Remote Sensing and Gis Investigations for Georesources Mapping, Southern Hamisana Area, Red Sea State, Ne Sudan

EL NEELAIN UNIVERSITY THE GRADUTE COLLEGE, DEPARTMENT OF GEOLOGY

REMOTE SENSING AND GIS INVESTIGATIONS FOR GEORESOURCES MAPPING, SOUTHERN HAMISANA AREA, RED SEA STATE, NE SUDAN

By: Elsheikh Edrees Alagab Hassan Omer B.Sc. (Hons.) in Hydrogeology, Al Neelain University, 2013

A Thesis Submitted to the Graduate College, Al Neelain University in Fulfilment of the Requirements for a Master Degree of Science in the Applications of Remote Sensing and GIS in Geology

Supervisor: Professor. Khalid Abdelrahman Elsayed

October 2018

EL NEELAIN UNIVERSITY THE GRADUTE COLLEGE, DEPARTMENT OF GEOLOGY

REMOTE SENSING AND GIS INVESTIGATIONS FOR GEORESOURCES MAPPING, SOUTHERN HAMISANA AREA, RED SEA STATE, NE SUDAN

By: Elsheikh Edrees Alagab Hassan Omer B.Sc. (Hons.) in Hydrogeology, Al Neelain University, 2013

A Thesis Submitted to the Graduate College, Al Neelain University in Fulfilment of the Requirements for a Master Degree of Science in the Applications of Remote Sensing and GIS in Geology

Supervisor: Professor. Khalid Abdelrahman Elsayed

Examination Committee

External Examiner Dr. Manal Awad Khairy Signature.……………………...

Internal Examiner Dr. Sami Omer Hag El Khidir Signature.……………………...

Supervisors: Professor. Khalid Abdelrahman Elsayed Signature.……………………...

October 2018

Dedication

To my father

To My Mother, my Brothers, Sisters,

Friends and

Every one searching for knowledge

ACKNOWLEDGEMENTS

Firstly, I would like to express my appreciation and special thanks to my supervisor Professor. Khalid Abdelrahman Elsayed Zeinelabdein, for his patience, persistence and valuable comments of the research, and for many hours of their time spent in discussions.

My appreciation extends to the Exploration Manager of Orshab Gold Mining Company, Dr. Abdalla Eltom Mohamed for his worthy support, guidance, continued interest, encouragement during this work.

My thanks, also, extend to Mr. Musab Awad Eljah for his assistance in the laboratory work and petrographic investigations.

Orshab Gold Mining Company is highly acknowledged for supporting the study by funding the chemical analysis and providing facilities during the field work.

Finally, this work would not be possible without the support and patience of my family to whom I am particularly grateful

Abstract

The study area is situated in northwestern flanks of the Red Sea Hills, in the south of Hamisana area, northeastern of Sudan, which is characterized by desert climate. Ample of digital image processing of Landsat 8 data and spectral analysis of semi-hyperspectral data (ASTER L1T) were conducted with the aim of geological mapping and mineral prospecting using remote sensing and GIS investigations, authenticated by field work. Geologically, the study area is dominated by metavolcanosedimentary sequence and mafic- ultramafic ophiolitic rock metamorphosed in greenschist facies. The layered sequences are intruded by the syn- to late-orogenic and post-organic igneous intrusions. The mafic-ultramafic having features characteristic of ophiolite sequences occur mostly in northeast of the study area and trend to the north as possible continuation of the Sol Hamid-Onieb ophiolitic mélange. They include serpentinites, listevinite, pyroxenite and gabbro. The most prevailing and discernable regional structure in the area under consideration is the Hamisana Shear Zone (HSZ) which extend in N-S direction and, by which most of the rock units are affected. The integration of remote sensing and GIS techniques facilitated the production of a geological map at the scale of 1:70 000. Digital image processing was conducted to enhance the visual interpretation of the Landsat 8 data. Band ratioing transformation revealed the presence of alteration halos related to mineralization in the area. The mineral indices revealed the wide distribution of indicator minerals in the study area, nevertheless, On the other hand, the results of SAM classification of ASTER data portrayed the highly probable alteration halos related mineralization in the area. The outcome of this study revealed that the results of mineral prospecting obtained from spectral analysis of ASTER data are more superior than those obtained from Landsat data, as the former is characterized by a higher spectral resolution and, accordingly, good capability in distinguishing the spectral signatures of the indicator minerals and in the delineation the alteration zones. Based on remote sensing and GIS investigation, gold had been detected in different forms such as gossanic bodies, auriferous quartz veins and stringers, rocks alteration zones and as residual deposits, where the gossanic bodies and auriferous quartz veins represent the most potential gold-bearing lithology in the study area. The gossans are found in different locations in this study area hosted in different rock type such as meta-dacite, andesite to rhyolite, mainly trending N-S and dipping to the east.

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المستخلص تقعععععقة القعععععشةال شاقعععععشةنععععع لجةنعععععش ةالقععععع ا ة ععععع ةال اعععععل ةالنععععع لل ةالغشبععععع ة ععععع ة بعععععلجةالب عععععشةا عععععش ة القععععععشة اعععععع وةال قععععععلال ة ةوعععععع ةتتلععععععنةب اععععععل ةلعععععع شا ةا ش عععععع ةال عععععع ة عععععع ةال لل ععععععل ةالشق ععععععشة للق عععععععشةاللعععععععال ة)Landsat-8( ةك عععععععلةا ش ععععععع ة ل عععععععل ةات ل عععععععجةالل ةععععععع ةللب لاعععععععل ةنعععععععب ة ل قعععععععشةالل ة عععععععشة للق عععععععععشةاللعععععععععال ة)ASTER-L1T(ة ذلعععععععععتةب ععععععععع نةالتلعععععععععش لةال لععععععععع ة ةالب ععععععععع ة ععععععععع ةال عععععععععل ة بلقتل امة ة جةاب ل ةا قتن لشة ةب ة ةاظمةال ل ل ةال غشا شة قةال جةال قل ة

ل ععععععلةال القععععععشة غلععععععل ةبللتتلب ععععععل ةالبشكلا ععععععشةالشقعععععع ب شةال ت لععععععشة ةاللععععععل شة عععععع ةالقل ععععععشة ت لععععععشة ععععععع ة ش ععععععععشةالن قععععععع ةا لطععععععععش ةوععععععععذزةالتتلب عععععععل ةال تلبقععععععععشة غعععععععع ةب تععععععع ل ة ت ععععععععشةة ة تعععععععع لش ة التكت ا ععععععشة ةال شاا عععععع ةال عععععع ةاللععععععل شة عععععع ةالقل ععععععشةتت عععععع ةبتتلب ععععععل ةا عععععع ة ةقعععععع ة عععععع ة عععععع ة ال ععععع ةالنععععع لل ةالنعععععشق ة ععععع ة القعععععشةال شاقعععععشةبل تععععع ا ةا ععععع ةالنععععع لجة ةوععععع ةا تععععع ا ةللععععع لةلععععع جة ل ععععع ة ا ا عععععععوةا ععععععع ل ت ةوعععععععذزةالتتلب عععععععل ةتطعععععععمةكعععععععجة ععععععع ةالقععععععع شب ات ال ة ةا قعععععععتة ال ة ةالبل ش كقععععععع ال ة ة القعععععلبش ةاكتعععععشةالتشاك عععععوةال ل عععععشةا قل عععععشة ةال ععععع ة ععععع ةال القعععععشةوععععع ةالعععععل ةقععععع ةو قعععععلالة ةالعععععذ ة تعععععع ةنعععععع لجة اعععععع وة ة ظععععععمةال عععععع ا ةاللععععععلش شة تععععععلتش ةبعععععع ة عععععع ةوععععععذزةال شاقععععععشةتععععععمة ععععععجةتقا ععععععل ة ا قتنعععععع لشة عععععع ةب عععععع ة ةاظععععععمةال ل ععععععل ةال غشا ععععععش ة عععععع ةا ش عععععع ةال لل ععععععل ةالشق ععععععشةللعععععع شةالق ععععععشة اللععععععععال ة)Landsat-8(ةبغععععععععشاة ععععععععل ةالقعععععععع ش ةالبلععععععععش شة لعععععععع ةالتةش عععععععع ةبعععععععع ةال عععععععع ا ةالقعععععععع ال ة اللععععععلش شةال لتلةعععععععشة عععععععلةقعععععع جة ععععععع ةااتعععععععلخةلععععععشا لةاللقعععععععل ةالتغ عععععععشة ةاللععععععشا لةال ل عععععععشةب ق عععععععل ة 1:70000 ك عععععلةتلب ععععع ةال ععععع ة عععع ةاقعععععبشةال ععععع مةالل ا عععععشةقعععععل ة عععع ةالكنعععععنة ععععع ةوعععععل ةالتغ عععععشةالتععععع ة ل ععععععلة قععععععشةبللت عععععع ال ة عععععع ة القععععععشةال شاقععععععش ةتلب عععععع ةال نععععععشا ةال ا ععععععشةكنععععععنة عععععع ةت ععععععقةال ععععععل ة ال شن ة ةال القش ة

عععع ة ععععشةالععععش ةا لعععع ةاتععععل جةتلععععا نةلععععشا لةالل ععععنةالعععع ا ةتةللعععع جة عععع ةت عععع ةوععععل ةالتغ ععععشةالتعععع ة ل عععععععلة قعععععععشةبللت ععععععع ال ة ععععععع ةكنعععععععة ةاتعععععععل جةت ق قعععععععل ةال شاقعععععععشةب اقعععععععلشةالت ل عععععععجةالل ةععععععع ةلب لاعععععععل ة ASTERةا طععععععجة عععععع ةتلععععععتةالتعععععع ةتععععععمةال لعععععع جة ل ععععععلة عععععع ةب لاععععععل ةالق ععععععشةLandsat-8ة ذلععععععتةل ععععععلةل عععععع ة ا طعععععععل شةالل ة عععععععشةال لل عععععععشة القععععععع ش ةال ععععععع ة ععععععع ةت ععععععع ةبلععععععع ل ةل ة عععععععشةلل عععععععل ةال شنععععععع ةةل عععععععل ة التغ عععععشةال ت لقعععععشةب اعععععلل ةالت ععععع ال ة ععععع ةلععععع جة لععععع ةا قتنععععع لشة ععععع ةب ععععع ة ةاظعععععمةال ل عععععل ةال غشا عععععشةة ععععع ةا ة ععععع ةالعععععذووة ععععع ة القعععععشةال شاقعععععشة ت ا ععععع ة ععععع ةانعععععكلجةت ا ععععع ة لتلةعععععشة تعععععجةت ا ععععع زة عععععقةلل عععععل ة الكبش ت ععععععع ا ة)الق قعععععععل (ة ة عععععععش ةالكععععععع اشت ة ةاللقعععععععل ةالتغ عععععععشة ععععععع ةاللعععععععل شةبل طعععععععل شةالععععععع ةت ا ععععععع زة بنعععععكجة ععععععشة عععععع ةالش اقعععععوةال ا ععععععشةال تللةععععععشة ةشقععععع ب ل ةال شاقعععععع ةا قععععععلمةالق قعععععل ة ة ععععععش ةالكعععععع اشت ة و عععععلة ععععع ةاوعععععمةاللل عععععل ةل ععععع ةالعععععذووة ععععع ة القعععععشةال شاقعععععش ةوعععععل ةتغ عععععشةاللعععععل شة غععععع ةب ش قعععععل ة الكععععع اشت ةاللعععععغ ش ة التععععع ةت تععععع ة لععععع ةتشاك ععععع ة لل عععععشةل ععععع ةالعععععذوو ةلعععععلمةالق قعععععل ة ععععع ة ععععع ة القعععععشة ال شاقعععععععشة ععععععع ةت تعععععععشة اقعععععععقة لتلةعععععععشة ععععععع ة ت ا ععععععع ة عععععععقةاللعععععععل شةال طععععععع ةشةلععععععع ة تعععععععجةال اقعععععععل ة ة الشا اقل ة ةا ا قل ة ظمةال اققة ت ةللمةالق قل ةن لج- ا وة ة ةا ةالنش

List of Contents

DEDICATION...... …...... i ACKNOWLEDGMENTS ……………...... ii ABSTRACT...... iii iv ...... الــمـــــســتـخـــــلــــــص LIST OF CONTENTS...... v LIST OF FIGURES...... ix LIST OF TABLES...... xi LIST OF PHOTOS...... xi LIST OF PLATES...... xi LIST OF ABBREVIATIONS...... xii

CHAPTER I: INTRODUCTION

1.1 Statement of the Problem.……………………………………………………... 1 1.2 Objectives of the Study………………………………………………………... 1 1.3 The Study Area………………………………………………………………... 2 1.3.1 Location and Accessibility………………………………………………...... 2 1.3.2 Physiographic Features………………………………………………………... 3 1.3.2.1 Topography……………………………………………………………………. 3 1.3.2.2 Drainage System………………………………………………………………. 3 1.3.2.3 Climate and Vegetation Cover………………………………………………… 3 1.4 Population and Socio-Economic Activities…………………………………… 6 1.5 Previous Studies…………………………………………………………….…. 6

CHAPTER II: METHODS OF INVESTIGATIONS

2.1 Introduction………………………………….……………...………….……… 10 2.2 Material and Data…………………………….……………………….………. 10 2.3 Landsat 8 Data (OLI)…………….……………………………...…………… 10 2.3.1 The Landsat 8 Spectral Bands and Sensors……………………………...... 11 2.3.2 Operational Land Imager………………………………………………...... 11 2.3.3 Thermal Infrared Sensor………………………………………………………. 12

2.4 Landsat 8 Pre-Processing and Preparation………………………………….…. 13 2.4.1 Image Mosaicking and Subsetting………………………………………….…. 14 2.4.2 Image Fusion ……………………………………………………………….…. 15 2.5 The ASTER Instrument…………………………………………………….…. 16 2.5.1 ASTER Data Preparation and Processing……………………………………... 17 2.5.1.1 ASTER Geometric Corrections…………………………………………….…. 17 2.5.1.2 Radiometric Calibration and Atmospheric Correction………………………... 17 2.6 Shuttle Radar Topography Mission and GIS ……………………………….…. 19 2.7 Field Works ………………………………………………………….…….…... 20 2.8 Tools and Data….……………………….……………………………………... 20 2.9 Software………………………………………………………………………... 20

CHAPTER III: LITERATURE REVIEW

3.1 Remote Sensing ……….………….……...... …...... …...... 22 3.1.1 Historical Overview of Remote Sensing…………….….……………….….…. 22 3.1.2 Fundamentals of Electromagnetic Radiation and Physical Concepts…………. 25 3.1.3 Interaction of Electromagnetic Radiation with the Atmosphere………………. 26 3.1.4 Interaction of Electromagnetic Radiation with Earth Surface Features…….…. 27 3.1.5 Sensors…….………….……...... …...... …...... 28 3.1.6 Image Resolution….………….……...... …...... …...... 29 3.1.7 The Landsat Systems Generations…….……...... …...... …...... 29 3.2 Geographic Information Systems….……...... ….…….…………………. 31 3.2.1 Introduction…….……...... ….…….……...... ………………….…. 31 3.2.2 Components of the Geographical Information Systems…….……...... 32 3.3 Tectonic and Geological Setting…….……...... ….…….……...... 33 3.3.1 Tectonic Setting…….……...... ….…….……...... ………………… 33 3.3.2 Regional Geology of Red Sea Hills…….……...... ….……………….…. 34 3.3.2.1 Basement Complex in Red Sea Hills…….……...... ……………………...... 37 3.3.2.2 Cenozoic Sediments…….……...... ……………………………………...... 40 3.3.2.3 Tertiary Volcanic and Related Minor Intrusions….……...... ……...... 41 3.3.2.4 Quaternary Sediments…….……...... ….…….…...... …………...... 41

CHAPTER IV: RESULTS AND DISCUSSIONS

4.1 Digital Image Processing of Landsat 8 data …….….…………………………. 39 4.1.1 Contrast Enhancement….……...... ….…….…...... ……… 42 4.1.2 Decorrelation stretching….……...... ….…...... ……………. 45 4.1.3 Color Compositing….……...... ….…...... …………………. 45 4.1.4 Band Ratioing Images….……...... ….…...... ……………… 49 4.1.5 Principal Component Analysis (PCA) ….……...... …....………………. 51 4.2 Geology of the Study Area (South Hamisana) ………………………………...54 4.2.1 Mafic Ultra-Mafic Rocks….……...... …....……………………………… 54 4.2.1.1 Basal Ultra-Mafic Tactonites. …...... …....……………………………… 56 4.2.1.2 Basic-Ultra Basic Cumulates…...... …....…………………………….…. 56 4.2.2 Metavolcanosedimentary Sequence…...... …....………………………… 59 4.2.2.1 Metavolcanics…...... …....………………………………………………. 59 4.2.2.2 Metasediments…...... …....……………………………………………….62 4.2.3 Intrusive Rocks…...... …....……………………………………………... 65 4.2.3.1 Syn-Orogenic Granites…...... …....……………………………………….…. 65 4.2.3.2 Post Orogenic Granites…...... …....……………………………………….….65 4.2.4 Oligocene Basalt…...... …....………………………………………………... 65 4.3 Digital Image Processing for Mineral Prospecting……………………………. 67 4.3.1 Overview…...... …....………………………………………………...... 67 4.3.2 Mapping of Alteration Zone via Band Rationing Technique….……………… 68 4.3.2.1 Hydrothermal Composite Ratio…...... …....………………………………… 69 4.3.2.2 Mineral Composite Ratio…...... …....………………………………...... 69 4.3.2.3 Sabins Ratio Images …...... …....……………………………………………. 69 4.3.2.4 Abrams’s Band Ratio…...... …....…………………………………… ……... 69 4.3.2.5 Iron Oxide Index…...... …....………………………………………………... 74 4.3.2.6 Al- OH-Bearing Minerals…...... …....………………………………………. 74 4.3.2.7 Al- OH-Bearing Minerals…...... …....………………………………………. 74 4.3.2.8 Chemical Analysis…...... …....……………………………………………… 74 4.4 Spectral Analysis of ASTER Data…...... ……………………………………...77 4.4.1 ASTER Mineral Indices…...... …....………………………………………... 77 4.4.1.1 Alunite Index (ALI) …...... …....……………………………………………. 77 4.4.1.2 Clay Minerals Ratio…...... …....………………………………………….…. 78

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4.4.1.3 Ferrous and Iron Oxides Ratio…...... …....………………………………...... 78 4.4.1.4 Alterations Image Ratio…...... …....………………………………...... 79 4.4.2 Spectra of Rocks and Minerals.…....………………………………….……… 79 4.4.2.1 Reference Spectra...... …....……………………………………….…………. 81 4.4.2.2 SMACC End Member Extraction...... ….... …………………….…………… 83 4.5 Spectral Angle Mapper Classifier……………………………………………... 83 4.5.1 Hematite Mapping ……………………………………………………………. 84 4.5.2 Dolomite Mapping ………………………………………………...……….…. 85 4.5.3 Buddingtonite Mapping …………………………………….………………… 86 4.5.4 Chlorite Mapping …………………………………………………...………… 87 4.5.5 Kaolinite Mapping…………………………………………………….………. 88 4.5.6 Muscovite Mapping ………………………………………….….……………. 89 4.6 Outcome of the Spatial and Spectral Analysis of ASTER Data………………. 90 4.7 Lineament Mapping and Analysis………………………………….…………. 92 4.8 Structural Deformations in the Study Area………………………………...... 94 4.8.1 Gold Occurrences Related to Structural Analysis in the Study Area…………. 95

CHAPTER V: CONCLUSION AND RECOMMONDATION

7.1 Conclusion. ……………………………………..…………………..……...... 97 7.2 Recommendations. ………………………………..…………………………... 98  References …………………………………………………………………….. 99  Appendix A. Gold Contents in Chip Samples in the Study Area……………... 109  Appendix B. Gold Contents in Soil Samples in the Study Area…..…………... 111

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List of Figure

Figure (1.1): Location map of the study area.…………………………….……….…….…. 2 Figure (1.2): 3D Terrane model displaying the main morphological features………….…. 4 Figure (1.3): DEM image general topography and drainage pattern………………………. 5 Figure (2.1): Compares the electromagnetic spectrum that Landsat 7 and landsat 8…...…. 13 Figure (2.2): Mosaic image covering, the area of interest…………………………………. 15 Figure (2.3): Flow chart illustrate the methodology adopted in the study………………… 21 Figure (3.1): The wavelength from gamma-rays to radio waves………………………..…. 26 Figure (3.2): Spectral reflectance curves of some land cover compared to the Landsat 8… 28 Figure (3:3): illustrate the Launched and Decommissioned of Landsat series………….…. 30 Figure (3.4): Geotectonic map of the Precambrian structures and the major sutures and shear zones of the Arabian-Nubian Shield…………………………………………………….…. 35 Figure (3.5): Terranes map of the Arabian-Nubian Shield………… ……………...... 36 Figure (4.1): Linear contrast enhancement applied on bands 7, 5, 2 in RGB ……...... 44 Figure (4.2): Decorrelation stretched OLI bands 7, 5, 3 in RGB, ………………………….46 Figure (4.3): OLI geological colour composite image of bands 7, 5, 3 in RGB……………48 Figure (4.4): Sultan ratio image obtained using ratios of 6/7, 6/2 and 6/5*4/5 in RGB…… 50 Figure (4.5): Principal components, PC1, PC2, PC3, PC4 PC5 and PC6 ………………… 52 Figure (4.6): Colour composite of principal components, PC2, PC3, PC4 in RGB……….. 53 Figure (4.7): Geological map of the study area created based on images processing……... 55 Figure (4.8) Model of the porphyry copper deposit and hydrothermal alteration………… 68 Figure: (4.9). Hydrothermal composite ratio image ………………………………………. 70 Figure: (4.10). Mineral composite ratio image ……………………..……………..…. 71 Figure (4.11): Sabin’s band ratio image ………………………………………….….. 72 Figure (4.12): Abrams’s band ratio images ………………………………………… 73 Figure (4.13): Mineral Indices images (a) Iron oxides (b) Al- OH-bearing minerals (c) Ferrous oxides ……………………………………………………………………………...... 75 Figure (4.14): Vector layer of mineral Indices images classifier alteration minerals……… 76 Figure (4.15): Mineral Indices images (a) Alunite index (b) Clay minerals index……… 78 Figure (4.16): The mineral Indices images (a) ferrous oxide minerals (b) Iron oxide….. 79

