Safety analysis is a crucial approach for improving safety in the aircraft system design process. Traditional methods for safety analysis often lag behind the design process and rely heavily on engineers' expertise, limiting their effectiveness for complex aircraft systems. This paper proposes an advanced safety analysis method for airborne systems, integrating formal verification with the Systems Modeling Language (SysML). A detailed SysML-based system model is constructed to align safety analysis with system design seamlessly. Additionally, We introduce a novel coding approach that translates SysML state machine models into symbolic Kripke structures, utilizing Boolean functions to implicitly represent graphical system behavior. To mitigate the state explosion issue common in complex systems, an improved reachability analysis algorithm utilizing partition sorting is proposed. Building on this foundation, symbolic model checking techniques are employed to automate safety analysis, including the verification of safety requirements and the derivation of minimal cut sets for airborne systems. The proposed method is validated with case studies on an electro-mechanical actuator and a flight control system. Compared to existing methods, this approach ensures consistency between safety and system design models, eliminates complex transformations between modeling languages, and improves both the efficiency and accuracy of the analysis.

There is a growing trend towards making Synthetic Aperture Radar (SAR), SAR Interferometry (InSAR), and their applications more accessible to end users. Directly delivering geocoded, co-registered, and flattened SLC data (GSLC) eliminates the need for the complex geocoding and co-registration procedures which require professional domain knowledge and software. GSLC products dramatically simplify InSAR processing flows, making InSAR products easily available to a wide range of users. However, challenges still exist for SAR/InSAR analysis using GSLC datasets. In this paper, we analyze the feasibility of using GSLC for deformation measurements based on offset tracking, both in theory and practice. We find that correct GSLC offset tracking requires the input GSLCs to be 1) unflattened, 2) deramped, and 3) adequately sampled. We also show that the direct result from GSLC offset tracking is a projection of displacement in the slant range and azimuth directions. We can transform the offset measurement from GSLCs to the deformation field, but the current transformation relation is not precise enough. This research may help deepen the understanding of GSLCs and their applications.

Interferometric Synthetic Aperture Radar (InSAR) has become an essential tool for monitoring surface deformation with high precision and wide spatial coverage. Among various InSAR techniques, the Stacking InSAR approach is widely used for geological hazard assessments due to its computational efficiency and robustness against decorrelation noise. However, Digital Elevation Model (DEM) errors remain a significant challenge, introducing spurious deformation signals and degrading deformation rate estimates. We present here a rigorous analysis of the impact of DEM errors on Stacking InSAR-derived deformation rates and introduce an enhanced method that effectively mitigates these errors. Unlike conventional correction techniques that require explicit DEM error estimation, the proposed method leverages perpendicular baseline averaging, eliminating DEM-induced biases while maintaining computational simplicity. The method is validated using both simulated and real Sentinel-1A datasets from two tracks, with results compared against conventional Stacking and Small Baseline Subset (SBAS) approaches. The findings demonstrate that the proposed method significantly improves deformation rate estimation by suppressing DEM-induced artifacts, thereby enhancing the reliability of InSAR applications in geological hazard monitoring and deformation assessment.

Heavy rainfall and earthquake in mountain areas usually trigger numerous landslides. Fast and accurate mapping of landslides is crucial for risk management and emergency rescue. Deep learning-based landslide detection methods can automate identification, but convolutional neural network (CNN) models focus primarily on local features, often missing crucial global context in landslide images. Conversely, Transformer-based models excel at capturing global features but are hindered by high computational complexity. As a result, existing detection models struggle to strike an effective balance between accuracy and efficiency. To address this issue, this article presents a lightweight landslide detection method based on the newly proposed Mamba network. Specifically, a landslide detection model named SegMamba2D with an encoder–decoder structure is proposed. In the encoder, the Mamba network is used to extract multiscale features. A state-space model (SSM) is employed to reduce computational complexity while maintaining accuracy. In the decoder, a multilayer perceptron is used to build a lightweight decoder, ensuring that the model’s overall complexity remains low. The experimental results on both public and new datasets demonstrate that SegMamba2D achieves a superior landslide detection accuracy, with an approximately 2% improvement in$F1$score across various scenarios over conventional models, while significantly reducing computational costs. Additionally, SegMamba2D demonstrates robust generalization performance across diverse research areas. These advancements highlight the model’s potential to enhance accuracy in creating landslide inventories and expedite emergency response times during landslide disasters. The source code is available athttps://github.com/xiaochuan-tang/SegMamba2D

We document one of several methodologies used to validate the NASA-ISRO Synthetic Aperture Radar (NISAR) mission requirements for solid earth deformation. NISAR’s deformation requirements cover steady-state, coseismic, and transient deformation processes and were designed to confirm that the mission is able to meet its solid earth science goals. We use independent observations of earth surface deformation from continuous Global Navigation Satellite System (GNSS) stations as ground truth for NISAR-observed deformation, and we provide a statistical framework to assess the quality of the associated NISAR data products. Our validation workflows have been developed as Jupyter Notebooks and are publicly available via GitHub/GitLab.

The application of multi-temporal interferometric synthetic aperture radar (MTInSAR) technology in bridge structural health monitoring often encounters considerable challenges due to the intricate nature of bridge structures. Notably, the thermal expansion and contraction (TEC) of bridges can lead to prominent interferometric phase jumps at the expansion joints. When the magnitude of the phase jump exceeds π, the continuity assumption required for phase unwrapping is no longer valid. To address this limitation, we propose an adaptive MTInSAR method that can partition the bridge into independent segments and concurrently estimate deformation from multiple reference points. We validate the effectiveness of the method using 23 TerraSAR-X images of the Shanghai Yangtze River Bridge. The results demonstrate the successful detection of expansion joints and reliable phase unwrapping in PS sub-networks.

Landslide hazards pose a great threat to the local residents and infrastructure in mountain areas. Numerous technologies have been invented to monitor landslides, and large amounts of high-resolution spatio-temporal data are consistently emerging. These data are highly related to the national security. The local governments release legislation to regulate the sharing of these data. However, the existing landslide detection models explicitly or implicitly assume that landslide monitoring and mapping data are directly shared in a centralized server. There is a gap between landslide detection models and landslide data sharing. To bridge this gap, this letter proposes a privacy-preserving machine learning method named federated learning-based landslide detection (FedLD) for landslide detection. First, horizontal federated learning (HFL) is introduced to protect the data privacy of the modeling process of landslide detection, enabling the development of landslide detection models without direct sharing of the original landslide monitoring data. Second, a new marginal contribution (MC) metric is proposed to measure the contribution of the participants of federated landslide detection models and is used to develop a model aggregation algorithm for federated landslide detection. Experimental results demonstrated that FedLD is able to protect the privacy of popular deep learning-based landslide detection models and achieves competitive landslide classification performance. Therefore, federated learning (FL) provides an effective solution for promoting data sharing in landslide detection.

