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SATELLITE REMOTE SENSING FOR HYDROTHERMAL ALTERATION MINERALS MAPPING OF SUBTLE GEOTHERMAL SYSTEM IN UNEXPLORED ASEISMIC ENVIRONMENT ALIYU JA’AFAR ABUBAKAR A thesis submitted in fulfilment of the requirements for the award of the degree of Doctor of Philosophy Faculty of Built Environment and Surveying Universiti Teknologi Malaysia SEPTEMBER 2018

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Page 1: SATELLITE REMOTE SENSING FOR HYDROTHERMAL …eprints.utm.my/id/eprint/81858/2/AliyuJaafarAbuBakarPFAB2018.pdf · geoterma kurang ketara menggunakan kaedah pemetaan konvensional. Penginderaan

SATELLITE REMOTE SENSING FOR HYDROTHERMAL ALTERATION

MINERALS MAPPING OF SUBTLE GEOTHERMAL SYSTEM IN

UNEXPLORED ASEISMIC ENVIRONMENT

ALIYU JA’AFAR ABUBAKAR

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy

Faculty of Built Environment and Surveying

Universiti Teknologi Malaysia

SEPTEMBER 2018

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iii

To my Father and Mother

For their endless love and prayers,

To my beloved wife, Asma’u,

and my kids; Ishaq, Abduljaleel, Zainab and Haidar,

For your patience, love and understanding

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iv

ACKNOWLEDGEMENT

Indeed all praises are due to Allah (SWT) from Who’s Infinite Grace and Mercy,

all things are made possible.

I would like to first and foremost express my deepest gratitude to my supervisor

Prof. Dr. Mazlan Hashim for his support, guidance, understanding, erudition, mentorship,

constructive critiques and purposive encouragement throughout the entire PhD Journey.

My Sincere appreciation also goes to Dr. Amin Beiranvand Pour who, despite his

relocation to South Korea, has been an important pillar and guide on technical aspects and

was instrumental whenever I had issues with software programs and analysis. I personally

acknowledge both their patience for accepting my shortcomings and gradually instilling

in me, the zeal to write academically and scientifically.

I would like to acknowledge the Kaduna State Scholarship Board Overseas

Scheme for funding my PhD studies, the Kaduna State University for granting me study

leave and the Universiti Teknologi Malaysia for giving me the IDF fellowship all of which

greatly supported my doctoral degree pursuit. The assistance of the technical staff of the

Nigerian Geological Survey Agency (NGSA) Kaduna, is also highly appreciated.

My profound gratitude to my father, Alhaji Ja’afaru Abubakar and mother, Hajiya

Zainab for their love and moral guidance. Esteem regards to my wife and kids for their

patience. My prayers for Allah’s Mercy and the reward of Jannatul Firdaus to Hajiya

Hannatu our eldest sister who died during my studies. Special regards to my brothers and

sisters and the entire Alhaji Ja’afaru Kauru family for their fervent prayers and

encouragement.

Special thanks to all my colleagues especially those at INSTeG Geospatial

Institute UTM for the experiences we shared.

Lastly, I am indebted to all those who in one way or the other have been of help

to me who are numerous to mention. Thank you

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ABSTRACT

Mapping prospective geothermal (GT) resources and monitoring associated surface manifestations can be challenging and prohibitively expensive in subtle systems especially when using conventional survey methods. Remote sensing offers a synoptic and cost- effective capability for identification of GT systems. The objective of this research is to refine and develop methods of identifying unconventional GT systems by evaluating the applicability of the ASTER, Landsat 8 and Hyperion satellite data for mapping hydrothermal alteration indicator minerals as proxy for detecting subtle GT targets in unexplored aseismic settings. The study area is Yankari Park in North Eastern Nigeria, characterized by the thermal springs; Wikki, Mawulgo, Gwana and Dimmil. Spectral Angle Mapper (SAM), Linear spectral Unmixing (LSU) and Mixture Tuned Matched Filtering (MTMF) were comparatively evaluated by using image derived spectra and corresponding library spectra for mapping pixel abundance of GT indicator minerals in a novel and efficient manner. The results indicated that employing image derived spectra from field validated and laboratory verified regions of interest as reference, gives more accurate results than using library spectra around known alteration zones remotely detectable on the imagery. The MTMF provided high performance subpixel target detection with an accuracy of 50-100% and 70-100% subpixel abundance for argillic- phyllic-silicic and propylitic alteration mineral assemblages respectively, as compared to less than 10% for the same endmembers when using library spectra. The MTMF is thus best suited for mapping alterations associated with subtle GT systems than the less selective LSU. The per-pixel SAM was unsuitable for target detection of alteration indicators of interest with poor overall accuracy of 33.81% and 0.24 Kappa coefficient at0.02 radian angle. Results of mapping thermally anomalous pixels do not conform to known locations of the thermal springs signifying the limitations of the current thermal sensors in mapping low temperature GT systems even at 60m spatial resolution. However, examining the spatial correlation of the anomaly areas with the major geologic structure systems from geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat insulating sedimentary formations. This study establishes the integrative applicability of Multispectral and Hyperspectral data for mapping subtle GT targets in unexplored regions using in-situ validated alteration mineral mapping and thermal anomaly detection. This has significant implication for the GT green energy industry as the developed methods and GT prospect map could aid the prefeasibility stage narrowing of targets for in-depth geophysical, geochemical, geothermometric and related surveys.

