Search Results (79)

In high-mountain canyon regions, many settlements are located on large, deep-seated ancient landslides. The deformation characteristics, triggering mechanisms, and long-term developmental trends of these landslides significantly impact the safety and stability of these communities. However, the deformation mechanism under the influence of human engineering activities remains unclear. SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar) technology, UAV LiDAR, and field surveys were utilized in this study to identify a large ancient landslide in the upper Jinsha River Basin: the Zhongxinrong landslide. It extends approximately 1220 m in length, with a vertical displacement of around 552 m. The average thickness of the landslide mass ranges from 15.0 to 35.0 m, and the total volume is estimated to be between 1.48 × 10⁷ m³ and 3.46 × 10⁷ m³. The deformation of the Zhongxinrong landslide is primarily driven by a combination of natural and anthropogenic factors, leading to the formation of two distinct accumulation bodies, each exhibiting unique deformation characteristics. Accumulation Body II-1 is predominantly influenced by rainfall and road operation, resulting in significant deformation in the upper part of the landslide. In contrast, II-2 is mainly affected by rainfall and river erosion at the front edge, causing creeping tensile deformation at the toe. Detailed analysis reveals a marked acceleration in deformation following rainfall events when the cumulative rainfall over a 15-day period exceeds 120 mm. The lag time between peak rainfall and landslide displacement ranges from 2 to 28 days. Furthermore, deformation in the high-elevation accumulation area consistently exhibits a slower lag response compared to the tensile deformation area at lower zones. These findings highlight the importance of both natural and anthropogenic factors in landslide risk assessment and provide valuable insights for landslide prevention strategies, particularly in regions with similar geological and socio-environmental conditions. Full article

Slow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability for slow surface deformation. However, landslides usually occur in alpine canyon areas and vegetation coverage areas where InSAR measurements are still limited by temporal and spatial decorrelation and atmospheric influences. In addition, there are several difficulties in monitoring the multiscale characterization of landslides from the InSAR results. To address this issue, this paper proposes a novel method for slow-moving landslide hazard assessment in low-coherence regions. A window-based atmosphere correction method is designed to highlight the surface deformation signals of InSAR results in low-coherence regions and reduce false alarms in landslide hazard assessment. Then, the deformation annual velocity rate map, coherence map and DEM are used to construct the InSAR sample set. A landslide hazard assessment model named Landslide-SE-Unilab is subsequently proposed. The global–local relationship aggregation structure is designed to capture the spatial relationship between local pixel-level deformation features and global landslides, which can reduce the number of missed assessments and false assessments of small-scale landslides. Additionally, a squeeze-and-excitation network is embedded to adjust the weight relationship between the features of each channel in order to enhance the performance of network evaluation. The method was evaluated in Kangding city and the Jinsha River Valley in the Hengduan Mountains, where a total of 778 potential landslides with slow deformation were identified. The effectiveness and accuracy of this approach for low-coherence landslide hazard assessment are demonstrated through comparisons with optical images and previous research findings, as well as evaluations via time-series deformation results. Full article

This study presents the geological and geomorphological characterization of the Pie de la Cuesta landslide, a large (>60 ha) slow-moving (up 4.5 m/month) landslide in Southern Peru. The landslide has been active since 1975 and underwent a significant re-activation in 2016; the mass movement has caused the loss of property and agricultural land and it is currently moving, causing further damage to property and land. We use a combination of historical aerial photographs, satellite images and field work to characterize the landslide’s geology and geomorphology. The landslide is affecting the slope of the Vitor Valley, constituted by a coarsening upward sedimentary sequence transitioning from layers of mudstone and gypsum at the base, to sandstone and conglomerate at the top with a significant ignimbrite layer interbedded within conglomerates near the top of the sequence. The landslide is triggered by an irrigation system that provides up to 10 L/s of water infiltrating the landslide mass. This water forms two groundwater levels at lithological transitions between conglomerates and mudstones, defining the main failure planes. The landslide is characterized by three main structural domains defined by extension, translation and compression deformation regimes. The extensional zone, near the top of the slope, is defined by a main horst–graben structure that transitions into the translation zone defined by toppling and disaggregating blocks that eventually become earth flows that characterize the compressional zone at the front of the landslides, defined by thrusting structures covering the agricultural land at the valley floor. The deformation rates range from 8 cm/month at the top of the slope to 4.5 m/month within the earth flows. As of May 2023, 22.7 ha of potential agricultural land has been buried. Full article

