units at the arc accretion stage. Moreover, they have duplexed and complexed core of high tighted antiformal structures mixed with serpentinites and metavolcanoclastics (Fig.3.4).

Fig. 3.4: Two minor inclined tight folds (marked by orange color) in the core of an open anticline (marked by black color) associating the thrust plane of the ophiolite over the metavolcanosedimentary rocks.

14


3.1.3.2. Minor folds Some minor parasitic (chevron) folds have been observed along Wadi Um Laseifa. These minor structures are often obtained as synchronous development of the final result of continuing compressional stage. They show Z shape (chevron). However, the high degree of angularity may suggest more stress action took place throughout the final result of this stage (Fig. 3.5).

Fig. 3.5: Minor chevron-shaped folds in Wadi Um Laseifa.

14


3.2. Early Extensional Phase (D2) Generally, this phase of deformation (D2) comprises the early extension features of NW-SE sinistral strike-slip faults of Najd Fault System. Many researches demonstrate the presence of a metamorphic core complex, developed in an extensional collapse (Blasband et al., 1997 and 2000). The extensional collapse took place throughout the entire Arabian-Nubian Shield during the later stages of East African Orogeny. During this phase, NW-SE directed extension in the Central Eastern Desert exhumed a number of basement complexes namely the Meatiq- Sibai and Hafafit dome and resulted in the formation of Kareem intermountain molasse basin between Meatiq and Sibai (Fritz et al., 2002) (Fig. 3.6). Four major NW-SE faults configurated as Najd Fault System are named here as El-Shush shear zone, Um Laseifa fault, Kab Ahmed fault and El Mirifiya fault. Some of them such as El-Shush shearing caused the mechanism driven exhumation, continuously accompanied by magmatic activity as that had been suggested by Fritz et al., (2002).

14


Fig.3.6: Distribution of metamorphic and magmatic core complexes (M, Meatiq; Si, Sibai; H, Hafafit) and structural basement in the Pan-African Orogen in the Eastern Desert of Egypt (after Fritz et al., 1996). P, Palestine; J, Jordan; NFS, Najd Fault System. 14


3.2.1. El-Shush shear zone The El-Shush shear zone appears as a wide shear zone comprising most the rock units of the lower crust. It can be considered as NW-SE oblique-slip shear zone parallel to the main trend of the Najd Fault System. It is characterized by comprising the high grade metamorphic rocks (gneisses and migmatites) related to the second stage of metamorphism (M2) as well as highly sheared granites of the second stage of foliation (S2). Moreover, alignment of the mafic minerals, in addition to the cataclastic deformation of rocks are the well preserved features for shearing (Fig. 3.7 a, b, c, d and e). Field observation revealed that the high grade metamorphic migmatitic rocks had been formed by partial melting of the lower part of buried island arc units of modern arc chemical affinities. Single evaporation ages from El-Shush gneisses interpreted as magmatic age (Bregar et al., 1996) indicate that the accretionary stage occurred at ~ 680 Ma. Succession and relative timing of deformational phase during extension and exhumation is constraint by interrelation between magmatic and tectonic event (Bregar et al., 1996).

45


Fig. 3.7a: Minor fault cutting the leuco band of the migmatites showing reverse movement. Fig. 3.7b: Alignment of the mafic minerals along El-Shush shear zone. Fig. 3.7 c & d: Photomicrographs showing the mylonite and augen structure in the deformed granite along El-Shush shear zone. Fig. 3.7e: Highly sheared granites along El-Shush shear zone.

44


Exhumation of core complexes had been a substantially debated topic within the scientific community in recent years. Despite the existence of several models, most authors outlined the importance of thickened crust by earlier plate convergence (Dewey, 1988; Lister and Davis, 1989; England and Houseman, 1989). These authors argue for a tectonically controlled process (such as orogenic collapse) where exhumation was initiated when lateral buoyancy forces exceeded horizontal driven forces. This gives rise to rapid exhumation with similar cooling ages throughout the orogeny and P-T loops that include isothermal decompression. This scenario had been suggested by Blasband et al., (2000). On the other hand, Fritz et al., (2002) suggested another mechanism deriving exhumation in the Central Eastern Desert, whereas exhumation was a long-term process that covered a time span of tens of millions of years continuously accompanied by magmatic activity. Field observation of the exhumed El-Shush migmatite core complex shows a gradational changing from the migmatite to gneissic tonalite and granodiorite. These evidences directly agree with the scenario of Fritz et al., (2002) that the exhumation of ElShush migmatites took place at long term process of time span such as another parts in the Eastern Desert. Generally, the exhumed migmatites of El-Shush Shear zone occurring as elongated NW-SE belt discordantly cut by the late orogenic granites of the Um 44


Shaddad and Abu Mrarkhat granites of crystallization age ranging between 650 and 640 Ma ( Fritz et al., 2002) defining a second stage of extension. It may cause NW- SE elongation of the basement outcrops, such as Um Shaddad granitic pluton (Abdeen, 2003), on one hand and it may show magmatic activity together with strikeslip tectonics on other hand. The occurrence of the highly sheared granite at the juxtaposition between the lower and upper crustal rock units may show that the sinistral strike-slip NW-SE oriented stretching combined as transpression-extrusion regime (Teyssier and Tikoff, 1999), whereas bulk convergence is balanced by both vertical thickening and horizontal NW-SE directed extension. The amount of vertical thickening may easily be compensated by stretch of parallel shearing (sheared granite) to NW-SE Najd Fault System. 3.2.2. Um Laseifa fault This fault runs along Wadi Um Laseifa with wide brecciated shear zone (Fig. 3.8). It extends NW-SE, bounded by Gebel Nusla from the western side, as well as it directly affects the synformal structures constructed by the action of the early compressional phase (D1).

44


3.2.3. Kab Ahmed fault This fault is NW-SE oblique-slip fault with about 80o dip to NE direction accompanied by some kinematic features such as (CS) fabric and slickensides indicating the sinistral movement combined by vertical component (Fig. 3.9a & b). It runs along Wadi Kab Ahmed separating the highly schistose rocks from the ironbearing metavolcanoclastic formation. 3.2.4. El-Mirifiya fault This fault is another NW-SE oblique-slip fault dipping 550 to SW. it runs along Wadi El-Mirifiya separating the highly sheared granite and gneissic tonalite. It has plane surface characterized by slickensides indicating the sinistral and vertical component movement (Fig. 3.10).

41


Fig. 3.8: The brecciated rocks along Um Laseifa fault zone.

Fig. 3.9a: Kab Ahmed fault plane with highly foliated shear zone. Fig. 3.9b: Slickensides in Kab Ahmed fault surface.

44


Fig. 3.10: El-Mirifiya fault surface showing sub horizontal slickensides of sinistral sense displacement.

3.3. Late Extensional Phase (D3) A number of geologic features, namely dykes, sedimentary basins and post-orogenic A-type granites were interpreted to have been formed in an extending crust (Blasband et al., 2000). These features were formed at the same time or slightly after the NW-SE shear zones of the core complex. In the mapped area two main trends of faults are related to the late extensional phase (D3). These are N-S and NW-SE reactivated faults. These two trends of (D3) are responsible for NS striking of bimodal mafic and felsic dykes along Kadabora granite pluton (Fig. 2.1), and the alignment of some granitic plutons such 44


as Um Shaddad granite (Fig. 3.11). Moreover, due the displacement of this N-S sinistral movement, particularly along Wadi Um Gheig, the movement was accompanied by the late compression action (D4) resulted as folded dykes (F2) and refolding of (F1) folds.

Fig. 3.11: Um Shaddad granite pluton intruded along the reactivated NW-SE sinistral fault system.

44


3.4. Late Compressional Phase (D4) This phase represents the later phase of deformation (D4) which caused the NW-SE upright open folding of some dykes (Fig. 3.12) as well as the refolding of (F1) folds. The late compressional phase most probably affected the area just after the complete cooling of the dykes, as the result of the later N-S sinistral extensional movement (Fig. 2.1). The refolding of (F1) folds is observed by some tight antiformal and synformal structures observed often in the core of the major folds and represented also by highly schistose rocks that were changed to NW-SE direction coinciding with the fold axes trend (Fig. 3.13).

Fig. 3.12: NW upright open folds affected the felsic dykes and the late orogenic granite.

44


Fig. 3.13: Density contour of the poles of the foliation planes indicating NWSE folds.

3.5. Fractures and Joints Joints are opening-mode fractures formed as a consequence of the deformation of brittle rock masses in Earth's crust (Pollard and Aydin, 1988). They are sensitive indicators of the paleo-stress field and can be used to infer the orientation of the regional stress field along the temporal spatial evolution (Dyer, 1988). The conditions of the joints in the various sets can vary greatly depending on their mode of origin and the type of rocks in which they occur. In the studied tonalite, two sets of joints perpendicular to each other (orthogonal joint sets) are well preserved (Fig. 3.14). In the other rock types, three sets are recorded and represented by NESW, NW-SE and E-W directions, revealing that all these trends are 44


coinciding with the main trends of structural features affecting the area under study.

Fig. 3.14: Orthogonal tensile joint system in the tonalite indicating extension forces along NW-SE direction.

These four deformational phases and associated tectonic events as well as their ages are given in Table (3.1). Figure (3.15) shows the suggested model for the study area that represents a part of the Eastern Desert of Egypt and the Arabian Nubian Sheild (ANS).

45


Table 3.1: Structural features, events and ages of the deformed phases affecting the study area. The ages are given from some available radiometric dates (Bregar et al., 2002 and Johnson et al., 2011). Deformation phase

Structure feature

Event

Early compressional 1-early foliation (S1). phase (D1). 2-Thrusting. 3-Early folding (F1).

age

Arc- arc accretion accompanied 750-660 Ma. by the first metamorphic event (M1).

