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The study investigates an earth slide type rain-induced landslide that occurred at Madithorai, Nilgiris District, India in 2009. It consists of detailed field investigations such as topographical survey and borehole investigation, laboratory and numerical investigations. Based on the topographical survey the slope was modelled in SEEP/W programme to perform the uncoupled transient seepage analysis. The cumulative rainfall of 586 mm was given as an input for a duration of 5 days that caused landslide at Madithorai. The results of transient seepage analysis were used to determine the stability of the slope based on the infinite slope method for unsaturated soil. Based on the analysis the failure mechanism of Madithorai landslide was observed. The influence of antecedent rainfall using three different idealized 5-day antecedent rainfall was studied. A threshold intensity was established from the different 5 days antecedent rainfall and the corresponding factor of safety which can be used as for landslide early warning. The factor of safety from deterministic analysis was greater than one which does not imply that the slope is stable. Therefore, the performance of the slope was studied based on the reliability index and probability of failure using the Monte Carlo simulation. The results highlight that the slope is in hazardous condition with a reliability index of 1.15 and the probability of failure of 11.7%. Hence, it is recommended to adopt a landslide early warning system and suitable remedial measures at Madithorai location. [ABSTRACT FROM AUTHOR]

The susceptibility mapping of rainfall-induced landslides is an effective tool for predicting and locating disaster-prone zones at the regional scale. One of the most important parts of landslide susceptibility models is the hydrological model. In this context, the present study considers three pore water pressure (PWP) profiles with surface runoff to estimate the spatiotemporal variation of wetting front depth (WFD) during rainfall episodes. To reasonably simulate the inherent uncertainty and variability involved in the hydrogeomechanical properties of the surficial soil layers at the regional scale, probabilistic analysis based on the recursive first-order reliability method (FORM) is employed to calculate the probability of slope failure. The regional time-dependent landslide susceptibility mapping is realised using a newly developed model called Physically-based probabilistic modelling of Rainfall Landslides using Simplified Transient Infiltration Model (PRL-STIM). The proposed model is applied in a representative area that suffered extensive rainfall-induced landslides in July 2013 (Niangniangba Town, Gansu Province, China). The results indicate that the PRL-STIM model achieved a satisfactory prediction accuracy of 75% AUC compared to existing models like transient rainfall infiltration and grid-based regional slope-stability model (72%) and the probabilistic analysis results based on the first-order second moment method (74%). It also performed well in predicting the spatial distribution of shallow landslides, with a success rate of 81.6%. Regarding the model efficiency, the completion of a raster file for calculating the landslide probabilities of the study area (including 711,051 cells) requires only 17.1 s. It is thus hoped that the proposed calculation framework of PRL-STIM that considers various uncertainties (e.g., nonlinearity of the physical model, non-normal probability distributions, random variable cross correlations, etc.) in geotechnical parameters is better suited for landslide susceptibility mapping at the regional scale, where only limited historical event data is available. [ABSTRACT FROM AUTHOR]

Monitoring slow-moving landslides in densely vegetated areas using X-band Synthetic Aperture Radar (SAR) data posed challenges due to the dramatic loss of coherence during SAR interferometry and the relative lower precision of sub-pixel offset tracking (SPOT). The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. The monitoring results revealed a maximum of 40 and 10 cm/year average deformation rates along the azimuth and range direction, respectively. This study demonstrates a powerful and efficient method for monitoring slow-moving landslides in vegetated terrain. [ABSTRACT FROM AUTHOR]

Owakudani (Owakuzawa Valley), the largest fumarole area in Hakone volcano, Japan, is a place where volcanic disasters occur frequently, such as the mudslide disaster that killed six people in 1910 and the phreatic eruption that destroyed hot spring supply facilities in 2015. Therefore, monitoring surface displacements that may lead to slope failure is important for protecting life and property. In this study, we estimated three-dimensional displacements from interferometric synthetic aperture radar (InSAR) analysis from the ALOS-2/PALSAR-2 satellite data and detected landslide displacement around Owakudani. Moreover, the temporal variation of the landslide displacement was clarified from the InSAR time series analysis results. The landslide displacement detected in this study corresponds to a pre-existing collapsed landform whose underground structure was unknown. Additionally, the results of finite element analysis clarified the subsurface deformation concentration zone, suggesting the displacement on the pre-existing sliding surface. The landslide displacement started after the 2015 phreatic eruption. Therefore, fluid injection during the eruption might be one of the triggers of the displacement. Moreover, the annual displacement variation corresponds to changes in precipitation, suggesting that underground pore pressure changes have accelerated the displacement. These results demonstrate that the series of our approach can contribute to understanding the causes and mechanisms of landslides for which underground geological surveys are inadequate. [ABSTRACT FROM AUTHOR]

The 6th World Landslide Forum (WLF6) took place in Florence, Italy from November 14 to 17, 2023. The forum focused on landslide science for sustainable development and aimed to contribute to global commitments and agreements related to disaster risk reduction and sustainable development. The event brought together researchers, stakeholders, and organizations from around the world to collaborate on priority actions for landslide risk reduction. The forum included presentations, workshops, and exhibitions, and was attended by over 1200 participants from 61 countries. The opening plenary session discussed the importance of sharing information, supporting research, and improving landslide preparedness and response. The Florence Declaration on Landslide Risk Reduction was adopted, committing to these goals. The recognition and awards ceremony honored individuals and organizations for their contributions to landslide science. The parallel sessions program covered various themes related to landslides, including international programs, multi-hazard risks, societal aspects, cultural heritage, and ground failures triggered by earthquakes. The document also provides an overview of different chapters in a book or report on landslides, covering topics such as regional and global landslide inventories, monitoring and predicting landslides, and the effects of climate change on landslides. [Extracted from the article]