Figure (4.17): Illustrate the band ratios (4/8, 4/2, and 8/9 in RGB), of ASTER imagery… 80 Figure (4.18): Spectral library profiles, (mineral jpl_beckman_826.sil and USGS minerals_beckman_421) Spectral for the alteration minerals…………………………… 82 Figure (4.19): Spectral library profiles, (mineral jpl_beckman_826.sil and USGS minerals_beckman_421,) after convolution and resampling to ASTER sensor…… ……... 82 Figure: (4.20): Alteration map produced from the SAM classifier, (a) SAM absorption rule images of Hematite (b) gray image superimposed by vectored layer of the hematite mineral displayed in yellow color………………………………………………...... 85 Figure (4.21): Alteration map produced from the SAM classifier, (a) absorption rule images of dolomite appears as dark tone (b) ASTER image superimposed by vectored layer of the dolomite mineral displayed in violet color.…………………………………………………86

Figure (4.22): Alteration map produced from the SAM classifier, (a) buddingtonite alterations mineral display the highest absorption areas as dark tone (b) vectored layer of the buddingtonite mineral is displayed in yellow color………………………………………………………. 87

Figure (4.23): Alteration map produced from the SAM classifier (a) SAM absorption rule images, the chlorite appears as dark tone (b) The chlorite mineral is shown in light green color in the vectored layer………………………………………………………………………... 88

Figure (4.24): Alteration map produced from the SAM classifier, (a) kaolinite brought from SAM classifier, appears as dark tone in the rule image (b) VNIR image superimposed by vectored layer of the kaolinite displayed in red color………………………………...……. 89

Figure (4.25): Alteration map produced from the SAM classifier, (a) SAM absorption rule images of muscovite appears as dark tone (b) VNIR image overlay by vectored layer of the muscovite mineral displayed in light green color…………………….………...... 90

Figure (4.26): Alteration zones maps of SAM classifier for ASTER data, created by matching image spectra to mineral spectra in the USGS spectral library and JPL reference spectra……………………………………………………………………………………… 91

Figure (4.27): Lineament classification map of the study area …………...... 93

Figure (4:28): Structural map of the study area prepared through the interpretation of Landsat imagery and limited filed work…………………………………………………………….. 96

List of Table

Table (2.1): The OLI and TIRS bands designations……………….………………………. 11 Table (2.2): Sensor characteristics of the ASTER instruments……………………………. 16 Table (3.1): Characteristics of the electromagnetic spectral regions………………………. 25 Table (3.2): Launched, decommission and sensors of Landsat Series…………………….. 31 Table (4.1) Summarizes the basic statistics of the bands involved in the transformation and gives the engine vector …………………………..…………………………………………51 Table (4.2): Summarizes the basic statistics of the Correlation Matrix …………………… 51 Table (4.3): ASTER mineral indices used in the present study……………………………. 77 Table (4.4): Details of the USGS and JPL reference spectra of alteration minerals………. 82

List of Photo

Photo (4:1): Contact between Meta-andesite and Meta-dacite…………………………….. 62 Photo (4.2): Meta greywacke in hand specimens……………….…………………………. 63 Photo (4.3): Outcrop of the marbles trended N-S… …………………..….……………….. 64 Photo (4.4): Illustrate outcrop of Post-orogenic granite……….…………………………… 66

List of Plate

Plate (4.1): Photomicrograph the serpentinites under XPL microscope…………………… 57 Plate (4.2): Photomicrograph of the listwanite rock under XPL microscope……………… 57 Plate (4.3): Photomicrograph of the pyroxenite. XPL microscope ……………………….. 58 Plate (4.4): Photomicrograph of the Meta-gabbro. XPL microscope ……………………... 58 Plate (4.5): Photomicrography of the andesite. XPL………………………………………. 60 Plate (4.6): Photomicrograph of dacite. XPL………………………………………………60 Plate (4.7): Photomicrograph of the Rhyolite. XPL……………………………………….. 61 Plate (4.8): Photomicrography of the Basalt. XPL………………………………………… 61 Plate (4.9): Photomicrograph of the meta-greywacke in XPL……………………………... 63 Plate (4.10): Photomicrograph of the meta- conglomerates in XPL area………………….. 64 Plate (4.11): Photomicrograph of post-orogenic granite.in XPL…………………………... 66

List of Abbreviation

ASPRS American Society of Photogrammetry and Remote Sensing ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer BIL Band Interleaved by Line BIP Band Interleaved by Pixel BSQ Band Sequential DIP Digital Image Processing DN Digital Number EAO East African Orogeny EMR Electro-Magnetic Radiation EMS Electromagnetic Spectrum EOS Earth Observation System ERTS Earth Resources Technology Satellite ESEI Environmental Systems Research Institute ETM+ Enhanced Thematic Mapper sensor FCC False Color Composite FLAASH Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes GIS Geographical Information Systems HIS Intensity Hue Saturation HSZ Hamissana Shear Zone IFOV Instantaneous Field of View ISA Italian Space Agency JERS-1 Japanese Earth Resource Satellite-1 JNASDA Japanese National Space Development Agency JPL Jet Proportion Library LDCM Landsat Data Continuity Mission MITI Ministry of International Trade and Industry MSS Multispectral Scanner System NASA National Aeronautics and Space Administration NDVI Normalized Difference Vegetation Index NIMA National Imagery and Mapping Agency NIR Near Infra-Red

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OLI Operational Land Imager PCA Principal Component Analysis QWIP Quantum Well Infrared Photo RBV Return Beam Vidicon SAM Spectral Angle Mapper SAR Synthetic Aperture Radar SMACC Sequential Maximum Angle Convex Cone SRTM Shuttle Radar Topography Mission SWIR Short Wave Infra-Red TCC True Color Composite TM Thematic Mapper USGS United States Geological Survey UTM Universal Transverse Mercator VNIR Visible Near Infra-Red WGS84 World Geographic System 84 WiFS Wide Image Field Sensor

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CHAPTER I: INTRODUCTION

1.1 Statement of the Problem The study area (Hamisana), is a vast stretched land characterized by high topographic rugged rocky terrain. This area is rich in its mineral resources. Nevertheless, difficulties in the ground- based geological mapping and exploration due to the inhospitable environments making traditional geological survey rather difficult. In such cases, the use of remote sensing represented in image processing of satellite imageries combined with GIS techniques are preferable to save time and effort and reduce costs expended by most traditional methods.

Satellite remote sensing images have been widely and successfully used for mineral exploration. Remote sensing relies mostly on the capability of the sensor to register spectral signatures and other geological features related to mineral deposits. Gold is one of the most important mineral commodities that have been searched with the use of satellite remote sensing, GIS can help in many aspects of the mineral exploration activities.

1.2 Objectives of the Study The main objective of this research is to apply remote sensing and GIS techniques in geological mapping and general prospecting for minerals in the study area. The targeted geological mapping is in a medium- scale of 1:70,000 based on integrated remotely sensed data of moderate spatial resolution, multispectral optical data, limited field work and extensive laboratorial investigations. The study aims to decipher the main geological elements of the study area in terms of lithological and alteration mineral's related mineralization, which represent prospective targets for gold mineralization.

The specific objectives of this research can be summarized as follows:

 To perform geological mapping and determine the main lithological variations.

 To prospecting for the economic potentiality of gold and related mineral resource.

 Use spectral analysis classification to identify alteration minerals related to gold.

 To identify the structural control on mineralization in the target area.

1.3 The Study Area 1.3.1 Location and Accessibility The study area is located in the northwestern flanks of the Red Sea Hills in the Hamisana area. It is considered as a part of Gebeit Al-Maadin District, which form a prominent physiographic feature in NE Sudan. The study lies about 400km northeast of Atbara town. It is bounded by latitudes: 20°28 00̎ - 21° 09 00̎ N and longitudes: 34°40̍ 00̎ - 35°14 00̎ E, (Figure 1.1). The area is accessible via unpaved roads through two highways: Khartoum - Atbara - Port Sudan - Gebeit Mine to the Hamisana area and through Khartoum - Atbara – Abidiya, and via unpaved road directly to the Hamisana.

Figure (1.1): Location map of the study area.

1.3.2 Physiographic Features 1.3.2.1 Topography Topographically, the Hamisana as a part of Red Sea Hills is characterized by undulating topographic surface and high relief rocky terrains that vary in height from place to another. a colour composite image of bands 7, 4, 2 as RGB, has been draped over the extracted DEM, the produced satellite image provided a three dimensional perspective view (Figure 1.2). The visualized (3D terrane) image is superior in describing regional geomorphological features, as the flat sand covered area in the southwest of the image terrane. Syn-orogenic are deeply eroded and are exposed in erosional windows restricted in the eastern part of the study area. Various ring complex of granitic intrusions with moderate- to deep erosion levels are displayed, such as J. El-Shareef and J. Abu Dueim intrusion in the western part of the study area.

1.3.2.2 Drainage System The drainage pattern of the Red Sea Hills is generally structurally controlled, faults and folds produce rectangular and angular drainage patterns, whereas large batholiths produce radial patterns. Numerous wadi deposits plains are bisected the rocky area. The area is approximately covered by parallel seasonal streams cutting in basement rocks, making rectangular. The main seasonal streams in the study area are wadi Orshab, wadi Kamoreib, wadi Eikwan and Wadi Abu Dueim (Figure 1.3). They generally run westwards towards the Nile, cutting through a vast peneplain in between. These wadies are dry most of the year due to the climatic conditions, described above.

1.3.2.3 Climate and Vegetation Cover The study area is characterized by a desert climate, where the rainfall ranges from 10 mm/y to about 30 mm/y. These rains are restricted to the summer season between August and September. The summer is very hot with temperature around 40°C, while winter (from November up to February) is dry and cold, where the temperature reaches less than 15 degrees in the night. Due to the influence of the climate, vegetation cover is poor and sparse, bushes and small trees such as Acacia Nilotica, Acacia Etabacia and Phretiophytes are confined to the seasonal water courses and some of the sandy plains. Short-lived grasses flourish following the occasional rains on the hillsides and in the flat areas (Abdel Rahman, 1993).

Figure (1.2): 3D Terrane model of the study area displaying the main morphological features.

Figure (1.3): DEM image general topography and drainage pattern in the area

1.4 Population and Socio-Economic Activities The study area of the present investigation is located in the Red sea Hills state of eastern Sudan. They are sparsely populated by the Bisharin and Atmans of the Beja tribe that dominate the whole Red Sea Region. The inhabitants are mainly nomads breeding camels, goats and sheeps. Some of them peruse seasonal agricultural activities in the wadis, depending on the scarce rainfall to grow sorgham (dura). However, some of the water wells which are dug into the thick Wadi-fill have dependable quantities of good water (Abdel Rahmm, 1993).

In the last decade these areas have witnessed a gold rush by indigenous population and foreigners, where they exploited gold mineralization, the local people used traditional and primitive mining methods to produced gold. They exploited gold mostly from the placers and primary deposits. where they exploited gold mineralization the gold exploitation leads to develop many localized mining centers as Noraya market. Recently, most these areas have been licensed to local mining companies such as Orshab Gold Mining and other start mining activities,

1.5 Previous Studies The geology of the Red Sea Hills Region of Sudan, has received considerable attention in comparison to the rest of the country, nevertheless, vast areas remained poorly known. Mining of gold in the region dates back to the Pharaonic ages, but systematic geological mapping was started by the Sudan Geological Survey in the 1950's.

Delany, 1955; Gass, 1955; Ruxton, 1956. apart from geological investigations by local and foreign individual geoscientists, the Red Sea Hills have also been an area of elaborated regional mapping and detailed mineral prospecting and exploration studies. This has been within the framework of joint-venture projects. The first major project started in 1970's with the Soviet technical aid (Techno-export). It was proceeded by a number of programs by other foreign mining companies and institutions, like the Bureau de Recherches Geologiques et Minieres (BRGM) of France.

Gass (1955) made the first attempt to class the basement rocks of the northern Red Sea Hills, into sedimentary-volcanic sequences, batholithic granites and younger granites. Ruxton (1956) tentatively classified the basement rocks of northern Red Sea Hills into three major

sedimentary-volcanic units: The Primitive System; the Nafirdeib Series; the Awat Series; and four periods of igneous intrusions.

Gabert et al., (1960) who introduced the term "Kashabib Series" to replace the "Primitive System". This three-fold system of classification by Gabret et al., (1960). has been used with minor modifications by most of the previous workers, e.g. Kabesh (1962),

Whiteman (1971), Vail (1972, 1978) and Ali (1979) who described the regional geology of the northern Red Sea Hills and showed their correlation with the southern Red Sea Hills and the other parts of the Sudan.

Bellevier et al. (1980) conducted the first studies of the HSZ, they mapped and undertook geochemical prospecting in a region bounded by 21°-22°N and 35°-35°.15'E. They demonstrated that this part of the HSZ was characterized by tabular bodies of greenschist facies volcanic rocks, gabbros, and serpentinites, all affected by a pervasive N-S trending foliation and upright isoclinals folds.

Fitches et al. (1983) identified the Sol Hammed-Onib ophiolite belt. Hussein et al. (1984) defined the southwestward extension of the Arabian Yanbu Suture, he suggested that the major N-S trending structure to the south ('Sol Hammed-Aberkateib Shear Zone' of Almond et al. 1984a) was a further continuation of this suture to the south, and termed the entire structure the Sol- Hammed Suture.

Almond et al. (1984a, b) reported on the structure around Aberkateib, which they called the Aberkateib shear zone. They noted that the shear zone was about 12 km wide, composed of mylonitized siliceous greenschist striking N15°W, dipping 70°W, and displaying sub horizontal lineation. Both sides of the shear zone at Aberkateib show a transition through less deformed rocks into the rocks of the Serakoit granite batholiths, demonstrating that shearing was younger.

Vail (1985) first appreciated the significance of the major N-S trending structure that we now call the HSZ. The northern part of the HSZ was first studied by Ball (1912) during regional topographical and geological surveys of the SE Desert of Egypt.

The ophiolite belts of the Red Sea Hills of the Sudan are considered to represent suture zones (Fitches et al., 1983; Vail, 1983) and were probably continuous with their equivalents on the

Arabian side of the Pan-African Nubian-Arabian Shield (Bakor et al., 1976; Camp, 1984; Vail, 1985b). Kröner et al., (1987), on the basis of ophiolite decorated sutures and associated major shear zones, subdivided the Nubian Shield into five terranes in the Red Sea Hills of Sudan

Recognizing a major belt of ophiolitic rocks along the Egyptian-Sudanese border, Kröner et al. (1987) identified the 'Allaqi-Heiani Belt' and suggested that this might be a major suture. If so, then the Allaqi-Heiani suture could be the western continuation of the combined Yanbu-Sol Hamed suture. This would require at least 70 km of dextral offset along the structure that Kröner et al. (1987) first called the HSZ. This terminology is preferred, because it clearly and for the first times in print distinguished the N-S trending HSZ from the E-W trending Allaqi-Heiani suture and NE-SW trending Sol Hamed suture.

The Hamisana zone characterized by east-west crustal shortening and by steep folds and thrust faults, Calc-alkaline magmatism, was contemporaneous with and outlasted main phase deformation, thus deformation occurred in an intra-arc setting. Thrust faults were reactivated after east-west contraction, compromising their usefulness in delineating “sutures” or as piercing points across older faults (Miller and Dixon, 1988).

Stern et al. (1989) investigated the Hamisana Shear Zone (HSZ) is a broad zone of deformation approximately 50 km wide and at least 300 km long, making it one of the largest basement structures in NE Africa. It has been interpreted as a Precambrian suture, as a zone of strike-slip displacement, or as a zone of crustal shortening.

The results of new Rb-Sr and U-Pb zircon geochronological studies indicate that the northern part of the HSZ was thermally active during the Pan-African event until 550 Ma ago; initiation of the structure may have begun 40-110Ma ago earlier. The timing of activity in the HSZ is 50- 150Ma younger than coalitional suturing and terrain assembly in the Arabian-Nubian Shield but is similar to the 655-540Ma Najd fault system of Egypt and Arabia. Deform most important deformation of the HSZ is unrelated to suturing, and at least one late Precambrian suture must extend west from Arabia into the interior of N. Africa (Stern et al., 1989).

Khalil (1973 & 1993) discussed the quantitative interpretation of the secondary dispersion pattern of gold deposits and the application of the geochemical criteria in prospecting for mineral deposits in the basement complex of the Sudan, with examples from the Red sea Hills.

Abdel Rahman (1993) investigated the regional extent, geotectonic environment and the related metallogeny of some of the ophiolites of Sudan. with examples from the Red sea Hills Hamissana.

Elsayed (2002) utilized the advantages of digital image processing techniques of satellite data and the GIS in geological mapping, hydro geological investigation and mineral prospecting in central Red Sea Hills of Sudan.

Babiker, (2006): Different digital image processing and geographic information system techniques, together with a limited reconnaissance ground truthing, to investigate the geology and lithostratigraphic units of the area around J. Erba and J. Oda of the Sudanese Red Sea Hills (RSH).

Elsayed et al. (2014) Investigated and assessment the mineral potentiality of Block (5) in the Hamisana Area, Red Sea Hills of Sudan: using a remote sensing and geo-statistical approach. to carry outgeneral prospecting on the remote sensing base, followed by the assessment and statistical analysis of the economic potentiality of gold and related mineral resources based on geochemical results of the analyzed samples

Eljah et al. (2015) investigated the geological and tectonic setting of the Kamoreib metavolcanic, southern Hamisana area. The metavolcanics include porphyritic dacite, amygdaloidal andesite, andesite, rhyolite and basalt, intercalated with tuffaceous materials. Petrographic and geochemical analysis revealed that the metavolcanics represent a mature island arc environment which is characterized by calc-alkaline geochemical affinity.

Elsheikh et al. (2015) investigated the structural evolution of the Hamisana Geodynamic Zone. According to Elsheikh et al. (2015), there are at least three episodes of tectonic events including five phases of deformations. The first episode is represented by the formation of foliations (D1), followed by the strong collision between Haya and Gebeit terrains (D2). The second episode is the collision of the mentioned arcs with Gabgaba terrain (D3). The third episode is represented by the open folding with E-W axis, and the reactivation of the N-S shear zone in the study area (HSZ) that represents the prominent structural feature in the area.

CHAPTER II: METHODS OF INVESTIGATIONS

2.1 Introduction Different methods have been conducted in this study to achieve the objectives stated in the previous chapter. A provisional collection of different digital and non-digital datasets was the first step done. Pre-field office and laboratory work included the revision of the previous relevant work, checking existing maps and charts and the preparation of different thematic maps (base maps, geological interpretation maps, digital elevation models…. etc.), using different techniques of digital image processing of satellite data and editing of maps using GIS software. Several reconnaissance field trips have been conducted to the study area. During which, the enhanced satellite images have been verified.

The post-field work included geochemical analysis of the collected representative samples, another phase of digital image processing of satellite data taking into account the obtained field data and the geochemical results. GIS procedures have been performed to construct a geodatabase for the study area; analyzing and modeling different spatial data and the final production of a geological map at the scale of 1:70.000. Details of the material, methods and tools used and utilized during this study are discussed below:

2.2 Materials and Data Different geological maps and charts have been used. These are obtained from the Geological Research Authority of Sudan (GRAS), in addition to structural map, geological map, and charts. (Published and unpublished).

2.3. Landsat 8 Data (OLI) Landsat 8 is an American Earth Observation Satellite launched on February 11, 2013, It is the eighth satellite in the Landsat program, the seventh to reach orbit successfully. Originally called the Landsat Data Continuity Mission (LDCM), it is collaboration between NASA and the United States Geological Survey (USGS). NASA Goddard Space Flight Center provided development, mission systems engineering, and acquisition of the launch vehicle while the USGS provided for development of the ground systems and will conduct on-going mission operations. Landsat 8 is equipped with the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), which are able to collect up to 550 multispectral images daily, each image covers an area with dimensions of 170 km (north-south) by 183 km (east-west; http://landsat.usgs.gov).