The application of multitemporal interferometric synthetic aperture radar (MTInSAR) technology in bridge structural health monitoring often encounters considerable challenges due to the intricate nature of bridge structures. Notably, the thermal expansion and contraction (TEC) of bridges can lead to prominent interferometric phase jumps at the expansion joints. When the magnitude of the phase jump exceeds$\pi $, the continuity assumption required for phase unwrapping is no longer valid. Consequently, classical phase unwrapping methods fail to accurately retrieve bridge deformation. To address this limitation, we propose an adaptive MTInSAR method that can partition the bridge into independent segments and concurrently estimate deformation from multiple reference points. The algorithm first identifies expansion joint locations using a mean square error threshold. Subsequently, reference point selection and segmental phase unwrapping are performed to derive displacement time series of persistent scatterers (PSs), where the mechanical properties of the bridge structure are considered. We validate the effectiveness of the method using 23 TerraSAR-X (TSX) images of the Shanghai Yangtze River Bridge. The results demonstrate the successful detection of expansion joints and reliable phase unwrapping in PS subnetworks. Moreover, a comparative analysis with the classical minimum cost flow (MCF) method highlights the superior adaptability and reliability of the proposed approach. Finally, threshold values for triggering conditions when phase jumps occur are quantified. The proposed work will enhance the robust monitoring of bridge motions, safeguarding the structural health of bridges.

NASA has committed to open-source science that enables Earth observation data transparency, inclusivity, accessibility, and reproducibility – all fundamental to the pace and quality of scientific progress. We have embraced this vision by producing standard InSAR science products that are freely available to the public through NASA Data Active Archive Centers (DAACs) and are generated using state-of-the-art open-source and openly-developed methods. The Advanced Rapid Image Analysis (ARIA) project’s Sentinel-1 Geocoded Unwrapped Phase product (ARIA-S1-GUNW) is a 90 meter InSAR product that spans major, land-based fault systems, the US Coasts, and active volcanic regions through the complete Sentinel-1 record. The products enable the measurement of centimeter-scale surface displacement with applications across the solid earth, hydrology, and sea-level disciplines. The ARIA-S1-GUNW also enables rapid response mapping of surface motion after earthquakes, landslides, and subsidence. The ARIA-S1-GUNW products are freely available through the Alaska Satellite Facility (ASF) DAAC. In the last year, we have successfully grown the archive to over 1.1 million products, a 6 fold increase, through NASA ACCESS by improving our processing workflow and leveraging HyP3, an AWS-based cloud processing environment. We are continuing to partner with researchers to generate more products over relevant areas of scientific interest. All the processing software and cloud infrastructure are open-source to ensure reproducibility and enable other scientists to modify, improve upon, and scale their own cloud workflows for related InSAR analyses. We have, in parallel, developed and supported open-source, well-documented tools to further streamline time-series analysis from the ARIA-S1-GUNW into deformation analysis workflows.

The Delaware sub-basin, a part of the Permian basin, located along the borders of New Mexico and Texas is known for its vast hydrocarbon reserves. The fluid exchange during hydrocarbon production and associated wastewater injection has resulted in surface deformation, endangering the infrastructure in the basin. In this work, we have estimated basin-wide time-series surface deformation over the Delaware basin between 2018-2021 using persistent scatterer interferometry technique with Sentinel-1 imagery. The results show that the Delaware basin is primarily affected by subsidence with several pockets deforming at a rate of up to 40 mm/yr. Further, we used scaled distributed point sources to model the deformation using the production and injection volumes. The modeled deformation is consistent with the observed except for a few small pockets with high injection volumes. In addition, the modeled results indicate the inelastic deformation or diffusion of the injected fluids over a large area.

Coherence estimation is crucial in Interferometric Synthetic Aperture Radar (InSAR) for various applications, including land cover classification, change detection, and multi-temporal InSAR techniques. However, estimation challenges often arise due to systematic phases caused by deformation and topography, leading to an underestimation. Existing FFT-based stripe correction methods have limitations in handling complex patterns and are noise-sensitive. We propose here a novel approach using local phase surface fitting to remove the trend component and thereby improve the accuracy of the coherence estimation. The algorithm involves adaptive window size selection based on varying phase patterns and joint estimation of surface model parameters using regional network adjustment. Comparative experiments with simulated and Sentinel-1A data demonstrate the superiority of the method in effectively removing trend components, especially from nonlinear stripe regions. This improvement is evident in the significantly enhanced average coherence, with increases of 37.4% and 60.1% observed in the two experimental areas, resulting in enhanced quality of interferogram coherence maps.

Multitemporal InSAR is a widely used geodetic technique for measuring ground deformation. However, assessing the accuracy of InSAR deformation results is challenging, especially when field measurements such as leveling are limited in coverage or unavailable. While many studies have attempted to calculate the uncertainty of deformation using a priori InSAR stochastic models to assess the deformation reliability, these models are often biased by various factors. In this letter, we propose a new method called the Sparse Parameter Model (SPM) for InSAR deformation retrieval and uncertainty assessment when instantaneous deformation is not the focus. The method estimates the sparser deformation time series and leverages redundant SAR observations for the deformation uncertainty assessment and decorrelation noise suppression. The proposed model is tested by both simulated and real Sentinel-1 datasets and the derived deformation was validated with GPS measurements in the real application. The results demonstrated that the overall uncertainty of InSAR deformation, as estimated by the SPM, is 5.4 mm, falling well within the expected range of uncertainty, which highlights the effectiveness of the SPM in retrieving InSAR deformation and assessing uncertainty.

The new generation of SAR satellites is serving our long-standing demand for high-resolution crustal deformation over various scales. However, the reliability of InSAR measurements is still limited by varying tropospheric conditions between acquisitions, especially when mapping slow-deforming interseismic deformation. We propose here a new phase-based approach for mapping interseismic deformation using short-period interferograms. Our method formulates the InSAR phase after topographic correction as the sum of three components: (1) spatiotemporally varied turbulent tropospheric phase, (2) topography-correlated stratified tropospheric phase, and (3) interseismic-related deformation assumed to be accumulated at a constant rate. We simultaneously solve for the parameters in the model to avoid overestimating the tropospheric phases, especially when interseismic deformation and tropospheric delays are both coupled with elevation in space. Synthetic tests and practical applications to easternmost Altyn Tagh fault demonstrate that the new method can effectively recover the small-amplitude interseismic deformation caused by fault motion even when the interferograms are dominated by strong tropospheric delays.

