A TEST OF THE VALIDITY OF MORPHOMETRIC ANALYSIS IN ...

A TEST OF THE VALIDITY OF MORPHOMETRIC ANALYSIS IN
DETERMINING TECTONIC ACTIVITY FROM ASTER DERIVED DEMs
IN THE JORDAN-DEAD SEA TRANSFORM ZONE

A TEST OF THE VALIDITY OF MORPHOMETRIC ANALYSIS IN
DETERMINING TECTONIC ACTIVITY FROM ASTER DERIVED DEMs
IN THE JORDAN-DEAD SEA TRANSFORM ZONE
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
By
Husam Abbas Ata, B. A., M. A.
Yarmouk University, 1995
Yarmouk University, 1998
May 2008
University of Arkansas

ABSTRACT
The Jordan-Dead Sea Transform (JDTZ) is an active tectonic zone which is
located between the Dead Sea in the north and the Gulf of Aqaba in the south and extends
approximately 245 km. The JDTZ has a complicated geomorphological setting and varied
geology and it contains a number of active faults that were associated with several
significant destructive earthquakes in the region. This research was initiated to develop a
morphometric digital approach to examine the mountain front geomorphic form along the
Jordan-Dead Sea Transform Zone to obtain intensive analysis of its potential as an
indicator of seismic activity.
Four ASTER scenes were used as a visual digital data reference to locate
geomorphic forms along the mountain fronts in the study area. Using the ASTER
generated DEMs of ±10m vertical accuracy as a base elevation reference layer, several
significant mountain fronts and valley profiles were digitized using GIS technology to
precisely determine their morphometric indices. The mountain front sinuosity (S mf ) and
the ratio of valley floor width to valley height (V f ) morphometric indices were utilized to
analyze the several mountain fronts and valleys in the JDTZ to determine their tectonic
activity.
The final morphometric analysis of S mf results indicated two major tectonic
activity classes within the JDTZ. The active tectonic class 1 values range from 1.00 to
1.30 while the moderate to less active class 2 values are >1.30. On the other hand, the V f
analysis results suggest the existence of three tectonic activity classes. The active tectonic
class 1 values range from ≤ 0.09 to 0.50, the moderate to less active class 2 values range
from 0.51 to 1.88, and the less active (inactive) class 3 values are >1.88.

This dissertation is approved for
Recommendation to the
Graduate Council
Dissertation Director:
_________________________________________________________
John C. Dixon, PhD
Dissertation Committee:
_________________________________________________________
Jackson D. Cothren, PhD
_________________________________________________________
Pamela E. Jansma, PhD
__________________________________________________________
Thomas R. Paradise, PhD

DISSERTATION DUPLICATION RELEASE
I hereby authorize the University of Arkansas Libraries to duplicate this dissertation
when needed for research and/or scholarship.
Agreed _____________________________________
Refused ____________________________________

ACKNOWLEDGEMENTS
“When I look back upon my early days, I am stirred by the thought of the number of
people I have to thank for what they gave me or for what they were to me. . . . . I think we
all live spiritually, by what others have given us in the significant hours of our lives” -
Albert Schweitzer (1875-1965), a German philosopher, physician, musician, theologian,
and the 1952 Nobel Peace Prize winner.
From the formative stages of this dissertation, to the final draft, I owe an immense
debt of gratitude to my supervisor, Dr. John C. Dixon, for his constant academic support
and his words of encouragement and help throughout my graduate study and research. I
would like to express thanks to Dr. Jackson Cothren, who never gave up on me. He was
always interested in the work and helped me find answers regardless of the research
challenges. I also want to thank Dr. Pamela Jansma and Dr. Thomas Paradise for their
scholastic assistance and contribution to my research. Put these scholars together and it is
the dynamic committee that words can never adequately describe. I would also like to
extend my appreciation to the King Fahd Center for Middle East and Islamic Studies for
the financial support they offered to conduct and maintain my graduate research.
Second, I would like to acknowledge all the professors, technicians, and staff of
the Center for Advanced Spatial Technologies (CAST) who were always willing to
answer questions, and helped me locate valuable information. In addition, I would like to
thank the environmental dynamics’ professors who taught and enlighten me. They were
interested as I in seeing this study done. Moreover, I would like to thank the geosciences
department’s staff and my fellow graduate students for their assistance and support.
Finally, I would like to thank my faithful wife Nadia Khrais, without her support
and steady encouragement this work would never have been completed. I also want to
thank my parents, sisters, precious son, and family who offered me unconditional love,
support, encouragement and surrounded me with their blessing and prayers.
v

DEDICATION
To my father and mother,
To my two sisters, and
To my wife and son,
With love and gratitude
vi

TABLE OF CONTENTS
Title
Page
Abstract..………………………………………………………..………………… ii
Acknowledgments...……………………………………………………………… v
Dedication………..…………………….…………………………………………. vi
Table of Contents………………………..…………………...…………………… vii
List of Figures…..………..……………..………………………………………… x
List of Tables…..…………………….…………………………………………… xvi
List of Abbreviations – Keywords……………...………………………………… xvii
1. Chapter One: Introduction…...………………………………………………… 1
1.1. Research objectives…..……………………………………………………… 1
1.2. Research problem…...……………………………………………………….. 1
1.3. Justification of the Study..…………………………………………………… 4
1.4. The Jordan-Dead Sea Transform Zone characteristics..…………………….. 6
1.4.1. Geomorphology of the JDTZ.…………………….…………………….. 6
1.4.1.1. Rifting Process and Extensional Tectonics..……………………… 16
1.4.2. Structure and Geology of the JDTZ…..………………………………… 20
1.4.2.1. Structure…..………………………………………………………. 20
1.4.2.1.a. Wadi Araba…...…………………………………………… 20
1.4.2.1.b. The Dead Sea…...………………………………………… 21
1.4.2.2. Geology…...………………………………………………………. 23
1.4.2.2.a. The Aqaba Complex...…………………………………….. 25
1.4.2.2.b. The Araba Complex...…………………………………….. 25
1.4.2.2.c. The Dead Sea..……………………………………………. 26
1.4.3. Seismicity of the JDTZ..……………………………………………...… 30
1.4.3.1. Regional tectonics of Jordan and its vicinity..………………….… 30
1.4.3.2. Seismotectonic maps of Jordan..….………………………………. 33
1.4.3.3. Seismic maps of the JDTZ..…….……………………………….... 37
1.4.3.4. Jordan/JDTZ earthquakes data..……………………………...…… 42
2. Chapter Two: Literature Review..…………………………………………...… 46
vii

2.1. Introduction..………………………………………………………………… 46
2.1.1. Morphometric analysis in geomorphology…......………………………. 46
2.1.2. Remote sensing and GIS uses in geomorphology..……………………... 48
2.2. Morphometric analysis approach..……………………………………...…… 50
2.3. Geomorphic indices of active tectonics..………………………………….… 51
2.3.1. The Hypsometric Curve and Hypsometric Integral (H i )..….…………… 52
2.3.2. Drainage Basin Asymmetry (AF)..…………………………………...… 54
2.3.3. Stream Length-Gradient Index (SL)..…………………………...……… 57
2.3.4. Triangular Facets Index (Pf)..…………………………………...……… 59
2.3.5. Mountain front sinuosity (S mf )..………………………………………… 62
2.3.5.1. Choosing mountain fronts……………………..….….…………… 64
2.3.6. Valley floor width to valley height ratio (V f )...……..……………...…… 65
2.3.6.1. Choosing valley profiles..………………………………………… 66
2.4. Satellite imagery and digital elevation models.…..…………..……………… 67
2.4.1. Digital Elevation Model………...………………………………….…… 68
2.5. Advanced Spaceborne Thermal Emission and Reflection Radiometer
Satellite..……………………………………………………………………... 72
2.5.1. ASTER data types..……………………………………………………... 73
2.6. Previous studies in morphometric analysis….………………...…………..… 74
2.7. Summary..…………………………………………………………………… 82
3. Chapter Three: Materials and Methods..…………………..…………………... 84
3.1. Introduction..………………………………………………………………… 84
3.2. The digital morphometric approach..……………………...………………… 84
3.2.1. Choosing mountain fronts..………………………………………...…… 85
3.2.2. Choosing valley profiles..………………………………….…………… 85
3.3. ASTER Stereo capability..……………………………………...…………… 85
3.4. Obtaining ASTER imagery data..……………….…………………………… 87
3.5. Viewing ASTER data in PCI Geomatica..……………………………...…… 89
3.6. Generating ASTER DEMs…..………………………………….…………… 91
3.6.1. Generating and extracting DEMs from ASTER data…..………..……… 92
3.6.1.1. PCI Geomatica software……..…………………………………… 92
viii

3.6.1.2. DEMs extraction process…..………………………………...…… 97
3.6.1.3. The math model….……………………………………..………… 100
3.6.1.4. DEMs editing process…..………………………………………… 118
3.6.1.5. Transferring ASTER DEM to GIS environment…..……...……… 127
3.7. Obtaining Landsat 7 ETM+ imagery…..………...……………………..…… 130
3.7.1. Creating ASTER and Landsat composite images…..…………………... 130
3.8. Obtaining vector data……..…………………………………………….…… 133
3.8.1. Converting Interchange files to Shapefiles in ArcToolbox…...………… 134
3.8.2. Digitizing vector data…...………………………………………….…… 137
3.8.3. Digitizing mountain fronts and measuring sinuosity…..……..………… 138
3.8.4. Digitizing valley profiles and measuring elevations and valleys’ widths. 153
4. Chapter Four: Results and Discussion…..……………..……………………… 162
4.1. Introduction…..……………………………………………...……….……… 162
4.2. The tectonic morphometric analysis…...…………………..………………… 163
4.2.1. Mountain front sinuosity results……...……………….………………… 166
4.2.2. The ratio of valley floor width to valley height results…..……...……… 166
4.3. Seismic activity at mountain fronts…..……………………………………… 168
4.4. The accuracy of ASTER DEM data.….………………...…………………… 172
4.5. ASTER DEM error test…..…………………………………………..……… 175
4.6. Final results and tectonic activity classes…..………...……………………… 190
5. Chapter Five: Conclusion…..……………………………...……...…………… 194
5.1. Conclusion…...………………………………………………………………. 194
5.2. Limitation of the Study…..…….……………………………..……………… 196
5.3. Future directions/remarks…..…………………………...…………………… 196
Bibleography……………………………………………………………………… 198
Appendix A…..…………………………………………………………....……… 216
ix

LIST OF FIGURES
Figure
Page
1.1: Earthquakes in the Jordan-Dead Sea Transform and adjacent areas, 1900-
1980……………………………………………………………………….…... 2
1.2: Jordan-Dead Sea Transform Zone (JDTZ) dimensions.….….……………... 7
1.3: East African Rift System …………………...………………...….................. 8
1.4: Tectonic setting of the Middle East......…………………...…………...…… 9
1.5: Wadi Araba Valley location map along the Jordan-Dead Sea Transform
fault system………...………………...…………………...…………………... 10
1.6: Block diagram along the central part of the JDTZ illustrating the tectonic
style and the continuous rise of the graben floor.…………………...………... 12
1.7: The Aqaba-Dead Sea-Jordan subgraben system….………………................ 13
1.8: A block diagram showing the strike-slip geometry along the JDTZ
representing the en echelon fault system.………………..………...…………. 15
1.9: The seven pillars of Wisdom of southern Jordan………..………………….. 16
1.10: The East Africa Rift Valleys as an example of the rifting process......……. 16
1.11: Rift structure.…...………….…………………...………………..…............ 17
1.12 Normal fault system..……………………………………………….……… 18
1.13 (a) Symmetric horst and graben system (b) Half-graben system above the
subhorizontal fault detachment.…………………...…….…………….…...…. 19
1.14 Alluvial fans (bajadas) at the southern end along the eastern side of Wadi
Araba…………………...…………………...………………………................ 20
1.15 Dead Sea location map showing the two basins.………………..……......... 23
1.16 Jordan-Dead Sea Fault simplified geological cross-section.……..……..….. 24
1.17: A simplified geological cross section across the southern section of the
Jordan-Dead Sea Transform.…………………...…………………................... 25
1.18: Rock types and the locations of all mountain fronts in the JDTZ.…...……. 29
1.19: Bouguer gravity anomaly map of Dead Sea transform, Jordan and Israel… 34
1.20: (A) The thirteen seismic zones and (B) major faults within the thirteen
seismic zones in Jordan and its vicinity………………..……………...…….... 35
x

1.21: The Dead Sea fault and the adjacent branching faults…..………………… 37
1.22: Seismic map of the Middle East 1900 – 1983…..………………………..... 38
1.23: Seismic zones and active faults of Jordan and surrounding countries.......... 41
1.24: Major earthquake events on Jordan, 19A.D. – August 1983….…………... 44
1.25: Major earthquake events on Jordan, September 1983 – 2005…………….. 45
2.1: Hypsometric curve derivation from drainage basin……….………………... 54
2.2: Block diagram shows the effect of an asymmetry factor with a left side tilt
on tributaries lengths…..…………………...…….……………........................ 55
2.3: An example of calculating a drainage-basin transverse topographic
asymmetry vector for a single stream segment.…………………………..…... 56
2.4: Map showing the Stream Length-Gradient Index (SL)…………..…….….... 57
2.5: Diagram shows the process of calculating the Stream Length-Gradient
Index (SL).…………………...…………………...…………………............... 59
2.6: Rapid and slow block uplift produces………..………...………………….... 60
2.7: Circular and elongated basins.…………………...…..……………………... 61
2.8: Location of older and younger triangular facets to mountain fronts……..…. 62
2.9: Calculating mountain front sinuosity (S mf ) index…………………………... 63
2.10: Mountain front sinuosity (S mf ) index...……………...…………...………... 63
2.11: Calculating valley floor width to height ratio (V f )..………………..….…... 66
2.12: Valley floor width to height ratio (V f ).…………………...………..……… 66
2.13: The Aqaba 1:50,000-scale topographic map with 20m intervals..….…….. 70
2.14: Flat map shows ASTER DEM global coverage of January 31,
2004…………………………………………………………………..……….. 73
2.15: Data structure of ASTER Level-1B granule…………….…………............ 75
3.1: Simplified diagram of imaging geometry and data acquisition timing for
ASTER along-track stereo image.…………………...………………….......... 86
3.2: Simplified diagram of the imaging geometry for ASTER along-track stereo
image “stereo configuration”...……………...……….………………….......... 87
3.3: The USGS global visualization viewer showing ASTER scenes of Jordan... 88
3.4: Focus is the first icon on the Geomatica Toolbar.………………..….……... 90
3.5: Selecting ASTER VNIR sensor images.……………………...…...………... 90
xi

3.6: Import File window.…………………………………..…………...………... 90
3.7: ASTER 3-2-1 composite image of Aqaba.…………………………..……... 91
3.8: Comparing raw images to epipolar images.…….…………..…………..…... 93
3.9: Measuring height from ASTER stereopair parallax difference..……..…….. 95
3.10: Summary of a standard ASTER DEM product generation process.…...….. 97
3.11: OrthoEngine on the Geomatica Toolbar…………....…………….…..….... 99
3.12: Opening new project from the processing step drop-down menu …...……. 100
3.13: Reading satellite data from hard drive…...…………………………….….. 100
3.14: Project Information window...…………………...………………………... 101
3. 15: Setting project projection to UTM………………………………………... 101
3.16: Setting the UTM zones……….…………………..........…………………... 102
3.17: Setting the UTM rows……………………...…………….………………... 102
3.18: Setting Earth Model (Ellipsoid)...………………...…...…………………... 102
3.19: Setting GCPs to match the output file projection………………………….. 102
3.20: Reading row ASTER data HDF files from the hard drive……………...…. 103
3.21: Importing GCPs from file…………………………………...…………..… 104
3.22: loading the 121 already available GCPs from image 3N file…………….... 104
3.23: Dead Sea band 3N GCPs……………………...……………………….…... 105
3.24: Dead Sea band 3B GCPs……………………...…………………….……... 105
3.25: The image layout of bands 3N and 3B showing the location of all 121
GCPs in the Dead Sea ASTER image..…………………...…………………... 105
3.26: Start the Collect Ground Control Points and Tie Points manually function. 106
3.27: An image show how two images connect through a tie point.…...…...…… 106
3.28: Collecting tie points manually from both images…......…………………... 107
3.29: Opening both uncorrected 3N and 3B images to manually collect tie
points.…………………...………………………………...…………………... 108
3.30: Collecting tie point window for Aqaba image 3N.……………..…......…... 108
3.31: Automatically Collect tie points.……………..…….....………….………... 109
3.32: Automatically Collect tie points uniformly over an entire image….…..….. 110
3.33: Band 3N tie points (North Araba).……………………..………………….. 111
3.34: Band 3B tie points (North Araba).……………...……...………………….. 111
xii

3.35: Displaying overall image layout from both images.…...…...……………... 111
3.36: Image layout of both bands 3N and 3B for the North Araba scene.…...….. 112
3.37: Running Model Calculation to perform bundle adjustment.…..………….. 112
3.38: Creating a DEM from stereo pairs using image correlation.………..…….. 113
3.39: Create Epipolar image icon.…………………...……………….…...……... 114
3.40: Generate Epipolar images window.…………………...…………..………. 114
3.41: Extract DEM Automatically icon.…………………...…………..………... 115
3.42: Automatic DEM extraction window showing all used options for Aqaba... 116
3.43: The multi-resolution image pyramids.…………………….......…...……… 118
3.44: The Dead Sea generated DEM.…………………...…………...…………... 119
3.45: North Araba generated DEM.…………………...……….....……………... 119
3.46: South Araba generated DEM.…………………...….…..……..…………... 120
3.47: Aqaba generated DEM.…………………...………………...…..………… 120
3.48: General view of the clipped GTOPO30 DEM of the Middle East showing
the JDTZ.…………………...…………………...……………………..……... 122
3.49: The XPace Tool……..…………………………………………………….. 124
3.50: The Dead Sea final DEM.…………………...………………...…………... 127
3.51: North Araba final DEM.…………………...……………….....…………... 127
3.52: South Araba final DEM.…………………...………………..……...……... 127
3.53: Aqaba final DEM.…………………...…………………...…...…………… 127
3.54: Portion of the North Araba converted GRID file format to ASCII format... 129
3.55: The converted North Araba DEM using ArcGIS ASCII to Raster
command line.…………………...………..……………...………………….... 129
3.56: Import from Interchange file window in ArcToolbox ………..…………... 134
3.57: The Coverage to Shapefile tool in ArcToolbox…………………..……….. 135
3.58: The Coverage to Shapefile tool window in ArcToolbox...………...……… 135
3.59: The Define Projection Wizard in ArcToolbox………………………...…... 136
3.60: The Define Projection Wizard window under Projections in ArcToolbox... 137
3.61: Map shows the Dead Sea shoreline and Jordan borders before and after
editing.…………………………………….…….…………………………….. 138
3.62: The Gulf of Aqaba generated DEM and shaded relief….……………….… 139
xiii

3.63: Dead Sea ASTER color composite 3-2-1………………………………….. 140
3.64: Dead Sea ASTER-derived DEM…………………………………………... 141
3.65: Dead Sea shaded relief…………………………………………………….. 142
3.66: North Araba ASTER color composite 3-2-1………………………………. 143
3.67: North Araba ASTER-derived DEM……………………………………….. 144
3.68: North Araba shaded relief…………………………………………………. 145
3.69: South Araba ASTER color composite 3-2-1………………………………. 146
3.70: South Araba ASTER-derived DEM……………………………………….. 147
3.71: North Araba shaded relief…………………………………………………. 148
3.72: Aqaba ASTER color composite 3-2-1…………………………………….. 149
3.73: Aqaba ASTER-derived DEM……………………………………………... 150
3.74: Aqaba shaded relief………………………………………….…………….. 151
3.75: An overall map of the mountain fronts and valley profiles…………….…. 152
3.76: Calculating the V f value for valley profile #11 in the Dead Sea area……... 155
3.77: The difference between the 2D image and the actual 3D topography of a
given surface………………...…………..…...………….……………………. 156
3.78: Collecting elevations from DEM as shapefile passes through raster
horizontally, diagonally, and vertically.…………………….………………… 157
3.79: Converting Aqaba 3D feature to points using XTools Pro...…………..….. 158
3.80: General view from the Dead Sea area……………………………………... 160
3.81: Collecting elevation data from profile #11 in the Dead Sea area...……..… 161
4.1: Valley profile display of a V-shaped valley in South Araba and a U-shaped
valley in the Dead Sea area……….…………………….……………….……. 162
4.2: The tectonic activity classes of the S mf and V f indices results…………….... 165
4.3: Network of earthquakes-mountain fronts’ relationship on the JDTZ
showing earthquake events of M L 4 to 6 and tectonic zones, 19A.D. to
August 1983…………………………………………………………………... 170
4.4: Network of earthquakes-mountain fronts’ relationship on the JDTZ
showing earthquake events of M L 4 to 6 and tectonic zones, September 1983
to 2005……………………………………………………………………...…. 171
xiv

4.5: The relationship between the ground coordinate system and the image
coordinate system.………….……………...……….………………….……… 173
4.6: The tectonic activity classes of all S mf and V f based on V f +σ Vf results….….. 189
4.7: The active tectonic classes of the northern and southern regions of the
JDTZ showing all S mf and V f +σ Vf results..………….………...……....……… 193
xv

LIST OF TABLES
Table
Page
1.1: Concise description of the mountain fronts rock types within the JDTZ...… 28
2.1: The S mf and V f indices ranges of selected literature...……………………… 83
3.1: ASTER scenes selected to cover the JDTZ study area...…………………… 89
3.2: Specification for standard ASTER DEM products…………….…………… 96
3.3: Number and type of tie points in each DEM coverage area...……………… 110
3.4: Options used for all DEMs extraction processes within the study area…….. 115
3.5: Overall number of digitized fronts in the JDTZ………...……………...…… 140
3.6: Digitized valley profiles number and their distances upstream from
mountain fronts in the JDTZ………………………………..………………… 154
4.1: Brief results of all mountain fronts sinuosity (S mf ) and valleys floor width
to valleys height ratio (V f ) analyses.………………………………..………… 164
4.2: The percentage of the U- to V-shaped valley profiles in the JDTZ……….... 167
4.3: MATLAB command lines……………………………………………...…… 183
4.4: Dead Sea final σ Vf results and V f tectonic classes.....…………..…………… 184
4.5: North Araba final σ Vf results and V f tectonic classes…..……..…………..… 185
4.6: South Araba final σ Vf results and V f tectonic classes………….…….……… 186
4.7: Aqaba final σ Vf results and V f tectonic classes...………………....………… 187
4.8: Percentages change of all valleys tectonic classes after applying the ± σ Vf
values.………………………………………………………………………… 191
4.9: Final S mf and V f tectonic activity classes…………………………………… 192
A.1: The Dead Sea S mf and V f results..…………………………..……………… 216
A.2: The North Araba S mf and V f results..…………………….………………… 217
A.3: The combined Dead Sea and North Araba S mf and V f results....…………… 218
A.4: The South Araba S mf ) and V f results……………………………..………… 219
A.5: The Aqaba S mf and V f results...……………..……………………………… 220
xvi

LIST OF ABBREVIATIONS
2D
3D
ASTER
DCW
DED
DEMs
DLG
DTD
DTM
EOS
ETM+
GCPs
GIS
GPS
JDT
JDTZ
JSO
RMSE
S mf
TPs
USGS
UTM
V f
WGS
Two Dimensional
Three Dimensional
Advanced Spaceborne Thermal Emission and Reflection Radiometer
Digital Chart of the World
Digital Elevation Data
Digital Elevation Models
Digital Line Graph
Digital Terrain Data
Digital Terrain Model
Earth Observing System
Enhanced Thematic Mapper Plus
Ground Control Points
Geographic Information Systems
Global Positioning System
Jordan-Dead Sea Transform
Jordan-Dead Sea Transform Zone
Jordan Seismology Observatory
Root Mean Square Error
Mountain Front Sinuosity
Tie Points
The United States Geological Survey
Universal Transverse Mercator
Valley Floor Width to Valley Height Ratio
World Geodetic System
KEYWORDS: ASTER imagery, digital elevation model, geographic information
systems, geomorphic indices, geomorphic analysis, Jordan, Jordan-Dead Sea
Transform Zone (JDTZ), morphometric analysis, mountain front sinuosity, satellite
remote sensing, tectonic geomorphology, valley floor width to valley height ratio.
xvii

1. Chapter One: Introduction
1.1. Research objectives
The objective of this research is to develop a digital remotely sensed approach to
characterize mountain front geomorphic form along the Jordan-Dead Sea Transform
Zone (JDTZ) to assess the current degree of tectonic activity and achieve detailed
analysis of the mountain front’s potential as an indicator of seismic activity.
1.2. Research problem
The relationship between earthquakes and faulting was examined by Allen
(1975). In his research conducted in California, using the San Andreas fault system as an
example, he indicated that nearly all large earthquakes with magnitudes greater than 6.0
on the Richter Scale have occurred along existing faults that are associated with active
mountain fronts. Such earthquakes occur at relatively shallow depths (less than 20km)
that are largely associated with subsurface rupture and surface faulting and folding. In
addition, he reported that larger earthquakes have generally occurred on the larger and
longer faults (Allen 1975).
Many studies around the world (Ganas et al. 2001, Tramutoli et al. 2001)
confirmed the strong relationship between active faults and earthquakes and the
usefulness of remote sensing techniques to recognize and analyze that relationship
(Dreger and Kaverina 2000, Karnieli et al. 1996, Sabins 1997, Süzen and Toprak 1998).
Such studies established that tectonism has a geomorphic expression in regions where it
occurs (Gerson et al. 1984).
In Jordan, the primary seismic and tectonic zones are the Dead Sea fault system,
and secondarily the Wadi Araba fault, including the Gulf of Aqaba region (Bender 1974a,
1

Degg 1990, Yücemen 1992). Within the Jordan-Dead Sea Transform (JDT), the majority
of the historical and the 20 th
century instrumental recorded earthquakes, often
accompanied with crustal deformation, are clustered on these two fault systems that are
believed to be associated with active mountain fronts (Abou Karaki 1995, Al-Tarazi
1992, Arieh and Rotstein 1985, Ben-Avraham et al. 2005, Ken-Tor et al. 2001, Klinger et
al. 2000a, 2000b, Olimat 2001) (figure 1.1).
Figure 1.1: Earthquakes in the Jordan-Dead Sea Transform and adjacent areas, 1900-
1980 (Modified from Arieh and Rotstein 1985, figure 3, P. 884).
2

The largest earthquake recorded in the Gulf of Aqaba was on November 22, 1995
and had a moment magnitude of 7.3 and a local magnitude of 6.2 on the Richter Scale
with a depth of 14km and an epicenter off-shore about 60km from the head of the gulf
where the cities of Aqaba and Elat are located (Al-Tarazi 2000, Klinger et al. 1999). This
earthquake was the beginning of a seismic swarm that occurred in the central part of the
Gulf of Aqaba. During this swarm, the Jordan Seismology Observatory (JSO) detected
2,089 earthquakes with magnitudes ranging between 2.0 to 6.2, which strongly affected
the near-shoreline cities (Al-Tarazi 2000).
More recently, on September 9, 2006 at 7:58 am, an earthquake with a local
magnitude of 4.5 on the Richter Scale struck the northern part of the Dead Sea with an
epicenter depth of 7.7km (Zgheylat 2006a). On September 18, 2006 near noon time,
another earthquake with a local magnitude of 4.2 on the Richter scale was detected in the
exact location as the previous quake with an epicenter depth of 7km (Zgheylat 2006b).
Both earthquakes were associated with the natural intersection of the River Jordan and
Al-Zarqa Faults (Zgheylat 2006a, 2006b). Earlier, on January 23, 2005, an earthquake of
local magnitude 3.5 on the Richter Scale occurred in the northern Dead Sea segment, one
of a series of two other quakes with local magnitudes of 2.7 each in the Dead Sea and
Tiberias Lake (also known as the Sea of Galilee or Lake Kinneret). The hypocenters of
the quakes were believed to be a few tens of kilometers below the northern section of the
Dead Sea level (Abu El-Zelouf 2005).
In a similar incident, an earthquake with a magnitude of 4.9 on the Richter Scale
struck Jordan on Wednesday, February 11, 2004. The quake occurred at 11:14am and
lasted for around 20 seconds. The hypocenter was located to the northeast of the Dead
3