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ABSTRAK

Memetakan sumber prospek geoterma (GT) dan pemantauan manifestasi permukaan berkaitan adalah sangat mencabar dan mahal terutamanya apabila melibatkan sistem geoterma kurang ketara menggunakan kaedah pemetaan konvensional. Penginderaan jauh menawarkan keupayaan sinoptik dan kos efektif untuk mengenal pasti sistem GT. Objektif kajian ini adalah untuk memperhalusi dan membangunkan kaedah mengenal pasti sistem GT yang tidak konvensional dengan menilai pemakaian data satelit ASTER, Landsat 8 dan Hyperion untuk pemetaan mineral penunjuk hidroterma sebagai proksi untuk mengesan sasaran GT yang halus dalam tetapan tidak-seismik yang belum diterokai. Kawasan kajian ialah Taman Yankari di Nigeria Timur Utara, yang dicirikan oleh beberapa mata air panas Wikki, Mawulgo, Gwana dan Dimmil. Teknik pengelasan data digital Spectrum Angle Mapper (SAM), spectrum Linear Unmixing (LSU) dan Mixture Tuned Matched Filtering (MTMF) dinilai secara relatif dengan menggunakan spektrum yang diperoleh dari imej dan spektrum rujukan yang bersesuaian untuk pemetaan semua piksel mineral penunjuk GT. Hasil kajian menunjukkan bahawa menggunakan spektrum yang diperoleh dari imej yang telah disahkan oleh analisis spektrum makmal bagi kawasan yang diselidiki memberi hasil yang lebih tepat daripada spektrum rujukan di sekitar zon perubah. MTMF menyediakan pengesanan sasaran sub-piksel prestasi tinggi dengan ketepatan 50-100% dan 70-100% sub-piksel yang berlimpah untuk perhimpunan galian alahan argillic-phyllic-silicic dan propylitic masing-masing, berbanding kurang daripada 10% untuk pengguna akhir yang sama apabila menggunakan spektrum rujukan. Oleh itu, MTMF adalah paling sesuai untuk pemetaan perubahan yang berkaitan dengan sistem GT- kurang ketara berbanding LSU yang kurang selektif. Sampel per piksel tidak sesuai untuk mengesan sasaran penunjuk perubahan dengan ketepatan keseluruhan 33.81% dan 0.24 pekali Kappa pada 0.02 radian sudut. Keputusan pemetaan piksel anomali termal tidak sesuai dengan lokasi yang diketahui dari mata air termal yang menandakan keterbatasan sensor termal semasa dalam pemetaan sistem GT suhu rendah walaupun pada resolusi spatial 60 m. Walau bagaimanapun, mengkaji korelasi spatial bagi kawasan-kawasan anomali dengan sistem struktur geologi utama dari peta geologi di kawasan kajian menunjukkan persamaan rapat bagi keduanya dengan kecerunan haba yang dinyatakan sebelum ini dalam kajian pembentukan sedutan haba. Kajian ini menunjukkan penerapan integratif data multispektral dan hiperspektral untuk pemetaan sesaran GT kurang ketara di kawasan yang belum diterokai, boleh di tentu sah melalui sampel lapangan dan pengesanan anomali termal. Hasil kajian mempunyai implikasi yang ketara untuk sumbangan ke industri tenaga hijau GT kerana kaedah yang dibangunkan dan peta prospek GT dapat membantu penelitian terperinci tahap sesaran haba bagi geofizik, geologi, geokimia, geotermometri dan kaji selidik yang berkaitan.

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TABLE OF CONTENT

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENT vii

LIST OF TABLES xiv

LIST OF FIGURES xvi

LIST OF ABREVIATIONS xxv

LIST OF APPENDICES xxvii

1 INTRODUCTION 1

1.1 Background of Study 1

1.2 Problem Statement 6

1.3 Research Objectives 8

1.4 Scope of the study 9

1.5 Significance of Study 10

1.6 Thesis outline 12

2 LITERATURE REVIEW 14

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2.1 Introduction 14

2.1.1 Definition of Frequently used Terms: 15

2.2 The geological setting of Nigeria and its implication for

geothermal assessments 17

2.3 Geothermal (GT) systems 20

2.4 Hydrothermal Systems and Alteration 22

2.5 Review of Concepts and Methods 27

2.5.1 Remote Sensing and Spectroscopy 27

2.5.2 Multispectral and Hyperspectral Sensors 30

2.5.3 Spectral Characteristics of specific Hydrothermal

Alteration Minerals associated with Geothermal Systems. 33

2.5.4 A Review of Spectral Processing Methods and

Models used for Spectral Information Extraction from

Remotely Sensed Data. 38

2.5.4.1 Comparative Characteristics of KB and DD

Approaches 42

2.5.4.2 Spectral Matching Models 46

2.5.4.3 Linear Mixture Models 47

2.5.4.4 Partial Sub-pixel Unmixing Models 50

2.6 Geothermal resource exploration using remote sensing

applications 52

2.6.1 Mapping Associated Alteration Minerals as Proxy

for geothermal exploration 53

2.6.1.1 Mapping geothermal indicator minerals

using hyperspectral data 53

2.6.1.2 Mapping geothermal indicator minerals

using multispectral data 56

2.6.2 Detection of GT anomalies using thermal infrared

remote sensing 60

2.6.2.1 Airborne thermal data applications 60

2.6.2.2 Spaceborne thermal data applications 63

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2.6.3 Challenges and limitations of GT detection and

exploration using remote sensing 66

2.6.4 Future Direction in the field 68

2.7 Previous Geothermal Related Studies in Nigeria 71

2.8 Observations and Inferences made from the review of

relevant literature 73

2.9 Summary 76

3 METHODOLOGY 77

3.1 Introduction 77

3.2 The Study Area 78

3.2.1 Geology of the Yankari Park 79

3.3 Research instruments and materials 81

3.4 Remote Sensing data and their characteristics 82

3.4.1 Landsat 8 data 83

3.4.2 ASTER data 85

3.4.3 EO-1 Hyperion data 86

3.5 Ancillary data 89

3.6 Methodology 91

3.6.1 Pre-processing of Landsat 8 data 91

3.6.2 Pre-processing of ASTER Level 1T data 92

3.6.2.1 Mosaicking 93

3.6.2.2 Masking of Yankari boundary 95

3.6.2.3 Colour Balancing of Mosaics 96

3.6.2.4 Atmospheric correction of ASTER,

Hyperion and Landsat 8 optical bands 97

3.6.2.5 Atmospheric Correction of ASTER TIR

bands 100

3.6.2.6 Crosstalk correction 102

3.6.3 Pre-processing of EO-1 Hyperion data 103

3.6.3.1 Removal of bad bands 105

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3.6.3.2 Destriping of vertical stripes 105

3.6.3.3 Smile effect correction 106

3.6.3.4 Geometric rectification 108

3.6.4 AIG-Developed Processing of Hyperion data 110

3.6.4.1 Minimum Noise Fraction 111

3.6.4.2 Pixel Purity Index 115

3.6.4.3 N-dimensional visualization and selection of

endmembers 117

3.6.5 Band ratio 120

3.6.6 Principal Component Analysis 121

3.6.7 Decorrelation Stretch 123

3.6.8 Normalized Difference Vegetation Index 123

3.6.9 Spectral Mapping Methods 124

3.6.9.1 Spectral Angle Mapper 125

3.6.9.2 Linear spectral unmixing 126

3.6.9.3 Mixture Tuned Match Filtering 127

3.6.10 Field validation and sample collection for laboratory

analysis 129

3.6.11 Spectral library 134

3.6.12 Extraction of Mean image spectra from ROIs

verified using field GPS sampling and laboratory

analysis 134

3.7 Temperature anomaly detection method 139

3.7.1 Theoretical basis of Land Surface Temperature

retrieval 140

3.7.2 LST retrieval methods 141

3.7.2.1 Conversion of digital numbers to Radiance

(Radiometric calibration) 142

3.7.2.2 Conversion of radiance to At-sensor

brightness temperature 143

3.7.2.3 Calculation of land surface emissivity 143

3.7.2.4 Calculation of LST using SCA 144

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3.7.2.5 Detection of temperature anomalous

