Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

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Synthetic aperture radar interferometry

http://www.gbv.de/dms/ilmenau/toc/024184004richa.PDF

Remote sensing digital image analysis

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

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Change detection techniques

The Nature of Remote Sensing: Introduction. Remote Sensing. Information Extraction from Remote-Sensing Images. Spectral Factors in Remote Sensing. Spectral Signatures. Remote-Sensing Systems. Optical Sensors. Temporal Characteristics. Image Display Systems. Data Systems. Summary. Exercises. References. Optical Radiation Models: Introduction. Visible to Short Wave Infrared Region. Solar Radiation. Radiation Components. Surface-Reflected. Unscattered Component. Surface-Reflected. Atmosphere-Scattered Component. Path-Scattered Component. Total At-Sensor. Solar Radiance. Image Examples in the Solar Region. Terrain Shading. Shadowing. Atmospheric Correction. Midwave to Thermal Infrared Region. Thermal Radiation. Radiation Components. Surface-Emitted Component. Surface-Reflected. Atmosphere-Emitted Component. Path-Emitted Component. Total At-Sensor. Emitted Radiance. Total Solar and Thermal Upwelling Radiance. Image Examples in the Thermal Region. Summary. Exercises. References. Sensor Models: Introduction. Overall Sensor Model. Resolution. The Instrument Response. Spatial Resolution. Spectral Resolution. Spectral Response. Spatial Response. Optical PSFopt. Image Motion PSFIM. Detector PSFdet. Electronics PSFel. Net PSFnet. Comparison of Sensor PSFs. PSF Summary for TM. Imaging System Simulation. Amplification. Sampling and Quantization. Simplified Sensor Model. Geometric Distortion. Orbit Models. Platform Attitude Models. Scanner Models. Earth Model. Line and Whiskbroom ScanGeometry. Pushbroom Scan Geometry. Topographic Distortion. Summary. Exercises. References. Data Models: Introduction. A Word on Notation. Univariate Image Statistics. Histogram. Normal Distribution. Cumulative Histogram. Statistical Parameters. Multivariate Image Statistics. Reduction to Univariate Statistics. Noise Models. Statistical Measures of Image Quality. Contrast. Modulation. Signal-to-Noise Ratio (SNR). Noise Equivalent Signal. Spatial Statistics. Visualization of Spatial Covariance. Covariance with Semivariogram. Separability and Anisotropy. Power Spectral Density. Co-occurrence Matrix. Fractal Geometry. Topographic and Sensor Effects. Topography and Spectral Statistics. Sensor Characteristics and Spectral Stastistics. Sensor Characteristics and Spectral Scattergrams. Summary. Exercises. References. Spectral Transforms: Introduction. Feature Space. Multispectral Ratios. Vegetation Indexes. Image Examples. Principal Components. Standardized Principal Components (SPC) Transform. Maximum Noise Fraction (MNF) Transform. Tasseled Cap Tranformation. Contrast Enhancement. Transformations Based on Global Statistics. Linear Transformations. Nonlinear Transformations. Normalization Stretch. Reference Stretch. Thresholding. Adaptive Transformation. Color Image Contrast Enhancement. Min-max Stretch. Normalization Stretch. Decorrelation Stretch. Color Spacer Transformations. Summary. Exercises. References. Spatial Transforms: Introduction. An Image Model for Spatial Filtering. Convolution Filters. Low Pass and High Pass Filters. High Boost Filters. Directional Filters. The Border Region. Characterization of Filtered Images. The Box Filter Algorithm. Cascaded Linear Filters. Statistical Filters. Gradient Filters. Fourier Synthesis. Discrete Fourier Transforms in 2-D. The Fourier Components. Filtering with the Fourier Transform. Transfer Functions. The Power Spectrum. Scale Space Transforms. Image Resolution Pyramids. Zero-Crossing Filters. Laplacian-of-Gaussian (LoG) Filters. Difference-of-Gaussians (DoG) Filters.Wavelet Transforms. Summary. Exercises. References. Correction and Calibration: Introduction. Noise Correction. Global Noise. Sigma Filter. Nagao-Matsuyama Filter. Local Noise. Periodic Noise. Distriping 359. Global,Linear Detector Matching. Nonlinear Detector Matching. Statistical Modification to Linear and Nonlinear Detector. Matching. Spatial Filtering Approaches. Radiometric Calibration. Sensor Calibration. Atmospheric Correction. Solar and Topographic Correction. Image Examples. Calibration and Normalization of Hyperspectral Imagery. AVIRIS Examples. Distortion Correction. Polynomial Distortion Models. Ground Control Points (GCPs). Coordinate Transformation. Map Projections. Resampling. Summary. Exercises References. Registration and Image Fusion: Introduction. What is Registration? Automated GCP Location. Area Correlation. Other Spatial Features. Orthrectification. Low-Resolution DEM. High-Resolution DEM. Hierarchical Warp Stereo. Multi-Image Fusion. Spatial Domain Fusion. High Frequency Modulation. Spectral Domain Fusion. Fusion Image Examples. Summary. Exercises. References. Thematic Classification: Introduction. The Importance of Image Scale. The Notion of Similarity. Hard Versus Soft Classification. Training the Classifier. Supervised Training. Unsupervised Training. K-Means Clustering Algorithm. Clustering Examples. Hybrid Supervised/Unsupervised Training. Non-Parametric Classification Algorithms. Level-Slice. Nearest-Mean. Artificial Neural Networks (ANNs). Back-Propagation Algorithm. Nonparametric Classification Examples. Parametric Classification Algorithms. Estimation of Model-Parameters. Discriminant Functions. The Normal Distribution Model. Relation to the Nearest-Mean Classifier. Supervised Classification Examples and Comparison to Nonparametric Classifiers. Segmentation. Region Growing. Region Labeling. Sub-Pixel Classification. The Linear Mixing Model. Unmixing Model. Hyperspectral Image Analysis. Visualization of the Image Cube. Feature Extraction. Image Residuals. Pre-Classification Processing and Feature Extraction. Classification Algorithms. Exercises. Error Analysis. Multitemporal Images. Summary. References. Index.

