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E-raamat: Advanced Remote Sensing Technology for Tsunami Modelling and Forecasting

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(FACULTY OF GEOSPATIAL & REAL ESTATE, University Geomatica College, Kuala Lumpur, Malaysia)
  • Formaat: 316 pages
  • Ilmumisaeg: 04-Jul-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351175531
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Advanced Remote Sensing Technology for Tsunami Modelling and ForecastingSuurem pilt
  • Formaat: 316 pages
  • Ilmumisaeg: 04-Jul-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351175531
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The innovation in space technologies has generated a new trend for observing and monitoring tsunamis from space. Most tsunami remote sensing studies focus on using classical image processing tools or conventional edge detection procedures. However, these methods do not use modern physics, applied mathematics, signal communication, remote sensing data and innovative space technologies. This book equips readers to understand how to monitor tsunamis from space with remote sensing technology art to create a better alarm warning system.

Dedication iii
Preface v
1 Principles of Tsunami
1(14)
1.1 Definition of Tsunami
1(1)
1.1.1 Comments on Tsunami Definition
1(1)
1.2 Tsunami Terminology
2(7)
1.3 Physical Characteristics of Tsunami
9(1)
1.4 Tsunami Classifications
9(2)
1.5 How do Tsunamis Differ from other Water Waves?
11(2)
1.6 The Wave Train
13(1)
1.7 The Shoaling Effect
13(2)
2 Tsunami Generation Mechanisms
15(19)
2.1 Causes of Tsunami
15(11)
2.1.1 Tsunami Generation
15(2)
2.1.2 Plate Tectonics: The Main Features
17(1)
2.1.3 Type of Plate Tectonic Boundaries
17(1)
2.1.3.1 Divergent Boundaries
18(1)
2.1.3.2 Convergence Boundaries
19(2)
2.1.3.3 Transform Boundaries
21(1)
2.1.4 Where is the Evidence for Plate Tectonics?
22(1)
2.1.5 Continental Drift
22(4)
2.2 How do Earthquakes Generate Tsunami?
26(1)
2.3 How do Landslides, Volcanic Eruptions, and Cosmic Collisions Generate Tsunamis?
26(1)
2.4 What Happens When a Tsunami Encounters Land?
27(1)
2.5 Tsunami Generation Mechanisms
28(3)
2.5.1 Fault Slip
28(1)
2.5.2 Split
29(1)
2.5.3 Amplification
29(1)
2.5.4 Tsunami Run-up
30(1)
2.5.5 Do Tsunamis Stop Once on Land?
31(1)
2.6 Historical Tsunami Records
31(2)
2.7 Why aren't Tsunamis Seen at Sea or from the Air?
33(1)
2.8 Combination of Tsunami, Tide, Sea Level, and Storm Surge
33(1)
3 Tsunami of Sumatra-Andaman Earthquake 26 December 2004
34(16)
3.1 Why Earthquakes and Tsunamis occur in the Sumatra Region
34(2)
3.2 Rupture of 2004 Earthquake and Tsunami
36(1)
3.3 How Earthquakes occur in the Sumatra Region?
36(2)
3.4 Mechanisms of Sumatran Earthquake and Tsunami
38(1)
3.5 Physical Characteristics of the 2004 Earthquake
39(1)
3.6 2004 Tsunami Beaming
40(1)
3.7 Energy of the Earthquake and its Effects
41(2)
3.8 Propagation of 2004 Tsunami
43(2)
3.9 Paths of Tsunami along Andaman Sea
45(2)
3.10 Retreat and Rise Cycle
47(3)
4 Novel Theories of Tsunami Generation Mechanisms
50(17)
4.1 5,000 Years of Tsunamis
50(2)
4.2 Tsunami Recurrence
52(1)
4.3 Can Tsunami Cause Marine Landslide?
52(2)
4.3.1 Mechanisms of Earthquake Causing Landslides
53(1)
4.4 Slow Slip and Tsunami
54(3)
4.5 Low-frequency Earthquake Event
57(2)
4.5.1 Characteristics of Low-frequency Earthquakes
57(2)
4.6 New Tsunami Generation Mechanisms and Models
59(2)
4.7 Molecular Hydrodynamic Tsunami Generation
61(1)
