Interferometric Synthetic Aperture Radar Data Research Articles

A Forest Height Joint Inversion Method Using Multibaseline PolInSAR Data

Estimating vegetation height from polarimetric interferometric synthetic aperture radar (PolInSAR) data using the random volume over ground (RVoG) model has long been used. Most of these methods propose models and apply them to real airborne data to demonstrate their potential. The single-baseline PolInSAR forest height estimation based on the RVoG model lacks sufficient observation information. For this reason, multibaseline data are introduced to address this. This paper fits the relationship of model parameters in multibaseline observation scenarios, and focuses the forest height inversion on the calculation of pure volume decorrelation. Subsequently, a multibaseline forest height joint inversion method based on the least squares principle is adopted. Finally, we use airborne PolInSAR data from the Lope and Mondah sites collected by UAVSAR and F-SAR systems during AfriSAR 2016 to verify the proposed method. The experimental results show that the accuracy of the proposed method (Lope: root mean square error (RMSE) = 5.8 m, Mondah: RMSE = 5.12 m) is 38.1% and 34.53% higher than the coherence separation product (Lope: RMSE = 9.37 m, Mondah: RMSE = 7.82 m).

Land Subsidence in the Texas Coastal Bend: Locations, Rates, Triggers, and Consequences

Land subsidence and sea level rise are well-known, ongoing problems that are negatively impacting the entire Texas coast. Although ground-based monitoring techniques using long-term global navigation satellite systems (GNSS) records provide accurate subsidence rates, they are labor intensive, expensive, time-consuming, and spatially limited. In this study, interferometric synthetic aperture radar (InSAR) data and techniques were used to map the locations and quantify rates of land subsidence in the Texas Coastal Bend region during the period from October 2016 to July 2019. InSAR-derived land subsidence rates were then validated and calibrated against GNSS-derived rates. The factors controlling the observed land subsidence rates and locations were investigated. The consequences of spatial variability in land subsidence rates in Coastal Bend were also examined. The results indicated that: (1) land subsidence rates in the Texas Coastal Bend exhibited spatial variability, (2) InSAR-derived land subsidence rates were consistent with GNSS-derived deformation rates, (3) land subsidence in the Texas Coastal Bend could be attributed mainly to hydrocarbon and groundwater extraction as well as vertical movements along growth faults, and (4) land subsidence increased both flood frequency and severity in the Texas Coastal Bend. Our results provide valuable information regarding not only land deformation rates in the Texas Coastal Bend region, but also the effectiveness of interferometric techniques for other coastal rural areas around the globe.

Surface Rupture on a Secondary Fault Associated with the 8 August 2020 Mw 5.1 Sparta North Carolina Earthquake

Abstract On 8 August 2020, northwest North Carolina experienced an Mw 5.1 earthquake that caused damage to buildings and roads in the city of Sparta. A regional centroid moment tensor solution shows that the earthquake was the result of slip on a reverse fault with a minor strike-slip component. Interferometric Synthetic Aperture Radar (InSAR) data, from the Japan Aerospace Exploration Agency’s Advanced Land Observing Satellite #2 (ALOS2) satellite, reveal a deformation field that is more complex than expected from a single reverse fault earthquake. The data also reveal an apparent fault rupture at the Earth’s surface that caused damage to local roads. Modeling of the InSAR deformation field indicates the fault rupture is associated with a very shallow normal faulting event with an equivalent Mw of about 5.1 that overprinted the reverse fault deformation field and possibly occurred aseismically.