zones 144

3.8 Summary 147

4 RESULTS AND DISCUSSIONS 148

4.1 Introduction 148

4.2 Results of established image enhancement processing

4.2.1 Results of Landsat 8 Band ratios processing 149

4.2.2 Result of Landsat 8 ICA using VNIR+SWIR+TIR

bands 155

4.2.3 Results of ASTER Band ratios and decorrelation

stretch 158

4.2.4 Results of ASTER Feature-oriented Principal

Component Selection 162

4.3 Results of Spectral Mapping Methods 171

4.3.1 Results of spectral analysis at Wikki (ROI) using

4.3.1.1 SAM using image spectra (Wikki roi) 172

4.3.1.2 SAM using library spectra (Wikki roi) 175

4.3.1.3 LSU using image spectra (Wikki roi) 177

4.3.1.4 LSU using library spectra (Wikki roi) 178

4.3.1.5 MTMF using image spectra (Wikki roi) 180

4.3.1.6 MTMF using library spectra (Wikki roi) 183

4.3.2 Results of spectral analysis at Mawulgo ROI using

SAM, LSU and MTMF 186

4.3.2.1 SAM using image spectra (Mawulgo roi) 187

4.3.2.2 SAM using library spectra (Mawulgo

roi) 189

4.3.2.3 LSU using image spectra (Mawulgo roi) 189

4.3.2.4 LSU using library spectra (Mawulgo roi) 190

methods 149

SAM, LSU and MTMF 172

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4.3.2.5 MTMF using image spectra (Mawulgo

roi) 191

4.3.2.6 MTMF using library spectra (Mawulgo

roi) 193

4.3.3 Result of Silicic alteration mapping using ASTER

TIR bands at Wikki and Mawulgo ROIs 195

4.3.3.1 LSU using image derived spectra (Wikki

roi) 196

4.3.3.2 LSU using image derived spectra (Mawulgo

roi) 197

4.3.3.3 MTMF using image derived spectra (Wikki

roi) 198

4.3.3.4 MTMF using image derived spectra

(Mawulgo roi) 199

4.4 Results of alteration mapping at SW overlapping ROIs (A)

using ASTER and Hyperion datasets 200

4.4.1 Mapping alteration minerals in overlapping ROIs

(A) using ASTER datasets 202

4.4.1.1 SAM using image derived spectra ROI (A) -

ASTER 204

4.4.1.2 LSU using image derived spectra ROI (A) -

ASTER 205

4.4.1.3 MTMF using image derived spectra ROI (A)

-ASTER 206

4.4.2 Mapping alteration minerals in overlapping ROIs

(A) using Hyperion datasets 208

4.4.2.1 SAM using image derived spectra ROI (A) -

Hyperion 209

4.4.2.2 LSU using image derived spectra ROI (A) -

Hyperion 210

4.4.2.3 MTMF using image derived spectra ROI (A)

-Hyperion 211

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4.5 Results of Temperature anomaly detection using Landsat 7

ETM+ thermal Band 214

4.5.1 Converting DN values to Radiance 215

4.5.2 Converting radiance to At-sensor brightness

temperature 216

4.5.3 Calculating Land Surface Emissivity (LSE) 217

4.5.4 Calculating LST using SCA 220

4.5.5 Detecting temperature anomalous zones 222

4.5.6 Verification of the LST anomaly results 222

4.5.7 Correlation between LST anomalous zones, fault

structures and heat sources 224

4.6 Results of Field survey and laboratory analysis 226

4.7 Integration of the results into a geothermal prospect map

using GIS 236

4.8 General Discussions 238

4.9 Summary 244

5 CONCLUSION AND RECOMMENDATIONS 245

5.1 Conclusion 245

5.1.1 Contributions of the study 248

5.2 Recommendations 249

REFERENCES 251

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LIST OF TABLES

TABLE NO. TITLE PAGE

2.1. Summary of Spectral Processing methods 40

2.2. A comparison between KB and DD approaches 43

2.3. Common hydrothermal alteration minerals identified

in remote sensing data of GT regions 59

3.1. Comparison of Hyperion and AVIRIS data 87

3.2. Summary of spectral and spatial characteristics of

the acquired remote sensing image data sets 88

3.3. Ancillary data specifications for topographic and geology

maps used in this study 90

3.4. Generated image to image GCPs list with RMSE for

ASTER and Hyperion overlap (A) region SW Yankari Park. 109

3.5. Result of the MNF eigenvalue percent of Hyperion VNIR

and SWIR bands 112

3.6. Field acquired GPS coordinates of sampling points of

hydrothermally altered rocks 132

4.1. Feature-oriented PCA Using ASTER SWIR bands (5, 6, 8)

& 1 of Yankari subset 163

4.2. Feature-oriented PCA Using ASTER VNIR bands (1, 2, and 3)

& 4 of Yankari subset 167

4.3. Error matrix for SAM at 0.10 angle (image spectra-Wikki roi) 174

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4.4.

4.5.

4.6.

4.7.

4.8.

4.10.

Error matrix for SAM at 0.04 angle (image spectra-Wikki roi) 174

Error matrix for SAM at 0.02 angle (image spectra-Wikki roi) 175

Error matrix for SAM at 0.10 angle (library spectra-Wikki roi) 176

Error matrix for SAM at 0.04 angle (image spectra-

Mawulgo roi) 188

Error matrix for SAM at 0.02 angle (image spectra-

Mawulgo roi) 189

Identified minerals using ASD and XRD analysis in

representative alteration zones associated with

Mawulgo and Wikki thermal springs. 229

xv

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FIGURE NO.

2.1:

2.2:

2.3:

2.4:

2.5:

2.6:

2.7:

2.8:

2.9:

2.9:

2.10:

LIST OF FIGURES

TITLE

Geological map of Nigeria

Schematic representation of a typical GT system

Simplified diagram of a hydrothermal system

A prismatic view of surface manifestations in active

geothermal systems from around the world

A view of hydrothermally altered surfaces around

geothermal systems from around the world

Diagram showing the Visible and Infrared (NIR, SWIR, TIR)

regions of the Electromagnetic Spectrum.

A diagrammatic representation of a multispectral and

hyperspectral image showing band arrangement and

pixel optical density

Diagram showing Hyperspectral imaging concept

in the remote sensing application context

(A) Laboratory spectra of epidote, calcite, muscovite,

kaolinite, chlorite and alunite. (B) Laboratory spectra

of limonite, jarosite, hematite and goethite

(C) TIR spectra of silicate minerals.