4.8 Can Gravity Cause Tsunami?
62(1)
4.8.1 Why Would Gravity and Topography be Related to Seismic Activity?
62(1)
4.9 Did Himalayan Mountain Cause 2004 Tsunami?
63(1)
4.10 Did Deep Heat Spawn the 2004 Tsunami?
64(1)
4.11 Can Nuclear Bomb Create a Tsunami?
65(1)
4.12 Can HAARP Technology Create a Tsunami?
66(1)
5 Modification of the Earth's Rotation by 2004 Earthquake
67(7)
5.1 Earth Rotation
67(1)
5.2 Forces Affecting the Length of the Earth's Day
68(2)
5.2.1 Tidal Forces and Earthquakes
68(1)
5.2.2 Wind Force
69(1)
5.2.3 Madden-Julian Cycle
69(1)
5.2.4 Climate Changes
70(1)
5.3 2004 Tsunami's Effects on Earth's Rotation
70(4)
5.3.1 Chandler Wobble or Variation of Latitude
70(1)
5.3.2 How Chandler Wobble is Impacted by Earthquakes?
71(3)
6 Principles of Optical Remote Sensing for Tsunami Observation
74(20)
6.1 Introduction to Remote Sensing
74(1)
6.2 Electromagnetic Spectrum
74(3)
6.2.1 Radio Waves
75(1)
6.2.2 Microwaves
75(1)
6.2.3 Infrared
75(1)
6.2.4 Visible Light
75(1)
6.2.5 Ultraviolet
75(1)
6.2.6 X-beams
76(1)
6.2.7 Gamma-rays
76(1)
6.3 Energy in Electromagnetic Waves
77(1)
6.4 Photoelectric Effect
78(1)
6.5 Young's Slits
79(1)
6.6 Electromagnetic-radiation-matter Interactions
80(4)
6.7 Interaction Processes on Remote Sensing
84(1)
6.8 Black Body Radiation
85(2)
6.9 Spectral Signatures
87(1)
6.10 Spatial Dimension
88(6)
6.10.1 Spectral Resolution
88(2)
6.10.2 Spatial Resolution
90(1)
6.10.3 Temporal Resolution
91(3)
7 Potential of Optical Remote Sensing Satellite for Monitoring Tsunami
94(13)
7.1 Introduction
94(1)
7.2 Tsunami Observation from High Resolution Satellite Images
94(6)
7.2.1 Spectral Signature Analysis using Optical High-resolution Satellite Imagery
94(2)
7.2.2 NDVI Analysis using Optical High-resolution Satellite Imagery
96(3)
7.2.3 Damage Index using High Resolution Satellite Data
99(1)
7.3 Tsunami Inundation Mapping using Terra-ASTER Images
100(4)
7.4 Tsunami Observation from Low Resolution Satellite Images
104(3)
8 Modelling Shoreline Change Rates Due to the Tsunami Impact
107(21)
8.1 Shoreline Definition Regarding Tsunami
107(3)
8.1.1 Optical Remote Sensing for Shoreline Extraction
109(1)
8.1.2 Hypotheses and Objective
110(1)
8.2 Study Areas and Data Acquisitions
110(2)
8.3 Automatic Detection of Shoreline Extraction
112(12)
8.3.1 Image Segmentation
112(2)
8.3.2 Theory of Edge Detection
114(1)
8.3.3 Sobel Algorithm
114(2)
8.3.3.1 Sobel Algorithm Output
116(3)
8.3.4 Canny Algorithm
119(1)
8.3.4.1 Apply Gaussian Filter to Smooth the Image in Order to Remove the Noise
119(1)
8.3.4.2 Finding the Intensity Gradient of the Image
119(2)
8.3.4.3 Non-maximum Suppression
121(1)
8.3.4.4 Double Threshold
121(1)
8.3.4.5 Edge Tracking by Hysteresis
121(1)
8.3.4.6 Canny Algorithm Output
121(3)
8.4 Tsunami Impacts on Shoreline Deformation
124(2)
8.5 The Role of Vegetation Covers on Tsunami Wave Energy Reduction
126(2)
9 Modelling of Tsunami Impacts on Physical Properties of Water using MODIS Data: A Study Case of Aceh, Indonesia
128(27)
9.1 Introduction
128(1)
9.2 Coastal Water of Aceh
129(1)
9.3 MODIS Satellite Data
130(2)
9.3.1 Comparison between MODIS and other Optical Satellite Sensors
131(1)
9.4 Impact of Tsunami on Coastal Physical Properties
132(20)