Early extensional phase (D2).

1-NW-SE sinistral strike-slip Partial melting of the island arc 660-630 Ma. faults of Najd Fault System. roots and intrusion of tonalite 2-Exhumation of migmatites. and granodiorite accompanied by the second metamorphic event (M2).

Late extensional phase (D3).

1-N-S extensional faults. 2-NW-SE reactivated faults.

Intrusion of late to post orogenic granite, younger gabbro and dykes.

630-570 Ma.

Late compressional phase (D4).

NW-SE upright open folds.

Refolding of (F1) and folding some dykes.

570-540 Ma.

44


Fig. 3.15: A cartoon display the different stages of the evolution of the Arabian Nubian Shield (modified after Blasband, 2000) with special reference to Wadi Um Gheig area. (1) The oceanic phase showing the island arc development in the Mozambique Ocean. (2) Multiple arc-accretion stage leading to the early compressional phase (D1) and first stage of metamorphism (M1). (3) The early extensional phase (D2) showing the exhumation of Wadi El-Shush migmatites along Najd Fault System and the second stage of metamorphism (M2). (4) The late extensional phase (D3) associated with the intrusion of the late to post magmatic rocks. (5) The late compressional phase (D4) associated with the refolding of (F1) folds and some dykes. 44

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________

CHAPTER IV REMOTE SENSING INTERPRETATION 4.1. Introduction Remote sensing is the science of acquiring information from an object without actually being in contact with it (Sabins, 1997), through the link of electromagnetic energy (Hunt, 1980). Landgrebe, (1978) defined the remote sensing as "the science of deriving information from an object from measurements made at a distance from the object," it means obtaining informations about an object without touching the object itself (Gupta, 1991). This is done by sensing and recording reflected or emitted energy and processing, analyzing and applying that information. Therefore, the term remote sensing is practically used to mean data acquisition of electromagnetic radiation (reflectance or emittance) from sensors on board of an aerial or space platform, as well as the interpretation of acquired data for deciphering ground object characteristics. This Electromagnetic Spectrum (EMS) commonly ranges from gamma-rays to microwave radiation (Drury, 1993). Remote sensing is a technology of determining characteristics of a distinct object from electromagnetic waves emanating from and reflecting from the object, augments the sensory perception and 63


archival capabilities of the field observer in at least one and sometimes all of those four ways (Vincent, 1997). The main basic principle of remote sensing methods is that each object reflects or emits a certain intensity of light in various wave length ranges of the EMS; this is dependent upon the physical and/or the compositional characters of the object. This reflected or emitted radiation represents the spectral fingerprint or what so called the spectral signature of the corresponding object. Thus, by using the spectral curve's information, it can differentiate between rock types of objects. This is the basis for discrimination of various objects from their spectral behavior (Gupta, 1991 and List, 1992-a). The process of remote sensing involves an interaction between incident radiation and the targets of interest. This is exemplified using imaging systems where the following seven elements are involved; however, that remote sensing also involves the sensing of emitted energy and the use of non-imaging sensors (Fig 4.1). 1. Energy Source or Illumination (A) - the first requirement for remote sensing is to have an energy source which illuminates or provides electromagnetic energy to the target of interest. 2. Radiation and the Atmosphere (B) - as the energy travels from its source to the target, it will come in contact with and interact in

64


the atmosphere it passes through. This interaction may take place a second time as the energy travels from the target to the sensor. 3. Interaction with the Target (C) - once the energy makes its way to the target through the atmosphere; it interacts with the target depending upon the properties of both the target and the radiation. 4. Recording of Energy by the Sensor (D) - after the energy has been scattered by, or emitted from the target, we require a sensor (remote - not in contact with the target) to collect and record the electromagnetic radiation.

Fig.4.1: Simplified scheme showing the main remote sensing elements.

5. Transmission, Reception, and Processing (E) - the energy recorded by the sensor has to be transmitted, often in an electronic form, to a receiving and processing station where the data are processed into an image (hardcopy and/or digital).

65


6. Interpretation and Analysis (F) - the processed image is interpreted, visually and/or digitally or electronically, to extract information about the target which was illuminated. 7. Application (G) - the final element of the remote-sensing process is achieved when we apply the information we have been able to extract from the imagery about the target in order to understand it, reveal some new information, or assist in solving a particular problem. These seven elements comprise the remote-sensing process from beginning to end. 4.1.1. The Electromagnetic Radiation Spectrum (ERS) The electromagnetic spectrum ranges from the shorter wavelengths (including gamma and X-rays) to the longer wavelengths (including microwaves and broadcast radio waves). There are several regions of EMS, which are useful for remote sensing (Fig 4.2). The sensors in remote-sensing platforms usually record electromagnetic radiation. Electromagnetic radiation (EMR) is energy transmitted through space in the form of electric and magnetic waves (Star and Estes, 1990). Remote sensors are made up of detectors that record specific wavelengths of the electromagnetic spectrum. EMR is the range of electromagnetic 66


radiation extending from cosmic waves to radio waves (Jensen 1996). All types of land cover (rock type, water bodies, etc.) absorb a portion of the electromagnetic spectrum, giving a distinguishable signature of electromagnetic radiation. Armed with the knowledge of which wavelengths are absorbed by certain features and the intensity of the reflectance, you can analyze a remotely sensed image and make fairly accurate assumptions about the scene. (Suite, 1983; Star and Estes, 1990) 4.1.2. Satellites and Sensors Most of the remote sensing satellite systems operate within the optical spectrum which extends from approximately 0.3 to 14µm. This range includes ultraviolet, visible, near, mid, and farinfrared wavelengths. Since the launch of the Spaceborne Landsat Thematic Mapper satellites, new techniques and methodologies have been developed for lithological mapping. The extraction of spectral information related to this type of target from different satellite sensors, such as; Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery, has been achieved. The use of image processing techniques, such as; band ratio and principal component analysis (PCA) (Sabins, 1999) have been approached.

67


Fig.4.2: Diagrams showing the electromagnetic spectrum ranges, where: (a): Range of ultraviolet, (b): Range of visible spectrum, (c): Range of infrared and (d): Range of microwaves.

68


The launch of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) in December 1999 provided higher spectral resolution data that enabled space-based mineral exploration, particularly for areas with little or no access as well as areas of lack detailed geologic base maps (Di Tommaso & Rubinstein, 2007). The ASTER data consists of 14 spectral channels that cover the visible near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) regions of the electromagnetic. The VNIR, SWIR and TIR bands have spatial resolution of 15, 30 and 90 m respectively (Fig 3.3). ASTER channels are more contiguous in the short wave infrared region than those of the Landsat (Zhang et al., 2007), which increase its accuracy in the spectral identification of rocks and minerals (Crosta & Filho, 2003).

Fig: 4.3: Comparison of spectral bands between Landsat-7 ETM and ASTER. 69


Landsat ETM+ and ASTER are common multispectral remote sensors, which are being used to acquire Earth's surface information, especially in arid and semi-arid areas. In this study various ASTER and Landsat wavelength regions have been used as a complement for lithological mapping. Both ASTER and Landsat ETM+ scene, were projected to the same projection system, UTM zone 36N and WGS84 datum. The radiometric characteristics of both ASTER and Landsat ETM are generally summarized in Table (4.1). Table 4.1: Radiometric characteristics of ASTER and Landsat ETM data. Region of

Spatial

Spectrum

Resolution

VNIR

15 m

Spectral range (µm)

TIR

30 m

90 m

Landsa

Bands

t Bands

Spectral Range (µm)

1

0.45-0.52

0.52-0.6

1

2

0.52-0.60

0.63-0.69

2

3

0.63-0.69

3N

4

0.76-0.90

3b

8

0.52-0.90

1.60-1.70

4

5

1.55-1.75

2.145-2.185

5

2.185-2.225

6

7

2.08-2.35

2.235-2.285

7

2.295-2.365

8

2.360-2.430

9

8.125-8.475

10

8.475-8.825

11

8.925-9.275

12

10.25-10.95

13

10.95-11.65

14

0.76-0.86

SWIR

ASTER

70

Spatial Resolution

30 m

15 m

30 m

6

10.4012.50

60 m


4.1.3. Methodology The remote sensing methods which used in the present study are mainly depending on Landsat ETM+ and ASTER images. Different processes had been done for them such as; various band selection techniques, color ratio, Principal Component Analysis (PCA) and the different classification techniques. The previous analysis make it easy for discrimination of the different lithological rock types which are represented in the study area. Finally, after we make all the available processes, we can produce the so called geosensing map for the study area (Fig. 4.4).

Fig: 4.4: Methodology flow chart.

71


On the basis of the path/row coordinate system, Earth Resources Observation System (EROS) data center archived all the Landsat satellite images (scenes) in a global indexing and referencing system named Landsat Worldwide Reference System (WRS). Accordingly, the (WRS) consists of a global network of 233 paths and 119 rows.

The path and row intersections correspond to geographic locations over which Landsat scenes are generally centered. These locations are identified by three-digit path and row numbers, and when combined to identify a nominal scene center. The study area is coordinated by (path 174/row-042) with acquired date on September 10, 2000.