Sea with a depth of 20km. The main quake was followed by seven aftershocks with
magnitude of two to three points on the Richter Scale (Husseini 2004). The Jordan
Seismology Observatory in the Dead Sea area detected five seismic events on December
31, 2003 and January 1, 2004. The five earthquakes had maximum magnitudes of 3.8 on
the Richter Scale. Such quakes are considered normal, weak, and occur frequently in this
area. The first quake was recorded at 1:30pm with a magnitude of 3.3 on the Richter
Scale. The strongest quake had a local magnitude of 3.8 while the others recorded 2.5
each, with a depth range of 19 to 8.5 kilometers (Al-Gharaibeh 2004).
Five large earthquakes with a local magnitude of 6.0 or higher are recorded for
the region between the Dead Sea and the Gulf of Aqaba. These include the earthquakes of
A.D. 1068, 1202, 1212, 1293, and 1458 (Ellenblum et al. 1998, Niemi et al. 2001). The
epicenters of the 1068 and 1212 quakes were probably in the southern Araba Valley
(Niemi et al. 2001). Clearly, the occurrence of earthquakes represents a significant
geologic event in the region, with potential significance for the associated human
population.
1.3. Justification of the Study
This study is believed to be the first attempt to apply two geomorphic indices,
namely mountain front sinuosity (S mf ) and valley height to valley floor width ratio (V f ) to
conduct morphometric analysis in the Jordan-Dead Sea Transform zone. Since these
geomorphic indices were first introduced as indicators of seismic activity (Bull and
McFadden 1977), normally all measurements have been collected using topographic and
geologic maps as sources of elevation then manually calculated to determine the indices
values. In this research a new methodology is applied using a digital approach, including
4

satellite imagery, multiple layers of geographic data, and digitized calculations of the two
geomorphic indices of seismic potential to achieve more precise determinations.
Previous studies focused on the Jordan-Dead Sea Transform, including the Dead
Sea and Wadi Araba, employed different analytical techniques and multiple sensors in
their applications, including Landsat TM (Gerson et al. 1984, Malkawi et al. 2000),
Radarsat (Abdelhamid 2001), SPOT imagery and SPOT derived DEM (Klinger et al.
2000a, 2000b), GTOPO30 dataset (Galli and Galadini 2001), digitized topographic maps
(Hall 1997), gravity anomaly maps (Brink et al. 1999), plus paleoseismic analyses
(Marco et al. 2005, Zilberman et al. 2000), GPS geodesy (Wdowinski et al. 2004), and
seismic anisotropy (Rümpker et al. 2003).
This study aims to accomplish the transition from a conventional to digital
morphometric analysis approach in order to determine tectonic activity along the
mountain fronts in the Jordan-Dead Sea Transform Zone (JDTZ) to examine their degree
of activity and add to the repertoire of geomorphic and environmental studies in Jordan.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)
imagery will be the main source of Digital Elevation Models (DEMs) and an input to
other digital data. Morphometric analysis is being utilized because the results of this
method have proven to be reliable and relatively accurate in the assessment of tectonic
activity of large-scale regional analysis of natural geomorphic forms (Azor et al. 2002,
Silva et al. 2003, Zovoili et al. 2004). In addition, this approach is easy, does not require
expensive equipment, and can be remotely performed without conducting field
measurements.
5

1.4. The Jordan-Dead Sea Transform Zone characteristics
The following section is a brief description of the geomorphology, geology, and
seismicity of the Jordan-Dead Sea Transform zone. Several seismic maps of Jordan and
its vicinity are included to illustrate the relationship between tectonic events and major
faults along the JDTZ, such as earthquakes in Jordan and the surrounding countries,
seismic zones in the area, and the major fault lines located in Jordan.
1.4.1. Geomorphology of the JDTZ
The study area extends from the northern shoreline of the Dead Sea Transform
System, Wadi Araba (Araba or Arava Valley), to the Gulf of Aqaba. The focus is on the
eastern portion of the region that falls only within the Jordanian territories defined by
Jordan’s western borders. Since the study area is part of the Jordan-Dead Sea Rift system
(Dead Sea Transform System, DST) and the Jordan-Wadi Araba Rift including the Gulf
of Aqaba area, it will be referred to as the Jordan-Dead Sea Transform Zone (JDTZ). The
JDTZ extends approximately 245km in length (N-S direction) and about 40km in width
(E-W direction), as illustrated in figure 1.2.
6

Figure 1.2: Jordan-Dead Sea Transform Zone (JDTZ) dimensions; 245km (N-S) and 40
km (E-W).
7

The Jordan-Dead Sea Rift system, that contains the JDTZ, extends over 1,000km,
which is a portion of the original 6,000km East African-North Syrian Fault System that is
also called the East Africa-Asia Rift System (Bender 1974a, 1975, Burdon 1959, Klinger
et al. 2000a) (figure 1.3). The Rift margins show extensive extensional rifts combining
normal faults and flexures. The movement along the Dead Sea Transform began in the
early Miocene during two stages of left-lateral displacement and counterclockwise
rotation of the Arabian plate relative to the African plate (Goudie 2002, Taqieddin et al.
2000), as shown in figure 1.4. This has led to the formation of several pull-apart basins
and push-up swells along the transform. The Dead Sea Basin is the deepest and largest
pull-apart basin along the Jordan-Dead Sea Rift system with active seismicity along the
transform area (Bender 19974b, Niemi et al. 2001, Taqieddin et al. 2000).
Figure 1.3: East African Rift System (Modified from Keller and Pinter 2002, figure 2.17,
p. 69).
8

The Red Sea is essentially a spreading center between the Arabian and Nubian
plates (Garfunkel 1997). Since is it located to the south of the Gulf of Aqaba, the latter is
directly affected by any tectonic activity in the Red Sea. The motion between the Arabian
and Nubian plates is parallel to the total motion of the Red Sea transform (Sultan et al.
1993).
Figure 1.4: Tectonic setting of the Middle East. Arrows show the motion between the
Arabian and African Plates along the Dead Sea Fault (DSF), the North Anatolian Fault
(NAF), and (EAF) East Anatolian Fault (Modified from Goudie 2002, figure 8.1, p. 216).
The breakup of the continuous Afro-Arabian continent during the Cenozoic Era
and the successive drift of Arabia from Africa created a series of rifts reflected by major
morphologic depressions. The Dead Sea rift is mainly a transform boundary, while the
9

Red Sea which is the longest and biggest basin within the Rift system has developed
through a stage of continental extension and is now a spreading boundary (Joffe and
Garfunkel 1987). In fact, most of the spectacular relief and structures in the Middle East
and northeastern Africa are related to the tectonic movements resulting from the opening
of the Red Sea basin (2000km long and 350km wide) that started in the Late Oligocene
about 30Myr (Goudie 2002) (figure 1.5).
Figure 1.5: (A) Wadi Araba Valley location map along the Jordan-Dead Sea Transform
fault system (JDT). (B) Generalized geologic map of Wadi Araba Valley showing
location of selected major active faults and juxtaposition of different bedrock lithologies
across the valley (Modified from Niemi et al. 2001, figure 1, p. 450).
10

Wadi Araba extends about 147km in length (Niemi et al. 2001) and is part of this
depression that is located between the Gulf of Aqaba and the Dead Sea to the northnortheast.
The transform fault strikes N15˚E - N5˚E (Bender 1974b, 1975, 1982). The rift
valley rises gradually from the Gulf to an altitude of about 250m at the watershed of Jebel
er-Risha (75-80km long) and continues about 1,000m from this divide to the southern
shore of the Dead Sea where it decreases to an altitude of 400m-409m below sea level
(Andrews 1996, Bender 1974b, 1975). The width of the valley averages 15km but varies
significantly between 9km at the narrowest part in the south about 16km NNE of Aqaba,
to about 25km in the north at Wadi Feinan (Bender 1974b, 1975, Niemi et al. 2001).
Unconsolidated sediments of Quaternary age and older clastic sediments occupy most of
the Wadi Araba floor (Bender 1974b, 1975, 1982).
The Wadi Araba-Jordan Valley is a transform valley and is distinguished from rift
valleys that are bound by normal faults and escarpments on both sides by having a strikeslip
fault that trends at an angle to the transform valley (Ginat et al. 1998). The highlands
bordering the east side of the Jordan rift valley rise 1,592m in the south at Jebel er-Risha,
about 1,500m above the valley floor, and 1,200m in the north at the east side of the
southern end of the Dead Sea about 1,550m above the level of the Dead Sea (Bender
1974b, 1975). The mountain ridge east of the rift slopes gently eastward in the direction
of the central plateau, while it is very steep on its western side towards the rift where it
reaches 1,734m in height (Bender 1974b, Royal Geographic Center 1992). Many wadis
and perennial streams cut farther eastward into the plateau capturing additional area in
the drainage basins of the rift (Bender 1974b, 1975, 1982).
11

Both the western and eastern sides of the rift valley demonstrate distinct, mostly
triangular-shaped morphological projections and niches of structural origin (Bender
1974b), which could reflect the disharmonic faulting in the Jordan-Wadi Araba graben
(Picard 1952). The eastern scarp of the Dead Sea basin in Jordan is relatively straight and
continuous and is bounded by a single bordering fault (figure 1.6), whereas, the western
side, located in Israel, is more complex with several step faults or fault splinters involved.
Left lateral movement along a curved strike-slip fault is believed to have opened the
Dead Sea pull-apart basin (Bartov and Sagy 2004, Goudie 2002).
Figure 1.6: Block diagram along the central part of the JDTZ illustrating the tectonic
style and the continuous rise of the graben floor (From Kashai and Croker 1987, figure
11, p. 50).
According to Picard (1987), the Aqaba (Elat)-Dead Sea-Jordan subgraben system
(i.e. Jordan-Dead Sea Transform) is divided into four distinct subgrabens namely (1) The
Gulf of Aqaba and South Araba, (2) The North Araba-Dead Sea-South Jordan, (3)
12

Central Jordan Valley and Lake Tiberias, and (4) North Jordan or Hula Valley (figure
1.7). The first two subgrabens are located within the JDTZ, therefore, a brief description
of both geomorphology and geology are included below according to Picard (1987)
interpretations.
Figure 1.7: The Aqaba-Dead Sea-Jordan subgraben system (From Picard 1987, figure 1,
p. 24).
13

1) The Gulf of Aqaba (Elat) graben extends about 180km in length to Tiran, is 15-
25km in width, and is up to 1,850m in depth. It is composed of Precambrian basement
rocks and surrounded by the 2,000m high Sinai and Hedjaz horsts. The Sinai slopes are
cut by Pliocene-Quaternary meridian (N-S) to submeridian (NNE-SSW) normal dip-slip
faults that are sometimes coupled with minor strike-slip faults. Subgraben faults have an
average dip of 70°. The mean width of the graben between the main marginal faults, now
covered by the Red Sea, is 20km (Picard 1987).
2) The North Araba-Dead Sea-South Jordan subgraben is north of the Aqaba-
South Araba subgraben. The former subgraben is distinguished by a 700m altitude
difference from the latter. It stretches roughly 80km in length from the Jebel er-Risha,
watershed that consists of Paleocene-Eocene limestone and marls, down to the Dead Sea
(400 below mean sea level). On the Jordanian (Transjordan) side, a large fault oriented
NNE (20° to 25°) extends from Jebel er-Risha area and is masked by the Araba alluvial
deposits (Picard 1987).
The Jordan-Dead Sea Transform has several structural characteristics which have
been highlighted by several scholars that can be summarized as follow:
1) A major left-lateral offset of about 105-110km has taken place along a belt of
strike-slip faults across the transform (Klinger et al. 2000b, Quennell 1959).
Most of the movement was over by the Quaternary. The offset occurred across
an area several kilometers wide near the Gulf of Aqaba (Picard 1987).
2) Grabens are rhomb-shaped. They appear as depressions in the transform floor
that were produced by strike-slip faults, many of which are en echelon (Picard
1987, Quennell 1959), as shown in figure 1.8.
14

3) Relief of several hundreds to a few thousands meters is present as a result of
normal faulting along the transform margins. As a result, an apparent contrast
between uplifted shoulders and downfaulted stepped blocks exist in a few
locations along the transform margins (Picard 1987) (figure 1.9).
Figure 1.8: A block diagram -not to scale- showing the strike-slip geometry along the
JDTZ representing the en echelon fault system (From Kashai and Croker 1987, figure 15,
p. 57).
15

Figure 1.9: The Seven Pillars of Wisdom of southern Jordan developed on the Cambrian-
Ordovician Sandstones in Wadi Rum located in the Aqaba area (Modified from Goudie
2002, Plate 8.2, p. 227).
1.4.1.1. Rifting Process and Extensional Tectonics
The Jordan-Dead Sea Transform Zone is a rift valley that separates the two
tectonic plates, the Arabian to the east and the African to the west. Rift valleys are
significant landforms associated with continental crust where the lithosphere is
dominated by tensional stress (Summerfield 1997, van der Pluijm and Marshak 1997)
(figure 1.10).
Figure 1.10: The East Africa Rift Valley as an example of the rifting process (From
Keller and Pinter 2002, figure 2.16 (a), p. 68).
16

The structure of rifts include complex normal faults of planar and listric patterns.
The widely accepted and traditional view of the geomorphology of normal faults within a
rift system shows downthrown blocks as grabens bounded by planar normal faults in
which horsts repeat in a symmetric pattern (figure 1.11a). On the other hand, listric faults
produce asymmetric structures with the downthrown block generating a half-graben
structure (figure 1.11b) (Park 1988, Summerfield 1997, van der Pluijm and Marshak
1997).
Figure 1.11: Rift structure (A) symmetric horst and graben structure (B) Asymmetric
listric fault half-graben structure (From Summerfield 1997, figure 4.9, p. 92).
Normal faults in rift systems are generated by tensional tectonic forces due to the
extension of lithosphere (i.e. crustal stretching) at divergent plate boundaries in the
oceanic lithosphere (e.g. Mid-Atlantic Ridge) and in the continental lithosphere (e.g. East
African Great Rift Valley) (Buck 1991, Park 1988, van der Pluijm and Marshak 1997). In
17

most cases, normal faults are composed of parallel arrangements that might be planar or
listric. The movement of a listric normal fault triggers rotation of the hanging wall around
a horizontal axis which forms a rollover anticline. Along listric normal faults, the hanging
wall sloping in the direction of the main fault generates half-graben depressions. This
formation is widely seen in the Basin and Range of the southwestern United States
topography where half-grabens form the basins and the tilted fault block forms the ranges
(figure 1.12a). If normal faults happen in conjugates, planar normal faults will form. In
this case, the block enclosed between them drops down creating a graben, while the
remaining uplifted block to its side will form a horst (figure 1.12b) (Park 1988, van der
Pluijm and Marshak 1997, Wernicke 1981, Wernicke and Burchfiel 1982).
Figure 1.12: Normal fault system (a) Half-graben system (b) Horst and graben system
(From van der Pluijm and Marshak 1997, figure 8.31, p. 173).
The traditional concept of active asymmetric arrangements of horsts and grabens
requires that at some point the displacement of faults will disappear or die out at depth.
The models of asymmetric faulting in rift systems indicate that the listric normal faults
merge at depth with a regionally extensive subhorizontal detachment fault (figure 1.13)
18

(Price 1990, van der Pluijm and Marshak 1997, Wernicke 1981, Wernicke and Burchfiel
1982).
Figure 1.13: (a) Symmetric horst and graben system (b) Half-graben system above the
subhorizontal fault detachment (From van der Pluijm and Marshak 1997, figure 15.5, p.
326).
The JDTZ is distinguished by its large alluvial fans that are normally found in arid
to semiarid regions. Alluvial fans are considered to be an erosional-depositional system
with a surface that has a cone-shape and concave cross-section. They spread out from the
point where the stream leaves the mountain to form cone-shaped bodies. The fan slope is
affected by the stream channel slope, width, and depth that changes discharge patterns
and depositional patterns. All alluvial fans have only one apex (head), which is the
attaching point with the mountain, and end at the toe (Bull 1968, 1977a, Bull and
McFadden 1977, Summerfield 1997). The western side of the JDTZ margins is delimited
by widespread alluvial fan bajadas, as illustrated in figure 1.14. The alluvial fans are
deformed by fault displacement in the JDTZ (Baker 1986b, Rockwell et al. 1984,
Summerfield 1997).
19

Figure 1.14: Alluvial fans (bajadas) at the southern end along the eastern side of Wadi
Araba (Left: Landsat 7 ETM+ 742-composite image; Right: Landsat 7 ETM+ Band-7 of
the JDTZ, 2002).
1.4.2. Structure and Geology of the JDTZ
1.4.2.1. Structure
1.4.2.1.a. Wadi Araba
The Jordan Rift Valley, that includes the Wadi Araba fault, is the single most
important structural feature that extends the whole length of the country and forms
Jordan’s western border (Andrews 1996). The meridional rift valley forms a 200km long
portion of the East African-North Syrian zone of structural weakness. The Wadi Araba
fault system strikes N15°E from the Gulf of Aqaba to the Dead Sea and separates the
Palestine Block in the west from the Transjordan Block in the east. In comparison with
the Palestine block, the Transjordan block is structurally higher, has steeper regional dips
to the north, and drops off more rapidly north of east-west fault zones (Bender 1974b,
1975, 1982). The Wadi Araba fault system illustrates an obvious left lateral strike-slip
fault system with a movement history primarily during mid Miocene and Pliocene-Recent
phases (Andrews 1996, Bender 1975). This strike-slip movement has also led to the
formation of discontinuous and irregular extensional grabens and compressional folds
20

along its length (Andrews 1996). A study conducted by Burdon (1959) suggests that the
tectonism of Jordan was divided into tensional and compressional structures. Several
studies including Burdon’s, have determined five major compressional features in Jordan
(30-80km long) primarily oriented in the NE-SW direction. These are the Wadi El-Yabis,
Wadi Shueib, Biren, Amman-Al-Hallabat, and Al-Shawbak structures that are
constructed of a series of folds and thrust faults (Al-Tarazi 1992). In general, continuous
fault zones exist along both sides of the Jordan-Dead Sea Transform. All major active
fault systems to the east of the rift strike at various angles to the direction of the Jordan-
Dead Sea Transform (Bender 1974b, 1982), forming cliffs and causing occasional
earthquakes in the area and its vicinity (Andrews 1996).
Within the rift valley itself, the major faults are parallel to the direction of the
Jordan-Dead Sea Transform. The network of faults dividing the mountain range east of
the western border faults has the same trend as the border fault system; north, northnorthwest,
northwest, and roughly east-west. The north-trending faults are much longer
than the others. The faults parallel to the direction of Wadi Araba and to a larger scale to
the Jordan-Dead Sea Transform have affected Miocene to Pliocene and Quaternary
sediments within the rift valley. Along the border faults, significant dip-slip movements
have occurred with the down-dropping of the western block often exceeding 500m
(Bender 1974b, 1982).
1.4.2.1.b. The Dead Sea
The Dead Sea, often called the Dead Sea Rift or the Dead Sea Depression (DSD),
is a transform feature between the Red Sea in the south and the collision zone of the
Taurus-Zagros Mountains in the north (Freund 1965). The Dead Sea is about 100km long
21

and 10-15km wide. Its catchment area has the capacity of 40,000km 2 of saline water
(Arkin and Gilat 2000, Garfunkel 1997, Taqieddin et al. 2000). It is a major plate tectonic
feature forming the boundary between the Arabian plate on the east and the African plate
(Sinai subplate) on the west. It is considered a major strike-slip fault that has about
100km of cumulative left-lateral slip. The Dead Sea is the lowest point on Earth (about
400-415m below mean sea level) and it is one of the many basins along the Dead Sea
Transform System that has up to 700m high escarpments. It developed as a wide pullapart
basin containing fluvial and evaporite beds of Miocene to Holocene age about 20-
25Myr to 10kyr (Frumkin 2001, Garfunkel 1997, Klinger et al. 2000a, Quennell 1983,
Taqieddin et al. 2000). The Dead Sea is considered to be a transform depression rather
than a rift because it is bounded by normal faults whose scarps reach 700m and are
accompanied with left lateral strike slip (Zilberman et al. 2000).
The Dead Sea consists of two basins, the northern and southern basins, which are
divided by the Lisan Peninsula. The northern basin, which is larger and deeper, is about
50-60km long and 700-730km below mean sea level. The southern basin is smaller and
shallower, about 30-40km long and 300-350km below sea level, and is almost dry at the
present time (Arkin and Gilat 2000, Garfunkel 1997, Goudie 2002, Taqieddin et al. 2000,
Yechieli et al. 1998) (figure 1.15). The level of the Dead Sea has dropped 20m during the
twentieth century (Arkin and Gilat 2000) with an average annual withdrawal rate of
0.8m/yr (Yechieli et al. 1998). In recent years, the Dead Sea’s southern basin is almost
dry due to mineral harvesting by Potash Companies both in Jordan and Israel. The
northern basin contains saline water with a catchment capacity of about 40,000-
42,000km 2 (Garfunkel 1997, Yechieli et al. 1998).
22

A
B
Figure 1.15: (A) Dead Sea location map showing the two basins (From Arkin 2000,
figure 2, p. 713). (B) Inset map shows the present watershed of the DSD and the general
location of major strike-slip faults along the Dead Sea transform. The DSD is enlarged
showing its flanked escarpment associated with normal faulting along the depression
border (From Frumkin 2001, figure 1, p. 80).
1.4.2.2. Geology
The geology of the Jordan-Dead Sea Transform Zone includes the youngest rocks
(i.e. lake sediments) to be found in Jordan. In general, these rocks were deposited 15000
years ago after extensive lakes diminished during the Late Pleistocene, which occupied
the whole area forming soft siltstones and mudstones. Such rocks have been eroded into a
landscape of gullies and cliffs (Andrews 1996, Ron et al. 2006). Along the fault systems,
younger rocks are down-faulted against older complexes to the east, forming the
23

triangular shaped structural niches, giving the east side of the Jordan-Dead Sea
Transform its distinct structure and morphological characteristics (Bender 1974b, 1982).
The JDTZ, especially Wadi Araba, is occupied entirely by unconsolidated
sediments of Quaternary age and older clastic sediments and limestones. Pre-Cambrian
igneous and metamorphic rocks cover an area of approximately 1,800km 2 in southern
Jordan. These rocks are exposed from the area south of Aqaba for approximately 70km
towards the north along the east side of the Wadi Araba Rift. They are also exposed east
of the Rift and south of the Ras en-Naqab escarpment, dipping east of Wadi Rum
underneath a thick sequence of Paleozoic sandstones (Bender 1974a, 1975, Burdon 1959)
(figure 1.16).
Figure 1.16: Jordan-Dead Sea Fault simplified geological cross-section. (Pε)
Precambrian, (P) Paleozoic, (K) Cretaceous, (E) Eocene, (M) Miocene, and (PL) Pliocene
rocks and sediments (From Ginat et al. 1998, figure 3, p. 154).
The Jordan-Dead Sea Transform Zone basement is mainly divided into the Aqaba
and the Araba complexes separated by a regional unconformity, where the former
complex consists of two units and the latter consists of three units. Figure 1.17 is a
24

simplified geological cross section across the southern section of the Jordan-Dead Sea
Transform near Al-Quwayra town which is located about 35km northeast of Aqaba (Sneh
et al. 1998).
Figure 1.17: A simplified geological cross section across the southern section of the
Jordan-Dead Sea Transform, about 40km northeast of Aqaba (Modified from Sneh et al.
1998).
1.4.2.2.a. The Aqaba Complex
1) Metamorphic Rocks: The metamorphic rocks are found as blocks within the
surrounding Neoproterozoic intrusive rocks. In Wadi Abu Burqa, at Gharandal area, they
were preserved from erosion before deposition of the Cambrian rocks. The metamorphic
rocks are dated as 700-800Myr (Jarrar et al. 1983). This age matches the age obtained for
the metamorphic rocks of the Elat area (Kroner et al. 1990).
2) Igneous Rocks: The igneous rocks consist of highly weathered granites,
granodiorites, and quartz diorite. The plutons of the Aqaba complex have an average age
of 630-580Myr (Ibrahim and McCourt 1995).
1.4.2.2.b. The Araba Complex
1) Sarmuj Conglomerates: The Sarmuj conglomerates occur in very small
outcrops along the lower course of Wadi Abu Burqa, with an exposed thickness of about
40m. They form the base of the Araba complex (Bender 1974a). The unit consists of
25

conglomerates with well rounded clasts of plutonic and metamorphic rocks in a partly
brecciated and arkosic-sandy matrix. The base of the Sarmuj conglomerates is
distinguished by the age of the underlying calc-alkaline granitoids and is dated at 625-
600Myr (Jarrar 1985).
2) Hayyala Volcaniclastic Unit: The volcaniclastic unit is a series of stratified,
steeply east dipping, green weathering tuffs with thin horizons of volcaniclastic
sandstone. They are exposed in the margin of Wadi Araba and overlain with an angular
unconformity by Cambrian sedimentary rocks (Bender 1974a). This unit is believed to be
Late Neoproterozoic in age, dated between 595-550Myr (Ibrahim 1993).
3) Rhyolite Volcanics: The upper part of the rhyolite is highly weathered and cut
by dykes and has intrusive joints that are overlain by Cambrian sandstones. This unit was
first recognized in Wadi Rum and believed to be 550-542Myr in age (McCourt and
Ibrahim 1990).
1.4.2.2.c. The Dead Sea
The Dead Sea floor is covered by Neogene and Quaternary sediments up to
several kilometers thick (Picard 1987). Several studies confirmed that the Dead Sea basin
was found to have 10km of sediments with ages from the Neogene to the present time
(Garfunkel 1997, Niemi 1997). According to Frumkin (2001), the macrostratigraphic
analysis of the sediment that has been recovered from the Cave of the Letters in Israel
(west of the transform) indicates a low deposition of the Dead Sea basin around 10-7Myr
ago.
The early formation of the Dead Sea rift took place at some stage in the
Oligocene. During the Miocene it developed as an elongated sedimentary basin with
26

primarily clastic and some volcanic input from the Oligocene to the Pleistocene, salt
lakes and evaporite deposits were formed, creating the present day Dead Sea. Clastic,
evaporite, alluvial, and lacustrine sediments continued to be deposited throughout the
Pleistocene until today (Arkin and Gilat 2000, Klinger et al. 2000a).
A brief geological description of the rock types and their abundance within the
JDTZ are listed in table 1.1 illustrating the location of the examined mountain fronts and
their proximity to different rock types that consequently will affect the final
morphometric analysis results. Harder rocks (e.g. limestone) are primarily located in the
northern region while mostly softer and weathered rocks (e.g. sandstone), with an
exception to the presence of a small number of fronts along granite mountains, are
present in the southern region, as shown in figure 1.18.
27

Location Rock Type Description
Q 4
Lisan Formation (Jordan Valley) deposited in a lacustrine
environment during the Quaternary that consists of sandstone,
shale, chalk, marl, and conglomerate
βq
Little quantities of olivine basalt, scoria, tuff, and agglomerate
(Quaternary, younger than 1.8Myr)
Dead Sea
Limestone, chalk, chert, marl, and clay (Senonian-Paleocene)
North
Araba
South
Araba
Aqaba
K S
β1
Ja
Q 4
T S
K J
P Mn
Q
P Mn
Q
K K
P Mn
G C
Magmatic intrusions of alkali basalt (Jurassic-Lower Cretaceous:
few scattered quantities within the Lisan Formation)
Limestone, dolomite, and sandstone (Upper Zarqa Group, Jurassic)
Lisan Formation (Jordan Valley) deposited in a lacustrine
environment during the Quaternary that consists of sandstone,
shale, chalk, marl, and conglomerate
Limestone, clay, marl, gypsum, and conglomerate (Late Eocene-
Pliocene)
Limestone, dolomite, and marl (Late Cretaceous: Cenomanian-
Turonian)
Sandstone (little quantity) mainly with existence of clay and
dolomite (Cambrian-Turonian)
Undifferentiated Alluvium (Quaternary), most abundant bedrock
Sandstone mainly with existence of clay and dolomite (Cambrian-
Turonian)
Undifferentiated Alluvium (Quaternary), most abundant bedrock
Sandstone: massive and brownish weathered sandstone (Lower
Cretaceous)
Sandstone mainly with existence of clay and dolomite (Cambrian-
Turonian)
Granite: gray granite and alkali granite (Precambrian)
Table 1.1: Concise description of the mountain fronts rock types within the JDTZ
(Modified from Bartov 1990 and Bender 1974b).
28

Figure 1.18: Rock types and the locations of all mountain fronts in the JDTZ study area.
29

1.4.3. Seismicity of the JDTZ
1.4.3.1. Regional tectonics of Jordan and its vicinity
The Levant is a unique region in terms of documenting historical earthquakes that
covers the time span of four millennia. This documentation is in a variety of forms that
includes biblical, Roman ecclesiastical, and Islamic and Arabic historical chronicles.
During the last 4,000 years, several destructive earthquakes occurred over large areas of
the Middle East region that caused loss of life and property (Abou Karaki 1995, Al-
Tarazi 1992, Degg 1990). A thorough analysis of the earthquakes that affected Northwest
Arabia (modern Jordan, Palestine, and Israel) from the 2 nd to the mid-8 th century A.D.
was conducted by Russell (1985) from historical records and archaeological evidence
collected in these countries. Destructive earthquakes occurred during the late Roman,
Byzantine, and Early Islamic periods in Northwest Arabia, and were generated as a result
of the existence of border faults and other structural elements along the rift valley that are
capable of producing seismic activity (Russell 1985).
The tectonism of Jordan is directly related to the regional tectonism in the Middle
East and the Eastern Mediterranean region, which is mainly controlled by the Jordan-
Dead Sea Transform fault system (Al-Tarazi 1992). Much of Jordan is subject to a
momentous seismic threat that has been reported in contemporary geological and
seismological investigations and also mentioned throughout ancient documents. During
the past 80 years, seismic activity in Jordan has been relatively low; however, the whole
region has been affected by earthquakes for thousands of years. An excellent example is
the 1927 earthquake with a local magnitude of 6.25 on the Richter scale, which was
devastating in several locations (Arieh et al. 1982, Avni et al. 2002, Ben-Menahem 1991,
30