InSAR Phase Unwrapping by Deep Learning Based on Gradient Information Fusion

Phase unwrapping (PhU) is an important step in interferometric synthetic aperture radar (InSAR) technology. At present, difficulties are encountered when using deep learning to solve the PhU problem because the fringe density of the actual interferogram varies, resulting in an imbalanced class of semantic segmentation. Deep learning cannot completely use gradient information, and it is difficult to address a large number of residues. In this letter, a PhU semantic segmentation model based on gradient information fusion and improved PhaseNet network is proposed to solve the problem of imbalanced classification and error propagation. 21 613 pairs of phase samples are constructed by using simulated and real Sentinel-1 InSAR Data. The experimental results show that the average classification accuracy of the method can reach 97%, and the mean square error is only 0.97. The average processing speed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256 \times256$ </tex-math></inline-formula> slices is only 0.5 s. Compared with the traditional methods and other deep learning methods, this method solves the problem of classification imbalance, and the use of fusion gradient information improves the efficiency of the algorithm as well as reduces the burden of network classification and the error propagation, showing increased robustness in the case of many residues and high fringe density.

Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

Human Activity Detection Based on Multipass Airborne InSAR Coherence Matrix

This letter focuses on the detection of subtle human activity, from multipass airborne interferometric synthetic aperture radar (InSAR) images, of such activities causing absolute decorrelation between two acquisitions. These activities can be vehicle track, footprint, grazing, and human construction. The millimetric sensitivity of the interferometric phase makes it valuable for detecting such activities. It can bring a large value to civil and military applications. However, there are always high false alarms in the conventional detection method. To solve this problem, a human activity detection method based on the coherence matrix is approached in this letter. In the final part of this letter, a preliminary validation of the method is provided by processing actual multipass airborne InSAR data stacks acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. It is noted that the proposed method reaches reliable results in comparison with the ground truth.

Atmospheric Phase Screen Compensation on Wrapped Ground-Based SAR Interferograms

This work proposes a method for estimating and compensating the atmospheric phase screen (APS) in sets of synthetic aperture radar (SAR) interferograms generated with a ground-based SAR (GB-SAR) instrument. We address the presented approach’s physical, statistical, and mathematical framework by discussing its potential and limitations. In contrast with other existing algorithms that estimate the APS from the unwrapped phase signals, our methodology is based on the straightforward analysis of the wrapped phases, directly. Therefore, the method is not affected by any potential phase unwrapping mistake, and it is suitable for multitemporal interferometric SAR (InSAR) applications. The effects of the local topography, the decorrelation noise, and the ground deformation on the APS estimates are deeply studied. Experiments performed on simulated and real GB-SAR InSAR data corroborate the validity of the theory. In particular, the simulated results show that the method is beneficial in zones with medium-to-high topographic slopes (e.g., for Alpine and mountainous regions).

The analysis on uncertainty resulting from method and wavelength selecting in forest height inversion using simulated polarimetric interferometric SAR data

The analysis on uncertainty resulting from method and wavelength selecting in forest height inversion using simulated polarimetric interferometric SAR data

Transtensional Rupture within a Diffuse Plate Boundary Zone during the 2020 Mw 6.4 Puerto Rico Earthquake

Abstract On 7 January 2020, an Mw 6.4 earthquake occurred in the northeastern Caribbean, a few kilometers offshore of the island of Puerto Rico. It was the mainshock of a complex seismic sequence, characterized by a large number of energetic earthquakes illuminating an east–west elongated area along the southwestern coast of Puerto Rico. Deformation fields constrained by Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data indicate that the coseismic movements affected only the western part of the island. To assess the mainshock’s source fault parameters, we combined the geodetically derived coseismic deformation with teleseismic waveforms using Bayesian inference. The results indicate a roughly east–west oriented fault, dipping northward and accommodating ∼1.4 m of transtensional motion. Besides, the determined location and orientation parameters suggest an offshore continuation of the recently mapped North Boquerón Bay–Punta Montalva fault in southwest Puerto Rico. This highlights the existence of unmapped faults with moderate-to-large earthquake potential within the Puerto Rico region.