A cloud-free ASTER image recorded on 19 January 2012 1b level was used in this study. First, Visible Near-Infrared (VNIR) and Short Wave- Infrared (SWIR) radiance at the sensor data was normalized and converted to relative reflectance; digital numerical (DN) data were converted to relative reflectance. We also consider that the image does not suffer atmospheric influences because study area is located in a dry, sparse vegetated area. The input radiance parameters of ASTER instrument constrain the radiance values to a reflectance of 70% to avoid signal saturation over bright targets. VNIR 15 m resolution spatially resample to 30 m of SWIR spatial 72


resolution to match the spatial resolution of the SWIR bands, thus forming a nine-band image data set VNIR+SWIR in 30 m spatial resolution. 4.2. Preprocessing Techniques Most satellite images are in the form of digital data. Digital image is therefore stored on a series of (DNs); each one is representing the intensity of EMR energy measured for a particular ground resolution cell (pixel). Each pixel covers one of the small equal areas of terrain called picture element, whose size is determined by the spatial resolution of the used imagery system. Digital numbers (DNs) range from 0 to 255 in the gray-scale intensity values (Sabins, 1997).

Therefore, because the image in the digital format exists as a simple array of numbers within the computer, various mathematical procedures can be performed to make the data more usable. These computer's

mathematical

techniques

of

manipulation

and

interpretation of the digital image is called a ''digital image processing'' (Lillesand and Kiefer, 1994). Any analog images such as photographs or maps can be converted into a computer compatible digital format by a digitizing system.

73


4.2.1. Calibrated Landsat Digital Number (DN) to Top of Atmosphere (TOA) Reflectance Conversion Firstly, Converting the Calibrated DN to Spectral Radiance using the following equation Lƛ= ((LMAXƛ- LMINƛ)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMINƛ

Where: Lƛ = spectral radiance at the sensor’s aperture QCAL = the quantized calibrated pixel value in DN LMINƛ = the spectral radiance scaled to QCALMIN in watts/(meter

squared * ster * μm) LMAXƛ = the spectral radiance scaled to QCALMAX in watts / (meter

squared * ster * μm) QCALMIN = the minimum quantized calibrated pixel value

(corresponding to LMINλ) in DN1 for LPGS products, 0 for NLAPS products QCALMAX = the maximum quantized calibrated pixel value

(corresponding to LMAXλ) in DN = 255 Then Converting Spectral Radiance to (TOA) Reflectance P= π* Lλ* d2/ ESUNλ* cos () S 74


Where: P = unitless TOA or planetary reflectance Lλ = spectral radiance at the sensor’s aperture d = Earth-Sun distance in astronomical units from nautical handbook or interpolated values. ESUNλ = mean solar exoatmospheric spectral irradiance Cos (ϑ) S = solar zenith angle in degrees

4.2.2. Calibrated ASTER Digital Number (DN) to Top of Atmosphere (TOA) Reflectance Conversion Same equation is used in Conversion from DN values to reflectance values for ASTER images. The calculation of ESUN is the same for whatever sensor you are using, as it is simply the convolution of the band's spectral response function with the Extraterrestrial Solar Spectral Irradiance function. Using this standard approach the calculated ESUN for each ASTER band is given in (Table 4.2)

75

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.2: ASTER Solar Spectral Irradiances for each band after Smith (2007).

Band#

Smith: ESUN

1 2 3N, 3B

1845.99 1555.74 1119.47 231.25 79.81 74.99 68.66 59.74 56.92

4 5 6 7 8 9

4.2.3. Geometric correction (image rectification) Geometric corrections include correcting for geometrical distortions due to sensor-Earth geometry variations, and conversion of the data to the real world coordinates (e.g. latitude and longitude) on the Earth's surface. Sources of distortions are: Variation in the altitude and velocity of the sensor platform, Earth curvature, atmospheric refraction, relief displacement and nonlinearities in the sweep of a sensor’s IFOV. Conversion of the data to the real world coordinates are carried out by analyzing well distributed Ground Control Points (GCPs). This is done in two steps.

76


A- Georeferencing: This step involves the calculation of the appropriate transformation from image to terrain coordinates (Fig. 4.5).

Fig.4.5: Diagram showing the georeferencing.

B-Geocoding: This step involves resambling the original image to obtain a new image corrected in which all pixels are correctly positioned within the terrain coordinate system. Resampling is used to determine the digital values to place in the new pixel locations of the corrected output image. The resampling process calculates the new pixel values from the original values in the uncorrected image. There are three common methods

for

resampling

(Nearest

Neighborhood,

Bilinear

Interpolation and Cubic Convolution). All the datasets have been rectified to common UTM (Universal

Transverse

Mercator)

WGS84

based

on

the

topographical map and by using GPS points and resampled using 77


the nearest neighbor algorithms. Nearest neighbor resampling uses digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the simplest method and does not alter the original values, but may result in some pixel values being duplicated while others are lost. This method also tends to result in a disjointed or blocky image appearance (Fig 4.6).

Fig.4.6: Diagram showing the nearest neighbor resampling.

4.3. Digital Image Processing's and Their Interpretations Multispectral remote-sensing has been successfully used for lithological and mineral mapping, especially with the development of the sensors and mathematical algorithms that provided detailed information of the mineralogy of the different rock types comprising the Earth’s surface (Zhang et al., 2007). Since the launch of the Spaceborne Landsat Thematic Mapper (TM) satellites, new techniques and methodologies have been developed 78


for mapping in arid regions using multispectral remote sensing (e.g. Sultan & Arvidson, 1986; Sabins, 1997 & 1999; Abdelsalam et al., 2000; Ramadan et al. 2001; Kusky & Ramadan, 2002; Liu et al., 2007). Spectral discrimination of different rock units is a common application of remote sensing (e.g. Abdelsalam et al., 2000; Zhang et al., 2007). Many authors have studied the extraction of spectral information related to differentiate between rock units (mapping) from various satellite sensors such as Landsat TM, Landsat Enhanced Thematic Mapper Plus (ETM+) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The use of different image processing techniques such as band rationing and principal component analysis (PCA) were reported by several authors (e.g. Crósta & Moore, 1989; Loughlin, 1991; Rokos et al., 2000; Ferrier et al., 2002; Crósta & Filho, 2003; Zhang et al., 2007). The launch of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) in December 1999 provided higher spectral resolution data that enabled better identification for rock units and structures, particularly for areas with poor background information (Di Tommaso and Rubinstein, 2007). ASTER data consists of 14 data channels that cover ranges of visible, near infrared (VNIR), shortwave infrared (SWIR) and 79


thermal infrared (TIR) regions of the electromagnetic spectrum. It provides higher spatial, spectral and radiometric resolutions than traditional Landsat data (Abrams & Hook, 2001). Each ASTER scene covers an area of 60×60 km2. VNIR bands have a spatial resolution of 15 m, the SWIR bands have a spatial resolution of 30 m, while the TIR bands have a spatial resolution of 90 m. Since the ASTER channels are more contiguous in the short wave infrared region than those of the Landsat (Zhang et al., 2007), which increase their accuracy in the spectral identification of rocks and minerals (Crosta & Filho, 2003), they provide a better understanding of the geology and soils of the earth surface. This has made ASTER data superior to other sensors for lithological mapping (Zhang & Pazner, 2007; Zhang et al., 2007; Gad & Kusky, 2007; Raharimahefa & Kusky, 2009). Image analysis techniques such as the band ratio are based on the spectral characteristics of surface types. Such expression demonstrates less variability over the spectral ranges in contrast to other land surface types. Most of the band ratio techniques are constructed by using laboratory measured spectral profiles. However, laboratory measured spectra (e.g., USGS spectral library) do not precisely represent the actual field spectrum due to many uncontrolled factors that make field conditions different from laboratory measurement. In addition, spectral unmixing usually

80


requires detailed spectral profiles of each element in a mixed pixel, and this becomes the bottle-neck, particularly for the use of middlescale satellite data. Therefore, developing techniques and/or methodologies entirely constructed on the image spectra can provide a simple yet efficient tool for feature's extraction. We carried out field data comparing ground-based geological mapping results with the spectral signature of the same areas using specially processed Landsat ETM+ and ASTER images. The unique perspective offered by comparing the processed ETM+, and ASTER imagery with the high-resolution field mapping provides a unique opportunity to test the utility of ETM+ and ASTER data to differentiate between rock units and produce more detailed and accurate maps. 4.3.1. Color Combination Images and Band Selections A particular aspect of remote sensing is that: it provides data in multiple spectral bands (Gupta, 1991). Landsat ETM+ and ASTER data contain a wide range of spectrally divers' data. To display a color image only three bands are required in a band combination, each directed to one of the color-guns Red, Green and Blue (RGB). The best band combinations are that enhances a desired target and includes the most informative bands with less redundancy of information contained in these bands and that has fewer inter-correlated bands. 81


Selection of appropriate triple color composite images was performed in two different ways; the correlation coefficient method and the Optimum Index Factor (OIF) method. The less correlated bands are good for image visualization in RGB color space (Drury, 1993). 4.3.1.1. Correlation coefficient method The correlation coefficient was calculated to the reflective bands of Landsat ETM+ data (Table 4.3). Band1 has shown fewer correlations with the other bands followed by band7 then band4 and band5. Therefore, the RGB images 7, 4, 1 and 7, 5, 1 are selected for better visual interpretation of the area. The correlation coefficient calculated for all the band-triplet possibilities and ordered to get the best combination (table 4.4).