On the surface of complex landslide areas, other types of slope processes often occur in addition to the landslide movements themselves. However, in order to define the interrelationships or analyse their triggers, it is necessary to have chronological information on their past occurrence. In this study, a spectrum of tree-ring-based (dendrogeomorphic) approaches for spatio-temporal analysis of these processes is presented. The different approaches are applied to a selected complex landslide area with the presence of both creep and rockfall movements. Tree-ring-based data were obtained from standard increment cores as well as from tree roots exposed in tension cracks and from the modified surface of stumps. The presented approaches allowed not only chronological reconstruction of past activity processes but also reconstruction of their spatial activity and even estimation of the rate of displacements of selected landslide blocks. For each methodological approach, their advantages and limitations were evaluated. In addition, the obtained chronological data were used to evaluate potential triggers. The uniqueness of the tree-ring-based data was evident in detecting a wide variability in both the preparatory (up to 2-year periods with above-average rainfall in the case of landslides) and triggering factors themselves (3–5-day rainfall totals for landslides, extreme river discharges for activity tension cracks, or the influence of low temperatures during the winter season). Thus, this study provides a comprehensive overview of dendrogeomorphic approaches that can be used to obtain unique data on the spatio-temporal activity of processes in landslide areas in the past, estimating their rates and preparatory and triggering factors. [ABSTRACT FROM AUTHOR]

The movement of landslide-debris flows is strongly affected by entrainment and runout-path terrain. Herein, mobility alteration was explored from an energy perspective via the discrete element method, using the Shaziba geohazard. The dynamics of the Shaziba geohazard were investigated based on field investigations and numerical simulations. The multiple energy evolutions, conversions, and dissipations of different initiation areas and distinct landslide processes, including kinetic, potential, frictional, and collisional energy, were quantitatively and systematically investigated. The simulation results provide a clear presentation of the inter-particle interactions and energy transfer behavior. Friction caused the most significant energy dissipation, yet collisions dominated the mobility reduction during the entrainment process and interactions with the runout-path terrain. The entrained area possessed higher mobility due to entrainment-induced energy transfer, with distinct forms of energy supplement at different depths. Moreover, the deflection and arching effects of runout-path terrains altered the velocity and direction of landslides and could be used to explain the dissipation of landslide energy as well as reduced mobility. At the cliff, the high-velocity and near-vertical impact on the sediment resulted in the rapid dissipation of landslide energy in collisions. The results provide a reference for analyzing and predicting the movement and mitigating threats of similar geohazards. [ABSTRACT FROM AUTHOR]

Tajikistan is an area characterized by several lakes created after rockfalls and moraine deposits collapsed due to its geographic position on the collisional area of the Euro-Asian and Indian tectonic plates. In 1911, a violent earthquake generated the Usoi dam, which led to the creation of Lake Sarez. The purpose of the article is to provide an overview of the ground deformation of the two landslides that affect and threaten the area of Lake Sarez exploiting the combination of two different techniques, the interferometric synthetic aperture radar (InSAR) method with the SqueeSAR approach and optical image analysis through the use of COSI-Corr software. The combination of these two techniques turned out to be useful and provide more information because InSAR method balances lack the optical analysis and vice versa giving the possibility to obtain a complete overview of the displacement of the area. Moreover, InSAR data have been used to reconstruct the geometry and depth of the sliding surface of a potential landslide that could affect the lake, generating a wave that could overtop the dam and affect the villages downstream. [ABSTRACT FROM AUTHOR]

According to studies by the Intergovernmental Panel on Climate Change (IPCC), tropical mountainous areas are experiencing increasingly unfavorable climatic conditions regarding geohazards due to a heightened occurrence of intense rainfall events. These climatic shifts contribute to heightened geological risks, notably an elevated frequency of landslides, exacerbating the challenges faced by these regions. There is an urgent need to understand and measure how rainfall variability affects geo-hydrological hazards, which remain difficult to determine and predict. The complex and non-linear space-time relationships and dynamics of rainfall, El Niño-Southern Oscillation (ENSO), and landslides in the Tropical Andean Mountain region in Colombia require an adequate analysis and understanding of their link in terms of its spatial and temporal component at different scales. By evaluation in annual, seasonal, and monthly scales, additional insights on the relationships using a wavelet spectral analysis and a space-time permutation scan statistics method using SaTScan™ are provided. In this study, a space-time and frequency analysis of landslides using a 42-year (1981–2022) rainfall and Multivariate ENSO Index v2.0 time series is presented. According to the results, landslides closely aligned with rainfall patterns, exhibiting a bimodal annual cycle. The ENSO added complexity, with La Niña years leading to more frequent landslides throughout the year and El Niño years showing concentrated occurrences in specific months. This study highlights the influence of rainfall patterns and antecedent rainfall on landslide occurrence, the impact of ENSO phases on rainfall and landslides, and the increasing trend of landslides in Colombia. [ABSTRACT FROM AUTHOR]