Coseismic and Early Postseismic Slip Models of the 2021 Mw 7.4 Maduo Earthquake (Western China) Estimated by Space‐Based Geodetic Data

AbstractThe May 22nd, 2021,7.4 Maduo earthquake occurred on an intraplate fault of Bayan‐Har block in the Tibet Plateau. Here, we derive the coseismic and early postseismic surface deformations from the Sentinel‐1 (S1) interferometric synthetic aperture radar (InSAR) data. We use the subpixel offsets of SAR and Sentinel‐2 (S2) optical images to determine the surface rupture traces. The fault geometry and coseismic fault slip distribution of multi‐segmented ruptures are estimated by inverting InSAR interferograms and SAR pixel offsets. We show that at least five fault segments with curved geometry are activated, with a peak coseismic slip of about 5 m. The geodetic data inversion suggests that an NW‐striking blind segment near the Eling Lake may have ruptured during the 2021 event. Postseismic slip inversion with the 60‐day cumulative line‐of‐sight deformations, shows that the early afterslip of 0.1 ∼ 0.3 m occurred mostly toward the downdip direction of the main coseismic slip asperities. The afterslip geodetic moment accounts for approximately 15.3% of the coseismic one. Coulomb stress analysis shows that the nucleation of the Maduo earthquake is partially facilitated by the 1947 M7.7 Dari earthquake.

The Global Water Body Layer from TanDEM-X Interferometric SAR Data

The interferometric synthetic aperture radar (InSAR) data set, acquired by the TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) mission (TDM), represents a unique data source to derive geo-information products at a global scale. The complete Earth’s landmasses have been surveyed at least twice during the mission bistatic operation, which started at the end of 2010. Examples of the delivered global products are the TanDEM-X digital elevation model (DEM) (at a final independent posting of 12 m × 12 m) or the TanDEM-X global Forest/Non-Forest (FNF) map. The need for a reliable water product from TanDEM-X data was dictated by the limited accuracy and difficulty of use of the TDX Water Indication Mask (WAM), delivered as by-product of the global DEM, which jeopardizes its use for scientific applications, as well. Similarly as it has been done for the generation of the FNF map; in this work, we utilize the global data set of TanDEM-X quicklook images at 50 m × 50 m resolution, acquired between 2011 and 2016, to derive a new global water body layer (WBL), covering a range from −60∘ to +90∘ latitudes. The bistatic interferometric coherence is used as the primary input feature for performing water detection. We classify water surfaces in single TanDEM-X images, by considering the system’s geometric configuration and exploiting a watershed-based segmentation algorithm. Subsequently, single overlapping acquisitions are mosaicked together in a two-step logically weighting process to derive the global TDM WBL product, which comprises a binary averaged water/non-water layer as well as a permanent/temporary water indication layer. The accuracy of the new TDM WBL has been assessed over Europe, through a comparison with the Copernicus water and wetness layer, provided by the European Space Agency (ESA), at a 20 m × 20 m resolution. The F-score ranges from 83%, when considering all geocells (of 1∘ latitudes × 1∘ longitudes) over Europe, up to 93%, when considering only the geocells with a water content higher than 1%. At global scale, the quality of the product has been evaluated, by intercomparison, with other existing global water maps, resulting in an overall agreement that often exceeds 85% (F-score) when the content in the geocell is higher than 1%. The global TDM WBL presented in this study will be made available to the scientific community for free download and usage.

Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry

Abstract. Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEMs) are measuring the radar scattering center height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for thick and deformed sea ice with snow cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer-plus-volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The experiments are performed across four polarizations: HH, VV, Pauli 1 (HH + VV), and Pauli 2 (HH − VV). The model-retrieved performance is validated with the optically derived DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.26 m in Pauli-1 (HH + VV) polarization.