Diagram showing satellite remote sensing and

unmixing of endmember signatures

PAGE

18

21

24

25

26

30

32

33

37

38

46

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2.11: Simplified diagram showing the basic assumptions of the LMM 49

2.12: Simplified diagram showing the process to extract and

estimate endmember abundances 49

2.13: Tincalconite spectra compared with the spectra of

selected minerals that are common to playa surfaces 57

2.14: Comparison of Night time uncorrected and corrected

AST_08 images (surface temperature) 64

3.1a: Study area map of Yankari Park 79

3.1b: Geological Map of the Yankari Park 81

3.2: OLI signal-to-noise ratio (SNR) performance at Ltypical 84

3.3: Comparison of Spectral Bands between ASTER and

Landsat-7 Thematic Mapper 86

3.4: Google earth image showing image footprints for the

three satellite data used in this study 89

3.5: Landsat 8 true color image scene of bands 4-3-2 as

RGB showing the study area 92

3.6: Four ASTER scenes covering Yankari Park prior to mosaicking. 94

3.7: Mosaic of 4 ASTER scenes covering Yankari Park study area 94

3.8: Masked ASTER image showing Yankari Park

boundary of Study Area 96

3.9: Colour balanced image of ASTER masked scenes 97

3.10: Hyperion image spectral profiles at a region of

interest before and after Atmospheric correction 100

3.11: Grayscale mosaicked and corrected ASTER thermal

bands of Yankari Park 101

3.12: Simplified flowchart for objectives 1 and 2 showing

methods used for hydrothermal alteration mapping

using multispectral satellite imagery 103

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3.13:

3.14:

3.15:

3.16:

3.17:

3.18:

3.19:

3.20:

3.21:

3.22:

3.23:

3.24:

3.25:

3.26:

3.27:

Hyperion false colour composite of bands 151, 205

and 218 loaded as RGB

Shows Hyperion subset image band 135 with (a)

vertical stripes and (b) after destriping.

Result of Hyperion data showing brigtness gradient

(smile) in MNF band 1 for (a) & (b) VNIR; (c) &

(d) SWIR; before and after smile correction

Image of (a) ASTER and (b) Hyperion showing generated

GCPs used for geo-rectification of Hyperion

MNF Bands 1, 2, 3, 5, 7 and 15 of SWIR Yankari (A)

subset (360 x 700 pixels)

MNF Eigenvalue plot for the SWIR bands indicating

coherent spectral information in only the first 11 bands

RGB using coherent SWIR MNF bands 2-3-5 of Hyperion

PPI plot showing 10000 iterations applied to identify the

purest pixels to be used in selecting endmembers for

the spectral analysis.

PPI result image showing bright pixels as spectrally pure

n-dimensional visualizer showing distribution of

endmembers employed for the analysis

Extracted EM from the Hyperion SWIR data from which

image spectra are selected

Flowchart of methods for Hyperspectral analysis, objective 3

SAM angle in n-Dimensional space showing observed and

reference endmember spectrum

Diagram showing the characteristics of Mixture Tuned

Matched Filtering (MTMF) algorithm

Laboratory preparation and analysis of altered rock

samples for ASD and XRD

104

106

107

109

114

114

115

116

117

118

119

119

125

129

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133

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Extracted image spectral plots (a), (b), (c) from designated

ROIs, compared with ASTER Spectral library plots

in (d); (e) emissivity spectral plots

ASTER false colour composite of bands 3-2-1 as

RGB showing 27 sampled locations used for field

verification of hydrothermal alteration.

Simplified flow chart for Single channel Algorithm

temperature retrieval method

Simplified methodology framework of the overall study

(a) Landsat 8 band ratio 7/4, 6/3, 5/7 image of complete

scene for Yankari and environs.

(b) Landsat 8 band ratio 7/4, 6/3, 5/7 image subset

of Yankari Park.

Landsat 8 band ratio 6/7, 4/3, 5/6 image for Yankari

scene and environs

Landsat 8 band ratio 7/5, 5/4, 6/7 as RGB image for

Yankari scene and environs.

Landsat-8 image map derived from IC5, IC6 and IC7 as

RGB color combination for the masked Yankari

Park study area.

ASTER band ratio 6-3-8 as RGB of Yankari Park

ASTER DCS image of ratio bands 6-3-8 in RGB of

Yankari Park

Gray scale image of Yankari Park showing spatial subsets

of focused areas for detailed analysis at district scale

PC3 showing dark pixels as possible alteration zones due

to hydroxyl-bearing minerals

PC3-PC4-PC2 as RGB to further highlight alteration

zones of interest

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PC2 showing bright pixels as possible alteration

zones due to iron oxide/hydroxide minerals

ASTER false colour composite (FCC) of bands 3-2-1 as

RGB showing GPS field sampled locations and Mawulgo

ROI-red box, and Wikki ROI-blue box, subsets used

for spectral analysis.

Results of SAM at Wikki ROI using image spectra showing

3 alteration zones at radian angles (a) 0.10 (b) 0.04 (c) 0.02

(d) NDVI

Results of SAM at Wikki ROI using library spectra

showing alteration zones. (a) 0.10 default radian angle

(b) NDVI image at Wikki ROI showing vegetated-exposed

zones

(a) LSU subpixel abundance image using image

spectra (Wikki roi). (b) RMSE for image in (a)

(a) LSU subpixel abundance image using library spectra

(Wikki roi). (b) RMSE image for image in (a)

(a) MF score images (image spectra) for 3 endmembers

loaded as RGB showing alteration zones at Wikki roi.

(b) MTMF sub-pixel abundance image for valid detections

produced from 2D scatter plot in figure 4.18

2-Dimensional scatter plot used to map valid alteration

detections (Wikki roi) for 3 endmembers (image spectra)

as reference

(a) MF score image (library spectra) for 3 endmembers

loaded as RGB showing alteration zones at Wikki roi.

(b) MTMF sub-pixel abundance image for valid detections

produced from 2D scatter plot in figure 4.20

2-Dimensional scatter plot used to map valid alteration

detections (Wikki roi) for 3 endmembers (library spectra)

as reference

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188

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199

Results of SAM at Mawulgo roi using image spectra

showing 3 alteration zones at radian angles (a) 0.04

(b) 0.02 and (c) NDVI image

(a) LSU subpixel abundance image using image spectra

(Mawulgo roi). (b) RMSE for image in (a)

(a) LSU subpixel abundance image (library spectra at

Mawulgo roi). (b) RMSE for image in (a)

(a) MF score image (image spectra) for 3 endmembers

loaded as RGB showing alteration zones at Mawulgo roi.

(b). MTMF subpixel abundance image produced

using 2D scatter plotting in fig. 4.25.

2-Dimensional scatter plot used to map valid alteration

detections (Mawulgo roi) for 3 endmembers

(image spectra) as reference

(a) MF score images (library spectra) for 3 endmembers

loaded as RGB showing alteration zones at Mawulgo roi.

