Displacement Time Series Research Articles

Daily to Centennial Behavior of Aseismic Slip Along the Central Section of the North Anatolian Fault

AbstractSlow, aseismic slip plays a crucial role in the initiation, propagation, and arrest of large earthquakes along active faults. In addition, aseismic slip controls the budget of elastic strain in the crust, hence the amount of energy available for upcoming earthquakes. The conditions for slow slip include specific material properties of the fault zone, pore fluid pressure, and geometrical complexities of the fault plane. Fine scale descriptions of aseismic slip at the surface and at depth are key to determine the factors controlling the occurrence of slow, aseismic versus rapid, seismic fault slip. We focus on the spatial and temporal distribution of aseismic slip along the North Anatolian Fault, the plate boundary accommodating the 2 cm/yr of relative motion between Anatolia and Eurasia. Along the eastern termination of the rupture trace of the 1944 M7.3 Bolu‐Gerede earthquake lies a segment that slips aseismically since at least the 1950s. We use Sentinel 1 time series of displacement and GNSS data to provide a spatio‐temporal description of the kinematics of fault slip. We show that aseismic slip observed at the surface is coincident with a shallow locking depth and that slow slip events with a return period of 2.5 years are restricted to a specific section of the fault. In the light of historical measurements, we discuss potential rheological implications of our results and propose a simple alternative model to explain the local occurrence of shallow aseismic slip at this location.

GNSS land subsidence observations along the northern coastline of Java, Indonesia

Land subsidence in cities along the northern coastline of Java has been at a worrying level. Monitoring efforts using geodetic data reveal that Jakarta, Pekalongan, Semarang, and Demak subside at least ~9x faster than the present-day rate of global sea level rise, which affects the cities’ future urban viability. In this study, we publish a time series of the precise 3D displacements observed by twenty continuous Global Navigation Satellite System (GNSS) stations between 2010 and 2021. These are the first open-to-the-public and rigorously processed GNSS datasets that are useful for accurately quantifying land subsidence in the densely populated sinking cities in Java. The data also provides a way to tie other geodetic observations, such as Interferometric Synthetic Aperture Radar (InSAR), to a global reference frame in an attempt to build worldwide observations of coastal land subsidence.

Understanding the Spatiotemporal Characteristics of Land Subsidence and Rebound in the Lianjiang Plain Using Time-Series InSAR with Dual-Track Sentinel-1 Data

The Lianjiang Plain, renowned for its position as ‘China’s textile hub’ and characterized by its high population density, has experienced considerable subsidence due to excessive groundwater extraction in recent years. Although some studies have investigated short-term subsidence in this plain, research on long-term subsidence and rebound remain understudied. In this paper, the characteristics of surface deformation in the Lijiang Plain during two periods (2015–2017 and 2018–2021) have been investigated using the time-series interferometric synthetic aperture radar (TS-InSAR) technique, and the correlation with the changes in groundwater level, geological factors, and urban construction are discussed. The InSAR-derived results are cross-validated with the adjacent orbit datasets. Large-scale and uneven subsidence ranging from −124 mm/year to +40 mm/year is observed from 2015 to 2017. However, a significant decrease in the subsidence rate during 2018–2021, with local rebound deformation up to +48 mm/year in three regions, is also observed. Groundwater level changes are found to be the major cause of the ground deformation, and the intercomparison between groundwater level and ground displacement time series from TS-InSAR measurements also indicates a clear relationship between them during 2018–2021. Geological factors control the range of deformation area over the study period. The impact of urban construction on surface subsidence is evident, contributing to high deformation. Our findings could improve the understanding of how deformation is affected by groundwater rebound and offer valuable insights into groundwater management, urban planning, and land subsidence mitigation.