Table 4.3: Correlation coefficient of Landsat ETM+ data in the study area. Band 1

Band 2

Band 3

Band 4

Band 5

Band 1

1

Band 2

0.9723

1

Band 3

0.9349

0.9781

1

Band 4

0.9082

0.9613

0.9908

1

Band 5

0.8651

0.9117

0.9252

0.9344

1

Band 7

0.8515

0.8964

0.9114

0.9196

0.9633

82

Band 7

1

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.4: Results of the Correlation coefficient method for best band triplet selection of Landsat ETM+ data in the study area. Rank

band-

Correlation

triplet

Coefficient

Rank

band-

Correlation

triplet

Coefficient

1

1,4,7

2.6794

11

3,5,7

2.7999

2

1,5,7

2.6799

12

2,4,5

2.8074

3

1,3,7

2.6979

13

2,3,5

2.8150

4

1,4,5

2.7078

14

4,5,7

2.8173

5

1,2,7

2.7202

15

3,4,7

2.8219

6

1,3,5

2.7252

16

1,3,4

2.8339

7

1,2,5

2.7491

17

1,2,4

2.8418

8

2,5,7

2.7713

18

3,4,5

2.8505

9

2,4,7

2.7773

19

1,2,3

2.8853

10

2,3,7

2.7860

20

2,3,4

2.9302

Similarly, Correlation coefficient analysis was applied to the nine reflective ASTER VNIR/SWIR bands after resampling the SWIR bands to the same resolution with VNIR bands (15m). A positive correlation has observed between the nine ASTER VNIR-SWIR bands (Table 4.5). But band4, band9 and band8 are relatively fewer correlations with the other reflective bands. The correlation

coefficient calculated

for all the band-triplet

possibilities are ordered from lesser to higher (Table 4.6).

83

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.5: Correlation coefficient of ASTER VNIR-SWIR data of the study area. Band1

Band2

Band3

Band4

Band5

Band6

Band7

Band8

Band1

1

Band2

0.9810

Band3

0.9656 0.9924

Band4

0.9835 0.9621 0.9477

Band5

0.9865 0.9534 0.9317 0.9602

Band6

0.9730 0.9469 0.9256 0.9235 0.9755

Band7

0.9771 0.9719 0.9611 0.9292 0.9619 0.9727

Band8

0.9472 0.9546 0.9484 0.8842 0.9251 0.9551 0.9855

Band9

0.9446 0.9315 0.9165 0.8814 0.9435 0.9566 0.9669 0.9671

Band9

1 1 1 1 1 1 1

4.3.1.2. Optimum Index Factor (OIF) Selection of better color composite for visual interpretation of images was also made by the statistical approach, Optimum Index Factor. OIF approach takes into account the variance within bands and the correlation coefficient between bands (Benomar & Fuling, 2005). The individual bands were enhanced separately before the technique was applied. OIF accounted for the Landsat ETM+ data and ordered from the highest value to the lowest (Table 4.7). The OIF technique also was applied to ASTER VNIR-SWIR bands (Table 4.8). Triplets with higher values of OIF were used for better extraction of lithological information since they use bands with

highest

variance

and least

Basavarajappa, 2008). 84

redundancy (Qaid

and

1

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.6: Results of the correlation coefficient method for best band triplet selection of ASTER VNIR-SWIR data in the study area. Rank

band-triplet

Correlation Coefficient

Rank

band-triplet

Correlation Coefficient

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

4,8,9 3,4,9 4,6,9 4,6,8 4,5,8 2,4,9 4,7,9 3,4,8 4,5,9 3,5,9 3,4,6 3,6,9 4,7,8 2,4,8 3,5,8 1,4,9 1,4,8 4,6,7 1,3,9 2,5,9 3,6,8 3,8,9 2,4,6 3,5,6 2,5,8 2,6,9 5,8,9 3,4,7 3,4,5 2,3,9 3,7,9 4,5,7 2,8,9 3,5,7 5,6,8 2,6,8 1,2,9 1,5,8 1,8,9 4,5,6 3,6,7 1,3,8

2.7327 2.7457 2.7615 2.7628 2.7696 2.7749 2.7775 2.7804 2.7850 2.7917 2.7969 2.7988 2.7989 2.8010 2.8052 2.8095 2.8149 2.8254 2.8267 2.8283 2.8291 2.8321 2.8325 2.8328 2.8332 2.8350 2.8357 2.8380 2.8396 2.8404 2.8445 2.8512 2.8532 2.8546 2.8557 2.8566 2.8571 2.8588 2.8589 2.8591 2.8594 2.8611

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84

2,4,7 1,3,6 2,3,6 2,7,9 5,7,9 5,7,8 1,6,9 1,5,9 1,6,8 5,6,9 2,4,5 2,5,6 2,3,5 6,8,9 1,4,6 1,2,8 1,3,5 2,5,7 1,7,9 1,4,7 2,6,7 3,7,8 2,3,8 6,7,9 1,3,4 1,2,6 2,3,4 1,3,7 1,7,8 5,6,7 2,7,8 6,7,8 7,8,9 1,2,5 1,6,7 2,3,7 1,5,7 1,2,4 1,2,7 1,4,5 1,5,6 1,2,3

2.8631 2.8642 2.8650 2.8702 2.8723 2.8725 2.8742 2.8746 2.8753 2.8755 2.8757 2.8758 2.8775 2.8788 2.8800 2.8827 2.8837 2.8872 2.8887 2.8898 2.8915 2.8950 2.8954 2.8962 2.8968 2.9009 2.9023 2.9038 2.9098 2.9101 2.9120 2.9133 2.9195 2.9209 2.9228 2.9254 2.9255 2.9266 2.9300 2.9302 2.9350 2.9389

85

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.7: OIF ranking of Landsat ETM+ data in the study area. Rank band-triplet O.I.F. Rank band-triplet O.I.F. 1 4,5,7 0.04266 11 1,4,7 0.03138 2 3,5,7 0.04059 12 1,3,5 0.03115 3 3,4,5 0.03820 13 2,3,7 0.03061 4 2,5,7 0.03752 14 1,3,7 0.02873 5 3,4,7 0.03597 15 2,3,4 0.02747 6 1,5,7 0.03590 16 1,2,5 0.02736 7 2,4,5 0.03534 17 1,3,4 0.02567 8 1,4,5 0.03377 18 1,2,7 0.02494 9 2,4,7 0.03306 19 1,2,4 0.02220 10 2,3,5 0.03291 20 1,2,3 0.01959

The highest band-triplet ranking by using correlation coefficient method and Optimum Index Factor OIF were inspected and compared visually (Table 4.9). These Landsat color composite images (Fig 4.7a, b & c) were used for better extraction of lithological information. As well as the ASTER VNIR-SWIR colored composite images also (Fig 4.8 a, b & c).

86

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.8: OIF ranking of ASTER VNIR-SWIR data in the study area. Rank

band-triplet

O.I.F.

Rank

band-triplet

O.I.F.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

1,2,3 1,2,4 2,3,4 1,2,6 1,2,9 1,2,8 1,2,5 1,2,7 2,3,6 2,3,5 2,3,9 2,3,8 2,3,7 1,3,4 1,3,6 1,3,9 1,3,5 1,3,8 1,3,7 2,4,6 2,4,8 2,4,9 2,4,7 2,4,5 2,5,8 2,5,6 2,6,8 2,5,7 2,6,7 2,5,9 2,6,9 2,7,8 2,7,9 2,8,9 1,4,8 1,4,6 1,4,9 1,4,7 3,4,6 1,4,5 3,4,8 3,4,9

0.001514 0.001179 0.001160 0.001109 0.001107 0.001107 0.001101 0.001096 0.001094 0.001088 0.001084 0.001073 0.001069 0.001050 0.000980 0.000974 0.000973 0.000972 0.000965 0.000753 0.000752 0.000749 0.000743 0.000741 0.000660 0.000660 0.000655 0.000655 0.000655 0.000652 0.000651 0.000641 0.000641 0.000637 0.000632 0.000627 0.000624 0.000624 0.000616 0.000616 0.000610 0.000608

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84

3,4,5 3,4,7 1,5,8 1,6,8 1,6,7 1,5,7 1,5,6 1,7,8 1,6,9 1,5,9 3,5,6 1,7,9 1,8,9 3,5,8 3,5,7 3,6,7 3,6,8 3,5,9 3,6,9 3,7,8 3,7,9 3,8,9 4,6,7 4,5,6 4,5,7 4,6,8 4,5,8 4,7,8 4,6,9 4,5,9 4,7,9 4,8,9 5,6,7 5,6,8 5,7,8 6,7,8 5,6,9 6,7,9 5,7,9 5,8,9 6,8,9 7,8,9

0.000606 0.000605 0.000540 0.000538 0.000536 0.000535 0.000535 0.000530 0.000528 0.000528 0.000525 0.000524 0.000522 0.000520 0.000519 0.000519 0.000517 0.000513 0.000513 0.000503 0.000503 0.000497 0.000171 0.000170 0.000169 0.000167 0.000166 0.000163 0.000158 0.000155 0.000155 0.000149 0.000085 0.000079 0.000077 0.000077 0.000069 0.000068 0.000068 0.000061 0.000060 0.000058

87

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.9: Results of visual inspection for best band-triplets selection of Landsat ETM+ and ASTER VNIR-SWIR data.

Visual inspection (RGB) band-triplet Rank Landsat color composite

ASTER color composite

1

ETM 751

ASTER 964

2

ETM 741

ASTER 463

3

ETM 543

ASTER 984

4

ETM 752

ASTER 942

5

ETM 541

ASTER 843

6

ETM 731

ASTER 854

Fig.4.7 (a): Landsat ETM 751color composite image in (RGB). 88


Fig.4.7 (b): Landsat ETM 741color composite image in (RGB).

Fig.4.7 (c): Landsat ETM 543 color composite image in (RGB). 89


Fig.4.8 (a): ASTER 964 color composite image in (RGB).

Fig.4.8 (b): ASTER 463 color composite image in (RGB). 90


Fig.4.8 (c): ASTER 984 color composite image in (RGB).