2.3.1 The Landsat 8 Spectral Bands

Referring to Table (2.1), Band 1 senses deep blues and violets. Blue light is hard to collect from space because it’s scattered easily by tiny bits of dust and water in the air, and even by air molecules themselves. It is also called the coastal/aerosol band, after its two main uses: imaging shallow water and tracking fine particles like dust and smoke. Bands 2, 3, and 4 are visible blue, green, and red. Band 5 measures the near infrared, or NIR. This part of the spectrum is especially important for ecology because healthy plants reflect it – the water in their leaves scatters the wavelengths back into the sky. By comparing it with other bands, we get indexes like NDVI, which let us measure plant health more precisely than if we only looked at visible greenness (LDCM, 2013)

Bands 6 and 7 cover different slices of the shortwave infrared, or SWIR. They are particularly useful for telling wet earth from dry earth, and for geology: rocks and soils that look similar in other bands often have strong contrasts in SWIR. Band 8 is the panchromatic – or just pan – band, it works just like black and white film: instead of collecting visible colors separately, it combines them into one channel, because this sensor can see more light at once, it’s the sharpest of all the bands, with a resolution of 15 meters (Table 2.1; LDCM, 2013)

Band 9 shows the least, yet it is one of the most interesting features of Landsat 8. It covers a very thin slice of wavelengths, few space-based instruments collect this part of the spectrum, because the atmosphere absorbs almost all of it. Bands 10 and 11 are in the thermal infrared, or TIR (Table 2.1) – they see heat. Instead of measuring the temperature of the air, like weather stations do, they report on the ground itself, which is often much hotter (http://landsat.usgs.gov).

2.3.2 Operational Land Imager The Operational Land Imager (OLI; Table 2:1) advances Landsat sensor technology using an approach demonstrated by the Advanced Land Imager sensor flown on NASA’s experimental Earth Observing-1 (EO-1) satellite. Earlier landsat satellites carried whiskbroom sensors that employed scan mirrors to sweep the instrument’s field of view across the surface swath width and transmit light to a few detectors. In contrast, the pushbroom sensor uses long detector arrays, with over 7000 detectors per spectral band, aligned across its focal plane to view across the swath. This pushbroom design results in a more sensitive instrument providing improved land surface information with fewer moving parts. Its images have 15 meter panchromatic and 30 meter multi-spectral spatial resolutions (including visible, near infrared, and shortwave- infrared;) along a 185-kilometer-wide swath, covering wide areas (LDCM, 2013).

Table (2.1): The OLI and TIRS bands designations, Source: http://landsat.usgs.gov

Spectral Band Wavelength Resolution

Band 1 - Coastal / Aerosol 0.433 - 0.453 30 m Band 2 – Blue 0.450 - 0.515 30 m Band 3 – Green 0.525 - 0.600 30 m Band 4 – Red 0.630 - 0.680 30 m Band 5 - Near IR 0.845 - 0.885 30 m Band 6 – SWIR 1 1.560 - 1.660 30 m Band 7 – SWIR-1 2.100 - 2.300 30 m Band 8 – Panchromatic 0.500 - 0.680 15 m Band 9 – Cirrus 1.360 - 1.390 30 m Band 10 – TIRS 1 10.30 - 11.30 100 m Band 11 – TIRS-2 11.50 - 12.50 100 m

The OLI collects data from nine spectral bands. Seven of the nine bands are consistent with the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors found on earlier Landsat satellites, providing for compatibility with the historical Landsat data, while also improving measurement capabilities. (Figure. 2.1) compares the portions of the electromagnetic spectrum that Landsat 7’s ETM+ observed to the parts of the spectrum that LDCM’s OLI observes. Note that, OLI detect two new spectral bands labeled 1 and 9. Previous Landsat sensors (i.e., MSS, TM, and ETM+) used mirrors that swept back and forth, across the swath like a “whiskbroom” to collect data. New technologies allow OLI to view across the entire swath at once, building strips of data like a “pushbroom.” The advantages are that pushbroom sensors require fewer moving parts and are more sensitive than whiskbroom sensors (USGS, 2013).

2.3.3 Thermal Infrared Sensor The Thermal Infrared Sensor (TIRS), built by the NASA Goddard Space Flight Center, conducts thermal imaging and supports emerging applications such as evapotranspiration rate measurements for water management. The TIRS focal plane uses Quantum Well Infrared Photo detector arrays (known as QWIPs) for detecting the infrared radiation—a first for the Landsat program. The TIRS data it is registered to OLI data to create radiometrically, geometrically, and terrain-corrected 12-bit Landsat 8 data products (USGS, 2013).

Figure (2.1): Compares the portions of the electromagnetic spectrum that Landsat 7’s (ETM+) and LDCM’(http://landsat.usgs.gov)

2.4 Landsat 8 Pre-Processing and Preparation

Applications of remote sensing in geology involve delineation of structures and discrimination of rock types. Therefore, the purpose of this chapter is to present the different digital image processing techniques used to enhance the geological details of the study area (lithological units, alteration zones, structural elements). The data used in digital image processing is illustrated using new generation of Landsat satellites (Landsat 8) which include band 1 through band 7 (Visible and Reflected Infrared), band 8 is (Panchromatic), band 9 (Cirrus Thermal Infrared) and bands 10, 11 (Thermal Infrared).

The preprocessing phase include the radiometric and geometric corrections. In the following section, the pre-processing techniques, image enhancement techniques and image interpretation used are described. Digital image processing involves the manipulation and interpretation of digital image with the aid of a computer (Lillesand and Kiefer, 2000). Based on the objectives and techniques, digital image processing can be categories into three main groups (Gupta, 2003):

i. Pre-Processing and Preparation Process The pre-processing operations include the geometric and radiometric corrections, radiometric image correction includes correction of the recorded digital image in respect of radiometric distortions, while geometric image corrections deal with digital processing for systematic geometric distortions caused by the sensors and platforms.

The image registration process is the superimpositions of images taken by different sensors from different platforms, and /or at different times, over one another based on a standard map projection.

ii. Processing Operations Processing operations are the main image processing and can be grouped into enhancement and classifications processes. Image enhancement, includes all processes that render certain features of interest in an image more interpretable and embraces various colour enhancement, contrast stretching, image filtering, image transformation, HSV Sharping, saturation stretching and image fusion. Image classification is the categorization of pixels of a scene into various thematic groups based on spectral response.

2.4.1 Image Mosaicking and Subsetting

Satellite imageries come in varying swath widths, depending on their source of acquisition. For example in this research the Landsat 8 data was used. Each image of Landsat 8 covers an area with dimensions of 170 km (North-South) by 183 km (East-West). These images cover large swathes of land, but in most of the cases the area of interest is not covered by a single scene. Accordingly, it may be need to assemble several satellite scenes in order to obtain full coverage of area of interest. This composition process is known as ‘mosaicking. This process requires very accurate radiometric and geometric corrections to the constituent imageries.

Image mosaicking is the process of overlaying two or more images that have overlapping areas or to put together a variety of non-overlapping images and/or plots for presentation. Image files that need to be mosaicked must have the same spatial resolutions (typically geo-referenced) and spectral resolutions (typically pixel-based) and the same number of bands. Following this procedures, a mosaic image was built form Landsat 8 OLI scenes: four scenes of Landsat 8 OLI “Operational Land Imager” sensor have been used within the course of this work (Figure 2.2). Details of the acquired scenes are provided bellow:

 Scene 1, defined with path 172, row 45, acquisition date: 14th January 2014  Scene 2, defined with path 172, row 46, acquisition date: 14th January 2014  Scene 3, defined with path 173, row 44, acquisition date: 05th January 2014  Scene 4, defined with path 173, row 46, acquisition date: 05th January 2014

This mosaic image is rather larger in size is very demanding on computing resources. Sometimes, for management or other reasons, only a part of the entire image needs to be displayed or processed to convey the information or to represent the whole. Therefore, it becomes necessary to extract the area of interest from the images/ mosaics, and this extraction process is known as ‘Subsetting’.

Figure (2.2): Mosaic of four scenes covering the Hamisana Shear Zone, the area of interest located in the central mosaic image.

2.4.2 Image Fusion The spatial resolution of a multispectral digital image is enhanced in a process of the type, wherein a higher spatial resolution panchromatic image is fused with a plurality of lower spatial resolution spectral band images. Resolution merge techniques involve the integration of various remotely sensed data delivered from different sensors having dissimilar spatial and spectral resolutions. The obtained resolution merge data has better and more information than that obtained from single sensor data. Thus increases confidence and reduces ambiguity in image interpretation and application. Resolution merge image can be applied in order to

sharpen images ‘increasing the spatial resolution’, improve geometric corrections and detect changes using multi-spectral data and other applications (Gupta, 2003). In this study resolution merge image is made on a pixel-based for the co-registered images to sharpen the Landsat 8 multispectral bands of 30-meter spatial resolution with the panchromatic band of the same satellite of 15-meter spatial resolution. The resulted image was used as an input for all the subsequent digital image processing procedures.

2.5 The ASTER Instrument The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument provided by the Ministry of International Trade and Industry (MITI), Tokyo, Japan to be launched on NASA’s Earth Observing System morning (EOS- AM1) platform in December 1999. The platform described as a Sun-synchronous. ASTER's data is considered as semi-hyperspectral sensor, where spectral bands and spatial resolution are generally more detailed than those of Landsat, meaning it is particularly useful for geological studies as well as environmental monitoring. ASTER consists of three different subsystems that provide a 14 narrow contiguous bands (Table 2.2). The Visible and Near-infrared (VNIR) has three bands with a spatial resolution of 15 m; the Shortwave Infrared (SWIR) has 6 bands with a spatial resolution of 30 m; and the Thermal Infrared (TIR) has 5 bands with a spatial resolution of 90 m. (ASTER Users Handbook date).

Table (2.2): Sensor characteristics of the ASTER instruments, (after Lillesand and Kiefer, 2000 ) Spatial Radiometric Spectral range Subsystem Band No Resolution Resolution (µm) (m) (bits) Band 1 0.52–0.60 VNIR Band 2 0.63–0.69 15 8 Band 3 0.76–0.86 Band 4 1.60–1.70 Band 5 2.145–2.185 Band 6 2.185–2.225 SWIR 30 8 Band 7 2.235–2.285 Band 8 2.295–2.365 Band 9 2.360–2.430 Band 10 8.125–8.475 Band 11 8.475–8.825 TIR Band 12 8.925–9.275 90 12 Band 13 10.25–10.95 Band 14 10.95–11.65

Each subsystem operates in a different spectral region, with its own telescope(s), and is built by a different Japanese company. All ASTER bands cover the same 60 Km imaging swath with a pointing capability in cross-track direction to cover 116 Km from Nadir at an altitude of 705 Km. The scan angle is 98.3°. This is accessible at least once every 16 days (ASTER Users Handbook Date).

Multispectral satellite remote sensing technologies have been commonly used for remotely sensed classification of vegetation since the early 1960s (Govender et al., 2008; Jensen, 2007). In a single observation, multispectral sensors generate three to six spectral bands of data that range from the visible to NIR of the EMS (Jensen, 2007).

Hyperspectral sensors commonly collect more than 200 spectral bands that range from the visible to SWIR section of the EMS. They provide extensive analyses of earth surface features that would be limited with coarser bandwidths collected by multispectral data. In this study ASTER images have been treated as semi- hyperspectral data as they provide narrow band width continuous spectrum especially in the SWIR spectrum. The preprocessing steps carried out in this study for the ASTER images include the resampling of the 30m spatial resolution SWIR bands to 15 m spatial resolution. The thermal TIR bands have been discarded, as they have coarser spatial resolution and pertaining information about the lithologies already examined by the optical data.

2.5.1 ASTER Data Preparation and Processing 2.5.1.1 ASTER Geometric Corrections ASTER’s geometric system correction primarily involves the rotation and the coordinate transformation of the line of sight vectors of the detectors to the coordinate system of the Earth. This is done as part of ASTER Level-1 processing at GDS using engineering data from the instrument (called supplementary data) and similar data from the spacecraft platform (called ancillary data). The geometric correction of ASTER data has evolved through elaborate processes of both pre-flight and post-launch calibration (http://speclab.cr.usgs.gov).

2.5.1.2 Radiometric Calibration and Atmospheric Correction The nature of remote sensing requires that solar radiation pass through the atmosphere before it is collected by the instrument. Because of this, remotely sensed images include information about both the atmosphere and the earth's surface. For those interested in quantitative analysis of surface reflectance, removing the influence of the atmosphere is a critical pre-processing step (Lillesand and Kiefer, 2000).

To compensate for atmospheric effects, properties such as the amount of water vapor, distribution of aerosols, and scene visibility must be known. Because direct measurements of these atmospheric properties are rarely available, there are techniques that infer them from their imprint on hyperspectral radiance data. These properties are then used to constrain highly accurate models of atmospheric radiation transfer to produce an estimate of the true surface reflectance. Moreover, atmospheric corrections of this type can be applied on a pixel-by-pixel basis because each pixel in a semi- hyperspectral image contains an independent measurement of atmospheric water vapor absorption bands (ENVI, 2008).

In order to carry out atmospheric correction, the input image must be a radiometrically calibrated radiance image. In this correction, the interleave of the raw input image has been converted into band-interleaved-by-line (BIL) format.

ASTER Atmospheric effects were corrected using the FLAASH, atmospheric correction software provided by ENVI. FLAASH is Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes module is a first-principles atmospheric correction modeling tool for retrieving spectral reflectance from hyperspectral radiance images. FLAASH can accurately compensate for atmospheric effects. FLAASH corrects wavelengths in the visible through near-infrared and short-wave infrared regions. The atmospheric correction within this study was conducted using the following parameters: i. Input Radiance Image (ASTER L1T) using single scale factor for all bands of 10.0. ii. Scene and sensor information as:  Sensor altitude: 705 km  Pixel size: (15m)  The Ground Elevation: 0.7 km (average scene elevation obtained from DEM)  Scene center location: 20.999498 N; 34.767981 E  Flight date: 15/3/2004  Flight time GMT (08:19:32) iii. Atmospheric model settings: Mid-Latitude Summer (MLS) iv. The Initial Visibility: 80 km, value is assumed for the atmospheric correction if the aerosol is not being retrieved.

2.6 Shuttle Radar Topography Mission and GIS

The Shuttle Radar Topography Mission (SRTM) produced the most complete, highest resolution digital elevation model of the Earth. The project was a joint endeavor of NASA, the National Geospatial-Intelligence Agency, and the German and Italian Space Agencies, and flew in February 2000. It used dual radar antennas to acquire interferometric radar data, processed to digital topographic data at 1 arc-sec resolution. The objective of this project is to produce digital topographic data for 80% of the Earth's land surface (all land areas between 60º north and 56° south latitude), with data points located every 1-arc-second (approximately 30 meters); on a latitude/longitude grid. The absolute vertical accuracy of the elevation data is 16 meters, at 90% confidence (SRTM, 2015). Digital Elevation Model (DEM) derived from topographic data from Shuttle Radar Topographic Mission data (SRTM) provides 3Dimensional views (which were lack in the Landsat 8 data). The information from the processed and interpreted satellite imageries were combined with fieldworks data and petrographic data were integrated in a GIS database. The GIS layers have been co-registered using the following parameters:

 Projection: Universal Transverse Mercator (UTM)  Grid: UTM-Zone 36 N  Spheroid: World Geographic System 1984 (WGS84)  Datum: WGS84  Units: Meter  False Easting: 500000  False Northing: 0  Central Meridian: 33  Scale Factor: 0.9996  Latitude of Origin: 0

All the spatial data have been organized into a one geo-database, interrelated spatial data are stored in a group layer; the following group layers have been designed:

i. Digitally processed satellite data are saved in as a raster format. ii. Digitized lithological map, structural data, lineaments, sample locations and drainage lines are saved in vector format. iii. Demographical data, which include locations of village and towns, names of hills and mountain, and roads are portrayed in vector format.

iv. Classification layer such as SAM classifier for alterations minerals produced in raster format and convoluted to vector format. v. Elevation data are saved as the DEM layer in grid format, and in vector format in the form of contour layer.

2.7 Field works Fieldwork usually represents the very important phase of any geological research. The typical geological survey has been conducted using a GPS- GIS navigation system in order to access the interested location and building an instance geodatabase for field location and tracking. Field observations, satellite imagery are tools used for describing, analyzing and interpreting the physiographic features observed, as well as geological mapping and sampling of the major rocks units in the studied area. This procedure was followed in order to identify rock types, mineralogical composition, macro-, meso-, and mega-field relationships. About 100 representative rock samples, collected in this study. Among which, about 20 thin sections have been studied under the microscope to determine their mineral composition and mineral assemblages, about 120 Quartz composite chip samples, collected for gold determination and 180 alteration samples collected for pathfinders

2.8 Tools and Data In addition to the basic geological tools e.g. geological hammers, compass, camera, lens …etc., the following tools helped to performing different tasks during this study:

 Global Positioning System (GPS) receiver for navigation and location sample.  Lietz Research Petrographical Polarized microscope for petrographic investigation  Measurements tools and measurements of structural elements.  Photographic Camera for digital micrograph images.

2.9 Software A group of software has been utilized in this study, they include:  ENVI 5.3, PCI Geomatics and ERDAS Imagine for digital images processing  GIS software packages, Global Mapper for mapping and digitization  Surfer 12, Corel draw X7 for drawing.  Microsoft office tools for writing and data interning

Figure (2.3): Flow chart illustrate the methodology adopted in the study

CHAPTER III: LITERATURE REVIEW

3.1 Remote Sensing Overview The American Society of Photogrammetry and Remote Sensing (ASPRS) adopted a combined formal definition of remote sensing and photogrammetry as: “the art, science, and technology of obtaining reliable information about physical objects and the environment, through the process of recording, measuring and interpreting imagery and digital representations of energy patterns derived from non-contact sensor systems” (Colwell, 1983).

The objective of remote sensing is, generally to obtain information about objects on the earth surface from a distance (List, 1993). Sabins (1997) defined remote sensing as a science of acquiring, processing and interpreting images, and related data, obtained from aircraft and satellites that record the interaction between matter and electromagnetic radiation.

Remote sensing can be defined as the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under study (Lillesand and Keifer, 2004).

3.1.1 Historical Overview of Remote Sensing The historical development of remote sensing started with and revolutionized the early aerial photography systems, which were based on optical camera-mounted aircraft. The space race to the moon culminated in the American side by the creation of the first continuous program of observation and data collection of the earth by satellite from the space ‘‘the Earth Observation system (EOS)’’ managed by NASA. The EOS C started in 1972 by launching the first satellite system ‘‘the Earth Resources Technology Satellite (ERTS-1), which was later called Landsat- 1 systems, which carried the Multispectral Scanner System (MSS) and the Return Beam Vidicon (RBV). The Landsat-1 was followed by Landsat-2, and Landsat-3 series. This system progressed by the exploitation of the Thematic Mapper (TM) sensor in Landsat-4 and Landsat -5, and witnessed great improvement in Landsat-7 by the utilization the Enhanced Thematic Mapper sensor (ETM+) in 1999 (Gupta, 2003).

Concurrently, The Russians developed their own intelligence and Earth Observation systems (Although little is known about their programmes). The Russians developed a series of RESURS-01 to -4, the last being launched in 1998. The RESOURS satellite has two sensors; the MSU-E (high- resolution) provides three spectral bands (Green, Red, and NIR) with ground resolution 35 x 45 m. The second sensor is MSU-SK (medium resolution) has four bands (G,

R, NIR, TIR) with pixel dimension of 140 m x 185 m in the VNIR, and 600 m x 800m in TIR band (Gupta, 2003)

In 1978 the French undertook the SPOT 1 program, which is another earth observation multispectral sensor that continued with SPOT-2, SPOT-3 and SPOT-4 having four (20meter) multispectral ‘‘XS’’ and one panchromatic band ‘‘HRV’’ (10 meter). SPOT-5 was launched in 1999 and is considered as a multispectral- high spatial- resolution sensor that provides a panchromatic band ‘‘HRVIR’’ with 10-meter resolution and 5 meter for the multispectral bands ‘‘XS’’, and can increase the resolution to 5 meters ground resolution for the multispectral, and 2.5 meter for the panchromatic band in the super mode (http://www.spot.com).

Many countries developed their own earth observation system. The Republic of India developed a moderate resolution satellite system called IRS program (Indian Remote Sensing Satellite) and began with IRS-1 in 1988, followed by IRS-1B in 1991. IRS-1 & - 1B satellites have a multispectral scanner with four visible and near infrared bands with 72.5 meters and 36.25 meters ground resolution, respectively. A second generation of IRS satellites, named IRS-1C and IRS-1D series was launched in 1995 and 1997, respectively. The second generation of IRS satellites is characterized by three multispectral bands in the visible and near infrared ranges with 23-meter ground resolution, 70 meters resolution MIR (Mid Infrared) band, 5.8 meter panchromatic band and two WiFS bands (Wide Image Field Sensor) of 188 meters spatial resolution

In 17 February 1999 the Japanese National Space Development Agency (NASDA) launched the first Japanese Earth Resource Satellite-1 (JERS-1), that carried two imaging systems, an Optical Sensor (OPS), which covered the reflected region of the Visible Near Infrared (VNIR) and the Short Wave Infrared (SWIR) in seven bands, the second system is the Synthetic Aperture Radar (SAR) (Lillesand and Kiefer, 2000). The Radar application was improved by the creation of the interferometric Synthetic Aperture Radar (SAR), which allowed monitoring of ground terrain and relief from space (SRTM program). The radar application was progressed by the development of radar-bearing satellite system as ERS-1, JERS-1 and Radarsat (Gupta, 2003).

The second generation of satellite systems started at end of the last century and the turn of the new millennium. These satellites are characterized by modern high spatial or spectral resolution systems as the IKONOS, SPOT-5 and Quickbird, which have high-spatial ground resolution sensors. Based on the use of the area-array chip technology, IKONOS-2 satellite was launched

in September 1999 and employs linear array technology and records data in four channels of multispectral data at 4-meter ground resolution and one panchromatic channel with 1-meter resolution. The Quickbird, which is a commercial system, was launched in October 2001, with 61-centimeter resolution panchromatic band and 2.44-meter multispectral imagery (Gupta, 2003).