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. Full article

Quantitative disaster risk studies for slow-moving rotational and translational landslides in small regions (e.g., cities and watersheds) are very scarce. The limitations of risk modeling associated with these hazards include (i) the lack of data for physical modeling, (ii) methodological restrictions on estimating landslide intensity with statistical models and determining the temporal probability of landslides, and (iii) the absence of characterizations of the physical vulnerability of exposed assets. The present study combines and updates different methodologies to overcome these limitations for quantitative landslide disaster risk estimation, creating a novel methodological approach that was applied in a pilot study in Tegucigalpa city, Honduras. Tegucigalpa, the capital city of Honduras, has the highest number of recorded landslides in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. As an input for the disaster risk assessment, this study generated landslide risk vulnerability functions based on empirical data. The application of the proposed methodology allowed us to estimate the average annual loss (AAL) caused by landslides in the study area—a key disaster risk metric that is lacking in other landslide disaster risk studies—enabling comparisons with disaster risk estimates associated with other hazards. In particular, the AAL value obtained for the study region was USD 7.26 million. Full article

In mountain environments, landslide activity can be assessed through a combination of remote and proximal sensing techniques performed at different scales. The complementarity of methods and the synergistic use of data can be crucial for landslide recognition and monitoring. This paper explored the potential of Multi-Temporal Differential Synthetic Aperture Radar Interferometry (MT-DInSAR) to detect and monitor slope deformations at the basin scale in a catchment area of the Northern Apennines (Italy) and verified the consistency between the landslide classification by the Inventory of Landslide Phenomena in Italy (IFFI) and displacements from the SAR data. In this research, C- and X-band SAR were considered to provide insights into the performances and suitability of sensors operating at different frequencies. This study provides clues about the state of activity of slow-moving landslides and critically assessed its contribution to the IFFI inventory update. Moreover, it demonstrated the benefits of the synergistic use of SAR and geomorphological analysis to investigate slope dynamics in clayey terrains by exemplifying the approach for a relevant case study, the Gaiato landslide. Notwithstanding the widespread use of MT-DInSAR for landslide kinematics investigations, the main limiting factors are discussed along with the expected improvements related to the upcoming new generations of L-band SAR satellites. Full article

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at $0.1^{\circ }\times 0.1^{\circ }$ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation ($r\ge 0.7$) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures. Full article

The EarthExplorer 10 mission Harmony by the European Space Agency ESA, scheduled for launch around 2029–2030, consists of two passive C-band synthetic-aperture-radar companion satellites flying in a flexible constellation with one Sentinel-1 radar satellite as an illuminator. Sentinel-1 will serve as transmitter and receiver of radar waves, and the two Harmonys will serve as bistatic receivers without the ability to transmit. During the first and last year of the 5-year mission, the two Harmony satellites will fly in a cross-track interferometric constellation, such as that known from TanDEM-X, about 350 km ahead or behind the assigned Sentinel-1. This constellation will provide 12-day repeat DEMs, among other regions, over most land-ice and permafrost areas. These repeat DEMs will be complemented by synchronous lateral terrain displacements from the well-established offset tracking method. In between the cross-track interferometry phases, one of the Harmony satellites will be moved to the opposite side of the Sentinel-1 to form a symmetric bistatic “stereo” constellation with ±~350 km along-track baseline. In this phase, the mission will provide opportunity for radar interferometry along three lines of sight, or up to six when combining ascending and descending acquisitions, enabling the measurement of three-dimensional surface motion, for instance sub- and emergence components of ice flow, or three-dimensional deformation of permafrost surfaces or slow landslides. Such measurements would, for the first time, be available for large areas and are anticipated to provide a number of novel insights into the dynamics and mass balance of a range of mass movement processes. Full article