The sliding process and characteristics of earthquake-induced landslides are complex, and many factors affect landslide movement. The damage caused by landslides is often closely related to their movement characteristics. Therefore, establishing an efficient and quick analysis model for earthquake-induced landslide movement is very important in considering multiple control factors in the sliding process. Based on the typical characteristics of earthquake-induced landslide movement, considering the influences of earthquake action, friction characteristics of sliding surfaces, topographic relief, and morphological changes in landslide masses, a widely applicable quick analysis model for earthquake-induced landslide movement (ELQA) is constructed based on energy conservation. Compared with the existing landslide movement models, this model can consider multiple control factors and significantly improve the applicability of the model. Through comparisons with theoretical analysis, model tests, and actual landslide (Kolka glacial detrital flow), the movement distance of the landslide front edge obtained by the ELQA model matches well with the comparison results, indicating that the ELQA model can reproduce earthquake-induced landslide movement more accurately and quickly. Based on the ELQA model, the influences of control parameters (terrain sampling point density, time step, calculated point number) and calculation parameters (internal friction angle, slip mass volume, terrain concavity, and convexity characteristics) on the calculation results are studied. The results show that control parameters such as terrain sampling point density, time step, and calculated point number have limited influence on the movement characteristics; as long as appropriate values are selected, the error rate can be controlled within 5%. The calculation parameters have a significant influence on the movement characteristics. For example, the influence of the internal friction angle on the coverage area reaches up to 83.75%. The slip mass volume has an insignificant influence on the maximum movement speed, but it on the coverage area reaches approximately 20%. The influence of the terrain concavity and convexity characteristics on the results is less than 10%. The ELQA model is suitable for in-situ estimation and can provide effective suggestions for disaster prevention and reduction in earthquake-prone areas. [ABSTRACT FROM AUTHOR]

Landslide research has benefited greatly from advances in remote sensing techniques. However, the recent increase in available data on land surface movement provided by InSAR techniques can lead to identifying only those areas that were active during data acquisition as hazardous, overlooking other potentially unsafe areas or neglecting landslide-specific geological settings in hazard assessments. Here, we present a case study that serves as a reminder for landslide researchers to carefully consider the geology and geomorphology of study areas where complex active movements are detected using InSAR technology. In an area extensively studied using InSAR and UAV-related techniques, we provide new insights by applying classical approaches. The area is the coastal stretch of La Herradura, and its importance lies in the fact that it has served as an illustrative example in the Product User Manual of the European Ground Motion Service, a platform that provides ground motion data on a European scale. Our approach is to revisit the area and carry out qualitative geological and geomorphological assessments supported by UAV surveys and GIS spatial analysis on a broader scale than previously published investigations. Our classical approach has yielded the following new observations, crucial for risk assessment and land management: active landslides identified by InSAR techniques since 2015 are bodies nested within large mass movements that affect entire slopes. A variety of processes contribute to slope dynamics, such as large slumps, marble rock spreading and block sliding, and surface rock falls and topples. The revised delineation of the landslide bodies reveals an area almost five times larger than previously mapped. These new findings in a well-known area highlight (1) the importance of updating and downscaling previous maps and (2) the ongoing importance of classical fieldwork and desk studies as basic complements to modern InSAR analyses. [ABSTRACT FROM AUTHOR]

Small-scale landslides that occur at unexpected locations and seasons can cause severe losses of life and property. Unusually, the Shidang landslide occurred prior to the rainy season in Jinkouhe County, Sichuan Province, China, resulting in 19 deaths and missing persons. In this paper, landslide characteristics and causes are revealed through a field investigation, laboratory experiments, and numerical calculations. The results show that the Shidang landslide was a small-scale landslide with a total volume of approximately 19500 m3, originating from hollow colluvial deposits. The geomorphological hollow above the landslide provided topographic conditions for the collection of subsurface runoff. Groundwater flow was transported downward along dolomite and accumulated in the area above shale, with a low conductivity. Groundwater table rise combined with the decrease in the soil strength led to landslide occurrence. Improper siting of residential areas and insufficient vigilance against post-rainfall landslides caused this tragedy. Considering the post-disaster lessons, it is strongly recommended that in the future, more attention should be given to the investigation of landslides triggered by groundwater, especially in determining landslide hydrological system boundaries. In mountainous areas where the developable space is limited, areas with hollow landforms are potentially at risk of geohazards. This paper could provide a reference for the prevention and management of similar landslides and new insights for updating landslide investigation systems and residential housing plans. [ABSTRACT FROM AUTHOR]

Landslide event inventories are one of the most critical datasets to increase knowledge on landslide occurrences. However, they are rarely available in various regions, especially in countries of the Global South. This study aims to generate rainfall-induced landslide event inventories and define the rainfall thresholds responsible for landslide occurrence at the national scale of Malawi, Africa. We mainly followed a three-step methodology to generate landslide inventories. First, we went through media reports to identify documented landslide events. Second, we used Sentinel-2 images to identify possible areas affected by landslides using automated change detection algorithms based on vegetation indices. Third, we manually went through optical images provided by Planet Lab and Google Earth and mapped landslides via visual image interpretation. Overall, we mapped 27 rainfall-induced landslide inventories between 2003 and 2022, with a total of 4709 individual landslides. We then analysed the Malawian terrain and identified two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing similar morphometric and climatic conditions. Ultimately, we calculated the rainfall threshold for each landscape cluster. The minimum rainfall amounts responsible for landsliding correspond to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively. In this context, our paper not only presents and shares the first national-scale, digital rainfall-induced landslide event inventory database of Malawi but also suitable rainfall thresholds to be potentially exploited for a national scale landslide early warning system. A similar framework could be applied to generate landslide inventories for other data scarce regions. [ABSTRACT FROM AUTHOR]