9.4.1 Retrieving Sea Surface Salinity and Suspended Sediment
132(4)
9.4.1.1 Tsunami Impact on Sea Surface Salinity and Suspended Sediment Variations
136(8)
9.4.1.2 Sediment Impacts on Sea Surface Salinity
144(1)
9.4.2 Tsunami Impact on Chlorophyll-a
145(1)
9.4.2.1 Chlorophyll Algorithm
146(1)
9.4.2.2 Tsunami Impact on Chlorophyll-a Variations
147(2)
9.4.3 Tsunami Impact on the Sea Surface Temperature
149(1)
9.4.3.1 Sea Surface Temperature Retrieving from MODIS Data
149(1)
9.4.3.2 Sea Surface Temperature Variations
150(2)
9.5 Mechanism of Upwelling by Tsunami
152(3)
10 Genetic Algorithm for Simulation of Tsunami Impacts on Water Mass Variations using MODIS Satellite Data
155(11)
10.1 Water Mass Definition
155(1)
10.2 Remote Sensing and Water Masses
156(1)
10.3 Genetic Algorithm
156(8)
10.3.1 Population of Solutions
157(1)
10.3.2 Fitness
158(2)
10.3.3 Cross-over and Mutation
160(4)
10.4 Tsunami Causes Water Masses Redistribution
164(1)
10.5 Can Water Masses Redistribution Affect Length of Day?
164(2)
11 Three-dimensional Tsunami Wave Simulation from Quickbird Satellite Data
166(18)
11.1 Introduction
166(1)
11.2 Theory of Wave Spectra in Optical Remote Sensing Data
167(3)
11.2.1 Kirchhoff Approximation for Sea Surface Reflection
169(1)
11.3 QuickBird and Kalutara, Sri Lanka
170(1)
11.4 Wave Spectra Estimation from QuickBird Satellite Data
171(2)
11.5 Numerical Model of Tsunami Run-up
173(1)
11.6 Fuzzy B-spline Method for 3-D Run-up Simulation
174(2)
11.7 Galerkin Finite Element
176(8)
11.7.1 Moving Least Square Method (MLSM)
176(1)
11.7.2 Weight Function
177(1)
11.7.3 3-D Waves and Run-up Study Case: Kalutara Coastline, Sri Lanka
177(3)
11.7.4 3-D Whirlpools and Solitary Waves
180(4)
12 Four-dimensional Hologram Interferometry of Tsunami Waves from Quickbird Satellite Data
184(25)
12.1 Introduction
184(1)
12.2 Physics of Hologram
184(4)
12.2.1 Definition of Hologram
184(1)
12.2.2 LASER
185(1)
12.2.3 Hologram and Holographic
185(2)
12.2.4 Duality of Hologram and Universe
187(1)
12.3 How Holography Works?
188(4)
12.3.1 Reflection and Transmission Holograms
188(3)
12.3.2 Capturing the Fringes
191(1)
12.3.3 Coherent and Incoherent Holography
191(1)
12.3.4 Hologram Classifications
192(1)
12.4 Four-dimensional
192(3)
12.5 Mathematical Model for Retrieving 4-D using Hologram Interferometry
195(7)
12.5.1 Hologram Interferometry to Reconstruct Fourth-dimensional of Tsunami Wave
195(2)
12.5.2 Fourier Computer Generated Hologram
197(1)
12.5.3 2-D Hologram
198(1)
12.5.4 4-D Phase Unwrapping
199(1)
12.5.5 Hybrid Genetic Algorithm (HGA)
200(1)
12.5.5.1 Initial Solution and Population
201(1)
12.5.5.2 Record Pareto Optimal Solutions
201(1)
12.5.5.3 Fitness Evaluation
201(1)
12.5.5.4 Crossover and Mutation
202(1)
12.5.5.5 Phase Matching
202(1)
12.6 4-D Hologram Visualization of QuickBird
202(3)
12.7 4-D and Relativity
205(4)
13 Principles of Synthetic Aperture Radar
209(17)
13.1 Principles
209(1)
13.2 Radio Detecting and Ranging
210(1)
13.3 Synthetic Aperture Radar and Radar Resolutions
211(5)
13.3.1 Spatial Resolution
211(2)
13.3.2 Slant and Ground Range Resolution
213(1)
13.3.3 Resolution Cell
214(1)
13.3.4 Ambiguous Range
214(1)
13.3.5 Range-Rate Measurement (Doppler)
215(1)
13.4 Radar Range Equation