Remote sensing, models, and methods for image processing

Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods.

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Digital change detection methods in ecosystem monitoring: a review

In the upper crust, where hydraulic gradients are typically 10 MPa km−1, the mean permeabilities required to accommodate the estimated metamorphic fluid fluxes decrease from ∼10−16 m² to ∼10−18 m² between 5- and 12-km depth Below ∼12 km, which broadly corresponds to the brittle-plastic transition, mean k is effectively independent of depth at ∼10−185±1 m² Consideration of the permeability values inferred from thermal modeling and metamorphic fluxes suggests a quasi-exponential decay of permeability with depth of log k ≈ −32 log z - 14, where k is in meters squared and z is in kilometers At mid to lower crustal depths this curve lies just below the threshold value for significant advection of heat Such conditions may represent an optimum for metamorphism, allowing the maximum transport of fluid and solute mass that is possible without advective cooling

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Permeability of the continental crust: Implications of geothermal data and metamorphic systems

Introduction Ege, J.R., 1984, Mechanisms of surface subsidence resulting from solution extraction of salt, in Holzer, T.L., ed., Man-induced land subsidence: Geological Society of America Reviews in Engineering Geology, v. 6, p. 203–221. Lucas, R.E., 1982, Organic soils (Histosols)—Formation, distribution, physical and chemical properties and management for crop production: Michigan State University Farm Science Research Report 435, 77 p. National Research Council, 1991, Mitigating losses from land subsidence in the United States: Washington, D. C., National Academy Press, 58 p. Stephens, J.C., Allen, L.H., Jr., and Chen, Ellen, 1984, Organic soil subsidence, in Holzer, T.L., ed., Man-induced land subsidence: Geological Society of America Reviews in Engineering Geology, v. 6, p. 107–122. White, W.B., Culver, D.C., Herman, J.S., Kane, T.C., and Mylroie, J.E., 1995, Karst lands: American Scientist, v. 83, p. 450–459.

Land subsidence in the United States

Interferometric synthetic aperture radar (InSAR) has great potential to detect and quantify land subsidence caused by aquifer system compaction. InSAR maps with high spatial detail and resolution of range displacement (±10 mm in change of land surface elevation) were developed for a groundwater basin (∼103 km2) in Antelope Valley, California, using radar data collected from the ERS-1 satellite. These data allow comprehensive comparison between recent (1993–1995) subsidence patterns and those detected historically (1926–1992) by more traditional methods. The changed subsidence patterns are generally compatible with recent shifts in land and water use. The InSAR-detected patterns are generally consistent with predictions based on a coupled model of groundwater flow and aquifer system compaction. The minor inconsistencies may reflect our imperfect knowledge of the distribution and properties of compressible sediments. When used in conjunction with coincident measurements of groundwater levels and other geologic information, InSAR data may be useful for constraining parameter estimates in simulations of aquifer system compaction.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/98WR01285

Detection of aquifer system compaction and land subsidence using interferometric synthetic aperture radar, Antelope Valley, Mojave Desert, California

Preface List of symbols 1. Groundwater flow 2. Solute transport 3. Heat transport 4. Regional-scale flow and transport 5. Ore deposits 6. Hydrocarbons 7. Geothermal processes 8. Earthquakes 9. Evaporites 10. Diagenesis and metamorphism References.

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Groundwater in Geologic Processes

The variation of permeability with depth can be probed indirectly by various means, including hydrologic models that use geothermal data as constraints and the progress of metamorphic reactions driven by fluid flow. Geothermal and metamorphic data combine to indicate that mean permeability (k) of tectonically active continental crust decreases with depth (z) according to log k ! )14‐3.2 log z, where k is in m 2 and z in km. Other independently derived, crustal-scale k‐z relations are generally similar to this power-law curve. Yet there is also substantial evidence for local-to-regional-scale, transient, permeability-generation events that entail permeabilities much higher than these mean k‐z relations would suggest. Compilation of such data yields a fit to these elevated, transient values of log k ! )11.5‐3.2 log z, suggesting a functional form similar to that of tectonically active crust, but shifted to higher permeability at a given depth. In addition, it seems possible that, in the absence of active prograde metamorphism, permeability in the deeper crust will decay toward values below the mean k‐z curves. Several lines of evidence suggest geologically rapid (years to 10 3 years) decay of high-permeability transients toward background values. Crustal-scale k‐z curves may reflect a dynamic competition between permeability creation by processes such as fluid sourcing and rock failure, and permeability destruction by processes such as compaction, hydrothermal alteration, and retrograde metamorphism.

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Steven E. Ingebritsen

Papers

Permeability of the continental crust: Implications of geothermal data and metamorphic systems

Land subsidence in the United States

Detection of aquifer system compaction and land subsidence using interferometric synthetic aperture radar, Antelope Valley, Mojave Desert, California

Groundwater in Geologic Processes

Permeability of the Continental Crust: Dynamic Variations Inferred from Seismicity and Metamorphism