Marco et al. 1996, Niemi and Ben-Avraham 1994, Vered and Striem 1977). The main
concern is that the major Jordanian cities and highly populated regions such as Amman,
Irbid, Salt, Aqaba, Jarash, Al-Karak, Al-Mafraq, Al-Tafila, Ghour es-Safi, and Petra are
located in earthquake-prone areas within proximity of active fault systems that branch off
the JDT such as Carmel, Al-Karak, Al-Fiaha, Wadi El-Sirhan, Zarqa Main, and Swaqa
faults (Abou Karaki 1995, Klinger et al. 2000b, Olimat 2001, Quennell 1958, Yücemen
1992). Obviously, the most populated areas within this region are vulnerable to strong
earthquakes, generating a real threat to the safety of population, infrastructure, social
integrity, and economics of Jordan and its surrounding countries (Olimat 2001).
The strongest earthquake activity zone in terms of magnitude and frequency is the
Jordan-Dead Sea Transform fault system, which is considered an active tectonic feature
based on the region’s seismicity (Yücemen 1992). The seismicity of Jordan-Dead Sea
Transform system is considered to be low to moderate; nevertheless, several destructive
earthquakes have occurred in the past that destroyed most of the urban areas in Jordan
and the surrounding countries such as those in A.D. 362, 748, 1033, 1034, 1070, 1182,
1201, 1759, 1837, 1927, 1956, 1995 (Al-Tarazi 1992, Olimat 2001).
The primary seismic and tectonic threat in Jordan is derived from the Dead Sea
fault system, and secondarily the Wadi Araba fault (Bender 1974a, Yücemen 1992).
Paleoseismic analysis of the Azaz fault segment of the Dead Sea Rift indicates that this
region was subjected to irregular periods of strong seismicity and quiescence during the
latest Pleistocene and the Holocene. At least two large earthquakes occurred in the Early
Holocene, each one resulting in a 1-1.5m high fault scarp (Zilberman et al. 2000). In a
recent study, paleoseismic analysis of the Bet-Zayda Valley in the delta of the Jordan
31

River at the north shore of the Sea of Galilee along the Dead Sea Transform exposed
several stream and surface offsets as a result of the earthquakes that occurred in the area.
The three-dimensional excavations of buried stream channels at Bet-Zayda demonstrate
primarily strike-slip displacement at a minimum rate of 3mm/yr during the Late Holocene
(Marco et al. 2005).
The displacement along the Jordan-Dead Sea Transform was measured from the
displacement of several alluvial fans within the transform. For example, the study of the
Dahal area along Wadi Dahal fault revealed an offset of the largest alluvial fan (i.e. Dahal
fan). The average slip rate of the Dead Sea fault is 4±2mm/yr, corresponding to about
500m of offset since the last interglacial period, consistent with the relative motion
between Arabia and Africa and regional kinematics measured by other techniques
(Klinger et al. 2000a) such as the GPS data north of the Dead Sea (Pe’eri et al. 1998) and
in northern Arabia (Reilinger et al. 1997, Pe’eri et al. 1998), the satellite laser ranging
(SLR) measurements in Israel (Smith et al. 1994), and the data from the offset Plio-
Pleistocene alluvial terraces analysis in the Wadi Araba (Ginat et al. 1998).
In a similar study, Niemi et al. (2001) measured cumulative displacement of 22-
54m from stream channels and alluvial fan surfaces across the Wadi Araba fault using
detailed geologic and topographic mapping. The northern Wadi Araba fault maintained a
relatively constant slip rate in the past 15kyr. The average slip rate of 4.7±1.3mm/yr was
measured from offset fault-scarp alluvial fans. The cumulative displacement as a result of
the last M W 7 earthquake was 3m. Using an average slip rate of 4.7±1.3mm/yr together
with a 3m slip-per-event suggests a maximum earthquake recurrence interval for M L 7
events of Wadi Araba fault of 500-885 years (Niemi et al. 2001). The total displacement
32

along the Jordan-Dead Sea Transform (JDT) caused by left-lateral strike-slip is 107km.
The left-lateral displacement along the JDT occurred in two steps: (1) 62km offset by
Early Miocene (about 18Myr), and (2) 45km offset since the Late Miocene or Pleistocene
to the present time (Niemi et al. 2001). The rotation rate of the Arabian plate was found
to be 0.396˚Myr -1 around a pole at 31.10˚N, 26.70˚E relative to Africa (Klinger et al.
2000a, Quennell 1983).
Regional seismicity studies in Wadi Araba and the Dead Sea fault systems reveal
much information regarding their activity and contribution to historical earthquakes in
Jordan, Palestine and Israel. In an attempt to shed view on the anatomy of the regional
plate boundary, a new gravity anomaly map of Wadi Araba and the southern half of the
Dead Sea transform was produced by Brink et al. (1999) as shown in figure 1.19. The
map highlights a string of numerous subsurface basins of various length, size, and depth
along the plate boundary and relatively short (25 to 55km) and discontinuous fault
segments that occupy the whole transform. Such structure suggests a dynamically
changing plate boundary with time with continuous small changes in relative plate
motion (Brink et al. 1999).
1.4.3.2. Seismotectonic maps of Jordan
The earthquake events that occurred in Jordan and the surrounding countries
between 19A.D. and the end of the year 2005 indicate a significant concentration along
the Jordan-Dead Sea Transform Zone. However, the collected seismic events show a
lower concentration in the eastern part of the Mediterranean Sea when compared to the
earthquake distribution of the major tectonic features in Jordan-Dead Sea Transform area.
According to Al-Tarazi (1992) and based on several earlier studies conducted by Abou-
33

Karaki (1987), Arieh (1991), Ben-Menahem (1979, 1981), Ben-Menahem et al. (1982),
El-Isa and Al-Shanti (1989), and Poirier and Taher (1980), thirteen seismic zones were
identified in Jordan and the surrounding countries of the Middle East. Below is a brief
description of the seismic zones as shown in the seismotectonic map of Jordan and its
vicinity (figure 1.20A) (Al-Tarazi 1992):
Figure 1.19: Bouguer gravity anomaly map of Dead Sea transform, Jordan and Israel,
corrected with density of 2670 kg/m 3 . Contour interval is 3 mGal. Terrain correction was
calculated from digital terrain model (DTM) with 25m grid using inhouse code.
Background: Shaded relief topography from DTM. Top inset is simplified plate geometry
and location of maps. Bottom inset is a regional Bouguer gravity map of Israel and
Jordan (From Brink et al. 1999, figure 1).
34

1. Dead Sea-Jordan River fault (zones 1 and 2): It extends about 200km from 30.90º
to 32.93ºN at a longitude of 35.50ºE that stretches from the Dead Sea along
Jordan River and ends at Tiberias Lake in the north. This fault is characterized by
high seismic activity. Many historical earthquakes occurred along its length. Also,
earthquake epicenters occur along several active faults that branch from the main
fault (figure 1.21).
2. Wadi Araba fault (zone 3): It extends about 174km from the Dead Sea to the Gulf
of Aqaba. The historic earthquake activity along this fault indicates that this fault
is seismically less active than the Jordan-Dead Sea fault.
A
B
Figure 1.20: (A) the thirteen seismic zones and (B) major faults within seismic zones in
Jordan and its vicinity (From Al-Tarazi 1992, map 2.13, p. 49 and map 2.17, p. 53,
respectively).
35

3. The faults of the northern zone (zone 4): The northern zone extends from 30.93º
to 35.00ºN and includes the Ed-Damur, Yammouneh, and Rachaya faults. The
historic earthquakes show that this zone used to be more seismically active than
the Jordan-Dead Sea fault but became less active with time.
4. The faults of the northern Red Sea and the Gulf of Aqaba (zones 5, 6, and 7): The
Red Sea Fault system includes both the faults of the Gulf of Aqaba and the Suez
Gulf that are characterized by seismic swarm activity. However, the seismicity of
the Gulf of Aqaba and the Red Sea zone is lower than of the Jordan-Dead Sea
fault as indicated from the recorded earthquakes.
5. The Wadi El-Sirhan Basalt Area (zone 8): This zone is located to the east of the
Jordan-Dead Sea Transform. It is completely covered by basaltic material and
characterized by slight seismicity.
6. The Wadi Farah-, Carmel-, and Al-Galiel-zones (zones 9, 10, and 11): These three
faults are considered secondary faults that are located to the west of the Jordan-
Dead Sea Transform. The Wadi Farah fault seems to be active compared to the
Carmel and Al-Galiel faults that are less seismically active. Most of the seismic
activity in these faults occurs in swarms that never produced a historic devastating
earthquake. However outlining them is essential as they are situated in a very
densely populated area.
7. The South-East Mediterranean zone (zone 12): This seismic zone was delineated
based on the instrumental earthquake data recorded from this area. The seismic
activity in this zone seems to be low and shallow in comparison to the Jordan-
Dead Sea Transform zones.
36

8. The Cyprus zone (zone 13): It covers the major earthquakes in and around Cyprus
Island. The historical earthquakes recorded in this zone indicate that the Cyprus
fault is active.
The lines in figure 1.20B represent the major faults in the region. According to the
seismotectonic maps of Jordan, the earthquake epicenters are well located along faults
number 1, 2, 5, 8, 9, 10 and 11, while there is random distribution of the earthquakes
epicenters in the proximity of the remaining faults (3, 4, 6, 7, and 12) (Al-Tarazi 1992).
Figure (1.21): The Dead Sea fault and the adjacent branching faults, 1900-1980
(Modified from Brew 2001, figure 4.9, e).
1.4.3.3. Seismic maps of the JDTZ
The Jordan Seismological Observatory began operation to collect and document
seismic data in September 1983. The earthquakes were recorded in digital mode and
processed at the Natural Resources Authority in Amman. All seismic data include the
origin times, magnitude, epicenters in geographic locations (degrees and minutes), and
37

focal depths (hypocenters) in kilometers (JSO 2005). After more than twenty years
(1983-2005) of continuous operation of the seismic network in the region, more than
10,000 earthquakes were recorded in Jordan, mostly distributed along the Jordan-Dead
Sea Transform Zone where most of the mapped active fault zones in the region are
(Olimat 2001) (figure 1.201). An earthquake catalog of the Middle East countries was
compiled from seismic events between 1900 and 1983. This catalog was used to create a
seismic map of the Middle East including the Arab countries located in northern Africa
(Riad and Meyers 1985, Riad et al. 1985). Figure 1.22 is a modified map from Riad and
Meyers (1985) that shows the seismic events in Jordan and surrounding countries during
that period of time.
Figure 1.22: Seismic map of the Middle East 1900 – 1983 (Modified from Riad and
Meyers 1985).
38

A new computerized regional tectonic map of Jordan and the neighboring
countries was produced, which shows the main regional structures in Jordan and the
surrounding area. The purpose of this map is to illustrate the seismic networks that exist
in Jordan and surrounding countries and to show the relationship between earthquakes
and active faults in the JDTZ. The maps shown in figure 1.20 were georeferenced in
ArcGIS. The active fault lines and seismic zones were then digitized to create layers for
more accurate manipulation that could be presented on a seismic map (figure 1.23). In
addition, using the provided earthquake data, two maps were generated of Jordan and the
surrounding countries that show the distribution of earthquake epicenters and their local
Richter magnitudes.
All earthquakes were recorded in local magnitude (M L ) scale which is basically
the Richter magnitude scale that quantifies the amount of seismic energy released by an
earthquake (Richter 1935). Earthquakes of local magnitude of four and greater (M L ≥ 4)
only were mapped. These types of events are noticeable and could cause shaking of
indoor items, rattling noises, and some damage, but no major destruction (Richter 1935).
The major earthquakes maps are presented to illustrate the magnitudes of the seismic
events over time based on the method of collecting the data (figures 1.24 and 1.25) where
all seismic events are represented using circle proportional symbols (Dent 1999).
Historical earthquake data as well as data from 19A.D. to August 1983 were
compiled from multiple published references that studied and recorded the seismicity of
the Middle East including Jordan. The magnitudes of these seismic events were translated
from the Mercalli Intensity Scale, which is based on the observed historic structural
destruction, to the approximate Richter Scale magnitudes (JSO, Personal
39

communication). Some of the previous earthquake catalogues and sources are:
Ambraseys (1978), Amiran (1951, 1952), Ben-Menahem (1991), Gutenberg and Richter
(1956), Karnik (1969), Poirier and Taher (1980), Riad and Meyers (1985), the Bulletin of
the International Seismological Centre (ISC), the Bulletin of Preliminary Determination
of Epicenters (PDE), and the National Earthquake Information Service (NEIS) Bulletin
provided by United States Geological Survey (USGS).
Earthquake data from September 1983 to 2005 were mostly recorded by the
Jordan Seismological Observatory (JSO) stationed at the Natural Resources Authority
(NRA) in Amman, Jordan. Data were delivered in a simple text format (*.txt) with
information such as date of activity (day, month, year), time of occurrence, latitude, and
longitude of earthquake events and their local magnitudes and depth in kilometers (JSO,
Personal communication).
In addition, contemporary earthquakes events of the Middle East region, in Jordan
in particular can be obtained from Jordan Seismological Observatory (JSO), the United
States Geological Survey (USGS), the British Geological Survey (BGS), Israel
Geological Survey (IGS), the Helwan Observatory, Cairo, Egypt (Degg 1990), and the
Institute of Petroleum Research and Geophysics (IPRG) bulletin in Holon (Al-Tarazi
1992).
40

Figure 1.23: Seismic zones and active faults of Jordan and surrounding countries.
41

1.4.3.4. Jordan/JDTZ earthquakes data
Earthquake data presented in the seismic maps of Jordan and within the JDTZ are
as accurate as their source material. Earthquake data included in the tectonic maps covers
Jordan and its vicinity that lie between longitude 32.00°-39.00° east and latitude 27.00°-
35.50° north (all coordinates are in decimal degrees). Magnitude measures the strength of
an earthquake as recorded by a seismometer (Richter 1935). In order to keep all map
earthquake data consistent, local magnitude (M L ) values were used due to their
availability in all seismic data sets. The local magnitudes (M L ) are given for every
recorded seismic event, while intensity values, if available, are in modified Mercalli
intensity scale. The accuracy in the focal depth is proportional to the date and size (i.e.
magnitude) of the recorded earthquakes. In case no determination is possible, depths were
recorded as 0km. The accuracy of the epicentral location is classified according to the
date as follows:
1) 1A.D.-1899: For this period, which includes the historical earthquakes, the
accuracy is expected to range between 50 and 150km.
2) 1900-1980: ±10 km for events lying in between longitude 35.00°-36.00°E and
29.50°-33.00°N, (longitude 53°03’60”E and latitude 29°53’30”N), while for the other
events ±15-25 km.
3) 1981-2005: ±5 km for events between longitude 35.00°-36.00°E and 29.50°-
33.00°N, (longitude 53°03’60”E and latitude 29°53’30”N), while for the other events
±10-15 km.
All earthquake raw data were converted from their formats into Microsoft Office
Excel format (*.xls). Redundant and missing values were eliminated. After that, data
42

were converted into database (dBase IV) format (*.dbf) to be readable by the Geographic
Information System software (i.e. ArcGIS). The earthquake data were imported into
ArcGIS using “Add XY Data” under the “Tools” function where the X axes were
assigned the longitude values and the Y axes assigned the latitude values. Finally,
shapefiles were created for each set of the seismic data (data were assigned the same
coordinate system as the geographic layers of the study area) and recorded on the seismic
maps of Jordan.
All seismic data are projected to the European Datum of 1950 Zone 36 North, the
mean datum for Jordan and Israel, as shown in figures 1.24 and 1.25. All vector data are
projected to the World Geodetic System (WGS) of 1984 ellipsoid and datum, Zone 36
North, under the Universal Transverse Mercator (UTM) coordinate system.
43

Figure 1.24: Major earthquake events on Jordan, 19 A.D. – August 1983.
44

Figure 1.25: Major earthquake events on Jordan, September 1983 – 2005.
45

2. Chapter Two: Literature Review
2.1. Introduction
2.1.1. Morphometric analysis in geomorphology
Mountain fronts, valleys, and alluvial fans are surface features that construct the
arid to semiarid landscape and exist at large or small scales. To understand the way
landforms evolve, it is essential to study the underlying geology. In general, landform
development implies deep structures of the earth; therefore there is always a strong
relationship between landscape and the geologic environment (Keller and Pinter 2002). In
recent years, the advancements in computer technologies and digital data
acquisition/processing has led to the improvement of the knowledge of geomorphic
processes and the development of the use of predictive models and quantitative
measurements to analyze, monitor, and understand landform changes (Summerfield 1997,
Wood 1996). This advancement has allowed geographers, geologists, and
geomorphologists to explore human/land interaction utilizing modeling and systems
analysis in their geomorphological studies that relied on sophisticated hardware and
software tools (Baker 1986a).
The study of the nature of landforms, landscapes, and surface processes including
their description, classification, origin, development, and history highlighting the
physical, biological, and chemical aspects is known as geomorphology, which may have
either a qualitative or quantitative representation (Baker 1986a, Easterbrook 1999, Keller
and Pinter 2002, Morisawa 1985). According to Morisawa (1985), quantitative
geomorphology represents a new subfield of geomorphology that is defined as “the
application of mathematics and statistical techniques to the study of landforms, their
46

description and the processes by which they are created and changed”. Hence, the
quantitative measurement and analysis of landforms and topography are the fundamental
factors of morphometry (Hayden 1986, Keller and Pinter 2002) or geomorphometry
(Summerfield 1997) that summarize numerical definitions of the Earth’s surface shape in
correlation with landscape processes. Morphometric analyses in tectonic geomorphology
studies basically refer to the measurement on topographic maps (recently DEMs) of
quantitative parameters (Wells et al. 1988).
Geomorphology is a significant tool in tectonic studies when using the
geomorphic record. Such record includes several landforms and the Quaternary deposits
that capture immense amount of information from the last few thousands and extend to
about two million years (Keller and Pinter 2002). Tectonic geomorphology focuses on
the contrast between topography and geomorphic features generated by tectonic
processes and the erosion factors caused by surface processes that tend to wear them
down. Defining the relationship between theses processes and interpreting the resulting
landscape features is the main focus of tectonic geomorphology (Baker 1986a, Bull 1984,
Burbank and Anderson 2001).
Major progress in the field of tectonic geomorphology has been made during the
last three decades, primarily due to the increased potential of evaluating the time factor in
landscape development (Bull 1984). Therefore, through the use of computerized models
of landform change, it is possible to determine the magnitude and frequency of
displacements along faults and allocating classes of relative tectonic activity (Baker
1986a). Consequently, tectonic geomorphology might be defined into two categories: (1)
the study of landforms formed by tectonic processes; focusing on the shapes and origins
47

of landforms as a result of tectonic activities, or (2) the application of geomorphic
principles to explain tectonic problems; analyzing landforms to evaluate the history,
magnitude, and rate of tectonic processes (Keller and Pinter 2002). The current research
mainly deals with the second definition of tectonic geomorphology that involves using
geomorphological indices to analyze existing landforms, namely mountain fronts and
their associated valleys, in an effort to determine their tectonic activity classes.
2.1.2. Remote sensing and GIS uses in geomorphology
Earth-observing satellites, airborne sensor systems and aerial and space
photography have nearly complete coverage of the Earth’s surface that provides images
of different formats and various scales. This permits not only interpretation of landscape
evolution, but rather offers the opportunity to integrate observation of a variety of
processes over a large region. Geomorphic analysis from space has the advantage of
allowing the use of quantitative methods for both data gathering and information
extraction. Thus, satellite images are becoming useful and necessary in geomorphology,
especially in obtaining quantitative measurements and performing geomorphic analyses
(Hayden et al. 1986, Ulrich et al. 2003).
Geographic Information Systems (GIS) have enhanced the applicability of
geologic mapping when integrated with data obtained by remote sensing using a wide
range of formats and scales. In addition, advancement in image analysis provides
geologists opportunity to enhance, manipulate, and combine digital remotely-sensed data
with several types of geographic information that in turn increases the amount of
extracted information related to topographic and geologic features (Horsby and Harris
48

1992). Satellite imagery permits research at different scales, which is valuable in the
investigation of lineaments and faults (Arlegui and Soriano 1998).
Digital enhancement of satellite images yields much information about image
features. GIS techniques enable the integration and analysis of multi spatial and nonspatial
data that have the same georeferencing scheme. Therefore, the integration of GIS
and remotely sensed data could be more informative and results would be more
applicable to image interpretation (Ehler 1992, Horsby and Harris 1992, Saraf and
Choudhury 1998). Within the context of GIS, surface geomorphology is most commonly
represented in Digital Elevation Models (DEMs) especially when quantitative
measurement using geomorphometry is necessary. DEMs are generally defined as a
regular two dimensional array of heights sampled above some datum that describes a
surface (Wood 1996).
On remotely sensed images, faults and edge segments (i.e. lineaments) are
generally formed by an assortment of landscape elements that represent rock features on
the land surface. Virtually all lineaments are discontinuous but due to the narrowly
spaced edge and line segments, the human eye tends to merge them together to make
them look continuous (Moore and Waltz 1983). Analyzing faults using remote sensing
data is essential to fields such as tectonics, engineering geology, and geomorphology
especially as they relate to tectonic analysis and earthquake hazards
assessment/mitigation (Süzen and Toprak 1998).
Tectonism in general has a geomorphic expression in the region where it occurs
and its adjacent areas (Gerson et al. 1984). Any subsurface features such as faults,
fracture zones, geological contacts, and numerous bedrock discontinuities have a
49

significant surface expressions that can be detected by extensive analysis of digital
satellite images and photographs that show linear or curvilinear topographic depression
(Hardcastle 1995).
The geomorphology of mountain fronts reveals much information regarding the
tectonic activity occurring along them as well as their past history. Typically, earthquakes
are concentrated on detached mountain fronts (Keller and Pinter 2002). In addition,
alluvial fans are conical landforms characteristic of arid to semiarid regions and are
associated with mountain fronts. They also commonly reveal insights into recent tectonic
activity (Bull 1968, 1977a, Bull and McFadden 1977). Sediments eroded from the
mountain are trapped at the mountain front to shape an endpoint of an erosionaldepositional
system that forms a fan-shaped body. The connecting link between the
depositional and erosional system is the stream. Many features contribute to the
morphology of an alluvial fan, including the size of the drainage basin supplying
sediments to the fan, source area geology, source area relief, vegetation, climate, and
tectonic activity (Bull 1968, 1977a, Bull and McFadden 1977, Keller and Pinter 2002).
2.2. Morphometric analysis approach
Calculation of geomorphologic indices has been applied at different places around
the world. However, there have been no serious attempts to employ this technique in a
digital format rather than applying the broadly used conventional methods of William B.
Bull (Bull 1968, 1977a). While this method of landform analysis has proven to be very
valuable in tectonic investigations, no studies have been found that were carried out in
the Middle East to examine its usefulness using either conventional or digital methods.
50

The study of landforms and deposits developed or modified by tectonic processes
can provide relevant information about the activity of the related tectonic structure. The
geomorphic analysis of mountain fronts, related drainage network, and alluvial fan
systems, provides valuable insights about the recorded tectonic history of any given
region. Therefore, such studies at a regional scale have been frequently undertaken using
morphometric analysis to calculate tectonic geomorphic indices. The most common
indices are mountain front sinuosity (S mf ) and valley floor width to height ratio (V f ) that
when combined together allow individual mountain fronts to be assigned to different
tectonic activity classes (Bull 1968, 1977a, 1978, Bull and McFadden 1977, Silva et al.
2003).
In this approach, the measurements are normally calculated manually from
topographic maps and/or aerial photographs. The elevation measurements for the valley
height are obtained by using topographic maps. In general, these measurements are
compared to measurements obtained in the field to determine their accuracy and
consistency (Bull 1968, 1977a, 1978, Bull and McFadden 1977).
2.3. Geomorphic indices of active tectonics
Geomorphic indices were developed to acquire tectonic information about active
tectonics in areas experiencing rapid deformation and to quantify the description of
landscape (Bull 1977b, Bull and McFadden 1977, Keller and Pinter 2002, Zovoili et al.
2004). In tectonic studies, geomorphic indices are valuable because the needed data can
be easily attained from topographic maps and aerial photographs, plus they can be
employed for evaluation of large areas. The results of the indices of an area might be
51

combined together, or along with other information such as uplift rates to construct its
tectonic activity classes (Bull 1977b, Keller and Pinter 2002).
Below is a brief description of the most common geomorphic indices used in
active tectonic studies. The description includes the index definition/explanation,
mathematical formula, and tectonic geomorphological application.
2.3.1. The Hypsometric Curve and Hypsometric Integral (H i )
The hypsometric curve portrays the distribution of elevation across an area of
land. One advantage of the hypsometric curve is that drainage basins of different sizes
can be compared with each other as a function of elevation and area based on total
elevation and total area plotted under the curve. This makes the hypsometric curve totally
independent of differences in basin size and relief. Thus, the hypsometric curve scale
could range from a single drainage to continents and even to the entire globe (Keller and
Pinter 2002, Strahler 1952).
The hypsometric curve is generated by plotting the relative drainage basin height
(h/H) that is known as the total basin height ratio against the relative drainage basin area
(a/A) which is the total basin area ratio (Keller and Pinter 2002, Strahler 1952). The
maximum height (H) equals the maximum elevation minus the minimum elevation and
represents the relief within the basin. The areas in between each pair of adjoining contour
lines symbolize the total surface area of the basin (A). The area (a) is the surface area
within the basin above a certain line of elevation (h). The relative area (a/A) value
measures between 1.0 at the lowest point in the basin where relative height (h/H) equals
zero, and zero at the highest point in the basin where relative height (h/H) equals 1.0
(Keller and Pinter 2002, Mayer 1990, Strahler 1952), as illustrated in figure 2.1.
52

Determining the hypsometric integral (H i ) is the simplest way to characterize the
shape of the hypsometric curve for a given drainage basin. It is simply defined as the area
under the hypsometric curve and calculated as follow:
H =
i
mean elevation - minimum elevation
maximum elevation - minimum elevation
Calculating the hypsometric integral (H i ) is achieve by deriving the maximum and
minimum elevation directly from a topographic map. The mean elevation is calculated by
obtaining the mean of at least 50 elevation values in the basin using point sampling on a
grid (Keller and Pinter 2002, Pike and Wilson 1971). It can also be evaluated directly
from the digital elevation model (DEM) of the basin (Keller and Pinter 2002, Luo 2002,
Luo and Howard 2005, Pike and Wilson 1971). The hypsometric integral and its
relationship to the degree of dissection allows it to be used as an indicator of a
landscape’s stage in the cycle of erosion. The theoretical evolution of the stage of a
landscape is: (1) youthful stage, characterized by deep incision and rugged relief, (2)
mature stage, where various geomorphic processes operate in near equilibrium, and (3)
old stage, distinguished by a landscape near base level with very subdued relief. High
hypsometric integral values indicate that most of the topography is high relative to the
mean representing a youthful topography stage. Intermediate to low hypsometric integral
values represent more evenly dissected drainage basins, indicating a mature stage of
development (Keller and Pinter 2002, Mayer 1990, Strahler 1952).
53

Figure 2.1: Hypsometric curve derivation from drainage basin (From Keller and Pinter
2002, figure 4.1, p. 122).
2.3.2. Drainage Basin Asymmetry (AF)
Active tectonic deformation has its effect on the development of adjacent
drainage basins. Such stream networks have distinctive patterns and geometries that can
be described both qualitatively and quantitatively (Hare and Gardner 1985, Keller and
Pinter 2002). Studying drainage systems provides information on the long-term evolution
of the landscape (Burbank and Anderson 2001).
The asymmetry factor (AF) was developed to detect tectonic tilting transverse to
flow at drainage-basin or larger scales (Hare and Gardner 1985, Keller and Pinter 2002).
The asymmetry factor is determined by the formula:
AF = 100 (A r / A t )
where A r is the area of the basin to the right of the trunk stream that is facing downstream
and A t is the total area of the drainage basin. The asymmetry factor (AF) for most stream
54

networks that formed and maintained flow in steady settings is 50. Since the asymmetry
factor is susceptible to any tilting perpendicular at the trunk of the stream, any AF values
greater or less than 50 indicate the possibility of tilting. Any drainage basin with a
flowing trunk stream that was subjected to a tectonic rotation will most likely have an
effect on the tributaries’ lengths. Assuming the tectonic activity caused a left dipping to
the drainage basin, the tributaries to the left of the main stream will be shorter compared
to the ones to the right side of the stream with an asymmetry factor greater than 50, and
vice versa (Hare and Gardner 1985, Keller and Pinter 2002), as shown in figure 2.2.
Figure 2.2: Block diagram shows the effect of an asymmetry factor with a left side tilt on
tributaries lengths (From Keller and Pinter 2002, figure 4.3, p. 125).
Another quantitative index to evaluate basin asymmetry is the Transverse
Topographic Symmetry Factor (T) that is defined as:
T = D a / D d
where D a represents the distance from the midline of the drainage basin to the midline of
the active meander belt, and D d corresponds to the distance from the basin midline to the
basin divide (figure 2.3). For diverse segments of valleys, the calculated T values indicate
55

migration of streams perpendicular to the drainage-basin axis. Thus, the Transverse
Topographic Symmetry Factor is a vector that has direction and magnitude that ranges
from zero to one (T = 0 to 1), which reflects a perfect asymmetric basin or a tilted one
respectively (Burbank and Anderson 2001, Cox 1994, Cox et al. 2001, Keller and Pinter
2002). In case of a negligible influence by the bedrock tilting on the relocation of the
stream channels, the direction of the regional migration is an indicator of the ground
tilting in that similar direction. The analysis of numerous drainage basins in an area
results in multiple spatially distributed T vectors, which, when averaged, define the
irregular zones of basin asymmetry. The calculation of both AF and T is a quantitatively
rapid method of identifying ground tilting (Cox 1994, Cox et al. 2001, Keller and Pinter
2002).
Figure 2.3: An example of calculating a drainage-basin transverse topographic
asymmetry vector for a single stream segment (From Cox 1994, figure 3, p. 574).
56

2.3.3. Stream Length-Gradient Index (SL)
The Stream Length-Gradient Index (SL) is calculated along a river and used to
evaluate the erosional resistance of the available rocks and relative intensity of active
tectonics. The SL index has sensitivity to channel slope changes, which makes it a good
evaluation tool for the relationship between potential tectonic activity, rock resistance,
topography, and length of the stream (Azor et al. 2002, Hack 1973, Keller and Pinter
2002, Zovoili et al. 2004), as illustrated in figure 2.4.
Figure 2.4: Map showing the Stream Length-Gradient Index (SL) for the South
Mountain-Oak Ridge, Ventura basin in south California (From Azor et al. 2002, figure 8,
p. 750).
57

