JournalTOCs

Landslides
Journal Prestige (SJR): 1.802

Citation Impact (citeScore): 4
Number of Followers: 27

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1612-5118 - ISSN (Online) 1612-510X
Published by Springer-Verlag

[2469 journals]

Outburst debris flow of Yusui Stream caused by a large-scale Silabaku
landslide, Southern Taiwan
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  Abstract: Abstract On August 7, 2021, a disastrous rainfall-induced landslide occurred at Mt. Silabaku in the Yusui Stream source area, Taiwan. The huge deposits from the Silabaku landslide severely damaged the Minbaklu Bridge, and minor collapses in the Yusui Stream disrupted the only major road, Expressway No. 20. More than 400 residents from the three adjacent villages were stranded for 19 days in the mountainous area. The analysis of rainfall data, seismic records, topographic and geologic data, and historical orthoimages of the slope conditions, landform evolution, and current status of the Silabaku landslide revealed that the landslide resulted from slate avalanche induced by heavy rains, with local failures during Typhoon Morakot in 2009. Based on the high-resolution topographic data of light detection and ranging and an unmanned aerial vehicle, the debris deposits accumulated along the Yusui Stream were estimated to be above 8 million m3. In addition, a fan volume of 2 million m3 changed the flow line of the Laonong River, and the thick deposits pose a considerable threat to the safety of the nearby villages and infrastructures.
  PubDate: 2022-07-01
Characteristics of landslides and debris flows triggered by extreme
rainfall in Daoshi Town during the 2019 Typhoon Lekima, Zhejiang Province,
China
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  Abstract: Abstract Climate change and rapid urban expansion can foster landslide disasters and cause severe damage in areas that are traditionally considered relatively safe from such hazards. In this regard, a recent event in Daoshi Town, China, is a good example that showcases the combined effect of extreme rainfall and urbanization in generating a first-time landslide disaster for a developing community. On 10 August 2019, Typhoon Lekima made its landing in Zhejiang Province, China. It generated a rainstorm close to Daoshi Town, inducing severe and unexpected damage from shallow landslides and debris flows (7 deaths, 25,560 affected and a direct economic loss of 1.5 hundred million dollars). In this region, similar rainfall has never been experienced before, and landslides have seldom been reported in the past. Field investigations and measurements were conducted to identify the landslide distributions, size characteristics, and mobility. A landslide inventory map, including 251 shallow landslides and 82 debris flows, was compiled to study the geological and topographical factors related to widespread failure. The results showed that shallow landslides and debris flows mainly occurred in the north-central part of the study area, in which the base was mainly composed of strongly weathered limestone. The elevation, mean slope, and slope aspect also contributed to the occurrence of landslides. In addition, 32.4% of the disasters were located close to buildings and roads, highlighting a relevant predisposing role played by human activity. This typhoon-bound rainstorm gave rise to a higher triggering condition than the normally required rainfall threshold in the study area. Under this circumstance, landslides were easily triggered and were able to transition into debris flows due to liquefaction. Field observations demonstrate that the erosion of slope toes caused by debris flows also facilitated the occurrence of other shallow landslides. A changing climatic setting is a never-before-experienced phenomenon for the study area. This research improves the understanding of landslide development in the area and prepares basic data and information for further studies and mitigation strategies.
  PubDate: 2022-07-01
The fault-controlled Chengtian landslide triggered by rainfall on 20 May
2021 in Songyang County, Zhejiang Province, China
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  Abstract: Abstract The geological structures of fault zones control the occurrence of landslides and determine their characteristics and motion styles, especially in igneous rock areas with high rock strength. On 20 May 2021, a fault-controlled landslide occurred during heavy rainfall near Chengtian village in Zhejiang Province, China. The landslide abruptly destroyed a provincial highway in front of the slope and rushed into a river resulting in a barrier lake, which blocked local traffic for 17 days. The pseudotachylites, scratches and striations observed on the exposed impermeable breccia bedrock confirmed the sliding surface as part of the Cretaceous fault plane. The original rhyolite slope located on the fault zone had been subjected to weathering, creating favourable geological and hydrogeological structures for a landslide. Due to the low fluidity of the sliding materials, the sliding mass decelerated quickly and blocked the river, resulting in a geo-disaster chain. This paper presents a typical instance of landslides controlled by geological structures on a fault zone.