4.3.2. Color Ratio Composite (CRC) Spectral rationing is a multispectral image processing method that involves the division of one band by another. The bands employed for a ratio image are usually selected such that one spectral band is inside, and the other is outside a wavelength region of spectral reflectance minimum or maximum of a particular target. Ratios are attractive because they enhance compositional information, while suppressing other types of information about Earth's surface, such as terrain slope and grain size differences. There are several desirable traits of spectral ratios;

91


1. Spectral ratios are easily related to reflectance spectra of terrain features, as measured by laboratory and field spectrometers. It is easier to predict a ratio from laboratory or field reflectance spectra. Thus, spectral ratio images are simpler to interpret for compositional information about materials on Earth's surface. 2. Spectral ratios are more robust because all illuminations, atmospheric, and electronic gain has no effects. In a ratio image, a particular rock or soil will appear the same whether it is on a hillside facing the sun or facing away from the sun, because a ratio suppresses brightness effects. 3. Spectral ratios tend to separate the effects of grain size and chemical composition in rocks, soils, and minerals by suppressing brightness differences. Spectral ratios are best adapted for the detection of absorption bands because they compare reflectance at one wavelength with reflectance at another wavelength. In fact, the greatest utility of ratio imaging can be attained for mapping a particular target by selecting a ratio of bands that are located inside and outside one or more absorption bands of the target material. This choice enhances the effect of chemical composition on the final spectral ratio image, while suppressing effects of grain size, topographic slope, sun position, and atmospheric state.

92


A simple way of trying to extract useful information from multispectral images is to perform band rationing. Band ratios describe the color to an object, although this color only corresponds to human perception when the three visible bands are considered. Ratio images are prepared by dividing the DN in another band for each pixel, stretching the resulting value, and plotting the new value as an image (Sabins, 1997). Ratioing is an effective method for distinguishing among rock types because the main differences in the visible and nearinfrared spectral regions are found along the slopes of the reflectivity curves. The rationing process removes first-order brightness effects due to topographic slopes and enhances subtle color variations between materials (Abrams et al., 1984). Band ratio images are used to suppress the topographic variation, and the brightness difference related to graining size variation (Adam and Felic, 1967; Sultan et al., 1987). Band ratios have been used successfully in lithological mapping for the Arabian Nubian Shield and for other areas worldwide. Band selection for the different ratio images are used based on the spectral signature of these rocks.

93


4.3.2.1. Landsat Band Ratio Composite They have been used successfully in lithological mapping for the Arabian Nubian Shield and for other worldwide areas. Band selection for the different ratio images are used based on the spectral signature of represented rocks. When ratioing techniques are applied, all the reasonable grouping of minerals are best discriminated by a combination of ratios that include shortwavelength bands (i.e. 3/1, 4/1 or 4/2), the ratio of the longwavelength bands (5/ 7) and a ratio of one band each from short and long wavelength band groups (e.g. 5/4 or 5/3) (Crippen, 1989). The distinctive spectral reflectance of serpentinites (Fig 3.9) is caused by the abundance of antigorite, lizardite, clinopyroxenite and magnetite in the mineral composition. Comparison with the spectral reflectance of other rock units, e.g. granites and metavolcanics, is also given in Fig (4.9). In the current study, we applied band ratio image enhancement techniques, (e.g. Sultan & Arvidson, 1986; Sabins, 1999; Gad and Kusky, 2006). Landsat Band Ratio Composites (7/1, 3/1 and 5/7) (Fig 4.10) are used in the current study and proved to be very effective in the lithological discrimination showing clear and obvious contacts for the main rock units in the study area. In this ratio the serpentinites appear in a dark-blue color while the metavolcanics have a dark-

94


red color and the granites have orange to yellowish brown and the gneisses and migmatites with bright-red color.

Fig.4.9: Spectral reflectance of the serpentinites, granites and metavolcanics (Andesite and amphipolite) for the Eastern Desert, Egypt (After Frei and Jutz, 1989).

Comparison with the previously used RGB band ratio images (5/7, 5/1 and 5/4 * 3/4) which are used by Sultan & Arvidson (1986), while (3/5, 3/1 and 5/7) band ratio are used by Sabins (1999) (Figs. 4.11 and 4.12 respectively). In addition, the band ratios (5/3, 5/1 and 7/5) and (7/5, 5/4 and 3/1) are used by Gad and Kusky (2006) (Figs. 4.13 and 4.14) respectively. The above band ratio images are applied in the study area. In Sultan’s ratio, the 95


serpentinites appeared bright in color (which is assigned to red) owing to the band 7 absorption by MgO– and OH– bearing minerals, while in Sabin's ratio, the serpentinites appears in violet color. In Gad and Kusky band ratio image (5/3, 5/1, 7/5), the serpentinite appears in a dark brownish green color while the metavolcanics have a pinkish yellow color. Whereas in the band ratio image (7/5, 5/4, 3/1) the serpentinites shown as dark brownish color while the associated metavolcanics rocks appear yellowish green. 4.3.2.2 ASTER Band Ratio Composite They have been successfully used in geological mapping since early 2000 similar to Landsat TM and ETM, ASTER bandratio combinations and band math are effective in emphasizing spectral characteristics of certain rocks and minerals and hence are more effective in lithological mapping compared with the Landsat RGB band combination images (Okada and Ishii, 1993; Hewson et al., 2001; Bedell, 2001; Abdeen et al., 2001; Velosky et al., 2003; Rowan and Mars, 2003; Rowan et al., 2003) New ASTER band-ratio image (4/7, 4/ 6 and 4/9) (Fig 4.15). Shows that the different lithological units, and the contact between them can be identified. The rock units that are successfully mapped include gneisses, and migmatites have sharp contact in the image 96


with bright color, volcano-sedimentary, talc schist and granitic rocks, while the ultra-mafic in this band ratio doesn’t have clear contacts. Comparison with the previously used Landsat RGB band ratio images (4/7, 3/4 and 2/1) (Fig 4.16) which is used by Abdeen et al. (2001) to map the Neoproterozoic Allaqi suture, southeastern Egypt. The comparison shows that the gneisses and migmatites are not that well distinguished from adjacent metavolcanic or granites while the granite is recognized in Abdeen ratio better than our ratio. In the same way, we compared our newly adopted ASTER band-ratio image with the ASTER band-ratio image (7/6, 6/5 and 6/4) (Fig 4.17) that has been used by Wolter et al., (2005) to map the gneiss domes and granitic rocks in southern Tibet. Gneiss and migmatites in this ratio are correctly recognized by orange color, and the ultramafic serpentinites are having fairly obvious contact with light blue color while the metavolcanic, metavolcanosedimentary and granites are not well recognized. Furthermore, the comparison between the same ratio and ASTER band-ratio image (4/7, 4/1, 2/3*4/3) (Fig 4.18) which has been done by Abrams et al., (1983) granites are recognized by green to dark-green color with relatively clear contacts, gniesses and migmatites are not well defined but still can be mapped while the metavolcanics and ultramafics not easy to be mapped in this ratio. 97


Fig.4.10: Landsat RGB color ratio image (7/1, 3/1 and 5/7) (GS: El-Sibai granite, GN: Nusla granite, GU: Um Shaddad ganite, GK: Kadabora granite, BG: biotite granite, TO: tonalite, MG: migmatites MV: metavolcanics, MVS: metavolcanosedimentary, Sch: schists, UM: ultramafic, DI: Meta gabbro and GB: Fresh Gabbro).

98


Fig.4.11: Landsat RGB color ratio image (5/7, 5/1, 5/4 * 4/3) for the study area (Sultan & Arvidson, 1986). Symbols as in Fig.4.10.

Fig.4.12: Landsat RGB color ratio image (3/5, 3/1, 5/7) for the study area (Sabins, 1999). Symbols as in Fig.4.10. 99


Fig.4.13: Landsat RGB color ratio image (5/3, 5/1, 7/1) for the study area (Gad and Kusky, 2006). Symbols as in Fig.4.10.

Fig. 4.14: Landsat RGB color ratio image (7/5, 5/4, 3/1) for the study area (Gad and Kusky, 2006). Symbols as in Fig.4.10. 100


Fig. 4.15: ASTER RGB color ratio image (4/7, 6/4, 4/9) for the study area. Symbols as in Fig.4.10.

Fig.4.16: ASTER RGB color ratio image (4/7, 3/4, 2/1) for the study area (Abdeen et al., 2001). Symbols as in Fig.4.10. 101


Fig.4.17: ASTER RGB color ratio image (7/6, 6/5, 6/4) for the study area (Wolter et al., 2005). Symbols as in Fig.4.10.

Fig.4.18: ASTER RGB color ratio image (4/7, 4/1, 2/3*4/3) for the study area (Abrams et al., 1983). Symbols as in Fig.4.10. 102


4.3.3. Principal Component Analysis (PCA) Principal Component Analysis is a technique for reducing the correlation between variables and commonly produces images with separate geologic features. Because geologically important information might occupy only a small portion of the spectral range of a band that is otherwise highly correlated with other bands, it is possible that such information will be lost through one of these other bands being chosen instead. Data compression techniques, such as PCA, take advantage of the underlying minimum dimensionality of the data and offer the opportunity of displaying a greater proportion of the original variance in a single image. In Landsat data, about 80 to 90 percent of the scene variability can be accounted for by the first PC. This variable is usually an average over the values of the bands. The components can be combined into a color display by assigning blue, green and red colors to any triplet of the available variables (Drury, 1993). For any pixel in a multispectral image, the DN (digital number) values are commonly high correlated from a band to band. This correlation means that the reflectance of a pixel in one band is known, one can predict the reflectance in adjacent bands. Any principal component image can be combined to create a color image by assigning the data to make up each image to separate primary color. As a result, the color PC image displays a great deal of spectral variations in the vegetation

103


and rocks, while the three bands of the false-color composite image constitute only 10.7 percent of the variation of the original data set. The principal component transformation is an image enhancement. The display of information in image processing becomes a more difficult task when the number of bands exceeds three because there are only three primary colors in a color image display. Besides the combination of three spectral ratios into a color ratio image, there are principal components and image classification. Principal component is an image classification technique for displaying the maximum spectral contrast from nspectral bands with just three primary display colors (Vincent, 1997). PCA allows redundant data to be computed into fewer bands. The bands of PCA data are non-correlated and independent and are often more interpretable than the source data (Jensen, 1996). 4.3.3.1. Principal Component Analysis for Landsat ETM Bands The standard principal component analysis for Landsat ETM bands was done using ENVI 4.7 software for 6 ETM bands excluding the thermal band ETM6. The relative weight of the original bands in each component (Table 4.10) are explained as loadings that show correlations in the data original bands. High positive loadings, high negative loadings, and zero loadings

104


indicate positive correlation, negative correlation and lack of correlation in the data in the original bands, respectively. The PC1 explains 95.24% of the whole variance, while the PC2 accounts for 3.06 % of the total variance, while the first three principal components typically contain over 98% of the variance. A false-color composite formed by projecting the first three principal components PC1 in red, PC2 in green, and PC3 in blue (Fig 4.19) thus contains most of the variance for the 6 input bands. Principal Components (PC1, PC2 and PC4) in RGB respectively also have been used in this study (Fig 4.20) the two images showing very good discrimination between different rock units. Table 4.10: Principal component analysis on Landsat ETM+ bands.