On other hand, high-spectral resolution sensors have been created at the same time. The advanced Earth Observation System (EOS) carried the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra platform with other instruments was launched in December 1999. The ASTER propped up the hyperspectral system from airborne to space platforms. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) obtains a high-resolution (15 to 90 square meters per pixel) images of the Earth in 14 different wavelengths of the electromagnetic spectrum, ranging from the visible to the thermal infrared light (http//: asterweb.jpl.nasa.gov/).

The NASA’s EO-1 platform is a multi- sensor belonging to the second generation was launched in November 2000, carrying Hyperion and ALI sensors from 705 Km orbit. Hyperion is the a satellite hyperspectral sensor, pushbroom scanner covering the 0.4 to 2.5 µm spectral range with 242 continuous spectral bands at approximately 10 nm spectral resolution and 30 meter spatial resolution in 7.5 Km wide swath perpendicular to the satellite motion. The ALI (Advanced Land Imager) represents the future of the multispectral- earth observation system that would replace the Landsat system. ALI sensor has nine channels in the range of VINIR & SWIR wavelength, all in 30-meter spatial resolution and a 10-meter panchromatic channel, with a full swath width of 36 Km. Hyperion is the first space-borne hyperspectral sensor onboard the EO-1 platform. The Hyperion gathers near-continuous data in 220 discrete bands at a 30- meter spatial resolution. The data is stored in 16- bit format (Kruse, 2003; Hubbard, et al., 2003; Hubbard, and Crowley, 2005)

Landsat-8 is an American Earth Observation Satellite launched on February 11, 2013. It is the eighth satellite in the Landsat program, the seventh to reach orbit successfully. It is originally called the Landsat Data Continuity Mission (LDCM). Landsat 8 images are available to the public since July 2013

3.1.2 Fundamentals of Electromagnetic Radiation and Physical Concepts

In remote sensing, Electromagnetic Radiation (EMR; Figure 3.1) is the main source of energy and communication between the sensors and the object under consideration. The transported energy can be measured and provide information on the nature of the radiant object (List, 1992). The Sun produces a continuous spectrum of electromagnetic radiation ranging from very short, extremely high frequency gamma and cosmic waves (<0.001 nm) to long, very low frequency radio waves (100 Km) the intervening parts being X-rays, ultra-violet, visible, near-infrared, mid-infrared, far-infrared, microwaves and radio waves Table (3.1). The EM spectrum can be divided into two parts: the optical range in which optical phenomena of reflection and refraction predominate and ranges from 0.02 µm to 1.00 µm; and the microwave range of wavelength between 1 mm to 1 meter (Gupta, 2003).

Table (3.1): Characteristics of the electromagnetic spectral regions (after Gupta, 2003)

Regions Wavelength Remarks

Incoming radiation completely absorbed by the upper Gamma - ray <0.03 nm atmosphere and not available for remote sensing Completely absorbed by the atmosphere. Not employed in X-ray 0.03 - 30 nm remote sensing 0.03 - 0.4 Incoming wavelengths <0.3 µm completely absorbed by Ultraviolet µm Ozone in the upper atmosphere Photographic Transmitted through the atmosphere. Detectable with film 0.3 - 0.4 µm UV band and photo-detectors, but atmospheric scattering is severe. Imaged with film and photo-detectors. Includes reflected Visible 0.4 - 0.7 µm energy peak of earth at 0.5 Infrared 0.7 - 100 Interaction with matter varies with wavelength. Atmospheric µm transmission windows are separated by absorption bands. Reflected solar radiation that contains no information about Reflected IR thermal properties of materials. The interval from 0.7 to 0.9 0.7 - 3.0 µm band µm is detectable with film and is called the photographic IR band Principal atmospheric windows in the thermal region. Images Thermal IR 3 to 5 µm, 8 at these wavelengths are acquired by optical mechanical band to 14 µm scanners and special vidicons systems but not by film 0.1 to 100 Longer wavelength that can penetrate clouds, fog, and rain. Microwave cm Images can be acquired in the active or passive mode Active form of microwave remote sensing. Radar images are Radar 0.1 - 100 cm acquired at various wavelength bands Radio >100 km Longest wavelength of the electromagnetic spectrum.

Figure (3.1): The wavelength from gamma-rays (< 0.001 nm) to the very long radio waves (>100 km; after, Drury 1997)

3.1.3 Interaction of Electromagnetic Radiation with the Atmosphere The electromagnetic radiation from the sun that is reflected by the earth and detected by the satellite or aircraft-borne sensor must pass through the atmosphere twice: one on its journey from the sun to the earth, and once after being reflected by the surface of the earth back to the sensor. During its passage through the atmosphere, electromagnetic radiation interacts with particulate matter suspended in the atmosphere and with molecules of the constituent gases. This interaction is usually described in terms of two processes called scattering and absorption. Both of these processes vary in their effect from one part of the spectrum to another region. The spectrum that are relatively free from the effects of the scattering and absorption are called atmospheric windows, and they cover the (visible/near infrared, middle-infrared and thermal- infrared) wavebands. The effect of the processes of scattering and absorption is to add a degree of haze to the image that is to reduce the contrast of the image and to reduce the amount of radiation returning to the sensor from the earth's surface and this will result in a decrease in the detectability of features present in the image. Therefore, they are known collectively as attenuation or extinction (Mather, 1999).

i. Absorption in the atmosphere Electromagnetic radiation of all wavelengths emitted by the sun reaches the top of the atmosphere. Only radiation with specific wavebands can pass through the atmosphere to reach the surface of the earth. This is because the gaseous component of the atmosphere acts as

selective absorbers. The most common absorbers are nitrogen, oxygen, carbon dioxide, water vapor and ozone (Gibson, 2000).

ii. Scattering in the atmosphere The scattering mechanism can be selective or non-selective. In Selective scattering, the relative size of the particles in the atmosphere and the wavelength are important. Rayleigh scattering occur when the dimensions of the particles (nitrogen and oxygen molecules) are small than the wavelength of the electromagnetic radiation. When the dimension of the aerosols such as dust, smoke and smallest pollen grains in the atmosphere are approximately the same as the wavelength of the radiation then the Mie scattering occurs. Non-selective scattering: scattering at all wavelength occurs equally with aerosols whose dimensions are greater than approximately ten times the wavelength of the radiation (Gibson, 2000).

3.1.4 Interaction of Electromagnetic Radiation with Earth Surface Features Electromagnetic energy reaching the earth surface may be reflected, transmitted or absorbed (Mather, 1999). Which processes actually occur depends on the wavelength of the radiation and the angle at which the radiation intersects the surface and the roughness of the surface. A surface that reflects all the incident energy is known as a specular reflector, while surface that scatters all the energy equally in all direction is a Lambertian reflector. However, in general, the more Lambertian a surface is, the better it is for remote sensing purposes. The albedo of the surface is given by the ratio of the electromagnetic radiation reflected from the surface to the total electromagnetic incident on the surface (Gibson, 2000).

There has been great interest in measuring the spectral signatures of surface materials, such as vegetation, soil, and rock, over the spectral range. A graphical representation of the reflectance variations as a function of wavelength is known as a spectral reflectance curve (Gibson, 2000). In other spectral regions, signatures of interest are temperature and emissivity (TIR) and surface roughness. The motivation of multispectral remote sensing is that different types of materials can be distinguished based on differences in their spectral signatures. An idealized spectral reflectance curve of vegetation, snow, soil and water is shown in (Figure 3.2). The vegetation curve shows relatively low values in the red and blue regions of the visible spectrum, with minor peak in the green spectral band. These peaks and the trough are caused by absorption of blue and red light by chlorophyll and other pigments (Mather, 1999).

Figure (3.2): Idealized spectral reflectance curves of some land cover types compared to the wavelengths of Landsat 8 OLI bands. (http://landsat.usgs.gov)

3.1.5 Sensors Sensors are classed according to the energy source into: I. Active Sensors

These sensors provide their own energy source for illumination. The sensor emits radiation which is directed toward the target to be investigated. The radiation reflected or scattered from that target is detected and measured by the sensor. Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season. Active sensors can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. However, active systems require the generation of a fairly large amount of energy to adequately illuminate targets (Gibson, 2000).

II. Passive Sensors

Remote sensing systems which measure energy that is naturally available are called passive sensors. These systems are used to detect energy when the naturally occurring energy is available. For all reflected energy, this can only take place during the time when the sun is illuminating the Earth. Energy that is naturally emitted (such as thermal infrared) can be detected day or night, as long as the amount of energy is large enough to be recorded (Gibson, 2000).

3.1.6 Image Resolution There are four types of resolutions in remote sensing including Radiometric, Temporal, Spatial and Spectral. The radiometric resolution is the sensors ability to distinguish the differences of intensity in an image, while temporal resolution is the repeat time for a sensor to pass over a geographic region. The most significant components of remotely sensed images are the spectral and spatial resolutions. These resolutions represent the geometric makeup of each pixel and relationship to surrounding neighbourhood pixels in a scene or image. Ultimately, the spatial and spectral resolutions are what set hyperspectral and multispectral imagery apart. Spatial resolution refers to the sharpness level of spatial detail shown in an image (Purkis; Klemas, 2011). It is the measure of the smallest object on the ground set by the sensor representing a single picture element (pixel) in the image. As a result, distance is associated with pixel size describing the side length of a pixel. Thus, the finer spatial resolution is associated with a smaller distance (i.e. 30m by 30m) making it easier for one to define features in a scene (Jensen, 1996).

Spectral resolution represents a particular range in wavelength of the Electromagnetic Spectrum (EMS) including the number and width of spectral bands measured by the sensor (Purkis; Klemas, 2011). If the sensor captures a small number of wide bands, it has a low spectral resolution. In contrast, if the sensor captures a large amount of narrow bands, the greater the spectral resolution. The advantage of a higher spectral resolution is for interpreters to distinguish between features in an image. The greater/finer detail in a scene, the more likely unique characteristics are to be defined. Based on spectral responses, hyperspectral imagery captures more narrow bands than multispectral in the same portion of the EMS (Jensen, 1996)

3.1.7 The Landsat Systems Generations The Landsat program was initiated by NASA as an experimental program aimed to earth’s resources observation and subsequently turned into commercial activities. The Landsat system described as unmanned medium resolution orbital sensor system. The platform described as a Sun-synchronous, near-polar orbit. Three generations of Landsat correspond to different technologies and platforms: The first generation is Landsat-1 (Figure 3.3) which was formerly called ERTS, Landsat -2 and -3. The second generation is Landsat-4 and 5 with Thematic Mapper ‘‘TM’’ sensor, whereas the third generation is Landsat-6 and –7 developed by the Enhanced Thematic Mapper (ETM+) sensor (Lillesand and Kiefer 2000)

Figure (3:3): illustrate the Launched and Decommissioned of Landsat series (http://landsat.usgs.gov)

The earliest generations of Landsat were equipped with a three-channel (except Landsat-3 that had high resolution panchromatic mode) Return Beam Vidicon (RBV) and widely used four- band Multi-Spectral Scanner (MSS) aimed at visible and VNIR bands. The MSS is a cross- track scanning system with an oscillating mirror that scans a swath 185 km wide, normal to the orbit path. The scan angle is 11.56°, and the image data is recorded only during the east-bound sweep. At an altitude of 918 km, the angle of Instantaneous Field of View (IFOV) is 0.087m.rad and produced a ground resolution cell of 7909 m². The data is either relayed directly to the ground receiving station or recorded by an on-board tape recorder until the platform is above a receiving station (Lillesand and Kiefer 2000)

In the second generation, Landsat-4 and - 5 carried the Thematic Mapper (TM) scanner, in addition to the MSS sensor. The TM sensor has a better spatial and spectral resolution than the MSS. The TM is a line scanner system with 7 channels, six in VIS, NIR, SWIR and one in TIR spectrum, which collects data in both east- and west-bound sweeps seven times per second. Thus improving radiometric accuracy. With a reduced altitude of 705 km, and a swath of 185 km, the ground resolution in TM bands is about 30 m² (except the thermal band which has 120 m²). The MSS on Landsat-4 and -5 are similar to those used during the first generation of Landsat (Gupta, 2003).

Landsat-6, carrying Enhanced Thematic Mapper (ETM), was launched in October 1993 (Table 3.2), but failed. The series is followed by Landsat-7, which was launched in April 1999, carrying the Enhanced Thematic Mapper Plus (ETM+). The main aim of Landsat-7 is to provide continuity of Landsat-4 and -5 TM data, with many improvements. Landsat-7 has eight spectral bands. Bands 1 to 6 are in blue, green, red, NIR, SWIR-I and SWIR-II are exactly the same as

those in Landsat TM with 30-meter ground resolution. The TIR band has been improved to 60- meter ground resolution and measured in two gains. Whereas, the panchromatic eighth band has 15-meter ground resolution. Landsat-7 also carries on-board solid state record for temporary storage of remote sensing data, which allows acquisition of the data over areas beyond the reach of ground receiving station (Gupta, 2003).

Table (3.2): Launched, decommission and sensors of Landsat Series (modified after Gupta, 2003)

Satellite Launched Decommissioned Sensor Orbit

Landsat-1 23-07-1972 6-01-1978 MSS/RBV 18 days / 900 km

Landsat-2 22-01-1975 25-02-1982 MSS/RBV 18 days / 900 km

Landsat-3 05-03-1978 31-03-1983 MSS/RBV 18 days / 900 km

Landsat-4 07-1982 14 -12-1993 MSS/TM 16 days / 705 km

Landsat-5 03-1984 05-06-2013 MSS/TM 16 days / 705 km

Landsat-6 05-10-1993 05-10-1993 ETM 16 days / 705 km

Landsat-7 06-04-1999 Still active ETM+ 16 days / 705 km

Landsat-8 11-02-2013 Still active OLI/ TIRS 16 days / 705 km

3.2 Geographic Information Systems 3.2.1 Introduction The appearance of Geographic Information Systems (GIS) in the mid-1960 reflects the progress in computer technology and the influence of quantitative revolution in geography. Geographical Information System is defined as "A computer-based technology and methodology for collecting, managing, analyzing, modeling and presenting geographic data for a wide range of applications" (Davis, 2001). It is a rapidly growing technology and a very powerful tool for processing, analyzing and integrating spatial data sets. GIS can accommodate and integrate limitless, multidisciplinary data sets provided that they are all controlled by the same georeferencing system. GIS as a computer-based system differs from other computer systems by

its capability to handle both locational data and attribute data about features (Lillesand and Kiefer, 2000).

3.2.2 Components of the Geographical Information Systems Successful Geographical Information Systems (GIS) implementation typically includes the following components (Laurini and Thompson, 1994). i. Data input: is the most critical stage. It involves the scanning and digitized maps, data conversion to a suitable format, georeferencing and registering the data to a common coordinate system, and entering tabulated data. ii. Data management: Input data are structured and integrated together in a geographic database to ease the data retrieval, updating and access. iii. Data processing and analysis: processing involves the operations performed on the available data using various techniques of data investigation, statistical procedures and other methodologies to produce insight and new information (Davis, 2001). Analysis is the qualitative and quantitative interpretation of the information and data. iv. Modeling: includes a combination of logical expression, analytical procedures, and criteria, which are applied for the purpose of simulating process, predicting an outcome, or characterizing phenomena (ESRI, 1995). v. Data output: includes simple display on computer monitors, high-quality large formats maps or printouts, printed reports, graphics and tabulated data in addition to various types of web based GIS products.

Conceptually, a GIS can be envisioned as a stacked set of map layers, where each layer is aligned or registered to all other layers (Davis, 2001). Each layer is characterized by a unique geographic data type. Geographic data in GIS comprises a spatial component that deals with the location, shape and size of features and a non-spatial component that deals with the description of data and also called attributes. Spatial data are of three main types.

i. Points: point features e.g. wells and sample locations are features that have no recognizable spatial dimension but does have specific location. ii. Lines: they are one-dimensional features with considerable length and no width and characterized by a beginning and ending node and a number of vertices defining a polyline e.g. stream, roads, faults trace .... etc.

iii. Polygons: these are 2D features defining an enclosed area with at least three sides and that have area and a perimeter e.g. lithological unit, parcels of land, political district...etc. (Drury, 2003).

GIS data features represent a discrete data that occur as distinct features, having definite location and shape and constitute spatially well-defined separate entities e.g. rivers, lineaments, wells, sample locations, etc. Continuous data display neither definite boarders nor distinctive values and instead values change gradually with transitions from one level to another e.g. surface representation, temperature zonation, etc. (El Khidir. 2006).

Spatial data representation models are of the three types: vector model, raster model and triangulated model. Vector model deals with modeling discrete features that are characteristically stored as points, lines and polygons. Although vector model is the best choice for drawing precise shape and position of features, it is not used for representing continuous phenomena. Raster model and their data are represented as digital image or continuous data. Raster data is constituted from two-dimensional grid that is constructed of an array of unit cells (pixels) arranged in rows and columns. Each cell has one measured quantity. Grid cells are of equal dimensions (width x length). GIS database can be defined as a collection of information about things and their relationship. The database is created to collate and maintain information (Drury, 2003).

3.3 Tectonic and Setting Regional Geology 3.3.1 Tectonic Setting The greater Gondwana land was formed due to the collision between east and west Gondwana (650-600 Ma) after the consumption of the upper Proterozoic Mozambique ocean basin (Stern, 1994). The collision formed A 5000 Km long orogenic belt named the East African Orogeny (EAO: Stern, 1994). The belt (EAO) consist of the Arabian-Nubian Shield in the north and the Mozambique belt to the south The Arabian-Nubian Shield formed during the Neoproterozoic time (Stern and Kroner, 1993). They comprise several terrines sutured together (700-800 Ma; Stern and Kroner, 1993). The Mozambique belt was formed as structural belt of N-S trend, extend from South of Zambezi River up to the north of Kenya and Uganda (Holmes, 1951).

Studies in northeast Africa and western Arabia show that the geology of the Red Sea Hills, as a part of the Arabian–Nubian Shield, can be interpreted in terms of general tectonic models, including the much later volcano-tectonic evolution of the Red Sea (Stern, 1994 and coleman, 1993; Figure 3.4). However, the detailed evolution of the Red Sea Hills is still poorly known.

The Pan-African tectonic-thermal event (Kennedy, 1964) is a term used to describe an overall volcano-tectonic episode which led to structural differentiation of the whole African continent into cartons and reactivated circum cratonic areas. Radiometric analyses indicate that this Pan- African tectonic-thermal event spans the period from 950 to 450 Ma (Kroner et al., 1987). During this episode the Arabian–Nubian Shield was formed and modified. This shield consists largely of island arc–back arc areas and continental micro plates welded together along east to northeast-trending ophiolite and suture zones. The Red Sea Hills are located within Nubian– Arabian shield in the northeast Sudan. Kroner (1994) divide the Nubian–Arabian Shield into five intra-oceanic and island arc areas, separated by suture zones and fault zones. These five areas, from north to south, are those of Gerf, Gabgaba, Gebeit, Haya, and Tokar (Figure 3.5).

The Tertiary opening of the Red Sea marked the next important tectonic event of the Arabian Nubian shield (Coleman, 1993), the rift system may have developed along pre-existing north- trending structures (Ghebreab, 1998), rift-related features occur on both sides of the Red Sea along its uplifted margins, the western margin being the Red Sea Hills, the geomorphology of the western margin is characterized by relatively steep slopes over a narrow area which is dissected by a number of normal faults, the normal faults strike parallel to the coastline and the NW-trending axis of the Red Sea.

3.3.2 Regional Geology of Red Sea Hills

The Red Sea Hills region of NE Sudan is part of the Arabian-Nubian Shield of Proterozoic era; it lies in the central part of the Nubian segment; it extends northwards through the eastern desert of Egypt and Southwards across the Sudan-Eritrean border into the Ethiopian plateau, to the east is bound by the Red Sea coastal plain and the Nubian Desert to the west, several geologists have described the geology of the Red Sea Hills of NE Sudan (ElNadi, 1984; Vail, 1982, 1979; Ali, 1979; Kabesh, 1962; Gabert et al., 1960; Ruxton, 1956; Gass, 1955)

Gass, (1955) made the first attempt to classify the basement complex in the Red Sea Hills region into sedimentary-volcanic sequences, batholithic granites and younger granites, (Ruxton, 1956) subdivided the basement complex of the region into three major sedimentary and volcanic subdivisions with four main periods of igneous intrusions, (Vail 1979 and 1982) introduced the name "Nubian shield" for the region between the Nile and the Red Sea and described the basement complex as greenschist assemblage. (Abdelsalam, 1993) described the geology of the southern central part of the Red Sea Hills east of long 35˚ 30˚ along the Nakasib Suture Zone.

Figure (3.4): Geotectonic map showing the Precambrian structures and the major sutures and shear zones of the Arabian-Nubian Shield (After Abdel Rahman, 1993).

Figure (3.5): Terranes map of the Arabian-Nubian Shield. (Modified after Kröner and stern, 2004).