Crustal Deformation of the Kendeng Fault Branches Area from GNSS and InSAR Data in Surabaya City, Indonesia

Slip rates on active faults derived from the interseismic velocity field are critical to understanding seismic hazards in metropolitan cities. This study integrated the data from the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) to evaluate the interseismic velocities in the second-largest city in Indonesia, Surabaya, where branches of the Kendeng fault (the Surabaya and the Waru faults) pass through. Data from 16 campaign-mode GNSS stations collected between 2017 and 2020 from previous research are reprocessed to estimate the velocity field. Horizontal velocities under the ITRF frame range between -23.8 mm/yr and 47.9 mm/yr toward the southeast. Vertical velocities generally range between -1.3 mm/yr and -112.2 mm/yr. Sentinel-1A SAR data of both ascending and descending tracks acquired between November 2014 and July 2020 were used to generate the interferograms with the InSAR Scientific Computing Environment (ISCE) software. Furthermore, cumulative displacement time series were constructed using the Small BAseline Subset (SBAS) technique within the Generic InSAR Analysis Toolbox (GIAnT). This study also carried out the detection of outlier SAR epochs to improve the precision of Line-of-sight (LOS) velocity estimates. The LOS velocities range from -14.8 to 10.8 mm/yr in the ascending track and from -12.7 to 9.5 mm/yr in the descending track. These results will facilitate the detection of coupling behaviors on the Kendeng fault branches, which can improve our understanding of seismic risks in the Surabaya area.

Atmospheric phase delay correction of PS-InSAR to Monitor Land Subsidence in Surabaya

Atmospheric phase delay is one of the most significant errors limiting the accuracy of Interferometric Synthetic Aperture Radar (InSAR) results. In this research, we used the Generic Atmospheric Correction Online Service for InSAR (GACOS) data to correct the tropospheric delay modeling from the persistent scatterers’ InSAR monitoring. Eighty-one (81) Sentinel-1A images and tropospheric delay maps from GACOS monitored land subsidence in Surabaya city between 2017 and 2019. InSAR processing was carried out using the GMTSAR software, continued with StaMPS and TRAIN, which were used to correct the tropospheric delay of PSInSAR-derived deformation measurements. The results before and after the atmospheric phase delay correction using GACOS were confirmed and analyzed in the main subsidence area. The findings of the experiments reveal that the atmospheric phase affects the mean LOS velocity results to some extent. The average difference between PS-InSAR before and after tropospheric correction is 1.734 mm/year with a standard deviation of 0.550 mm/year. The significance test of the two variables, 95%, showed that the tropospheric correction with GACOS data could affect the PS-InSAR results. Furthermore, GACOS correction may increase the error at some points, which could be due to its turbulence data’s low accuracy.

Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

Deformation monitoring and thematic mapping of the Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data

The preventive monitoring and sustainable conservation of large-scale cultural heritage sites require satellite-based Earth observations. In this study, we present the first monitoring and thematic mapping results of the Badaling Great Wall (Beijing, China) using deformation indicators calculated from very high-resolution (VHR) multi-temporal spotlight TerraSAR-X data. The proposed coarse–fine search algorithm achieved high computational efficiency for calculating the unknown parameters of the two-tier network persistent scatterer synthetic aperture radar (SAR) interferometry (PSInSAR) approach. The spatiotemporal deformation anomalies, characterized by the absolute velocity, deformation deviation and acceleration, are informative to identify suspected hotspots for prioritizing monitoring activities. We provide an understandable method for thematic mapping and subsequent sustainable conservation of heritage sites by synergistically exploiting impacts from natural degradation and the tourism industry. We determine that the optimum tourist capacity of the site could be 1.0 million per month based on comparing pre- and post-COVID data (2019–2020). This study demonstrates the potential and performance of spaceborne PSInSAR tools for the intelligent management of large-scale architectural heritage sites by integrating InSAR deformation products with environmental and social data.

