SAR Techniques Research Articles

Detecting infrastructure hazard potential change by SAR techniques on postseismic surface deformation: A case study of 2016 Meinong earthquake in southwestern Taiwan

Over the past two decades, multi-temporal InSAR techniques have been successfully utilized to monitor surface deformation throughout earthquake cycles. As sensor resolution and observation frequency of radar satellites have significantly improved, the spatial monitoring capability has expanded from tectonic to regional and local scales. Although regions with higher deformation rates are generally considered to have greater hazard potential, from an engineering perspective, locations with abrupt displacement rate changes may pose greater risks. This paper presents a robust method to transform tectonic-scale geodetic data into engineering-scale deformation tolerance ratios for assessing infrastructure safety during the postseismic period. Using the persistent scatterer InSAR (PSI) method, we measured line-of-sight (LOS) displacements from Sentinel-1 satellite images. By correcting LOS velocities with GNSS data and constraining the velocity in the N-S direction, we inverted the 2D (E-W and UD) postseismic deformation rates for the three years following the 2016 Meinong earthquake. The 2D rates were then converted into annual deformation tolerance ratios (ADTR), with a statistically established threshold to identify infrastructure segments with high hazard potential. Additionally, we conducted time-series variation analysis on the inherent pixel unit to evaluate the state of hazard potential and characterize the spatiotemporal behavior of fault systems during the postseismic period. Our results indicated that the postseismic deformation rates in the E-W and UD directions are 1.5 and 2–3 times higher, respectively, than those in the interseismic period. The horizontal ADTR index of high-speed railway infrastructures identified a high-hazard potential segment located on the Chegualin fault in Kaohsiung, while the vertical ADTR index highlighted a high-hazard potential segment at a junction between multiple fault extensions in Tainan. Moreover, statistical time-series variation analysis revealed a gradual decrease in hazard potential after 2017, which is related to a slow earthquake that occurred in early to mid-2018, modulating regional stress during the postseismic period. Our novel method not only highlights the impact of postseismic deformation on infrastructure but also provides a crucial basis for future earthquake hazard prevention and mitigation.

Investigating compressed SAR technique efficiency for land deformation analysis along Hwanggang dam

Abstract Monitoring and determining the land deformation in dam reservoirs is very crucial as it is one of the main factors of dam failure around the world. Hwanggang Dam is located at a strategic location near the inter-Korean border. North Korea frequently released water from the Hwanggang Dam, which often triggers floods and landslides in areas near the inter-Korea border in South Korea. Consistent monitoring of the Hwanggang Dam is very crucial to avoid catastrophic loss of infrastructure and life. This research paper exploits the potential of the Compressed SAR (ComSAR) technique for deformation analysis along Hwanggang Dam. A total of 171 Sentinel-1 images of Hwanggang Dam were downloaded from Alaska Satellite Facility in ascending track acquired between 2016 and 2023 to generate a time-series interferogram stack. The generated interferogram stack was exploited by using the ComSAR technique to estimate deformation along Hwanggang Dam. The resultant estimated deformation shows instability along the Hwanggang Dam embarkment.

An improved time series SAR interferometry (TSInSAR) for investigating earthquake-induced active unstable slopes (AUS) in Pakistan

ABSTRACT Time series interferometric SAR (TSInSAR) techniques face challenges related to the availability of highly coherent point targets in non-urban, vegetated, and mountainous regions. In this study, we tested an improved TSInSAR approach with the help of distributed scatterers (DS) detected through the fast statistically homogeneous pixel selection (faSHP) approach and jointly processed with persistent scatterers (PS) within the interferometric point target analysis (IPTA) framework. The method is applied to a seismically active and highly gradient terrain to investigate the earthquake-induced active unstable slopes (AUS) in the Muzaffarabad-Balakot region of northern Pakistan, particularly using sixteen PALSAR-2 images acquired between October 2014 and May 2020. However, its effectiveness was also assessed by using Sentinel-1 data from the same time period. The quantitative assessment showed the effectiveness of the method in delineating the sliding surfaces of the unstable slopes and achieving approximately four times higher point density as compared to the standard PSI approach when applied to PALSAR-2 data and approximately nine times higher point targets when Sentinel-1 data is used. Exhibiting an average deformation rate varying between −40 mm yr−1 and 20 mm yr−1 along the line of sight (LOS) direction, the obtained results from PALSAR-2 data revealed the existence of 451 AUS in the region. The study also reports the discovery of a giant destabilized slope (~2.30 sq. km) located near Patika Village along the Neelam River. The majority of these unstable slopes are found at altitudes above 780 m, with slope angles ranging from 15 to 50 degrees. The study also found a significant correlation between deformation patterns and local precipitation. The rainwater gradually penetrates into the joints and cracks, accelerating the processes of deformation and slope failure. The research and its findings could help decision-making entities by providing first-hand information for managing the risks associated with slope failure.

