InSAR-based landslide hazard mapping and identification of at-risk infrastructures in a selected district of North Shewa Zone, Ethiopia.

The devastating nature of landslides demands a thorough understanding of their spatial distribution and the risks they pose to human settlements and infrastructural assets. In this study, we employed a combination of Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information System (GIS) techniques to explore the western escarpment of the Main Ethiopian Rift, with a focus on selected districts within the northern Shewa Zone, Ethiopia. By analyzing the SAR data, we derived 28 displacement maps and utilized them to create a comprehensive landslide hazard zonation map. The results indicated significant ground displacement, particularly along the rift margins and areas characterized by rugged terrain. The hazard zones were classified based on their level of risk, with 44% classified as very low, 24% as low, 5% as moderate, 13% as high, and 14% as very high hazard zones. The accuracy of our results was evaluated using receiver operating characteristic (ROC) analysis, which was conducted utilizing landslide inventory data. The analysis demonstrated a remarkable area under the curve (AUC) value of 0.848, providing strong evidence for the validity of our findings. Additionally, our study involved a spatial and statistical assessment of major infrastructure, revealing that 20 to 28% of these properties were in hazard zones ranging from moderate to very high levels, which calls for efficient risk-reduction actions. Therefore, this finding enables stakeholders to identify high-risk areas, prioritize mitigation efforts, and minimize the impact of landslide disasters.

A comprehensive dataset of surface deformation satellite maps of the valley of Toluca, Mexico.

This work presents a substantial compilation of ground deformation maps portraying the dynamic evolution of the Valley of Toluca (VT) in Mexico. The dataset comprises a repository of 1121 BEAM-DIMAP formated maps obtained by the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique. Leveraging satellite image pairs from the Sentinel 1-A and Sentinel 1-B satellites, the dataset spans intervals of 1, 3, 6, and 12 months between each image acquisition and covers a panoramic timeframe from October 2014 to December 2022. This compilation provides an in-depth chronicle of the VT's ground transformations over a span of eight years that could be of interest to various disciplines. To enhance the dataset's robustness, a supplementary comma-separated values (CSV) dataset includes the coherence statistics from the satellite image pairs, substantiating the precision and dependability of the ground deformation maps presented herein.

Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computing.

Land subsidence is a huge challenge that land and water resource managers are still facing. Radar datasets revolutionize the way and give us the ability to provide information about it, thanks to their low cost. But identifying the most important drivers need for the modeling process. Machine learning methods are especially top of mind amid the prediction studies of natural hazards and hit new heights over the last couple of years. Hence, putting an efficient approach like integrated radar-and-ensemble-based method into practice for land subsidence rate simulation is not available yet which is the main aim of this research. In this study, the number of 52 pairs of radar images were used to identify subsidence from 2014 to 2019. Then, using the simulated annealing (SA) algorithm the key variables affecting land subsidence were identified among the topographical parameters, aquifer information, land use, hydroclimatic variables, and geological and soil factors. Afterward, three individual machine learning models (including Support Vector Machine, SVM; Gaussian Process, GP; Bayesian Additive Regression Tree, BART) along with three ensemble learning approaches were considered for land subsidence rate modeling. The results indicated that the subsidence varies between 0 and 59 cm in this period. Comparing the Radar results with the permanent geodynamic station exhibited a very strong correlation between the ground station and the radar images (R2 = 0.99, RMSE = 0.008). Parsing the input data by the SA indicated that key drivers are precipitation, elevation, percentage of fine-grained materials in the saturated zone, groundwater withdrawal, distance to road, groundwater decline, and aquifer thickness. The performance comparison indicated that ensemble models perform better than individual models, and among ensemble models, the nonlinear ensemble approach (i.e., BART model combination) provided better performance (RMSE = 0.061, RSR = 0.42, R2 = 0.83, PBIAS = 2.2). Also, the distribution shape of the probability density function in the non-linear ensemble model is much closer to the observations. Results indicated that the presence of significant fine-grained materials in unconsolidated aquifer systems can clarify the response of the aquifer system to groundwater decline, low recharge, and subsequent land subsidence. Therefore, the interaction between these factors can be very dangerous and intensify subsidence.

An Enhanced Offset Tracking Method: Providing Auxiliary Information for DInSAR Phase Filtering in Urban Areas.

In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results.

Coseismic Deformation Field and Fault Slip Distribution Inversion of the 2020 Jiashi Ms 6.4 Earthquake: Considering the Atmospheric Effect with Sentinel-1 Data Interferometry.