A Multivariate Time Series Analysis of Ground Deformation Using Persistent Scatterer Interferometry

Ground deformations in urban areas can be the result of a combination of multiple factors and pose several hazards to infrastructures and human lives. In order to monitor these phenomena, Interferometric Synthetic Aperture Radar (InSAR) techniques are applied. The obtained signals record the overlapping of the phenomena, and their separation is a relevant issue. In this framework, we explored a new multi-method approach based on the combination of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Hierarchal Clustering (HC) on the standardized results to distinguish the main trends and seasonal signals embedded in the time series of ground displacements, to understand spatial-temporal patterns, to correlate ground deformation phenomena with geological and anthropogenic factors, and to recognize the specific footprints of different ground deformation phenomena. This method allows us to classify the ground deformations at the site scale in the metropolitan area of Naples, which is affected by uplift cycles, subsidence, cavity instabilities and sinkholes. At the local scale, the results allow a kinematic classification using the extracted components and considering the effect of the radius of influence generated by each cavity, as it is performed from a theoretical point of view when the draw angle is considered. According to the results, among the classified cavities, 2% were assigned to subsidence and 11% to uplift kinematics, while the remaining were found to be stable. Furthermore, our results show that the centering of the Spatial-PCA (S-PCA) is representative of the region’s main trend, whereas Temporal-PCA (T-PCA) gives information about the displacement rates identified by each component.

A Stochastic Dynamical Model of Slope Creep and Failure

AbstractWe propose a stochastic dynamical model to simulate slope secondary and tertiary creep phenomena. The slope secondary creep is represented by the Kesten process defined as a stochastic affine auto‐regressive process involving both multiplicative and additive random variables. The Kesten process can realistically capture the co‐existence of a background deformation and intermittent displacement bursts, which are together well characterized by an inverse gamma velocity distribution. The slope tertiary creep is modeled by a nonlinear stochastic dynamical equation embodying a nonlinear feedback mechanism and a nonlinear random effect, which can mimic the development of slow or catastrophic landslides. For catastrophic landslides, the probability density function of slope velocities tends to deviate from the inverse gamma distribution by populating the “dragon‐king” regime, although sometimes they may grow undetectably in the “black‐swan” regime. Our model provides a quantitative framework to understand, simulate, and interpret complex landslide displacement time series.

An Investigation of Volcanic Ground Deformation Using InSAR Observations at Tendürek Volcano (Turkey)

Although approximately one-tenth of the world’s population lives near volcanoes, most of the 1500 active volcanoes are not monitored by ground-based instruments because of the cost and difficulty of access. Since the development of interferometric synthetic aperture radar (InSAR) in the 1990s, recent advances have allowed the near-real-time detection of surface deformations, one of the earliest markers of volcanic activity. According to the Global Volcanism Program, Turkey’s most recent eruption (involving gas and ash) occurred in the Tendürek volcano in 1885. An explosion in the Tendürek volcano, which continues to actively output gas and steam, would be a critical issue for the life and property of the people living nearby. In this context, we processed the Sentinel-1 data collected by the European Space Agency using the Stanford Method for Persistent Scatterers, and the surface deformations of the Tendürek volcano were investigated. In addition, we applied two different atmospheric correction approaches (linear phase-based tropospheric correction and the Generic Atmospheric Correction Online Service for InSAR) to reduce atmospheric effects and found that the linear phase-based tropospheric correction model produced lower standard deviation values. Subsequently, the mean deformation velocity maps, displacement time series, and deformation components in the line-of-sight direction were calculated. The results showed that the most severe subsidence was −11 mm/yr on the upper slopes of the Tendürek volcano. Although the lower slopes of the subsidence region have a lower settlement rate, the subsidence has a peak-caldera-centered location.

The Precursory 3D Displacement Patterns and Their Implicit Collapse Mechanism of the Ice-Rock Avalanche Events Occurred in Sedongpu Basin Revealed by Optical and SAR Observations

