Surface Displacement Measurement from Remote Sensing Images

Cover
Half-Title Page
Title Page
Copyright Page
Contents
Preface
Part 1. Theory, Principles and Methodology
Chapter 1. Relevant Past, On-going and Future Space Missions
1.1. Some key parameters for space missions
1.1.1. Parameters for both SAR and optical missions
1.1.2. Parameters specific to SAR missions
1.1.3. Parameters specific to optical missions
1.2. Past and on-going SAR missions
1.2.1. ERS-1, ERS-2 and Envisat
1.2.2. Canadian C-band satellites: Radarsat-1, Radarsat-2 and RCM
1.2.3. Japanese L-band satellites: JERS-1, ALOS and ALOS-2
1.2.4. SRTM and X-SAR
1.2.5. TerraSAR-X, TanDEM-X and PAZ
1.2.6. COSMO SkyMed constellations
1.2.7. SAOCOM1
1.2.8. Sentinel-1
1.2.9. ICEYE
1.3.Future SAR missions
1.3.1. TerraSAR-NG
1.3.2. ALOS-4
1.3.3. NISAR
1.3.4. Biomass
1.3.5. ROSE-L
1.4. Optical imaging missions
1.4.1. Past optical missions
1.4.2. On-going optical missions
1.4.3. Future optical missions
1.5. Conclusion
1.6. Acknowledgments
1.7. References
Chapter 2. Image Matching and Optical Sensors
2.1. Introduction, definition and applications
2.1.1. Problem definition
2.1.2. Geometry of matching
2.1.3. Radiometric and geometric hypothesis
2.2. Template matching
2.2.1. Matching as a similarity measure
2.2.2. Basic template approach
2.2.3. Normalized template approach
2.3. Handling “large” deformations
2.3.1. Introduction
2.3.2. A posteriori filtering and regularization approach
2.3.3. Multi-resolution approach
2.4. Discrete nature of the image and sub-pixel matching
2.5. Optical imaging sensors
2.5.1. Sensor geometries
2.5.2. Sensor orientation in 3D space
2.6. Acknowledgments
2.7. References
Chapter 3. SAR Offset Tracking
3.1. Basics of SAR imaging
3.1.1. Imaging geometry and resolution of SAR systems
3.1.2. Radar speckle and speckle reduction with multi-looking
3.1.3. Spectral support of the backscatter intensity
3.1.4. Offsets due to radar penetration
3.2. SAR offset tracking
3.2.1. Cross-correlation
3.2.2. SAR offset tracking using image pairs
3.2.3. SAR offset tracking using image series
3.2.4. Tracking from single and multiple orbits
3.2.5. Summary
3.3. Acknowledgments
3.4. References
Chapter 4. SAR Interferometry: Principles and Processing
4.1. Introduction
4.2. Principle and limits of SAR interferometry
4.2.1. Geometrical information
4.2.2. How to choose an interferometric pair?
4.2.3. Phase and coherence estimation
4.2.4. Loss of coherency: several reasons
4.2.5. Other InSAR limitations
4.3. Atmospheric corrections
4.3.1. Compensation for tropospheric delays
4.3.2. Estimation of and compensation for ionospheric propagation delays
4.4. InSAR processing chains
4.4.1. Main steps in InSAR processing
4.4.2. Illustration of the DInSAR processing chain: a case study with DiapOTB
4.4.3. Available InSAR software: a quick overview
4.5. Conclusion
4.6. Acknowledgments
4.7. References
Chapter 5. Advanced Methods for Time-series InSAR
5.1. Introduction
5.2. Background of time-series InSAR analysis
5.3. A review
5.3.1. Small baseline methods for time-series analysis
5.3.2. From PS to PSDS
5.4. The SBAS technique
5.4.1. Principle and definition of a “small baseline” network of interferograms
5.4.2. Multi-looking and filtering atmospheric or DEM error corrections and unwrapping
5.4.3. Reconstruction of the time series
5.4.4. Source separation, atmosphere versus deformation
5.5. The PSDS technique
5.5.1. The PS algorithm
5.5.2. PS selection
5.5.3. DS selection
5.5.4. Phase linking
5.5.5. Tools supporting PS and PSDS processing
Author contributions
5.6. Acknowledgments
5.7. References
Chapter 6. The Interferometric Phase: Unwrapping and Closure Phase
6.1. Introduction
6.2. Phase unwrapping algorithms and limitation of phase unwrapping errors
6.2.1. The problem of phase unwrapping and some prerequisites
6.2.2. A short history of unwrapping methods in InSAR
6.2.3. Residue-cut tree algorithm, variants and 3D generalization
6.2.4. Least-squares method
6.2.5. Network-based approaches
6.2.6. Methods for correcting unwrapping errors
6.2.7. Summary and comparison
6.3. The (re)discovery of closure phases and their implications for SAR interferometry
6.3.1. Introduction to closure phases
6.3.2. Mathematical properties of closure phases
6.3.3. Physical misclosures
6.3.4. Implications for interferometric phase estimation
6.4. Acknowledgments
6.5. References
Part 2. Applications for Surface Displacements
Chapter 7. Remote Sensing of the Earthquake Deformation Cycle
7.1. Introduction
7.2. What have we learned about faults from nearly three decades of tectonic InSAR?
7.2.1. Coseismic deformation
7.2.2. Inter-seismic deformation
7.2.3. Post-seismic deformation and aseismic deformation transients
7.3. Investigating earthquake surface ruptures with optical image correlation
7.3.1. A history of the optical correlation technique in the study of earthquakes
7.3.2. Measuring earthquake surface displacements from optical images: methodology
7.3.3. Some examples of near-fault displacement fields from recent and historical earthquakes