The Red Sea Hills are dominated by a Precambrian crystalline basement complex overlain by a variety of Phanerozoic cover rocks, from Paleocene to Holocene. The general regional geology of the red sea could be divided into major groups:

i. Quaternary Sediments ii. Tertiary Volcanics iii. Cenozoic Sediments iv. Basement Complex

3.3.2.1 Basement Complex in Red Sea Hills The Sudanese Red Sea Hills is known to be the central part of the Nubian segment of the Proterozoic Arabian – Nubian shield one of basis of plate tectonics. The Red Sea Hills have been interpreted by many authors (Vail, 1983; Kroner, 1984; Almond et al., 1984; Kroner et al., 1987) as a complex system of accreted island arc, characterized by distinct cycles of magmatic activities separated by ophiolitic suture belts. The lithological units of basement complex of the Red Sea Hills can be subdivided into following major group: i. High-Grade Gneisses (Kashabib Series) This lithological unit was first described by (Gabert et al., 1960) who named it “Kashabib Series” after the type locality of J. Kashabib. The high-grade lithological units that occur within the Red Sea Hills have been interpreted differently by previous workers; four different interpretations have been descried for this unit:

i. Ahmed 1979= The high grade of metamorphism are remnants of ancient continental basement. ii. Kröner, 1987= The high grade rock wide aureoles around granitoids batholiths iii. Embleton et al., 1983= The high grade rock are local areas of increased deformation and metamorphism within the low grade arc assemblage iv. El Nadi (1984; 1987) = The high grade rock are as shelf metasediments of the Nile Craton

The rocks of this unit occur at Sasa plain west of Mohamed Qol village and southeast of Gebeit Mine. They show clear foliation. Gneissic structure is not always prominent. They include paragneisses, paraschists in addition to ortho-amphibolites. Their petrography revealed a wide range of mineral composition of the rocks belonging to the unit. Medium-grained hornblende represents the dominant mineral. Subdominant minerals include quartz and plagioclases (Oligoclase –Andesine composition), in addition to orthoclase. These rocks were subjected to metamorphism that ranges from the upper to lower amphibolite facies. The medium- to high- grade rocks of this unit are considered to represent the roots of the arc assemblage. ii. Lower Volcano-Sedimentary Unit (Nafirdeib Series) They represent the most widespread unit in the Red Sea Hills. The volcanic rocks range in composition from basalt and basaltic-andesite to andesite and rhyolite, which have calc-alkaline affinity (ElNadi, 1987). The metamorphic grade is green schist facies to epidote-amphibolite facies. These metavolcanic with their associated metasediments (Meta greywackes, marble,

meta-pelites and meta-psammites) in the area correlated with other volcanosedimentary rocks in the adjacent areas in the Arabian-Nubian Shield. Kroner et al. (1987) correlated the whole Gebeit volcanic terrain with the 700-850 Ma old Hejaz arc of western Arabia as interpreted by (Camp, 1984). He suggested that Gebeit arc volcanics and the Hejaz arc form a broad NE trending arc complex.

Also Haya terrane (900-850 Ma) have been correlated with Asir terrane in the Arabian Shield. Alteration zones with massive volcanogenic sulphide mineralization is found associated with volcanic rocks along the northern Margin of the Haya terrane. The most extensive exposure of this unit in the Red Sea Hills occur in the Gebeit terrane. The rapid lateral and vertical variation and changes in lithologies of the lower volcano-sedimentary units clearly indicate composite volcanoes (ElNadi, 1984). It has been suggested that the metavolcanic structurally overlie the low-grade metasediments. Geochemistry of these rocks indicates a predominant calc-alkaline affinity. The lavas of these volcanics have been suggested to have been erupted in an island-arc tectonic setting (ElNadi, 1984; Klemenic, 1984). iii. Upper Volcano-Sedimentary Unit (Awat Series) The upper volcano-sedimentary unit has been previously named Awat series by (Ruxton, 1956) to designate the volcanosedimentary sequence unconformably overlying the Nafirdeib series. The volcanic parts of this unit are dominantly acidic which includes rhyolites, rhyo-dacites and dacites constitute the major lithology in addition to ignimbrites and other acidic pyroclastic. The sedimentary part of the upper volcano-sedimentary unit comprises siltstone, greywackes and red conglomerates, limestones and quartzite and mudstones. The rocks of this unit are less altered, less metamorphosed and less deformed compared to the lower volcano-sedimentary unit. Embleton et al. (1983) dated the Awat volcanic sequence on the basis of presumed contemporaneous intrusive at 740 Ma. This unit was correlated with the molasses-type sediments of the Hammamat group of eastern Egypt (Ries et al., 1983) and the J. Balah group in Saudi Arabia (Delfour, 1970). The geochemistry revealed calc-alkaline affinity, indicating that they are subduction related volcanics. iv. Ophiolitic Complex This unit represents the mafic-ultramafic rocks in Abu Samar area north of Derudeb village. These rocks consist of well crystallized pyroxenite and hornblendite, in addition to massive basalt and doleritic dikes, most of these rocks have less deformation (Abdel Rahman, 1993). In the Red Sea Hills there are other ophiolitic complexes such as Sol Hamid, Nakasib and Wadi

Onib (Babiker, 1977). Fitches et al. (1983) described that the Sol-Hamid ophiolite complex had been partly eroded and is overlain by the Nafirdeib series (Nubian arc metavolcanics) and both were affected by an intrusion of batholithic granitoids. Field observations and ages of ophiolite emplacement are fixed older age for ophiolite and solve its presence structurally overlie younger units.

Hussein et al. (1982) explained that an ophiolite complex in Wadi Onib is in a tectonic contact with Nubian arc metavolcanics. Mafic-ultramafic rocks having features characteristic of ophiolite sequences occur mostly in the northeastern part of the study area around Kiaw. These mafic-ultramafic rocks may represent the southern part of the Onib ophiolite which was studied by Abdel Rahman (1993). It forms elongated surface and hilly outcrops trending N-S. Their contact with the meta-volcano sedimentary unit is structural. v. Intrusive Rocks Main igneous intrusive rocks are granites, granodiorites/diorites, gabbros and ultramafic rocks cover about 50% of the total area of the Red Sea Hills terrain (Ahmed, 1979). They vary in mode of occurrence from huge batholiths to stocks and plugs. Ring complexes and dyke swarms are also well known. Intrusive of the Red Sea Hills terrain are believed to have been emplaced at different times during the Pan-African age. Accordingly, they have been classified as syn-, syn- to late- and post-orogenic granitoids or intrusions.

- The Syn- to Late-Orogenic Intrusions They occur as high rugged mountains, but when they suffer from deep weathering they appear as subdued oval sandy plains. They are heterogeneous batholiths of calc-alkaline rocks, which range in composition from gabbros to diorites, granodiorites and granites. Tonalite and adamellites are less common. The calc-alkaline geochemical affinities characteristically indicate a subduction related geotectonic environment. An age of 815-724 Ma has been assigned for the syn-collisional intrusives by (Klemenic and Pooles, 1988; Brown 1980) suggested that the syn- to late-tectonic batholiths of the Nubian-Arabian Shield have been emplaced between 960-520 Ma.

- The Post-Orogenic Igneous Intrusions They occur as high rugged circular plutons and plugs with clear sharp outer boundaries cutting through the older lithologies and structural trends. When they are deeply weathered they form subdued circular sandy plains. Post-orogenic ring complexes are well documented in the Red Sea Hills terrain (Ellabib, 1991; Ahmed, 1979; Vail, 1976).

vi. Dyke Swarms the dyke swarms represent one of the most striking igneous features exhibited by the Red Sea Hills terrain. They invade the whole older lithostratigraphic units at different times and have different directions. Abu Fatima (1992) classified the dyke swarms into two major groups: older dykes and younger dykes: the older dykes were mostly emplaced at 600-700 Ma, mainly less than 4-meter-thick, and many are related to nearby igneous complexes, while some are likely to be feeders of the volcanics in the area (Babiker and Gudmundsson, 2004). According to Abu Fatima (1992) the age of the younger dykes is 130-140 Ma and their thickness may reach 30 m and they commonly form conspicuous ridges. The dyke swarms range in composition from acidic to mafic lithologies including granite, microgranite, rhyolite, granodiorites, diorite gabbro and basalt. The major trends are NE, ENE, NNE and NNW, other common trends are NW, E-W and to a lesser extent N-S.

3.3.2.2 Cenozoic Sediments Siliciclastic and shallow marine, rift-related Cenozoic sediments have been witnessed all along the Sudanese coastal plain, both in drilled boreholes and as surface outcrops. They have a maximum thickness of 4162m as encountered in Durwara II well, 70 km south of Port Sudan. They have been divided into: Mukawar Formation, Hammamat Formation, Maghersum Formation, Khor Eit Formation, Abu Imama Formation and Dungonab Formation, in addition to the older gravel (Sestini, 1965). Bunter and Abdel Magid (1989) have recognized the close similarities between the gulf of Sues sediments and those of the Sudanese littoral zone. They discussed the tectonic and paleogeographic evolution of the Red Sea-Gulf of Suis graben and divided it into two major tectonic phases: pre-rift and syn-rift and further divided the latter phase into syn-rift pre-salt and syn-rift post-salt phases. These sediments vary from Upper Cretaceous continental sandstone, Eo-Oligo-Miocene shallow marine limestones to thick Miocene evaporites sequences with coarse to fine clastic sediments.

The older gravels unit (a few to 25m thick), uncomfortably overlies the Miocene sediments and they represent the different alluvial fan cycles that occurred during the Pliocene-Pleistocene times. The emergent Pleistocene reefs occur as linear terraces, running parallel to the shoreline. Four distinct reef terraces have been identified by (Sestini, 1965). Their formation was attributed to eustatic changes in sea – level (Babiker, 1994; Whiteman, 1971).

3.3.2.3 Tertiary Volcanic and Related Minor Intrusions

Tertiary volcanics and related minor intrusions are widely distributed in Sudan. These volcanic have been reported from various localities within the Red Sea hills terrain (Kenea, 1997; ElNadi, 1984; Whiteman, 1968; Kabesh, 1962; Gass, 1956; and 1955; Delany,). Some of them have been related to the Red Sea Rift (Vail, 1978), while others have been considered to be part of the Ethiopian volcanic plateau. The available field radiometric evidence is sparse but sufficient to show that the volcanicity in north and northeastern Sudan began in upper Cretaceous times and continued until the Recent (Almond et al., 1984). Related dykes and sills are mainly basaltic in composition.

3.3.2.4 Quaternary Sediments Recent sediments represent the superficial deposits that cover large area from the Red Sea region. They occur as a result of the interaction between the aeolian and fluvial sedimentary processes acting within the Red Sea Hills terrain and the coastal plain. Alluvial fans, wadi deposits and fan-delta deposits represent the products of the fluvial processes. Recent marine sediments include: supratidal sand/mud facies (sabkha) intertidal sand bars and beach facies and the subtidal coralline limestones and lagoonal carbonate muds (Babikir, 1994). The clearly recognizable Hamada and Serir lag deposits cover extensive areas within the Sudanese coastal plain and within the Red Sea hills terrain (Babiker, 2006).

CHAPTER IV: RESULTS AND DISCUSSIONS

4.1 Digital Image Processing of Landsat 8 data

Image enhancement is the process of making an image more interpretable for a particular application (Faust, 1989). It is obvious that image enhancement should be carried out after image correction to avoid the enhancement of the different image distortions and noises. Image enhancement is a point involves the modification of the original set of digital numbers. Point operations modify the digital numbers of each pixel of the image under enhancement, while local operations modify the digital numbers in the context of the surrounding ones (Jehsen, 1996). Enhancing certain features of an image may occur at the expense of other features which may become relatively subdued (Gupta, 2003).

Geological mapping has been increasingly supported by the use of remote sensing and GIS techniques, which improve the quality and timely availability of basic information for exploration activities (List and Squares, 1996). Based on their spectral signatures in different colour composite imageries, the discriminated lithological units represent the geological units of the map. In the present study remote sensing techniques were used to generate a geological map, alteration maps, lineament map

4.1.1 Contrast Enhancement The raw satellite data are usually dim and lack contrast, because natural features have a low range of reflectance in a specific wave band (Gibson and Power, 2000). Contrast enhancement is a mapping of brightness values to produce a more useful image (Richards, 1999). Generally, the enhancement is not done until the restoration processes are completed (Jensen, 1996). Some objects reflect a certain amount of energy in a certain wavelength, while other objects reflect much less energy in the same wavelength. This results in a contrast between any two types of materials when recorded by a sensor. When different materials reflect similar amount of energy, low contrast images result.

The three OLI visible bands (2, 3 and 4 are visible blue, green, and red) have low contrast and poor spectral resolution because the moisture content of the atmosphere strongly scatters these short wavelengths. The three OLI reflected infrared bands (5, 6 and 7) have much better contrast and spectral resolution because their longer wavelengths are less affected by the atmospheric scattering. The contrast stretching enhancement expands the original input brightness values to make use of the total range of the gray values (0 – 255; LDCM 2013).

There are several types of contrast stretching techniques, but here linear stretching has been used. The contrast-stretched images are more informative than the original unscratched images. The procedure is to convert the minimum DN in image to zero and the maximum DN to 255. The purpose of the enhancements is to render the image more interpretable and the features better discernible. Linear contrast stretching is the simplest and frequently applied, where the transformation is applied to the old grey range of the image’s Digital Number (DN) and is linearly expanded to occupy the full range of the grey levels in the new scale. Image statistics, namely the mean and the standard deviation significantly influence this enhancement, since they regulate the brightness of the different bands and thus the corresponding colours (Gupta, 2003).

An example of a simple stretching is the linearly Saturation stretched of Landsat 8 OLI bands 7, 5 and 2 in the R, G and B colour composite image (Figure 4.1). This bands combination is well known in the remote sensing studies as geological image, since it good in delineating the geological and structural features. In this image, it clearly that the hues are affected by the superficial covers as manifested in the Aeolian sand in the southwestern and eastern parts of the study area. Generally, it is clear that the reflected hues of the various rock units affected by their mineralogical composition.

On this image(Figure 4.1) the syn-orogenic granites appear in dark Red to Brown color with coarse texture register in eastern part of the image area post-orogenic intrusions have Brown hues but they can be easily delineated by their morphological expression when the watering results of this rock appear in White color, the metavolcanosedimentary appear in light Brown hues has founded in the central part of the image area with hilly sheared (N-S) direction when the watering results appear in Pale Violet Red color. On other hand the mafic ultramafic rock appears in the Royal Blue color registered in the Medill north of the study area

Figure (4.1): Linear contrast enhancement applied on bands 7, 5, 2 in RGB, respectively. Rock typing: sGr: Syn-orogenic granites, pGr: Post-orogenic intrusion, uBr: Ultrabasic rock, MVS: Metavolcanosedimentary

4.1.2 Decorrelation stretching

Decorrelation stretching is a process that enhances the color display of highly correlated raster sets. The process performs a principal components transformation on the set of input bands, applies a contrast stretch to the components, then reverses the transformation,

When the output rasters are displayed in RGB, hue and intensity are usually similar to the original image, but the color saturation is greatly increased. (Gupta, 2003). In the present study the decorrelation stretching technique was applied on the pan-sharpened image to expand the variability of the selected bands of 7, 5, 3 in RGB, respectively colour composite (Figure 4.2).

The resulted image improved the range of intensities and saturations of colors. Moreover, it well discriminated the different lithological units and enhanced the appearance of the structural elements within the investigated area. This image displayed in 7, 5, 3 in RGB, respectively,

In this image (Figure 4.2) the metamorphosed basic and ultrabasic rocks display in Light-Sky- Blue to Cyan hues. This is due to its contents of magnesium- and the hydroxyl- bearing minerals, which because absorption features in the range of the SWIR band. The metavolcanosedimentary as well as the syn-orogenic granites are difficult to discriminate as they both reflect Pinkish to Violet hues, but from the watering results of the syn-orogenic granites appear in yellowish color register in eastern part of the image when the watering results of the metavolcanosedimentary appear in the Pinkish hues has founded in the central part of the image area. The post-orogenic intrusions have Dark-Olive-Green hues but they can be easily delineated by their circular morphological expression, when the watering results of this rock in appear Sea Green color, the basic metavolcanic appear in cyan to light green color founded as sporadic in western part of the image area.

4.1.3 Color Compositing Satellite image can be displayed in gray scale using any one of the available bands, as well as can be in a colour image. To display a colour satellite image, only three bands are needed. These bands should be of the same resolution and registered to the same geographic coordinate system. Each of the three bands is displayed in one of the three major colour channels, Red, Green and Blue; then the resultant is a colour composite image. RGB colour composite images are simple and effective as the mixing of the three primary colors (red, green & blue) can produce a wide range of colors (Harris, et. al, 1999).

Figure (4.2): Decorrelation stretched OLI bands 7, 5, 3 in RGB, respectively. Rock typing: sGr: Syn-orogenic granites, pGr: Post-orogenic intrusion, uBr: Ultrabasic rock, MVS: Metavolcanosedimentary

A natural colour composite image result from real red, green and blue wavelengths bands when represented in R, G, and B respectively. Other band combinations produce false-colour composite images, any three bands of the six OLI bands can produce color composite image. It is obvious that high spectral resolution is important when producing color composite images. The rule of color composites is to set the most informative band for a particular purpose in the red, the next in green and the least informative band in blue (Drury, 1993). The selection of bands has been based on the statistical method called the Optimum Index Factor (OIF).

This statistical method has been applied to the study area sub-scene using Landsat 8 bands (2, 3, 4, 5, 6 and 7). The different RGB combinations helped in discriminating the rock types, which is useful in the geological application. Because Landsat 8 OLI images are delivered with additional bands, different colour composites were created in this study. For instance, (Figure 4.3) shows a geological colour composite of bands 7, 5, 3 in RGB respectively. the mafic ultramafic rocks display in Deep Steel-Blue hues. This is due to its contents of magnesium- and the hydroxyl- bearing minerals, which because absorption features in the range of the SWIR of band 7. the ophiolitic rocks restricted to the northern middle eastern parts of the image area. The Oligocene basalt (un-mappable scale) appear in light blue with their cone and lava flow structures. The metavolcanosedimentary sequence appears in brown colour with highly sheared and generally trend in N-S direction restricted to the central part of study area. The weathering products of the volcanosedimentary sequence rocks are clearly seen in floral white colour. The post-orogenic intrusions have Dark to Saddle brown but they can be easily delineated by their circular morphological expression, when the watering results of this rock in appear white color, the basic metavolcanic appear in gray color founded as sporadic in western part of the image area.

Figure (4.3): OLI geological colour composite image of bands 7, 5, 3 in RGB, respectively.

4.1.4 Band Ratioing Images Topographic slope shadows and seasonal changes in sunlight illumination angle and intensity cause differences in brightness values from identical surface materials. Band ratioing technique is applied to extract information not really seen in single image (Gupta, 2003). Ratio images are prepared by dividing the DN value in one band by the corresponding DN value in another band for each pixel (Sabins, 1996; Drury, 1993) where resulting values are representing the ratio image. The general formula is:

Where:  DNnew is DN of the new obtained image.

 DNA and DNB values are DN values of A and B input images.

 K1 and K2 are the factors which take care of the path radiance present in the two input images.  m and n are scaling factors for the grey–scale.

The band ratios and multiplication techniques were used following the general methodology of (Sultan et al., 1986; Abdelsalam and Stern, 1999) who noted that certain band ratios are particularly useful for lithological discrimination. The individual Sultan ratio images of bands (6/7, 6/2 and 6/5*4/5) in RGB, respectively are used in lithological discrimination of different rock types in the study area. On this image (Sultan at al., 1987; Figure 4.4), it is clear that the lithological discrimination of different rock types in the study area was increased, while the alteration zones are displayed violet red colour. The resulted ratio image shows that the volcano-sedimentary sequence appears in blue colour, which is highly sheared, where the general trend of shear is in N-S direction restricted to the central parts of study area. The sheared granite appears in medium orchid colour restricted to the eastern part of study area, while the post-orogenic granite is displayed in deep violet with their circular structures. The weathering products of the post-orogenic granite (sand cover) are clearly seen in bluish yellow, the ophiolitic rocks appear in orange red, while the weathering results of these rocks are clearly seen in coral colour restricted to the northern east part of the image area. Oligocene basalt (un- mappable scale) appear in orange to red colour with their cone and lava flow structures.

Figure (4.4): Sultan ratio image obtained using band ratios of 6/7, 6/2 and 6/5*4/5 in RGB. Rock typing: sGr: Syn-orogenic granites, pGr: Post-orogenic intrusion, uBr: Ultrabasic rock, MVS: Metavolcanosedimentary.

4.1.5 Principal Component Analysis (PCA) Principal component analysis is a multi-variate statistical technique commonly used in a digital image processing of remotely sensed data (e.g. Singh and Harrison, 1985; Loughlin, 1991; Eklundh and Singh, 1993; Chavez and Kwarteng, 1989; Roger, 1996 and Kenea, 1997). The method represents a powerful means of suppressing irradiance effects that dominate all bands, so that geologically interesting spectral reflectance features of surface materials can be examined (Sabins, 1999). PCA reduces both the dimensionality of a multi-spectral data and the high degree of band-to-band correlation inherited in such data sets (Sabins, 1999). Principal Component (PC) transformation is a linear transformation that depends on the statistics of the image data to define a rotation of the original data axes by calculating a new orthogonal coordinates system that point in the direction of decreasing order of variances (Ekhundh and Singh, 1993). To perform principal component analysis, fundamental statistics of the original data were computed (Tables 4.1, 4.2). Using these statistical terms, the new axes of principal component analysis were computed.