Quantifying the kinematic evolution patterns of mountain glaciers near Yarlung Tsanpo River performs a major role in evaluating the glacial instability and the secondary disasters. For the Sedongpu Basin near the Yarlung Tsanpo River Valley, the dramatic geomorphic landscape variations triggered by the ice-rock avalanche events were visually identified as the dominant texture deficiencies in time-series optical images. To improve the image correlation quality broken by these image texture deficiencies, the Landsat-8/Sentinel-2 optical images were divided into different groups, then a stepwise combination strategy was innovatively proposed to derive the glacier time-series displacement velocities in different temporal stages. The standard deviations (STD) of the optical measurements in the stable area maintained around 0.04 m/yr for the normalized displacement velocity and maintained from 0.6 to 1.7 m for the cumulative displacement time series. The obvious variations in glacier displacement velocity were identified before each collapse event. Subsequently, the offset-tracking procedures were performed on 7 Sentinel-1A Synthetic Aperture Radar (SAR) images to acquire the range and azimuth displacement velocities. To better reveal the dynamic mechanism of the glacier activity, the three-dimensional (3D) glacial displacement velocity was also derived by using optical and SAR results. The precipitation, temperature, and seismic activities were assumed as the main triggering factors of controlling the glacial dynamic mechanism and final collapse events. Additionally, the dynamic mechanism of the active glaciers in Sedongpu Basin conformed to a power law, which was limited by the changes of the internal stress friction force on the sliding base surface. The aim of this study is to shed a light on interpreting the precursory displacement patterns and their implicit failure mechanism of these ice-rock avalanche events with the conventional freely optical and SAR observations.

Quantifying seasonal ground deformation in Taiyuan basin, China, by Sentinel-1 InSAR time series analysis

The presence of seasonal surface deformation in areas that are subsiding/uplifting due to groundwater pumping/recovery can indicate aquifer recharge. Understanding seasonal surface deformation and the existing relationship between triggering factors and the deformation supports effective groundwater management and subsidence mitigation. Here, we investigated seasonal ground displacement in response to the aquifer compaction and expansion due to groundwater extraction and recharge within the Taiyuan basin, Northern China. We obtained Sentinel-1 InSAR displacement time series data from 2017 to 2020 and quantitatively analyzed relationships between triggering factors (e.g., groundwater level and precipitation) and the associated displacements using wavelet-based methods. The results show that the seasonal deformation is concentrated in the irrigated areas within the central basin, with a 1-year periodicity. The annual peak-to-peak amplitude was up to 2 cm, peaking in March with troughs in August. This seasonality present in the displacement time series is found to be ‘in-phase’ with respect to groundwater levels and ‘anti-phase’ in relation to natural precipitation. These results are consistent with the irrigation pumping practices in the basin, with increased groundwater withdrawals to meet irrigation water demand prior to the monsoon precipitation season, resulting in water level decline and land subsidence from mid-March to mid-August. During the non-irrigated season, groundwater extraction decreases, and the aquifer systems are replenished by precipitation, causing water levels to recover and the ground to uplift from late August to mid-March. A geological analysis indicates that the seasonal deformation is controlled by the thickness of Quaternary strata and fault systems, which play a critical role in forming the earth fissures in the basin. The elastic and inelastic deformation was separated; the estimated elastic/inelastic ratio was about 0.6–0.7, indicating that the inelastic component dominates the main aquifer compaction in the central basin. Our analysis is of hydrologic importance which could be used to guide groundwater management and subsidence mitigation, given the inter-basin water diversion projects in this arid basin.

Real-time high-precision landslide displacement monitoring based on a GNSS CORS network

GNSS technology is an effective all-weather, continuous and real-time method of detecting three-dimensional landslide displacement. However, current positioning methods such as the network real-time kinematic (RTK) technique based on virtual reference station (VRS) technology have some limitations, particularly a sensitivity to atmospheric modeling error. In this study, taking advantage of existing GNSS continuously operating reference stations (CORS), we propose a network RTK algorithm (virtual atmosphere constraint (VAC)) for high-precision landslide monitoring. Network modeling atmosphere delays were used as virtual observations for the terminal position solution, by which their accuracy information can be considered. Additionally, a noise reduction method that combines complementary ensemble empirical mode decomposition and median filtering was proposed to reduce GNSS signal residual errors in the original RTK coordinate time series. To validate the effectiveness of the algorithm, we processed one week of data from two monitoring receivers on the Heifangtai landslide and four surrounding CORS using the VAC and VRS methods. Results show that the original displacement time series processed with VRS was prone to jitter, whereas the VAC method exhibited better accuracy, stability, and ambiguity fixing rates. Horizontal and vertical RMS errors at a stable monitoring point were 1.7 cm and 3.9 cm for VRS-RTK and 0.9 cm and 2.3 cm for VAC-RTK; thus, VAC reduced the error by 40.1% and 20.5%, respectively. After noise reduction, the RMS values of the VAC method at the stable and the other collapsed monitoring point were both reduced to an mm-level accuracy.