  PubDate: 2022-07-01
UAV applications to assess short-term dynamics of slow-moving landslides
under dense forest cover
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  Abstract: Abstract The paper presents a methodology to rapidly assess and map the landslide kinematics in areas with dense vegetation cover. The method uses aerial imagery collected with UAVs (Unmanned Aerial Vehicles) and their derived products obtained from the structure from motion technique. The landslide analysed in the current paper occurred in the spring of 2021 and is located in Livadea village from Curvature Subcarpathians, Romania. This landslide affected the houses in the vicinity, and people were relocated because of the risk of landslide reactivation. To mitigate the landslide consequences, a preliminary investigation based on UAV imagery and geological-geomorphological field surveys was carried out to map the active parts of the landslide and establish evacuation measures. Three UAV flights were performed between 6 May and 10 June using DJI Phantom 4 and Phantom 4 RTK UAVs (Real-Time Kinematic Unmanned Aerial Vehicles). Because it is a densely forested area, semi-automated analyses of the landslide kinematics and change detection analysis were not possible. Instead, the landslide displacement rates and the changes in terrain morphology were assessed by manually interpolating the landmarks, mostly tilted trees, collected from all three UAV flights. The results showed an average displacement of approximately 20 m across the landslides, with maximum values reaching 45 m in the transport area and minimum values below 1 m in the toe area. This approach proved quick and efficient for rapid landslide investigations in a densely forested area when fast response and measures are necessary to reduce the landslide consequences.
  PubDate: 2022-07-01
Fatal landslides in Colombia (from historical times to 2020) and their
socio-economic impacts
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  Abstract: Abstract Landslides typify one of the most hazardous natural phenomena fostering economic and even human losses worldwide. Several countries like Colombia, in South America, are hotspots for fatal landslides. In this contribution, we thoroughly reviewed four available databases, articles, grey literature and web resources, in order to build up a new catalogue of fatal landslides in Colombia. We gathered a catalogue of 2351 individual fatal landslides which caused about 37,959 deaths. Of these, we found 11 fatal events in historical times (pre-twentieth century). In modern times (1912–2020), we analysed landslides’ spatial and temporal distribution, finding that in central-western Colombia, particularly in the departments of Caldas, Risaralda, Quindío and Antioquia, these kinds of events are more frequent. Upward trends in these areas and a nationwide increase in the number of events in the last 20 years suggest that fatal landslides are far from being effectively mitigated. Our findings also show a strong correlation between the climate variability phenomenon known as El Niño Southern Oscillation (ENSO) and fatal landslides, particularly during those years when strong La Niña (cold phase of ENSO) events occur. Despite rainfall being the most common trigger for fatal landslides, we observed an increasing trend in anthropogenically related events in the last decade. Finally, we obtained multiple socio-economic indices and ran a statistical analysis at the departmental level in order to assess whether impoverished and vulnerable people are more affected by fatal landslides. We propose that in most cases, departments with low income, high levels of corruption and inequality are usually more affected.
  PubDate: 2022-07-01
MFFENet and ADANet: a robust deep transfer learning method and its
application in high precision and fast cross-scene recognition of
earthquake-induced landslides
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  Abstract: Abstract Automatic recognition and segmentation methods have become an essential requirement in identifying large-scale earthquake-induced landslides. This used to be conducted through pixel-based or object-oriented methods. However, these methods fail to develop an accurate, rapid, and cross-scene solution for earthquake-induced landslide recognition because of the massive amount of remote sensing data and variations in different earthquake scenarios. To fill this research gap, this paper proposes a robust deep transfer learning scheme for high precision and fast recognition of regional landslides. First, a Multi-scale Feature Fusion regime with an Encoder-decoder Network (MFFENet) is proposed to extract and fuse the multi-scale features of objects in remote sensing