The Stream Length-Gradient Index (SL) is calculated using the following
formula:
SL = (∆H / ∆L) L
where SL is the Stream Length-Gradient Index, L is the total channel length from the
midpoint of the reach -where the index is calculated- upstream to the highest point on the
channel, and ∆H/∆L is the channel slope or gradient of the reach, where ∆H represents
the change in elevation for a particular channel of the reach with respect to ∆L that
symbolizes the length of the reach. The calculation of the SL index is typically achieved
by obtaining the needed parameters that are directly measured from topographic maps
(Azor et al. 2002, Hack 1973, Keller and Pinter 2002).
The SL index is associated with stream power, which is a product of unit weight
of water, discharge, and energy slope. Total stream power available at a particular reach
of a channel is correlated to the ability of a stream to erode its bed and transport
sediments. Hence, the total stream power is a significant hydrologic variable that is
proportional to the slope of water surface and discharge. In addition, discharge generally
correlates with upstream channel length and the energy slope is estimated by the slope of
the channel bed, which is essential for forming and preserving rivers (figure 2.5). In
landscape evolution, it is assumed that stream profiles adjust quite rapidly to rock
resistance. Therefore, the SL index is applied to identify recent tectonic activity by
recognizing high index values variations on a particular rock type. For this purpose, the
SL index is generally calculated for a number of reaches along major streams that erode
the area and results merged for analysis. Commonly, high SL index values are present
where rivers cross hard rocks and reflect relatively high tectonic activity, while low SL
58

index values indicate relatively low tectonic activity and suggest less-resistant and softer
rock types (Hack 1973, Keller and Pinter 2002).
Figure 2.5: Diagram shows the process of calculating the Stream Length-Gradient Index
(SL) for a given creek (From Keller and Pinter 2002, figure, 4.6, p. 128).
2.3.4. Triangular Facets Index (Pf)
The topography of the mountain fronts is affected by factors such as the relative
rates of faulting, erosion and deposition that determine its evolution. Rivers that flow
from uplifted footwall across faults have the tendency to dissect and divide mountain
fronts; on the other hand, active faulting tends to reshape their linear characters. For
example, simple block uplift with the existence of two sloping sides bordered with faults
will produce regularly spaced, similarly sized, and shaped basins on each side that are
characterized by valleys with wide basins and narrow throats -referred to as “wine glass”-
59

as they pass across the active range front. In addition, such uplift will also create a linear
rang front, large triangular facets, and small piedmont fans. Therefore, the spacing of
facets along range fronts reveals the evolution of drainage basins within the footwall
block (Burbank and Anderson 2001, Mayer 1986), as shown in figure 2.6.
Figure 2.6: (A) Rapid block uplift produces linear range front, large facets, and small
fans. (B) Slow deformation cause by uplift produces irregular range front, dissected
facets, and large fans (From Burbank and Anderson 2001, figure 10.1, p. 202).
The actual spacing of the basins relies on their shape being circular or elongated.
The spacing can be measured as the ratio (i.e. index) between the basins mean length -
which is the mean distance from the main drainage divide to the mountain front- to the
mean spacing of the mouths of the basins along the range front. Thus, circular basins will
create broader triangular facets, while more elongated basins will produce smaller and
60

more closely spaced facets (Burbank and Anderson 2001, Mayer 1986, 1990), as shown
in figure 2.7. Tectonically active fault blocks will usually have higher index values that
are characterized by shorter facets, elongated basins with short drainages and more
closely spaced rivers. Less active tectonic fault blocks which usually are associated with
older mountains, on the other hand, are distinguished by longer facets, circular basins and
irregular widely-spaced rivers that show low index values. Hence, the Triangular Facets
Index (Pf) is a good indicator of tectonic activity (Burbank and Anderson 2001).
Figure 2.7: Circular and elongated basins (From Burbank and Anderson 2001, figure
10.2, p. 203).
Triangular facets are interpreted as variably degraded remnants of fault-generated
footwall scarps where the degraded scarp defines the height of the facet, and the spacing
of drainages incised into the footwall defines the facet width (Wallace 1978). Usually, the
apex of the triangular facet is the crest of the divide between dissected drainage basins
and the base corresponds to the faulted mountain front (Yeats 1997). Accordingly, the
older triangular facets (i.e. first generation facets) are located away from the active
mountain front, whereas the younger facets (i.e. second generation facets) are positioned
more closely to the active mountain fronts (Zovoili et al. 2004), as illustrated is figure
2.8.
61

Figure 2.8: Location of older and younger triangular facets to mountain fronts (Modified
from Zovoili et al. 2004, figure 6, p. 1720).
2.3.5. Mountain front sinuosity (S mf )
Mountain Front Sinuosity, a widely used geomorphic measure of seismic activity,
simply reflects the balance between the tendency of uplift to maintain a fairly straight
front and erosion caused by streams that tend to generate irregularities in the front over
time creating a sinuous topographic structure. The degree of erosional modification of
tectonic structures is measured by the mountain front sinuosity index (Bull 1977a, 1978,
Bull and McFadden 1977, Keller and Pinter 2002, Rockwell et al. 1984, Silva et al. 2003,
Wells et al. 1988). Mountain front sinuosity (S mf ) is defined as the ratio between (L mf ) the
length of the mountain front along its base at the distinct break in slope and (L s ) the
straight line length of the whole mountain front (Bull 1977b, 1978, Bull and McFadden
1977, Keller and Pinter 2002) and is expressed in the formula:
S mf = L mf / L s
where S mf is the mountain front sinuosity index, L mf is the length along the edge of the
mountain-piedmont junction, and L s is the overall length of the mountain front.
62

Typically, lower values of S mf indicate active uplift processes, while higher values signify
relatively less tectonic activity (Bull 1977b, 1978, Bull and McFadden 1977, Burbank
and Anderson 2001, Keller and Pinter 2002, Wells et al. 1988) (figures 2.9 and 2.10).
Figure 2.9: Calculating mountain front sinuosity (S mf ) index (Modified from Keller and
Pinter 2002, figure 4.14, p. 137).
Figure 2.10: Mountain front sinuosity (S mf ) index (Modified from Burbank and Anderson
2001, figure 10.5, p. 205).
63

The sinuosity (S mf ) values can be calculated from topographic maps or aerial
photographs. Because S mf values are scale dependent, it is more useful to calculate them
using larger scales that accentuate the irregularity of the mountain fronts. Lower values of
S mf index indicate relatively active mountain fronts, while higher values signify a
relatively moderate to less active (inactive) mountain fronts (Bull 1977b, 1978, Bull and
McFadden 1977, Burbank and Anderson 2001, Keller and Pinter 2002).
2.3.5.1. Choosing mountain fronts
Both L mf and L s are measured manually in the same units as the topographic map
scale (e.g. meters, feet, etc.) then fed into the equation to obtain results, thus the S mf index
is unitless (Bull 1977a, 1978, 1984, Bull and McFadden 1977, Keller and Pinter 2002,
Rockwell et al. 1984, Silva et al. 2003, Wells et al. 1988).
Mountain fronts are defined as major fault-bounded topographic escarpments with
measurable relief exceeding one contour interval of 20m (Wells et al. 1988). Mountain
fronts can be calculated as one front divided into segments of approximately 1Km long
(Azor et al. 2002) or as several continuous fronts of various lengths (Bull 1978, 1984,
Silva et al. 2003, Wells et al. 1988). The latter approach has been adopted in this research
since it is widely used and is more suitable to the current study area. According to Wells
et al. (1988) and based on the method produced by Bull (1978, 1984), longer mountain
fronts are subdivided into discrete segments with generally similar geologic and
physiographic characteristics, based on one or more of the following criteria: (1)
Intersection with cross-cutting drainages large in scale relative to the front, (2) Abrupt
deflections in mountain front orientation, (3) Abrupt changes in lithology, and (4) Abrupt
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changes in the main geomorphic characteristics of a mountain front relative to adjacent
front segments such as relief, steepness, or dissection (Wells et al. 1988).
2.3.6. Valley floor width to valley height ratio (V f )
The other important stability index is the ratio of valley floor width to valley
height (V f ). This index reflects the differences between the V-shaped valleys downcutting
in response to active uplift, where the stream is governed by the influence of a base level
fall at some point downstream that indicates a relatively high tectonic activity, and the U-
shaped broad-floored valleys with principally lateral erosion into the adjacent hillslopes
in response to relative base-level stability or tectonic quiescence that signifies a relatively
low tectonic activity. Therefore, this index uses one vertical and one horizontal
dimension at a given point along the stream in the erosional system. The ratio of valley
floor width to valley height is defined as:
V
f
2V
fw
=
[( E − E ) + ( E − E )]
ld sc rd sc
where V fw is the width of the valley floor, E sc is the elevation of the valley floor or stream
channel, and E ld and E rd are the elevations of the left and right valley divides respectively,
as shown in figures 2.11 and 2.12. Similar to the S mf index, lower values of the V f index
indicate relatively active mountain fronts and reflect deep valleys with active incision
related to uplift, whereas higher V f index values are associated with relatively moderate
to less active mountain fronts that represent low uplift rates (Bull 1977a, 1978, Bull and
McFadden 1977, Burbank and Anderson 2001, Keller and Pinter 2002, Rockwell et al.
1984, Silva et al. 2003, Wells et al. 1988).
65

Figure 2.11: Calculating valley floor width to height ratio (V f ) (From Keller and Pinter
2002, figure 4.15, p. 139).
Figure 2.12: Valley floor width to height ratio (V f ) (From Burbank and Anderson 2001,
figure 10.6, p. 205).
2.3.6.1. Choosing valley profiles
Similar to the S mf index, all V f index measurements are usually obtained from
topographic maps and aerial photographs (Bull 1977a, 1978, 1984, Bull and McFadden
1977, Keller and Pinter 2002, Rockwell et al. 1984, Silva et al. 2003, Wells et al. 1988).
The location of the cross-valley (i.e. valley profile) transects within a drainage basin
affect the values of V f . Valley floors tend to become gradually narrower upstream from
the mountain front and for a given stream the values of V f ratios tend to become
increasingly larger downstream from the headwaters (Bull and McFadden 1977). In
66

addition, the values of V f may also vary widely among streams with different drainage
basin areas, discharge, and underlying bedrock lithology (Wells et al. 1988).
Consequently, each study area will dictates its own valley profile measurement
distance from a given mountain front based on its distinctive geomorphology and the
geospatial distribution of valleys. The V f measurements were taken at a distance of about
200m from mountain fronts (Zovoili et al. 2004), 250 m upstream from the mountain
front (Silva et al. 2003), at a distance of 0.1 of the drainage basin length (Rockwell et al.
1984), and approximately upstream at a distance of 1Km from the mountain front (Bull
and McFadden 1977).
Both S mf and V f values have been used to evaluate the relative degree of tectonic
activity of related mountain fronts (Bull and McFadden 1977, Keller and Pinter 2002,
Silva et al. 2003). However, it is very important to specify that only the combination
between S mf and V f indices, especially in arid to semiarid regions are able to provide
semi-quantitative information of the relative degree of tectonic activity of the examined
mountain fronts and assigning them to different tectonic activity classes (Bull and
McFadden 1977, Silva et al. 2003). Therefore, in this research both of these geomorphic
indices were used.
2.4. Satellite imagery and digital elevation models
The main focus in this research is to calculate these two tectonic geomorphic
indices, namely mountain front sinuosity and the valley floor width to valley height ratio,
to indicate the relative seismic activity levels of the mountain fronts in JDTZ. The
calculation of measurements requires elevation data that is obtainable using remote
67

sensing imagery and digital elevation models (DEMs) of the study area as input layers
which will be integrated using geographic information system (GIS).
In previous studies conducted in different countries, the values of S mf and V f were
calculated manually from topographic and geomorphological maps and aerial
photographs of various scales (chapter 2, section 2.6). The best topographic maps of the
JDTZ are at a scale of 1:50,000 with 20m contour intervals (figure 2.13). Producing
DEMs from digitized topographic maps is time consuming and produces less-accurate
results than digital topographic maps and satellite derived DEMs (Toutin and Cheng
2001). Therefore, using available commercial DEMs data generated from satellite data is
more convenient and has better resolution.
2.4.1. Digital Elevation Models
Digital elevation models offer the most common methods for extracting vital
elevation and topographic information. DEMs are increasingly used for visual analysis of
topography, landscapes and landforms, in addition to modeling of surface processes
(Hirano et al 2003, Kamp et al. 2003, 2005, Welch 1990). Currently, DEMs have been
the main source for the extraction of different geomorphological and topographic features
depending on altitude and its spatial distribution and variation (Felicísimo 1994). Digital
Elevation Model (DEM), Digital Elevation Data (DED), Digital Terrain Data (DTD)
(Campbell 2002), or Digital Terrain Model (DTM) all include various arrangements of
individual points of x (east-west direction) and y (north-south direction) coordinates of
horizontal geographic locations. Z is the vertical elevation value that is relative to a given
datum for a set of x, y points (Bernhardsen 1999, Bolstad and Stowe 1994, Welch 1990).
They consist of samples array of elevations for a number of ground positions at equally-
68

spaced intervals (USGS 1990). DEM provides a digital representation in threedimensions
of a portion of Earth’s terrain. The resolution of DEMs depends on scale and
resolution of the data source (digital satellite images, aerial photographs) and the spatial
resolution (i.e. grid spacing) of the data samples, as well as other variables like data
structure and algorithms used during the extraction process (Campbell 2002, Cuartero et
al. 2004, Sabins 1997, USGS 1990).
DEMs are most commonly prepared in raster data structure, which are compatible
with remotely sensed data. They are represented in an array of equally spaced grids
having values of the topographic elevation observed and recorded of the earth’s surface.
It is similar to any remote sensing data except that each pixel shows an elevation
measurement in the center of the pixel instead of brightness values (i.e. digital numbers
or DNs). Using this format makes the process of manipulations, classification, analysis,
and display of DEMs similar to that of remote sensing imagery (Campbell 2002). The
form of surface model used for this entire study is that of the Digital Elevation Model
(DEM). Although the term is used inconsistently in the literature (Burrough 1986, Weibel
and Heller 1991) it is defined here consistently with the terms of Burrough (1986), as a
regular gridded matrix representation of the continuous variation of relief over space
(Wood 1996).
69

Figure 2.13: The Aqaba 1:50,000-scale topographic map with 20m intervals. Produced by
the Unites States Army, Sheet 3049 III, Series K737.
70

In the last decade several developments have been introduced to satellite sensors
to produce data for digital elevation model generation. Nowadays many satellites provide
stereo images with high potential of producing DEMs that can be integrated in
visualization software or GIS environments with available geodata and cartographic
information (i.e. existing vector data) for landscape and geomorphic analysis (Poli et al.
2005). However DEMs of usable details are still not available for much of the Earth, high
accuracy determination and visualization of topography of the Earth’s surface is still very
essential for local and national level environmental applications (Chrysoulakis et al.
2004).
The use of three-dimensional terrain modeling in GIS applications was made
possible by the advancement in computer and database technology. The primary
requirement for such application is using a DEM of the terrain within the region of
interest. When combines with satellite images and GIS coverages, a DEM becomes more
useful in terrain visualization and geomorphic terrain analyses (Welch 1990).
The accuracy of DEMs depends on the level of detail of the source and the grid
spacing used to sample the source. The scale of the source material is the main limiting
factor for the level of detail of the source. DEMs are classified into three levels of quality
that characterizes the model’s accuracy (USGS 1990, 1997-1998): (1) Level-1 DEMs are
the standard format elevation datasets. They have the desired accuracy standards of 7m
vertical root mean square error (RMSE) that doesn’t exceed the maximum 15m RMSE,
(2) Level-2 DEMs are elevation datasets that have been processed and smoothed for
consistency and edited to remove systematic errors. The maximum vertical RMSE is onehalf
contour interval with no errors greater than one contour interval, and (3) Level-3
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DEMs are elevation datasets derived from digital line graph (DLG) data using selected
elements from hypsographic and hydrologic data. The maximum vertical RMSE is onethird
contour interval with no errors greater than two-third of the contour interval.
2.5. Advanced Spaceborne Thermal Emission and Reflection Radiometer Satellite
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is
an advanced multispectral imaging satellite, boarded on Earth Observing System EOS-
AM1 platform (platform altitude of 705km) that was launched on board NASA’s Terra
spacecraft in December, 1999 and placed in orbit in February 2000. ASTER is an alongtrack
imager with a circular, near polar orbit (to increase the ground coverage), low
inclination satellites for long-term global observations of the land surface, biosphere,
solid Earth, atmosphere, and oceans at an altitude of 705km. The orbit is sunsynchronous
with an equatorial crossing at local solar time of 10:30am, returning to the
same orbit every 16 days. It has two visible bands, seven IR bands, and five thermal IR
bands. The spatial resolution varies with wavelengths, 15m in the visible and the nearinfrared
(VNIR), 30m in the short wave infrared (SWIR), and 90m in the thermal infrared
(TIR). It is also equipped with off-nadir backward-viewing sensor pointing feature that
has the capability to produce stereo images and in turn generate a high resolution DEMs
(Abrams et al. 2003, Abrams and Hook 1995, 2002, ASTER websites, Childs 2003,
Fujisada 1994, 1998, Fujisada et al. 1998, 2001, Kamp et al. 2003, 2005, Lang et al.
1996, Selby 2003, Tokunaga et al. 1996, Toutin 2001, Toutin and Cheng 2001,
Yamaguchi et al. 1998).
All ASTER bands are designed to collect data over a swath (i.e. ground resolution
cell) of 60km x 60km that requires nine seconds for each scene, and approximately 60-
72

seconds for a stereo pair (Fujisada 1994, Lang et al. 1996). However, the data coverage is
restricted to about 650-scenes per day that are processed to Level-1A; of these, about 150
are processed to Level-1B, working under a limited 8% duty cycle (i.e. 8 minutes/orbit)
and priority data acquirement with a global coverage between 85ºN and 85ºS (Abrams et
al. 2003, Abrams and Hook 1995, 2002, ASTER websites, Childs 2003, Fujisada 1994,
1998, Fujisada et al. 1998, 2001, Lang et al. 1996, Lang and Welch 1999, Tokunaga et al.
1996, Toutin 2001, Toutin and Cheng 2001, Yamaguchi et al. 1998), as shown in figure
2.14.
Figure 2.14: Flat map shows ASTER DEM global coverage of January 31, 2004.
2.5.1. ASTER data types
The ASTER instrument has two types of data levels, Lavel-1A and Level-1B data.
Level-1A data is basically defined as reconstructed, unprocessed instrument data at full
resolution. Consequently, the Level-1A data consist of the image data, the radiometric
coefficients, the geometric coefficients, and other supplementary data without applying
73

any of the coefficients to preserve the original data values. The L1A data is used as
source data to generate DEM products because of the many useful instrument parameters
and information that are incorporated inside them. In addition, due to the maintained
ancillary data which include the spacecraft position or the geometric correction table that
contains the latitudes and longitudes of the collected pixels, high quality DEM data
products can be generated (Abrams et al. 2003, Abrams and Hook 2002, Fujisada 1998,
Fujisada et al. 2001, Yamaguchi et al. 1998).
Alternatively, Level-1B data have no ancillary data, instrument geometric
parameters, and the spacecraft information. The L1B data are normally generated
applying these coefficients for radiometric calibration and geometric resampling. The
L1B data product is produces, by default, in the UTM projection, in swath orientation,
and Cubic Convolution resampling. However, the georeferenced and georectified L1B
data is still able to produce practical quality DEM products for ASTER individual scenes
(Abrams et al. 2003, Abrams and Hook 2002, Fujisada 1998, Fujisada et al. 2001, Poli et
al. 2005, Yamaguchi et al. 1998). The data structure of an ASTER L1B data product is
illustrated in figure 2.15.
2.6. Previous studies in morphometric analysis
In this section, a number of studies utilizing geomorphological indices as a tool
for tectonic investigation will be described. Starting with the early studies that introduced
quantitative analysis to geomorphology and ending with the most recent.
The earliest application of geomorphology to the assessment of tectonic stability
began with Bull and McFadden (1977). In their research conducted at the north and south
ends of the Garlock fault in the Mojave Desert in California, the researchers employed
74

S mf and V f index analysis to determine the Quaternary tectonic activity of the area. Based
on the sinuosity values the tectonic activity of the area was classified into three classes.
The area north of the Garlock faults has relatively low values of S mf index, while the area
north of the fault shows a relatively high S mf index values. A transitional area in the
central part of the northern area nearby the Garlock fault shows relatively high values of
S mf index. Tectonic indices were calculated using elevation data obtained directly from
aerial photographs, 1:62,000 scale topographic maps, and 1:250,000 topographic and
geologic maps (Bull and McFadden 1977).
Figure 2.15: Data structure of ASTER Level-1B granule (From Abrams et al. 2003.
figure 8, p. 22).
75

This study concluded that active tectonic terrains (class 1) are characterized by
unentrenched alluvial fans, elongated drainage basins with narrow valley floors and steep
hillslopes even in soft rock types, S mf index values of 1.0 to 1.6, and V f index values of
0.05 to 0.9. Moderate to slightly active tectonism terrains (class 2) are differentiated by
entrenched alluvial fans, large drainage basins that are more circular than class 1 basins,
steep hillslopes, valley floors that are wider than their floodplains, S mf index values
ranges 1.4 to 3.0, and V f index values of 0.5 to 2.0. The tectonically inactive terrains
(class 3) are distinguished by pedimented mountain fronts and embayments, steep
hillslope only on hard rock types, few large integrated stream systems in the mountains,
S mf index values of 1.8 to more than 5, and V f index values greater than 2 (Bull and
McFadden 1977).
In a similar study conducted by Bull (1978) at the south front of the San Gabriel
Mountains in California, geomorphic indices were calculated using 1:24,000-scale
topographic maps with contour intervals ranging from 10 to 40 feet. This study concluded
similar results to the north and south of the Garlock fault research and mountain fronts
were represent the three tectonic activity classes (Bull 1978).
In a parallel study, the mountain fronts and alluvial fans of the Ventura area in
California were analyzed using S mf and V f indices (Rockwell et al. 1984). Eight range
fronts were defined in the study area based on geographic location, geomorphic
expression, and presence of known active faults and folds. This study yields two tectonic
activity groups; group (1) has very active fronts while group (2) is associated with less
active tectonism. The very active tectonism fronts (groups 1) are associated with
faults/folds that were uplifted and tilted basinward generating a relatively large alluvial
76