Table (4.1) Summarizes the basic statistics of the bands involved in the transformation and gives the engine vector loadings of both band sets. Eigenvector Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 PC1 0.176 0.288 0.406 0.478 0.532 0.455 PC2 0.283 0.360 0.420 0.336 -0.438 -0.554 PC3 0.379 0.317 0.080 -0.424 -0.477 0.583 PC4 0.309 0.342 0.035 -0.591 0.541 -0.378 PC5 -0.636 0.011 0.689 -0.341 -0.040 0.030 PC6 -0.493 0.753 -0.417 0.108 -0.037 0.016

Table (4.2): Summarizes the basic statistics of the Correlation Matrix of Landsat 8

Correlation Band 2 Band 3 Band 4 Band 5 Band 6 Band 7

Band 2 1.000 0.996 0.990 0.984 0.960 0.953

Band 3 0.996 1.000 0.998 0.992 0.974 0.966 Band 4 0.990 0.998 1.000 0.996 0.980 0.972 Band 5 0.984 0.992 0.996 1.000 0.985 0.977 Band 6 0.960 0.974 0.980 0.985 1.000 0.994 Band 7 0.953 0.966 0.972 0.977 0.994 1.000

This technique was applied in order to enhance the differences between the lithological units present in the study area for improved geological mapping. This procedure has resulted in the production of six principal component images PC1, PC2, PC3, PC4 PC5 and PC6 (Figure 4.5). Hence, the principal components contain order variance properties with the maximum in PC1. It is well known that the PC1 contain most of the variance of the images and contain significant albedo and topographic information. The PC2 appears to display more spectral –related to lithological contrast and known to discriminate between the VNIR and SWIR bands. PC3 and PC4 display fair spectral-related to lithological contrast information. Whereas, PC5 and PC6 with very variance and normally show more noises (El Khidir, 2006).

PC1 PC2 PC3

PC4 PC5 PC6 Figure (4.5): Show the principal components, PC1, PC2, PC3, PC4 PC5 and PC6 of Landsat 8

The six produced PCs were used to produce a colour composite image. The colour composite images have been produced and have been found useful in geological interpretation,

The colour composite image obtained using PC1, PC2 and PC3 in the RGB (Figure 4.6).

Figure (4.6): Colour composite of principal components, PC1, PC2 and PC3 in RGB, respectively. Rock typing: sGr: Syn-orogenic granites, pGr: Post-orogenic intrusion, uBr: Ultrabasic rock, MVS: Metavolcanosedimentary

On this image, the syn-orogenic granite appears in pale green to sea green colour, which is distributed in the southeastern part of the image area. while the weathering products of these rocks have yellowish colour The post-orogenic granite appears in violate color. Moreover, the metavolcanics appear in royal blue color, the metasediments appear in light green colour. The ophiolitic rocks appear in deep blue colour, while the weathering products of these rocks have coral colour, restricted to the northeastern part of the image area. The Oligocene basalt (un- mappable scale) appear in light green colour with their characteristic cone structures

4.2 Geology of the Study Area (South Hamisana)

The interpreted lithological units have been combined in a GIS database which has been designed to combine the various geological and topographical al data about the study area. All the spatial data have been organized into a one geo-database, interrelated spatial data are stored in a group layer; the following group layers have been designed:

i. Digitally processed Satellite data are saved in as a raster format. ii. Digitized lithological units, structural data, lineaments, sample locations are saved in vector format. iii. Drainage lines are saved in vector format.

On-screen digitization has been carried out to trace the different geological features. The final geological map was produced at the scale 1:70,000 (Figure 4.7). From the prepared geological map, the study area it is dominated by meta-sedimentary, meta-volcanic units mainly meta- andesite with less common other volcanic varieties and ophiolitic rocks, these layered sequences are intruded by the syn to late-orogenic and post-organic igneous intrusions, in which the granitic composition is dominant. In the following sections, detailed accounts on the various lithological units are provided:

4.2.1 Mafic Ultramafic Rocks Mafic-ultramafic rocks having features characteristic of ophiolite sequences occur mostly in the northeastern part of the study area around Kiaw. These mafic-ultramafic rocks represent the southern part of the Onib ophiolite which was studied by Abdel Rahman (1993), it forms elongated surface and hilly outcrops trending N-S, their contact with the meta-volcano sedimentary unit is structural contact; the rocks encountered are serpentinites, listwanite, pyroxenite and gabbro, the mafic-ultramafic rocks are classified into compositionally two distinctive assemblages consisting from:

Figure (4.7): Geological map of the study area

4.2.1.1 Basal Ultra-Mafic Tactonites This rock types include mainly serpentines, listwanite, and talc and chlorite schists.

I. Serpentinites The serpentinites are composed exclusively of the serpentine group minerals formed by the hydrothermal alteration of the previously existing olivine and pyroxenes of the ultramafic rocks; they are produced when hot sea water circulates through the lithosphere at spreading ocean ridges, or in regions where mountain building activities occur in response to the closing of an ocean basin. In the field, the serpentinites are generally massive, folded and highly sheared, the predominant of the asbestos (chrysotile) indicate that the original rock is probably dunite, the serpentinites are subjected to hydrothermal alteration resulting in the formation of chlorite schist; talc carbonate and quartz carbonate rocks (listwanite). In the hand specimen, serpentinites are black in color, fine texture and smooth, they are composed predominantly of serpentine minerals (from the alteration of the olivine or pyroxene. under microscope, they are composed of serpentine minerals (antigorite and lizerdite). Asbestoses (chrysotile) occur as well as highly birefringence veins cutting the other serpentine minerals (Plate 4.1).

II. Listwanite The listwanite represents an unusual rock type that forms when ultramafic rocks are completely carbonated. The carbonate alteration of the ultramafic rocks to listevinite is well known for it is association with lode gold mineralization making it an attractive prospect for exploration. It consists of rusty- red quartz, talc, carbonate, Cr-muscovite assemblage. In hand specimen, this rock is massive (very hard), reddish in colour, coarse in texture, composed of ankerite (red), quartz, malachite (green) and pyrite. In thin section, it is composed of quartz, fuchsite (green muscovite) and ankerite, (Plate 4.2), the accessory minerals are chromite and in addition to other opaques.

4.2.1.2 Basic-Ultra Basic Cumulates The cumulate mafic-ultramafic units comprise a variety of rock types including pyroxeneites and gabbro.

I. Pyroxenites The pyroxenites in outcrop are generally massive, highly sheared, altered to talc in some locations and cut by quartz veins, in hand specimen, it is dark in color (black) with brownish

weathered surface, medium to course in texture composed mainly of pyroxene mineral, in thin section, and they show mainly clinopyroxenes (Plate 4.3).

Plate (4.1): Photomicrograph of the serpentinites under XPL, mineral type: Sps: Asbestos

Plate (4.2): Photomicrograph mineral composition of the listwanite rock under XPL, mineral type: Qtz: quartz, Fuc: fuchsite, Chr: Chromite, Ank: ankerite

Plate (4.3): Photomicrograph plate of the pyroxenite. XPL, mineral type: Agu: Augite, Pl: plagioclase, Di: Diopside II. Gabbro The gabbros are massive, folded, sheared and cut by quartz veins in some places. In hand specimens, they are grayish green in color, medium to course in texture, composed of plagioclase and pyroxene, in thin sections, they are composed of nearly equal amount of altered plagioclase and pyroxene (coarse grain, high interference color), secondary minerals are zoisite blue color. Iron oxides are the accessories (Plate 4.4).

Plate (4.4): Photomicrograph of the Meta-gabbro. XPL, mineral type: Pl: plagioclase, Px: Pyroxene, Zoi: zoisite, Cpx: clinopyroxenes

4.2.2 Metavolcanosedimentary Sequence This sequence covered a broad region of the study area, it forms as surface to hilly outcrops, consisting of greenschist facies meta-volcanics and meta-sedimentary rocks forming marked physiographic continuous N-S trending belts, the low grade metavolcanic and metasediments comprise stratified successions of volcanics, volcano-clastic and sedimentary rocks all being metamorphosed at low greenschist facies. They are intensively sheared, folded and cut by quartz veins and dykes of various compositions.

4.2.2.1 Metavolcanics This unit comprises a wide range of rock compositions including meta-rhyolite, meta-dacite, meta-andesite and meta-basalt. they are varying in texture from phyric to aphyric. In this study the main occurrence of this unit was found in Kamoreib area.

I. Meta-Andesite In hand specimen is meso-type, fine grained, both phyric and aphyric varieties are common, the phyric type is composed of phenocrysts of plagioclase in nearly glassy groundmass, the plagioclase occurs as subhedral altered grains, most of which show deformation twining, epidote occur as secondary mineral (Plate 4.5).

II. Meta-Dacite In the field; the meta-dacite in hand specimen is leucocratic green; porphyritic consists of phenocrysts of quartz and plagioclase in a fine grained groundmass, in thin sections; it is composed of quartz, plagioclase and few sanidine phenocrysts. Secondary minerals are epidote and chlorite while iron ores are the accessory minerals (Plate 4.6).

III. Meta-Rhyolite In hand specimen is leucocratic brown and fine-grained. Under microscope, the rock is made up of quartz phenocrysts embedded in quartz sericite groundmass (Plate 4.7); probably iron oxides exist as opaque's.

IV. Meta-basalt In hand specimen is melanocratic very fine grained, in thin sections it made up of plagioclase phenocrysts set in a fine groundmass composed of plagioclase laths, epidote, chlorite and calcite. accessory iron ores are present (Plate 4.8).

Plate (4.5): Photomicrography of the andesite, Ander XPL microscope, mineral type: Pl: plagioclase, Ep: Epidot

Plate (4.6): Photomicrograph of dacite rock in the XPL microscope, mineral type: Pl: plagioclase, Ep: Epidot, Qtz: Quartz Or: Orthoclase

Plate (4.7): Photomicrograph of the Rhyolite. XPL microscope, mineral type: Qtz: Quartz

Plate (4.8): Photomicrography of the Basalt. XPL. Microscope, mineral type: Pl: plagioclase, Oli: olivine, Px: pyroxene, Ep: Epidot.

Photo (4:1): Outcrop illustrate contact between Meta-andesite and Meta-dacite

4.2.2.2 Metasediments It varies in texture from fine, coarse and very course (clay to gravel), this unit consists of meta- pelites, meta-greywacke and meta-conglomerates, intercalated with thin layers of marbles, in some places they display graded bedding which suggest formation in the turbidity environment.

I. Meta-Greywacke In hand specimens are varying in color from greenish, brownish to reddish; they are, also, varying in texture from fine- to coarse-grained. In some outcrops, the grains are rounded to sub rounded composed of quartz, feldspar, plagioclase, and rock fragments dominantly andesite and rhyolite. Under microscope, they are composed of quartz, plagioclase, and rock fragments in clayey matrixes >25% transform partially or completely to chlorite (Photo 4.2; Plate 4.9)

II. Meta-Conglomerates In this study area are found approximately 120m high from the surrounding surface and it's elongated with trend in the N-S direction parallel to the general trends of the region, the conglomerates are composed of rounded to sub rounded gray to black rock fragments of basic to acidic metavolcanic, the poorly sorted nature of these rocks suggests a high energy depositional environments and a proximal source, because of the coarse texture of these rocks, they are avoiding the shearing making a distinct fish like shape (Photo 4.4; Plate 4.10)

Photo (4.2): Meta greywacke in hand specimens

Plate (4.9): Photomicrograph of the meta-greywacke in XPL; Microscope, mineral type: Pl: plagioclase, Qtz: Quartz, Rf: Rock fragment

I. Marbles In hand specimens are black, grayish or reddish in color, medium grained, composed essentially of calcite, it found as thin layer intercalated with the other meta-sediments. Photo (4:3).

Photo (4:3): Outcrop of the marbles trended N-S

Plate (4.10): Photomicrograph of the meta- conglomerates in XPL Microscope, mineral type: Qtz: Quartz, Rf: Rock fragment

4.2.3 Intrusive Rocks The main igneous intrusive rocks in the study area are granites and tonalite. They form as hilly, bouldary, and ridge outcrops, these rocks covered a broad region of the study area intruded in the meta-volcano sedimentary and the ophiolitic rocks, they are divided into two distinctive suites:

4.2.3.1 Syn-Orogenic Granites The outcrops are highly sheared and foliated. they are conformable with general trend of the country rocks, this unit is cut by many quartz veins and dykes of various compositions, most of these rocks are surrounded by sand and gravels derived from them usually obscure their contacts with the host country rocks, close to the contacts between the granitic rocks and the host country rocks there are a number of xenoliths of different size and compositions, the xenoliths are elliptical to spherical in shape with the gradual contacts with their host rocks, in the study are the most dominant rocks are tonalite, these rocks are leucocratic coarse-grained composed essentially of altered plagioclase and quartz.

4.2.3.2 Post Orogenic Granites These rocks are easily identified in the Landsat image by their circular shape crossing the regional shear trend, they are generally massive, with sharp contacts with their host rocks, and characterized by marginal facies (chilled margin) compared with the syn-organic granites, they are characterized by lack of foliation shearing because they post-date the Hamisana shearing. Onion skin weathering, boiler plate and Aeolian weathering are well developed. In hand specimen, they are leucocratic (grayish), coarse and often porphyritic in texture, in thin sections, they are composed of k-feldspar phenocrysts set in a coarse groundmass composed of quartz, alkali feldspar and plagioclase. Biotite is the mafic constituents (Photo 4.4; Plate 4.11)

4.2.4 Oligocene Basalt Numerous small dark hills of alkali-olivine basalts have been observed in central part of the study area, (un-mapped) they rest on the low-grade meta-volcano sedimentary rocks. The basalts are fresh, massive, fine grained and dark in color. They are olivine-phyric rocks consist of olivine phenocrysts set in very fine groundmass composed of plagioclase

Photo (4.4): Illustrate outcrop of Post-orogenic granite

Plate (4.11): Photomicrograph of post-orogenic granite.in XPL Microscope, mineral type: Pl: plagioclase, Qtz: Quartz, Bi: Biotite, Or: Orthoclase

4.3 Digital Image Processing for Mineral Prospecting 4.3.1 Overview

Exploitation of remote sensing images in mineral prospecting is well understood by enhancing geological mapping, mapping the regional lineaments, structural trends, local fracture and faults to delineate localized ore deposits and recognize hydrothermally altered rocks by their spectral signatures (Sabins, 1997; 1999). Satellite imagery can provide direct indicators for minerals deposits.

Rocks are aggregates of minerals, while minerals are constituted of anions and/or cations bonded together at certain chemicophysical conditions. Therefore, rock spectra are a composite of their constituent particles (Drury, 1999; List, 1992). Remote sensing advances in recent years have helped earth science researchers to identify and map the distribution of target minerals on the Earth’s surface (Sadeghi et al., 2013). Moreover, the location of the study area in arid region make the use of remote sensing a powerful tool for general prospecting due to the absence of vegetation cover, which generally obscure the geological formations.

Alteration is a physical and chemical change in rock units. Alteration of successive propylitic, argillic, phyllic and potassic zones often accompany mineral deposits (Figure 4.8), especially those related to sulphides. These successive zones are rich in alteration minerals such as, the iron oxides, the hydroxyl- bearing minerals, carbonates and quartz-feldspar minerals (framework silicates) constitute one of the most important guide and criteria for mineral exploration by their impact and changes observed in satellite imagery. In this context, remote sensing techniques play a significant role in locating mineral deposits and it effectively reducing the prospecting and exploration costs by the swift depiction of mineral deposits guides and indicators in their metallogenic provinces/ belts/ sites (Gupta, 2003). Most epithermal vein deposits are accompanied by hydrothermal alteration of the adjacent country rocks, not all alteration is associated with ore bodies, and not all ore bodies are accompanied by alteration, but the presence of altered rocks is a valuable indicator of possible deposits. Prospectors have long been aware of the association between hydrothermally altered rocks and ore deposits. Prior to remote sensing, altered rocks were recognized by their appearance in the visible spectral bands. The country rock is altered to the clay minerals illite, kaolinite, and montmorillonite plus alunite. This assemblage of alteration minerals is called the argillic zone (Vitaliano, 1964).

Figure (4.8) Model of the porphyry copper deposit (a) cross section of hydrothermal alteration minerals and types, which include propylitic, argilic, phyllic and potassic alteration (b) cross section of ores associated with cache alteration (Modified from Lowell and Guilbert, 1970)

4.3.2 Mapping of Alteration Zone via Band Rationing Technique Band rationing results from the division of brightness values in one band by the corresponding values in another band on a pixel by pixel basis. Black and white pixels displayed in a ratio image represent pixels having the greatest difference in reflectivity between the two spectral bands. The major advantage of band ratioing is that they convey the spectral or color characteristics of image features, regardless of variations in scene illumination conditions (Sabin’s, 1997). Ratio images can be displayed in gray-scale or as color composites.

Some of the digital image processing was directed towards the mapping of alteration zones through the band ratioing techniques. Band ratioing is widely used to enhance the spectral feature of the alteration zones depending on the absorption bands of their iron oxide and hydroxyl bearing minerals. Using the spectral characteristics of the iron oxide and hydroxyl- bearing mineral during this study, several ratio images were prepared for the purpose of alteration zones delineation. These band ratios are intended to enhance the iron oxides and clay minerals which are common in alteration zones (Elsayed, et al., 2014).

4.3.2.1 Hydrothermal Composite Ratio This ratio image was prepared by assembling the band ratios: 6/7, 4/2, 5/4 in RGB, respectively (Figure 4.9). This image is termed the hydrothermal composite. on this image, the hydrothermal alteration was delineated in reddish hues, and the gossans ridge appear in reddish orange to red colors. This color is restricted to the gossan ridges and some other altered rock type shares this color. This bands ratio color composite can enable the delineation of the gossan ridges. Therefore, an on-screen digitizing process was conducted in order to delineate these ridges. The Gossan type of gold mineralization is found in different location in the study area as Orshab, Kamoreib and Birhindeb area which occurs as a lenses with N-S direction (Figure 4.9).

4.3.2.2 Mineral Composite Ratio In this study an attempt was made for the application of the band ratioing, a well-known technique of remote sensing in geology. Accordingly, a band ratio colour composite was prepared by assembling the ratios of OLI bands 6/7, 6/4, 4/2 in RGB, respectively (Figure 4.10). This image is termed the mineral composite. As it is clear from this image, the gossans ridge appears in orange to yellow hue, but the delineation of these ridges is rather difficult. This is because this color is common in the image and many rock types are displayed in the same color. From the result of this band ratio technique, the gossan type gold mineralization found in Orshab, Kamoreib and Birhindeb areas (Figure 4.10).

4.3.2.3 Sabins Ratio Images Based on the above assumptions, the technique is used to produce a false colour composite image of the adjusted band ratioing of bands 4/2, 6/7, 4/6 as RGB, respectively (Figure 4.11). This image is called Sabin’s image (Sabin’s, 1987). On this image Sabin’s ratio, as it is clear the alteration zone appears in light green hue, but the delineation alterations are rather difficult, because this color is common in the image and many rock types are displayed in the same color, on other hand the gossan ridge appear in light green to cyan

4.3.2.4 Abrams’s Band Ratio This image is prepared using the band ratio combination of bands 6/7, 6/5 and 4/2 in RGB, respectively. The orange-yellow colors in this image are due to iron oxides rich area and the red hues are due to intensive clay minerals. This ratio image called Abrams image (Abrams et al., 1983). On this image the alterations zone was delineated in light green hues to cyan, and the gossans ridge appear cyan colors. (Figure 4.12).

Figure: (4.9). Hydrothermal composite ratio image obtained using band ratios of 6/7, 4/2, 5/4 in RGB, respectively.

Figure: (4.10). Mineral composite ratio image of bands ratio: 6/7, 6/4, 4/2 in RGB

Figure (4.11): Sabin’s ratio image obtained using band ratios 4/2, 6/7, 4/6 in RGB, respectively

Figure (4.12): Abrams’s band ratio color composite images obtained using band ratios of 6/7, 6/5 and 4/2 in the RGB, respectively

4.3.2.5 Iron Oxide Index The band ratio image 4/2, was prepared to delineate the iron oxide, on this image the iron oxide shows high indexing as brighter tone (Figure 4.13a). a density slicing image produced from this ratio image demonstrating the zones of the iron oxide minerals in a green colour (Figure 4.13d). The iron probably hematite oxides is revealed strongly around J. Abu Dueim in the western part of the study area and J. Al-Shareef.

4.3.2.6 Al- OH-Bearing Minerals The clay minerals and the hydroxyl- bearing minerals such as sericite and muscovite minerals are characterized by strong absorption feature in the SWIR range of band 7. Hence, the band ratio 6/7 was used to delineate areas rich in clay and hydroxyl-bearing minerals. However, the gray scale band ratio images disclosed as a brighter tone in the grey scale image (Figure 4.13b). a density slicing image produced from this ratio image demonstrating the zones of the clay minerals in a red colour (Figure 4.13e). The high indexing were revealed around the mafic ultramafic rock in the northeast of the study area.

4.3.2.7 Ferrous oxide index In geology, it is well known that rocks bearing especially the ferrous oxides have low reflectance (absorption feature) in the ultraviolet and violet range and high reflectance in the red spectrum of the visible light, in the present study the band 6/5 a grey scale image (Figure 4.13c). a density slicing image produced from this ratio image demonstrating the zones of the ferrous oxide minerals in a yellowish colour (Figure 4.13f). is used to detect ferrous oxide minerals, on this image the hydrothermally altered rocks with the ferrous minerals in bright hues, the iron probably goethite, oxides

4.3.2.8 Chemical Analysis Within the study area, gold had been detected in different forms such as; in quartz veins, volcanic massive sulphide and alteration zones. The auriferous quartz veins represent one of the most potential gold bearing lithology in the area. More than 180 chip samples for gold analysis were taken from: quartz veins, shear zones and weathered rocks. Gold content in chip samples vary from 0.05 ppm to 23.4 ppm. A number of 120 Soil samples have been taken from alteration zones contiguous to the quartz veins. All these samples were analyzed for gold content. Gold content in alteration samples vary from 0.06 ppm to 19.4 ppm. From different digital image processing was prepared. The alterations zones in the various ratio images have been converted to vector and combined in the GIS. the spatial analysis is used to separate the high probable alteration zones obtained from three images ratio (Al-OH-Bearing minerals, Ferrous and Iron oxide ratio) defines the intersected alteration zones polygons as the high

probable alteration zones (Figure 4.14) and overlay geochemical analysis for chip and soil sample.