216(1)
13.5 Radar Backscattering
217(6)
13.5.1 Characteristics of Radar Backscattering
218(1)
13.5.2 Surface Scattering
219(1)
13.5.3 Backscatter Coefficient
219(1)
13.5.4 Incident Angle
220(1)
13.5.5 Polarization
221(1)
13.5.6 Speckles
221(2)
13.6 SAR Imagine Sea Surface
223(3)
14 Detection of Internal Wave from Synthetic Aperture Radar Post Tsunami
226(21)
14.1 Internal Wave
226(1)
14.2 Internal Wave Imaging in SAR
227(4)
14.2.1 SAR Imaging of Internal Solitons
228(1)
14.2.2 Mathematical Model of Internal Wave Radar Cross Section
229(2)
14.3 Algorithms
231(2)
14.3.1 Wavelet Transform for Internal Wave Detection
231(1)
14.3.2 Particle Swarm Optimization (PSO)
232(1)
14.4 Tsunami Derived Internal Wave in SAR Data
233(4)
14.4.1 SAR Data Acquisition
233(1)
14.4.2 Andaman and Nicobar Islands
234(1)
14.4.3 Backscatter Distribution in ASAR Data
235(2)
14.5 Automatic Detection of Internal Waves
237(3)
14.5.1 Wavelet Transformation for Automatic Detection of Internal Wave
237(1)
14.5.2 PSO for Automatic Detection of Internal Wave
238(2)
14.6 Internal Wave Variations with Physical Water Properties
240(3)
14.7 Tsunami Deriving Internal Wave from Optical Satellite Data
243(4)
15 Altimeter Satellite Data Observed Tsunami Spreading
247(16)
15.1 Microwave Altimeter
247(1)
15.2 Principles of Altimeter
247(5)
15.2.1 Sort of Radar Altimeter
248(1)
15.2.2 The Geoid
248(1)
15.2.3 Reference Ellipsoid
248(1)
15.2.4 Range and Azimuth Resolutions
249(1)
15.2.5 Satellite Altitude
250(1)
15.2.6 Surface Height
250(1)
15.2.7 Pulse-limited Altimetry
250(1)
15.2.8 Frequencies used and their Impacts
251(1)
15.3 Altimetric Measurements over the Ocean
252(1)
15.4 Altimeter Sensors for 2004 Tsunami
253(10)
15.4.1 Retrieving Tsunami Wave Height and Propagation from Altimeter Data
254(1)
15.4.2 Jason-1
255(2)
15.4.3 Topex-Poseidon Satellite
257(1)
15.4.4 ENVISAT
258(2)
15.4.5 Geosat Follow-on
260(3)
16 Schrodinger Theory for Future Tsunami Forecasting in Malacca Straits, Indian Ocean, Red Sea and Nile River
263(24)
16.1 Quantum for Wave Propagation
263(1)
16.2 Schrodinger Equation for Tsunami Propagation
264(2)
16.3 Numerical Model of Tsunami Travelling
266(3)
16.3.1 Finite Difference Model Method
268(1)
16.3.2 Grid Generation
269(1)
16.4 Different Study Cases
269(13)
16.4.1 Malacca Straits
269(8)
16.4.2 Red Sea and Indian Ocean Tsunami by Grand Ethiopian Renaissance Dam (GERD)
277(5)
16.5 Tsunami from Point View of Quantum Mechanics
282(1)
16.6 Quantum Viewpoints of GERD Impacts
283(4)
References 287(10)
Index 297(4)
Author's Biography 301
Maged Marghany has a Ph.D. in Environmental Remote Sensing from the University Putra Malaysia where he now works as a researcher. He has conducted extensive research on the application of SAR data to coastal studies. He has been leading several projects related to the application of SAR to Malaysian coastal waters funded by Ministry of Science and Technology, Malaysia (MOSTE). His research is directed towards the use of SAR data for modeling shoreline changes and developing a new approach for forecasting oil slick trajectory movements. He has taught extensively and published over 200 papers on the topic.
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