Inversion of seismic source parameters from satellite InSAR data based on deep learning

Satellite interferometric synthetic aperture radar (InSAR) has been playing an important role in the earthquake surface deformation observation and source model inversions with its advantage of fine spatial resolution in comparison with seismological data. At present, the two-step inversion approach, including a nonlinear inversion step for determining the fault geometry (e.g., length, width, depth, strike, dip, rake, slip) and a linear inversion step for estimating the slip distribution, is widely used to obtain seismic source parameters from satellite InSAR data. However, the nonlinear inversion step has some weaknesses, such as a prior knowledge requirement, local minima solution, complex and time-consuming. Coseismic differential interferogram is input into back propagation neural network (BPNN) to perform real time inversion of fault geometry in the previous study. But due to the simpleness of network structure, rake and slip parameters is fixed and inversion accuracy is limited. In this paper, we propose a deep learning approach of Earthquake Source Parameters Inversion using ResNet (abbreviated as ESPI-ResNet) from satellite InSAR data. ESPI-ResNet is able to firstly classify the 4 fault types and then invert the 7 fault geometry parameters based on uniform slip model. We train our model by firstly creating a large dataset of simulated interferograms with source parameters ranges confined by seismological and geological data. The accuracy of fault type classification is 99.6% and root mean squared error (RMSE) of inversed fault geometry parameters is low in the simulated test dataset. To find most suitable model for seismic source parameters inversion, we further compare the inversed accuracy of different networks, including BPNN, VGG-16, ResNet-18, ResNet-34, ResNet-50 and DenseNet. We find that moderate increase of network depth and the use of convolution, deep residual learning can improve the model's performance for source parameters inversion, and therefore ResNet-34 is chosen as the backbone network in this study. Finally, real differential interferograms of Yutian earthquake (2020 Mw 6.3), Jiuzhaigou earthquake (2017 Mw 6.5) and Menyuan earthquake (2016 Mw 5.9) are used to validate the proposed method. Real earthquakes validation shows all of them have correct fault type classification, and the inverted results are consistent with the current seismic source parameters data.

Estimating Forest Stand Height in Savannakhet, Lao PDR Using InSAR and Backscatter Methods with L-Band SAR Data

Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above-ground biomass for greenhouse gas emissions reporting and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R2 of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission.

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

Evolution of Deep-Seated Gravitational Slope Deformations in Relation with Uplift and Fluvial Capture Processes in Central Eastern Sardinia (Italy)

Connections between Plio-Pleistocenic tectonic activity and geomorphological evolution were studied in the Pardu Valley and Quirra Valley (Ogliastra, East Sardinia). The intensive Quaternary tectonic activity in Sardinia linked to the opening of the Tyrrhenian Basin is known. In Eastern Sardinia, it manifests with an uplift that is recorded by geomorphological indicators, such as deep-seated gravitational slope deformation, fluvial captures, engraved valleys, waterfalls, and heterogeneous water drainage. The Pardu River flows from the NW toward the SE and then abruptly changes direction toward the NE. At this point, a capture elbow adjacent to the current head of the Quirra River is well developed. The Quirra River, in its upstream part, flows at altitudes approximately 200 m higher than the Pardu River. It also shows an oversized and over-flooded valley with respect to the catchment area upstream. This setting indicates that the Pardu River, which previously flowed south along the Quirra River, was captured by the Pelau River. We analyzed long-term landslides with lateral spreading and sackung characteristics, which involve giant carbonate blocks and underlying foliated metamorphites in both valleys. The use of LiDAR, high-resolution uncrewed aerial vehicle digital photogrammetry (UAV-DP), and geological, structural, and geomorphological surveys enabled a depth morphometric analysis and the creation of interpretative 3D models of DGSDs. Space-borne interferometric synthetic aperture radar (InSAR) data using ERS and Sentinel-1 satellites identified downslope movement of up to 20 mm per year in both Pardu Valley flanks. Multi-source and multi-scale data showed that the state of activity of the DGSDs is closely linked to the geomorphological evolution of the catchment areas of the Rio Pardu and Rio Quirra. The intense post-capture erosion acted in the Rio Pardu Valley, giving it morphometric characteristics that were favorable to the current evolution of the DGSDs, while the Rio Quirra Valley presents paleo-DGSDs that have been fossilized by pre-capture terraced alluvial deposits.