The joint NASA/ISRO SAR (NISAR) satellite mission is anticipated to provide routine L-band coverage of most of the Earth's land surface every 12-days for both ascending and descending orbits. In terms of impact on solid earth science (SES), the primary measurement will be Interferometric SAR (InSAR) observations of ground deformation in two satellite line-of-sight (LOS) directions. Key observation characteristics include acquisitions with small interferometric baselines to maximize interferometric coherence and decrease sensitivity to topography, wide bandwidth allowing for split-band processing to model out the impacts of the ionosphere, and joint L- and S-band observations in selected regions. We describe here the key measurement requirements for solid earth science, as well as our approach to validating these requirements once the mission is underway.

Phase decorrelation, as one of the main error sources, limits the capability of interferometric synthetic aperture radar (InSAR) for deformation mapping over areas with low coherence. Although several methods have been realized to reduce decorrelation noise, for example, by phase linking and spatial and temporal filters, their performances deteriorate when coherence estimation bias exists. We present an arc-based approach that allows reconstructing unwrapped interval phase time-series based on iterative weighted least squares (WLS) in temporal and spatial domains. The main features of the method are that phase optimization and unwrapping can be jointly conducted by spatial and temporal iterative WLS and coherence matrix bias has negligible effects on the estimation. In addition, the linear formation makes the implementation suitable with small subset of interferograms, providing an efficient solution for future big SAR data. We demonstrate the effectiveness of the proposed method using simulated and real data with different decorrelation mechanisms and compare our approach with the state-of-art phase reconstruction methods. Substantial improvement can be achieved in terms of reduced root-mean-square error (RMSE) in the simulation data and increased density of coherent measurements in the real data.

InSAR technique is a powerful remote sensing tool to detect potentially unstable slopes at a catchment scale and to monitor surface displacements of a single landslide. However, decorrelations caused by complex terrain and vegetation coverage in southwest mountainous areas of China, make it almost impossible for conventional time series InSAR methods (e.g. PSI and SBAS) to identify sufficient measurement points and get desired results. In this paper, a new InSAR approach named coherent scatterer (CS) -InSAR is proposed to map landslide surface displacements by joint exploitation of persistent scatterers (PS) and distributed scatterers (DS). Case study of Danba County using both L-band ALOS PALSAR images (2006-2011) and C-band Sentinel IW images (2017-2018) demonstrate the effectiveness of our CS-InSAR method in detecting and monitoring the activity of potential landslide in China's mountainous west.

The synthetic aperture radar (SAR) interferometry (InSAR) has been developed for more than 20 years for historical surface deformation reconstruction. In particular, the onboard Sentinel-1/A/B satellite, newly planned NASA-ISRO SAR (NISAR), and Germany Tandem-L will continue to provide unprecedented SAR data with an increased number of acquisitions. However, processing of real-time SAR data has been experiencing challenges regarding the InSAR deformation parameter estimation over a long time with the small baseline subsets (SBAS) InSAR technology. We use sequential adjustment for the estimation of the deformation parameters, which uses Bayesian estimation theory under the least square criteria to inverse long time-series deformation dynamically. Finally, both simulated and real Sentinel-1A SAR data verify the performance of the sequential estimation. It can be regarded as an effective data processing tool in the coming era of SAR big data.

A broad range of studies have been conducted so far to quantify the effect of soil moisture on synthetic aperture radar (SAR) intensity and interferometric synthetic aperture radar (InSAR) phase. The introduced models are either intensity or interferometric models, and there is no single scattering model that can estimate both intensity and phase changes, indicating the subject is poorly understood. Here, we quantify the influence of soil moisture on InSAR phase and SAR intensity by employing a volume scattering model. We model soil as a collection of randomly distributed independent point scatterers embedded in a homogeneous background. Our volume scattering model successfully estimates SAR intensity and InSAR phase changes due to soil moisture changes. In addition to soil moisture changes, the model also takes into account the scatterers' size and their volumetric fraction. This may open a new window in the study of soil structure using SAR images and InSAR methods. Our results indicate that the structure of soil manipulates the way soil moisture alters the SAR intensity and InSAR phase. The model has been evaluated against field soil moisture measurements and shown to be successful in modeling InSAR phase and SAR intensity.

Nonzero closure phase exists in multilooked pixels. We study the influence of the statistical properties of the intensity and phase changes of single-looked pixels on multilooked phase and coherence. By quantifying the extent of their influences on phase triplet, we show in this article that the statistical properties of the intensity of pixels within a multilooking window can induce changes in interferometric phase and coherence, and contribute to the nonzero closure phase. We demonstrate that the intensity-induced changes increase by increasing the standard deviation of the phase changes, dispersion index of intensity, and the correlation between the intensity and phase changes. We have used ALOS Phased Array type L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2, and Sentinel-1 images to generate real and semisynthetic interferograms to assess our findings. The semisynthetic interferograms are produced by pairing real SAR data and synthetic SAR data; the synthetic SAR data are generated from the real data by adding random vectors with predefined average changes of phase and intensity. Our results show that closure phase is only a function of the statistical properties of the phase and intensity of pixels, and does not possess the information about the magnitude of physical changes. This casts doubt on the effectiveness of methods that exploit phase triplet as a means to estimate soil moisture or any other deforming or nondeforming changes.

Phase unwrapping (PU) and phase filtering are the key procedures for the interferometric synthetic aperture radar (InSAR) technology. As one of the most popular multibaseline PU (MBPU) algorithms, the cluster-analysis (CA)-based MBPU algorithm still has some problems that need to be improved. To begin with, the cluster ambiguity vector is obtained by searching the nearest integer point to the cluster centerline with known slope and intercept in the search space. It will be time-consuming and inconvenient when the number of baselines or the search space is too large. In addition, they do not have the capacity of phase filtering. Moreover, they do not consider the impact of different baseline combinations on the performance of the CA-based MBPU algorithm. For these reasons, a novel CA-based MBPU and filtering (MBPUF) algorithm is proposed in this article. The main contributions of this article are that it gives the closed-form solving formulas of the cluster ambiguity vector to improve the efficiency of the CA-based MBPU algorithm, proposes a novel MB InSAR phase-filtering strategy that makes the CA-based MBPU algorithm capable of solving the phase-discontinuity problem and improving the height-reconstruction accuracy simultaneously, and utilizes the optimal baseline combination to improve the robustness of the CA-based MBPU algorithm. Theoretical analysis and experiments on both simulated and real MB InSAR data sets show the effectiveness and robustness of the proposed closed-form robust CA-based MBPUF algorithm.