Observation of spatial and temporal patterns of seasonal ground deformation in central Yakutia using time series InSAR data in the freezing season

The seasonal freeze/thaw variations of the active layer cause seasonal ground deformation, such as thaw subsidence in summer and frost heave in winter due to the cyclic phase changes of soil water. The Synthetic Aperture Radar (SAR) remote sensing, especially interferometric SAR (InSAR) techniques, has been proven to be a very useful tool for observing surface displacements of permafrost environments. Most previous studies on seasonal InSAR displacement have been concentrated in several study sites in the Arctic tundra regions. The objective of this study is to extend the InSAR analysis of seasonal ground deformations to boreal forest regions, where monitoring ground deformation is particularly important in terms of better understanding active layer dynamics under various ecological and geological conditions. In this study, we presented a strategy to analyze the time series SAR data in the freezing season to estimate the differential phase for seasonal ground deformation while overcoming the problem of coherence losses due to the rapid growth of vegetation during the short growing season. We applied the SBAS-based InSAR processing method to Sentinel-1 SAR in the central Yakutian lowlands, where boreal forests and thermokarst landforms are widely distributed, and retrieved frost-related displacement signals successfully. In addition to the magnitude of ground deformation in the freezing season, we further examined the temporal deformation pattern related to the progression of the freezing front in the soil during active freezing. Based on the Stefan solution, we proposed a piecewise linear model for the time series deformation. The proposed model divides the time series displacement according to the progression of freezing into three linear segments, and the slope parameter of each linear model can be interpreted as indicating the hydrological characteristics of the upper, middle, and lower parts of the soil. The results for the Yakutian lowlands illustrated the potential of SAR remote sensing to observe and quantify spatial details of the thermal and hydrological properties inside the active layer soil even in highly vegetated subarctic boreal forest areas.

Sinkhole risk mapping and early warning: the case of Camaiore (Italy)

Introduction: Sinkholes are ground collapses that can cause significant damage to infrastructure and buildings. Part of the risk represented by sinkholes is related to their abruptness and the difficulty in spotting in advance their exact location within a sinkhole-prone area. For this reason, urban planning informed by an accurate risk mapping and monitoring is one of the most effective ways to reduce the risk.Methods: In this study, we propose a two-folded procedure based on the examination of ground displacement data measured by a ground-based interferometric radar and on the generation of a sinkhole risk zonation map. We examined 11 years’ worth of ground displacement data measured by a ground-based interferometric radar to search for sinkhole precursors. The analysis was based on averaged displacement time series retrieved from high-coherence pixels scattered around Camaiore, Italy, a test site where a catastrophic sinkhole occurred in 1995. To generate a sinkhole risk map, we evaluated the susceptibility map as derived from a set of predisposing environmental parameters, the vulnerability derived from the thickness of the sedimentary cover that can be linked to the abruptness of the collapse, and the value of the elements at risk from the Italian Real Estate Market Observatory integrated with land cover information for the non-built up areas.Results: The analysis of ground displacement data revealed that Camaiore had not experienced subsidence relatable to incoming sinkholes in the monitored period. However, few cm of vertical movements, which are well correlated with water table oscillations, have been measured and are expected to be of the same order of magnitude of sinkhole precursor deformations. This implies that a phenomenon of the size and velocity of the 1995 event could have likely been detected before its final collapse. The sinkhole risk map identified specific areas that should be closely monitored using in situ and remote sensing instrumentation.Discussion: The sinkhole risk zonation map generated in this study can be used to inform urban planning and risk management strategies. The study also shows the potential of ground-based interferometric radar to detect sinkhole precursors and the importance of integrating different mitigation approaches. Overall, this study can provide insights for sinkhole risk assessment and management in sinkhole-prone areas.