Due to some limitations associated with the atmospheric residual phase in Sentinel-1 data interferometry during the Jiashi earthquake, the detailed spatial distribution of the line-of-sight (LOS) surface deformation field is still not fully understood. This study, therefore, proposes an inversion method of coseismic deformation field and fault slip distribution, taking atmospheric effect into account to address this issue. First, an improved inverse distance weighted (IDW) interpolation tropospheric decomposition model is utilised to accurately estimate the turbulence component in tropospheric delay. Using the joint constraints of the corrected deformation fields, the geometric parameters of the seismogenic fault and the distribution of coseismic slip are then inverted. The findings show that the coseismic deformation field (long axis strike was nearly east-west) was distributed along the Kalpingtag fault and the Ozgertaou fault, and the earthquake was found to occur in the low dip thrust nappe structural belt at the subduction interface of the block. Correspondingly, the slip model further revealed that the slips were concentrated at depths between 10 and 20 km, with a maximum slip of 0.34 m. Accordingly, the seismic magnitude of the earthquake was estimated to be Ms 6.06. Considering the geological structure in the earthquake region and the fault source parameters, we infer that the Kepingtag reverse fault is responsible for the earthquake, and the improved IDW interpolation tropospheric decomposition model can perform atmospheric correction more effectively, which is also beneficial for the source parameter inversion of the Jiashi earthquake.

Ice thickness distribution of Himalayan glaciers inferred from DInSAR-based glacier surface velocity.

Retrieval of glacier ice thickness is extremely important for monitoring water resources and predicting glacier dynamics and changes. The inter-annual glacier ice thickness observations (more than 5 years) exploit the glacier mass changes. Ice thickness is one of the important parameters to predict the future sea-level rise. Without adequate knowledge and precise information of glacier ice thickness distribution, future sea-level changes cannot be accurately assessed. In this study, we use an existing flow model to estimate the ice thickness of the High Mountain Asia (HMA) glaciers, using remote sensing techniques. The glacier ice velocity is one of the significant parameters in the Laminar flow model to retrieve the ice thickness. The glacier ice velocity is derived by utilizing the Differential SAR Interferometry (DInSAR) technique. The most optimum DInSAR data (ALOS-2/PALSAR-2) is used for estimating the ice velocity of the HMA glaciers. The ice thickness is mainly estimated for five different states in the HMA region, namely Himachal Pradesh, Uttarakhand, Sikkim, Bhutan, and Arunachal Pradesh. Most of the states are observed with a mean ice thickness of 100 m. Five benchmark glaciers (Samudra Tapu, Bara Shigri, Chhota Shigri, Sakchum, and Gangotri glaciers) are also selected for validating our results with the existing thickness information. The issues related to velocity-based ice thickness inversion are also emphasized in this study. The high-velocity rate due to the influx of melting water from adjacent glaciers causes an increment in the flow rate. This abnormal velocity derives erroneous ice thickness measurements. This is one of the major problems to be considered in the velocity-based thickness-derived procedures. Finally, the investigation suggests the inclusion of the velocity influencing parameters in the physical-based models for an accurate ice thickness inversion.

3D Co-Seismic Surface Displacements Measured by DInSAR and MAI of the 2017 Sarpol Zahab Earthquake, Mw7.3.

On 12 November 2017, an earthquake occurred in Sarpol Zahab city, located on the Iraq/Iran boundary, with a moment magnitude (Mw) of 7.3. Advanced Land Observing Satellite 2 (ALOS-2) L-band (23.6 cm wavelength) and C-band Sentinel-1A data (ascending and descending) were used to detect the co-seismic displacements maps caused by this earthquake. The ALOS-2 data was utilized to reconstruct the 3D co-seismic displacements maps, as well as estimate the fault-dip and slip distribution along the rupture. The results showed the maximum surface displacement in the north, east, and up directions to be 100, 50, and 100 cm, respectively. The best-fit faulting geometry had a strike of 337.5° and a dip of 11.2° toward the northeast, at a depth of 8 km. The predicted geodetic moment was 1.15 1020 Nm, which corresponds to a magnitude of Mw 7.31. There were two significant slip sources: one in the shallower depth range of 8.5-10 km, with a peak slip of 5 m, and another in the depth range of 10.5-20 km, with a peak slip of 5.3 m. Both controlled the principal deformation signals in geodetic images. The slip was concentrated, along with a strike distance of 20 to 40 km, at a depth of 10 to 20 km. The earthquake was caused by the Zagros Mountains Front Fault (ZMFF), based on the results of 3D co-seismic deformation, inferred slip, preliminary investigation, and interpretation of the mainshock, as well as aftershock distributions.

