Currently, there are efforts to classify and predict future displacement events (e.g., landslides, infrastructure collapse) using machine learning from PSInSAR-derived deformation maps. This paper proposes two workflows for creating databases with information about disasters from scanned documents and online news sources. The databases can be used as ground truth to confirm that the deformation profiles from the training datasets are related to actual events or as input in future machine learning algorithms.

Kepler Space Inc. is creating a wide area Canadian Ground Motion Service (C-GMS), initially based on time series (every 6/12 days, between 2015 and 2022) of Sentinel-1 (S-1) satellite radar data and a modified Persistent Scatterers Interferometry (PSInSAR) approach to detect and map ground motion over time, with mm/year accuracy over wide areas (up to 700 km swaths). The information available through C-GMS follows the general format of the European Ground Motion Service, but with additional layers depicting seasonal motion, coherent after/before date, changes in displacement rates and break events. Presently C-GMS covers the Windsor-Quebec City corridor (roughly 1100 km in the East-West direction and around 250-500 km in the NS direction and is publicly available for viewing and data download at http://c-gms.com/.

Inspection of temporal deformation profiles from the Luțca bridge collapse, in Romania, showed that before the disaster, some PSI deformation profiles exhibits a pattern of subsidence/uplift followed by loss of coherence. In this work, it is proposed a new methodology for detecting such profiles, based on the removal of the seasonal component and piecewise linear modeling of deformation profiles. The methodology is not limited to the aforementioned types of profiles. The method is validated on an area including the Luțca bridge.

A remote operated C-band SAR sensor for agriculture applications was developed and deployed in the field. The sensor operates at a central frequency of 5.400 GHz. It comprises a 2D scanning system and monolbi-static channels which allow the operation in single- and repeatpass interferometry modes. Sensor is designed and developed as a ground-based configuration, simulating the measurement geometry of Sentinel-1 and a potential companion satellite, intended to provide single-pass interferometric products for 3D imaging applied on agricultural crops, in order to retrieve the biophysical parameters of plants and their development status.

In this paper an analysis of the temporal profiles from the national ground motion map of Romania is presented. The map was built based on Sentinel-1 A/B synthetic aperture radar images acquired between 2015 – 2019, using the persistent scatterer technique, and to date, it is the first nationwide deformation map for Romania. Two types of common deformation patterns were analyzed: periodic deformation, associated with seasonal thermal dilation, and deformation with changes in the linear trend, i.e., steps and piecewise linear deformation profiles with changes of the linear trend above a threshold. The patterns were detected using methods based on statistics and signal processing.

In this paper, the ground stability of the city of Constanta and surrounding areas (in Romania) is analyzed using the persistent scatterer interferometry technique, applied to Sentinel-l SAR data spanning an interval of two years. Two deformation maps are computed: one where the atmospheric phase screen is estimated and removed using ERA-Interim data and spatial filtering, and another using only spatial filtering.

The inundation extent is derived using brightness temperature data acquired by the L-band Soil Moisture Active Passive (SMAP) satellite, to support boreal carbon studies. Exploiting the L-band capabilities to penetrate clouds and vegetation and SMAP's 3-day revisit, the product may complement high-spatial resolution optical products in the high latitudes. The quality of the inundation extent is assessed by comparing with the following data sets: 3-m resolution maps derived using Radarsat synthetic aperture radar (SAR) data in northern Canada and multi-sensor climatology over Siberia. Initial results show encouraging comparisons. SMAP describes the seasonality of inundation more realistically compared with the climatology.

This paper presents the first outdoor measurements with the experimental ground based C-band synthetic aperture radar developed by the authors. A 3 meters long rail was used to generate the synthetic aperture. The results depict the first outdoor measurements, i.e. synthetic aperture radar imaging and interferometry.

The temporal evolution of pixel values in Satellite Image Time Series (SITS) is considered criterion for the characterization, discrimination and identification of terrestrial objects and phenomena. Due to the exponential behavior of sequences number with specialization, Sequential Data Mining techniques need to be applied. The spatial aspect of the data was taken into account by the introduction of connectivity measures that characterize the pixels tendency to form objects. The conjunction of corresponding Connectivity Constraints (CC) with the Support Constraint (SC) leads to the extraction of Grouped Frequent Sequential Patterns (GFSP), a concept with proved capability for preliminary description and localization of terrestrial events. This work is focused on efficient SITS extraction of evolutions that fulfil SC and CC. Experiments performed on Bucharest urban interferometric SITS are used to illustrate the potential of the approach to find interesting evolution patterns.

A specific double-bounce radar backscattering mechanism is used to measure water level changes in areas of wetland or inundated vegetation. The backscattered SAR signal from the water-vegetation double bounce mechanism is usually much stronger than the backscattered signal from the double bounce from dry surfaces. As an effect, the SAR amplitude from inundated vegetation is stronger than the one related to dry vegetation and the interferometric phase maintains coherence in time, making possible accurate phase measurements related to water level changes.