Multi-source Synthetic Aperture Radar (SAR) datasets are used to investigate the deformation history and failure mechanism of small-scale loess landslide in the Heifangtai loess terrace, Gansu province, China. A total of 65 SAR datasets acquired by L-band ascending ALOS/PALSAR, X-band descending TerraSAR-X and C-band descending Sentinel-1A/B covering the different periods of Heifangtai terrace are fully exploited. In addition, groundwater level data are also involved to analyze the failure mechanism of loess landslide. The displacements occurred during the past eleven years were quantitatively identified for firstly by InSAR technique. The results show that three slopes, failed on October 1, 2017 had large cumulative deformations from January 2016 to November 2016. The acceleration dates of the deformation for the three slopes were successfully captured by Sentinel-1A/B data. Furthermore, the result shows that the magnitude of the landslide deformation is closely correlated to the groundwater level variation.

Tropospheric delays (TDs) in differential interferometric synthetic aperture radar (InSAR) measurements are mainly caused by spatial and temporal variation of pressure, temperature, and humidity between SAR acquisitions. These delays are described as one of the primary error sources in InSAR observations. Although independent atmospheric measurements have been used to correct TDs, their sparse spatial or temporal resolution requires interpolation, leading to uncertainties in the corrected interferograms. The performance of the conventional phase-based correction method is weakened by the presence of confounding signals (e.g., TDs, deformation, and topographic errors) and spatial variability of the troposphere. Here, we propose a method that can simultaneously estimate stratified TDs together with parameters of deformation and topographic error based on their distinct spatial-temporal correlation. Spatial variability of the relationship between TDs and topographic height is addressed through localized estimation in windows divided by quadtree according to height gradient. We demonstrate the performance of the proposed method with both simulated and real data sets. In addition, both advantages and disadvantages of this method are addressed.

Isolating phase residuals due to inaccurate external digital elevation model (DEM) is important in retrieval and interpretation of deformation behavior from interferometric synthetic aperture radar (InSAR) observations. Multitemporal InSAR (MTInSAR), by taking DEM error as a parameter, can make the isolation possible. However, due to the presence of atmospheric artifacts in observations and improper deformation model employed in the observation system, accurate retrieval of DEM error cannot be guaranteed in current MTInSAR techniques. Considering that the DEM error has a fixed spatial pattern and its contribution to interferometric phase-only changes with spatial baselines, we propose here a nonparametric method that can estimate the DEM error in a more robust way. To retrieve signals having a fixed spatial pattern from unwrapped MTInSAR measurements, the independent component analysis (ICA) is used. Experiments with synthetic and real data sets indicate the proposed method is able to estimate DEM error with no a priori information about deformation. Moreover, experiments also show that the method can provide a more robust estimation when the observed phase observations are affected by atmospheric delays and/or the number of interferograms used is limited.

Removing the topographic component in the interferometric synthetic aperture radar (InSAR) phase is conventionally conducted using an external digital elevation model (DEM). However, with an increasing spatial resolution of SAR data, the external DEM is becoming less qualified for this purpose, resulting in notable phase residues and even decorrelation in differential interferograms. Although topographic residuals can be parameterized and estimated by multi-temporal InSAR (MTInSAR) techniques, its accuracy is limited by several factors. Instead of providing accurate height information, shortening the length of baselines is an alternative for DEM phase mitigation. We propose here an MTInSAR processing framework that can retrieve the deformation time series without the estimation of topographic residuals. Within the framework, we generate a set of pseudo interferograms with near-zero baselines by integer combination and take these pseudo interferograms as observations of MTInSAR model, where deformation becomes the only signal that needs to be parameterized. The deformation time series is then retrieved directly from wrapped phases by ridge estimation with an integer ambiguity detector. It is noted that although atmospheric artifacts might be magnified during the combination, their differential components at arcs constructed with neighboring points that are not significantly enlarged. The proposed method is particularly suitable for infrastructure deformation monitoring in urban areas where no accurate external DEM is available. It also has promising potential for retrieving deformation from SAR data stacks with short acquisition intervals since the combination can enlarge the signal of interests in pseudo-observations. Semisynthetic and real data tests indicate that the proposed method has satisfied performance on DEM error mitigation and deformation time series estimation.

Stratified tropospheric delays (TDs) in differential interferometric synthetic aperture radar (InSAR) result from the temporal variation of vertical stratification in the lower part of the troposphere. Although an approximately model can be made by assuming a linear relationship between topography and delayed phase in the interferogram, the estimation is weakened by the spatial variability of troposphere and the interference from other confounding signals (e.g., deformation, topographic error and orbit error, etc.). In this contribution, a jointly tropospheric correction scheme is proposed to simultaneously estimate stratified tropospheric delays with deformation and topographic errors. Spatial variability of tropospheric properties is addressed through a localized estimation which is derived by quadtree segmentation according to height gradient. The performance of the proposed method is validated and compared with the conventional linear and weather-model-based methods using Sentinel-1 dataset.

For airborne repeat-pass synthetic aperture radar interferometry (InSAR), precise trajectory information is needed to compensate for deviations of the platform movement from a linear track. Using the trajectory information, motion compensation (MoCo) can be implemented within SAR data focusing. Due to the inaccuracy of current navigation systems, residual motion errors (RMEs) exist between the real and measured trajectory, causing phase undulations in the final interferograms. Up to now, MoCo and RME estimation have usually been combined in airborne InSAR to estimate ground deformation. Conventional MoCo methods generally involve azimuthal and range resampling and phase correction. Then frequency-domain focusing techniques can be used to generate the SAR images. After focusing SAR images with MoCo, both multisquint and autofocus approaches can be used to estimate RME. In addition to the MoCo-based frequency-domain focusing, the time-domain backprojection (BP) technique can also focus the SAR data obtained from highly nonlinear platform trajectories. In this paper, we present, for the first time, the combination of BP and multisquint techniques for RME estimation. A detailed derivation of the implementation of the multisquint approach using the BP-focusing images is presented. Repeat-pass data from the SlimSAR system over Slumgullion landslide are used to demonstrate the feasibility of RME estimation for both stationary and nonstationary scenes. We conclude that the proposed method can effectively remove the RME.