Outlier Detection Based on Nelder-Mead Simplex Robust Kalman Filtering for Trustworthy Bridge Structural Health Monitoring

Structural health monitoring (SHM) is vital for ensuring the service safety of aging bridges. As one of the most advanced sensing techniques, Global Navigation Satellite Systems (GNSS) could capture massive spatiotemporal information for effective bridge structural health monitoring (BSHM). Unfortunately, GNSS measurements often contain outliers due to various factors (e.g., severe weather conditions, multipath effects, etc.). All such outliers could jeopardize the accuracy and reliability of BSHM significantly. Previous studies have examined the feasibility of integrating the conventional multi-rate Kalman filter (MKF) with an adaptive algorithm in the data processing processes to ensure BSHM accuracy. However, frequent parameter adjustments are still needed in tedious data processing processes. This study proposed an outlier detection method using a Nelder-Mead simplex robust multi-rate Kalman filter (RMKF) for supporting trustworthy BSHM using GNSS and accelerometer. In the end, the authors have validated the proposed method using the monitoring data collected at the Wilford Bridge in the UK. Results showed that the accuracy of the total dynamic vibration displacement time series has been improved by 21% compared with the results using the conventional MKF approach. The authors envision that the proposed method will shed light on reliable and explainable data processing policy and trustworthy BSHM.

Characterization of landslide displacements in an active fault zone in Northwest China

AbstractLandslides can be caused by natural forcing and anthropogenic activities. Zhouqu County (China) on the eastern margin of Qinghai‐Tibet Plateau is set within the active Pingding‐Huama fault zone with evident fractures on the land surface. Frequent landslides and debris flows have occurred in this region due to river erosion, rainfall and deforestation. Here we quantified the slope movements using time‐series synthetic aperture radar interferometry (InSAR) based on the ascending and descending Sentinel‐1 satellite images acquired between October 2014 and August 2020. We observed distinct displacements in the highly fractured fault zone. The eastward and vertical displacement time series between February 2017 and July 2020 were constrained by the common‐day ascending and descending acquisitions. The eastward rates (461 mm/year) were greater than those in the vertical direction (−185 mm/year). We also note displacement discontinuities across the thrust faults beneath the Suoertou and Zhongpai landslides. Seasonal variations in the displacement time series suggest that the cyclic rainfall is the primary driver for the mass wasting processes rather than the tectonic loading. As a complement to in situ observations, our results demonstrate that InSAR is an effective tool to characterize the spatio‐temporal nature of landslide displacements in complicated geological environments.Plain Language SummaryZhouqu County in the Pingding‐Huama fault zone in the eastern margin of Qinghai‐Tibet Plateau is identified as a high priority site to research on clusters of landslides and debris flows in a mixed geodynamic setting of active tectonics, seasonal rainfall, river erosion and anthropogenic activities. However, our knowledge about landslide kinematics in this complicated region is still limited. We relied on remote sensing images from one ascending and one descending Sentinel‐1 satellite tracks to constrain the spatial–temporal displacement dynamics of active landslides from 2014 to 2020. The spatial patterns of displacements are determined by thrust faulting, river erosion, and anthropogenic activities. The temporal variations of landslide speed are mainly controlled by the seasonal rainfall rather than the tectonic loading.

Dynamics of a hanging fluid-discharging pipe subjected to reverse external flow: An experimental investigation

The present paper discusses and analyzes experiments on the dynamics of hanging cantilever tubular beams conveying fluid downwards and subjected to partially-confined reverse external axial flow. A bench-top-size facility, consisting of a reservoir filled with water, a hanging flexible pipe conveying fluid downwards and a shorter outer rigid tube surrounding the pipe at its upper portion containing an upwards flow, was utilized. A non-contacting optical approach was employed to obtain the displacement time-series of pipe motions from which, using qualitative and quantitative nonlinear dynamics tools, the nature of the motions was explored. The oscillatory motions observed were found to be unsteady with both periodic and chaotic content. The influence of some system parameters on the dynamics, namely, external flow confinement, pipe slenderness, pipe material, placement of a constraint at the external annular flow inlet/outlet, and eccentric positioning of the outer rigid tube relative to the central pipe, was examined. It was found that varying the system parameters in some cases gives rise to quite interesting qualitative or quantitative changes in this Fluid–Structure Interaction (FSI) system.

Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation

Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings.