fans for given basin areas. The Less active tectonism fronts (group 2), on the other hand,
has relatively small alluvial fans that are more constrained in their depositional areas. The
alluvial fans with larger fan areas are associated with mountain fronts having higher rated
of tectonic activity (Rockwell et al. 1984).
The relatively active fronts produced lower S mf and V f values in comparison with
the less active fronts that show higher values of both S mf and V f . The V f index values
varies from 0.43 to 1.91, while the tectonically active fronts S mf index values ranging
1.01 to 2.72 indicating an active uplift in the study area. The geomorphic analysis results
indicated that tectonically active fronts have low S mf range from 1.01 to 1.34 with an
average of 1.14, while the less active fronts have sinuosities that range from 1.57 to 2.72
with an average of 2.04. The study concluded that both S mf and V f indices are useful
indicators of relatively tectonic activity in the Ventura area. All Indices were calculated
from the United States Geological Survey 7.5-minute topographic map (1:24,000-scale)
with 40 foot contour intervals (Rockwell et al. 1984).
Another study conducted by Wells et al. (1988) using tectonic geomorphology
indices as an indicator to relative tectonic activity in the Pacific coastal mountains and
piedmonts of Costa Rica. The research utilized morphometric analyses of 100 mountain
fronts and abundant river long-profiles along with field studies and radiometric dating
over two study areas (regions I and II) located within the subduction zone between the
Cocos and the Caribbean tectonic plates (Wells et al. 1988).
The geomorphic analysis results of the two regions have shown differences in
values based on the region. Region I, located to the northern coastal areas within the
transitional plate boundary zone, where region II is located in the southern segment that
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indicates an occurrence of high degree of tectonic activity on-shore opposite to the
subduction seismic ridge. The S mf index values of region I range from 1.0 to 3.1
indicating relatively high sinuosity values of mountain fronts that are more sinuous and
dissected. On the other hand, region II S mf index results range from 1.0 to 2.2 that signify
relatively low sinuosity values of mountain fronts -compared to region I- that are less
sinuous and dissected due to their location in the interior mountainous regions. In
general, low mean values of mountain front sinuosity were clustered around 1.2 to 1.5 in
the more tectonically active region I and its subregions, while the mean values were
between 1.1 to 1.5 in the less active region II and its subregions. On the other hand, the
V f index values ranging from 1.1 to 32, with typical values of 0.2 to 7. Individual stream
in upstream regions (less active) show low V f values of less than 1 to 2. Where individual
streams in downstream regions (more active) show higher V f ratios. The wide-floored
valleys were located one or more kilometers from the escarpment and have high Vf
values generally more than 2, where V-shaped valleys associated with development of
deep and narrow canyons close or immediately upstream from the intersection with the
fronts indicated low V f values generally less than 1. The entire geomorphic
measurements were acquired from 1:50,000 scale topographic map with 20m contour
intervals (Wells et al. 1988).
This study emphasizes the practicality of geomorphic analysis for detecting
spatial variation in plate tectonic framework within convergent tectonic plate boundaries
(e.g. subduction ridges). Such morphometric analysis being normally utilized in arid and
semiarid regions of compressional and extensional terrains along mountain fronts would
yields satisfactory results when used in the coastal regions (Wells et al. 1988).
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In an attempt to provide information concerning the active fold growth in the
South mountain-Oak Ridge, a tectonic geomorphic analysis study using several
geomorphic indices was carried out by Azor et al. (2002). The South mountain-Oak
Ridge near Ventura basin, southern California, is an asymmetric anticlinal uplift above
the active and buried Oak Ridge reverse fault. The shortening along the fault started to
accumulate since the Quaternary time and is responsible for the growth and current
topography of the westernmost 15km of the ridge during the past 0.5Myr (Azor et al.
2002).
To quantify the geomorphology of the South Mountain-Oak Ridge, several
geomorphic indices were involved in this research including stream length-gradient index
(SL), mountain front sinuosity (S mf ), ratio of valley floor width to valley height (V f ), and
hypsometric integral index (Hi). The S mf index values roughly decreased from
approximately 2 to 1 along the northern slope of the anticlinal ridge towards the
westernmost 10km of observed surface folding indicating a lower S mf values and active
uplifting. In general, V f index values along the northern side of the ridge decreased
westward from about 1.5 to 0.5 indicating rapid uplift and valley incision. Values of the
Hi index along the northern side of the ridge increase considerably from roughly 0.35 to
0.4 with a maximum of around 0.55. In addition, the northern, eroded fold scarp of the
ridge represents were relatively high SL index values, which is a pattern consistent with
the existence of active and rapid slip on the Oak Ridge fault. Evidently, the overall results
obtained from geomorphic indices of the western segment of the South mountain-Oak
Ridge emphasize the westward lateral growth of the underlying anticline. In conclusion,
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the tectonic geomorphic analysis of the ridge confirms the usefulness of geomorphic
indices of active tectonics to identify and evaluate active fold growth (Azor et al. 2002).
Recent research was conducted by Silva et al. (2003) to determine the tectonic
activity over 17 different mountain fronts in southeastern Spain. The fronts are
distributed along the Valencia Trough and the Eastern Betic Shear Zone (EBSZ), which
are the two most prominent crustal-scale structures of the Mediterranean sector of Spain.
Mountain front sinuosity values were calculated using 1:50,000-scale topographic and
geomorphic maps with four contour intervals (100m height) produced by the authors of
the study area, while the valley floor width/height ratio values were calculated from
1:25,000-scale topographic maps (Silva et al. 2003).
The geomorphic index values for the mountain fronts under study were classified
into three tectonic classes and associated with an estimate of tectonic uplift rates. Class 1
indicating tectonically active fronts with S mf index values ranging from 1.17 to 1.53
(mean values = 1.4) and V f index values < 0.5. Class 2 indicating moderately active
fronts with S mf index values ranging 1.8 to 2.30 and V f index values ranging 0.3 to 0.8.
Finally, class 3 indicating inactive fronts with S mf values ranging 2.8 to 3.5 and V f index
values that is > 0.7 and ranging 0.8 to 1.2 (Silva et al. 2003).
After the application of S mf and V f analysis over the 17 mountain front in SE
Spain, the study concluded the occurrence of different morphometric properties for
different tectonic landscapes (i.e. tectonic classes) and faulting styles that could only be
practically distinguished by means of statistical analysis using S mf /V f regression. These
tectonic classes could be linked to the available uplift rates from the last 100kyr in both
regions under study in SE Spain mountain fronts for better understanding of their tectonic
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activity. As a result, each tectonic activity class was assigned uplift rates and recurrence
intervals of earthquake activities as follow, class 1 an uplift rate of > 0.15 to 0.08 m/kyr,
class 2 an uplift rate of 0.07 to 0.03 m/kyr, and class 3 an uplift rate of < 0.03 m/kyr
(Silva et al. 2003).
Another recent study was undertaken by Zovoili et al. (2004) to study the tectonic
geomorphology of escarpments of two faults located in mainland Greece. The
Kompotades and the Nea Anchialos faults in the Sperchios and South Thessaly rift zones,
respectively, were studied using morphometric analysis. Three geomorphic indices were
applied namely mountain front sinuosity (S mf ), the valley floor width to valley height
ration (V f ), and the stream length-gradient (SL) index (Zovoili et al. 2004).
In order to gather more information about the tectonic activitie of the two faults,
the historical seismicity of the region was taken into consideration. The V f index values
ranged between 0.4 to 1.2, with more significant values close to 0.7, implying high uplift
rates. While the S mf index values concentrated around ≈ 1 demonstrating relatively high
tectonic activity in both faults that decreased toward the west. On the other hand, the SL
index, which is more sensitive to non-tectonic processes such as rock resistance and
stream length were found to be less indicative of tectonic activity (Zovoili et al. 2004).
The recurrence interval of the Nea Anchialos fault according to the historical
seismicity is about 1500 years and in case of the Kompotades fault the interval is more
than 2500. Given that both faults have the same V f index values, this suggest that the Vf
index seems to be sensitive for longer periods than 2500 years. This study concluded that
the combined values of the S mf , V f , and SL indices calculations indicate that both faults
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are very active and they belong to the first class (i.e. class 1) of tectonic activity (Zovoili
et al. 2004).
Several studies in different places in the world have utilized tectonic geomorphic
analysis approach to investigate tectonic activities in multiple geomorphic and geologic
settings. For example, researches took place in the Gorajec river basin in Roztocze in SE
Poland (Brzezińska-Wójcik et al. 2002), eastern California shear zone and parts of the
basin and range located in eastern Mojave Desert (Dudash et al. 2003), southern
interpolate Shillong Plateau in Bangladesh/India (Biswas and Grasemann 2005), the SW
border of Sierra Nevada in Granada, Spain (El-Hamdouni et al. 2006), and the Alborz-
Central Iran border zone, from the east of Varamin of the east of Semnan (Arian and
Faranak 2006) all verified the usefulness of tectonic geomorphic index analyses in
determining and classifying relative tectonic activity along mountain fronts and river
basins.
2.7. Summary
Detailed quantitative measurements of landforms are able to provide essential
information to objectively compare and calculate geomorphic indices that are practical
for identifying the characteristics of a particular area such as its level of tectonic activity.
Therefore, combined evaluation of several indices would seemingly develop a system of
relative tectonic activity classes. Generally, the classification of areas being very active,
moderately active, and less active (inactive) is useful in locating active structures and the
calculation of active tectonic processes rates (Keller and Pinter 2002). A summary of the
S mf and V f indices ranges of selected significant studies are listed in table (2.1).
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Literature Tectonic Classes S mf ranges V f ranges
1 1.0 - 1.6 0.05 - 0.9
Bull & McFadden (1977)
2 1.4 - 3.0 0.5 - 2.0
3 1.8 - >5 >2.0
Rockwell et al. (1984)
Active fronts 1.01 – 1.34 (Ave. 1.14)
Less active fronts 1.57 – 2.72 (Ave. 2.04)
0.43 - 1.91
Wells et al. (1988)
More active 1.0 – 3.1 (mean 1.2 – 1.5) < 1.0, V- valley
Less active 1.0 – 2.2 (mean 1.1 – 1.5) > 2.0, U- valley
Azor et al. (2002) Active uplift Values Decreased ≈ 2 to 1 decreased ≈ 1.5 to 0.5
1 1.17 – 1.53 (mean 1.4) < 0.5
Silva et al. (2003)
2 1.8 – 2.30 0.3 – 0.8
3 2.8 – 3.5 > 0.7 (mostly 0.8 – 1.2)
Zovoili et al. (2004) High upllifting Around 1.0 0.4 – 1.2 (significant 0.7)
Table 2.1: The S mf and V f indices ranges of selected literature.
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3. Chapter Three: Materials and Methods
3.1. Introduction
This chapter provides details of the methods and procedure used to collect the
data for study. This includes the acquisition, preparation, and processing of the digital
data, generating ASTER DEMs, and morphometric analyses. In addition, the digitizing of
mountain front sinuosity and valley profiles over the areas that fit the geomorphic indices
criterion applying the digital approach is explained. Finally, all analytical results are
depicted in maps, three-dimensional images, and tables to ease the method of comparing
results and extracting conclusions.
3.2. The digital morphometric approach
In general, all index measurements in the previous morphometric studies were
manually obtained from topographic maps and aerial photographs of various scales where
available. Therefore, the digital approach adopted in this study will try to replace
topographic maps by digital elevation sources of the study area to calculate both S mf and
V f indices. Toward this, digital elevation models (DEMs) and shaded relief maps of the
study area replace topographic maps as elevation sources.
Integrated layers of vector and raster data of both delineated mountain fronts and
valley profiles that comprises digitized fronts and their overall lengths will operate as
digital measurement tools. In addition, a three-dimensional cross section of each
designated valley profile is used as an elevation reference for the right and left valley
divides and its floor as well as a measurement tool to calculate each valley floor width.
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3.2.1. Choosing mountain fronts
In this current research, the mountain fronts were distinguished and delineated
based on the presence of any intersection with large scale cross-cutting drainages.
Generally, all selected mountain fronts were acknowledged as a single continuous front
until they encountered major interruption by large-scale drainages or valleys. Hence, new
mountain fronts start at another front that is discontinuous and/or trends in a different
direction (Bull 1978).
3.2.2. Choosing valley profiles
In this study, the transect distance of valley profiles for determining V f values
ranged between 45m to 1,200m (only two cases reached about 2,500m) upstream from
the mountain fronts, and approximately parallel, for each given mountain front.
Accordingly, a number of mountain fronts were associated with several valley profiles
while other fronts were linked to at least one profile based on the ability of identifying
pronounced valleys due to the DEM resolution and the availability of the digital data at
the selected valley locations.
3.3. ASTER Stereo capability
The ASTER stereo subsystem that has the capabilities of generating DEMs
include the nadir-looking and backward-looking telescopes that yields a base-to-height
ratio (B/H) of 0.6 (Hirano et al. 2003, Lang et al. 1996, Tokunaga et al. 1996, Yamaguchi
et al. 1998), which is close to ideal for generating DEMs with automated techniques for a
variety of terrain conditions (Hirano et al. 2003). The visible/near-infrared (VNIR)
subsystem has both nadir-looking (band 3N) and backward-looking telescopes (band 3B)
pair in the near-infrared spectral band that is used for same orbit stereo imaging. The two
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telescopes are designed to facilitate stereoscopic viewing capability in the along-track
direction and to enable the large B/H ratio to be fixed at 0.6 (Fujisada 1998, Fujisada et
al. 1998).
One of the advantages of the along-track data acquisition system is that the
images creating the stereopairs are obtained a few seconds apart under consistent lighting
and environmental conditions, producing a stereopairs of homogeneous quality that are
suitable for generating DEMs employing automated stereocorrelation techniques (Hirano
et al. 2003) (figure 3.1). ASTER is capable of producing 771 digital stereopairs per day
(Lang and Welch 1999, Welch et al. 1998). To produce a stereo pair image, there is about
a 60-second interval between the time the ASTER nadir telescope passes over a ground
location and the backward telescope (27.6° or 27.7° off nadir) records the same location
on the ground path of the satellite. By that time, the satellite travels an estimated distance
of 370km over the earth’s surface (ground speed is 6.7km/sec) producing the 60km stereo
scene (Chrysoulakis 2004, Fujisada 1994, Hirano et al. 2003, Kamp et al. 2003, Lang et
al. 1996, Lang and Welch 1999, Toutin 2001, Welch et al. 1998), as shown in figure 3.2.
Figure 3.1: Simplified diagram of imaging geometry and data acquisition timing for
ASTER along-track stereo image (From Welch et al. 1998, figure 2, p. 1283).
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Figure 3.2: Simplified diagram of the imaging geometry for ASTER along-track stereo
image “stereo configuration” (From Hirano et al. 2003, figure 1, p. 358).
3.4. Obtaining ASTER imagery data
The choice of satellite imagery mostly depends on the availability of data for a
certain location, time, price, and the required scale of the application (Poli et al. 2005).
ASTER imagery was specifically chosen in this research for four main reasons: (1) its
worldwide coverage (2) the capability of generating DEMs from its stereo images, (3)
high quality ground resolution of 15m which is suitable for landforms identifications, and
finally (4) the affordable prices of ASTER images per scene.
ASTER satellite images are available at the United States Geological Survey
(USGS) website (http://glovis.usgs.gov) that easily allows browsing, purchasing, and
downloading satellite data. Multiple sensors are listed as separate links enabling viewing
and purchasing of scenes utilizing the USGS global visualization viewer, they can be
done by visiting the desired sensor link. ASTER data are available at a separate link
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(http://glovis.usgs.gov/ImgViewer/ImgViewer.html?lat=&lon=&mission=ASTER&senso
r=ASTERVNIR) and scenes are provided as mosaics each show the acquisition date, its
unique reference number, longitude and latitude, satellite path and row, and cloud cover
percentage as illustrated in figure 3.3.
Figure 3.3: The USGS global visualization viewer showing ASTER scenes of Jordan.
Four ASTER Level-1B data scenes of the same swath with 14 spectral bands were
purchased representing the Gulf of Aqaba (one scene), Wadi Araba (two scenes), and the
Dead Sea (one scene) in Jordan. The browser allows choosing from multiple satellite
platforms and scenes acquired in different years and seasons of nearly the entire world.
The satellite data chosen for the study area were acquired in September 16, 2002 with a
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zero percent (0%) cloud cover for the entire four scenes. The four ASTER scenes were
delivered as a Pull-FTP site sent through email after ordering. The total price is 225 USD
with a price of US $55 for each scene and US $5 data processing and handling (Kamp et
al. 2003, 2005, Poli et al. 2005). Table 3.1 shows the area coverage of ASTER imagery of
Jordan-Dead Sea Transform Zone (JDTZ) area and the type of data products (i.e.
granules) and their reference numbers listed from north to south.
The coordinates for all ASTER images for areas around the world are recorded in
the Universal Transverse Mercator (UTM) coordinate system in meters and referenced to
the World Geodetic System of 1984 (WGS-84) ellipsoid (Hirano et al. 2003, Lang et al.
1995, Lang and Welch 1999, Fujisada 1998, Fujisada et al. 2001).
Area coverage Data Type Granule/Product Cost
The Dead Sea ASTER L1B registered SC: AST_L1B.003:2008495456 $55
radiance at the sensor V003
North Wadi Araba ASTER L1B registered SC: AST_L1B.003:2008495466 $55
radiance at the sensor V003
South Wadi Araba ASTER L1B registered SC: AST_L1B.003:2008495566 $55
radiance at the sensor V003
Aqaba ASTER L1B registered
radiance at the sensor V003
SC: AST_L1B.003:2008495567 $55
Table 3.1: ASTER scenes selected to cover the JDTZ study area.
3.5. Viewing ASTER data in PCI Geomatica
PCI Geomatica software (Geomatica 2003) facilitates working with imagery,
vectors, graphical bitmaps, and other geospatial using its Focus application. Focus is a
visual environment with many useful display tools that enables viewing, editing, and
enhancing remotely sensed data of a variety of satellite sensors and aerial photography.
All data is stored together as the native PCIDSK file format using a single file name
extension of PIX that contains all features and database tables. Moreover, the image data
are stored as channels and auxiliary data are stored as segments which makes it easier for
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the PCI Geomatica software application tools to perform searching, sorting, and
recombining operations of such data type (Geomatica 2003).
Viewing ASTER images begins with starting up Geomatica Focus from the
Geomatica Toolbar (figure 3.4). To view any ASTER images, the first step would
normally be to import the HDF files using Geomatica Focus into PCIFDSK then save
them as *.PIX format. In Focus window, File> Utility> Import to PCIDSK. Next browse
for the source file and set the options to default. Then chose the ASTER sensor
instrument needed to create the image from (in this case VNIR) and click OK (figure
3.5). Finally, set the output file destination path then click Import (figure 3.6).
Figure 3.4: Focus is the first icon on the Geomatica Toolbar.
Figure 3.5: Selecting ASTER VNIR sensor images.
Figure 3.6: Import File window.
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To view the final imported ASTER VNIR PIX image(s) composite in Focus
window, select File> Open> and navigate to the *.PIX file location. Other methods to
view the ASTER PIX image would be adding three layers of different bands to create the
composite image [Layer> Add> RGB> bands 3N, 2, and 1> Finish]. Both methods will
convey the same results as shown in figure 3.7.
Figure 3.7: ASTER 3-2-1 color composite image of Aqaba recorded to the UTM
coordinate system and referenced to WGS-84 ellipsoid.
3.6. Generating ASTER DEMs
In this section, the processes of generating ASTER DEMs and all associated
stages such as importing/exporting and converting satellite images, creating epipolar
images, generating and editing final DEMs, and photogrammetric software involved in
the process are explained in details and depicted in figures. In addition, an in-depth detail
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of the process of obtaining Landsat 7 enhanced thematic mapper plus (ETM+) images
and vector data to facilitate digitizing mountain fronts and related valleys to calculate the
S mf and V f geomorphic index values is also explained.
3.6.1. Generating and extracting DEMs from ASTER data
3.6.1.1. PCI Geomatica software
PCI OrthoEngine software is an efficient photogrammetric tool that handles a
wide range of dataset formats with the capability to produce quality geospatial products
(Geomatica 2003). OrthoEngine supports reading of ASTER data, ground control points
(GCPs) collection, geometric correction, ortho-rectification, DEM generating and
modeling, and either manual or automatic mosaicking. In addition, this software also has
the capability to automatically generate DEMs from either aerial photographs or satellite
stereoscopic sensors (Geomatica 2003, Toutin and Cheng 2001). PCI OrthoEngine
software was developed at the Canada Center for Remote Sensing (CCRS), Natural
Resources in Canada. Currently, this software is extensively used by the NASA EOS
Land Processes Distributed Active Archive Center (DAAC) located at the USGS EROS
Data Center to produce EOS Standard Product DEMs from stereo ASTER data (Toutin
and Cheng 2001).
In the process of DEM extraction, the selection of very important points known as
tie points (TPs) is common in DEM manual processing. Usually, manual selection of
points produces low quality DEMs with a lot of redundant or irrelevant information
potentially being lost, which is omitted when using automated DEM extraction. The basic
conditions necessary for generating an accurate DEM are: (1) using high accuracy control
points, and (2) using enough tie points to guarantee error control dependability (Cuartero
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et al. 2004). Commonly, the use of accurate tie points will speed up and would result in
significant enhancement of the DEM generation process (Lang and Welch 1999, Selby
2003), as the quality of TPs is crucial for the final DEM quality (Kamp et al. 2003). Tie
points can be chosen manually or automatically and are used to refine the relative
orientations of the stereo images. This is the triangulation process and once it is complete,
the DEM is created by finding conjugate points in the stereo pair in a manner similar to
automatic tie point finding but much, much more dense. The tie points then guide the
DEM point finder enabling it to better find DEM points (figures 3.8 and 3.27).
Figure 3.8: Comparing raw images to epipolar images (From Geomatica 2003, figure 6.2,
p. 69).
OrthoEngine allows work with specific modules for large set of spatial data
including ASTER. The influence of software is very obvious in terms of DEM results.
The PCI Geomatica software includes an ASTER specific model that compensates for the
shortage of orbital parameters that uses the ephemeris and attitude information recorded
in the ASTER metadata which accordingly produces better-quality DEMs (Cuartero et al.
2004, Geomatica 2003, Lang and Welch 1999). Basically, the ASTER satellite orbital
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math model is able to compensate for the effects of varying terrain and for the distortions
inherent to the camera. Such parameters include the curvature of the lens, the focal
length, the perspective effects, and the camera’s position and orientation. In addition, the
calculated math model calculates the camera’s position at the time when the images were
taken (Geomatica 2003).
In case of ASTER data products, the software uses a minimum of six tie pints (or
GCPs) located in both 3N and 3B images to generate a pair of epipolar images in order to
retain elevation parallax in only one direction (i.e. y-axis, N-S direction). Epipolar images
are stereo pairs that are reprojected to have a common orientation and matching features
between the right and left stereo images (Geomatica 2003, Selby 2003). Then an
automatic image-matching procedure is used to produce the DEM through a comparison
of the individual gray values of these images. This procedure utilizes a hierarchical subpixel
normalized cross-correlation matching method to find the corresponding pixels in
the left and tight epipolar images. The difference in location between images gives the
parallax arising from the terrain relief, which is then converted to elevation values above
the local mean sea level of the given datum (Geomatica 2003, Lillesand et al. 2004,
Toutin and Cheng 2001). Figure 3.9 is a simplified scheme showing the algorithm for
measuring height (∆h) from parallax difference in an ASTER stereopairs where B is the
base and equals to X 1 . For the nadir (vertical-looking) and the aft (backward-viewing)
cameras configuration, ∆h is related to the camera orientation angle (α) and the time
interval (∆t) required to record both the top and the bottom of the object, which is
represented by (X 1 -X 2 ) = ∆p in the nadir/aft stereopair (Lang and Welch 1999).
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Figure 3.9: Measuring height from ASTER stereopair parallax difference (From Lang and
Welch 1999, figure 2.0-3, p. 13).
PCI Photogrammetric software uses automatic stereo image correlation approach
to extract DEMs by calculating the parallax differences from ASTER 3N and 3B
channels (Chrysoulakis et al. 2004, Lang and Welch 1999, Poli et al. 2005). This method
is able to automatically produce relative DEMs from ASTER stereopairs in PCI
OrthoEngine tool using simply tie points to adjust the images together (Chrysoulakis et
al. 2004, Kamp et al. 2003). In this research both TPs and GCPs were utilized in
generating epipolar stereopairs and in turn the final DEMs for the JDTZ. The use of TPs
alone has the advantage of speeding up the process but the accuracy for the resulting
DEM will be inferior to the one created using both GCPs and TPs. Once any TPs and
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GCPs have been collected, then a bundle adjustment operation is performed to computes
a photogrammetric model using the orbital and sensor ephemeris information, plus the
GCPs and TPs, so that images are located relative to each other and to the ground (Lang
and Welch 1999, Selby 2003). The only difference between these points’ types is that
TPs are used to match points to relate stereo images to each other, which can be
calculated either manually or automatically. While GCPs are used to adjust the images to
the shape and orientation of the earth’s surface which typically come from an
independent source such as GPS.
The process of generating DEMs using PCI Geomatica software requires the
construction of a stereo pair by registering two images of the same ground area recorded
from different positions in space. Any positional differences in the stereo pair that are
parallel to the direction of satellite travel (i.e. parallax difference) are attributed to
displacements caused by relief on the ground (figure 3.9). Relative ground elevations are
determined by measuring the parallax difference in the registered images that eventually
are converted to relative or absolute (if GCPs available) elevations (z-coordinate values)
in the final DEM (Lang and Welch 1999). The specifications of a standard ASTER DEM
data product are illustrated in figure 3.10 and table 3.2.
Standard ASTER DEM Products
Unit of coverage 60km x 60km ASTER Scene
Format
Elevations in meters; UTM; WGS-84
Resolution X-Y = 15, 30m, or 90m; Z = 1m
Product Name GCP Accuracy DEM Accuracy
Relative DEM None 10-30m
Absolute DEM 15-30m (or 5-15m) 15-50m (or 7-30m)
Table 3.2: Specification for standard ASTER DEM products (Modified from Lang and
Welch 1999, table 3.0-1, p. 19).
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Figure 3.10: Summary of a standard ASTER DEM product generation process (Modified
from Lang and Welch 1999, figure 3.0-2, p. 17).
3.6.1.2. DEMs extraction process
Whatever the image data are used, the main digital processing steps for DEM
generation are: (1) setting-up the stereo images, (2) data extraction by image matching,
(3) the 3D stereo intersection, and (4) the DEM editing (Toutin 2001). In this research,
some guiding principles were followed for DEM generation that includes: (1) generating
DEMs automatically, if possible, with minimal manual intervention to reduce induced
inaccuracy, (2) GCPs were only exploited to match the JDTZ generated DEMs to
GTOPO30 based on the satellite ephemeris data, no elevation values were used due to the
lack of such data from the source, (3) coarse DEM GTOPO30 database are utilized to fix
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possible missing data within generated ASTER DEMs, and (4) DEMs of 30m grid are
generated (Fujisada et al. 2001, Tokunaga et al. 1996).
The extraction of ASTER DEMs process is explained in more details in the
literature provided by Geomatica (2003, chapter 6) and Selby (2003). These tutorials
illustrate the relative straightforward procedures of the automatically extraction of DEMs
and orthorectified images from ASTER satellite imagery utilizing PCI Geomatica
OrthoEngine. The output of the automatic extraction of ASTER images is orthorectified
DEM and images which are automatically geocoded that can be used with other datasets
and to orthorectify other images within ASTER files or other satellite images (Selby
2003). The step-by-step DEM extraction methods derived from ASTER purchased
images of the JDTZ study area in addition to all input options and final editing are
explained in this section.
All of the L1B data (and L1A data) are basically stored together with metadata in
one HDF file (Abrams et al. 2003, Abrams and Hook 2002, Selby 2003). The stereo
images corresponding to bands 3N and 3B are extracted from the original hierarchical
data format (HDF) file in PCI Geomatica 9.1 environment. Both channels have different
size due to the different detector size (Chrysoulakis et al. 2004, Fujisada 1994, Fujisada
et al. 1998, Poli et al. 2005).
In general, ASTER L1B VNIR subsystem dataset band 3N has 4200 (rows) x
4980 (columns) pixels and band 3B has 4600 (rows) x 4980 (columns) pixels (Abrams et
al. 2003). Therefore, the ground coverage in across-direction is the same, while in the
along-track direction, the scene 3B covers a longer area than 3N (Poli et al. 2005). A time
delay occurs between the acquisition of the backward-viewing image and the nadir
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image. During this time Earth rotation displaces the image center. The VNIR subsystem
automatically extracts the correct 400 pixels based on the orbit position information
supplied by the EOS platform. The pixels of 400 lines on band 3B equals to about 8.7%
of the whole band 3B image, plus both sides shift. The estimated 3N and 3B overlap area
is approximately 85% of the image area (JPL, personal communication).
For the georeferencing of all ASTER imagery the position and attitude of the
sensors at the acquisition time of each image line is required. The metadata contained in
the HDF file supplied this information (Poli et al. 2005).
-Step One: Create a new project
Initially click the OrthoEngine icon on the Geomatica 9.1 Toolbar to start
OrthoEngine (figure 3.11). Creating a new project starts with opening a new file from the
OrthoEngine File menu. Each ASTER scene in the study area requires a separate project,
thus, four projects were created for this purpose. Then, construct a new project from the
processing step drop-down menu (figure 3.12). After that, the software gives the option to
read the satellite data from a variety of data storage media such as CD-ROM, hard drive,
or a tape (figure 3.13). This will lead to a new window where the project information
needs to be entered. After filling in the filename and the project description in the
designated boxes, the next step would be choosing the math modeling method and setting
up the options. The options include the high resolution satellite being used to create the
DEMs from; in this case ASTER was selected (figure 3.14).
Figure 3.11: OrthoEngine (4 th icon) on the Geomatica Toolbar.
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Figure 3.12: Opening new project from the processing step drop-down menu.
Figure 3.13: Reading satellite data from hard drive using the 3 rd icon.
3.6.1.3. The math model
The math model is the mathematical relationship used to correlate the pixel of the
used satellite image to correct locations on the ground considering know distortions. The
math model directly impacts the outcome of the final project; therefore, choosing the
correct model that matches the imaging sensor is essential. The satellite orbital math
modeling is a rigorous model developed to compensate for distortion cause by sensor
geometry, orientation, and integration time, satellite orbital and altitude variations, earth
relief, shape, and rotation, the platform position, velocity, and orientation. The ASTER
math model principally calculates the position and orientation of the sensor at the time
the images were taken. The accuracy of the ASTER satellite orbital math model is about
one-third of a pixel in the VNIR satellite images (Geomatica 2003).
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Figure 3.14: Project Information window including project name and setting the
mathematical model method and the satellite options.
Afterward, the output projection for the output DEM and orthoimages should be
set as a first step (Geomatica 2003, Selby 2003) to UTM zone-36: 30E to 36E, Row: R-
24N to 32N, and WGS-84 ellipsoid (D-000-Global Datum Definition or Ellipsoid E012).
Since ASTER VNIR data are used to generate the DEMs, the output pixels spacing
should match the used 3N and 3B bands which is set to 15m. In the presence of GCPs, it
is recommended to set their projections based on the output file, as shown in figures 3.15
to 3.19.
Figure 3.15: Setting project projection to UTM.
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Figure 3.16: Setting the UTM zones.
Figure 3.17: Setting the UTM rows.
Figure 3.18: Setting Earth Model (Ellipsoid).
Figure 3.19: Setting GCPs to match the output file projection.
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Finally, the project is ready to read the raw ASTER data HDF files in order to
process the stereo images and create the DEMs (figure 3.20). Select both 3N and 3B
bands to be read and create 3N.pix and 3B.pix and their resultant report files 3N.rpt and
3B.rpt, respectively. Then provide an output filename for each band. It is important to
read band 3N first as one file then band 3B as another. The reason for not reading them
into a single file is due to the different pixel/line size of band 3B to band 3N (Selby
2003).
Figure 3.20: Reading row ASTER data HDF files from the hard drive.
-Step Two: Collecting ground control points and tie points
Selecting the right number and the best ground control points locations directly
affect the satellite orbital math model and eventually the accuracy and quality of the
generated DEMs. It is essential to choose features that are recognized accurately at the
resolution of the raw image. To improve the accuracy of the output DEMs generated
from ASTER images the minimum GCPs recommended are six points per image,
however, collecting more than the minimum number of points uniformly throughout the
images ensure the overall DEMs accuracy (Geomatica 2003).
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All images have 121, evenly spaced GCPs with x and y locations on the ground.
However, the z-values are not provided, thus, all elevation values are not available and
shown as zero. These GCPs coordinates included with the ASTER scene are calculated
from the sensor and ground processing and are used to transfer these values to the
ground. They are only as accurate as the satellite ephemeris information. To view the
existing GCPs, choose the import GCPs from file icon (figure 3.21), then select image 3N
(similar GCPs number in image 3B) to load the available GCPs on the image (figure
3.22). The 121 GCPs are evenly distributed in a grid form over the entire image of each
ASTER scene as shown in figures 3.23 to 3.25.
Figure 3.21: Importing GCPs from file using the 8 th icon on the toolbar.
Figure 3.22: loading the 121 already available GCPs from image 3N file.
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Figure 3.23: Dead Sea band 3N GCPs.
Figure 3.24: Dead Sea band 3B GCPs.
Figure 3.25: The image layout of bands 3N and 3B showing the location of all 121 GCPs
in the Dead Sea ASTER image.
The following step in the process is collecting tie points which aims to adjust and
correlate the two stereo images together. Using tie points allows automatic extraction of
the DEM from the generated 3N and 3B images only (figure 3.262). However, the
addition of GCPs to these images will permit precision geocoding and scaling of DEMs
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in the z direction (Selby 2003). Tie points are basically features that are clearly
recognizable in two or more images and can be used as reference points to identify how
images relate to each other (Geomatica 2003), as illustrated in figure 3.27.
Figure 3.26: Start the Collect Ground Control Points (GCPs) and Tie Points (TPs)
manually function from bands 3N and 3B by using the 2 nd icon on the OrthoEngine
toolbar.
Figure 3.27: An image shows how two images connect through a tie point (From
Geomatica 2003, figure 5.2, p. 51).
Selecting a set of quality tie points (minimum of six) which are spread over the
images is vital to the accuracy of the final DEMs. Therefore, tie points should be features
that can be accurately identified at the resolution of the raw image. It is important to
choose tie points that are close to the ground and away from features that rise above the
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ground such as buildings and mountains because high features may appear to lean in one