In rural areas, landslides can bury houses and result in major disasters in affected areas, which can greatly hinder local economic development and construction. When landslides occur, sliding debris can result in large impact forces, causing varying degrees of damage to building structures. Given the great harm caused by landslides, this paper studies the prediction of dynamic response characteristics of building structures under the impact of the Zhengjiamo landslide. The engineering geological background of the Zhengjiamo landslide is analyzed in detail via field investigations. Then, a numerical method is presented to simulate the runout process of the sliding debris, and the runout process and final deposition area are studied. A node-to-surface contact algorithm is adopted to transfer the displacements and contact forces between the sliding debris and the building. The building structure is simulated via the finite element method (FEM). The sliding debris is simulated via the smoothed particle hydrodynamics (SPH) method. An element erosion algorithm is adopted to simulate the destruction process. The SPH-FEM fluid–structure coupling method is implemented to simulate the landslide dynamic disaster process (the impact behavior for the building). The destruction process is considered in the interactions between the sliding debris and the building on the three-dimensional terrain. The landslide motion during each stage is analyzed, including the sliding, energy dissipation, impact, and damage to the building structure. The maximum runout of the landslide is about 280 m. The maximum stress of the impact on the building is 2 × 106 Pa. The impact speed of the sliding body is generally 16.5–24.07 m/s. On the basis of this, the disaster background, disaster status, and prediction of the landslide disaster effect are studied to provide new insights into landslide disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

In the current era, remote sensing is a powerful platform for detecting and predicting landslides. Moreover, the advancement in computing technologies has proven significant in artificial intelligence (AI) research. Researchers have made significant attempts in the existing literature by introducing landslide detection procedures from remote sensing images (RSIs) through deep learning (DpLr) algorithms. This research work aims to survey those methods. Our database consists of 204 published research articles. In addition, 50% (approximately) of the papers are directly related to landslide information extraction from satellite and unmanned aerial vehicles (UAV) images exploiting DpLr models. The suggested methods have been categorized into seven parts based on the applied model. Further, the evaluation methods have been discussed. The quantitative results are based on the following parameters: (1) contributing nations, (2) key study locations, (3) data set distribution, and (4) model utilization. Lastly, challenges in the studies of DpLr algorithms and the opportunities in landslide detection problems are discussed to motivate future research. [ABSTRACT FROM AUTHOR]

Fractures are considered the most characteristic property of expansive soils distinguishing it from other soils. The cause of many problems in engineering geology is directly or indirectly attributed to the existence of fractures, which makes the soil poor in mechanical properties and damages the overall structure. To this end, based on the detailed information obtained from on-site geological and engineering investigations, we studied the formation mechanisms of an expansive soil trench landslide under a roadbed and the resulting damages in the Ningming area of Guangxi, China. The landslide inversion was carried out with discrete element numerical simulation, and the relationship between fracture development and expansive soil slope instability was explored. The consistent results of field investigation and discrete element numerical simulation show that under alternating seasonal dry-wet stimulation, fractures appear at the surface of the expansive soil slope, which is then more conducive to rainwater infiltration. Under this cycle, the soil gradually softens, and its stability reduces. Additionally, the influence of human activities, such as traffic loading at the trailing edge, assisted in the formation of the landslide. The results of this study provide valuable references for the prevention of subgrade landslide and expansive soil-related damages in other expansive soil areas. [ABSTRACT FROM AUTHOR]

Global warming is causing unprecedented changes to permafrost regions with amplified effects in the Arctic through a phenomenon known as Arctic amplification. This intensified climate warming thaws both the discontinuous and continuous permafrost resulting in changes in the mechanical properties of the soils found in these regions. Since permafrost regions constitute nearly 24% of the Northern Hemisphere, understanding the strength of soils in thawed conditions is essential to analyze the stability of existing structures, and to design safer and more economical infrastructure in these regions. Specifically, thawing of the permafrost is causing considerable reductions in its strength of soils, which may lead to massive landslides, foundation failures, and so forth. Since frozen soil is a multiphase structure that consists of soil particles, unfrozen water, ice, and air, each constituent will influence the mechanical properties. This paper reviews the current state of knowledge of the impact of temperature, volumetric ice content, unfrozen water content, and frozen density on the compressive strength, peak shear strength, residual shear strength, undrained shear strength, and tensile strength of soils. The undrained shear strength of soil is said to have a linear correlation with temperature. In addition, the undrained cohesion of soil was found to depend on the temperature, whereas the undrained friction angle of soil was significantly influenced by volumetric ice content. An increase in the volumetric ice content up to 80% to 90% will cause a reduction in the peak and residual deviatoric stresses. In addition, an increase in volumetric ice content resulted in an increase in the compressive strength of the soil. The tensile and compressive strengths were found to be functions of the unfrozen water content. Global warming is causing the temperature of the permafrost, which is permanently frozen ground, to rise. This paper provides valuable insights into the impact of the changes in this ambient temperature on the strength of frozen soils in permafrost regions for a wide range of applications. Such insights are crucial for the design of resilient and stable infrastructure, such as foundations, embankments, and retaining walls, in which consideration of the reduced strength of thawed soils due to climate change will be necessary. In addition, the knowledge will allow for better management of vulnerable areas prone to landslides and erosion caused by the weakened soil strength permitting the implementation of mitigation measures before lives are lost and costly economic damages are incurred. Finally, this information will aid in early warning systems, emergency planning, and decision making to minimize the impact of hazards on human settlements and infrastructure. In this paper, a review of the current state of knowledge regarding the strength of frozen soils and the associated fluctuations in these strengths because of a rise in temperature are presented. Guidelines on the best practices for sample preparation and testing along with correlations to estimate various strength parameters are also provided. [ABSTRACT FROM AUTHOR]