Geodetic and Remote-Sensing Sensors for Dam Deformation Monitoring.

In recent years, the measurement of dam displacements has benefited from a great improvement of existing technology, which has allowed a higher degree of automation. This has led to data collection with an improved temporal and spatial resolution. Robotic total stations and GNSS (Global Navigation Satellite System) techniques, often in an integrated manner, may provide efficient solutions for measuring 3D displacements on precise locations on the outer surfaces of dams. On the other hand, remote-sensing techniques, such as terrestrial laser scanning, ground-based SAR (synthetic aperture radar) and satellite differential interferometric SAR offer the chance to extend the observed region to a large portion of a structure and its surrounding areas, integrating the information that is usually provided in a limited number of in-situ control points. The design and implementation of integrated monitoring systems have been revealed as a strategic solution to analyze different situations in a spatial and temporal context. Research devoted to the optimization of data processing tools has evolved with the aim of improving the accuracy and reliability of the measured deformations. The analysis of the observed data for the interpretation and prediction of dam deformations under external loads has been largely investigated on the basis of purely statistical or deterministic methods. The latter may integrate observation from geodetic, remote-sensing and geotechnical/structural sensors with mechanical models of the dam structure. In this paper, a review of the available technologies for dam deformation monitoring is provided, including those sensors that are already applied in routinary operations and some experimental solutions. The aim was to support people who are working in this field to have a complete view of existing solutions, as well as to understand future directions and trends.

Advanced SBAS-DInSAR Technique for Controlling Large Civil Infrastructures: An Application to the Genzano di Lucania Dam.

Monitoring surface deformation on dams is commonly carried out by in situ geodetic surveying, which is time consuming and characterized by some limitations in space coverage and frequency. More recently microwave satellite-based technologies, such as advanced-DInSAR (Differential Synthetic Aperture Radar Interferometry), have allowed the integration and improvement of the observation capabilities of ground-based methods thanks to their effectiveness in collecting displacement measurements on many non-destructive control points, corresponding to radar reflecting targets. The availability of such a large number of points of measurement, which are distributed along the whole structure and are characterized by millimetric accuracy on displacement rates, can be profitably adopted for the calibration of numerical models. These models are implemented to simulate the structural behaviour of a dam under conditions of stress thus improving the ability to maintain safety standards. In this work, after having analysed how advanced DInSAR can effectively enhance the results from traditional monitoring systems that provide comparable accuracy measurements on a limited number of points, an FEM model of the Genzano di Lucania earth dam is developed and calibrated. This work is concentrated on the advanced DInSAR technique referred to as Small BAseline Subset (SBAS) approach, benefiting from its capability to generate deformation time series at full spatial resolution and from multi-sensor SAR data, to measure the vertical consolidation displacement of the Genzano di Lucania earth dam.

A-Differential Synthetic Aperture Radar Interferometry analysis of a Deep Seated Gravitational Slope Deformation occurring at Bisaccia (Italy).

This paper presents the results of an investigation on a Deep Seated Gravitational Slope Deformation (DSGSD), previously only hypothesized by some authors, affecting Bisaccia, a small town located in Campania region, Italy. The study was conducted through the integration of conventional methods (geological-geomorphological field survey, air-photo interpretation) and an Advanced-Differential Interferometry Synthetic Aperture Radar (A-DInSAR) technique. The DSGSD involves a brittle lithotype (conglomerates of the Ariano Irpino Supersynthem) resting over a Structurally Complex Formation (Varycoloured Clays of Calaggio Formation). At Bisaccia, probably as a consequence of post-cyclic recompression phenomena triggered by reiterated seismic actions, the rigid plate made up of conglomeratic sediments resulted to be split in five portions, showing different rates of displacements, whose deformations are in the order of some centimeter/year, thus inducing severe damage to the urban settlement. A-DInSAR techniques confirmed to be a reliable tool in monitoring slow-moving landslides. In this case 96 ENVIronmental SATellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) images, in ascending and descending orbits, have been processed using SUBSOFT software, developed by the Remote Sensing Laboratory (RSLab) group from the Universitat Politècnica de Catalunya (UPC). The DInSAR results, coupled with field survey, supported the analysis of the instability mechanism and confirmed the historical record of the movements already available for the town.

Evaluation of a Cubature Kalman Filtering-Based Phase Unwrapping Method for Differential Interferograms with High Noise in Coal Mining Areas.