image but may not in the other. In addition, shadows are not suitable to be used as tie
points since they are not a permanent feature and may move from one image to another.
Streets and highways intersection are the best features to be selected as tie pints since
they are permanent feature and easy to identify in the images (Geomatica 2003).
To start collecting tie points manually (figure 3.28), both images should be active
during this process where the one from which points are being collected would be labeled
Working while the other is labeled Reference (figure 3.29). After choosing a single tie
point from the working image, it is easy to collect the same point in the other image by
switching to the reference image and select the same point. All tie pints are recorded in
the tie point collection window (figure 3.30 and table 3.3). It is highly recommended to
zoom in to see the detail in the image and use both Auto Locate and Bundle Update
features during tie point collection. The auto locate feature is employed by OrthoEngine
to estimate the position of the points by using an automatic correlation method once it has
adequate information to calculate the math model. Usually, this function works best when
there are three tie points existing per image. On the other hand, the bundle update feature
allows OrthoEngine to perform the bundle adjustment every time a tie point is added to
the project. This can assist to determine the dependability of the point being selected for
the project (Geomatica 2003).
Figure 3.28: Collecting tie points manually from both images by choosing the 9 th icon on
the OrthoEngine toolbar.
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Figure 3.29: Opening both uncorrected 3N and 3B images to manually collect tie points.
Figure 3.30: Collecting tie point window for Aqaba image 3N including the type of tie
point, their residual errors, their x, y coordinates, and z values on each image.
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DEM Coverage Area Dead Sea North Araba South Araba Aqaba
Number of tie points 24 23 17 20
Manual tie points (T) 20 18 11 15
Automated tie points (AT) 4 5 6 5
Table 3.3: Number and type of tie points in each DEM coverage area.
When done collecting tie points manually, running the Automatically Collect Tie
Points from the 10 th icon on the GCP/TP Collection list of the OrthoEngine (figure 3.31)
using the default inputs increase the chances of finding more tie points to enhance the
overall DEM resolution. Since tie points are simply matching points in two images,
collecting points can be automated in OrthoEngine utilizing image correlation techniques.
On the automatic tie point collection window, setting the tie point distribution pattern will
enable better automatic tie point matching. The number of tie points per area was set to
nine points to be distributed uniformly over the entire image. While the matching
threshold that indicates the minimum correlation score that will reflect a successful
match, which is represented by any value ranging from zero indicating no correlation to
one signifying the best correlation has been set to 0.75 (Geomatica 2003, Selby 2003), as
demonstrated in figure 3.32. Further extensive information regarding image correlation
principles and image matching techniques can be found for instance in (Ebner and
Heipke 1988, Förstner 1992, Hannah 1988, Heipke 1996, 1992, 1989, Mahn 1989).
Figure 3.31: Automatically Collect tie points.
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Figure 3.32: Automatically Collect tie points uniformly over an entire image.
On the tie point collection window (figure 3.30), the residual error indicates the
residual difference in pixels between the coordinates that were entered for the tie points, a
typographical mistake, or an error in the position of the tie points on the raw image.
Typically, for every tie-point a 3D point on the ground is computed. This 3D ground
point is reprojected through the rigorous ASTER math model to a new image coordinate.
All the tie points are used to estimate the exterior orientation of the stereopair and so the
reprojected 3D point will not fall exactly where you measured it. Residual errors do not
necessarily reflect errors in the tie points, but rather the overall quality of the math model.
Such errors might denote bad points, but usually they indicate the quality of the
computed math model to fit the ground control system. In general, high residual errors
suggest a poor model solution which is a result of inaccurate tie points, errors of the
projection or datum, inadequate tie points, or unsatisfactory distribution of the tie points
over the image (Geomatica 2003).
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Figures 3.33 and 3.34 show tie points in both 3N and 3B image of the North
Araba area. The T indicates manual tie point while TA indicates an automated tie pint
collection. Finally, to view the distribution of the collected tie pints within the 3N and 3B
images, choose the display overall image layout from the 11 th icon on the OrthoEngine
GCP/TP Collection list toolbar as in figures 3.35 and 3.36.
Figure 3.33: Band 3N tie points (North Araba). Figure 3.34: Band 3B tie points (North
Araba)
Figure 3.35: Displaying overall image layout from both images.
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Figure 3.36: Image layout of both bands 3N and 3B for the North Araba scene.
-Step Three: Math model calculations
Once all of tie points have been collected from stereo images, then a bundle
adjustment ought to be performed to compute the photogrammetric model using the
orbital and ASTER sensor ephemeris information in addition to tie points and GCPs if
available (Geomatica 2003, Selby 2003). The bundle adjustment uses the tie points and
the knowledge of the sensor geometry and orientation to calculate the best fit for all
images used in the project to generate the DEM simultaneously (Geomatica 2003), as
shown in figure 3.37.
Figure 3.37: Running Model Calculation to perform bundle adjustment.
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-Step Four: Creating epipolar images
As mentioned earlier, OrthoEngine uses image correlation to extract matching
pixels in the two stereo images and they apply the season geometry computed from the
math model to calculate the x, y, and z positions, as shown in figure 3.38. Epipolar
images are stereo pairs that are reprojected in order that the left and the right images have
an ordinary orientation, and matching features within the images appear a long a common
x axis. The main purpose of creating epipolar images is to eliminate any offset between
them in the y axis direction. Besides, epipolar images accelerate the autocorrelation pixel
matching algorithm and process that creates DEMs exploiting the stereo overlap area
between the epipolar images and diminish the possibility of incorrect matches due to the
fewer pixels it searches to find a match (Geomatica 2003, Selby 2003).
Figure 3.38: Creating a DEM from stereo pairs using image correlation (From Geomatica
2003, figure 6.1, p. 69).
To create epipolar images start with initiating the Create epipolar image window,
which is the 1 st icon located under the DEM from Stereo list on OrthoEngine toolbar
(figure 3.39). Using the User Select option will allow the manual selection of the stereo
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pairs for each DEM to be extracted. In the left and right image window, normally band
3N is assigned as the left image while band 3B to the right. After that, add the 3N and 3B
stereo images to the Epipolar Pairs Table using all channels. Then select epipolar images
from the List of Epipolar Pairs and set the working cache to at least 256MB, then click
save setup to store all entered options. Finally, click on the Generate Pairs button to start
the epipolar images generating process (Geomatica 2003) (figure 3.40).
Figure 3.39: Create Epipolar image icon.
Figure 3.40: Generate Epipolar images window.
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-Step Five: Extracting DEMs
Automatic DEM extraction is the final step in the DEM generating process.
Starting the function using the 2 nd icon on the OrthoEngine toolbar under DEM from
Stereo list (figure 3.41) will launch the Automatic DEM extraction window (figure 3.42).
To finalize the extraction process, the output DEM file requires a name and a path to be
saved to. Afterward, choose all images in the DEM Bounds to use the extents of all the
images in the Stereo Pair Selection table as the extents of the DEM then click the
Recompute icon to adjust the image size, the DEM resolution (30m), and recalculate
extents, then finally click Start DEM Extraction to generate the geocoded DEM file
(Geomatica 2003). Table 3.4 summarizes the options used in DEM automatic extraction
of all epipolar images for the study area. All elevations were based on the topographic
maps of southern Jordan available at scales of 1:50,000, 1:100,000, and 1:250,000.
Figure 3.41: Extract DEM Automatically icon.
DEM Coverage Area Dead Sea North Araba South Araba Aqaba
Minimum Elevation -650m -600m 0m 0m
Maximum Elevation 1800m 1950m 1800m 1800m
Failure Value -600 -9998 -100 -100
Background Value -9999 -9999 -150 -150
DEM Detail Medium Medium Medium Medium
Output DEM Channel Type 16-bit Signed 16-bit Signed 16-bit Signed 16-bit Signed
Pixel Spacing Interval
(DEM resolution)
2 (30m) 2 (30m) 2 (30m) 2 (30m)
Fill Holes and Filter Yes Yes Yes Yes
Create Score Channel Yes Yes Yes Yes
Create Geocoded DEM Yes Yes Yes Yes
Table 3.4: Options used for all DEMs extraction processes within the study area.
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Figure 3.42: Automatic DEM extraction window showing all used options for Aqaba
DEM.
Failures in the DEM extraction occur in areas with very low contrast such as
shadows, clouds, snow, and water bodies. Usually, small failure holes in the DEM are
filled automatically by interpolation, while large failure areas necessitate manual editing
using filters to complete the DEM (Selby 2003). Specifying failure values is very useful
when interpolating these pixels in subsequent DEM editing. The Background value
categorizes the pixels with no data that lies outside the extracted DEM overlap area so
they could be distinguished from elevation values. Alternatively, the maximum and
minimum elevations are employed to estimate the search area for the correlation, which
increase the speed of the correlation process and reduce error (Geomatica 2003).
Therefore, due to the high contrast between the mountains and valleys elevations in the
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North Araba and the Dead Sea images, the minimum and maximum elevation values in
these areas have been expanded by ±150m to enable enhanced DEM results.
The Fill Holes and Filter option is used to enhance the quality of the DEM
product by automatically filtering the elevation values and interpolating the failed areas.
Also, turning on the Create Score Channel option generates an extra image channel that
classifies the failed pixels during correlation to the ground for each DEM pixel, which
assist evaluating the success of the procedure. Finally, choose the Create Geocoded DEM
to merge and geocode the epipolar DEMs (Geomatica 2003).
During DEM extraction, image correlation is used to locate similar features on
both the left and right stereo images. Matching these features is normally achieved by a
hierarchical approach utilizing a pyramid of reduced resolution images. This method
creates a multi-resolution image pyramid that consists of a base image and a series of
sequentially smaller sub-images, each at half the resolution of the previous image (figure
3.43). This correlation technique expedites the image correlation operation and
diminishes the mismatches measures. The first correlation attempt is executed on very
coarse versions of the images. This facilitates OrthoEngine matching major features by
precisely constructing the foundation for advanced correlation attempts. The next
attempts are carried out on higher resolution images until finally the correlation is
performed on images at full resolution to provide the highest accuracy of the terrain in
the DEM (Geomatica 2003, Ismert et al. 2003).
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Figure 3.43: The multi-resolution image pyramids method of creating reduced resolution
images to optimize display performance (Modified from Ismert et al. 2003, figure 5, p.
6).
3.6.1.4. DEMs editing process
During the DEM extraction process few missing data and little failure holes with
unsatisfactory resolution were produced in dispersed locations. Most failure holes are a
few meters wide, the largest is about 4.5km across located in South Araba area. The
interpolation of ASTER data normally solves such problems by restoring the failure holes
of 1km or lees with elevation values. In this case, since there are several failure holes of
dissimilar sizes that were scattered randomly in the highest and lowest elevation areas
within the JDTZ, the procedure of editing DEM were applied to substitute for these
missing data.
These missing data occurred when using the full resolution DEM (pixel sampling
of 1, yielding 15m DEM) due to the high contrast in elevation of the terrain relief
between mountains and valleys. Nevertheless, the major mountains and valleys in the
study area are identifiable and well defined to proceed with digital geomorphic analysis.
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Thus, medium DEM detailed resolution (pixel sampling of 2, yielding 30m DEM) was
generated, which is suggested for most satellite images to accelerate the process and
produce smooth DEMs (Geomatica 2003). Due to the geographic nature of the JDTZ, it
was difficult to recognize and allocate mutual features in both epipolar images during the
process of generating DEM. Therefore, employing the full resolution 15x15m pixel
sampling window increased the probability of image-to-image correlation failure creating
many and larger failure holes that were fewer when using the medium 30x30m pixel
sampling window. Since ASTER is an optical sensor, it is impossible to generate DEMs
in cloud-covered areas or water areas (Tokunaga 1996); as a result, both the Dead Sea
and the Mediterranean Sea show no elevation data. The generated geocoded 30m
resolution DEMs for each ASTER scene are presented in figures 3.44 to 3.47, where the
missing data can be noticed as black holes (i.e. no data) within the DEMs.
Figure 3.44: The Dead Sea generated DEM. Figure 3.45: North Araba generated DEM.
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Figure 3.46: South Araba generated DEM.
Figure 3.47: Aqaba generated DEM.
Following the generation of the DEM, editing can be performed, if required, to
correct out and fill in the failures and missing data. Consequently, the final DEMs and
orthorectified images are valuable for interpretation and analyses of landforms and
geology (Selby 2003). The DEM editing process necessitates another DEM data source in
order to retrieve the missing data. Usually GTOPO30 coarse DEM data is used due to its
global coverage (Fujisada et al. 2001). The DEMs editing method using both Geomatica
Focus and XPace Tools are explained below in details.
-Step One: Importing and subsetting the global DEM
In order to fix the missing data in the generated DEMs of the study area, coarse
DEM data of known elevation values is being used. The global DEM GTOPO30 image
of the Middle East with a horizontal grid spacing of approximately 1km (Campbell 2002)
and has a file extension of HDR is accessible from the USGS website available at
(http://edcdaac.usgs.gov/gtopo30/dem_img.asp), as shown in figure 3.48.
1. Importing the GTOPO30 DEM to PCI Geomatica 9.1 and convert the image
to *.PIX with a new file name and path [File> Utility> Import to PCIDSK>
GTOPO30.HDR> file name].
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2. Subsetting the whole Middle East GTOPO30 DEM to fit the JDTZ study area.
Using Tools in Focus window [Tools> Clipping/Subsetting> Enter file name>
Set dimensions to subset].
-Step Two: Reprojecting the global DEM
The GTOPO30 DEM data is projected as latitude/longitude, thus, to best fit the
study area the DEM needs reprojection to the UTM zone-36: 30E to 36E, Row: R-24N to
32N, and WGS-84 (E012) ellipsoid.
1. To do so, in Focus [Tools> Reprojection> Enter the GTOPO30 file name> Set
the projection parameters> Resampling = Nearest> Transform Order = Exact>
Sampling interval = 1> Source layer = All> Reproject].
-Step Three: Adding New Channels
1. All DEM scenes should be viewed as Pseudocolored images to facilitate
editing, this is done in Focus window as follow: [Layer> Add>
Pseudocolored> File name].
2. Adding new channels to each study area scene should be done before starting
fixing any of the DEMs.
3. One bitmap channel that is specified using the special symbol (%%) and two
raster 16-bit signed channels, which is specified using the special symbol (%)
should be added for each DEM file (signed channel allow reading data as
integers of ± values).
4. On the maps tree menu, click on file to add extra channels to the exiting DEM
files [Right click on the file name> New> Raster Layer x2] then repeated the
same step for the Bitmap Layer.
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Figure 3.48: General view of the clipped GTOPO30 DEM of the Middle East showing
the JDTZ study site (Map not to scale).
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-Step Four: Filling the missing values in the study area DEMs
1. This step aims to fill in the missing values of each DEM of the study area by
known values obtained from the GTOPO30 DEM.
2. Using XPace Tool from the Geomatica toolbar (figure 3.49).
3. Each DEM file of the study area is separately combined with GTOPO30 DEM
by performing image mosaicking using the Geometric Correction package and
Image Mosaicking task as follow: [XPace> Geometric Correction> Image
Mosaicking]. Normally, a mosaic composite image is made of joining together
individual images covering adjacent regions on the ground (Campbell 2002,
Sabins 1997). Thus, mosaicking process is basically piecing together several
contiguous, overlapping, or bordering images to ultimately create a uniform
image (Geomatica 2003).
4. Filling the parameters window as indicated below then RUN the application.
FILI: (GTOPO30 DEM file name)
OBIC: 1
FILO: (study area DEM file name)
OBOC: 2
Where FILI indicates the name of the PCIDSK file (i.e. DEM) from which the
input image data is to be read. FILI cannot be the same as FILO, because the bounds of
the two images stored in their georeferencing segment are needed to determine their
overlap, OBIC is the input channel(s) with integer numbers to read from input files on
FILI and stored to output file on FILO, and OBOC is the output channel(s) on FILO to
write the image data to (Geomatica 2003).
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5. Remove all files from the maps tree menu and then reload again as
pseudocolored layers [for each DEM in the study area load only channel 2
(%2)].
6. Repeat procedure 3 to 5 for each of t he four DEMs.
Figure 3.49: The XPace Tool, the 10 th icon on Geomatica Toolbar.
-Step Five: Creating a mask for the missing values
A mask categorizes specific pixels over areas that need editing. Normally, the
mask doesn’t change the values in the areas that it covers, although it assists in
identifying these areas to be replaced with true (or known) elevations from other sources
(Geomatica 2003).
1. Using the EASI modeling to create a mask to the already created bitmap
channel (%%2). In Focus window [Tools> EASI Modeling> Enter file name
of the study area DEM].
2. Run the EASI modeling using the following syntax:
if % 1 = (enter the failed value of each study area DEM e.g. -9998) then
%% 2 = 1
else
%% 2 = 0
endif
-Step Six: Filling the missing values within the mask
1. Using the EASI modeling to create a mask to the already created bitmap
channel (%%2) and the new raster channel (%2). In Focus window, [Tools>
EASI Modeling> Enter file name of the study area DEM].
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2. Run the EASI modeling using the following syntax:
if %% 2 = 1 then
% 1 = % 2
else
% 1 = % 1
endif
3. Remove all images from the maps tree menu and reload the first channel (%1)
of the study area DEM.
-Step Seven: Smoothing the mask and finishing up the whole DEM scene
Filtering of missing data within DEMs will eventually alter the original elevation
values. Thus, filtered portions of the DEMs are used for as a cosmetic procedure for
display purposes and not as an elevation source for the morphometric analysis.
Smoothing the mask was done by utilizing convolution filters which involve the
movement of filter window throughout an image pixel-by-pixel and line-by-line until
covering the whole image. Low pass filter enhances remotely sensed images by blocking
the high spatial frequency details utilizing different size mask of a particular brightness
value and outputs a new images with new brightness values. The size of the mask varies
but usually 3 x 3, 5 x 5, 7 x 7, or 9 x 9 window. A low pass images for each DEM were
produced using a 9 x 9 averaging function mask. Ultimately, the central pixel value
within the mask is replaced by an average of the surrounding nine pixels in the
convolution filter (Campbell 2002, Jensen 1996, 2004, Mather 1999, Moore and Waltz
1993, Sabins 1997).
1. This process is achieved using XPace Tool.
2. Using Average Filter to smooth the mask of the added pixel values for
GTOPO30 DEM due to their coarser resolution compared to the ASTER
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DEM resolution using Image Processing package and Average Filter (FAV)
task [XPace> Image Processing> FAV].
3. Run the application after entering the following data in the parameters
window:
FILE: (file name of the study area DEM)
DBIC: 1
DBOC: 1
FLSZ: 9, 9
MASK: 2
Where FILE is the input DEM file of, DBIC specifies the input channel to be
filtered, DBOC specifies the output channel for the filtered result, FLSZ is the filter size
in units of pixels and lines, in this case a filter size of 9 x 9 pixels/lines window is used,
and MASK indicates the area in the input channel which should be processed, in this case
use the bitmap channel (%%2) to only process pixels under it (Geomatica 2003).
-Step Eight: Loading final DEM
Remove all images from the maps tree menu then reload the raster channel (%1)
of each DEM in the study area. At this point, the DEMs should be completely fixed and
ready to be transferred as an ASCII file to be readable in ArcScene program within the
ArcGIS software for further analyses. The difference between the generated ASTER
30m-pixel resolution DEM(s) and the coarser pixels of the 1Km resolution mask derived
from GTOPO30 DEM could be notices as shown in figures 3.50 to 3.53. Four transfers
are needed for each repaired DEM where only channel (%1) of each fixed DEM being
transferred. This procedure is carried out in Focus window as follow: [File> Utility>
Translate> Browse: DEM File name> Destination> Type new DEM ASCII File Name>
GRD: Arc/Info Grid (ASCII) as the new file extension].
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Figure 3.50: The Dead Sea final DEM.
Figure 3.51: North Araba final DEM.
Figure 3.52: South Araba final DEM.
Figure 3.53: Aqaba final DEM.
3.6.1.5. Transferring ASTER DEM to GIS environment
GIS has the capability to integrate multiple map layers including DEMs and
satellite imagery and run several analyses to the spatial relationship within these maps
quickly and with no effort (Horsby and Harris 1992). In addition to that, viewing data in
three dimensions in GIS environment give new perspective about the derived DEM data,
adding insights that wouldn’t be readily visible from a planimetric view (Poli et al. 2005).
The three dimensional vector and raster data and DEMs generated in ArcGIS depicting
DEMs and digitized valleys and mountain fronts were imported to ArcScene for further
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analysis and visualization. Particularly, ArcScene application available with ArcGIS
(ESRI) 3D Analyst extension, allows creating three dimensional maps for visualization
and rendering with a static level of detail control for large datasets (Chrysoulakis et al.
2004, Poli et al. 2005). The incorporation of elevation and terrain data, generally,
improves the management and visualization of geographic data and can enhance
information extraction (Poli et al. 2005). Thus, the developed three dimensional DEM
views using ArcScene will represent the high quality of the generated DEMs and their
potential for more thorough image interpretation (Kamp et al. 2003).
Converting the DEM ASCII files to raster files in the form of ESRI GRID format
to be readable by ArcGIS software and its extensions is achieved using a specific
command line for this purpose. The Command Line window is located under the Window
tab in the ArcMap main menu (within the ArcGIS 9 software). The command line is
simply identifies the current ASCII file format location and convert it into a grid file
format to the same or another path creating a floating-point raster dataset. Floating-point
number (or real number) is a sort of numeric field used for measuring and for storing real
number that can contain a fractional part and a decimal point (e.g. 34.824, 0.0004, and -
9873.21). The decimal point can be in any position in that field, thus, there is no fixed
number of digits before and after the decimal point, that is, the decimal point can float
from one place to another for different valued stored in the filed (Mather 1999). To
perform conversion, insert the following case sensitive command line
ASCIIToRaster_Conversion
FLOAT. An example of the Dead Sea DEM
conversion using this command line would be ASCIIToRaster_Conversion
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C:\All_DEMs\aster_dem_D-S.ascC:\All_DEMs\aster_dem_D-S_newFLOAT. After this
conversion, the DEM data can be imported to GIS environment for any further analysis,
as illustrated in figures 3.54 and 3.55.
Figure 3.54: Portion of the North Araba converted GRID file format (*.GRD) to ASCII
format, notice the failure value (-9999) on the top and the elevation values on the rest of
the file.
Figure 3.55: The converted North Araba DEM using ArcGIS ASCII to Raster command
line.
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3.7. Obtaining Landsat 7 ETM+ imagery
The Landsat imagery could be obtained and downloaded free of charge from the
University of Maryland Institute for Advanced Computer Studies website provided by the
Global Land Cover Facility, Earth Science Data Interface available at
(http://glcf.umiacs.umd.edu/index.shtml). When clicking the “Download Data” button
located on the upper-right part of the screen, a new webpage that has all the available
satellite sensors scenes will appear. For the purpose of this research, Landsat 7 ETM+
sensor was chosen to download the imagery of the JDTZ study area in Jordan that is
available at (http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp).
Two Landsat scenes were downloaded that cover the study area located within the
JDTZ. Each image carries a unique identification number and the size of the image as
follow: The Dead Sea ETM+ image data is (p174r038_7x20020308.ETM-EarthSat-
Orthorectified) and the Wadi Araba ETM+ image data is (p174r039_7x20020308.ETM-
EarthSat-Orthorectified). The data acquisition date of both scenes is March 8, 2002 and
the data processing date is February 12, 2004. All Landsat data are in the form of
compressed files (*.gz file extension) that are downloadable from the FTP files site
available at (http://glcfapp.umiacs.umd.edu:8080/esdi/ftp?id=37264) where each Landsat
band could be individually downloaded from the FTP link denoted for each band. The
final Landsat imagery is a georectified TIFF images that are projected to the WGS-84
datum and ellipsoid and are compatible with all ESRI GIS products.
3.7.1. Creating ASTER and Landsat composite images
To view and analyze both ASTER and Landsat images in a GIS environment, the
images would be more informative and useful if they are in the form of a color composite
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image. The best combination of three bands of Landsat satellite imagery forming a false
color image of an arid to semiarid region would be 7, 4, and 2 as Red, Green, and Blue,
respectively (Arlegui and Soriano 1998). Toward the differentiation between the Landsat
and ASTER color composite images, the former sensor images were displayed as 7-4-2
composites while the latter as 3-2-1 true color composite images. Having two different
color combination of satellite images will allow flipping back and forth between the
images, which will assist in highlighting mountain fronts and valleys in the digitizing
process and accentuating the differences between the different color image combinations.
The procedures to create color composite images from Landsat and ASTER scenes area
as follow:
-Step One: Import images to the new composite image file
The creation of a composite image depends firstly on the input file initial format.
In case the input image in PIC format there is no need to perform importing, otherwise,
the common Landsat and ASTER images formats such as HDF and/or TIFF need to be
imported to PCIDSK format.
a) Importing the HDF or TIFF files to PIX files first using PCI Focus (File>
Utility> Importing to PCIDSK> Source File Name> Destination File Name> Import).
b) In XPace, use the PIX file that you created in the first step as an input to the
composite image that you need to produce. Make sure to import and transfer files in the
order desired to create the composite image (RGB). If an image composite of bands 1, 2,
and 3 needed, then, import band 3 (Red) first, then transfer bands 2 (Green) and 1 (Blue)
respectively. (File> File Utility> Importing to PCIDSK> type the Source File Name>
enter the Destination File Name (the name of the RGB composite final image)> Import).
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-Step Two: Transfer imported images
a) In XPace, transfer the other two layers (bands Green and Blue) to the imported
PIX file (the Red band already exists) in step 2. The process is carried out as follow:
(Tools> Transfer Layers> enter the Source File Name (band Red from step 2)> type in
the Destination File Name (band Green)> Select the new image> Add> Select the added
image> Transfer Layers).
b) Repeat the same procedures as in the previous step (2/a) for the last layer (band
Blue).
-Step Three: Create the final composite image
a) In Focus, open the created composite RGB image. Right click on the image>
Enhance> Edit LUTs> Click on the Red band histogram once> Save> Save image
w/LTU> Overwrite existing channel> OK). Repeat the same procedures for the other
Green and Blue band histograms.
Lookup tables (or LUTs) consist of an array of the original input pixels and the
corresponding array of enhanced output pixels values that are used to produce the new
composite image. It contains the exact disposition of each combination of red, green, and
blue (RGB) values associated with each 8-bit pixel. Normally, the color of each pixel in
the image is determined by matching it to a set of colors stored in tables that show their
numerical values. Usually there are three LUTs for each of the composite color that is
presented in the form of a graphic display or a histogram (Campbell 2002, Jensen 1996,
2004, 2004, Mather 1999, Sabins 1997). Whereas, histogram is known as a statistical
graph represents the distribution of data points and content of a remotely sensed image as
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a function of some attribute, such as brightness values (Campbell 2002, Jensen 1996,
2004, 2004, Mather 1999, Sabins 1997).
-Step Four: Transfer the final composite image to new format
a) In Focus, transfer the created composite image to TIFF or JPG using (File>
Utility> Translate> Source File Name> Destination File Name> Set output format as
TIF/TIFF 6.0> Select all source layers> Add> Select all destination layers> Export). By
the end of this process the final satellite images are ready to be imported to ArcGIS
software and viewed as raster layer with the same projection as of the vector data in the
map project.
3.8. Obtaining vector data
The vector data were obtained from the Digital Chart of the World (DCW). The
scale of the thematic dataset or layers is 1:1,000,000 (or 1cm: 10km), which was
produced in 1993 (Campbell 2002). All layers are compatible with any geographic
information system (GIS) software in the market. The downloaded coverages data are in
interchange file format (*.E00 file extension) originally designed for ArcInfo that is also
readable by ArcView and ArcMap. Interchange files could be converted into coverage
file and in turn into Shapefiles using ArcToolbox, and then by using the projection tool
we can define the coordinate system (e.g. WGS-84) to the Shapefiles. The original DCW
data is projected to geographic longitude/latitude and to decimal degrees and the entire
database is provided by Penn State University Libraries webpage available at
(http://www.maproom.psu.edu/dcw/).
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3.8.1. Converting Interchange files to Shapefiles in ArcToolbox
In order to import the vector data into any GIS environment, the data should be
readable by such software. The downloaded interchange files from the DCW much be
converted into a compatible file format, namely shpafiles, to be loaded into the GIS
software. The steps of converting interchange files to shapefiles are listed below.
-Step One: Importing interchange files (*.00E)
-Under Import to Coverage use the Import from interchange file tool (figure 3.56).
-Browse to input the needed interchange file (in this case road layer was selected)
and assign the output file name and path, then click OK to execute file conversion.
-The result of this conversion is a coverage file of the actual interchange file.
Coverage is a vector-based data storage file that usually represents a single theme and
used to store the location, shape, and attributes of geographic features. It is one of the
main and native vector data formats for ArcGIS that combines spatial data and attribute
data and stores topological relationship among features. Normally, coverage file saves
spatial data in binary files and attribute and topological data is kept in tables. The related
feature attribute tables normally describe and store attributes of the geographic features
(Zeiler 1999).
Figure 3.56: Import from Interchange file window in ArcToolbox.
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-Step Two: Convert Coverage to Shapefile (*.shp)
-Under Export from Coverage use the Coverage to Shapefile tool (figures 3.57
and 3.58).
-Navigate to the roads coverage file created in the previous step to serve as the
input file. Set the feature type to line. Then give the shapefile output file name and path,
and then click OK to perform file conversion using default setting.
-The output file is a shapefile that represents the roads of Jordan.
Figure 3.57: The Coverage to Shapefile tool in ArcToolbox.
Figure 3.58: The Coverage to Shapefile tool window in ArcToolbox.
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-Step Three: Defining Shapefile projection
-Under Projections tool use the Define Projection Wizard (shapefile, geodatabase)
(figure 3.59).
-Browse to the input file (i.e. roads shapefile), highlight the file in the data
window and then click next (figure 3.60).
-Select the coordinate system that suites the study area to be assigned to the input
data then click OK to complete. The coordinate system was set to the spheroid-based
Clarke Geographic Coordinate Systems of 1866 (i.e. spatial reference). This is the default
projection of the downloaded interchange files which is the only projection that is wellsuited
the UTM Projected Coordinate Systems of WGS-84, zone-36 North. The finale
shapefile is ready to be imported into ArcGIS to overlay other layers and images or for
any further analyses.
Figure 3.59: The Define Projection Wizard in ArcToolbox.
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Figure 3.60: The Define Projection Wizard window under Projections in ArcToolbox.
3.8.2. Digitizing vector data
Obviously, as a result of the scale difference between the vector data obtained
form the DCW (scale 1:1,000,000) and the satellite imagery (scale 1:50,000), some
editing to the vector data was necessary to match significant features on the satellite
imagery. Toward this, the shapefiles of Jordan western borders and the Dead Sea
shorelines boundaries (figure 3.61) were modified, as close as possible, to fit the similar
features on ASTER imagery using the Editor tool that exists in ArcMap software.
On the other hand, the capitals and major cities in all of the produced maps were
manually digitized as points. Capital and major cities of the surrounding countries to
Jordan were digitized utilizing the seismic map of the Middle East (Chapter 1, figure
1.22), while the major Jordanian cities were manually digitized using the geologic maps
of Jordan. The digitized points were first created in ArcCatalog as point shapefile and
then projected to the UTM zone-36N and to the WGS-84 ellipsoid. Using the Editor tool
available within the ArcMap software, the capitals and cities were digitized over each
point on the designated map that indicate each location.
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Figure 3.61: Map shows the Dead Sea shoreline (left) and Jordan borders (right) before
(red line) and after (gray line) editing.
3.8.3. Digitizing mountain fronts and measuring sinuosity
Due to the relatively large imagery sizes and the presence of various mountain
fronts and valleys within the study area dictate large processing time, digitizing, and
computations will be performed on a scene-by-scene basis (Lang and Welch 1999). To
increase the possibility of recognizing mountain fronts and valley profiles within the
study area, shaded relieves (Hillshades) of each of the ASTER DEM scenes were created.
Towards this, exchanging the view back and forth between the DEMs and shaded relieves
took place during digitizing mountain fronts and valley profiles process. Shaded relieves
were generated using the 3D Analyst tool in ArcMap based on the elevation data
available in each ASTER DEM scene. In 3D Analyst, pick the raster layer needed to be
generated as shaded relief then click on Surface Analysis and choose Hillshade. Browse
to the destination DEM in the Hillshade window and use default values for both azimuth
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and altitude and 30m for the output cell size. Select a destination to save the Output
shaded relief raster then generate by clicking the OK button. Repeat the same process for
all ASTER DEM scenes, as shown in figure 3.62, an example from the Gulf of Aqaba
area.
Figure 3.62: The Gulf of Aqaba generated DEM (left) and shaded relief (right).
Prior to digitization process it is necessary to create two new polylines (i.e. lines)
shapefiles for each ASTER scenes area using ArcCatalog. One of these lines represents
the vector layer of the mountain fronts and the other serves as the straight front lengths of
each mountain front. The digitization of mountain fronts sinuosity and their
correspondent total straight lengths were based entirely on the criterion mentioned in
section (3.2.1). Using the digitization tool, each mountain front was created
independently, in some cases, pixel-b-pixel to get as close as possible to the mountain
front in each scene of the imagery, until finishing all available fronts in each scene.
Figures 3.63 to 3.74 illustrates the ASTER color composites (3-2-1 as RGB), derived
ASTER DEMs, generates shaded relieves, digitized mountain fronts and valley profiles
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of each individual ASTER scene in the JDTZ. The overall number of mountain fronts in
the study area are listed in table 3.5 and illustrated in figure 3.75.
Geomorphic form Dead Sea North Araba South Araba Aqaba
Fronts 1 1 4 9
Table 3.5: Overall number of digitized fronts in the JDTZ.
Figure 3.63: Dead Sea ASTER color composite 3-2-1.
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Figure 3.64: Dead Sea ASTER-derived DEM.
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Figure 3.65: Dead Sea shaded relief.
142