(b) MTMF subpixel abundance image produced using

2D scatter plotting in fig.4.27

2-Dimensional scatter plot used to map valid

alteration detections (Mawulgo roi) for 3 endmembers

(library spectra) as reference

(a) LSU subpixel abundance image (silicic) using

image spectra (Wikki roi). (b) RMSE for image in (a)

(a) LSU subpixel abundance image (silicic) using

image derived spectra (Mawulgo roi). (b) RMSE for

image in (a)

(a) MTMF subpixel abundance image using image

spectra (Wikki roi) produced from 2D plots in (b)

(a) MTMF subpixel abundance image using image

spectra (Mawulgo roi) produced from 2D plots in (b)

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4.32:

4.33:

4.34:

4.35:

4.36:

4.37:

4.38:

4.39:

4.40:

4.41:

ASTER and Hyperion image footprints showing

overlapping areas in SW Yankari Park and ROI (A)

location where analysis is conducted using image

derived spectra

ASTER PC3 & PC4 showing areas of possible hydroxy

land carbonate alterations as dark and bright pixels

Image derived spectra for selected endmembers

from ASTER and Hyperion images used for spectral analysis

(a) Results of SAM at ROIs (A) showing phyllic

(illite group) alteration. (b) NDVI image at ROI

(A) showing vegetated-exposed zones

(a) Results of LSU at ROIs (A) showing alteration

mineral assemblages. (b) RMSE image showing high

and low error zones

(a) MF score RGB for selected endmembers (ASTER)

showing incoherent image with errors. (b) MTMF

subpixel abundance image produced using mixture

tuning from fig. 4.38

2-Dimensional scatter plots used to map valid

alteration detections (A) for 4 endmembers (ASTER

image derived spectra) as reference

(a) Result of the SAM using the image derived spectra

on Hyperion, pixels classified as Kaolinite (red), Alunite

(green), Illite (Blue) and Calcite (yellow). (b) NDVI image

indicating altered-vegetated zones

(a) Result of the LSU using the image derived spectra on

Hyperion, more pixels were classified as Kaolinite (red),

Alunite (green), Illite (Blue) and Calcite (yellow) (b). RMSE

showing bright and dark pixels

(a) MF score RGB for selected endmembers (Hyperion)

showing incoherent image with errors. (b) MTMF subpixel

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4.42:

4.43:

4.44:

4.45:

4.46:

4.47:

4.48:

4.49:

4.50:

4.51:

4.52:

4.53:

abundance image produced using mixture tuning

from fig. 4.42

2-Dimensional scatter plot used to map valid alteration

detections (A) for 4 endmembers in figure 4.41 (b)

Calculated at-sensor brightness temperature image map

Calculated NDVI image used to determine

proportion of vegetation

Calculated land surface emissivity (LSE) image

Retrieved LST map showing low and high temperature

at 14.23°C and 34.52°C, three identified anomalous

areas; A, B, and C, and the coordinate points of known

thermal springs

Brightness temperature pattern between the warmest

and coolest month, red areas indicate anomalous zones

LST map showing major fault-fracture systems in

relation to identified anomaly zones

Field observed altered rocks and exposed alteration

zones around Mawulgo thermal springs

Field observed altered rocks and alteration zones

around Wikki thermal springs

ASD Laboratory reflectance spectra of altered rock samples,

arrows pointed the maximum absorption.

Result of XRD analysis showing the presence of associated

geothermal alteration minerals in samples M1 and followed

by other samples M2, M3, M4, M5, around Mawulgo

thermal spring

Result of XRD analysis showing the presence of

associated geothermal alteration minerals in samples

W1 and followed by other samples W2, W3, W4, W5,

around Wikki thermal spring.

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4.54: Geothermal Prospect Map of Yankari Park showing

prospective zones based on verified alteration mapping

and LST anomaly results 237

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LIST OF ABREVIATIONS

ASD - Analytical Spectral Device

ASTER - Advanced Spaceborne Thermal Emission and ReflectionRadiometer

AVIRIS - Advanced Visible & Infrared Imaging spectrometer

BHT - Borehole Temperature

DCS - Decorrelation Stretch

EGS - Enhanced Geothermal System

EMS - Electromagnetic Spectrum

ENVI - Environment for Visualizing Images

ETM+ - Enhanced Thematic Mapper+

FLAASH - Fast Line-of-sight Atmospheric Analysis of SpectralHypercubes

FLIR - Forward Looking Infrared Radiometer

FPCS - Feature-oriented Principal Component Selection

GCP - Ground Control Points

GPS - Global Positioning System

GHF - Geothermal Heat Flux

GT - Geothermal

GIS - Geographical Information Systems

HYMAP - Hyperspectral Mapper

HyspIRI - Hyperspectral Infrared Imager

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IARR - Internal Average Relative Reflection

IFOV - Instantaneous Field of View

LUT - Look-Up-Table

LSE - Land Surface Emissivity

LST - Land Surface Temperature

LSU - Linear Spectral Unmixing

LWIR - Long Wave Infrared

MAGI - Mineral and Gas Identifier

MNF - Minimum Noise Fraction

MODIS - Moderate Resolution Imaging Spectroradiometer

MTMF - Mixture Tuned Matched Filtering

NDVI - Normalized Difference Vegetation Index

PCA - Principal Component Analysis

PPI - Pixel Purity Index

ROI - Region of Interest

SAM - Spectral Angle Mapper

SCA - Single Channel algorithm

SEBASS - Spatially Enhanced Broadband Array Spectrograph

SWIR - Shortwave Infrared

TIMS - Thermal Infrared Multispectral Scanner

TIR - Thermal Infrared

USGS - United States Geological Survey

UTM - Universal Transverse Mercator

VNIR - Visible Near Infrared

XRD - X-Ray Diffraction

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APPENDIX

A

B

LIST OF APPENDICES

TITLE

List of Publications

Supplementary Maps, Images, Tables and Materials

PAGE

274

276

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CHAPTER 1

INTRODUCTION

1.1 Background of Study

Globally, there is a serious energy concern. This is partly as a result of the

combustion of fossil based carbon fuels causing climate change, global warming and

environmental pollution (Seinfeld and Pandis, 2012). According to the International

Energy Agency (IEA), the total world energy comes from 80% fossil fuels, 10%

biofuels, 5% nuclear and 5% renewable (hydro, wind, solar, geothermal), only 18% of

the total world energy was in the form of electricity (Obama, 2017). In addition,

fluctuating oil prices due to security concerns in mostly politically unstable oil

producing regions and the exhaustibility of greenhouse gas emitting fuels such as oil,

natural gas, coal etc. has prompted a search for alternative energy sources.

Consequently, the need for renewable energy systems which are not only reliable but

also environmentally sustainable has become imperative (Omer, 2008).

While most advanced countries like the United States, United Kingdom are

drawn into the race for renewable energy and alternatives as a result of climate change

and the need to move towards a low-carbon society, third world countries are yet to

even satisfy the minimal of their energy needs (Newell and Bulkeley, 2017). There is

a serious energy poverty in developing and especially poor countries in Africa (Salazar

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et al., 2017). This further worsens environmental degradation through fuel wood

extraction, deforestation, consequently reducing carbon sink and increasing global

warming (Chiesa et al., 2009).