Eigenvector

Band1

Band2

Band3

Band4

Band5

Band7

Variance%

PC1

-0.1219

-0.2243

-0.3492

-0.4343

-0.6054

-0.5078

95.24

PC2

0.1908

0.3152

0.4781

0.5001

-0.3734

-0.4962

3.06

PC3

-0.0323

-0.0487

-0.0890

-0.0475

0.7005

-0.7041

1.07

PC4

0.5901

0.5813

0.0150

-0.5579

0.0457

0.0139

0.5

PC5

-0.3804

-0.1139

0.7704

-0.4976

0.0347

-0.0040

0.08

PC6

0.6743

-0.7050

0.2189

-0.0147

0.0094

0.0004

0.05

105


Fig.4.19: PC Landsat ETM image (PC1 PC2 PC3 in RGB respectively) for the study area. Symbols as in Fig.4.10.

Fig.4.20: PC Landsat ETM image (PC1 PC2 PC4 in RGB respectively) for the study area. Symbols as in Fig.4.10. 106


4.3.3.2. Principal Component Analysis for ASTER data Similarly, Principal Component Analysis was done for nine bands of ASTER. The relative weights of the bands in each component (Table 4.11) are explained as loadings that show a correlation between original bands. The PC1 explains 98.71% of the total variance and has positive loadings in all bands. The PC1 Is generally weighted average of all data and represents topography and structure effects in the Scene. The first four principal components typically contain over 99.99 % of the variance. PC4 is a good representative to granites in the study area so PC1 and PC4 were used in the false-color composites. A false-color composite formed by projecting the first four principal components PC4 in red, PC1 in green, and PC2 in blue (Fig 4.21) thus contains most of the variance for the 9 input bands. Principal Component analysis (PC4, PC1 and PC3) in RGB respectively also were used in this study (Fig 4.22). The two images are showing very good discrimination between the different rock units in the study area.

107

CHAPTER IV REMOTE SENSING INTERPRETATION___________________________ Table 4.11: Principal Component Analysis on ASTER VNIR-SWIR bands.

Band1

Band2

Band3

Band4

Band5

Band6

Band7

Band8

Band9

Variance%

PC1

0.5345

0.6650

0.5043

0.1195

0.0310

0.0311

0.0301

0.0213

0.0113

98.71

PC2

-0.7694

0.2413

0.5520

-0.1908

-0.0691

-0.0588

-0.0205

0.0004

-0.0129

1.06

PC3

-0.2215

0.7029

-0.6615

-0.1370

-0.0040

0.0198

0.0037

0.0095

0.0067

0.17

PC4

0.1917

-0.0735

0.0506

-0.8664

0.0893

0.2636

0.2282

0.2396

0.1335

0.05

PC5

0.0358

-0.0047

-0.0307

0.0665

-0.5948

-0.4597

0.4316

0.4838

0.0888

0.1

PC6

0.0229

0.0046

-0.0029

-0.1158

0.6046

-0.7373

0.1205

-0.0932

0.2317

0

PC7

-0.0323

0.0005

-0.0010

0.0299

0.1657

0.0451

0.6584

-0.2144

-0.6994

0

PC8

0.0048

0.0007

0.0034

-0.0124

-0.2802

0.0879

0.3889

-0.7016

0.5197

0

PC9

-0.1835

0.0000

0.0000

0.4013

0.4013

0.4013

0.4013

0.4013

0.4013

0

108


Fig.4.21: PC ASTER image (PC4, PC1 and PC2 in RGB respectively) for the study area. Symbols as in Fig.4.10.

Fig.4.22: PC ASTER image (PC4, PC1 and PC3 in RGB respectively) for the study area. Symbols as in Fig.4.10. 109


4.3.4. Classification Classification is the process by which pixels have similar spectral characteristics and consequently, assumed to belong to the same class that can be identified and assigned a unique color. Multispectral classification is the process of sorting pixels into a finite number of individual classes or categories of data, based on their data file values. If a pixel satisfies a certain set of criteria, the pixel is assigned in the class that corresponds to those criteria (ERDAS, 1997). The most common classifiers operate on the basis of color alone, in the sense that they operate on the individual pixel values at each wavelength. Each pixel is assigned to a class, feature or cover type based on its own spectral properties, without any consideration of surrounding pixels. These per pixel classifiers can be divided into two main groups, the unsupervised and supervised classifiers. 4.3.4.1. Unsupervised Classification Unsupervised classification demands no prior knowledge of the image but effects a sub-division based on the intrinsic properties of the digital data. It is a technique that groups the pixels into clusters based on the distribution in the image. Unsupervised classification program may require the operator to specify a number 110


of classes, a maximum number of iterations and a threshold value. Unsupervised classification operates in an iterative fashion. Initially, the computer program assigns arbitrary means to the classes and allocates each pixel in the image to the class mean to which it is closest. New class means are then calculated, and each pixel is then again compared to the new class means. This procedure can be repeated as many times as the number of iteration's input to the program. However, pixels move between clusters following each iteration. Once the user defined threshold is reached, the program terminates even if the maximum number of iterations has not been reached. A threshold of 98 is meant that the program terminates when less than 2 percent of the pixels move between adjacent iterations. The classes produced from unsupervised classification are spectral classes and may not correlate exactly with information classes as determined by supervised classification. ENVI 4.7 software k-Means unsupervised classification was used. K-Means classification calculates initial class means evenly distributed throughout the data space then iteratively clusters the pixels into the nearest class using a minimum distance technique. Each iteration recalculates class means and reclassifies pixels with respect to the new means. All pixels are classified to the nearest class unless a standard deviation or distance threshold is specified, in which case some pixels may be unclassified if they do not meet the chosen criteria. This process continues until the number of 111


pixels in each class is changed by less than the selected pixel change threshold or the maximum number of iterations is reached. Thus unsupervised k-means classification is applied to the Landsat ETM data in the study area, where the number of classes is (8), the maximum number of iteration (10) and the threshold value is 95% (Fig 4.23). The same parameters were used as ASTER data for the study area (Fig 4.24)

Fig.4.23: Landsat unsupervised classification image for the study area. Symbols as in Fig.4.10.

112


Fig.4.24: ASTER unsupervised classification image for the study area. Symbols as in Fig.4.10.

4.3.4.2. Supervised Classification Supervised classification involves a considerable amount of input from the image analyst and knowledge about the types of surface that are found within the study area. This information can be obtained from maps or from field work where different surface classes are identified then entered into the software as Regions Of Interest (ROIs), information also can be obtained by individual spectra for every rock unit or by collecting endmembers. In the present study, supervised classification was applied depending on Endmembers, individual spectral signature and Regions Of Interest (ROI), With different algorithms such as 113


Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM), Minimum Distance (MD) and Spectral Information Divergence (SID) depending upon the ability of a different algorithm to distinguish between different rock units. 4.3.4.2.1. Supervised Classification Using EndMembers Endmember is an idealized pure signature for a class. In the present study, we applied ENVI Spectral Hourglass to extract endmembers from the images (Fig 4.25). The Hourglass processing flow uses the spectrally over-determined nature of hyper spectral data to find the most spectrally pure, or spectrally unique, pixels (endmembers) within the data set and to map their locations and sub-pixel abundances. This processing flow begins with reflectance or radiance input data. We can spectrally and spatially a subset the data, visualizes the data in n-D space and clusters the purest pixels into endmembers, and optionally supplies our own endmembers. In this study, we apply the Spectral Angle Mapper (SAM) algorithm on the Landsat bands using the selected endmembers (Fig 4.26). The SAM method produces a classified image based upon the value specified for SAM Maximum Angle. Decreasing this threshold usually results in fewer matching pixels (better matches to the reference spectrum). Increasing this threshold may result in a more spatially coherent image; however, the overall pixel matches will not be as 114


good as for the lower threshold. The same processes were done on the ASTER VNIR-SWIR bands (Fig 4.27).

Fig 4.25: The Spectral Hourglass Wizard Flow Chart.

115


Fig.4.26: Spectral Angle Mapper classification for the Landsat bands of the study area using endmembers collection. Symbols as in Fig.4.10.

Fig.4.27: Spectral Angle Mapper classification for the ASTER VNIR-SWIR bands of the study area using endmembers collection. Symbols as in Fig.4.10. 116


4.3.4.2.2.