(a) Iron oxide index (b) Clay index (c) Ferrous oxide index

(d) Density slice of Iron (e) Density slice of Clay (f) Density slice of Ferrous oxide oxide

Figure (4.13): Mineral Indices images (a) Iron oxides displayed in brighter tone (band 4/ band 2), and (b) Al- OH-bearing minerals appear in brighter tone, (c) Ferrous oxides displayed brighter tone (band 6/ band 5). Figure (d, e and f are density slice of Iron oxide, Clay and Ferrous oxide respectively.

Figure (4.14): Vector layer of mineral Indices images classifier alteration minerals related to gold, superimposed by the results of chemical analysis of chips samples of quartz veins and alteration samples.

4.4 Spectral Analysis of ASTER Data 4.4.1 ASTER Mineral Indices Mineral indices are one of the widely used analysis methods. The indices have been utilized for minerals, rock units and hydrothermal alterations zones mapping by using the differences between the spectral reflectance properties of minerals, rocks and alteration zones. The mineral indices obtained by applying spectral band ratio of specific absorption features band(s) of the selected mineral (Rowan and Mars, 2003; Rowan et al., 2003).

This technique was used in the current research in order to pave the way for the use of another method i.e. the spectral analysis of ASTER data. The spectral bands of the ASTER SWIR subsystem were designed to measure reflected solar radiation in one band centered at 1.65 μm, and five bands range in the (2.10–2.45μm) region in order to distinguish the Al-OH, Fe- Mg-

OH, H-O-H, and CO3 absorption features. Several investigators have documented the identification of specific minerals, such as calcite, dolomite, alunite, kaolinite and muscovite as well as mineral groups, through spectral analysis of ASTER data (Pour, 2012; Rowan and Mars, 2003 and Rowan et al., 2003). Al-OH bearing minerals such as kaolinite, muscovite and alunite show distinguishable absorptions in bands 5 and 6, as well as calcite in bands 8 and 9 of ASTER data (Ninomiya, 2003). The minerals indices aimed to demark the alteration zones related to mineralization. They are a combination of ratios from VNIR and SWIR ASTER bands for different minerals. In this context, much type of spectral indices was computed during the present study as shown in (Table 4.3).

Table (4.3): ASTER mineral indices used in the present study

Feature Band Ratio in ASTER Alunite index (ALI) (Band 7/Band 5)*(Band 7/ Band 8) Alterations minerals Band 4/8, Band 4/2, Band and 8/9 in RGB Respectively Clay Ratio Band 4/Band 5 Ferrous minerals (goethite) Band 4/Band 3 Iron oxides index (hematite) Band 2/Band 1

4.4.1.1 Alunite Index (ALI) Alunite mineral in the alunite index image appears in brighter tones in gray image. a density slicing image produced from this ratio image demonstrating the zones of the alunite minerals in a cyan colour in most central part of the image, and as narrow belts of the metavolcanic east of J. Abu Dueim intrusion in the southern part of the image area. However, the halos can be

attributed to the underlying rocks, since they are highly eroded and could indicate original alteration zones on these areas (Figure 4.15a).

4.4.1.2 Clay Minerals Ratio The clay ratio images have been obtained by the band ratio transformation. The clay index grey scale image shows high indexing as brighter tone (Figure 4.15b); The high indexing and brighter tones were revealed around the plutonic intrusion and ultrabasic, a density slicing image produced from this ratio image demonstrating the zones of the clay minerals in a yellowish colour rock in the eastern part of the study area.

a b Figure (4.15): Mineral Indices images (a) Alunite index image (band7/ band 5) *(band 7/ band 8) density sliced image of the alunite ratio illustrating the high values in a cyan colour (b) Clay minerals index image (band 4/ band 5) the clay ratio illustrating the high values in a yellowish colour 4.4.1.3 Ferrous and Iron Oxides Ratio The ferrous minerals were extracted by dividing band 4 over the band 3, where a grey scale image was produced demonstrating the hydrothermally altered rocks with the ferrous minerals appear in bright tones, a density slicing image produced from this ratio image demonstrating the zones of the ferrous oxide minerals in a yellowish colour (Figure 4.16a). The iron oxide index grey scale image shows high indexing as brighter tones. a density slicing image produced from this ratio image demonstrating the zones of the iron oxide minerals in a green colour (Figure 4.16b). In this image, the iron probably hematite, is revealed strongly around the eastern

part of J. Abu Dueim intrusion situated in the western side of the study area forming a ring complex,

a b Figure (4.16): The mineral Indices images (a) ferrous oxide mineral appear in a bright tone, a density sliced image of the ferrous oxide illustrating in red colour (b) Iron oxide minerals image (Band 2/Band 1), the alteration of iron oxide appears in a bright tone, density sliced image of the iron oxide ratio illustrating in a green colour

4.4.1.4 Alterations Image Ratio The band ratio colour composite of 4/8, 4/2, and 8/9 in RGB, respectively of ASTER imagery (Figure 4.17) have been applied for mapping alteration zone related gold mineralization. Their results indicated that these methods are successful and promising for identifying alteration zones associated with gold mineralization (Gabr et al., 2010). In this band ratio, the alteration zones related to mineralization are displayed in reddish brown to orange hues, and basic ultrabasic rock appear in red color whereas, the weathering results of the post orogenic granite appear in bluish color and metasedimentary appear in light green to cyan color, the metavolcanic appear in light yellow to orange color

4.4.2 Spectra of Rocks and Minerals The variable interactions of EMR with different material in the earth’s surface result in variable response from these materials with the incident radiation (reflected, absorbed, transmitted and emitted). This different behavior is a function of the wavelength of the incident radiation and the specific properties of the material, and can be envisage in the spectral signal (response)

curve. The spectral signal curve defines the reflectance (absorption) or emitted energy from the material at each specific wavelength of the EMR (Gupta, 2003).

Figure (4.17): Illustrate the band ratios (4/8, 4/2, and 8/9 in RGB), of ASTER imagery, for mapping alteration zone

In mineral prospecting alteration zone can be defining as any change in mineralogical composition of the rock brought about by physical or chemical means (Guilbert and Park, 1986). Alteration zones often accompany mineral deposits, especially those related to Sulphide. These successive zones, which are rich in alteration minerals such as, the iron oxides, the hydroxyl-bearing minerals, carbonates and quartz-feldspar minerals constitute one of the most important guides for mineral exploration. The alteration minerals can be broadly categorized into four groups; iron oxides; hydroxyl-bearing minerals including clays and sheet silicates; carbonates; and quartz-feldspars (framework silicates). The spectral features of the hydroxyl bearing and iron oxide minerals are the main indicators for mineral deposits in multispectral remote sensing context and are widely applicable in the prospecting studies (El-Khidir, 2006).

The most common type of alteration is the breakdown of feldspars and ferromagnesian minerals into a variety of clays and other hydroxyl bearing minerals (Drury, 1993). Since most alterations involve some or all of these minerals their detection has been used for many exploration studies (Kenea, 1997). In this context, semi hyperspectral analyses have been carried to enhance the alteration zones related to gold mineralization in selected sites in the study area. The technique is adopted to pinpoint mineralized zones by their spectral signatures of the alteration products.

4.4.2.1 Reference Spectra Common spectral libraries contain laboratory spectral data for different standard minerals, rocks, soil, plants and various objects, such as the USGS and JPL. Reference spectra of alteration minerals including hematite, Dolomite, Buddingtonite, Diopside, Chlorite, Kaolinite, and Muscovite, are obtained from the USGS and JPL spectral libraries (Table 4.4). The JPL spectral library provides reflectance spectra of 160 minerals, most of them are presented at three different grain sizes to demonstrate the effect of particle size on reflectance spectra; the USGS mineral spectral library contains reference spectra for about 481 minerals that represent different localities around the world (Figure 4.18). But most of them are presented in one particle size (Clark et al., 2007; Grove et al., 1992). Spectral analysis of the semi-hyperspectral data aimed to compare and match the spectral curve of a particular mineral, rock and material with standard curve for similar standard object. In the present study the standard USGS spectral library (mineral jpl_beckman_826.sil; USGS minerals_beckman_421, ENVI, 2015) both are convolved with the spectral response function and resampled in accordance to the band widths and distribution of the ASTER VNIR and SWIR nine bands. The results are ASTER customized USGS spectral library for specific minerals (Figure 4.19).

Figure (4.18): Spectral library profiles, (mineral jpl_beckman_826.sil and USGS minerals_beckman_421) Spectral for the alteration minerals

Figure (4.19): Spectral library profiles, (mineral jpl_beckman_826.sil and USGS minerals_beckman_421,) after convolution and resampling to ASTER sensor (VINR+SWIR)

Table (4.4): Details of the USGS and JPL reference spectra of alteration minerals. Wave- Spectrom Mineral name Sample ID Formula Class length eter (μm) (Mg,Fe,Al) (Si,Al) Chlorite Fine-ps12e 6 4 Phyllosilicate O10(OH)8 JPL- 0.40 beckman To Dolomite Corse-c05c CaMg(CO3)2 Carbonate 826.sil 2.50

Muscovite Corse-ps16a KAl2 (AlSi3O10)(OH)2 Phyllosilicate

Buddingtonite NHB2301 AlSi3O8.0.5H2O Tectosilicate USGS_ 0.395 Hematite FE2602 Fe2O3 Iron oxide Beckman To 421.sil 2.56 Kaolinite H89-FR-250K Al2Si2O5 (OH)4 Phyllosilicate

4.4.2.2 SMACC End Member Extraction The Sequential Maximum Angle Convex Cone (SMACC) spectral tool finds spectral End member and their abundances throughout an image. This tool is designed for use with previously calibrated semi-hyperspectral data. In comparison to ENVI’s Spectral Hourglass Wizard, SMACC provides a faster and more automated method for finding spectral End member, but it is more approximate and yields less precision (ENVI, 2015).

SMACC uses a convex cone model (also known as Residual Minimization) with these constraints to identify image end member spectra. Extreme points are used to determine a convex cone, which defines the first end member. A constrained oblique projection is then applied to the existing cone to derive the next end member. The cone is increased to include the new end member. The process is repeated until projections derive an end member that already exists within the convex cone (to a specified tolerance) or until the specified number of End members are found.

End members have been extracted in the present study via SMACC using the parameters:  Number of End member: 30.  RMS Error Tolerance: 100  The Un-mixing constraint for End Member Abundances: Sum to Unity or Less, this constrains the sum of the fractions of each material in each pixel to one or less.  Coalesce Redundant End members: SAM coalesce default value

The application of this process resulted in three file as flow: i. Endmember Location ROIs, this output file contains point ROIs indicating the pixels from which the resulting End member spectra are derived. ii. Abundance image file, this file contains the shadow and End member abundance images. iii. Reflectance Spectral Library, this contains spectral library of the extracted End members.

Despite the fact that the end members were entered as 30, the execution of this process managed to extract only eight End members. The extracted end members will be used in the consequent Spectral Angle Mapper Classification.

4.5 Spectral Angle Mapper Classifier For the mapping of the surface composition, the Spectral Angle Mapper (SAM) was chosen. SAM is a physically-based classification algorithm that compares the spectral similarity between (surface) reflectance image spectra and reference spectra. Spectral Angle Mapping

(SAM) is one of the spectral analysis and classification method, it considers reference spectrum from the known library and an unknown End member spectrum from a two or more band data represented as a two (or multi) dimensional plot of a two points. In order to extract thematic information from the ASTER image, it is often necessary to compare individually each unclassified pixel of the image with the image-derived reference spectra and then determine which reference spectrum most closely resembles the spectral characteristics of the pixel (Kruse et al., 1993). This technique, when used on calibrated reflectance data, is relatively insensitive to illumination and albedo effects. End member spectra used by SAM can come from ASCII files, spectral libraries, or can be extracted directly from the image (as ROI average spectra). SAM compares the angle between the End member spectrum vector and each pixel vector in n- dimensional space. Smaller angles represent closer matches to the reference spectrum. Pixels further away than the specified maximum angle threshold in radians are not classified (ENVI, 2015).

In the present study SAM was applied for ASTER VNIR–SWIR spectral subset using the customized ASTER library data. The spectral angle is determined for every image spectrum (pixel). This value in radians is assigned to the corresponding pixel in the output SAM image. The SAM classifiers used different rule threshold in this study for the alteration minerals as hematite, dolomite, buddingtonite, kaolinite, and muscovite, that has been extracted as alteration minerals related to mineralization. A mask layer signifies the area covered by the alluvial deposits and wadi has been used in all steps of the spectral analysis to avoid the placer alteration minerals and detect only the in situ alteration minerals. In general, most of the absorption band rule images yield spectral features in zones that are clustered around the metavolcanic belt in the western boundary of the Hamisana Shear Zone (the distinguishable signature feature appears in dark tone due the absorption of energy by the selected minerals), the rest are located in northeastern and eastern parts of the study area.

4.5.1 Hematite Mapping Hematite is one of the most abundant minerals on earth surface. It is iron oxide with the chemical composition (Fe2O3). Hematite is found as primary minerals and as alteration product in igneous and metamorphic rock. The hematite alteration product was identified in this study using SAM classification method. The hematite is shown in high values as dark tone in the grey scale SAM image rule, the saturated black tone in the rule image represents the mask (Figure 4.20a) and as vector overlay with high values in yellow color, (Figure 4.20b). SAM was mapped the highest hematite values in many placed and it demarked most the outcrop lithology in the

study area such as the southeastern part of the study area in the metavolcanic rocks and, also, in the western parts, representing the weathering results of the sheared granite.

Figure: (4.20): Alteration map produced from the SAM classifier, using rule threshold of 0.650 (left) SAM absorption rule images of Hematite, the high values (absorption mineral) appears as dark tone in the grey scale, while the unclassified (masked) superimposed by vectored layer displayed in the stander black color (right) gray image superimposed by vectored layer of the hematite mineral displayed in yellow color.

4.5.2 Dolomite Mapping Dolomite is one of the carbonate minerals. Dolomite alteration product occurs in many igneous and metamorphic rocks as an accessory mineral, found together with many ore minerals. In this study, the SAM classification technique has been used to identify the dolomite alterations. The results show that this alteration displays the highest absorption areas as dark tone in the grey scale SAM rule image, while the saturated black tone in the rule image represents the mask. (Figure. 4.21a), which is presented as violet color in the vectored layer demonstrates the highest absorption values of SAM classified image (Figure 4.21b). However, the dolomite mineral was mapped in the southwestern part of the study area, together with the weathering results of syn to late- orogenic intrusion.

a b Figure (4.21): Alteration map produced from the mineral absorption features images of SAM classifier, using rule threshold of 0.600 (a) SAM absorption rule images of dolomite the high values appears as dark tone in the grey scale, while the unclassified (masked) overlaid as vectored layer in stander black color (b) ASTER image superimposed by vectored layer of the dolomite mineral displayed in violet color.

4.5.3 Buddingtonite Mapping Buddingtonite is ammonium feldspar; it forms by hydrothermal alteration of primary feldspar minerals, it is an indicator of possible gold deposits, as they can become concentrated by hydrothermal processes. The buddingtonite mineral was detected in this study by SAM classification. The buddingtonite alteration minerals display high absorption areas as dark tones in the grey scale of rule image (Figure 4.22a). The minerals is shown in yellow color in the vectored layer demonstrates the highest absorption values of SAM classified image (Figure 4.22b). In the study area, buddingtonite mineral was mapped in the northeastern part of the study area, this area covered by mafic-ultramafic rock, and lesser in the medial west of the study area.

a b Figure (4.22): Alteration map produced from the mineral absorption features images of SAM classifier, using rule threshold 0.650 (a) buddingtonite alterations mineral display the highest absorption areas as dark tone in the rule image, while the unclassified (masked) overlaid by vectored layer displayed in black color (b) The buddingtonite mineral is shown in yellow color in the vectored layer demonstrates the highest absorption values of SAM classified image.

4.5.4 Chlorite Mapping The chlorite alteration minerals most often form in rock environments where minerals are altered by heat, pressure, and chemical activity. After examining the rule image of the chlorite in the grey scale, which is showing the highest absorption zones in a dark tone, the saturated black color in the rule image represents the mask. (Figure 4.23a) while they are displayed in green color in the SAM classification vector layer (Figure 4.23b). The chlorite mineral has been mapped extensively in the medial part of the study area. Chlorite is commonly found in basic to ultrabasic rocks existing in the northwestern and northeastern parts of the study area, as an alteration product of mafic minerals such as pyroxene and amphibole.

a b Figure (4.23): Alteration map produced from the mineral absorption features images of SAM classifier, using rule threshold 0.600 (a) SAM absorption rule images of chlorite brought from SAM classifier, the chlorite appears as dark tone in the rule images, while the unclassified (mask) superimposed by vectored layer in the black color (b) The chlorite mineral is shown in light green color in the vectored layer demonstrates the highest absorption values of SAM classified image.

4.5.5 Kaolinite Mapping

The kaolinite is Al-rich clay minerals with the chemical composition Al2Si2O5(OH)4. It is mostly formed by the alterations of feldspars. In the present study after applying the SAM classification technique, in the rule image the kaolinite appears in the highest absorption zones in a dark tone in the rule image (Figure 4.24a). This mineral is furthermore displayed as red color in the SAM classification as vector layer (Figure 4.24b). The spatial distribution of the Kaolinite mineral is sporadic in nature. It appears around the sheared granites and also mapped over basic-metavolcanic rock in the northeastern of the study area.

a b Figure (4.24): Alteration map produced from the mineral absorption features images of SAM classifier, using rule threshold 0.600 (a) SAM absorption rule images of kaolinite brought from SAM classifier, appears as dark tone in the rule image, while the unclassified masked area displayed in the stander black color (b) VNIR image superimposed by vectored layer of the kaolinite alteration mineral displayed in red color.

4.5.6 Muscovite Mapping

Muscovite belongs to the mica group of minerals, with the chemical composition KAl3Si3O10

(OH)2. The identification of muscovite in this study has been carried out using the SAM classifier technique. Muscovite alteration is appearing as the highest absorption areas via dark tone in the rule image, where the black tones represent the unclassified masked area (Figure 4.25a). The mineral is presented in light green color in the vectored layer demonstrates the highest absorption values of SAM classified image (Figure 4.25b). The distribution of the muscovite mineral is strongly mapped over granitic rock and. Often the sericite mineral gives absorption signatures on the SAM image similar to the muscovite mineral due to similar spectral carve on the spectral library. This means this area was identified by SAM probable to be alteration of sericite or muscovite mineral, since both have similar spectral signatures and chemical composition, and vary only in particle size.

a a Figure (4.25): Alteration map produced from the SAM classifier, using rule threshold 0.600 (a) SAM absorption rule images of muscovite appears as dark tone in the grey scale, while the (masked area) displayed in the stander black color (b) VNIR image overlay by vectored layer of the muscovite mineral displayed in light green color.

4.5.7 Outcome of the Spatial and Spectral Analysis of ASTER Data Mapping hydrothermal alterations products such as clays, iron oxide and carbonate, associated with gold mineralization in the study area has been conducted through the processing Landsat- 8 and ASTER imagery data. On the other hand, spectral analysis of ASTER data has been carried out using Spectral Angle Mapper (SAM) classification to identify the alteration minerals related gold mineralization, such as kaolinite, muscovite, dolomite, hematite, and Buddingtonite. The results rule images displaying the spatial distribution of concerned mineral by its distinguishable absorption features. Besides, vector layers (each represent one mineral), extracted from the rule images were prepared and superimposed over Landsat grey scale ASTER image (Figure 4.26). The resulting SAM mineral maps determine the distribution of the various minerals as vector layers of the predefined End member minerals in the concerned pixels within the area.

Figure (4.26): Alteration zones maps of SAM classifier for ASTER data, created by matching image spectra to mineral spectra in the USGS spectral library and JPL reference spectra.

The result of the spectral and spatial analysis of ASTER data indicate the probable target zone of alteration related to mineralization existing in the study area. Accordingly, the potential targets are located in the northeastern parts of the study area that is covered by ultramafic rock, and in the western part of the Hamisana shear zone that is occupied by metavolcanic association. Note: the SAM classification failed partly to give satisfactory mineral map; this can be ascribed to the masking effect of the sand sheets and alluvial deposits in the stream and in areas characterized by intensive weathering.

Moreover, many exploration targets have been identified within the study area on the basis of the results of the spectral analysis. The targets are mostly gold mineralization bearing quartz veins. The majority of the quartz veins are located in the eastern part of the study area, where the Hamisana Shear Zone crosses the investigated area. These targets contain old mine workings and artisans mining, that are considered to be particularly important, since they strongly indicate the presence of gold of sufficiently high grade to be amenable to extraction by ancient mining technique. Moreover, the results of the geochemical analysis of chips samples of quartz veins and soil samples taken from these targets authenticate the presence of gold mineralization in the determined sites according to spectral analysis of ASTER data.

4.7 Lineament Mapping and Analysis The term lineament has been used in the literature to designate different meaning. It has been applied to alignment of natural as well as man-made features. In geology, lineaments might indicate shear zones; fold axial traces, joints, fractures, faults, dykes, streams or layering. Terrain-related lineaments might occur as straight, curvilinear, parallel or en-echelon patterns, and are generally related to fracture systems, discontinuity planes, fault traces and shear zones (Gupta, 2003).