A lot of progress has been made in research and innovation in renewable

energy systems and technologies including solar, wind, hydropower, and bioenergy

(Johansson et al., 2004; Noailly and Shestalova, 2017). However, the lack of an

efficient storage systems, intermittent nature and the dependence of these Energy

sources on climatic fluctuations and uncertainties have been the bane of most

renewable energy systems (Denholm et al., 2010).

Geothermal (GT) energy (resource), which is the energy derived from the

earth’s heat, offers a renewable and a reliable source of energy (Glassley, 2014).

However, like most renewable energies it is inherently regional and site specific,

mostly associated with areas of magmatic episodes and crustal plate movements.

Depending on the amount of heat that can be harnessed, GT resources have been used

in many areas around the world for power, home heating, industrial/commercial,

aquaculture, greenhouses, recreational/spa, balneology/medicinal therapy, tourism and

several others (Lund et al., 2011; Lund and Boyd, 2016). In many countries around

the world such as Australia, USA, Iceland, and Japan, GT resources like hotsprings

are a multi-billion dollar industry for spa/recreation, balneology among others, which

attracts millions of tourists’ worldwide (Clark-Kennedy and Cohen, 2017).

Renewable energies are inherently regional and site specific. While solar and

wind energy could be harnessed effectively in sunny and windy areas, geothermal

potential areas have been associated with areas of crustal manifestations and magmatic

episodes. Identifying new prospective areas of geothermal resources requires

information on their location, depth, temperature and surface manifestations such as

hot springs, fumaroles, Geysers and associated minerals (Prakash, 2012).

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Remote sensing offers a great deal of data that can be used for mapping

prospective geothermal locations using both optical and thermal infrared remote

sensing which map GT indicator alteration minerals and detect heat and subtle

temperature anomalies that serve as clues at geothermal locations. While not directly

involved in the generation of energy, application of this complementary technology is

in the supply of information and spectral reflectance data which when interpreted and

analyzed can aid in determining the optimal location of potential targets for geothermal

resource exploration, exploitation, monitoring and development (Nishar et al., 2016a).

Remotely sensed satellite data such as; the thermal infrared multispectral

scanner (TIMS), Advanced Space borne Thermal Emission and reflection Radiometer

(ASTER), MODIS/ASTER or MASTER and Landsat TM, Landsat 7 ETM+ and

Landsat 8, have thermal infrared (TIR) bands, and have been used in the prefeasibility

stages of geothermal exploration and monitoring GT systems (Calvin et al., 2015;

Heasler et al., 2009). The Thermal Infrared (TIR) portion of the electromagnetic

spectrum (EMS) (10um to 12.5um) offers the possibility of sensing surface

temperature anomalies and heat fluxes emitted from prospective GT locations which

serves as targets for exploration and monitoring (Bromley et al., 2011; Coolbaugh et

al., 2007; Dean et al., 1982; Eneva et al., 2006; Fitts, 2013; Haselwimmer et al., 2013;

Heasler et al., 2009; Hochstein and Dickinson, 1970; Hodder, 1970; Mia et al., 2014;

Nishar et al., 2016b; Prakash, 2012; Qin et al., 2011; Tian et al., 2015)

Surface manifestations of GT areas have been successfully detected and

mapped using hyperspectral data including; spatially enhanced broadband array

spectrograph-SEBASS, hyperspectral mapper-HYMAP, ProspecTIR, advanced

visible & infrared imaging spectrometer-AVIRIS and multispectral data such as

ASTER, Landsat and MASTER for remote sensing of hydrothermally altered mineral

indicators as proxy for exploration of GT systems (Calvin et al., 2015; Hanson et al.,

2014; Hellman and Ramsey, 2004; Kratt et al., 2006a; Kratt et al., 2010; Kratt et al.,

2006b; Kruse, 2002; Reath and Ramsey, 2013; Van der Meer et al., 2012; Vaughan et

al., 2003; Vaughan et al., 2005; Waswa, 2017; Adams et al., 2017)

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The techniques involved in GT exploration using remote sensing, despite being

complementary to in-depth geological surveys, has nevertheless established itself as

an invaluable step in the prefeasibility stages of GT mapping due to its synoptic

capability of covering large and inaccessible areas cost effectively and by narrowing

targets prior to a substantial survey. However, while thermal infrared imagery have

been used for sensing temperature anomaly related to GT systems, its usefulness in

exploration has been constrained by the requirement for an extended calibration for

detecting subtle temperature anomalies (Calvin et al., 2015). Consequently, to date

surface thermal anomalies have only been detected within proximate areas of known

surface expressions like geysers, hotsprings and fumaroles (Coolbaugh et al., 2007).

Hydrothermal systems are GT systems characterised with hot water, steam and

permeable faults which serve as conduits for fluid circulation. Geysers and hotsprings

are typical examples of hydrothermal systems (Heasler et al., 2009). The nature of

active hydrothermal processes in many GT systems is similar to the processes that

generate alteration mineral deposits (Carranza et al., 2008). Thus mineral deposit

exploration concepts are applicable and have been used for exploration of GT

resources for example by identifying Hydrothermal Alteration (HA) zones and

minerals (Bogie and Lawless, 2000).

Detailed mineralogical studies of GT fields have been done by previous studies

which revealed an array of alteration minerals related to GT settings (Calvin et al.,

2015; Littlefield and Calvin, 2014; Vaughan et al., 2003). The use of portable field

spectrometers and laboratory spectral libraries have also been employed in studies to

validate results from remotely sensed data (Kratt et al., 2006b; Calvin and Pace,

2016a). The class of common alteration minerals associated with GT systems which

are detectable in remote sensing are limited. Many minerals have diagnostic spectral

properties and features such as; band center, strength, shape and width which are used

to identify mineral species with high confidence (Hunt, 1977). Laboratory and remote

sensing spectral data are usually separated into wavelength ranges on the basis of their

absorption features and the atmospheric windows through which the earth surface is

measured (Calvin et al., 2015). In the visible near infrared (VNIR) and short-wave

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infrared (SWIR) wavelengths (0.4 to~2.5 um), moderate and low-temperature surfaces

are sensed because of the sunlight they reflect (Clark et al., 2003). Absorption features

occur as a result of electronic orbital configuration of transition metals (generally iron

or copper) in various crystallographic sites and from the combination and overtones of

molecular vibrations from species such as hydroxyl, water, carbonate, and sulfate

(Clark et al., 2003). This region of the electromagnetic spectrum is most sensitive to

iron oxides, oxy-hydroxides, and ligands resulting from high or low temperature

alteration (Clark, 1999). The ability to readily discriminate minerals by their unique

spectral characteristics has generally been used as the basis for the techniques applied

in economic mineral exploration (Pour and Hashim, 2015b) and in particular, the basis

for application in geothermal exploration and mapping using associated

hydrothermally altered minerals as surrogates (Calvin et al., 2015).