Synthetic Aperture Radar for Geosciences

AbstractSynthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR‐related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in‐depth reviews SAR applications for geosciences, broadly including various aspects in air‐sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability.

Detection and Mechanical Interpretation of Surface Displacement by Volcanic and Anthropogenic Interactions around Bandung Basin, through Multi Temporal Interferometric SAR (MT-InSAR) Technique

Abstract This study aims to evaluate capability of MT-InSAR (Multi Temporal interferometric synthetic aperture radar) technique for measuring the surface displacement caused by tectonic, volcanic, and anthropogenic activity around the Bandung Basin, West Java, Indonesia. For this, persistent scatterer (PS), small baseline subset (SBAS), and combined PS and SBAS techniques are adopted. The results reveal different displacement patterns within two areas anthropogenic-caused subsidence in the middle Bandung city and geothermal production field, and volcanic-caused uplift in the northern area around the Tangkuban Parahu volcano. In addition, each InSAR tehnique shows unique spatial deformation. A significant pattern is located around the Wayang Windu geothermal field that large displacements has been occurred around injection, production wells. Those detected displacements are crucial for mitigating geological hazards as well as geothermal production monitoring.

SAR Features and Techniques for Urban Planning—A Review

SAR Features and Techniques for Urban Planning—A Review

The first demonstration of DEM generation using stereo SAR technique with ascending and descending LuTan-1 SAR data

The first demonstration of DEM generation using stereo SAR technique with ascending and descending LuTan-1 SAR data

Improved reptile search algorithm with sequential assignment routing based false packet forwarding scheme for source location privacy protection on wireless sensor networks

Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models.

Co-seismic surface displacement of the June 21, 2022 MW6 Khōst, Afghanistan earthquake from InSAR observations

A robust estimation of the earthquake location, seismic moment, and fault geometry is essential for objective seismic hazard assessment. Seismic events in a remote location, specifically in the absence of seismic and GNSS networks, can be investigated effectively using the InSAR-based technique. This study adopts the Differential Interferometric SAR (DInSAR) technique to quantify the co-seismic surface displacement caused by the June 21, 2022, Khōst MW6 earthquake that occurred along the western plate boundary between the Indian and Eurasian plate. The interferograms show that the maximum surface deformation occurred on the northwest and southwest of the fault line. From coherence, the Line of Sight (LOS) displacement, and the co-seismic surface displacement analysis, it has been observed that surface deformation was most pronounced in the southwest region of the fault line, and the surface has moved to the opposite direction along the fault line, which indicates a sinistral slightly oblique strike-slip movement. This InSAR-based observation appears consistent with the seismic waveforms derived from co-seismic surface displacements. Further, it has been argued that the slip deficit accumulated during the period of the last about 48 years along the frontal region of the northward extension of the Suleiman range and associated fault zone is qualitatively estimated at about 1.5 m, which is consistent with the seismic waveforms derived finite slip model.

Reconnaissance to characterisation of land subsidence due to groundwater overdraft and oil extraction in and around Mehsana City, Gujarat, India by long-term hybrid differential interferometric SAR technique