Methods for analyzing micropile groups considering complex pile–soil interaction have received less attention. This paper proposes a calculation method to study the internal force of micropile groups considering shadow effects of pile groups and nonlinear soil pressure. The nonlinear load transfer mechanism of micropile groups based on the concept of p-multipliers is studied, and the relationship between the p-multipliers of nonlinear landslide thrust and soil resistance and the normalized pile spacing are established. The new governing equations and calculation models of the micropile groups considering the possible failure modes and the p-multiplier method are established. Finally, the proposed method is verified by two case studies. The study results show that the established p-multiplier model of landslide thrust and soil resistance is closely related to soil properties and normalized pile spacing. Comparing the measured results, it is noted that the newly proposed model improves accuracy by an average of 29% over the traditional method. The effect of bending stiffness on the internal forces of piles is more significant than the normalized pile spacing. [ABSTRACT FROM AUTHOR]

We identify and analyze a large shortening structure (surface expression of a thrust fault) in western Arabia Terra, Mars, exhibiting recent, repeated, and long‐lasting tectonic activity. Where the fault system deforms Marsabit crater rim, four landslides with differing degradation states extend onto the crater floor. We propose these were triggered by episodic re‐activation of the thrust system. Using a morphological map and crater size frequency statistics we show that the fault system experienced at least four landslide‐inducing events during the Middle to Late Amazonian. We note that 1.4 km total displacement on the fault plane must have required many events to accumulate if motion was by brittle failure rather than continuous creep. The current understanding of tectonic activity and stress‐sources since 3.6 Ga, cannot account for these repeated and large Amazonian marsquakes—suggesting revaluation of sources of stress to account for a more active and complex Amazonian tectonic history. Plain Language Summary: We identify a large fault system, called a shortening structure, that has been repeatedly active during the last 2 billion years. This is suggested by a series of landslides with a variety of degradation states. These originate from where a large‐scale shortening structure deforms the wall of Marsabit crater. We propose these were triggered by at least four large tectonic events. The shortening structure has a scarp face height of about 700 m, implying that about 1.4 km of movement has occurred along the fault; requiring tens to thousands of marsquakes to accumulate. Our evidence that multiple marsquakes occurred "recently" (in the last 2 billion years) is important because Mars is thought to have been barely tectonically active during this time. This raises questions about the regional/global processes responsible for recent tectonic activity of this magnitude which would have likely re‐activated other local tectonic structures. Our study shows that repeated and long‐lasting tectonic activity occurred in the last 2 billion years, and finding more young tectonic structures would help to better understand the spatial and temporal extent of Mars' recent tectonic history. Key Points: A large shortening structure in western Arabia Terra is associated with four landslides of various degradation states inside a single craterWe propose that the landslides were tectonically‐triggered and formed at different times during the Middle to Late Amazonian epochThe shortening structure shows evidence of repeated, long‐lasting, and recent compressional tectonic activity in western Arabia Terra [ABSTRACT FROM AUTHOR]

Slope instability due to tectonic, hydrological and anthropogenic activities cause severe landslides in Himalaya. Joshimath, a densely populated Himalayan town witnessed a catastrophic landslide event during December 2022 and January 2023 causing damages to ∼700 buildings. We use Interferometric synthetic aperture radar, Global Positioning System and rainfall measurements to probe the kinematics of the Joshimath landslide. We separate the seasonal and episodic deformation components using singular spectrum analysis. While the low amplitude annual landslide motions are modulated by seasonal precipitation, acceleration phases are triggered by extreme rain events. Our analysis revealed episodes of cascading motions triggered by extreme rain events resulting an overall increase in landslide velocity from −22 mm/yr during 2004–2010 to −325 mm/yr during 2022–2023. We estimate the landslide depth (∼30 m) and hydraulic diffusivity (∼3 × 10−5 m2/s) using a 1‐D pore‐water pressure diffusion model. Our study reveals the importance of systematic monitoring of ground deformation and weather parameters for landslide hazard mitigation. Plain Language Summary: The Himalayas, world's largest and youngest mountain range formed as a result of the India‐Eurasia continental collision hosts thousands of landslides each year related to tectonic and anthropogenic activities. It is important to characterize the landslide kinematics, especially the slow moving landslides at the fast‐developing Himalayan urban centers for hazard assessment. A recent catastrophic landslide event in the Joshimath town in NW Himalaya occurred between December 2022 and January 2023 caused damages to ∼700 buildings leading to mass evacuation. We map the spatio‐temporal evolution of land deformation at Joshimath using about two decades of geodetic measurements and singular spectrum analysis technique. While the decadal‐scale landslide motions match well with the overall increase in rainfall intensity, the episodic accelerations are triggered by extreme rain events. The extreme rainfall event on 19 October 2021, triggered the latest episode of landslide acceleration, which eventually led to the tragic disaster. The present study, for the first time, provides constraints on the landslide depth and hydraulic diffusivity. Our study also emphasis on the systematic monitoring of ground deformation and weather parameters at the Himalayas for an effective landslide hazard preparedness and mitigation. Key Points: Joshimath landslide displays episodic and seasonal kinematic changes; episodic accelerations are triggered by extreme rain eventsThe 19 October 2021 extreme rain event triggered the latest episode of landslide acceleration culminating to the 2022 disasterDepth and hydraulic diffusivity of the landslide is constrained using 1‐D pore‐water pressure diffusion model [ABSTRACT FROM AUTHOR]