The structural analysis within the frame of the present study is directed mainly to the fractures analysis through the use of the stress-strain ellipsoid to classify the main fracture types and quartz veins orientation related to the deformation phases, which are reflected in the form of faults and tectonic fractures. The Hamisana Shear Zone is extending in N‐S trend. Accordingly, the Stress‐Strain analysis was applied to differentiate the potential open fractures (extensional, tensional and release) from the closed shear fractures in the study area.

Based of structural analysis in the study area, the fractures in the study area were classified into extension and tensional fractures (σ1). This kind of fractures is parallel to the pressure force that

affects the body that is parallel to the greatest stress and perpendicular to the axis of the greatest strain (Figure 4:27).

Figure (4.27): Lineament classification map of the study area extracted from Landsat 8 images.

This fracture is represented in the study area in NW-SE direction. Two shear fracture systems: the first shear (+σ2) N-S / 0 to 10° trend is dominant in the study area; being parallel to sub parallel to the major Hamisana Shear Zone. The second shear (-σ2) 300° to 310° trend is less dominant. Release fractures (σ3) occurs when removing the pressure force and it is perpendicular to the greatest stress axis and parallel to the maximum strain axis. It is represented in the study area in NE-SW direction.

4.8 Structural Deformations in the Study Area The vein-quartz (lode) gold deposits are more commonly associated with metamorphic terrains. Whereby they are hosted especially by low- to medium-grade metasupracrustal sequences with minor intrusion granitoids (granitoid- greenstone association) of Archean to Cenozoic age. These gold deposits are controlled by a variety of structural styles, vein textures, and alteration mineralogy, depending on many factors, such as the composition and metamorphic grade of the host rock, timing of gold deposition with respect to metamorphism and deformation, structural regime, and depth of formation (Park 1997).

In the present study, the remote sensing investigations were supplemented by field observations and intense structural measurements. Ductile deformation has been obtained in macro- and meso-scales under different folding styles representing different deformation phases. In Landsat OLI imagery, many folds are observed; where the N-S trending fold is the common folding direction. However, the NE-SW, NW-SE and E-W folding directions are also frequently encountered. The common types of faults in the study area are strike slip faults, where the dextral sense of movements is dominant. The Hamisana Shear Zone (HSZ) is extending in N‐ S trend. Accordingly, from the Stress‐Strain analysis applied, the extensional fractures are parallel to the greatest stress axis (σ1) in NW-SE direction, the release fractures are in NE-SW normal to the acting stress (σ3), where the shear fractures are in N-S direction parallel to the positive and negative shear arms (σ2s±), respectively.

 Greatest Stress: (σ1) = 330˚±15˚  Minor Stress: (σ3) = 330˚±90˚ or (60˚ ± 15˚)  The Positive Shear: (+σ2) = N-S = 360˚ ± 15˚ = Hamisana Shear Zones ~ N-S  The Negative Shear: (-σ2) = 300˚ ± 15˚= conjugate set Hamisana Shear Zones  The Extensional Fractures: (σ1) = 330˚ ± 15˚  The Release Fractures: (σ3) = 60˚ ± 15˚.

At least three episodes of tectonic events involving five phases of deformations are recognized. The first episode is represented by the formation of foliation (D1), followed by the strong collision between Haya and Gebeit terrains (D2) resulted in tight folding. The second episode is the collision of the mentioned arcs with Gabgaba terrain (D3). The third episode is represented by the open folding with E-W axis, and the reactivation of the N-S shear zone in the study area (HSZ) that represents the prominent structural feature in the area. The HSZ represents a geodynamic zone, referred to as the Hamisana Geodynamic Zone (HGZ). (Elsheikh et al., 2015). 4.8.1 Gold Occurrences Related to Structural Analysis in the Study Area The study area abodes a variety of rock types and structures such as ophiolitic units, greenstone, post orogenic granite and sheer zones. This geologic and tectonic setting is suitable for gold mineralization. Gold mineralization in the district is associated quartz veins hosted within reactivated second order shear zones. The N-S shear deformational phase has a strong potentiality for gold mineralization, in which the trans-tensional shearing generates open fractures parallel to the shear trend. They could be injected by hydrothermal solutions and/or redistribute older gold bearing bodies in the hostile rocks during the shearing activities.

The Hamisana Shear Zone forms an important boundary between the mafic/ultramafic dominated sequence of possible ophiolitic affinity to the east and the metavolcanic/ meta- sedimentary sequence to the west. faults and shear zones located within deformed terrains of ancient to recent greenstone belts commonly metamorphosed at greenschist facies. This type of deposits was encountered in different part of the study area, such as Abirkateib, Eikwan, Kiaw and Balandalawateib, where many veins have been identified. veins are most prevalent to the west of the HSZ. They typically trend N-S and often lie subparallel to the adjacent shears, some may be syn-orogenic.

The principal structural feature within the area is the regional scale Hamisana Shear Zone, this has a (360° ±15°). From the field work and subsequent analysis, it was evident that some mineralized veins are developed along the shear zone parallel to the axial plain of tight upright isoclinal folds which is a result of E-W compressional force. in the second deformation phase within the first episode, mineralized quartz veins in Balandalawateib area were found having the N-S direction hosted by granitoids which intruded in the metavolcanics of the greenschist metamorphic facies. The Mineralized quartz veins with NE-SW trend are found in northern part area hosted by ophiolite complex and metavolcanosedimentary sequences of the green schist metamorphic facie

Figure (4:28): Structural map of the study area prepared through the interpretation of Landsat imagery and limited filed work

CHAPTER v: CONCLUSIONS AND RECOMMONDATIONS

5.1 Conclusion The present work was based on remote sensing and GIS techniques implemented with limited fieldwork, using the optical multispectral and semi-hyperspectral satellite data. The research is focused on an area in the northwestern part of the Red Sea Hills, lies within the Hamisana Shear Zone. The investigations are directed towards mineral exploration for suspected alteration zones related to mineralization. In addition to geological studies, with generation of medium scale geological map.

This study revealed that the area is dominated by metavolcanosedimentary sequence and mafic- ultramafic rocks with ophiolitic rocks. These layered sequences are intruded by the syn-to late- orogenic and post-organic igneous intrusions. The mafic-ultramafic having features characteristic ophiolite sequences occur mostly in north part of the study area and trended to the north as the continuation of the Sol Hamid-Onieb ophiolitic Mélange, forming elongated surface and hilly outcrops. The ophiolite sequences are comprised from serpentinites, listevinite, pyroxeneites and gabbro’s. These rocks have structural contact with the metavolcanosedimentary rocks. The metavolcanosedimentary sequence covers a broad region of the study area. It forms surface to hilly outcrops consisting of greenschist facies, metamorphosed rocks forming marked physiographic continuous N-S trending belts. They are intensively sheared, folded and injected by quartz veins and dykes of various compositions. The main igneous intrusive rocks in the HSZ are syn-orogenic and post-orogenic granites.

The area under consideration witnessed multi-deformation stages. Ductile deformation has been obtained in macro- and meso-scales under different folding styles representing different deformation phases. In Landsat imagery, many folds are observed; where the N-S trending fold is the common folding direction. The common types of faults in the study area are strike slip faults, where the dextral sense of movements are dominant.

Field description of the lithological rock units, petrographic studies and structural measurement have been combined together using GIS applications to produce a set of geological maps, involving structural and lithological element

Different processed digital images of the Landsat-8 have been accomplished in this study using different techniques such as decorrelations stretching, principal component analysis and image transformation. Spectral enhancement techniques for the optical multispectral data of Landsat 8 have been integrated with geochemical analysis in order to detect the alteration zones related to mineralization. Band ratioing processing technique was used to demarcate clay minerals, iron oxides and ferrous oxides by their spectral criteria. The outcome of band ratioing revealed the wide spread indicator of mineralization in the study area.

The ASTER semi-hyperspectral data has been used for spectral analysis technique through the Spectral Angle Mapping (SAM) classification. ASTER data gave more details than the that of the Landsat-8 in the context of delineating the alteration halos related to mineralization zones. The outcome of the SAM classification portrays the highly probable alteration halos of mineralization zones. Moreover, the application of SAM on ASTER data was found to be better and more reliable in defining the alteration zones related to mineralization than the mineral indices which are normally blurred by the spectral signatures from the country rocks.

The outcome of digital image processing, spectral analysis and geochemical analysis demonstrated that the gold mineralization is detected in different forms such as: alteration zone companionship with quartz veins, residual gold deposit, alteration zones related gossan. The auriferous quartz veins and gossan represent the most potential target for gold bearing formation in the study area. The most potential quartz veins are hypothermal to meso-thermal in origin. The rock alteration exerted by quartz veins and stringers is impregnated with the highest gold concentrations.

5.2 Recommendations

Based on the results of this study, the following points are recommended:

i. The integration of the high spectral with high spatial resolution remotely sensed data, such as Quickbird, are highly needed for the detailed mapping and detailed exploration work. ii. Detailed geophysical survey can help in delineating the hidden and buried structural patterns, and other geological manifestation for mineral exploration, and depths of the mineralized zones using suitable geophysical method. iii. More detailed geological exploration focusing on the identified potential target zones mentioned in this study is highly recommended. 98

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Website http://landsat.usgs.gov http://speclab.cr.usgs.gov http://www.spot.com http//: asterweb.jpl.nasa.gov

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Appendix A. Gold contents in chip samples in the study area.

SID East North Au ppm SID East North Au ppm B.d.1 709800.2 2282012 0.4147 Bc67 704012 2289245 0.7048 B.d.2 691945.6 2279032 1.0849 Bc68 704004 2289237 0.1564 B.d.3 716027.2 2280949 0.289 Bc69 703996 2289237 0.4931 B.d.4 716243.1 2279557 0.2932 Bc7 704003 2289246 1.1354 B.d.5 716565.4 2278808 0.2749 F1 702656 2287715 0.289 B.d.6 716996.5 2276897 0.2794 F10 702692 2287545 0.7284 B.d.7 716299.9 2271817 0.2494 F11 702707 2287531 0.4693 B.d.8 716326.2 2271385 0.0855 F1-1 700591 2285817 0.1445 BA1 719406.4 2269553 0.5775 F1-10 701260 2285832 0.0845 BA10 720739.7 2268917 0.7897 F1-11 701131 2285529 1.4628 BA11 715184 2283552 0.2937 F1-12 701101 2285455 0.5775 BA2 708127.5 2280695 0.4859 F1-13 701099 2285539 0.4859 BA3 704580.2 2277452 0.5046 F1-14 701033 2285602 0.1682 BA4 715921.7 2281048 0.8049 F1-15 701013 2285577 0.2117 BA5 693319.2 2283831 0.7207 F1-16 700938 2285534 3.3233 BA6 708860 2304948 0.4148 F1-17 700895 2284559 0.1631 BA7 707575.4 2300898 0.489 F1-18 700895 2284559 1.243 BA8 707575.4 2300898 5.2678 F1-19 700895 2284559 0.1966 BA9 703332.6 2287741 0.4603 F12 702593 2287506 0.2861 Bb1 703154.2 2288189 0.2454 F1-2 700610 228556 0.338 Bb10 703154.2 2288189 0.5142 F1-20 702633.1 2305404 2.021 Bb11 703957.4 2283714 0.5464 F13 701467 2286468 0.1838 Bb12 703874.3 2289228 0.699 F1-3 700678 2285852 0.2016 Bb13 707984.9 2286954 0.8964 F14 701454 2286439 0.5778 Bb14 706815.5 2287385 0.4818 F1-4 700687 2285977 0.2855 Bb15 708860 2304948 1.5874 F15 701416 2286435 0.2146 Bb16 714200.7 2287692 0.2827 F1-5 700658 2285994 0.1682 Bb17 700635.4 2286529 0.7518 F16 701017 2286356 0.4154 Bb18 700635.4 2286529 0.7707 F1-6 700912 2285252 0.2433 Bb19 702773.4 2291261 0.3974 F17 700985 2286312 0.2226 Bb2 705041.9 2298593 0.5241 F1-7 700945 2285257 0.0567 Bb3 708466.1 2307564 12.5972 F18 700958 2286301 0.1867 Bb4 702633.1 2305404 0.5587 F1-8 700969 2285413 0.1616 Bb5 709113.6 2310980 0.4824 F19 701038 2286306 0.3312 Bb6 709942.6 2310187 0.5023 F1-9 700288 2285815 0.2871 Bb7 711354.3 2307843 0.7737 F2 702643 2287705 0.2932 Bb8 703947 2289108 0.4351 F20 701068 2286468 0.011 Bb9 704103 2289088 0.5023 F21 700995 2286499 0.295 Bc1 704148 2289135 0.1374 F22 700985 2286633 1.165 Bc10 703983 2289325 0.4047 F23 701010 2286738 0.002 Bc11 703983 2289325 0.4157 F24 699826 2286099 1.165 Bc12 703986 2289350 0.2539 F25 698851 2284686 0.665 Bc13 703986 2289350 0.4851 F26 698851 2284686 0.011 Bc14 704089 2289124 0.9347 F27 698967 2284961 0.295 Bc15 704085 2289132 0.1738 F28 698967 2284961 1.165 Bc16 704091 2289140 0.7284 F29 696349 2283553 0.527 Bc17 704067 2289129 0.4693 F3 702643 2287705 0.2749

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Bc18 704071 2289139 0.2861 F30 696349 2283553 1.208 Bc19 704076 2289149 0.1838 F31 696349 2283533 0.011 Bc2 704148 2289135 0.1143 F32 696349 2283533 0.076 Bc20 704076 2289149 0.5778 F33 696340 2283553 1.093 Bc21 704080 2289157 0.2146 F34 695915 2283241 0.092 Bc22 704080 2289157 2.4154 F35 695821 2283141 0.087 Bc23 704076 2289166 18.216 F41 697264 2285844 0.127 Bc24 704076 2289166 3.527 F42 697318 2285770 0.513 Bc25 704045 2289132 0.6434 F43 697364 2285788 0.18 Bc26 704043 2289135 0.6009 F44 697450 2285871 0.085 Bc27 704051 2289148 0.4814 F45 697457 2285886 0.263 Bc28 704049 2289158 0.6128 F46 697450 2285978 0.318 Bc29 704060 2289164 0.667 F47 697357 2286119 2.289 Bc3 704056 2289161 0.1181 F48 697359 2286058 0.206 Bc30 704064 2289172 0.2874 F49 697077 2285209 0.297 Bc31 704021 2289134 0.3429 F5 702581 2287742 0.6322 Bc32 704030 2289143 0.1733 F50 697122 2285224 1.296 Bc33 704031 2289151 0.5176 F51 697144 2285230 0.296 Bc34 704034 2289159 0.5474 F52 697140 2285239 0.2086 Bc35 704039 2289165 0.1891 F53 697140 2285239 0.654 Bc36 704044 2289174 0.4642 F54 697157 2285296 0.296 Bc37 704047 2289183 0.9012 F55 697185 2285347 0.843 Bc38 704056 2289183 0.4493 F56 697194 2285356 0.1793 Bc39 704061 2289190 21.4913 F57 697210 2285387 0.299 Bc4 704056 2289161 0.2676 F58 697220 2285392 7.717 Bc5 704034 2289108 1.0368 F59 697227 2285416 1.031 Bc50 704006 2289148 3.1928 F6 702543 2287743 0.5764 Bc51 703993 2289156 0.2866 F60 697227 2285416 2.118 Bc52 703997 2289163 0.2867 F61 698612 2285971 0.891 Bc53 704000 2289173 0.3492 F62 698612 2285971 0.1528 Bc54 704004 2289182 0.3669 F63 698601 2285951 7.717 Bc55 704009 2289187 0.3613 F64 698601 2285951 4.4591 Bc56 704012 2289198 0.3325 F65 698586 2285924 0.1751 Bc57 704017 2289207 0.3219 F66 698690 2285914 0.0845 Bc58 704021 2289212 4.1533 F67 698690 2285914 0.3117 Bc59 704027 2289219 0.2623 F68 698764 2285817 0.1721 Bc6 704034 2289108 0.3551 F69 696532 2285727 0.7451 Bc60 704027 2289219 0.209 F7 702543 2287747 0.1867 Bc61 704036 2289237 0.3891 F70 696625 2285727 0.2871 Bc62 704041 2289242 0.4344 F71 696654 2285707 0.0845 Bc63 704034 2289260 0.2417 F72 696826 2285476 0.1012 Bc64 704029 2289256 5.2467 F73 696803 2285471 0.3692 Bc65 704022 2289249 0.4734 F74 696803 2285471 0.1793

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Appendix B. Gold Contents in Alteration Samples in the Study Area.

Au SID East North Au ppm SID East North ppm R-R-24 699735 2269565 1.414 HN.1 707789 2271934 0.650 R-R-20 708961 2283252 0.038 HN.2 720739 2268916 0.005 R-R-23 691053 2305919 13.46 R-C-22 699566 2272932 0.003 D-R-15 698395 2315114 11.08 CHIP-4 706438 2278116 0.005 D-R-12T 703957 2283714 1.44 D-C-3 709042 2307057 0.005 RES-16 716326 2271385 2.55 R-C-24 699735 2269565 15.05 D-R-4 705914 2303847 2.029 D-C-2B 709170 2305598 0.002 D-R-6B 708610 2308210 0.214 D-C-11 706227 2287972 0.011 D-R-10H 707575 2300897 0.02 D-C-18 710545 2285004 2.01 D-R-9 709942 2310187 0.06 D-C-2 709170 2305598 0.001 RES-17 719406 2269552 1.013 D-C-12 703332 2287740 5.801 RES-18 720739 2268916 0.008 D-C-21 702511 2286518 0.002 D-R-3 706308 2304083 3.018 D-C-1 708860 2304948 0.011 D-R-2 707602 2304136 0.016 D-C-6 702633 2305404 0.798 R-R-22 697734 2322191 29.4 CHIP-2 709800 2282011 0.022 R-2 709800 2282011 1.72 D-C-24 702153 2292218 2.001 D-R-13B 706815 2287385 0.026 D-C-19 703899 2287243 0.006 RES-5 706390 2277595 0.043 D-C18 710545 2285004 2.184 D-R-13 706815 2287385 1.037 Alt1-6 700630 2285995 0.217 D-R-10F 707575 2300897 0.015 Alt1-7 700989 2285735 0.333 RES-18A 720739 2268916 0.006 Alt1-8 700958 2285704 0.242 D-R-1 708859 2304948 2.031 Alt1-9 700951 2285793 0.242 D-R-8 709113 2310979 0.123 Alt1-10 701012 2285828 0.067 RES-19 698065 2295604 2.073 Alt1-11 701021 2285912 0.336 D-R-12 689863 2315909 0.41 Alt1-12 700866 2284175 0.347 R-R-21 700829 2275034 1.012 Alt1-13 700819 2284155 1.286 D-R-8B 709113 2310979 0.534 Alt1-14 700715 2284069 0.935 D-R-7 708543 2310848 0.017 Alt1-15 700676 2284091 0.367 D-R-14 710545 2285004 1.997 Alt1-16 700641 2284113 3.363 RES-5A 706390 2277595 0.015 Alt1-17 700641 2284113 0.360 D-R-10 707575 2300897 0.049 Alt1-18 700770 2284258 2.233 D-R-6 708610 2308210 6.13 Alt1-19 700720 2284278 0.360 D-R-11 703332 2287740 1.62 Alt1-20 700637 2284319 0.523 D-R-16 679412 2305919 0.048 Alt1-21 700686 2284406 0.968 D-R-1B 708860 2304948 0.119 Alt1-22 700683 2284423 0.628 D-C-22B 682388 2325101 1.305 Alt1-23 700701 2284401 0.127 D-C-20 682653 2313189 2.205 Alt1-24 700737 2284372 0.333 D-C-13S 677096 2325035 0.07 Alt1-25 700745 2284376 0.242 R-C-23 689875 2324705 2.83 Alt1-26 700764 2284388 0.393 Ar.1 697355 2262497 0.266 Alt1-27 700794 2284345 0.733 Ar.2 697355 2262497 0.178 Alt1-28 700716 2284553 1.261 Ar.3 697579 2262787 0.6328 Alt1-29 700742 2284561 0.782 Ar.4 697579 2262787 1.6497 Alt1-30 700733 2284629 0.855 Ar.5 697839 2263158 0.2093 Alt1-31 700789 2284665 1.148 Ar.6 698062 2263331 0.9527 Alt1-32 700846 2284650 0.177 Ar.7 698062 2263331 0.7514 Alt1-33 700890 2284626 0.879 Ar.8 698374 2263719 0.2543 Alt1-34 700880 2284524 2.242 111

Ar.9 699122 2266777 0.2662 Alt1-35 700927 2285194 2.365 Ar.10 699199 2268058 0.5183 Alt1-36 700894 2285142 0.612 Ar.11 699199 2268058 8.743 D-C-19 703899 2287243 0.06 Ar.12 700026 2267153 1.4294 R-R-24 699735 2269565 1.414 Ar.13 699749 2269528 1.4628 R-R-20 708961 2283252 0.038 OR. A 712762 2282057 3.9227 R-R-23 691053 2305919 13.46 OR.B 712762 2282057 7.051 D-R-15 698395 2315114 1.078 HN.A 707789 2271934 0.5604 D-R-12 703957 2283714 1.44 HN.B 707789 2271934 0.4922 RES-16 716326 2271385 2.55 HN.C 707789 2271934 0.5271 D-R-4 705914 2303847 2.029 HN.D 707789 2271934 1.1032 D-R-6B 708610 2308210 0.214 HN.E 707789 2271934 0.7884 D-R-.F 707789 2271934 0.579

112