EZ-InSAR: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar

Satellite Interferometric Synthetic Aperture Radar (InSAR) is a space-borne geodetic technique that can map ground displacement at millimetre accuracy. Via the new era for InSAR applications provided by the Copernicus Sentinel-1 SAR satellites, several open-source software packages exist for processing SAR data. These packages enable one to obtain high-quality ground deformation maps, but still require a deep understanding of InSAR theory and the related computational tools, especially when dealing with a large stack of images. Here we present an open-source toolbox, EZ-InSAR (easy-to-use InSAR), for a user-friendly implementation of InSAR displacement time series analysis with multi-temporal SAR images. EZ-InSAR integrates the three most popular and renowned open-source tools (i.e., ISCE, StaMPS, and MintPy), to generate interferograms and displacement time series by using these state-of-art algorithms within a seamless Graphical User Interface. EZ-InSAR reduces the user’s workload by automatically downloading the Sentinel-1 SAR imagery and the digital elevation model data for the user’s area of interest, and by streamlining preparation of input data stacks for the time series InSAR analysis. We illustrate the EZ-InSAR processing capabilities by mapping recent ground deformation at Campi Flegrei (> 100 mm·yr−1) and Long Valley (~ 10 mm·yr−1) calderas with both Persistent Scatterer InSAR and Small-Baseline Subset approaches. We also validate the test results by comparing the InSAR displacements with Global Navigation Satellite System measurements at those volcanoes. Our tests indicate that the EZ-InSAR toolbox provided here can serve as a valuable contribution to the community for ground deformation monitoring and geohazard evaluation, as well as for disseminating bespoke InSAR observations for all.

Patterns and rates of soil movement and shallow failures across several small watersheds on the Seward Peninsula, Alaska

Abstract. Thawing permafrost can alter topography, ecosystems, and sediment and carbon fluxes, but predicting landscape evolution of permafrost-influenced watersheds in response to warming and/or hydrological changes remains an unsolved challenge. Sediment flux and slope instability in sloping saturated soils have been commonly predicted from topographic metrics (e.g., slope, drainage area). In addition to topographic factors, cohesion imparted by soil and vegetation and melting ground ice may also control spatial trends in slope stability, but the distribution of ground ice is poorly constrained and hard to predict. To address whether slope stability and surface displacements follow topographic-based predictions, we document recent drivers of permafrost sediment flux present on a landscape in western Alaska that include creep, solifluction, gullying, and catastrophic hillslope failures ranging in size from a few meters to tens of meters, and we find evidence of rapid and substantial landscape change on an annual timescale. We quantify the timing and rate of surface movements using a multi-pronged, multi-scalar dataset including aerial surveys, interannual GPS surveys, synthetic aperture radar interferometry (InSAR), and climate data. Despite clear visual evidence of downslope soil transport of solifluction lobes, we find that the interannual downslope surface displacement of these features does not outpace downslope displacement of soil in locations where lobes are absent (downslope movement means: 7 cm yr−1 for lobes over 2 years vs. 10 cm yr−1 in landscape positions without lobes over 1 year). Annual displacements do not appear related to slope, drainage area, or modeled total solar radiation but are likely related to soil thickness, and volumetric sediment fluxes are high compared to temperate landscapes of comparable bedrock lithology. Time series of InSAR displacements show accelerated movement in late summer, associated with intense rainfall and/or deep thaw. While mapped slope failures do cluster at slope–area thresholds, a simple slope stability model driven with hydraulic conductivities representative of throughflow in mineral and organic soil drastically overpredicts the occurrence of slope failures. This mismatch implies permafrost hillslopes have unaccounted-for cohesion and/or throughflow pathways, perhaps modulated by vegetation, which stabilize slopes against high rainfall. Our results highlight the breadth and complexity of soil transport processes in Arctic landscapes and demonstrate the utility of using a range of synergistic data collection methods to observe multiple scales of landscape change, which can aid in predicting periglacial landscape evolution.