The crack resistance of concrete structures with low reinforcement ratios requires a broader examination. It is particularly important in the case of foundations working in changing subsoil conditions. Unfavorable phenomena occurring in the subsoil (e.g., ground subsidence, landslips, non-uniform settlement) can lead to unexpected cracking. Therefore, it is necessary to check the effectiveness of the low reinforcement provided. As there are limited studies on lightly reinforced concrete structures, we performed our own experimental investigation and numerical calculations. In the beams analyzed, the reinforcement ratio varied from 0.05% to 0.20%. It was found that crack resistance in concrete members depends on the reinforcement ratio and steel bar distribution. A comprehensive method was proposed for estimating the crack resistance of lightly reinforced concrete members in which both the reinforcement ratio and the reinforcement dispersion ratio were taken into account. Furthermore, the method considered the size effect and the fracture properties of concrete. The proposed method provides the basis for extrapolation of the test results obtained for small elements and conclusions for members with large cross-sections, such as foundations, which frequently use lightly reinforced concrete. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future. [ABSTRACT FROM AUTHOR]

Over the past three decades, Sri Lanka has observed a substantial rise in landslide occurrences linked to intensified rainfall. However, the lack of comprehensive landslide inventories has hampered the development of effective risk analysis and simulation systems, requiring Sri Lanka to rely heavily on foreign-developed models, despite the difficulty of fully examining the similarities between the characteristics of landslides in Sri Lanka and the areas where the model has been developed. Satellite images have become readily available in recent years and have provided information about the Earth's surface conditions over the past few decades. Thus, this study verifies the utility of satellite images as a cost-effective remote-sensing method to clarify the commonalities and differences in the characteristics of landslides in two regions Ikawa, Japan, and Sabaragamuwa, Sri Lanka, which exhibit different geological formations despite similar annual rainfall. Using Google Earth satellite images from 2013 to 2023, we evaluated land-slide density, types, and geometry. The findings reveal that Ikawa exhibits a higher landslide density and experiences multiple-type landslides. In contrast, both areas have similar initiation areas; however, Sabaragamuwa predominantly experiences single landslides that are widespread and mobile. The findings also reveal that various characteristics of landslides are mainly influenced by varied topography. Here, we confirmed that even in areas where comprehensive information on landslides is conventionally lacking, we can understand the characteristics of landslides by comparing landslide geometry between sites using satellite imagery. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. [ABSTRACT FROM AUTHOR]

Landslide disasters have garnered significant attention due to their extensive devastating impact, leading to a growing emphasis on the prompt and precise identification and detection of landslides as a prominent area of research. Previous research has primarily relied on human–computer interactions and visual interpretation from remote sensing to identify landslides. However, these methods are time-consuming, labor-intensive, subjective, and have a low level of accuracy in extracting data. An essential task in deep learning, semantic segmentation, has been crucial to automated remote sensing image recognition tasks because of its end-to-end pixel-level classification capability. In this study, to mitigate the disadvantages of existing landslide detection methods, we propose a multiscale attention segment network (MsASNet) that acquires different scales of remote sensing image features, designs an encoder–decoder structure to strengthen the landslide boundary, and combines the channel attention mechanism to strengthen the feature extraction capability. The MsASNet model exhibited an average accuracy of 95.13% on the test set from Bijie's landslide dataset, a mean accuracy of 91.45% on the test set from Chongqing's landslide dataset, and a mean accuracy of 90.17% on the test set from Tianshui's landslide dataset, signifying its ability to extract landslide information efficiently and accurately in real time. Our proposed model may be used in efforts toward the prevention and control of geological disasters. [ABSTRACT FROM AUTHOR]

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is "first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions". This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall. [ABSTRACT FROM AUTHOR]

In civil engineering, stability analysis of slope is one of the main content of design. By using the finite element limit analysis software OptumG2, a landslide geological model is established to simulate the failure process of the landslide in Huadu District, Guangzhou City, China. The analysis focused on the deformation and failure characteristics, as well as the mechanical mechanism of landslide; the landslide mode of homogeneous soil is circular sliding. Additionally, investigating the influencing factors affecting slope stability is crucial in engineering implementation; in which the five influencing factors are considered as follow: slope height, slope gradient, soil cohesion, soil internal friction angle, and soil unit weight, respectively. A stability calculation model for a soil slope is established under 25 working conditions based on strength reduction method and orthogonal experimental design, in which the relationship between the safety factor and slope height, slope gradient, soil cohesion, soil internal friction angle, and soil unit weight is obtained. As the slope height increases from 5m to 45m, the safety factor of soil slope gradually decreases from 2.21 to 0.94; As the slope gradient increases from 20° to 60°, the safety factor of soil slope decreases approximately linearly from 1.80 to 0.95; As the cohesion of soil increases from 10kpa to 30kpa, the safety factor of soil slope increases approximately linearly from 1.04 to 1.60; As the internal friction angle of soil increases from 10° to 30°, the safety factor of soil slope increases approximately linearly from 1.00 to 1.81; As the unit weight of soil increases from 13kN/m3 to 21kN/m3, the safety factor of soil slope decreases approximately linearly from 1.50 to 1.21. The influencing factors affecting the safety factor of soil slope in descending order are slope height, slope angle, soil internal friction angle, soil cohesion, and soil unit weight. The research has reference significance for studying the stability and failure laws of soil slopes and conducting landslide control on soil slopes. [ABSTRACT FROM AUTHOR]