This study evaluated the applicability and performance of satellite

multispectral and hyperspectral remote sensing data for mapping subtle GT systems in

an uncharted tropical savanna region. Subtle GT systems are characterized by low

temperature thermal springs, and sometimes may not necessarily have clear

materialization of GT manifestations but however, indicate signs, imprints and relics

of past GT activity such as altered rock deposits that can be used to identify and or

infer on their characteristics. The Yankari Park is an area in northeastern Nigeria

characterized by several hotsprings and consequently hydrothermally altered rocks.

These provides a suitable test area for investigation of subtle GT features which are

challenging to detect using conventional methods. Advanced and innovative digital

image processing techniques and spectral information extraction algorithms are

assessed in sieving out relevant data to explore the peculiarities provided by this

unique environmental setting.

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1.2 Problem Statement

The long term economic progress and development of a country is usually

hinged on its ability to provide unhindered supply of not only accessible and affordable

but also environmentally friendly energy sources (Brimmo et al., 2017). Despite

having a population of over 170 million, Nigeria produces only 4000MW of electricity

compared to Brazil, which produces 24 times as much for almost similar population

(Garba, 2017). This challenge emphasized the need to explore alternative renewable

energy sources. Consequently, the country is seriously hampered in terms of economic

development and relies mostly on epileptic thermal to hydropower sources. This is

notwithstanding efforts towards renewable alternatives which have largely remained

at experimental stages (Adenikinju, 2008; Emodi and Yusuf, 2015). Geothermal

energy could be an important long term vision for clean sources of energy (Abraham

et al., 2015). Several GT manifestations have been identified circumstantially and are

mostly exploited for direct use (Abdullahi et al., 2014). GT resources could provide

sustainable alternatives if new potential sites can be identified. Surprisingly however,

there has been limited studies to evaluate GT prospects using remote sensing and

geospatial techniques in spite of their cost effectiveness and synoptic capabilities. The

few previous GT assessment studies in the country have been focused on using

conventional methods which are cumbersome, time consuming and expensive with

small area coverage and undependable outcomes (Kurowska and Krzysztof, 2010;

Nwankwo and Ekine, 2009; Nwankwo and Shehu, 2015). Consequently, an evaluation

study of this nature is imperative, timely and worthwhile.

In general, mapping and identifying prospective GT resources can be

challenging. This is especially the case in subtle systems not easily identifiable using

conventional survey techniques. As observed from literature, different methods have

been employed using remote sensing to identify GT targets, including; mapping

thermal anomalies, minerals by proxy and structural faults (Coolbaugh et al., 2007;

Eneva et al., 2006; Haselwimmer et al., 2013; Hellman and Ramsey, 2004; Hochstein

and Dickinson, 1970; Hodder, 1970; Kratt et al., 2006a; Littlefield and Calvin, 2014;

Mia et al., 2014; Mongillo, 1994; Calvin et al., 2015; Prakash, 2000; Qin et al., 2011;

Vaughan et al., 2003; Vaughan et al., 2005; Macharia et al., 2017; Saepuloh et al.,

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2015; Saepuloh A. et al., 2012). These studies are mostly in volcanic and tectonically

active locations. It is however, not fully understood how these techniques could be

implemented in aseismic environments with characteristically subtle GT features

(Littlefield and Calvin, 2014). This signifies the need for more studies in such regions

and their identification using state of the art techniques of remote sensing and

spectroscopy. This is imperative, particularly with the promising advances expected in

the enhanced geothermal systems (EGS) which could make many regions of the world

exploitable for GT renewable resources (Olasolo et al., 2016) and the recent

appreciable global increase in areas of GT exploitation and direct use (Lund et al.,

2011; Lund and Boyd, 2016). This study is thus premised on the need to improve and

refine methods of mapping unconventional GT targets in order to fully realize the

potentials of GT resources as a competitive renewable alternative with diverse

exploitive uses.

In the context of Hydrothermal alteration mapping as proxy for identification

of indicator minerals for characterization of GT systems, there has been in the last few

years a large increase in performance especially for narrowing targets in the

prefeasibility stages of exploration (Calvin et al., 2015; Calvin and Pace, 2016; Van

der meer et al., 2014). This is as a result of the successful application of both

multispectral and hyperspectral airborne and spaceborne remotely sensed data coupled

with innovative spectral information extraction algorithms for robust characterization

of associated surface compositional features in proximate areas of GT systems

(Hamilton et al., 2016). However, severe remaining limitations still exist in terms of

the requirement for the use of library spectra as reference for analysis as observed from

most previous studies (Kratt et al., 2010) (Kennedy-Bowdoin et al., 2004; Kratt et al.,

2006a; Kratt et al., 2010; Kratt et al., 2006b; Kruse, 2013; Littlefield and Calvin, 2014;

Nash et al., 2004; Reath and Ramsey, 2013; Vaughan et al., 2003; Vaughan et al.,

2005). Hydrothermally altered minerals rarely occur purely to match its corresponding

library spectra in all situations and in peculiar environmental settings because rock

alteration vary from one region to another depending on prevailing conditions which

results in unique hydrothermal alteration (Masoumi et al., 2017). Consequently

variable mineral mixtures may result which could affect the diagnostic spectral

reflectance or emittance characteristics of endmembers of interest (Hosseinjani and

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Tangestani, 2011). Thus, Surface components in unique settings may not be adequately

represented in existing spectral libraries which in most cases contain modeled spectra

that are acquired and generated under controled conditions different from those of

satellite image data (Hosseinjani and Tangestani, 2011). Hydrothermal alteration

mapping of GT systems as proxy requires endmember extraction for both linear and

non-linear unmixing using image and or modeled spectra, this is however, still an

ongoing effort and not a fully resolved problem (Boardman and Kruse, 2011). The use

of verified image spectra in unmapped regions has also not been adequately explored

which could improve the accuracy of spectral analysis for mapping and narrowing

potential GT sites. Based on the above identified research issues, limitations and gaps,

it is arguably imperative and worthwhile to explore new insights and refine methods.

This research proposed an improved method of mapping subtle GT systems by

exploring and evaluating the utility of image endmember spectra extracted from

regions of interest (ROIs) which have been field validated and laboratory verified for

spectral analysis, as compared to corresponding library spectra.

1.3 Research Objectives

The aim of this study is to evaluate the applicability of satellite multispectral

and hyperspectral remote sensing data in mapping hydrothermal alteration indicators

and anomalies as proxy for characterization of subtle GT systems in unexplored

aseismic settings.