This paper deals with the investigation of factors influencing the movement patterns of a reactivated slow-moving landslide situated in the eastern Liguria region (NW Italy) through the analysis of extensive ground-based hydrological and geotechnical monitoring data. Subsurface horizontal displacement and pore water pressure data were acquired simultaneously by means of automatic sensors positioned at pre-existing and localized failure zones. The joint examination of field measurements enabled us to explore the connections between rain, pore water pressure, and displacements. The results of continuous displacement monitoring showed that the landslide kinematics involved phases of extremely slow movements alternated with periods of relative inactivity. Both stages occurred prevalently at seasonal scale displaying similar durations. The slow-motion phases took place at relatively constant pore water pressure and were ascribed to mechanisms of viscous shear displacements along failure surfaces. Inactive phases entailed no significant deformations, mostly corresponding to prolonged dry periods. The two motion patterns were interrupted by episodic sharp deformations triggered by delayed (preparation periods from 4 to 11 days) rainfall-induced pore water pressure peaks, which were ascribed to sliding mechanisms taking place through rigid-plastic frictional behaviour. During these deformation events, hysteresis relationships between pore water pressure and displacement were found, revealing far more complex hydro-mechanical behaviour. Full article

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. Full article

Detecting slow-moving landslides is a crucial task for mitigating potential risk to human lives and infrastructures. In this research, Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) time series, provided by the European Ground Motion Service (EGMS), for the province of Frosinone in Italy are employed, and Sequential Turning Point Detection (STPD) is applied to them to estimate the dates when the displacement rates change. The estimated dates are classified based on the land cover/use of the province. Moreover, local precipitation time series are employed to investigate how precipitation rate changes might have triggered the landslides. Full article

Slow-moving landslides often occur in areas of high relief, which are significantly affected by tropospheric delay. In general, tropospheric delay correction methods in the synthetic-aperture radar interferometry (InSAR) field can be broadly divided into those based on external auxiliary information and those based on traditional empirical models. External auxiliary information is hindered by the low spatial–temporal resolution. Traditional empirical models can be adaptable for the spatial heterogeneity of tropospheric delay, but are limited by preset window sizes and models. In this regard, this paper proposes an improved tropospheric delay correction method based on the multivariable move-window variation model (MMVM) to adaptively determine the window size and the empirical model. Considering topography and surface deformation, the MMVM uses multivariate variogram models with iterative weight to determine the window size and model, and uses the Levenberg–Marquardt (LM) algorithm to enhance convergence speed and robustness. The high-precision surface deformation is then derived. Combined with hotspot analysis (HSA), wide-area potential landslides can be automatically identified. The reservoir area of the Baihetan hydropower station in the lower reaches of the Jinsha River was selected as the study area, using 118 Sentinel-1A images to compare with four methods in three aspects: corrected interferograms, derived deformation rate, and stability of time-series deformation. In terms of mean standard deviation, the MMVM achieved the lowest value for the unwrapped phase in the non-deformed areas, representing a reduction of 56.4% compared to the original value. Finally, 32 landslides were identified, 16 of which posed a threat to nearby villages. The experimental results demonstrate the superiority of the proposed method and provide support to disaster investigation departments. Full article

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide’s movements and rainfall records in the period 2000–2016 was performed, respectively, through archive investigations and the application of “Moving Sum of Daily Rainfall” method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016–2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations. Full article

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. Full article

The Austin metropolitan area has experienced unprecedented economic and population growth over the past two decades. This rapid growth is leading communities to settle in areas susceptible to landslides, necessitating a comprehensive analysis of landslide risks and the development of early warning systems. This could be accomplished with better confidence for slow-moving landslides, whose occurrences could be forecasted by monitoring precursory ground displacement. This study employed a combination of ground- and satellite-based observations and techniques to assess the kinematics of slow-moving landslides and identify the controlling and triggering factors that contribute to their occurrence. By closely examining landslide events in the Shoal Creek area, potential failure modes across the study area were inferred. The findings revealed that landslide-prone areas are undergoing creep deformation at an extremely slow rate (up to −4.29 mm/yr). These areas lie on moderate to steep slopes (>22°) and are predominantly composed of clay-rich units belonging to the Del Rio and Eagle Ford formations. Based on the incidents at Shoal Creek, episodes of intense rainfall acting on the landslide-prone areas are determined to be the main trigger for landslide processes in the region. Full article