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The deep-seated gravitational slope deformation (DSGSD) triggered by impoundment has attracted worldwide attention. After impoundment of Wudongde reservoir downstream of Jinsha River, China, Zaogutian DSGSD occurred with an estimated volume of 129 million m3, which provided an opportunity for in-depth analysis and cognition of its mechanism and risk. The DSGSD sits on a chair-shaped bedding bank slope with multi-weak interlayers at the bottom, forming a lithology structure of brittle cap overlying a ductile substratum. Based on the traditional engineering geological exploration, 3D observation of the DSGSD was carried out by UAV photogrammetry, and the cracks, discontinuities, and macroscopic deformation characteristics of different areas were identified. Combining the InSAR survey and surface-parallel flow assumption, the mm-level 3D deformation rate field and time-series displacement from December 2019 to December 2020 were reconstructed. Finally, the following conclusions were arrived at: according to the cracks and the boundary between rock mass and deposit, the DSGSD could be divided into four zones: the loose deposit near the bank, the scarp area in the front part, the major sliding area in the middle part, and the stable area. The loose deposits were deforming by uplifting with a maximum rate of 70 mm/year. The deformation rate in the western part of the major sliding area was the fastest, and the rates of the maximum settlement and southward deformation peaked at 100 mm/year and 250 mm/year, respectively, which were 2–3 times data in the scarp area. Under the soaking of reservoir water, the mechanical magnitude of the weak layer in the lower Dengying Formation and the Guanyinya Formation got reduced, which was the triggering factor of Zaogutian DSGSD. As a result, the major sliding area pushed the scrap area to creep along with the weak layer, and sliding accompanied by tensile fracturing is the instability mode in case of failure. The combination of InSAR and UAV observation provides a deeper insight into the deformation mechanism of DSGSD, which is not only conducive to slope stability evaluation but also demonstrates the role of remote sensing technology in the study of DSGSD.

Large landslides and deep-seated gravitational slope deformations (DSGSD) represent an important geo-hazard in relation to the deformation of large structures and infrastructures and to the associated secondary landslides. DSGSD movements, although slow (from a few millimetres to several centimetres per year), can continue for very long periods, producing large cumulative displacements and undergoing partial or complete reactivation. Therefore, it is important to map the activity of such phenomena at a regional scale. Ground surface displacements at DSGSD typically range close to the detection limit of monitoring equipment but are suitable for synthetic aperture radar (SAR) interferometry. In this paper, permanent scatterers (PSInSAR™) and SqueeSAR™ techniques are used to analyse the activity of 133 DSGSD, in the Central Italian Alps. Statistical indicators for assigning a degree of activity to slope movements from displacement rates are discussed together with methods for analysing the movement and activity distribution within each landslide. In order to assess if a landslide is active or not, with a certain degree of reliability, three indicators are considered as optimal: the mean displacement rate, the activity index (ratio of active PS, displacement rate larger than standard deviation, overall PS) and the nearest neighbor ratio, which allows to describe the degree of clustering of the PS data. According to these criteria, 66% of the phenomena are classified as active in the monitored period 1992–2009. Finally, a new methodology for the use of SAR interferometry data to attain a classification of landslide kinematic behaviour is presented. This methodology is based on the interpretation of longitudinal ground surface displacement rate profiles in the light of numerical simulations of simplified failure geometries. The most common kinematic behaviour is rotational, amounting to 41 DSGSDs, corresponding to the 62.1% of the active phenomena.