Differential interferometric synthetic aperture radar (InSAR) time-series processing relies on identifying coherent pixels in SAR image stacks that show the persistent scatterer (PS) or distributed scatterer (DS) behavior. Accuracy of InSAR time-series estimates is dependent on the quality of selected PS/DS pixels. Current pixel selection techniques perform well when identifying highly coherent pixels but produce many false alarms in low coherence regions due to the inherent bias in residual phase estimation. Therefore, pixels with low coherence may have the appearance of noise and be rejected if the coherence threshold is too high. In contrast, lowering the threshold increases the number of false alarms introduced in processing giving noisier time-series as a result of incorrect phase unwrapping. The multidimensional SAR data acquisition can be described as a zero mean Gaussian process fully described by the covariance matrix. In this paper, we investigate the covariance matrix using a random matrix theory approach to find the statistical properties of the eigenvalues for simulated and real SAR data. The probability distribution of all the eigenvalues in this case is limited by the Marcenko-Pastur distribution. The histogram of the highest eigenvalue follows a Tracy-Widom distribution. Thus, by adopting a pixel selection strategy based on a threshold on the highest eigenvalue of the coherence matrix, we can differentiate between low coherence and noise pixels. In addition, our technique provides a methodology to detect the number of targets present in multiscatterer layover pixels and extract time-series information from double bounce response of bridges. Applying the technique for TerraSAR-X data over Berlin shows the effectiveness of the algorithm.

Sinkhole is ground depression and/or collapse over the subsurface cavity in the karst terrain underlain by the carbonates, evaporites, and other soluble soils and rocks. The geohazards have been considered as a “hidden threat” to human life, infrastructures and properties. Sinkholes in West Texas have developed due to the dissolution of the subsurface evaporite deposits in contact with groundwater. Two Wink sinkholes in Wink, Texas, collapsed in 1980 and 2002, respectively. However, monitoring the sinkholes in no man's lands has been challenging due to the lack of availability of appropriate ground-based or remote sensing observations. We employ spaceborne SAR imagery to capture the status quo and forthcoming evolution of Wink sinkholes and neighboring regions. Finally, our study discusses the relationship among the sinkhole deformation, anthropogenic activities, and natural causes taking place in dense oil patch region and with low precipitation.

In this study, we present a case study of the Slumgullion landslide conducted in July 2015 to demonstrate the feasibility of deformation mapping with an airborne synthetic aperture radar (SAR) system known as ARTEMIS SlimSAR, which is a compact, modular, and multi-frequency radar system. For this study, the L-band SlimSAR was installed on a Cessna 206 aircraft and data were collected on July 3, 7, and 10 of 2015 and processed using the time-domain backprojection algorithm. Airborne light detection and ranging (LiDAR) campaign, GPS surveys and spaceborne InSAR analysis using COSMO-SkyMed images were also conducted to verify the performance of the airborne SAR system. The airborne InSAR results showed satisfying agreement with the GPS and spaceborne InSAR results. A 3-D deformation map over Slumgullion landslide was also generated, which displayed distinct correlation between the landslide motion and topography.

Tailings impoundment failures may lead to catastrophically fatal, environmental and financial consequences. However, field investigation and geotechnical analysis are limited by sparse instrumentation and high cost. Here we use time-series Interferometric Synthetic Aperture Radar (InSAR) method to map out the settlement over the entire tailings impoundment area in the vicinity of Great Salt Lake (Utah) using multiple SAR data. We show that the south pond is experiencing quasi-linear settlements with the largest rate of 200+ mm/yr at the northeast corner. InSAR observations can be well-explained by geotechnical consolidation model, which reveals and predicts the gradually decelerated settlement process. InSAR-derived displacement maps also highlight active motions of surrounding infrastructures, such as some highway segments. Nevertheless, there is no clear evidence that the fluctuating deformation at those locations and seasonal varied water level are correlated.

Detection of slow landslide movement in forested terrains has long been problematic, particularly for Cascade landslide complex in Washington, USA. Although parts of the landslide have been found reactivated, the timing and magnitude of landslide motions have not been systematically monitored. Here we apply time-series interferometric synthetic aperture radar (InSAR) strategies to map the landslide movement using two overlapping L-band ALOS-1 PALSAR-1 tracks. Our results show that the reactivated part of the landslide moved ~700 mm downslope between 2007 and 2011. The seasonal oscillations are correlated with precipitation records, suggesting the landslide motions are hydrologically driven. The temporal frequency of landslide motions is similar to that of GPS-derived regional ground oscillations but the former has much larger magnitude, suggesting stronger hydrological loading effects. Analysis of time-series radar amplitude on the headscarp allows us to re-evaluate the incipient motion of 2008 Greenleaf Basin rock avalanche.

Growth faults are common and continue to evolve throughout the unconsolidated sediments of Greater Houston region. Property damages due to faulting have become more evident during the past few years. The constant damages and the rapid rate of the fault movements portray the necessity of further study of the faults over this area. Locating the active faults is crucial for protecting people and infrastructures from severe damages. However, the mechanism of a majority of geo-hazards caused by faulting is still unclear, and many relatively small faults or fissures are still unrecognized. This paper aims to position and monitor the faults activity in Greater Houston region using an improved MTI technique. The improved MTI method, with maximized usable signal and correlation, has ability to identify and monitor the active faults by a detailed monitoring of differential vertical displacements. Not only those previously known faults position but also the new fault traces that have not been recognized or mapped by other methods are imaged.

Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Post-Classification Comparison (PCC) methods based on supervised change detection are widely used in change detection for remote sensing images, but are easily affected by a significant cumulative error of single remote sensing image classification. Unsupervised change detection methods are affected by the speckle noise and cannot explicitly identify the types of land cover or land use transitions. To solve those problems, this paper proposes a change detection method based on similarity measure and joint classification. The similarity measure is obtained by test statistic and Kittler and Illingworth minimum-error thresholding algorithm (TSKI), which is used to automatically control the joint-classification classifier. The efficiency of the proposed method is demonstrated by the polarimetric synthetic aperture radar (PolSAR) images acquired by Radarsat-2 over Wuhan of China. The experimental results show that the method can identify different types of land cover changes and reduce the false alarms in the change detection.

Topographic residuals in differential interferometric synthetic aperture radar (InSAR) measurements are mainly caused by inaccurate external digital elevation model (DEM). Accurate separation of the phase component contributed by topographic residuals plays an important role in the retrieval of deformation time series from InSAR observations. Even though the residuals can be modeled and estimated in the framework of multitemporal SAR interferometry (MTInSAR), it is not clear what an optimal processing strategy is and how accurate the estimation can reach. We analyze here the factors that affect the accuracy of the retrieved DEM residuals by applying four commonly used MTInSAR methods in a series of simulated scenarios. The results indicate that besides the quality of interferometric observations, the thresholds of spatial and temporal baselines, the diversity of spatial baseline lengths, the connectivity of interferogram network, and improper deformation model also fluctuate the accuracy of the retrieved topographic residuals. According to these affecting factors, this paper sheds light on an optimal approach to reliably retrieve accurate topographic residuals under MTInSAR framework.