7.3.4. New insights from high-resolution near-fault displacement maps
7.4. Conclusion
7.5. Acknowledgments
7.6. References
Chapter 8. Volcanology: The Crucial Contribution of Surface Displacement Measurements from Space for Understanding and Monitoring Volcanoes
8.1. Introduction
8.2. Origin of surface displacement and topographic changes in a volcano
8.2.1. Typical deformations in a basaltic system
8.2.2. Typical deformations in an andesitic stratovolcano
8.3. Techniques used to measure displacements and topographic changes in volcanoes
8.3.1. Pixel offset tracking
8.3.2. Measurement by interferometry (phase difference)
8.3.3. Measurement of topographic changes from differences in digital elevation models
8.4. Main limitations of measurements obtained by remote sensing
8.4.1. Temporal resolution
8.4.2. Cloud cover (optical imagery)
8.4.3. Atmospheric artifacts (radar imagery)
8.5. Main contributions of spatial geodesy for monitoring and studying volcanoes
8.5.1. The development of global and statistical studies
8.5.2. Improving knowledge of the magma supply system
8.5.3. Growth and stability of volcanic edifices
8.6. Recent progress
8.6.1. Integrating satellite imagery with field data
8.6.2. Automating processing chains for real-time detection
8.6.3. Integration of all satellite sources in multi-parameter studies
8.7. Volcanic crisis management: the contribution of displacement measurements obtained using spatial imagery
8.7.1. Piton de la Fournaise, Réunion: benefits of spatial imagery for a highly active, closely monitored volcano
8.7.2. Mount Agung, Indonesia: using InSAR data in real time in a crisis management situation
8.7.3. Taal, Philippines: innovative use of InSAR information in real time
8.8. Conclusion
8.9. Acknowledgments
8.10. References
Chapter 9. Anthropogenic Activity: Monitoring Surface-Motion Consequences of Human Activities with Spaceborne InSAR
9.1. Introduction
9.2. Characteristics of subsidence/uplift phenomena associated with human activity
9.2.1. Underground activities
9.2.2. Cavities
9.2.3. Suitability of the interferometric SAR techniques
9.2.4. Optimum SAR data properties for monitoring anthropogenic activities
9.3. Application examples: urban underground activities
9.3.1. Urban underground construction works
9.3.2. Urban groundwater extraction
9.4. Applications in mineral resources extractive activities
9.4.1. Brine, oil and gas extraction/injection
9.4.2. Mining activities
9.5. Conclusion
9.6. Acknowledgments
9.7. References
Chapter 10. Measuring Kinematics of Slow-Moving Landslides from Satellite Images
10.1. Introduction
10.2. Image correlation applied to satellite optical images
10.2.1. Landslide detection with optical remote sensing
10.2.2. Landslide characterization with optical remote sensing
10.2.3. Landslide monitoring with optical remote sensing
10.3. Offset tracking of SAR images applied to landslides
10.4. InSAR for landslide studies
10.4.1. Standard versus multi-temporal InSAR analyses
10.4.2. Limitations in the use of SAR interferometry for landslide applications
10.4.3. Landslide detection with InSAR
10.4.4. Landslide characterization with InSAR
10.4.5. Landslide monitoring with InSAR
10.5. Conclusion
10.6. References
Chapter 11. Remote Sensing of Glacier Motion
11.1. Introduction
11.2. What is glacier motion?
11.2.1. Physical basis of ice flow
11.2.2. Scales and processes of displacement in glaciology
11.3. Measuring glacier displacement from satellite data
11.3.1. Image preprocessing
11.3.2. Offset-tracking algorithms
11.3.3. InSAR
11.3.4. Filtering and corrections of displacement fields
11.3.5. Working with time series
11.3.6. Summary of benefits and drawbacks of the different methods
11.4. What can we learn from glacier displacement?
11.4.1. From a topological perspective
11.4.2. From a climatological perspective
11.4.3. From a mechanical perspective
11.4.4. From a hydrological perspective
11.4.5. From a geomorphological perspective
11.4.6. From a hazards perspective
11.5. Perspectives and future directions
11.5.1. What will we learn from future missions?
11.5.2. What future sensors are needed for glaciology?
11.6. Acknowledgments
11.7. References
Chapter 12. New Applications of Spaceborne Optical Image Cross-Correlation: Digital Elevation Models of Volcanic Clouds and Shallow Bathymetry from Space
12.1. Introduction
12.1.1. General introduction
12.2. Digital elevation models of volcanic ash clouds
12.2.1. Introduction: can we precisely measure the height of a volcanic ash cloud and what physical process controls the injection height and
the speed of a volcanic ash plume?
12.2.2. Principles
12.2.3. Applications
12.3. Shallow bathymetry: measuring wave characteristics from space
12.3.1. Introduction: can we map tectonic fault motion under shallow water and can we measure differential bathymetry?
12.3.2. Principles
12.3.3. Applications
12.4. Concluding remarks
12.5. Acknowledgments
12.6. References
List of Authors
Index
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