Large slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

Structure from Motion (SfM) is a powerful tool to provide 3D point clouds from a sequence of images taken from different remote sensing technologies. The use of this approach for processing images captured from both Remotely Piloted Aerial Vehicles (RPAS), historical aerial photograms, and smartphones, constitutes a valuable solution for the identification and characterization of active landslides. We applied SfM to process all the acquired and available images for the study of the Champlas du Col landslide, a complex slope instability reactivated in spring 2018 in the Piemonte Region (north-western Italy). This last reactivation of the slide, principally due to snow melting at the end of the winter season, interrupted the main road used to reach Sestriere, one of the most famous ski resorts in north-western Italy. We tested how SfM can be applied to process high-resolution multisource datasets by processing: (i) historical aerial photograms collected from five diverse regional flights, (ii) RGB and multi-spectral images acquired by two RPAS, taken in different moments, and (iii) terrestrial sequences of the most representative kinematic elements due to the evolution of the landslide. In addition, we obtained an overall framework of the historical development of the area of interest, and distinguished several generations of landslides. Moreover, an in-depth geomorphological characterization of the Champlas du Col landslide reactivation was done, by testing a cost-effective and rapid methodology based on SfM principles, which is easily repeatable to characterize and investigate active landslides.

There has been an increasing demand for detailed and accurate landslide maps and inventories in disaster-prone areas of subtropical and temperate zones, particularly in Asia as they can mitigate the impacts of landslides on social infrastructure and economic losses. Hence, in this study, models using automatically constructed high-performing convolutional neural network (CNN) architectures for landslide detection were applied and their outcomes were compared for landslide susceptibility mapping at the Kii peninsula, Japan. First, a total of 38 landslide and 63 non-landslide points were identified and divided into 70% and 30% of training and validation datasets, respectively. Eight landslide influence factors were used: slope angle, eigenvalue ratio, curvature, underground openness, overground openness, topographic witness index, wavelet, and elevation. These factors were selected using a 1-m DEM, which is easy to acquire and process data. Experimental results of model evaluation using receiver operating characteristics (ROC), area under the curve (AUC), and accuracy showed that the optimal models (ROC = 96.0%, accuracy = 88.7%) were more accurate than initial models (ROC = 91.1%, accuracy = 80.7%) in predicting landslides spatially. Furthermore, the landslide susceptibility mapping is consistent with the trends in the distribution of gentle slopes and knick lines unique to the study area and can be used as a powerful method for predicting landslides in future.

The long-term evolution of slopes affected by Mass Rock Creep deformations is controlled by both time-invariant predisposing factors, such as the geo-structural inheritance, and time-dependent preparatory conditions, including regional uplift and landscape evolution rates. However, the relationship among Deep-seated Gravitational Slope Deformations, drainage network evolution, and tectonics remains poorly defined. Here, we focused on an undocumented Deep-seated Gravitational Slope Deformation affecting an area of about 8 km2 in the SE tip termination of the Siah Kuh anticline in the Lorestan arc (Zagros Mts., Iran), upstream to the Mountain Front Fault. To assess the evolution processes which involved the slope up to the present, we integrated quantitative geomorphic analysis, optically stimulated luminescence dating of geomorphic markers, and SAR interferometry techniques. In detail, we semi-automatically extracted the river terrace treads to which we associated an elevation above the thalweg based on the Relative Elevation Model allowing the order definition. The plano-altimetric distribution of the treads and the OSL ages of two levels of strath terraces sampled in the field have been correlated along the river longitudinal profile, allowing the estimation of an uplift rate of 2.8 ± 0.2 mm year−1 and 0.42 ± 0.03 mm year−1, respectively upstream and downstream of the Mountain Front Fault. SAR interferometry was used to spot present-day shallow ground displacements associated with the ongoing slope deformation, by processing 279 satellite Sentinel-1 (A and B) radar images of the ascending and descending orbit spanning from 06 October 2014 to 31 March 2019. Different landslide mechanisms were distinguished across the fold axis, rototranslative to lateral spreading interpreted as two different evolutionary stages of the same process transposed spatially through the fold axis. Indeed, the rototranslative mechanism represents an advanced stage of the strain evolution while the lateral spreading is an earlier one. Finally, we infer that the variability in the spatial distribution of the slope deformation styles and patterns in the Lorestan arc is strictly related to the coupled evolution of the drainage system and tectonics. Involved volumes (from 0.6 up to 44 km3), local relief (from 400 up to 2000 m), incision rates (from 0.8 to 2.8 ± 0.2 mm year−1), and persistence time (from 104 to 105 years) represent the most important preparatory conditions and are predisposed by a moderately dipping downslope (from 8 to 25°) sedimentary sequence characterised by units with significantly different rheological behaviour.