We present a framework for studying reconnaissance to characterisation of land subsidence in the slowly subsiding city of Mehsana, Gujarat, India, due to groundwater overdraft and oil extraction. Considering interferometric synthetic aperture radar (InSAR) data availability constraints, we first conducted a long-term conventional Differential InSAR (DInSAR) investigation followed by time-series advanced DInSAR analyses by Persistent Scatterer InSAR (PSInSAR). We identified land subsidence in different parts of the study area, with subsidence rates ranging from ≤ -1 cm/yr to ≥ -3 cm/yr. During the entire observation period (2004–2019), the core urban area has been consistently subsiding with differential subsidence rates of −1.7 to −2.1 cm/yr during 2004–2006, −1.6 to −1.7 cm/yr during 2007–2011 and −2.6 to −2.9 cm/yr during 2016–2018 in conventional DInSAR and −2.5 to −3.2 cm/yr in advanced DInSAR. The subsidence measurements by collateral DInSAR observations in multifrequency and/or multi-imaging modes during 2016–2017 agree well in terms of the spatial extent of subsiding areas and the rates of subsidence. We characterised land subsidence in four type areas due to the diverse causes based on the temporal behaviour of land subsidence: (a) densely-populated city centre, (b) dairy plants and other industries in urban/peri-urban areas, (c) emerging urban clusters in peri-urban/rural areas, and (d) oilfields with agri-horticulture farms in the rural areas. We found a consistently increasing land subsidence trend in ‘a’, reducing land subsidence in ‘b’, newly emerging and rapidly growing land subsidence in ‘c’, and spatially-extensive subtle land subsidence in ‘d’. From the temporal nature of groundwater level (GWL) decline and land subsidence, we inferred that the land subsidence has been occurring primarily due to groundwater overdraft associated with urban growth, industry and agri-horticultural farms and secondarily due to oil extraction in the southeastern part of the study area. A comparison of annual rainfall difference from the long-term average vs GWL change vs land subsidence shows that the rainfall deficit during 2–3 consecutive years and groundwater overdraft are primarily related to land subsidence in the study area.

Tropical forest AGB estimation based on structure parameters extracted by TomoSAR

Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of practical value. As we know, forest AGB relates with height, density and diameter at breast height, and how to relate the ecophysical parameters with remote sensing images is vital for forest AGB estimation. In this paper, we aim to explore structure parameters about forest density and height, extracted by tomographic SAR (TomoSAR) techniques, for further improving the precision of AGB estimation models. Firstly, vertical structure profiles are constructed via TomoSAR, and the structure features are extracted. Secondly, the correlation between these features and the in-situ forest maximum height, tree density, and average AGB in plot scale is analyzed. Thirdly, the 8-fold cross-validation and step-wise regression methods were utilized to construct the tropical forest AGB models. Finally, the results of these models have been presented and analyzed. Based on the analysis, it indicates that “Model 7” is the most effective model, and its performance at both plot and pixel scales indicates a high level of accuracy for predicting forest AGB. These findings suggest that the proposed method can be effectively applied to tropical forested areas and has good scalability.

UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece

Areas in Western Greece are particularly prone to landslides. Usually triggered by earthquakes or intense rainfalls, they cause damage to infrastructure (roads, bridges, etc.) and human properties. Hence, there is an urgent need for the implementation of monitoring and landslide prevention methodologies. In the last years, Unmanned Aerial Vehicles (UAVs), Global Navigation Satellite Systems (GNSS), and Interferometric SAR (InSAR) techniques have been applied for landslide mapping and monitoring. The current study focuses on the systematic and long-term analysis of a landslide that occurred in Ano Kerassovo village, within the region of Western Greece. To precisely measure the current evolution of the landslide, we performed repetitive UAV campaigns in conjunction with corresponding GNSS surveys, covering a time period between February 2021 and April 2023. The identification of surface modification was based on a change detection approach between the generated point clouds. The results are validated through GNSS measurements and field observations. Added to this, we collected archived Persistent Scatterer Interferometry (PSI) measurements derived from the European Ground Motion Service (EGMS) to extend the observation period and gain a more complete understanding of the phenomenon. It is proven that archived PSI measurements can be used as an indicator of possible landslide initialization points and for small-scale large coverage investigations, while UAVs and GNSS data can precisely identify the microscale deformations (centimeter scale).