The specific objectives of the study are:

i. To identify hydrothermal alteration zones by applying image

transformation methods to VNIR+SWIR+TIR bands of Landsat 8 and

ASTER Multispectral data at regional scale.

ii. To discriminate/map hydrothermal alteration indicator mineral

assemblages associated with geothermal systems using spectral per-pixel

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and sub-pixel mapping techniques to SWIR & TIR bands of ASTER data

at district scale.

iii. To examine and identify specific alteration indicator minerals of

geothermal activity using EO-1 Hyperion hyperspectral data at district

scale.

iv. To detect and map temperature anomalies associated with thermal springs

using the Single Channel Algorithm (SCA) for Land Surface Temperature

retrieval on Landsat 7 ETM+ thermal band.

v. To verify image processing results through; field validations, laboratory

analysis, accuracy assessments and integrate the results using GIS into a

geothermal prospect map.

1.4 Scope of the study

1. This study investigated subtle GT systems by mapping associated

hydrothermal alteration minerals as proxy and detecting thermal anomalies.

The study is confined to the identification of specific alteration indicator

mineral assemblages such as; clays, sulfates, carbonates which mostly manifest

diagnostic spectral features in the shortwave infrared (SWIR) and silicates in

the thermal infrared (TIR) region of the electromagnetic spectrum. The visible

(VNIR) portions of the spectrum is however used in some initial stages for

qualitative mapping to sieve out background spectral information such as

vegetation and for identification of secondary diagnostic spectral features of

indicator minerals. Analysis of thermal anomalies is confined to the use of the

TIR for land surface temperature (LST) retrieval, however, related optical

bands are also used for Normalized Difference Vegetation Index Analysis.

2. The study employed 14 bands of ASTER Level 1B, 13 bands of Landsat 8,

Landsat 7 ETM+ thermal band 6 and 162 bands of EO-1 Hyperion satellite

data. The multispectral data covers the Yankari Park and its environs including

a whole Landsat 8 and a mosaicked ASTER scene. The hyperspectral Hyperion

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covers the southwestern part of the Yankari Park study area in Northeastern

Nigeria. Details of acquired data characteristics is given in chapter 3.

3. Relevant softwares were employed for analysis. The ENVI (Environment for

Visualizing Images) version 5.1 (Classic and Standard) was used for image

rectification and detailed digital image processing, spectral information

extraction using the Spectral Hour Glass approach, creation and comparison of

spectral libraries and production of raster maps. The ArcGIS version 10.2 was

used for thermal anomaly analysis, creation of shapefile and digitization,

conversion of raster maps to vector formats, and final data integration and

visualization.

4. Field survey was comprehensively done to validate remote sensing and image

processing results. This involved the use of hand held GPS MONTERRA@ to

identify locations where rock samples were obtained for laboratory analysis

and identifying hydrothermal alteration and GT related minerals in the

samples. Samples from hot spring sites such as altered rocks were analyzed

using X-Ray Diffraction (XRD) and Analytical Spectral Device (ASD)

spectroradiometer equipment. Photographs were taken of the

geomorphological features, rocks and hydrothermal alteration at the hot spring

sites.

1.5 Significance of Study

The study made significant contribution in terms of improving the techniques

for GT exploration, monitoring and narrowing of targets at the prefeasibility level

especially in unexplored regions characterized by subtle anomalous features which are

very difficult to identify using conventional techniques. The use of available but

improved spectral and spatial resolution satellite data in mapping GT features in

regions where expensive airborne surveys are not affordable could encourage further

interest in GT characterization and eventual inclusion of GT resource exploitation for

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either power or for other resource utilization such as; tourism, industrial, commercial,

greenhouses, agricultural, balneology, medicinal therapy and recreational purposes.

The Yankari Park serves as an important test ground for evaluating satellite

multispectral and hyperspectral data to help introduce a geospatial component to

earlier GT assessment efforts which have largely been conventional, thus showcasing

and encouraging further investigations in similar environmental settings elsewhere.

Mapping suitable targets synoptically and cost effectively as applied and demonstrated

in this study using satellite remote sensing data, geographic information system tools

and innovative digital image processing techniques could usher in a renewed interest

and aid decision making in GT resource exploration and exploitation. This is especially

so in less developed countries in dire need of alternative sources of energy and

economic development such as Nigeria. Recently, it was established that there are no

comprehensive data on renewable resources especially GT in Nigeria and the few

available data are incomplete and outdated (Brimmo et al., 2017). This indeed is a

challenge that foreshadow any practical investments in the country’s energy sector.

Hence there is the urgent need for nationwide resource investigations and assessments

as a policy to effect appropriate enabling factors to attract investments. This study is

also premised in furtherance of such a call.

In general, this research has significantly contributed to new knowledge by

improving our understanding as regards the applicability of satellite sensors in

mapping hydrothermal features and the effectiveness of spectral characterization of

subtle GT systems using spectral matching and sub-pixel abundance estimation of

associated surface compositional features by employing in-situ verified image derived

spectra especially in uncharted regions. The study discovered that surface alteration

mapping and detection of temperature anomalies in relation to fault structures can

serve as a significant prefeasibility step for identification of interest areas.

Consequently, identified and verified zones were subsequently integrated into a

geothermal prospect map which can aid in depth geochemical, geophysical and

geothermometric surveys thereby cost-effectively narrowing targets. The study also

made significant contribution by extracting and updating the Yankari Park Geological

Map which was unavailable previously. This was carried out using the Bauchi state

geological map as reference, and field surveys guided by technical staff from the

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Nigerian Geological Survey Agency and from the results of image transformations

particularly BR, DCS, FPCS, ICA and MTMF analysis. The geological map serves as

an important tool for identification of thermal anomalies in relation to identified major

fault structure systems in the Park and can aid future related research particularly as

regards; geological, lithological, geophysical, geothermometric and geothermal

investigations.

1.6 Thesis outline

The thesis comprise of five chapters:

• Chapter 1 explains a general background of the study and gives the problem

statement, research objectives, and scope of the study and finally the significance of

the study.

• Chapter 2 gives a review of relevant literature in the field of applications of

remote sensing in geothermal resource exploration, GT systems, hydrothermal systems

and alteration, concepts and methods, remote sensing and spectroscopy, multispectral

and hyperspectral sensors, spectral processing methods and models, GT mapping

using alteration indicator minerals, thermal anomaly detection, spaceborne and

airborne applications, Characteristic radiation spectra of Hydrothermal alteration

minerals, previous studies and methods of mapping, inherent limitations of the

techniques and observations on further research needs and future prospects in the field.

Finally, Inferences made from the review

• Chapter 3 describes the characteristics of the acquired satellite multispectral

and hyperspectral data, instruments and methods, spectral information extraction

methods used for the study, image enhancements, preprocessing and processing

methods, field validations and laboratory verifications used to achieve the objectives

of the study.

• Chapter 4 describes the presentation and discussion of the results of image

processing, spectral mapping, field and laboratory analysis.

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• Chapter 5 gives conclusions based on the results of the analysis and processing

and recommendations for future work in the field of research.

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