A new approach is presented for mapping 3-D surface displacement caused by subsurface fluid volumetric change based on 1-D interferometric synthetic aperture radar (InSAR) line-of-sight measurements and surface deformation modeling. The relationship between surface deformation and source fluid volumetric change is modeled according to elastic half-space theory. A distinctive advantage of the proposed approach is that it effectively extends the capability of the sun-synchronous orbit side-looking synthetic aperture radar that has been essentially only able to measure 1-D displacements accurately or at most 2-D displacements when InSAR measurements from more than one orbit or platform are combined. Experimental studies are carried out with both simulated and real data sets to test the performance of the method. The results have demonstrated that the approach works very well.

Underground mining induced displacements in most areas of China amount to meter-level while with small spatial coverage, spatially discontinuous and temporally nonlinear features. Traditional phase-based InSAR methods can hardly obtain large deformation in the center of land subsidence area due to the phase noise, maximum monitoring ability. This paper systematically studies the offset tracking technique based on SAR image intensity maps with and without pre-installed Corner Reflectors (CRs).The results of this experiment indicate that the high resolution SAR data with the aid of pre-installed CR points can better solve the large gradient deformation monitoring problem. In addition, offset tracking method has the potential to achieve two-dimensional deformation field along the line-of-sight and along the azimuth directions, which can provide more detailed information regarding mining induced deformation, which is complimentary to the traditional phase-based and intensity-based techniques.

InSAR has been widely used in monitoring land subsidence over large area. However, many factors in InSAR processing, such as decorrelation error, atmosphere error, height error and thermal noise limit the accuracy of InSAR measurements. The height error over urban area is particularly the most difficult issue in TerraSAR-X data processing for its shorter wavelength and higher spatial resolution. We employed a Multi-temporal InSAR (MTI) method based on the PS and SBAS method proposed by Hopper to estimate height of urban building and ground deformation simultaneously. By means of MTI method, the first raw urban DSM with 1,500 km2areas over urban area has been mapped with a height accuracy of about 5 m. The MTI-derived deformation shows that the established TerraSAR DSM reduced the height error influence on deformation phase effectively. GPS and leveling measurements are applied to calibrate the InSAR results. Precision of our InSAR annual subsidence can reach 6 mm.

Sinabung volcano in Indonesia is a part of the Pacific Ring of Fire, formed due to the subduction between the Eurasian and the Indo-Australian plate. We study the deformation of Sinabung volcano using ALOS/PALSAR interferometric synthetic aperture radar (InSAR) images acquired from Feb. 2007 to Jan. 2011. Based on multi-temporal InSAR processing, we have mapped the ground surface deformation before, during, and after the 2010 eruption. During the 3 years before the 2010 eruption, the volcano inflated at an average rate ∼1.7 cm/yr with marked higher rate of 6.6 cm/year during the 6 months prior to the 2010 eruption. The inflation is constrained to the top of the volcano. Since the 2010 eruption to Jan. 2011, the volcano has subsided for about 3 cm. The observed inflation and deflation are modeled with a Mogi and Prolate spheroid source. The source of inflation is located about 0.3–1.3 km below sea level directly underneath the crater. On the other hand, deflation source is modeled about 0.6–1.0 km depth with coeruption period. The average volumetric change was about from −2.7×10−5to 1.9×10−6km3/yr during the deformation event. Modified Laharz model compare to Landsat-7 ETM+ image through supervised classification method. We interpret the inflation was due to magma accumulation at a shallow reservoir beneath Sinabung. Pyroclastic flow's inundation area is highly matched between two different methods with about 86 % common region inserting for deflation pattern of volume by Mogi model.

In this work, we investigate the temporal evolution of the ground deformation over the ocean-reclaimed area of the Shanghai megacity (China). To this aim, we performed an integrated SBAS-DInSAR analysis by benefiting from the availability of SAR data collected at C-band by the ENVISAT ASAR sensor, and at X-band by the COSMO-SkyMed and TerraSAR-X SAR constellations, spanning the overall time interval between 2007 and 2015. The joint exploitation of different SAR datasets and the knowledge of time-dependent models for the expected subsidence in ocean-reclaimed platforms is helpful to retrieve long-term displacement time series, thus allowing us to get new insights about the future evolution of the reclamation area settlements over the next years.

The SuperSAR imaging system, a novel multi-azimuth synthetic aperture radar (SAR) system capable of detecting Earth surface deformation in three dimensions from a single satellite platform, has recently been proposed. In this paper, we investigate the feasibility of detecting precise 3-D surface displacement measurements with the SuperSAR imaging system using a point target simulation. From this simulation, we establish both a relationship between the interferometric SAR phase and the across-track displacement and a relationship between the multiple-aperture interferometry phase and the along-track displacement based on the SuperSAR imaging geometry. The theoretical uncertainties of the SuperSAR measurement are analyzed in the across- and along-track directions, and the theoretical accuracy of the 3-D displacement measurement from the SuperSAR system is also investigated according to both the decorrelation and the squint and look angles. In the case that the interferometric coherence is about 0.8 and that five effective looks are employed, the theoretical 2-D measurement precision values are about 3.67 and 6.35 mm in the across- and along-track directions, respectively, and the theoretical 3-D measurement precision values for 3-D displacement are about 4.05, 4.56, and 3.45 mm in the east, north, and up directions, respectively. The result of this study demonstrates that the SuperSAR imaging system is capable of measuring the 3-D surface displacement in all directions with subcentimeter precision.

Orbital errors, characterized typically as longwavelength artifacts, commonly exist in interferometric synthetic aperture radar (InSAR) imagery as a result of inaccurate determination of the sensor state vector. Orbital errors degrade the precision of multitemporal InSAR products (i.e., ground deformation). Although research on orbital error reduction has been ongoing for nearly two decades and several algorithms for reducing the effect of the errors are already in existence, the errors cannot always be corrected efficiently and reliably. We propose a novel model that is able to jointly estimate deformation rates and orbital errors based on the different spatial-temporal characteristics of the two types of signals. The proposed model is able to isolate a long-wavelength ground motion signal from the orbital error even when the two types of signals exhibit similar spatial patterns. The proposed algorithm is efficient and requires no ground control points. In addition, the method is built upon wrapped phases of interferograms, eliminating the need of phase unwrapping. The performance of the proposed model is validated using both simulated and real data sets. The demo codes of the proposed model are also provided for reference.