The objective of this study was to identify the locations of deep-seated gravitational slope deformations (DGSDs) and define the numerical characteristics of these deformations to contribute to the sustainable management of social infrastructure in the event of an increased disaster. The topographic features of the DGSDs were quantitatively characterized based on their surface morphologies. Topographic features indicative of gravitational deformation in pre-slide topographic maps, such as terminal cliff failures, irregular undulations, and gullies, suggest that progressive deformation occurred over a prolonged period. To track the gravitational deformation over time, we interpreted aerial photographs of DGSDs from 1948 and 2012 associated with deep-seated landslides on the Kii Peninsula induced by Typhoon Talas on 2–5 August 2011. Corresponding numerical analysis of the gravitational deformations using 1 m digital elevation models reveals that landslide areas exhibit eight characteristic influencing factors, demonstrating that characteristic morphologies exist in areas that eventually experience landslides. One such morphological feature is the existence of a gently sloping area in the upper section of the deep-seated landslide mass, which comprises a catchment basin without a corresponding valley or gully. These findings suggest that rainwater penetrates the ground, and degrades and deforms the rock within the landslide mass, causing the slope to fail after torrential rainfall. This study holds great significance for advancing sustainable infrastructure development and management and mitigating environmental changes.

This study examines the geomorphology of deep-seated landslides (DSLs) in the Western Carpathians, Slovakia, focusing on five key DSL areas along the Váh River Valley within the northern Veľká Fatra Mountains. Through an integrated approach combining fieldwork, GIS analysis, and Red Relief Image Map (RRIM) visualization, the research identifies and analyzes geomorphic features such as deep-seated landslide blocks, earthflows, and gravitational scarps. GIS methods reveal the spatial distribution and morphological characteristics of these mass movements, while RRIM emphasizes key gravitational features such as scarps, trenches, and cliffs. The study further differentiates between types of mass movement, ranging from slow, continuous deformation to catastrophic slope failures. Results emphasize the crucial role of geological and tectonic conditions in influencing landslide initiation, propagation, and evolution. The findings explain the complex interplay between geological structures and geomorphic processes that shape the Western Carpathian landscape. This work contributes to advancing the understanding of DSL dynamics and offers essential insights for regional land-use planning and hazard mitigation. It provides a foundation for applying similar methodologies to comparable geomorphic phenomena globally, addressing both scientific and practical challenges in landslide-prone regions.

Deep-seated large-scale toppling failure presents unique challenges in the study of natural slope deformation process in mountainous regions.An active deep-seated toppling process was identified in the Erguxi slope located in southwest China,which affected a large area and damaged critical transportation infrastructure with the volume of the deforming rock mass exceeding 24×10~6 m~3.It poses significant risks to the downstream Shiziping Hydropower Station by damming the Zagunao River.Field investigation and monitoring results indicate that the deformation of the Erguxi slope is in the advanced stage of deep-seated toppling process,with the formation of a disturbed belt but no identifiable master failure surface.It was postulated that the alternating tensile and shear strength associated with the hard/soft laminated rock strata of metasandstone and phyllite layers preclude the development of either a tensile or shear failure surface,which resulted in the continuous deformation and displacement without a catastrophic mass movement.The slope movement is in close association with the unfavorable geological conditions of the study area in addition to the construction of transportation infrastructure and the increase of the reservoir level.On the basis of the mechanism and intensity of the ongoing toppling deformation,a qualitative grading system was proposed to describe the toppling process and toevaluate the slope stability.This paper summarized the field observation and monitoring data on the toppling deformation for better characterizing its effect on the stability of the Erguxi slope.The qualitative grading system intends to provide a basis for quantitative study of large-scale deep-seated toppling process in metamorphic rocks.

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