Supervised

Classification

using

Spectral

Signature In the present study supervised classification is applied using spectral signatures of the different rock units we, depending on this units signature three different algorithms were applied. 1. Spectral Angle Mapper (SAM): as mentioned before used to produce a classified image based on the value specified for SAM Maximum Angle. This technique was applied on the Landsat ETM+ bands (Fig 4.28), as well as ASTER VNIRSWIR bands (Fig 4.29). 2. Minimum Distance (MD): This technique uses the mean vectors of each endmember and calculates the Euclidean distance from each unknown pixel to the mean vector for each class. All pixels are classified to the nearest class unless a standard deviation or distance threshold is specified. This technique was applied on the Landsat ETM+ bands (Fig 4.30), as well as ASTER VNIR-SWIR bands (Fig 4.31). 3. Spectral Information Divergence (SID): is a spectral classification method that uses a divergence measure to match pixels to reference spectra. The smaller the divergence, the more likely the pixels are similar. Pixels with a measurement greater than the specified maximum divergence threshold are not classified. This technique was 117


applied on the Landsat ETM+ bands (Fig 4.32), as well as ASTER VNIR-SWIR bands (Fig 4.33). 4.3.4.2.3. Supervised Classification using Regions Of Interest (ROIs). Regions Of Interest (ROIs) are portions of images, either selected graphically or selected by other means such as thresholding. The regions can be irregularly-shaped and are typically used to extract statistics for classification. Regions of interests were selected carefully for every rock unit from different localities in the scene of the study area depending on the field observation and the old maps. Two algorithms were used successfully to classify the images with this (ROIs). 1. Maximum Likelihood (ML): This classification assumes that the statistics for each class in each band are normally distributed and calculates the probability that a given pixel belongs to a specific class. Unless we select a probability threshold, all pixels are classified. Each pixel is assigned to the class that has the highest probability (that is, the maximum likelihood). If the highest probability is smaller than a threshold, we specify, the pixel remains unclassified (Richards, 1999).

118


Fig.4.28: Spectral Angular Mapper classification for Landsat bands of the study area using spectral signatures. Symbols as in Fig.4.10.

Fig.4.29: Spectral Angular Mapper classification for ASTER 9 bands (VNIRSWIR bands) of the study area using spectral signatures. Symbols as in Fig.4.10. 119


Fig.4.30: Minimum Distance classification for Landsat bands of the study area using spectral signatures. For symbols referee to (Fig.4.10).

Fig.4.31: Minimum Distance classification for ASTER 9 bands (VNIR-SWIR bands) of the study area using spectral signatures. Symbols as in Fig.4.10. 120


Fig.4.32: Spectral Information Divergence classification for Landsat bands of the study area using spectral signatures. Symbols as in Fig.4.10.

Fig.4.33: Spectral Information Divergence classification for ASTER 9 bands (VNIR-SWIR bands) of the study area using spectral signatures. Symbols as in Fig.4.10. 121


In the present study Maximum Likelihood was applied to the Landsat ETM+ bands in the study area using (ROIs) with overall accuracy of 90.6% (Fig 4.34), while the same algorithm was applied to the ASTER VNIR-SWIR bands with overall

accuracy of

83.6%(Fig 4.35). 2. Support Vector Machine (SVM): we perform supervised classification on images using a support vector machine (SVM) to identify the class associated with each pixel. SVM provides good classification results from complex and noisy data. SVM is a classification system derived from statistical learning theory. It separates the classes with a decision surface that maximizes the margin between the classes. The surface is often called the optimal hyperplane, and the data points closest to the hyperplane are called support vectors. The support vectors are the critical elements of the training set. In the present study, SVM was applied to the Landsat ETM+ bands in the study area using (ROIs) with overall accuracy of 90.04% (Fig 4.36), while the same algorithm applied to the ASTER VNIR-SWIR bands with overall accuracy of 83.04 % (Fig 4.37).

122


Fig.4.34: Maximum Likelihood classification for Landsat bands of the study area using (ROIs). Symbols as in Fig.4.10.

Fig.4.35: Maximum Likelihood classification for ASTER 9 bands (VNIRSWIR) of the study area using (ROIs). Symbols as in Fig.4.10. 123


Fig.4.36: Support Vector Machine classification for Landsat bands of the study area using (ROIs). Symbols as in Fig.4.10.

Fig.4.37: Support Vector Machine classification for ASTER 9 bands (VNIRSWIR bands) of the study area using (ROIs). Symbols as in Fig.4.10. 124


4.4. Lineament Extraction The term “lineament” is one of the most commonly used terms in the geology. O’Leary et al. (1976) described the term lineament as a mappable, simple or composite linear feature of a surface whose parts are aligned in a rectilinear or slightly curvilinear relationship and which differs from the pattern of adjacent features and presumably reflects some sub-surface phenomenon. Lineaments can be defined as linear topographical or tonal features on the terrain representing zones of structural weakness (Williams, 1983). Gupta (1991) summarized the definition of lineament in different geological features, such as (1) shear zones/faults; (2) rift valleys; (3) truncation of outcrops; (4) fold axial traces; (5) joint and fracture traces; (6) topographic, vegetation, soil tonal changes alignment etc. Lineaments are natural crustal structures that may represent a zone of structural weakness (Masoud and Koike, 2006). Lineament identification via remotely sensed data is achieved by using two principal techniques. First, lineament data can be visually enhanced using image enhancement techniques and a lineament vector map can be produced using manual digitizing techniques (e.g., Suzan and Toprak, 1998). Second, a lineament map may be produced using computer’s software and algorithms (Baumgartner et al., 1999; Hung et al., 2002; Kim et al., 2004; 125


Abdullah et al., 2010). In this study we identified the lineament map using computer software and algorithms. 4.4.1. Data Used and Their Processing Digital Elevation Model (DEM) data can be displayed in forms i.e. grid, contour, profile, and TIN (Triangulated Irregular Network) which are not completely continuous. In this study, these data are displayed in the form of grid. The DEM was acquired from contour map and was originally generated by digitizing the contour lines and give the contour lines the height value they represent, then converted it into grid form. The DEM was geo-rectified using topographic maps with points collected at road intersections and other landscape features throughout the area. Shaded relief images were derived from a digital elevation model (DEM) are used for linear extractions. In order to identify linear topographic features from the DEM, eight shaded relief images were generated. The first step is the production of eight separate shaded relief images with light sources coming from eight different directions. The first shaded relief image created had a solar azimuth (sun angle) of 0°, and the other seven shaded relief images were created with seven contrasting illumination directions 45°, 90°,135°, 180°, 225°, 270° ,and 315° (Fig. 4.38). The second step is to combine eight shaded relief image to produce one shaded relief image. For this purpose, the combinations of the eight shaded relief maps are computed by 126


using GIS overlay technique, where the eight shaded relief images are overlaid to produce one image with multi-illumination directions (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) (Fig. 4.39). This image have been used for automatic lineaments extraction over the study area. 4.4.2. Automated Lineament Extraction The automated lineament extraction is carried out by using algorithms and computer’s software (e.g.,Costa and Starkey, 2001; Kim et al., 2004;; Hung et al., 2005; Abdullah et al., 2009; Abdullah et al., 2010 and Zohier and Emam, 2013). In this study, the PCI Geomatica software is used for automated lineament extraction. The output is a vector segment which contains linear features as extracted from the image (Fig. 4.40). 4.4.3. Lineament Directional Analysis Lineament analysis of the study area shows that the area is highly affected and dissected by different oriented faults. These faults are different in their magnitudes ranging from prominent such as NW-SE (Najd fault system) and N-S (East African) to less prominent NE-SW (cross fault), WNW-ESE and E-W (Tethyan) (Fig. 4.41).

127


Fig. 4.38: Eight shaded relief images derived from DEM with illumination directions (sun azimuth),0°,45°,90°,135°, 180°,225°, 270° ,and 315°. 128


Fig. 4.39: Shaded relief image created by combining eight shaded relief images with sun angle of 0°,45°,90°, 135o, 180°,225°,270° and 315°.

Fig. 4. 40: Automatic lineament map of combining eight shaded relief images with sun angle of 0°,45°,90°, 135°, 180°, 225°, 270° and 315°. 129


(a)

(b)

Fig. 4.41: Rose diagram of automatic Lineament map (a) used the lineation frequency. (b) used the lineation lengths.

130

CHAPTER V SUMMERY AND CONCLUSIONS________________________________

CHAPTER V SUMMERY AND CONCLUSIONS The study area of Wadi Um Gheig is situated in the central part of the Eastern Desert of Egypt that represents the western part of the Arabian-Nubian Shield (ANS). It is bounded by Latitudes 25o 32' and 25o 42' N. and Longitudes 34o 13' and 34o 28'E, covering about 455 km2. It is easily accessible through the Red Sea coast asphaltic road and then along Wadi Um Gheig of about 50 km south of Qusier city. The area under investigation is characterized by wide variation in relief; it reaches a maximum of 1490 m above sea level at Gabal El-Sibai. Generally, the granitic plutons form the highest relief. The area is traversed by numerous valleys; such as Wadi Um Gheig, that represents the longest one in a NE-SW direction, as well as several tributaries known as Wadi Um Laseifa, Wadi El-Shush, Wadi Kab Ahmed and Wadi Kab El-Rekab. The present work is an integrated studies of the field geologic investigation, the structural pattern and the remote-sensing interpretation in order to prepare a new map, to define the field relationship of the different rock units and to identify the structural features and to give the tectonic model of the study area. Wadi Um Gheig area represents a part of the crustal structure of the Eastern Desert terrain comprising the lower crustal rock units 131