This work focuses on monitoring the ground motion and infrastructure stability in an urban environment, namely in the city of Bucharest. Bucharest is a fast developing city with the average construction rate of 8-20% new buildings with respect to the existing ones. Consequently, the civil engineering industry faced new challenges related to the need of having taller buildings with deeper underground levels, a developing network of subway lines and more bridges with large diameter piles' foundations. All these new works have an important impact upon the upper ground stability. The PSI (Persistent Scatterer Interferometry) technique is used to extract the ground deformation in Bucharest from archived C-band ESA data and from more recent TerraSAR-X data. The conducted analysis constitutes historical case studies for Bucharest City covering the intervals 1992-1999, 2003-2009 and 2011-2012.

Land covers and uses are dynamically being changed over the time. Detection and identification of these changes is necessary and is the first step of any study or planning for natural resource management. Synthetic Aperture Radar (SAR) imagery, thanks to its independence to weather conditions and sun illumination, is a powerful tool for these studies. In this research an unsupervised change detection framework based on the kernel-based clustering technique is presented. Kernel C-means algorithm is employed to separate the changes classes from the no-changes. This method is a non-linear algorithm which considers the contextual information. Using the kernel functions, the projecting of the data into a higher dimensional space helps to make the non-linear features more separable in a linear space. The proposed methodology has applied to dual-pol L-band SAR images acquired by the ALOS from Urmia Lake. Results show because of non-linear behavior of changed phenomenon, the algorithm leads to more reliable results.

A specific double-bounce radar backscattering mechanism observed in C-band Radarsat-2 data is used to measure water level changes in areas of wetland and inundated vegetation. The backscattered SAR signal from the water - vegetation double bounce mechanism is usually much stronger than the backscattered signal from the double bounce from dry surfaces. This is because the smooth water level reflects most of the signal along a preferential direction towards the vegetation canopy, as opposed to the dry ground which scatters the signal in multiple direction. As an effect, the SAR amplitude is stronger than the one related to dry vegetation and the interferometric phase maintains coherence in time, making possible accurate phase measurements related to water level changes.

Presently, a standard SAR image is obtained using two dimensional information acquired in a single pass monostatic acquisition. The third dimension of the image is folded in the 2D space based on the range information. With the availability of large datasets acquired with high resolution and from slightly different positions it was shown that amplitude and phase variation of persistent targets can be mapped to the relative target position within the pixel. Persistent Scatterers Interferometry solved the mapping problem for singe targets and Tomography is trying to solve it for multiple targets per pixel. Current challenges for the tomographic techniques are the insufficient and irregular baselines spread supplied by SAR missions not designed with this particular application in mind. Based on SAR sensor and orbits parameters, each mission has different tomographic capabilities. This SOAR-DLR project will explore the tomographic capabilities of Radarsat-2 in comparison and eventually in synergy with TerraSAR-X.

The region of interest is located approximately in the downstream part of the Mackenzie River, Canada. Our study focuses on guidelines to process RADARSAT-1 interferometric C-band data in a permafrost environment and monitor permafrost activity and landslide motion over 18 month period. From the interferometric method (D-InSAR) on ~100 interferograms, the main conclusions are (1) a high resolution Digital Elevation Models (DEM) and a shorter satellite revisit time intervals are essential to ensure InSAR processing accuracy for small deformations associated with permafrost activity; (2) the permafrost has sustained several changes over a year of monitoring (thaw starting during end of April-mid of May, and freezing around mid October). The deformation peak is reached in the summer time between mid of May and July (up to 15 plusmn 2 mm). This method will provide a guideline for In SAR monitoring of similar areas in permafrost environment.

The Coherent Targets Monitoring technique is providing superior ground deformation mapping compared with standard interferometry based on one pair of Master/Slave scenes. This is because using a larger data set it is possible to estimate and correct for additional phase error sources. Also the point targets detected as coherent targets are generally characterized by stronger signal that provides more accurate phase information than the clutter present in the rest of the scene. This paper reviews previous work in SAR noise estimation and shows the advantages of point targets versus distributed targets. Results from the Turtle Mountain/Frank Slide site show that the measured phase/deformation errors at the point target positions are within the estimated range. Corner reflectors with a high SNR are expected to improve the accuracy of the method.

In this study we used differential InSAR techniques to monitor landslide slide and permafrost activity at a site along the Mackenzie Valley Pipeline Corridor. Our results that motion are about 3 times more on exposed burnt slopes than the adjacent areas. The maximum activity is in September, probability related to gradual accumulated increases in soil temperatures.

In this study we used differential InSAR techniques to monitor current post slide activity at several landslides along transportation and energy corridors. The landslide materials vary from rock debris, glacial till to permafrost alluvium. Our results show that motion is triggered by spring melt and heavy rainfall events. In the northern Mackenzie Valley pipeline corridor seasonal landslide activity is related to permafrost melt during warm summer months.