Refined mapping and kinematic trend assessment of potential landslides associated with large-scale land creation projects with multitemporal InSAR

Since 2012, a mega-project, “Mountain Excavation and City Construction” (MECC), has been underway on the Chinese Loess Plateau, aiming to create extra flat ground for urban growth. However, this large-scale land creation has led to dramatic changes in the local geological environment, forming a large number of engineered loess slopes. This study aims to map and assess the boundaries and stability of these potentially unstable engineered slopes (potential landslides). Based on spatio-temporal deformation patterns of landslides prior to failure, a novel interferometric synthetic aperture radar (InSAR)-based procedure for potential landslide mapping and stability evaluation was constructed in this study. The proposed procedure was employed to map and evaluate the potential landslides in the Yan’an New District (YND), the largest land-creation area in the Loess Plateau. First, the spatial locations and boundaries of the potential landslides were determined by combining optical image interpretation with differential deformation maps derived from Sentinel-1 small baseline subset InSAR (SBAS-InSAR) analysis. Then, the kinematic evolution trend of the mapped potential landslide was evaluated by quantitative analysis of the time-series displacements using an exponential model. Finally, the potential influencing factors and failure modes of the potential landslide were examined and discussed. The results indicate that the InSAR-derived differential deformation maps reflect the external boundary of the potential landslides well, and a total of 18 potential landslides are identified and mapped for the first time in the large-scale land-creation area of the YND. Over 82% of the coherent targets (CTs) within these potential landslides showed primary or steady creep during the InSAR observation period; however, tertiary creep was also observed on several slopes. Thus, it is crucial to utilize multitemporal InSAR and/or ground-based equipment for the continuous monitoring of engineered slopes in large-scale land-creation areas. The high correlation between spatiotemporal deformation patterns and environmental factors indicates that the activity of potential landslides is mainly controlled by the thickness of filling loess, while rainfall accelerates the displacement of slopes. These findings can contribute to the development of landslide disaster prevention and mitigation measures in the YND.

Iceland Kinematics From InSAR

AbstractLarge‐scale ground deformation in Iceland is dominated by extensional plate‐boundary deformation, where the Mid‐Atlantic Ridge crosses the island, and by uplift due to glacial isostatic adjustment (GIA) from thinning and retreat of glaciers. While this deformation is mostly steady over multiple years, it is modulated by smaller‐scale transient deformation associated with, for example, earthquakes, volcanic unrest, and geothermal exploitation. Here, we combine countrywide Sentinel‐1 interferometric synthetic aperture radar (InSAR) data (from six tracks) from 2015 to 2021 with continuous Global Navigation Satellite System observations to produce time series of displacements across Iceland. The InSAR results were improved in a two‐step tropospheric mitigation procedure, using (a) global atmospheric models to reduce long‐wavelength and topography‐correlated tropospheric signals, and (b) modeling of the stochastic properties of the residual troposphere. Our results significantly improve upon earlier countrywide InSAR results, which were based on InSAR stacking, as we use more data, better data weighting, and advanced InSAR corrections to produce time series of ground displacements instead of just velocities. We fuse the three ascending and three descending track results to estimate maps of East and Up velocities, which clearly show the large‐scale extension and GIA deformation. Using revised plate‐spreading and GIA models, based on these new ground velocity maps, we remove the large‐scale and steady deformation from the InSAR time series and analyze the remaining transient deformations. Our results demonstrate the importance of (a) mitigating InSAR tropospheric signals over Iceland and of (b) solving for time series of deformation, not just velocities, as multiple transient deformation processes are present.

Geomorphological characterization, remote sensing monitoring, and modeling of a slow-moving landslide in Alcoy (Southern Spain)

In the present paper, an active landslide located to the southeast of the urban area of Alcoy (Alicante, Spain) is analysed. This landslide affects the N-340 road, as well as an area in which some industrial buildings dated from the fifteenth century and catalogued as Places of Cultural Interest are placed. This region is characterised by the existence of a steep relief composed of materials from the Miocene to the present, on which many gravitational movements are developed. The displacements have been analysed using high-resolution spotlight mode SAR images captured by the Spanish SAR satellite PAZ (which operates at X-band), which were processed by advanced differential SAR Interferometry (A-DInSAR). The behaviour of the instability has been determined using InSAR-derived time series displacement maps. Additionally, the study has been completed with a detailed geomorphological mapping at 1:2000 scale and an analysis of structural damage carried out in situ. The landslide was modelled in 3D, from a deterministic and probabilistic point of view, confirming the global instability of the area and the relationship of the landslide with rainfall and the construction of a new road. The integration of all this information obtained and its subsequent analysis has made possible to precisely define the extension of the instability, its kinematics, and to identify the main triggering factors. This information will be crucial for the proper management of this landslide by the local authorities.