(Lower crust) of higher grade crust dominant with gneissic rocks (structural basement), structurally covered by nappe of the ophiolites and the island arc assemblage forming the upper crustal units (Upper crust). The juxtaposition of the low grade sequence of the ophiolite and the metavolcanosedimentry rocks against the high grade metamorphic gneisses is represented by an extensional NWSE shear zone accompanied by highly sheared granites. According to the field studies of the different rock units, the Wadi Um Gheig area is represented by the rock units forming up the upper crust, lower crust and late to post magmatic rocks. 1-Upper Crust The upper crust comprises the older rock units of ophiolite and island arc assemblage. The ophiolite rock units are thought to represent the oceanic crust of Mozambique Ocean that were formed upon the rifting of Rodinia, such as other parts of ANS. In the study area the ophiolites are represented by serpentinites and talc carbonate bodies overthrusted the island arc assemblage of lowgrade metamorphic rocks (M1) of the metavolcanosedimentary, metavolcanic and metagabbro sequences. The metavolcanosedimentary rocks form the upper horizon of the island arc rocks assemblage, that can be differentiated into three rock varieties (i) The first comprises the highly folded metavolcanosedimentary rocks, representing the upper folded rock units directly over 131


thrusted by ophiolite rock units. (ii) The second comprises the highly schistose rocks intercalated often with talc carbonate rocks. (iii) The third comprises the metavolcanosedimentary rocks associated with the banded iron formation (BIF). 2- Lower Crust The lower crust comprises mainly the high grade metamorphic rocks (M2) of gneisses and migmatites, directly concordant with the syntectonic tonalite-granodiorite. Three main layers are subsequently distinguished for the lower crust. The deeper layer has the tonalite-granodiorite and gneissose tonalite gradually concordant with the migmatites and gneisses forming the middle layer, while the upper layer comprises the highly sheared granites in structurally contact with them. Generally, these rocks constitute a NW-SE belt, configurated with the main structure trends of the area. 3- Late to post orogenic magmatism The late to post orogenic magmatism comprises the calcalkaline and alkaline granites, younger fresh gabbro and post tectonic dykes. The late to post orogenic granites are represented by four plutons known as Um Shaddad, Abu Marakhat, El-Sibai and Kadabora. These plutons have circular and oval like shapes, with exception of Um Shaddad and Abu Marakhat of NW-SE strike 133


plutons. These granites

intrude directly the surrounding

metavolcanics and the metagabbro as well as the gneissic tonalite and the granodiorite. Nearly these granites have the same mineral composition with some little differences in the content of the mafic minerals, K-feldspars and quartz. Most of these granites were emplaced at time span ~ 650 and 640 Ma to suggest that they were placed prior the fresh gabbro emplacement of crystallization age ~ 545-540 Ma. The investigated fresh gabbro of Kab Moussa represents one of the youngest undeformed post tectonic isometric unlayered small mass intruded by some trachytic dykes dated at ~ 540 Ma. The fresh gabbro is often emplaced after El-Sibai and Abu Marakhat granites of determined age ~ 650 and 640 Ma. Generally the investigated granitic rocks and fresh gabbro were emplaced during CryogenianEdiacaran conforming geochronologic age ~ 650-540 Ma. The dykes represent the post tectonic intrusions of the youngest Neoproterozoic (Ediacaran) rocks, cross-cutting most of the investigated rocks with linear N-S features, less common E-W and NW-SE. Generally the N-S trends can be considered as a common theme of Ediacaran upper crustal extension parallel to the orogeny, whereas the less E-W directed dykes traverse to the orogeny.

131


The study of the structural features demonstrates four main phases of deformation affected the study area during the development of the Eastern Desert as a part of the Arabian-Nubian Shield and the East African Orogeny (EAO), during the late Cryogenian–Ediacaran period. These are: 1-Early Compressional Phase (D1) The early compressional phase (D1) is attributed to the compressional regime in the late Proterozoic due to arc accretion with a WNW-ESE to NW-SE compressional stress recorded in the upper crustal rock units as (1) Early foliation (S1) that was formed in the metavolcanoclastic rocks and associated schists, parallel with the main trend of the thrust fault zone and the associated fold axial planes of NNE-SSW trend and WNW dip. (2) One thrust fault is recorded along Wadi Um Gheig with NNE-SSW trend and WNW dip. (3) Early folding (F1) comprising the major and the minor folds, that can be considered the most predominant features in this phase. The major folds are represented by antiformal and synformal structures with parallel axial planes in NNE-SSW direction indicating the WNW-ESE compressional stress due to thrusting of the ophiolite rock units (serpentinites) over the metavolcanosedimentary rocks during the arc accretion stage. On the other hand, the minor folds are represented by number of chevron folds

131


showing often Z shape obtained often as synchronous development of the final result of continuing compressional stage. 2-Early Extensional Phase (D2) This phase comprises the early extensional NW-SE sinistral strike-slip faults of Najd Fault System. Four major NW-SE faults configured as Najd Fault System are named here as El-Shush shear zone, Um Laseifa fault, Kab Ahmed fault and El Mirifiya fault. Some of them such as El-Shush shearing caused the mechanism driven exhumation continuously accompanied by magmatic activity. El-Shush shear zone appears as a wide shear comprising most all the rock units of the lower crust of high grade metamorphic migmatitic rocks that had been formed by partial melting of the lower part of buried island arc units of modern arc chemical affinities. The exhumed El-Shush migmatite core complex of gradational changing to gneissic tonalite and granodiorite, indicates that the exhumation of El-Shush migmatites took place at long term process of time span such as another parts in the Eastern Desert. Generally the exhumed migmatites occur as elongated NW-SE belt discordantly cut by the late orogenic granites of the Um Shaddad and Abu Marakhat granites of crystallization age ranging between 650 and 640 Ma. Moreover, the occurrence of the highly sheared granite at the juxtaposition of the lower and upper crustal rock units may show 131


that the sinistral strike-slip NW-SE stretching combined as transpression-extrusion regime, whereas bulk convergence is balanced by both vertical thickening and horizontal NW-SE directed extension. The amount of vertical thickening may easily be compensated by stretch of parallel shearing (sheared granite) to NW-SE Najd Fault System. 3- Late Extensional Phase (D3) A number of geologic features such as late to post orogenic granites and dykes have been formed in this phase, most probably at the same time or slightly after the NW-SE shear zones and exhumation of the core complex. Two main fault trends are related to the late extensional phase (D3). These are N-S trend and the reactivated NW-SE trend. 4- Late Compressional Phase (D4) This phase represents the later phase of deformation caused the NW upright open folding of some dykes as well as the refolding of (F1) folds. Lineament analysis of the study area shows that the area is highly affected and dissected by different oriented faults. These faults are different in their magnitudes ranging from prominent such as NW-SE (Najd Fault System) and N-S (East African) to less prominent NE-SW (cross fault), WNW-ESE and E-W (Tethyan). 131


The multispectral remote sensing techniques have been done for the study area using Landsat Enhanced Thematic Mapper data (ETM+) data, the Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) data. Four different techniques have been implemented for mapping the different lithological units using ENVI 4.7 software. 1- The band combination images have been chosen by using correlation coefficient, Optimum Index Factor (OIF) and visual interpretation. The result shows that ETM 751, ETM 741 and ETM 543 are the best band combination images for Landsat ETM+ data, while ASTER 964, ASTER 463 and ASTER 984 are the best band combination images for ASTER data. 2- The band ratio composite technique has been used by new proposed Landsat ETM+ ratio (7/1, 3/1 and 5/7 in RGB), and pronounced the ability for mapping the studied rock units. It is noticed that the given new band ratio provides a great advantage than the previously published band ratios. New ASTER band-ratio image is also deduced in this study (4/7, 4/ 6and in 4/9 RGB). This ratio image is successfully used for mapping the gneisses and the migmatites. 3- The Principal Component Analysis (PCA) has been done on Landsat ETM+ data. The result shows that the first three principal components contain over 98% of the variance. A false 131


color composite formed by projecting the first three Principal Components (PC1, PC2 and PC3 in RGB) and (PC1, PC2 and PC4 in RGB) contains most of the variance for the 6 input bands. The two images show very good discrimination between different rock units. Similarly, Principal Component Analysis was done on ASTER VNIR-SWIR data. The first four principal components typically contain over 99.99 % of the variance, where PC4 is a good representative to granites. Two false color composite formed by projecting the first four Principal components (PC4, PC1 and PC2 in RGB) and (PC4, PC1 and PC3 inRGB) thus contain most of the variance for the 9 input bands. The two images show very good discrimination between the different rock units in the study area. 4- Multispectral classification techniques have been applied. The classification techniques are divided into two main groups. (1) Unsupervised Classification is applied for Landsat ETM+ and ASTER data, where the number of classes is (8), the maximum number of iteration is (10) and the threshold value is 95%. (2) Supervised Classification is used depending on Endmembers, individual spectral signature and Regions Of Interest (ROI), with different algorithms. Moreover, Maximum Likelihood (ML) classification was applied on Landsat ETM+ and ASTER VNIR-SWIR bands using (ROIs) with over all accuracy 90.6% and 83.6% respectively. Also 131


Support Vector Machine (SVM) classification applied on Landsat ETM+ and ASTER VNIR-SWIR bands using (ROIs) with over all accuracy 90.04% and 83.04% respectively. These two algorithms show the best visual discrimination for the studied rock units. Maximum Likelihood (ML) classification using endmembers shows a good visible contacts as well as Spectral Angle Mapper (SAM) using the spectral signature. On the other hand, the Spectral Information Divergence (SID) and Minimum Distance (MD) algorithms depending on the ground spectral signatures show less accuracy to define the contacts between the rock units. Finally, remote sensing techniques, which have been done in this Thesis are very important, where it gave us a good clues about the different rock units which forming the study area. More addition, it produced a new detailed lithological and structural trends which led us to construct the so called geo-sensing map.

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