The accuracy of L-band synthetic aperture radar (SAR) differential interferometry (InSAR) on crustal deformation studies is largely compromised by ionosphere path delays on the radar signals. The ionosphere effects cause severe ionospheric distortion such as azimuth streaking and long wavelength phase distortion similar to orbital ramp error. Effective detection and correction of ionospheric phase distortion from L-band InSAR images are necessary to measure and accurately interpret surface displacement. In this paper, we investigate the performance improvement of L-band InSAR interseismic deformation measurements in southern California through the joint correction of both ionosphere noise and orbital error. Our results show that this method can effectively remove orbit and ionosphere phase distortions. In comparison with in situ GPS measurements, the achieved InSAR measurement accuracy is improved from ~ 30 mm to ~ 10 mm by the proposed joint correction method. We show that, after the joint correction, the remaining atmosphere noise can be further mitigated through stacking, leading to an RMS error of ~ 4.7 mm/year in resultant line-of-sight velocity, as compared with ~ 11.3 mm/year before the correction. Our results demonstrate that the proposed joint correction technique provides a promising way to jointly correct orbital and ionospheric artifacts in L-band InSAR studies of crustal deformation.

The European Space Agency's Sentinel-1, a C-band imaging radar mission to be launched in mid-2013, will provide a continuity of radar data for monitoring the changing Earth. The azimuth resolution of Sentinel-1's background mode, interferometric wide-swath (IW) mode, is four times lower than that of European remote-sensing satellite (ERS) and Envisat systems. Therefore, the measurement accuracy of along-track displacement from Sentinel-1 IW images presumably will be significantly reduced. In this paper, we test the feasibility of along-track displacement measurement from Sentinel-1 IW mode. We simulate Sentinel-1 IW synthetic aperture radar (SAR) images from the ERS raw data that captured the coseismic deformation of the 1999 Hector Mine earthquake in California. Along-track displacement maps are generated using multiple-aperture interferometric SAR (MAI) and intensity tracking techniques, respectively, and are compared with GPS measurements. The root-mean-square (rms) error between the synthetic Sentinel-1 MAI and GPS measurements is about 9.6 cm, which corresponds to only 0.5 % of the azimuth resolution. The rms error between the along-track displacements from synthetic Sentinel-1 offset tracking and GPS is about 27.5 cm, which is about 1.4 % of the azimuth resolution. These results suggest that the MAI method will still be useful to measure along-track displacements from Sentinel-1 IW InSAR imagery and that it would be difficult to effectively measure the along-track displacements by the Sentinel-1 offset tracking method.

Interferometric synthetic aperture radar (InSAR) is a powerful technique that precisely measures surface deformations at a fine spatial resolution over a large area. However, the accuracy of this technique is sometimes compromised by ionospheric path delays on radar signals, particularly with L- and P-band SAR systems. To avoid ionospheric effects from being misinterpreted as ground displacement, it is necessary to detect and correct their contributions to interferograms. In this paper, we propose an efficient method for ionospheric measurement and correction and validate its theoretical and experimental performance. The proposed method exploits the linear relationship between the multiple-aperture interferometry phase and the azimuth derivative of the ionospheric phase. Theoretical analysis shows that a total electron content (TEC) accuracy of less than$1.0 \times 10^{-4}$TEC units can be achieved when more than 100 neighboring samples can be averaged (multilooked), and the coherence is 0.5. The regression analysis between the interferometric phase and the topographic height shows that the root-mean-square error can be improved by a factor of two after ionospheric correction. A 2-D Fourier spectral analysis indicates that the ionospheric wave pattern in the uncorrected power spectrum has disappeared in the power spectrum of the corrected interferogram. These results demonstrate that the proposed method can effectively remove ionospheric artifacts from an ionosphere-distorted InSAR image. Note that the method assumes that there is no appreciable surface displacement in the along-track dimension of the interferogram.

When a Synthetic Aperture Radar (SAR) signal propagates through the atmosphere on its path to and from the sensor, it is inevitably affected by atmospheric effects. In particular, the applicability and accuracy of Interferometric SAR (InSAR) techniques for volcano monitoring is limited by atmospheric path delays. Therefore, atmospheric correction of interferograms is required to improve the performance of InSAR for detecting volcanic activity, especially in order to advance its ability to detect subtle pre-eruptive changes in deformation dynamics. In this paper, we focus on InSAR tropospheric mitigation methods and their performance in volcano deformation monitoring. Our study areas include Okmok volcano and Unimak Island located in the eastern Aleutians, AK. We explore two methods to mitigate atmospheric artifacts, namely the numerical weather model simulation and the atmospheric filtering using Persistent Scatterer processing. We investigate the capability of the proposed methods, and investigate their limitations and advantages when applied to determine volcanic processes.

Surface deformation caused by an intrusion and small eruption during June 17-19, 2007, along the East Rift Zone of Kilauea Volcano, Hawaii, was three-dimensionally reconstructed from radar interferograms acquired by the Advanced Land Observing Satellite (ALOS) phased-array type L-band synthetic aperture radar (SAR) (PALSAR) instrument. To retrieve the 3-D surface deformation, a method that combines multiple-aperture interferometry (MAI) and conventional interferometric SAR (InSAR) techniques was applied to one ascending and one descending ALOS PALSAR interferometric pair. The maximum displacements as a result of the intrusion and eruption are about 0.8, 2, and 0.7 m in the east, north, and up components, respectively. The radar-measured 3-D surface deformation agrees with GPS data from 24 sites on the volcano, and the root-mean-square errors in the east, north, and up components of the displacement are 1.6, 3.6, and 2.1 cm, respectively. Since a horizontal deformation of more than 1 m was dominantly in the north-northwest-south-southeast direction, a significant improvement of the north-south component measurement was achieved by the inclusion of MAI measurements that can reach a standard deviation of 3.6 cm. A 3-D deformation reconstruction through the combination of conventional InSAR and MAI will allow for better modeling, and hence, a more comprehensive understanding, of the source geometry associated with volcanic, seismic, and other processes that are manifested by surface deformation.

In this paper, we propose a least-squares-based method for multitemporal synthetic aperture radar interferometry that allows one to estimate deformations without the need of phase unwrapping. The method utilizes a series of multimaster wrapped differential interferograms with short baselines and focuses on arcs at which there are no phase ambiguities. An outlier detector is used to identify and remove the arcs with phase ambiguities, and a pseudoinverse of the variance-covariance matrix is used as the weight matrix of the correlated observations. The deformation rates at coherent points are estimated with a least squares model constrained by reference points. The proposed approach is verified with a set of simulated data.

A numerical method called cross-step iteration is proposed to calculate the maximal/minimal received power for polarized imagery based on a target's Kennaugh matrix. This method is much more efficient than the systematic method, which searches for the extrema of received power by varying the polarization ellipse angles of receiving and transmitting polarizations. It is also more advantageous than the Schuler method, which has been adopted by the PolSARPro package, because the cross-step iteration method requires less computation time and can derive both the maximal and minimal received powers, whereas the Schuler method is designed to work out only the maximal received power. The analytical model of received-power optimization indicates that the first eigenvalue of the Kennaugh matrix is the supremum of the maximal received power. The difference between these two parameters reflects the depolarization effect of the target's backscattering, which might be useful for target discrimination.