In areas where loess is distributed, landslides represent a significant geohazard with severe implications. Among these events, loess-mudstone landslides are particularly prevalent, posing substantial risks to the safety and property of local residents, and moisture plays a pivotal role as a key factor in causing these disasters. In this study, the hydraulic properties of the soils along the longitudinal section of an ongoing loess-mudstone landslide are investigated through the variation of soil water characteristic curves, which are subsequently fitted by utilizing van Genuchten model. Moreover, a comprehensive experimental investigation was conducted on the loess, mudstone, and loess-mudstone mixtures to facilitate analysis, including X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM) observation, particle size distribution (PSD) analysis, along with fundamental geotechnical tests for parameter determination. It is found that mudstone and loess have distinct SWCC distribution. The SWCC of loess at various depths exhibits a similar distribution pattern due to the occurrence of landslide. The SWCC distribution of loess-mudstone mixture displays a transitional trend between the SWCC of mudstone and that of loess, and the water retention capacity increases as the mudstone content increases. The experimental findings have demonstrated notable agreement between each other and exhibited a satisfactory level of concurrence with the observed phenomena in geological surveys. [ABSTRACT FROM AUTHOR]

"The Urban Rehabilitation of Post-Disaster Scapes" is a book that examines the use of urbanscapes during the disaster process, focusing on the pre-disaster, disaster event, and post-disaster phases. The book includes 18 case studies of natural and man-made disasters from 1991 to 2022, covering various types of disasters such as floods, storms, earthquakes, wildfires, and sociological and technological disasters. The authors analyze the role of the urbanscape in each phase of the disaster process and discuss urban rehabilitation strategies. While the book provides a comprehensive discourse on disaster resilience and green growth approaches, it lacks case studies from the African continent. Overall, the book is recommended for researchers, students, and professionals in academia, disaster management, urban planning, and architecture. [Extracted from the article]

Predictive displacement models (PDMs) based on the Newmark sliding block method have been widely applied in earthquake-induced landslide hazard assessment (ELHA). Pulse-like ground motions (PLGMs) can cause significant structural damage. In this study, four PDMs that consider the probability of occurrence of PLGMs (PP) were created for strike-slip and non-strike-slip events. The results demonstrate that the model with PP has greater accuracy in non-strike-slip events than the model without PP, however, the effect of PP may be disregarded in strike-slip events. This result is applicable to the ELHA and can provide a basis for post-earthquake emergency and geological disaster mitigation. [ABSTRACT FROM AUTHOR]

Drawing upon a comprehensive global database of landslides and utilizing high-resolution IMERG satellite precipitation data, this study investigates the spatial and temporal variations of landslide occurrences across the member states of the Association of Southeast Asian Nations (ASEAN). This study constructs a region-specific, graded warning system by formulating an average effective intensity–duration (I–D) rainfall threshold curve for each ASEAN member. Examination of 1747 landslide events spanning from 2006 to 2018 illustrates a significant association between the frequency of landslides in ASEAN regions and the latitudinal movement of local precipitation bands. Incidences of landslides hit their lowest in March and April, while a surge is observed from October to January, correlating with the highest mortality rates. Geographical hotspots for landslide activity, characterized by substantial annual rainfall and constrained landmasses, include the Philippine archipelago, Indonesia's Java Island, and the Malay Peninsula, each experiencing an average of over 2.5 landslides annually. Fatalities accompany approximately 41.4% of ASEAN landslide events, with the Philippines and Indonesia registering the most substantial numbers. Myanmar stands out for the proportion of large-scale landslide incidents, with an average casualty rate of 10.89 deaths per landslide, significantly surpassing other countries in the region. The I–D rainfall threshold curves indicate that the Philippines experienced the highest precipitation levels before landslide initiation, whereas Myanmar has the threshold set at a considerably lower level. [ABSTRACT FROM AUTHOR]

This study identified the practices of adaptation strategies to climate change in Jimma, Bedelle, Bonga, and Sokorru urban centers using a survey of 384 households, 55 key informant interviews, 4 focus group discussions, and field observations. A cross-sectional study design was employed from 2019 to 2021. The adaptive capacity of municipalities to reduce climate extreme events was rated as poor by the majority (51%), mostly reactive measures (76%). The climate hazards identified in four urban centers were riverine and flash floods, urban heat waves, landslides, and windstorms. The urban households practiced lifestyle modification, reduce paved surfaces, the use of air conditioner, planting trees, and multiple windows. The adaptation strategies practiced by municipalities include the relocation of prone areas, the support of basic amenities, the construction of protection walls, diversion ditches, the clearance of waterways and rivers, greenery, and park development. The adaptation actions were constrained by a lack of awareness, commitment, cooperation and coordination, adaptive capacity, and participation. Gray/physical infrastructures (costly but important) as adaptation actions were hampered by the low municipal capacity. We recommend that urban authorities should incorporate climate change adaptation strategies into urban planning and development proactively to ensure future resilient climate smart urban centers of southwest Ethiopia. [ABSTRACT FROM AUTHOR]