Land subsidence analysis using synthetic aperture radar data

Land subsidence is considered a threat to developing cities and is triggered by several natural (geological and seismic) and human (mining, groundwater withdrawal, oil and gas extraction, constructions) factors. This research has gathered datasets consisting of 80 Sentinel-1A ascending and descending SLC images from July 2017 to July 2019. This dataset, concerning InSAR and PS-InSAR, is processed with SARPROZ software to determine the land subsidence in Gwadar City, Balochistan, Pakistan. Later, the maps were created with ArcGIS 10.8. Due to InSAR’s limitations in measuring millimeter-scale surface deformation, Multi-Temporal InSAR techniques, like PS-InSAR, are introduced to provide better accuracy, consistency, and fewer errors of deformation analysis. This remote-based SAR technique is helpful in the Gwadar area; for researchers, city mobility is constrained and has become more restricted post-Covid-19. This technique requires multiple images acquired of the same place at different times for estimating surface deformation per year, along with surface uplifting and subsidence. The InSAR results showed maximum deformation in the Koh-i-Mehdi Mountain from 2017 to 2019. The PS-InSAR results showed subsidence up to −92 mm/year in ascending track and −66 mm/year in descending track in the area of Koh-i-Mehdi Mountain, and up to −48 mm/year in ascending track and −32 mm/year in descending track in the area of the deep seaport. From our experimental results, a high subsidence rate has been found in the newly evolving Gwadar City. This city is very beneficial to the country’s economic development because of its deep-sea port, developed by the China-Pakistan Economic Corridor (CPEC). The research is associated with a detailed analysis of Gwadar City, identifying the areas with significant subsidence, and enlisting the possible causes that are needed to be resolved before further developments. Our findings are helpful to urban development and disaster monitoring as the city is being promoted as the next significant deep seaport with the start of CPEC.

Glacier retreat analysis in the context of climate change impact over the Satopanth (SPG) and Bhagirathi-Kharak (BKG) glaciers in the Mana basin of the Central Himalaya, India: A geospatial approach

• Glacier retreat footprints over the Mana Basin. • Spatio-temporal changes in the snout/glacier over the period of 50 years. • Estimation of ice velocity using the SAR technique. • Negative trend in snow cover area and increase of temperature. This study explores the dynamics of the Satopanth (SPG) and Bhagirathi-Kharak (BKG) glaciers of the Mana basin in the Central Himalayan region of India under the scenario of climate change. The spatio-temporal changes in the glacier area, volume, and shift in snout positions during the period from 1968 to 2017 were assessed based on the CORONA photograph and satellite data . The study also intended to identify the common drifts in glacier areas for observing temporal and seasonal (during the winter-spring-summer transition) changes in the glacial landscapes and ably evaluate glacier velocity and ice-marginal lake stage. The ice velocity was found to vary from 0.117 md −1 to 0.165 md −1 for January to October months. It was found to have increased during the winter-spring-summer transition periods used to estimate the ice flow intensity and its phase information regular basis. Besides this, the linear trends of the MOD11A2 land surface temperature product were obtained and temperature variability has been analyzed from 2001 to 2020. The results show significant warming over the basin along with a significant negative trend in snow cover area during different seasons. The derived information is quite useful in analyzing the climate change footprints such as snowline movement, snow volume changes, and glacier mass balance-related studies.

Integration of multi-sensor MTInSAR and ground-based geomatic data for the analysis of non-linear displacements affecting the urban area of Chieuti, Italy

Slow instability phenomena can turn into rapid events, showing sudden accelerations and potentially developing in a threat for structures and people. In such scenarios, an in-depth understanding of the spatial and temporal evolution of the ground surface displacement field becomes essential for preventing potential catastrophes. In this work, Multi-Temporal Interferometry SAR (MTInSAR) technique based on COSMO-SkyMed and Sentinel-1 SAR acquisitions and ground measurements have been used to study an ongoing instability occurrence, affecting the urban area of Chieuti, a town located in the Southern Italy. Archives of C and X-band SAR data and geomatic monitoring observations spanning seven, five and one year, respectively, have been analyzed exploiting the complementary characteristics of these datasets. This enabled the accurate spatial-temporal characterization of the ground displacement field in the study area, the identification of sectors evidencing instability problems and a comprehensive reliability assessment of the detected displacements trends, characterized by strong non-linearities. Moreover, the multi-geometry DInSAR analysis allowed to evaluate the horizontal and vertical components of the detected motion, confirming the nature of the instability process, related to a deep landslide mechanism affecting the western slope of the town.

ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging With Adaptive Threshold

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel and efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-calculated as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. Furthermore, the introduction of an adaptive threshold for each azimuth-range pixel permits the threshold shrinkage to be not only layer-varied but also element-wise. Additionally, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.