Differential interferometric synthetic aperture radar has been shown to be effective for monitoring subsidence in coal mining areas. Phase unwrapping can have a dramatic influence on the monitoring result. In this paper, a filtering-based phase unwrapping algorithm in combination with path-following is introduced to unwrap differential interferograms with high noise in mining areas. It can perform simultaneous noise filtering and phase unwrapping so that the pre-filtering steps can be omitted, thus usually retaining more details and improving the detectable deformation. For the method, the nonlinear measurement model of phase unwrapping is processed using a simplified Cubature Kalman filtering, which is an effective and efficient tool used in many nonlinear fields. Three case studies are designed to evaluate the performance of the method. In Case 1, two tests are designed to evaluate the performance of the method under different factors including the number of multi-looks and path-guiding indexes. The result demonstrates that the unwrapped results are sensitive to the number of multi-looks and that the Fisher Distance is the most suitable path-guiding index for our study. Two case studies are then designed to evaluate the feasibility of the proposed phase unwrapping method based on Cubature Kalman filtering. The results indicate that, compared with the popular Minimum Cost Flow method, the Cubature Kalman filtering-based phase unwrapping can achieve promising results without pre-filtering and is an appropriate method for coal mining areas with high noise.

Observation of a Large Landslide on La Reunion Island Using Differential Sar Interferometry (JERS and Radarsat) and Correlation of Optical (Spot5 and Aerial) Images.

Slope instabilities are one of the most important geo-hazards in terms of socio-economic costs. The island of La Réunion (Indian Ocean) is affected by constant slope movements and huge landslides due to a combination of rough topography, wet tropical climate and its specific geological context. We show that remote sensing techniques (Differential SAR Interferometry and correlation of optical images) provide complementary means to characterize landslides on a regional scale. The vegetation cover generally hampers the analysis of C-band interferograms. We used JERS-1 images to show that the L-band can be used to overcome the loss of coherence observed in Radarsat C-band interferograms. Image correlation was applied to optical airborne and SPOT 5 sensors images. The two techniques were applied to a landslide near the town of Hellbourg in order to assess their performance for detecting and quantifying the ground motion associated to this landslide. They allowed the mapping of the unstable areas. Ground displacement of about 0.5 m yr(-1) was measured.

Two-dimensional Co-Seismic Surface Displacements Field of the Chi-Chi Earthquake Inferred from SAR Image Matching.

The Mw=7.6 Chi-Chi earthquake in Taiwan occurred in 1999 over the Chelungpu fault and caused a great surface rupture and severe damage. Differential Synthetic Aperture Radar Interferometry (DInSAR) has been applied previously to study the co-seismic ground displacements. There have however been significant limitations in the studies. First, only one-dimensional displacements along the Line-of-Sight (LOS) direction have been measured. The large horizontal displacements along the Chelungpu fault are largely missing from the measurements as the fault is nearly perpendicular to the LOS direction. Second, due to severe signal decorrelation on the hangling wall of the fault, the displacements in that area are un-measurable by differential InSAR method. We estimate the co-seismic displacements in both the azimuth and range directions with the method of SAR amplitude image matching. GPS observations at the 10 GPS stations are used to correct for the orbital ramp in the amplitude matching and to create the two-dimensional (2D) co-seismic surface displacements field using the descending ERS-2 SAR image pair. The results show that the co-seismic displacements range from about -2.0 m to 0.7 m in the azimuth direction (with the positive direction pointing to the flight direction), with the footwall side of the fault moving mainly southwards and the hanging wall side northwards. The displacements in the LOS direction range from about -0.5 m to 1.0 m, with the largest displacement occuring in the northeastern part of the hanging wall (the positive direction points to the satellite from ground). Comparing the results from amplitude matching with those from DInSAR, we can see that while only a very small fraction of the LOS displacement has been recovered by the DInSAR mehtod, the azimuth displacements cannot be well detected with the DInSAR measurements as they are almost perpendicular to the LOS. Therefore, the amplitude matching method is obviously more advantageous than the DInSAR in studying the Chi-Chi earthquake. Another advantage of the method is that the displacement in the hanging wall of the fault that is un-measurable with DInSAR due to severe signal decorrelation can almost completely retrieved in this research. This makes the whole co-seismic displacements field clearly visible and the location of the rupture identifiable. Using displacements measured at 15 independent GPS stations for validation, we found that the RMS values of the differences between the two types of results were 6.9 cm and 5.7 cm respectively in the azimuth and the range directions.