Figure 3.66: North Araba ASTER color composite 3-2-1.
143

Figure 3.67: North Araba ASTER-derived DEM.
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Figure 3.68: North Araba shaded relief.
145

Figure 3.69: South Araba ASTER color composite 3-2-1.
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Figure 3.70: South Araba ASTER-derived DEM (portion of the North Araba and Aqaba
DEMs are shown to the north and south, respectively, to cover the fronts and valleys
extension).
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Figure 3.71: North Araba shaded relief (portion of the North Araba and Aqaba shaded
relieves are shown to the north and south, respectively, to cover the fronts and valleys
extension).
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Figure 3.72: Aqaba ASTER color composite 3-2-1.
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Figure 3.73: Aqaba ASTER-derived DEM.
150

Figure 3.74: Aqaba shaded relief.
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Figure 3.75: An overall map of the mountain fronts and valley profiles within the JDTZ.
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The achieved calculations of both values were computed in meters without human
intervention using XTools Pro. This appropriate tool provides an accurate length and
elevation measurements (in addition to many other features) and delivers the results in
the form of columns added directly to the specific shapefile attribute table. The XTools
Pro is a freeware ArcMap add-on provided by Data East GIS software development that
functions as any other ArcMap extension and is available to download at
(http://www.xtoolspro.com/). To calculate all lengths using this tool, the XTools Pro
main drop-down menu use the Table Operations, then Calculate Area, Perimeter, Length,
Acres and Hectares. After that a new window appears asking to enter the shapefile need
to be calculated and the desired outcome units. This process creates new columns in the
selected shapefile’s attribute table demonstrating the exact calculated lengths of both
sinuosity and straight length for each mountain front. Finally, calculating the S mf values
for all mountains is achieved by feeding all the data into a Microsoft Excel spreadsheet
and program the mountain front sinuosity formula to compute the final values.
3.8.4. Digitizing valley profiles and measuring elevations and valleys’ widths
After valleys were being identified in the study area, the digitizing process or their
profiles took place. Quite similar to mountain fronts digitization procedure, the digitizing
tool was used to create a straight two dimensional line -that will be turned into a three
dimensional profile in subsequent steps- covering the valley and the mountain peaks on
both sides. Based on the criteria of choosing valley profiles mentioned in section (3.2.2),
all valleys were digitized according to their visual identification that was entirely
restricted to the generated DEMs resolution. The number of the digitized valleys and the
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minimum and maximum valley profiles distances upstream from associated mountain
fronts are listed in table 3.6.
Distance Dead Sea North Araba South Araba Aqaba
Valleys # 22 15 25 36
Minimum 150m 220m 215m 45m
Maximum 2500m 2500m 1100m 1100m
Table 3.6: Digitized valley profiles number and their distances upstream from mountain
fronts in the JDTZ.
When creating valley profile shapefiles in ArcCatalog, it is important to check the
Coordinates will contain Z values box to enable the shapefiles to store three dimensional
data. The three dimensional data generally has an extra z-value for each x,y coordinate
that is typically used to store the vertical height of the coordinate. The z-values can be
viewed and modeled using the ArcGIS 3D analyst extension, in addition the converted
valley profile three dimensional shapefiles would be rendered using ArcScene program to
measure the main three elevations of each valley (i.e. E ld , E rd , and E sc ). The digitization of
valley profiles will serve the purpose of calculating the four unknown elevations of E ld ,
E rd , E sc and V fw values in order to calculate the V f values of each individual valley.
Towards this, the valley profiles will be first converted into a two dimensional cross
section profile to calculate the V fw values. Then, all the valley profiles will be converted
into three dimensional profiles and viewed in ArcScene to measure the elevation values
of each valley individually. Since all the valleys in the study area run approximately in
the east-west direction, as a result, the valley profiles are all oriented in the north-south
direction. Therefore, during the calculation of the V f values, the north end of each valley
profile will represent the east elevation of the valley divide (E ld ), while the south end will
represent the right valley elevation divide (E rd ).
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To calculate the V fw values of each individual digitized valley, the “Create Profile
Graph” located in the ArcGIS 3D Analyst extension is being employed to create a profile.
This graph-like profile will have x and y that symbolize terrain distance and elevation,
respectively. Setting the DEM of the related area to the valley profiles being graphed is a
crucial step to acquire the exact ground measurements. Simply, clicking on the Create
Profile Graph button enables drawing a straight line over each already digitized valley
shapefile. After completing drawing all valley profiles in a specific ASTER DEM
imagery, the drawn profiles could be viewed as graphs by double click on each individual
profile. As illustrated in figure 3.76, the valley floor width can be calculated by
subtracting the values of the beginning and ending of the valley floor using the reference
distance on the x-axes.
Figure 3.76: Calculating the V f value for valley profile #11 in the Dead Sea area (green
and red lines manually added for illustration purposes).
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To collect the right and left divide and floor (i.e. E ld , E rd , and E sc ) elevation values
of each individual digitized valley, the 3D Analyst “Convert Features to 3D” tool being
utilized. This tool and the conversion procedure allow deriving existing features height
from a surface. For the purpose of this research, the study area generated ASTER DEMs
are being used as the surface to obtain elevation data from. The Convert Features to 3D
process allows the manually digitized polyline, which represent each valley profiles, to
mimic the topography of the valley using the elevation data of the DEM to create a 3D
profile of the actual landscape, as illustrated in figure 3.77.
Figure 3.77: The difference between the (a) 2D image (straight-line) and (b) the actual
3D topography (dotted-line) of a given surface (Modified from ArcGIS Desktop Help,
Linear Interpolation).
The process of conversion two dimensional (2D) shapefile to three dimensional (3D)
features in 3D Analyst is as follow:
1. Add the shapefile of each individual valley profile, which is in the form of a
two dimensional feature and the elevation reference surface to an existing map
in ArcGIS.
2. Click 3D Analyst and choose Convert, and then click Features to 3D.
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3. Locate the valley profile shapefiles that need to be converted into 3D in the
Input Features menu.
4. Click the Raster or TIN Surface button and set the source to the Valley
profiles related DEM (i.e. raster data) to gather the features heights.
5. Type the name of the output 3D shapefile and allocate a destination.
6. Finally, Click the OK to execute.
Following the conversion all valley profiles in to 3D features in the study area, the
three dimensional shapefiles need to be converted into points. This important additional
step is performed using the XTools Pro in order to assign each point in the 3D shapefile
to its matching elevation value on the ground. The way this method function is by
collecting the elevation value from the center of each pixel in the DEM where the
polyline passed through (figure 3.78). From the XTools Pro main menu, simply choose
“Feature Conversions”, and then Convert Features to Points. This tool is provides ArcGIS
users with capabilities to convert polygons and polylines to point features with
customizable options. The conversion process is explained below and illustrated in
figure 3.79.
Figure 3.78: Collecting elevations from DEM as shapefile passes raster (a) horizontally,
(b) diagonally, and (c) vertically.
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1. In ArcGIS, add all valley profiles of a single ASTER DEM scene at the time
into the map.
2. Select the "Convert Features to Points" item from the XTools Pro Feature
Conversions menu.
3. From the drop-down list select the “Input feature layer” and select the valley
profiles shapefiles (i.e. polylines) for one of the four DEMs in the study area
to be converted to points.
4. Assign a destination and name to the output layer file to be saved in a
shapefile format, then press the OK button.
Figure 3.79: Converting Aqaba 3D feature to points using XTools Pro.
After performing all point conversions, the XTools Pro “Table Operations” is
being used to translate the created data points of each individual three dimensional
shapefiles into readable tables. From the XTools Pro main menu, choose the Table
Operations option, and then click the “ADD X, Y, Z Coordinates”. After that, a new
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window will appear asking to enter one of the already converted 3D-shapefiles into
points to create provide their X, Y, and Z values. The elevation values are created in the
form of additional three data fields automatically added to the correlated shapefile
attribute tables.
The developed 3D valley profiles exhibit the highest quality of the generated
DEMs in identifying the geomorphologic forms and providing extra details of the exact
points of collecting essential elevation data. Hence, they are best viewed using ArcScene
to extract the elevation values of E ld , E rd , and E sc for each valley in the study area (3.80).
After importing all valley profile 3D shpafiles and DEMs of the study area into
ArcScene, collecting the elevation data (i.e. z-values only) of E ld , E rd , and E sc of each
profile is performed using the information button located in the main toolbar, as
illustrated in figure 3.81. After obtaining the elevation data from the entire valley profiles
in all four DEMs within the study area, this information combined with the already
calculated data of the V fw values of all valleys from the previous step are transferred into
Microsoft Excel spreadsheet. Utilizing the function tool in Excel, the V f equation is being
programmed to perform an automated calculation of the V f values for the entire valley
profiles in the study area.
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Figure 3.80: General view from the Dead Sea area (Looking east, hillshade shows 2xelevation
exaggeration, map not to scale).
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Figure 3.81: Collecting elevation data from profile #11 in the Dead Sea area (Looking
east, DEM shows 2x-elevation exaggeration, map not to scale).
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4. Chapter Four: Results and Discussion
4.1. Introduction
The geomorphic analysis of landscape forms exhibits a great potential for
assessing the tectonic activity of the JDTZ. Theoretically, the morphometric analysis of
mountain front sinuosity (S mf ) and valley floor width to valley height ratio (V f ) indicate
the tectonic activity of the mountain front and its adjacent province. Generally, in arid to
semiarid regions, lower values are associated with V-shaped valleys and reflect high
tectonic activity while higher values are linked to U-shaped valleys indicating a moderate
to low tectonic activity (figure 4.1).
Figure 4.1: Valley profile display of a V-shaped valley in South Araba (left) and a U-
shaped valley in the Dead Sea area (right).
In this chapter the results of the geomorphic indices derived from each ASTER
scene in the JDTZ will be individually discussed and the tectonic activity categories of
the study area presented and discussed. The tectonic activity classes of all mountain
fronts and valley profiles were initially designated based on the tectonic activity ranges
established by Bull and McFadden (1977). Such categorization was adopted because it
has proven to be practical in various regions of different geologic settings. Finally, the
accuracy of the ASTER-derived DEM will be tested based on the elevation accuracy of
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the ASTER satellite data provided in the literature in order to examine their reliability
and effect on the final S mf and V f results. Therefore, the results of these indices will be
examined and compared with the valley profile shapes to generate appropriate tectonic
activity classes of the analyzed landforms in the study area.
4.2. The tectonic morphometric analysis
In previous research (chapter two, section 2.3), none of these studies specified a
precise method of differentiating tectonic activity classes. In contrast, the study
conducted by Bull and McFadden (1977) illustrates a significant overlapping in all of the
three tectonic classes. This research aims to generate discrete ranges of the tectonic
activity classes of both S mf and V f results. Furthermore, the three tectonic activity classes
of the valley profiles were determined entirely according to the assigned tectonic activity
class based totally on the S mf values of the mountain front that these valleys are
associated with. Therefore, in this research the definition of each tectonic class will not
only rely on the S mf calculated values of individual mountain fronts but also will be
derived from the individual valley profile V f results.
Table 4.1 lists concise results of both mountain front sinuosity (S mf ) and the
valley floor width/height ratio (V f ) computations. The tectonic activity classes of the V f
index results are assigned based on the V f mean values as presented in previous studies
(Bull and McFadden 1977, Rockwell et al. 1984, Silva et al. 2003, Wells et al. 1988).
Despite the length of the mountain front of the Dead Sea area that is connected to the
North Araba mountain front, it is still fits the criterion of being a continuous mountain
front and will be considered as one entity referred to as the Dead Sea and North Araba
mountain front. In this regard, both mountain fronts were initially measured separately to
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check their individual S mf and V f values then were measured as a single mountain front in
the subsequent morphometric analysis computations. Detailed results of all S mf and V f
calculations and description of individual valleys shapes are presented in appendix A, and
illustrated in figure 4.2.
Fronts/Valleys Location
Front#
Length
(Km)
S mf
S mf
Class
# of Valley
Profiles
V f
Mean
Dead Sea 1 67.70 1.07 1 22 0.54 1
North Araba 1 64.63 1.21 1 15 0.50 1
Dead Sea & North Araba 1 132.32 1.14 1 37 0.53 1
South Araba
Aqaba
V f
Mean
Class
1 35.94 1.16 1 6 0.56 1
2 3.62 1.21 1 2 0.75 1 - 2
25
3 32.14 1.28 1 15 0.35 1
4 15.26 1.17 1
2 0.47 1
1 10.52 1.12 1 2 0.16 1
2 14.60 1.22 1 3 0.32 1
3 12.03 1.21 1 2 0.43 1
4 19.98 1.71 2 4 0.67 1 - 2
5 8.07 1.17 1 36 1 0.55 1 - 2
6 10.12 1.07 1 2 0.18 1
7 10.62 1.22 1 4 0.14 1
8 21.31 1.25 1 10 0.26 1
9 22.30 1.13 1
8 0.21 1
Table 4.1: Brief results of all mountain fronts sinuosity (S mf ) and valleys floor width to
valleys height ratio (V f ) analyses.
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Figure 4.2: The tectonic activity classes of the S mf and V f indices results.
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4.2.1. Mountain front sinuosity results
The mountain front sinuosity analysis shows the lowest results as 1.07 of two
mountain fronts in both the Dead Sea and Aqaba regions and the highest S mf result as
1.71 in the Aqaba region. The results of the S mf analysis of all mountain fronts within the
JDTZ study area indicate that the majority of mountain fronts are highly tectonically
active representing the tectonic activity class 1. On the other hand, only one mountain
front (front 4) in the Aqaba region yield S mf result of 1.71 which symbolizes the moderate
to low tectonic activity class 2. This particular circumstance might have occurred as a
result of the following two principal reasons. (1) The ASTER satellite imagery and the
generated DEM of the Aqaba region did not extend to cover the total length of mountain
front number 4. Therefore, the analysis of the partial length of the mountain front
sinuosity could not provide the actual S mf value precisely. (2) The mountain front has
experienced modification to its shape due to the prolonged erosion caused by the seasonal
flash floods often occurred in this area causing it to be more sinuous (USGS 1998), where
the latter explanation is interpreted to be the main cause.
4.2.2. The ratio of valley floor width to valley height results
The ratio of the valley floor width to valley height analysis reveals the lowest
value as 0.09 in the Aqaba region while the highest V f result as 1.24 in the South Araba
region. Theoretically, the U-shaped valleys indicate relatively low tectonic activity, while
the V-shaped valleys, as a response to uplift, are associated with high tectonic activity
(Bull 1977b, 1978, Bull and McFadden 1977, Burbank and Anderson 2001, Keller and
Pinter 2002). To examine the relationship between the valley profile shapes and tectonic
166

activity, the percentage of the U- and V-shaped valley profiles were calculated and
summarized in table 4.2.
Valley shape/region Northern region Southern region
U-shaped valley 9/37 = 24.4% 26/61 = 42.6%
V-shaped valley 28/37 = 75.6% 35/61 = 57.4%
Table 4.2: The percentage of the U- to V-shaped valley profiles in the JDTZ.
The valley shape percentage calculations were carried out based on dividing the
JDTZ study area into two regions the southern (South Araba and Aqaba) and northern
(Dead Sea and North Araba) regions with a total of 61 and 37 valleys, respectively. The
separation of the study area into two regions had been implemented due to the
continuation and overlapping of a number of fronts into other adjoining areas (e.g. Dead
Sea into North Araba and South Araba into Aqaba). This method of dividing the study
area into two regions based on the spatial distribution of the mountain fronts and their
initial morphometric analysis was adopt by Rockwell et al. (1984) and Wells et al. (1988)
and found to be valuable for the final evaluation of the tectonic activity classes of the
mountain fronts in these regions. Evidently, the V-shaped valleys in both regions are
abundant, however, the U-shaped valleys in the southern region are more frequent
(42.6%) showing a higher percentages (almost twice the value) in comparison to the
northern region (24.4%) signifying a moderate to less tectonic activity in that particular
region.
Practically, it would be quite difficult to distinguish which area is more
tectonically active than the other since the majority of S mf and V f value results represent
the tectonic activity class 1. Nevertheless, according to the morphometric analysis results
the southern region is being slightly less tectonically active than the northern region.
167

Furthermore, the valley profile shapes in the southern region demonstrate a higher
number of U-shaped compared to V-shaped profiles, which may also be partly
attributable to the presence of the largely sandstone and less granite mountains in the
southern area compared to the predominantly limestone in the Dead Sea and northern
Araba areas (Bender 1974a, 1974b, 1975, 1982, Burdon 1959).
Another factor that might have a minimal impact on the alteration of the valley
shapes is the seasonal flash floods in southern Jordan. As a matter of fact, continuing
flash floods in the dry to semi-arid climates, similar to Jordan, drive the primary incisions
incidents that result in extensive erosion in mountain fronts and valleys based on the
difference in their rock resistance (de Jaeger and de Dapper 2002). Practically, in the
absence of tectonic activity, the weathering and erosion processes have a significant
impact on all rocks regardless of their type and resistance. Thus, not only the tectonic
activity of the region has contributed to the alteration of valley shapes which in turn have
a direct effect of the V f values, but the geology of the area has also played an additional
role due to the presence of relatively soft rock type mountains.
4.3. Seismic activity at mountain fronts
Although, the combined data from all tectonic morphometric index analyses
highlight significant variations in relative and numerical values of the JDTZ tectonic
activity; it is very substantial to integrate current field and historical seismological data in
order to examine the position of recorded earthquakes relative to mountain fronts, and
variations in rocks and weathering processes that have their input to the variations of the
overall calculations (Wells et al. 1988).
168

According to Allen (1975), major earthquakes with high magnitudes have
occurred along faults that are correlated to active mountain fronts. The major earthquake
events in the study area have taken place within or in close proximity to the active
seismic zones that are characterized by several active faults. These events were primarily
distributed along and at the intersections of the major active faults in the JDTZ that
comprises the Dead Sea area in the north, the Aqaba area in the south, and Wadi Araba
region. To better illustrate the distribution of earthquake events in the JDTZ, a tectonic
map has been generated utilizing all available historic and recent earthquake events
recorded in Jordan demonstrating the earthquakes network and mountain fronts
relationship, as shown in figure 4.3.
It is obvious that the recorded earthquake events occurred in the northern and
southern regions of the JDTZ are concentrated within the active tectonic zones. In
addition, the tectonic events occurred close to the examined mountain fronts, which are
also adjacent to the active Dead Sea-Jordan River (zone 1) and the northern part of the
Wadi Araba fault lines (zone 3) indicate that this region has experienced larger
magnitude tectonic activity. On the contrary, frequent small magnitude earthquakes
occurred in the southern region nearby the examined mountain fronts in the southern part
of the Wadi Araba fault line (zone 3), whereas the large number of events occurred
further south in the vicinity of the main fault line of the northern Red Sea and the Gulf of
Aqaba (zone 5), as illustrated in figures 4.3 and 4.4.
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Figure 4.3: Network of earthquakes-mountain fronts’ relationship on the JDTZ showing
earthquake events of M L 4 to 6 and tectonic zones, 19A.D. to August 1983.
170

Figure 4.4: Network of earthquakes-mountain fronts’ relationship on the JDTZ showing
earthquake events of M L 4 to 6 and tectonic zones, September 1983 to 2005.
171

4.4. The accuracy of ASTER DEM data
Elevation is one of the most important datasets in many natural resource and
geomorphological spatial databases that is often used for DEM construction. The
accuracy of these DEMs and their derived products are of critical significance because
errors in the base data will propagate through morphometric and spatial analyses. This is
principally correct where elevation is derived from DEMs and used with other spatial
data. Therefore, errors in elevation will often cause errors in the generated model outputs
(Bolstad and Stowe 1994, Kamp et al. 2003, 2005).
ASTER sensors provide images with a scale of 1:50,000 (Hirano et al. 2003, Lang
and Welch 1999, Lang et al. 1996, Poli et al. 2005, Toutin and Cheng 2001). Moreover,
the along-track stereo data acquisition eliminates the radiometric variations caused by
multi-date stereo data acquisition, which compensate for the weaker stereo geometry and
ultimately improves the image matching performance. It is possible to produce stereo-
DEMs with excellent accuracy when using ground control points (GCPs) measure with
high precision methods. The GCPs are features that could be clearly identified in the
satellite image which have a known ground coordinate. The relationship between the raw
satellite image and GCPs are determined by associating the pixels in the image to the x,
y, and z coordinated system on the ground (figure 4.5). The main source for the
horizontal and vertical values of such check points come from variety of sources such as
GPS, ground surveys, existing topographic maps, and geocoded images (Chrysoulakis et
al. 2004, Cuartero et al. 2004, Fujisada et al. 2001, Poli et al. 2005, Toutin and Cheng
2001). This is widely used in cloud-free images in arid to semi-arid regions where the
elevation accuracy could reach up to 10m (Fujisada et al. 2001, Toutin and Cheng 2001).
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Figure 4.5: The relationship between the ground and the image coordinate systems
(Geomatica 2003, figure 5.1, p. 33).
Nevertheless, a significant feature of the ASTER stereo system concept is to
generate high quality DEM data without referring to GCPs for individual scenes
depending on the spacecraft ephemeris and the instrument parameters (Fujisada et al.
2001, Kamp et al. 2003, 2005). Therefore, relative DEM is defined as elevation data
generated by not using GCPs, and derived from ASTER stereo pair image using stereo
matching method. On the other hand, absolute DEM requires referencing to a map
coordinate system using GCPs of know locations on the ground to enhance the elevation
data generated from ASTER stereo pair image (Lang and Welch 1999, Tokunaga et al.
1996). Normally, ASTER DEMs with relative accuracy can be used productively to assist
mapping, geomorphic, geologic, tectonic, landform, and a range of environmental studies
in remote areas of rugged terrain (Hirano et al. 2003, Lang and Welch 1999).
ASTER is a relatively recent sensor and there is little research focusing it its DEM
accuracy. In fact, most researches that analyses the accuracy of DEM generated were
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performed mainly on simulated ASTER data (Abrams and Hook 1995, Lang and Welch
1999, Welch et al. 1998, Cuartero et al. 2004). However, Lang and Welch (1999) suggest
that the root mean square error (RMSE) for elevation values in ASTER DEM should be
in the range of ±10m to ±50m, which is a broad range to define the accuracy of a product,
other researches suggest a DEM elevation accuracy of less than one pixel size (15m) and
up to ±7m (Cuartero et al. 2004, Hirano et al. 2003), where normally the RMSE values
are on the order of ±7m to ±30m (Kamp et al. 2005). Commonly, a DEM created from
ASTER imagery can be expected to have a vertical accuracy of about ±25m. However, in
areas with less vegetation or man-made features, the accuracy can rise to approximately
±10m. Such DEMs are generated with scales of 1:50,000 to 1:100,000 that are useful for
small to medium scale mapping applications and the interpretation of macro- and
mesorelief in areas where high-accuracy commercial DEMs data products are not
available (Kamp et al. 2003, Selby 2003).
ASTER DEMs which were created using GCPs typically produce absolute
elevations (Chrysoulakis et al. 2004) and fully scaled and precision located DEMs and
orthoimages (Selby 2003). In general, the elevation error when using high accuracy GCPs
is less than 15m (Fujisada et al. 2001). Assuming the parallax difference in the range of
0.5 to 1.0 pixels (i.e. 7-15m), the root mean square error (RMSE) of the elevation error is
expected to be in the ±12m to ±26m range. The planimetric and elevation accuracy of the
ASTER produced DEM were found to be ±15m and ±12.4m, respectively, and
considered quite satisfactory for large study areas (Chrysoulakis et al. 2004). According
to previous studies on ASTER derived DEMs, the vertical accuracy is approximately
between ±7 to ±15m and horizontal accuracy of about one pixel with a mean values
174

smaller than one pixel, while the RMSE and standard deviation are slightly larger than
one pixel size (Cuartero et al. 2004, Hirano et al. 2003, Poli et al. 2005).
Based on the study conducted by Hirano et al. (2003), the evaluations of ASTER
DEMs vertical accuracy created by automatic stereo correlation method using PCI
OrthoEngine indicate that an approximate RMSE for z-values of ±7m to ±15m and up to
±10m, where occasionally ±8.6m can be expected when using good quality and adequate
tie pints or GCPs (Hirano et al. 2003).
4.5. ASTER DEM error test
The extraction of topographic information from generated DEMs is becoming a
common method in geomorphic analysis and surface modeling processes. Therefore, it is
essential to generate a DEM with great precision that is able to represent the terrain as
accurately as possible, which eventually will determine the reliability of the
morphometric analysis results (Kamp et al. 2003).
In the case of DEMs, the errors are of attributive type implying an incorrect
assignment of altitude and they modify the z-values. These errors commonly appear in
the creation process of DEMs, both by automatic and manual procedures. The automatic
errors are generated mainly by automatic stereo correlation methods that may have
operative problems as a result of low contrasts in images, ambiguities due to the
repetition of objects of periodic patterns (Felicísimo 1994). Previous researches (Cuartero
et al. 2004, Hirano et al. 2003, Poli et al. 2005) have pointed out that the vertical accuracy
(i.e. elevation) of the ASTER generated DEMs in areas of less vegetation could be on the
order of ±7m to ±30m and can roughly reach ±10m. As an example, if the elevation in a
given location is 1000m in the generated ASTER DEM, this means that the elevation
175

could either be 990m or 1010m. Therefore, the ASTER derived DEMs will definitely
have a direct impact on the overall results of the tectonic geomorphic analysis of the
mountain fronts and valleys used in the research.
In most articles, critical analyses on the error sources using GIS application are
noticeably absent, and that derived products are presented without any estimate of their
accuracy (Felicísimo 1994). Although, testing the accuracy of ASTER elevations is
beyond the scope of this research; using variance-covariance error propagation to
calculate uncertainties (or errors) and the variance values will shed the light on the
accuracy of the calculated tectonic geomorphic indices results in terms of vertical
elevation accuracy and suitability to the actual study area elevation. This means,
evaluating the generated DEMs quality and measuring their reliability in providing the
needed results to serve the purpose of the research.
Error propagation techniques are used to evaluate the resulting errors (e.g.
standard deviation) in quantities that are calculated from a set of measurements (Mikhail
and Gracie 1977, Taylor 1997). This method of computing the magnitudes of errors in
measurements are based upon the assumption that the measurement errors are already
known and given. Practically, if the errors are know they could be simply eliminated by
applying accurate corrections leaving nothing to propagate. Alternatively, if random
errors were considered, even though the specific values of the random errors are not
know, studying the effect of their effect is still possible. In this case, the probability
distributions of the errors are used instead of dealing with the actual error values, or,
equally, working with the probability distributions of the corresponding measurements
and calculated quantities. Therefore, if the set of measurements are represented by the
176

andom factor x, and the calculated quantities are represented by the random vector y
such that y = f ( x)
, then the propagation involves finding the combined probability
distribution of y by specifying the joint probability distribution of x. This can be
accomplished by limiting the consideration to a linear function of x or to linearized forms
of nonlinear functions of x simply by employing the propagation of variances and
covariances (Mikhail and Gracie 1977).
Since variances and covariances are expectations, therefore calculating the
variance-covariance propagation is best accomplished using the approach of defining
variances and covariances in terms of sums calculated from samples of infinite size, in
our case of only four variables (Mikhail and Gracie 1977). Consider a dependent variable
(i.e. random variable) y as a linear combination of an independent variable x that consists
of four variables namely V fw , E ld , E rd , and E sc representing the acquired vertical
(elevations) and horizontal (valley floor width) measurements of the V f index equation
expressed in a linear regression equation such that:
y = Ax + b
In which A is a 4 x 4 coefficient matrix, and the b is the regression coefficient constant
(Mayer 1990, Mikhail and Gracie 1977). Simply, the y value equals the sum of the
constant b that represents the point, at which the line intercepts the y-axis, plus the
product of the slope x times the A value. Similarly, the line’s y intercepts b demonstrates
the y value when A = 0. The line’s slope x shows the amount of change in y units for oneunit
change in A (Knoke et al. 2002, Pipes and Harvill 1970). So, the previous equation is
written more formally given a functional relationship between several measured variables
as:
177

y = Vfwx + Eld x + Erd x + Escx + b
If the deviation of the calculated values of y from its mean value µ y is:
∆ y = y − µ y
It can be show that:
∆ y = V
fw∆ x + Eld ∆ x + Erd∆ x + Esc∆
x
Defining the variances and covariances of y in terms of the existing four random
variables q representing V fw , E ld , E rd , E sc by applying the ith sample component such as:
σ
1
q
2 2
y
= lim ∑ ∆yi
q→∞
q i=
1
The variation in dV f as a function of the uncertainties in V fw , E ld , E rd , and E sc can
be derived by taking the partial derivative of the V f index equation (Pipes and Harvill
1970, Ruffhead 1998, Skoog and Leary 1992, Taylor 1997). Partial derivative of a
function of multiple variables involves the derivative with respect to one of those
variables with the others held constant, that is:
2 Vfw
V
f
=
[(E - E ) + (E - E )]
ld sc rd sc
⎡ 2 ⎤ ⎡ ∂V
fw ⎤ ⎡ ∂V
fw ⎤
dV
f
= ⎢ ⎥ dV
fw
+ ⎢ ⎥ dEld + ⎢ ⎥ dErd
+
⎣(E ld
- E
sc
) + (E
rd
- E
sc
) ⎦ ⎣( ∂Eld
) ⎦ ⎣( ∂Erd
) ⎦
⎡ ∂V
fw ⎤
⎢ ⎥ dE
⎣( ∂Esc
) ⎦
sc
⎡ 2 ⎤ ⎡ −2V
fw ⎤
dV
f
= ⎢ ⎥ dVfw + ⎢ 2 ⎥ dEld
+
⎣(E ld
- E
sc
) + (E
rd
- E
sc
) ⎦ ⎣(E ld
- E
sc) + (E
rd
- E
sc)
⎦
⎡ −2V
fw ⎤ ⎡ 4V
fw ⎤
⎢ 2 ⎥ dEld
+ ⎢ 2 ⎥ dE
⎣(E ld
- E
sc) + (E
rd
- E
sc
) ⎦ ⎣(E ld
- E
sc) + (E
rd
- E
sc)
⎦
sc
178