Detailed analysis of C-band European Remote Sensing 1 and 2 (ERS-1/ERS-2) and Radarsat-1 interferometric synthetic aperture radar (InSAR) imagery was conducted to study water-level changes of coastal wetlands of southeastern Louisiana. Radar backscattering and InSAR coherence suggest that the dominant radar backscattering mechanism for swamp forest and saline marsh is double-bounce backscattering, implying that InSAR images can be used to estimate water-level changes with unprecedented spatial details. On the one hand, InSAR images suggest that water-level changes over the study site can be dynamic and spatially heterogeneous and cannot be represented by readings from sparsely distributed gauge stations. On the other hand, InSAR phase measurements are disconnected by structures and other barriers and require absolute water-level measurements from gauge stations or other sources to convert InSAR phase values to absolute water-level changes.

Differential interferometric synthetic aperture radar (SAR) (DInSAR) has proven to be very useful in mapping and monitoring land subsidence in many regions of the world. Shanghai, China's largest city, is one of such areas suffering from land subsidence as a result of severe withdrawal of groundwater for different usages. DInSAR application in Shanghai with the C-band European Remote Sensing 1 & 2 (ERS-1/2) SAR data has been difficult mainly due to the problem of decorrelation of InSAR pairs with temporal baselines larger than 10 months. To overcome the coherence loss of C-band InSAR data, we used eight L-band Japanese Earth Resource Satellite (JERS-1) SAR data acquired during 2 October 1992 to 15 July 1998 to study land subsidence phenomenon in Shanghai. Three of the images were used to produce two separate digital elevation models (DEMs) of the study area to remove topographic fringes from the interferograms used for subsidence mapping. Six interferograms were used to generate 2 different time series of deformation maps over Shanghai. The cumulative subsidence map generated from each of the time series is in agreement with the land subsidence measurements of Shanghai city from 1990 - 1998, produced from other survey methods.

Augustine volcano is an active stratovolcano located at the southwest of Anchorage, Alaska. Augustine volcano had experienced seven significantly explosive eruptions in 1812, 1883, 1908, 1935, 1963, 1976, and 1986, and a minor eruption in January 2006. We measured the surface displacements of the volcano by radar interferometry and GPS before and after the eruption in 2006. ERS-1/2, RADARSAT-1 and ENVISAT SAR data were used for the study. Multiple interferograms were stacked to reduce artifacts caused by different atmospheric conditions. Least square (LS) method was used to reduce atmospheric artifacts. Singular value decomposition (SVD) method was applied for retrieval of time sequential deformations. Satellite radar interferometry helps to understand the surface displacements system of Augustine volcano.

We present a digital elevation model (DEM) of King Edward VII Peninsula, Sulzberger Bay, West Antarctica, developed using 12 European Remote Sensing (ERS) synthetic aperture radar (SAR) scenes and 24 Ice, Cloud, and land Elevation Satellite (ICESat) laser altimetry profiles. We employ differential interferograms from the ERS tandem mission SAR scenes acquired in the austral fall of 1996, and four selected ICESat laser altimetry profiles acquired in the austral fall of 2004, as ground control points (GCPs) to construct an improved geocentric 60-m resolution DEM over the grounded ice region. We then extend the DEM to include two ice shelves using ICESat profiles via Kriging. Twenty additional ICESat profiles acquired in 2003-2004 are used to assess the accuracy of the DEM. After accounting for radar penetration depth and predicted surface changes, including effects due to ice mass balance, solid Earth tides, and glacial isostatic adjustment, in part to account for the eight-year data acquisition discrepancy, the resulting difference between the DEM and ICESat profiles is -0.57/spl plusmn/5.88 m. After removing the discrepancy between the DEM and ICESat profiles for a final combined DEM using a bicubic spline, the overall difference is 0.05/spl plusmn/1.35 m.

The deformation of Aniakchak volcano is investigated using 19 ERS-1/2 interferometric synthetic aperture radar (InSAR) data from 1992 through 2002. InSAR images from the different time intervals, reveal that the 10-km-wide caldera has been subsiding during the time of investigation. The pattern of subsidence does not following the pyroclastic flows from the last eruption of the caldera in 1931. The maximum subsidence is near the center of the caldera, with a rate of up to 13 mm/yr. Deformation outside the caldera is insignificant. Least squares inversion of the multitemporal deformation maps indicates that the subsidence rate has been relatively constant. Field observations have identified numerous fumaroles inside the caldera. In 1973, temperatures of 80/spl deg/ C were measured at a depth of 15 cm in loose volcanic rubble adjacent to the small cinder cone (about 1.5 km northeast of the vent of the 1931 eruption), whereas springs near a caldera lake had a temperature of 25/spl deg/ C in July 1993, Therefore, we suggest the observed subsidence at Aniakchak caldera is most likely caused by the reduction of pore fluid pressure of a hydrothermal system located a few kilometers beneath the caldera.

Interferometric synthetic aperture radar (InSAR) techniques are used to calculate the volume of extrusion at Okmok volcano, Alaska by constructing precise digital elevation models (DEMs) that represent volcano topography before and after the 1997 eruption. The posteruption DEM is generated using airborne topographic synthetic aperture radar (TOPSAR) data where a three-dimensional affine transformation is used to account for the misalignments between different DEM patches. The preeruption DEM is produced using repeat-pass European Remote Sensing satellite data; multiple interferograms are combined to reduce errors due to atmospheric variations, and deformation rates are estimated independently and removed from the interferograms used for DEM generation. The extrusive flow volume associated with the 1997 eruption of Okmok volcano is 0.154/spl plusmn/0.025 km/sup 3/. The thickest portion is approximately 50 m, although field measurements of the flow margin's height do not exceed 20 m. The in situ measurements at lava edges are not representative of the total thickness, and precise DEM data are absolutely essential to calculate eruption volume based on lava thickness estimations. This study is an example that demonstrates how InSAR will play a significant role in studying volcanoes in remote areas.

Interferometric synthetic aperture radar (InSAR) imaging is a recently developed geodetic technique capable of measuring ground-surface deformation with centimeter to subcentimeter vertical precision and spatial resolution of tens-of-meter over a relatively large region (/spl sim/10/sup 4/ km/sup 2/). The spatial distribution of surface deformation data, derived from InSAR images, enables the construction of detailed mechanical models to enhance the study of magmatic and tectonic processes associated with volcanoes. This paper summarizes our recent InSAR studies of several Alaska volcanoes, which include Okmok, Akutan, Kiska, Augustine, Westdahl, and Peulik volcanoes.