Biological Potential of Thiazolidinedione Derivatives: A Review

Background: The authors of the current review aspire to inspire researchers by using the SAR technique to discover a promising and novel therapeutic agent. Currently, we don’t have a drug that is both active and non-toxic for the treatment of severe diseases like cancer, diabetes, hypertension, and neurodegenerative diseases. In a summary, thiazolidinedione (TZD) heterocyclic ligands have a spectacular standing in medicinal chemistry's synthetic and pharmacological approaches. Thiazolidinedione (TZD) core upon the replacement of different substitutes, gives a wide range of organic action by the utilization of various components on various objective destinations.
 Methods: We looked through the logical data set utilizing relevant keywords. Among the looked through writing, certain research papers were gathered which resolved our inquiries. The vital discoveries of the key findings of these studies were incorporated alongside their significance. 
 Results: There has been an explosion in the introduction of new classes of pharmacological agents having Thiazolidinedione moieties. Subsequently, new medications with Thiazolidinedione moieties came up with improved compliance and reduced side effects.
 Conclusion: The present review describes the significance of the Thiazolidinedione nucleus and its derivatives as a therapeutic agent with an emphasis on the past as well as recent developments.

Biological Potential of Thiazolidinedione Derivatives: A Review

Background: The authors of the current review aspire to inspire researchers by using the SAR technique to discover a promising and novel therapeutic agent. Currently, we don’t have a drug that is both active and non-toxic for the treatment of severe diseases like cancer, diabetes, hypertension, and neurodegenerative diseases. In a summary, thiazolidinedione (TZD) heterocyclic ligands have a spectacular standing in medicinal chemistry's synthetic and pharmacological approaches. Thiazolidinedione (TZD) core upon the replacement of different substitutes, gives a wide range of organic action by the utilization of various components on various objective destinations. Methods: We looked through the logical data set utilizing relevant keywords. Among the looked through writing, certain research papers were gathered which resolved our inquiries. The vital discoveries of the key findings of these studies were incorporated alongside their significance. Results: There has been an explosion in the introduction of new classes of pharmacological agents having Thiazolidinedione moieties. Subsequently, new medications with Thiazolidinedione moieties came up with improved compliance and reduced side effects. Conclusion: The present review describes the significance of the Thiazolidinedione nucleus and its derivatives as a therapeutic agent with an emphasis on the past as well as recent developments

Automated classification of A-DInSAR-based ground deformation by using random forest

ABSTRACT Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

Damage to anthropic elements estimation due to large slope instabilities through multi-temporal A-DInSAR analysis

Deep-seated gravitational slope deformations (DsGSDs) are widespread phenomena in the Alpine environment. Their dynamics, although very slow, endanger human settlements and connecting infrastructures. Monitoring such phenomena is mandatory to evaluate the impact on infrastructure networks and inhabited areas. Nowadays, the implementation of a tool useful to define and manage the interactions of DsGSDs evolution and the anthropic element remains a challenge, particularly in land use planning. Apart from on-site monitoring, which is commonly poorly used for DsGSDs observation, satellite-based interferometry represents the most comprehensive instrument for an effective spatial and temporal characterization of these phenomena. This paper provides a dedicated procedure to assess the usability of Advanced Differential Interferometric SAR (A-DInSAR) techniques to explore the DsGSDs behaviour and investigate their local interaction along anthropic elements. Combining multi-temporal A-DInSAR data, ERS-1/2 (1992–2000), Radarsat-1/2 (2003–2010), COSMO-SkyMed (2011–2018) and Sentinel-1 (2014–2018), over the Motta de Pletè and Champlas du Col DsGSDs, north-western Italy, a line-of-sight displacement investigation over a long-time span is implemented. Multi-temporal deformation maps are generated to define the deformation pattern and DsGSDs evolution over time. Subsequently, a local-scale analysis along the main anthropic elements is performed, exploiting Vslope values and ground deformation time series, integrated with ground-based ones, where available. This local analysis is aimed to recognize the most critical sections of the anthropic elements along with an higher level of damage, and potential risk is expected. Moreover, the obtained results are compared with a survey damage of the anthropic elements for a local cross-check and to strengthen the A-DInSAR methodology. Overall, the presented methodology provides a powerful tool to better define the DsGSDs local dynamics in correspondence of the main strategic infrastructures and inhabited areas, for a proper infrastructure maintenance and territorial planning strategy.