Consequently, to develop a relationship between the standard deviation and the V f
and the standard deviations of V fw , E ld , E rd , and E sc it is necessary to square the previous
equation (this equation is true if there is no correlation), thus the linear function is
presented as:
σ = a σ + b σ + c σ + d σ
2 2 2 2 2 2 2 2 2
Vf Vf Eldf Erd Esc
Generally, it is more convenient to express the regression equations in matrix
form (Mayer 1990), considering the covariance matrix of the random four variables as:
S
xx
2 2 2 2
⎡σVfw σ
Eld
σ
Erd
σ ⎤
Esc
⎢ 2 2 2 2 ⎥
σ
Eld
σ
Eld
σ
EldErd
σ
EldEsc
= ⎢
⎥
⎢
2 2 2 2
σ
Erd
σ
EldErd
σ
Erd
σ
ErdEsc ⎥
⎢
2 2 2 2
⎥
⎢⎣
σ
Esc
σ
EldEsc
σ
ErdEsc
σ
Esc ⎥⎦
2
The preceding equation can be easily expressed in the matrix form where σ
Vf
is
the variance of V f such that:
σ = AS A'
2
Vf
xx
where A is the coefficient matrix and
A ' is the coefficient matrix transpose (Mayer 1990,
Mikhail and Gracie 1977). Substituting the coefficient matrix A into the foregoing
equation yields (this equation is true if there is correlation):
σ
2
Vf
=
[ a b c d ][ S ]
xx
⎡a⎤
⎢
b
⎥
⎢ ⎥
⎢c
⎥
⎢ ⎥
⎣d
⎦
This result in:
179

σ
2
Vf
=
[ a b c d ]
⎡σ σ σ σ
⎢
⎢σ σ σ σ
⎢σ σ σ σ
⎢
⎢⎣
σ σ σ σ
2 2 2 2
Vfw Eld Erd Esc
2 2 2 2
Eld Eld EldErd EldEsc
2 2 2 2
Erd EldErd Erd ErdEsc
2 2 2 2
Esc EldEsc ErdEsc Esc
⎤ ⎡a⎤
⎥ ⎢
b
⎥
⎥ ⎢ ⎥
⎥ ⎢c
⎥
⎥ ⎢ ⎥
⎥⎦
⎣d
⎦
All elevation values for the independent variables E ld , E rd , and E sc were obtained
using the same DEM to calculate valley profile values (V f ) in each satellite scene of the
JDTZ. Presuming no fluctuation in the acquired elevation values of these independent
variables, logically, any error in the DEM will affect all calculated measurements, and as
a result the correlation value of these variables will be very high. Therefore, when the
components of the random variables are independent, the covariance matrix
Sxx
is
diagonal (Mikhail and Gracie 1977). Ideally, the observed errors are obtained by
collecting control points using GPS and compare it to the generated DEM to create an
absolute DEM. Nevertheless, since the assumed ASTER vertical elevation error is ±10m
(i.e. E ld , E rd , E sc ), and the ASTER generated DEMs horizontal accuracy (i.e. pixel size,
2
V fw ) might shift in the order of half a pixel (30m/2 = 15m), thus, the V f variance σ
Vf
is
2
equal to σ (10m 2 = 100) and
Eld , Erd , Esc
matrix is expressed as follow:
2
σ
Vfw
(15m 2 = 225), respectively, and the covariance
S xx
⎡225 0 0 0 ⎤
⎢
0 100 95 95
⎥
= ⎢
⎥
⎢ 0 95 100 95 ⎥
⎢
⎥
⎣ 0 95 95 100⎦
Assuming the correlation between the independent variables E ld , E rd , and E sc is
0.95 (Cor Eld, Erd, Esc = ρ = 0.95). Therefore, the covariance of the elevation of the
independent variables is expressed as:
180

( σ
ei
)
ej
ρ = where i = 1,2,3 and j = 1,2,3
( σ xσ )
eiej
ei
ej
σ = ρ σ xσ )
(
ei ej
σ
eiej
= 0 .95(10x10)
= 95
Thus, after substituting this value the final integrated equation is expressed as:
σ
2
Vf
=
[ a b c d ]
⎡σ
⎢
⎢
⎢
⎢
⎢⎣
0 0 0
2
Vfw
0
2
σ
Eld
95 95
0 95
2
σ
Erd
95
2
0 95 95 σ
Esc
⎤ ⎡a⎤
⎥ ⎢
b
⎥
⎥ ⎢ ⎥
⎥ ⎢c
⎥
⎥ ⎢ ⎥
⎥⎦
⎣d
⎦
This result in the following equation after substituting the covariance matrix values:
σ
2
Vf
=
[ a b c d ]
⎡225 0 0 0 ⎤ ⎡a⎤
⎢
0 100 95 95
⎥ ⎢
b
⎥
⎢
⎥ ⎢ ⎥
⎢ 0 95 100 95 ⎥ ⎢c
⎥
⎢
⎥ ⎢ ⎥
⎣ 0 95 95 100⎦ ⎣d
⎦
To calculate the uncertainty
σ
Vf
values (the deviation from the mean) for each
individual valley profile, the square root (SQR, square terms are always positive) must be
2
calculated for the V f variance values σ
Vf
to obtain the standard deviation σ
Vf
values of all
valley profiles. Finally, the
σ
Vf
values will be added to and subtracted out of the V f index
values of each valley profiles to determine the effect of the assumed ASTER elevation
errors on the final results and in turn establish the finals active tectonic classes and their
ranges.
To perform the variance-covariance error propagation analysis, MATLAB 7.0
software has been employed for this purpose using the previously calculated V f results.
The MATLAB command lines listed in table 4.3 explains the step-by-step procedures to
181

obtain the standard deviation (i.e. σ Vf ) and the variance results of each valley profile. The
MATLAB commands are color-coded whereas each set of commands are highlighted, the
main command lines are black and preceded with (-) sign, instructions in green explain
the MATLAB command, and instructions in red explain commands procedures where
Vdata = the partial derivative calculations of ABCD (new output data), Obsdata = the V f
calculations (input or observation data), Sxx = the covariance matrix, Syy = the
covariance matrix of all profile ratios, Std = the standard deviation values, Columns 1 and
2 = the fronts and valley profiles numbers, respectively, Columns 3 to 6 = the A, B, C,
and D calculation values, respectively, Column 7 = the ABCD * Sxx * ABCD'
calculation results, Column 8 = the SQR (results of the final dV f equation), and Column 9
= the V f mean values for each valley profiles set related to a mountain front.
Tables 4.4 to 4.7 illustrate the final V f tectonic classes and the σ Vf values that
represent the final results of the ASTER accuracy equation. In addition, the final
+ ) and ( V
f
− σ ) results for all regions within the study area are listed
Vf
( V
f
σ
Vf
considering the vertical accuracy ±10m and the horizontal pixel size 30m of the ASTER
derived DEMs (figure 4.6). The lowest and highest final values of 0.02 and 2.25 were
found in Aqaba and the Dead Sea area, respectively. Since the V f index is a ratio and its
analysis normally expressed as absolute values (in this case as positive numbers),
therefore, all negative values after subtracting the DEM accuracy results (σ Vf ) out of the
V f values ( V
f
− σ ) were out of range (i.e. equal zero) and considered as class 1
Vf
throughout the final V f tectonic activity class classification. The presence of negative
values is donated to the shallowness of the valley bottom creating small differences in the
elevations of the valleys divides compared to the valley floors. This has only occurred in
182

a small number of valley profiles within the JDTZ (Dead Sea = 6 profiles, North Araba =
1 profile, South Araba = 4 profiles, and Aqaba = 2 profiles).
01
02 %% Clear data (erases previous calculations)
03
04 -
clear abcd i vdata Sxx (abcd = partial derivative results, vdata = outputs,
and Sxx = covariance matrix)
05
06 %% Read xls file (reading data from an Excel file)
07
08
%reads abcd values for a single front (specify new valley by changing the range values
and spreadsheet number, e.g. sheet #3 )
09 - vdata = xlsread (All_Calculations' , 3, 'S180 : X187');
10
11 %reads observation covariance matrix (same for all valleys)
12 - Sxx = xlsread (All_Calculations' , 3, 'AC13 : AF16');
13
14 %raw observatoins and vf calculations (different range for each valley profile data)
15 - obsdata = xlsread ('All_Calculations' , 3, 'B180 : J187');
16
17 % covariance matrix of all profile ratios
18 - Syy = abcd * Sxx * abcd’;
19
20 %% compute variance of each profile (numbers indicate the output table columns)
21 - abcd = vdata (:, 3, 6);
22 - vdata (:, 7) = diag (abcd * Sxx * abcd’);
23 - vdata (:, 8) = sqrt (vdata (:, 7));
24
25 %% compute mean of a front
26 - V f _mean = mean (obsdata (:, 9));
27
28 %% compute standard deviation of a front
29 - V f _std = sqrt (V f _var);
30 - V f _std_excel = std (obsdata (:, 9));
31
32 %% compute variance (V f _var) of a front assuming correlations among profiles
33 - V f _var = ((obsdata (:, 9)’ – V f _mean) * Syy * (obsdata (:, 9) – V f _mean))/
34 (nume1 (obsdata (:, 9)) – 1);
35
36 %% Draw histogram of V f (creates a histogram of each valley profile)
37 - hist (obsdata (:, 9));
38 - axis ([0 2 0 10])
39 - grid on
40
41 %% Write new data to a new spreadsheet (creates a new sheet and writes the new data)
42
43 %xlswrite (vdata,' spreadsheet-name' , 'sheet#' , 'range ## : ##');
44
Table 4.3: MATLAB command lines (each set of commands are highlighted, command
lines are black and start with (-), instructions in green explain the MATLAB commands,
and instructions in red explain commands procedures).
183

184

185

186

187

188

Figure 4.6: The tectonic activity classes of all S mf and V f based on V f +σ Vf results.
189

In table 4.8, the percentages of the valleys tectonic activity classes of the study
area are presented subsequent to employing the ASTER DEM error calculation results
(i.e. ±σ Vf values). In all four regions of the JDTZ, a significant increase (higher
percentages) of the final results are illustrated after adding the final V f accuracy test
values (i.e. ±σ Vf ) to the previously calculated V f values of each individual valley profile
compared to lower values (decreased percentages) when perform substituting. This
indicates the importance of using both addition and subtracting values of the DEM
accuracy test to determine the final tectonic activity classes in these four regions. Thus,
the minimum and maximum values of this accuracy test will establish the range of the V f
final tectonic classes within the JDTZ study area.
4.6. Final results and tectonic activity classes
Generally, the S mf results in the northern area show lower values compared to the
southern area which indicates a slightly more tectonic activity in this part of the JDTZ
region. However, the V f values in the northern area demonstrate a higher values in
comparison with the southern area, which is primarily related to the difference in the
geological setting and rock types between the two regions. The southern area, on the
other hand, has more U-shaped valleys as a result of relative tectonic quiescence
compared to the northern area that has largely V-shaped valleys indicating more tectonic
activity. These tectonic activity results match the recorded field and historical seismic
data of the region as previously illustrated in the earthquake network and active faults
relationship maps (section 4.3, figures 4.3 and 4.4).
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Dead Sea (22 valleys)
Class change Initial class to New class Percentage of valleys
1-2 → 1 5 / 22 = 22.7%
Decreased
2 → 1 or (1-2) 4 / 22 = 18.2%
Not changed (decreased) 13 / 22 = 59.1%
Total decreased 40.9%
1 → 2 or (1-2) 8 / 22 = 36.4%
1-2 → 2 or 3 4 / 22 = 18.1%
Increased 2 → 3 2 / 22 = 9.10%
Not changed (increased) 8 / 22 = 36.4%
Total increased 63.6%
Not changed (increased or decreased) 5 / 22 = 22.7%
North Araba (15 valleys)
Class change Initial class to New class Percentage of valleys
1-2 → 1 6 / 15 = 40.0%
Decreased
2 → 1 or (1-2) 1 / 15 = 6.70%
Not changed (decreased) 8 / 15 = 35.3%
Total decreased 46.7%
1 → 2 or (1-2) 6 / 15 = 40.0%
1-2 → 2 or 3 4 / 15 = 26.7%
Increased 2 → 3 0 / 15 = 0.00%
Not changed (increased) 5 / 15 = 33.3%
Total increased 66.7%
Not changed (increased or decreased) 2 / 15 = 13.4%
South Araba (25 valleys)
Class change Initial class to New class Percentage of valleys
1-2 → 1 3 / 25 = 12.0%
Decreased
2 → 1 or (1-2) 3 / 25 = 12.0%
Not changed (decreased) 19 / 25 = 76.0%
Total decreased 24.0%
1 → 2 or (1-2) 5 / 25 = 20.0%
1-2 → 2 or 3 4 / 25 = 16.0%
Increased 2 → 3 0 / 25 = 0.00%
Not changed (increased) 16 / 25 = 64.0%
Total increased 36.0%
Not changed (increased or decreased) 12 / 25 = 48.0%
Aqaba (36 valleys)
Class change Initial class to New class Percentage of valleys
1-2 → 1 2 / 36 = 5.60%
Decreased
2 → 1 or (1-2) 0 / 36 = 0.00%
Not changed (decreased) 34 / 36 = 94.4%
Total decreased 5.60%
1 → 2 or (1-2) 2 / 36 = 5.60%
1-2 → 2 or 3 1 / 36 = 2.80%
Increased 2 → 3 0 / 36 = 0.00%
Not changed (increased) 33 / 36 = 91.6%
Total increased 8.40%
Not changed (increased or decreased) 31 / 36 = 86.2%
Table 4.8: Percentages change of all valleys tectonic classes after applying the ±σ Vf
values.
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The final morphometric analysis of the S mf and V f indices revealed that the JDTZ
is tectonically active with slightly more activity in its northern part (figure 4.7). Theses
results uphold the historic and recorded earthquake data of the region where the northern
part of the JDTZ has experienced more noticeable tectonic activities than its southern
part. Based on the available digital data, the S mf and V f results indicate that there are two
main tectonic activity classes: more active (class 1), and moderate to less active (class 2).
The active class 1 S mf result ranges from 1.00 to 1.30, whereas class 2 values are > 1.30.
The V f results show the possibility of having an additional less active (or inactive)
tectonic class (class 3) that is still considered as a component of the moderate to less
active class 2. The V f active class 1 ranges from ≤ 0.09 to 0.50, the moderate to less
active class 2 ranges from 0.51 to 1.88, and the less active to inactive class 3 values are >
1.88 (table 4.9).
Tectonic activity classes and description S mf V f
Class 1 More active 1.00 – 1.30 ≤ 0.09 – 0.50
Class 2 Moderate to Less Active > 1.30 0.51 – 1.88
Class 3 Less Active (inactive) - > 1.88
Table 4.9: Final S mf and V f tectonic activity classes.
Similar to any other digital data, the generated DEMs primarily depend on the
resolution of the satellite imagery source. Thus, the better the initial ASTER imagery
resolution, the more enhanced DEMs quality could be created, hence, extra accurate
calculations of both S mf and V f indices. The values of S mf and V f indices analysis in the
JDTZ are reasonably close to the morphometric analysis results reached by other
researchers (chapter 2, table 2.1). These tectonic indices results were based on the
proposed ASTER vertical accuracy of ±10m and the generated DEMs horizontal pixel
size of 30m. This indicates the morphometric analysis of S mf and V f using the digital
192

approach has great potential in determining the tectonic activity classes in the JDTZ
study area.
Figure 4.7: The active tectonic classes of the northern and southern regions of the JDTZ
showing all S mf and V f +σ Vf results.
193

5. Chapter Five: Conclusion
5.1. Conclusion
The mountain front sinuosity index (S mf ) and the ratio of valley floor width to
valley height (V f ) are useful indicators of relative tectonic activity. Nevertheless, utilizing
the new digital approach as presented in this research, based on satellite imagery and
geographic digital data of land features, requires thorough examination and additional
research. Generally, remote sensing has limitations, and to get accurate geomorphic
analysis results it would be highly recommended if carried out together with ground
truthing (i.e. ground referencing data) and obtaining precise GPS elevation data of the
JDTZ study area.
The relative 30m-DEMs of the JDTZ were successfully generated from ASTER
imagery using PCI OrthoEngine software. ASTER digital data are available for many
parts of the Earth for a reasonable purchase price of US $55 per scene. ASTER imagery
and the derived DEMs provide good sources for mapping at scales in the range of
1:100,000 and 1:50,000 and for obtaining relative details about topography and elevation
information for a wide range of terrains (Kamp et al. 2003).
The morphometric analysis of mountain front sinuosity and the ratio of valley
floor width to valley height have proven to be useful in determining the tectonic activity
of several regions of different geological settings (Chapter two, section 2.3). The results
of the digital morphometric analysis approach of the S mf and V f indices in the JDTZ are
consistent with the northern area been of somewhat more tectonically active than the
southern area. Based on the available digital data, the S mf and V f results indicate that
there are two main tectonic activity classes: more active (class 1), and moderate to less
194

active (class 2). The active class 1 sinuosities (S mf ) result ranges from 1.00 to 1.30,
whereas class 2 values are > 1.30. The V f index results illustrate the possibility of having
an additional less active (inactive) tectonic class (class 3), but it is still considered as a
component of the moderate to less active tectonic category as in class 2. The V f active
class 1 ranges from ≤ 0.09 to 0.50, the moderate to less active class 2 ranges from 0.51 to
1.88, and the less active class 3 values are > 1.88.
Although the new digital approach has only been tested in Jordan on an arid to
semi arid region, it has the potential of providing reliable results for morphometric
analysis and quantifying the tectonic activity in the JDTZ study area. Indeed, further
studies using the new approach are highly recommended in different places and terrains
of different geological settings to examine this method’s efficiency.
However, the combined data from all analysis of morphometric landscape
parameters highlight significant variations in relative and numerical values of the JDTZ
tectonic activity. It is critical to integrate field data in order to examine other nontectonic
factors such as drainage basin size, stream power, position of fluvial systems relative to
mountain fronts, and variations in bedrock and weathering processes that have their input
to the variations of the overall calculations (Wells et al. 1988).
In conclusion, with the available ASTER satellite imagery and the resolution of
the ASTER derived DEM (30m pixel size and ±10m vertical accuracy), the digital
morphometric analysis approach of mountain fronts (S mf ) and valley profiles (V f ) has the
potential of providing results that are useful in determining the tectonic activity of the
JDTZ study area. More accurate and reliable results could be obtained utilizing the digital
195

S mf and V f indices analysis with the use of smaller DEM ground resolution of 15m to
10m or less.
5.2. Limitation of the Study
Since this research is believed to be among the first attempts to use a digital
geomorphic analysis approach for calculation of morphometric indices, the literature
discussing this method was scarce and quite limited. In addition, ASTER imagery data
has never been used in any previous research in Jordan, thus, the accuracy of the obtained
ASTER digital data have never been tested against the various existing terrains in Jordan
and the JDTZ.
One of the major inadequacies in this research that would have added valuable
information is the lack of digital data and comprehensive tectonic databases of Jordan.
Furthermore, the absence of the large scale topographic maps (1:25,000 or larger) of the
JDTZ study area hindered their comparison with the ASTER derived DEMs to perform
DEM accuracy test by using a simple map algebra operator (Cuartero et al. 2004). In
addition, there is a lack of ground control points (GCPs) and other elevation data such as
that can be obtained by the use of differential global positioning system (GPS) method
within the JDTZ study area (Chrysoulakis et al. 2004, Leick 2004, Poli et al. 2005).
5.3. Future directions/remarks
It has been studied that various indices have different sensitivities to specific
types and rates of tectonic activity, in addition to variables such as lithology, alluvial
fans, and drainage basin size (Wells et al. 1988). Moreover, both mountain front sinuosity
(S mf ) and stream length-gradient index (SL) are potentially valuable reconnaissance tools
used to identify areas of relative tectonic activity when jointly exploited. As a result, this
196

study stresses the value of incorporating different types of landscape parameters in the
JDTZ geomorphic analyses.
Most of the geomorphic indices, such as the drainage basin asymmetry (AF),
work best where each drainage basin is underlain by the same rock type (Keller and
Pinter 2002). Therefore, conducting research focusing on analyzing several geomorphic
indices in limited areas within the JDTZ might deliver significant relative tectonic
activity results and may possibly expand the range of tectonic activity classes.
Obtaining GPS elevation data of prominent landscape features and locations (i.e.
actual GCPs) in the JDTZ are highly recommended to generate absolute DEMs of the
study area in order to accurately compute the S mf and V f morphometric indices to
precisely determine the tectonic activity in the JDTZ.
Another point, which should be investigated in more detail, is the comparison of
the new digital morphometric approach to an existing locations with known tectonic
activity (i.e. activity classes) that has been determined by the morphometric analysis of
landforms and topography (e.g. the Mojave Desert, California) to specify the difference
in results between the two methods. In addition, this digital morphometric method in the
JDTZ and other locations should be tested by using multiple DEMs produced by other
sensors and of several ground resolutions.
197

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Internet Resources
ASTER Websites. Selected websites created by the USGS and NASA Jet Propulsion
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imagery.
http://asterweb.jpl.nasa.gov/
http://eospso.gsfc.nasa.gov/
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http://www.terrainmap.com/rm22.html#top
http://edcdaac.usgs.gov/aster/ast_l1b.asp
http://edcdaac.usgs.gov/aster/ast14dem.asp
http://edcdaac.usgs.gov/aster/asterprocessing.asp
http://www.satimagingcorp.com/satellite-sensors/aster.html
http://www.gds.aster.ersdac.or.jp/gds_www2002/index_e.html
215

Appendix A
Tables A.1 to A.5 list detailed results of both mountain front sinuosity (S mf ) and
the valley floor width/height ratio (V f ) computations. In addition, the shape of each
individual valley profile is described in an attempt to find the relationship between valley
shapes and V f index results.
Fronts/Valleys
Location
Front#
Length
(Km)
S mf
S mf
Class
Valley
Profile#
V f
V f
Class
V f
Mean
V f
Mean
Class
Dead Sea 1 67.70 1.07 1 1 1.18 2 0.54 1 U
Valley
Shape
2 1.03 2 U
3 0.51 1 - 2 U
4 1.19 2 V
5 0.19 1 V
6 0.77 1 - 2 U
7 0.47 1 V
8 1.19 2 U
9 0.28 1 V
10 0.64 1 - 2 U
11 0.47 1 V
12 0.25 1 V
13 0.32 1 V
14 0.73 1 - 2 V
15 0.18 1 V
16 0.27 1 V
17 0.41 1 V
18 0.20 1 V
19 0.36 1 V
20 0.30 1 V
21 0.25 1 V
22 0.65 1 - 2 V
Table A.1: The Dead Sea mountain front sinuosity (S mf ) and valley floor width to valley
height ratio (V f ) results.
216

Fronts/Valleys
Location
Front#
Length
(Km)
S mf
S mf
Class
Valley
Profile#
V f
V f
Class
V f
Mean
V f
Mean
Class
North Araba 1 64.63 1.21 1 1 0.38 1 0.50 1 V
Valley
Shape
2 0.40 1 U
3 0.38 1 V
4 0.26 1 V
5 0.34 1 V
6 0.57 1 U
7 0.69 1 V
8 1.22 2 V
9 0.85 1 V
10 0.53 1 V
11 0.61 1 V
12 0.37 1 V
13 0.26 1 V
14 0.54 1 V
15 0.33 1 U
Table A.2: The North Araba mountain front sinuosity (S mf ) and valley floor width to
valley height ratio (V f ) results.
217

Fronts/Valleys
Location
Front#
Length
(Km)
S mf
S mf
Class
218
Valley
Profile#
V f
V f
Class
V f
Mean
V f
Mean
Class
Dead Sea 1 132.32 1.14 1 1 1.18 2 0.53 1 U
& North
Araba
2 1.03 2 U
3 0.51 1 U
Valley
Shape
4 1.19 2 V
5 0.19 1 V
6 0.77 1 - 2 U
7 0.47 1 V
8 1.19 2 U
9 0.28 1 V
10 0.64 1 - 2 U
11 0.47 1 V
12 0.25 1 V
13 0.32 1 V
14 0.73 1 - 2 V
15 0.18 1 V
16 0.27 1 V
17 0.41 1 V
18 0.20 1 V
19 0.36 1 V
20 0.30 1 V
21 0.25 1 V
22 0.65 1 - 2 V
1 1 0.38 1 V
2 0.40 1 U
3 0.38 1 V
4 0.26 1 V
5 0.34 1 V
6 0.57 1 U
7 0.69 1 V
8 1.22 2 V
9 0.85 1 V
10 0.53 1 V
11 0.61 1 V
12 0.37 1 V
13 0.26 1 V
14 0.54 1 V
15 0.33 1 U
Table A.3: The combined Dead Sea and North Araba mountain front sinuosity (S mf ) and valley
floor width to valley height ratio (V f ) results.

Fronts/Valleys
Location
Front#
Length
(Km)
S mf
S mf
Class
Valley
Profile#
V f
V f
Class
V f
Mean
V f
Mean
Class
South Araba 1 35.94 1.16 1 1 0.35 1 0.56 1 V
Valley
Shape
2 1.24 2 U
22 0.29 1 V
23 0.33 1 V
24 0.15 1 V
25 1.02 2 V
2 3.62 1.21 1 3 0.58 1 0.75 1 - 2 U
4 0.91 1 - 2 U
3 32.14 1.28 1 5 0.53 1 0.35 1 U
6 1.07 2 U
7 0.79 1 - 2 U
8 0.49 1 V
9 0.11 1 V
10 0.13 1 V
11 0.21 1 V
12 0.12 1 V
13 0.26 1 V
14 0.15 1 V
15 0.41 1 U
16 0.11 1 V
17 0.42 1 U
18 0.19 1 V
19 0.29 1 V
4 15.26 1.17 1 20 0.52 1 0.47 1 V
21 0.42 1 V
Table A.4: The South Araba mountain front sinuosity (S mf ) and valley floor width to
valley height ratio (V f ) results.
219

Fronts/Valleys
Location
Front#
Length
(Km)
S mf
S mf
Class
Valley
Profile#
V f
V f
Class
V f
Mean
V f
Mean
Class
Aqaba 1 10.52 1.12 1 1 0.17 1 0.16 1 U
Valley
Shape
2 0.15 1 U
2 14.60 1.22 1 3 0.38 1 0.32 1 U
4 0.36 1 U
5 0.23 1 U
3 12.03 1.21 1 6 0.29 1 0.43 1 V
7 0.58 1 - 2 U
4 19.98 1.71 2 8 1.03 2 0.67 1 - 2 U
9 1.02 2 U
10 0.40 1 U
11 0.24 1 U
5 8.07 1.17 1 12 0.55 1 0.55 1 - 2 U
6 10.12 1.07 1 13 0.15 1 0.18 1 V
14 0.21 1 U
7 10.62 1.22 1 15 0.07 1 0.14 1 V
16 0.16 1 V
17 0.18 1 U
18 0.15 1 V
8 21.31 1.25 1 19 0.82 1 - 2 0.26 1 U
20 0.16 1 V
21 0.13 1 V
22 0.23 1 U
23 0.19 1 V
24 0.27 1 V
25 0.15 1 V
26 0.25 1 V
27 0.25 1 U
28 0.12 1 V
9 22.30 1.13 1 29 0.09 1 0.21 1 V
30 0.09 1 V
31 0.55 1 U
32 0.18 1 V
33 0.40 1 U
34 0.13 1 V
35 0.12 1 V
36 0.15 1 V
Table A.5: The Aqaba mountain front sinuosity (S mf ) and valley floor width to valley
height ratio (V f ) results.
220