Timely evaluation of landslide risk based on displacement data is crucial for the effective warning of landslide events. However, the complexity and diversity of displacement curves obtained in the field make the selection of evaluation criteria and thresholds challenging and subjective. Thus, this study proposes a new early warning criterion called the deformation probability index (DPI) to overcome this limitation. By analyzing features of landslide displacement curves obtained from the field, four linetypes were identified, and differences in velocity data between pre-failure and post-failure landslides were summarized from a probabilistic perspective. Subsequently, the DPI is defined to assess the early warning level of a landslide by converting observed velocity values into standardized velocity values using cumulative distribution function standardization. The resulting DPI values are divided into five early warning intervals with three alerting levels to indicate the relative stability of the landslide. Applying the proposed method to 16 landslides from around the world demonstrates the effectiveness of the DPI criterion in evaluating landslide risk, with a high correlation between alerting levels and observed landslide occurrences. The DPI criterion demonstrates notable sensitivity exclusively to displacement velocity and monitoring frequency, exhibiting remarkable generality and accuracy, thus offering a new method for landslide early warning. [ABSTRACT FROM AUTHOR]

Virtual reality is a technological development that, among others, has revolutionized Earth sciences. Its advantages include an opportunity to examine places otherwise difficult or impossible to access and it may also become an important component of education, fostering a better understanding of processes and landforms, geohazard awareness, and an appreciation of geoheritage. This paper reports on the GeoVT project, which aims to create a platform to build and disseminate Virtual Field Trips (VFTs) focused on geomorphology, natural hazards associated with geomorphological processes, and geoheritage sites. To put the GeoVT project in context, an overview of applications of VR in geosciences is provided. This paper subsequently proceeds with a presentation of the project and the GeoVT Authoring application, which is an innovative platform designed to help teachers and students, followed by brief presentations of a number of VFTs developed within the project. They address themes such as fluvial landforms and valley development, coastal landforms, evidence of past glaciation, coastal erosion, wildfire effects, mud volcanoes, and landslides. [ABSTRACT FROM AUTHOR]

Terrain generation methods have long been divided between procedural and physically‐based. Procedural methods build upon the fast evaluation of a mathematical function but suffer from a lack of geological consistency, while physically‐based simulation enforces this consistency at the cost of thousands of iterations unraveling the history of the landscape. In particular, the simulation of the competition between tectonic uplift and fluvial erosion expressed by the stream power law raised recent interest in computer graphics as this allows the generation and control of consistent large‐scale mountain ranges, albeit at the cost of a lengthy simulation. In this paper, we explore the analytical solutions of the stream power law and propose a method that is both physically‐based and procedural, allowing fast and consistent large‐scale terrain generation. In our approach, time is no longer the stopping criterion of an iterative process but acts as the parameter of a mathematical function, a slider that controls the aging of the input terrain from a subtle erosion to the complete replacement by a fully formed mountain range. While analytical solutions have been proposed by the geomorphology community for the 1D case, extending them to a 2D heightmap proves challenging. We propose an efficient implementation of the analytical solutions with a multigrid accelerated iterative process and solutions to incorporate landslides and hillslope processes – two erosion factors that complement the stream power law. [ABSTRACT FROM AUTHOR]

In the Shahroud Watershed, there has been an increasing occurrence of landslides that have caused a lot of human and financial losses. Therefore, landslide susceptibility zonation is crucial for reducing landslide risk. The aim of this study was to compare the Landslide Susceptibility Maps (LSMs) of different methods. Therefore, thematic layers of the ten causal factors were prepared. Then, a landslide inventory map consisting of 104 landslides covering 1401 hectares was compiled and partitioned into two subsets including 70% for training and 30% for testing purposes. Three landslide susceptibility maps were prepared using the Frequency ratio (Fr), Statistical index (Si), and Analytic Hierarchy Process (AHP) methods. The validation process showed that the Si [Area Under the Curve (AUC)= 0.732] and Fr (AUC= 0.707) models presented a more valid LSM than AHP (AUC= 0.651) method. The Qs (Quality sum) index values also confirmed the results of the ROC (Receiver Operating Characteristic) curve such that the Qs index values of 1.71, 1.43, and 0.62 for, respectively, Fr, Si, and AHP models implied a more accurate LSMs of the Fr and Si models than the one from the AHP. The results of this study can be used as a basic step for landslide risk management in the study area. [ABSTRACT FROM AUTHOR]

This article presents a methodology for evaluating the susceptibility of landfill areas to develop landslides by analyzing Synthetic Aperture Radar (SAR) satellite products. The deformation velocity of the landfills is computed through the Persistent Scatterer Method on SAR imagery. These data, combined with a deformation model based on the shallow water equations (SWE), form the foundation for a Monte Carlo experiment that extrapolates the current state of the landfill into the future. The results of this simulation are then employed to determine the probability of a landslide occurrence. In order to validate the methodology effectiveness, a case study is conducted on a landfill in Zaldibar, Spain, revealing its effectiveness in estimating the probability of landfill landslides. This innovative approach emerges as an asset in large landfill management, acting as a proactive tool for identifying high-risk sites and preventing potential landslides, ultimately safeguarding human life and the environment. By providing insights into landslide probabilities, this study enhances decision-making processes and facilitates the development of intervention strategies in the domain of landfill risk assessment and management. [ABSTRACT FROM AUTHOR]

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons. [ABSTRACT FROM AUTHOR]

Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 106 m3) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 106 m3 of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 105 m3 of that sediment was redeposited inside the study area. About 5.8 × 105 m3 of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems. [ABSTRACT FROM AUTHOR]

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results. [ABSTRACT FROM AUTHOR]

The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control. [ABSTRACT FROM AUTHOR]