images, in which a novel and practical Adaptive Triangle Fork (ATF) Module is designed to integrate the useful features across different scales effectively. Second, an Adversarial Domain Adaptation Network (ADANet) is developed to perform different seismic landslide recognition tasks, and a multi-level output space adaptation scheme is proposed to enhance the adaptability of the segmentation model. Experimental results on standard remote sensing datasets demonstrate the effectiveness of MFFENet and ADANet. Finally, a comprehensive and general scheme is proposed for earthquake-induced landslide recognition, which integrates image features extracted from MFFENet and ADANet with the side information including landslide geologic features, bi-temporal changing features, and spatial analysis. The proposed scheme is applied in two earthquake-induced landslides in Jiuzhaigou (China) and Hokkaido (Japan), using available pre- and post-earthquake remote sensing images. These experiments show that the proposed scheme presents a state-of-the-art performance in regional landslide identification and performs stably and robustly in different seismic landslide recognition tasks. Our proposed framework demonstrates a competitive performance for high-precision, high-efficiency, and cross-scene recognition of earthquake disasters, which may serve as a new starting point for the application of deep learning and transfer learning methods in earthquake-induced landslide recognition.
  PubDate: 2022-07-01
A methodological approach of QRA for slow-moving landslides at a regional
scale
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  Abstract: Abstract Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results.
  PubDate: 2022-07-01
Application of a fuzzy verification framework for the evaluation of a
regional-scale landslide early warning system during the January 2020
Gloria storm in Catalonia (NE Spain)
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  Abstract: Abstract The Gloria storm rainfalls affected Catalonia from 20 to 23 January 2020 and triggered multiple landslides, some of which affected buildings and infrastructures (such as roads and railways). This paper presents the rainfall and landslide datasets collected during the event, and evaluates the performance of a regional landslide early warning system (LEWS) during the Gloria storm applying a fuzzy verification method. The majority of the inventoried landslides can be classified as slides, involving a limited volume of sediment (up to 10 m3), and were triggered in cut slopes along linear infrastructures. Rainfall accumulations were significant in the whole region, especially in the Montseny area, where over 450 mm were registered in 96 h. Generally, the LEWS computed moderate and high warnings in the areas where large rainfall amounts were recorded, and showed good correspondence with the locations where landslides were reported. The fuzzy verification method has been applied using neighbouring windows of different sizes to obtain scale-dependant information on the LEWS performance. The skill of the LEWS considerably improves when enlarging the neighbouring window size from 500 m to 1 km.
  PubDate: 2022-07-01
Preliminary establishment of a mass movement warning system for Taiwan
using the soil water index
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  Abstract: Abstract The soil water index (SWI) represents the estimated soil moisture content and is an important indicator as a reference for issuing mass movement warnings nationwide in Japan. Many studies recently have pointed out that the occurrence of mass movements is highly related to whether the value of SWI is within the top historical rankings for the same location. Some studies have proposed a quantitative method, which is to calculate the ratio of the SWI at a certain location to the maximum value of the past decade (i.e., the normalized soil water index; NSWI). This study used 279 mass movements that occurred in Taiwan during 2006–2017 to analyze SWI and NSWI during the rainfall events that caused those mass movements. The results showed that the SWI at the times of mass movements was between 61.10 and 700.93, while the NSWI was between 0.14 and 1.87. On average, mass movements occurred within 1.71 h after the maximum SWI and NSWI appeared. The NSWI can effectively reflect the historical ranking of the current SWI value in a location, so different NSWI ranges could be used to define different warning levels for mass movement occurrence. This study also used an integrated system to link historical rainfall data and establish an interface that allows easy exploration of NSWI during rainfall events. In the future, further connecting this system with real-time rainfall data and even the estimated rainfall data should provide a major breakthrough for the mass movement warning system in Taiwan.
  PubDate: 2022-07-01
A complex slope deformation case—history
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  Abstract: Abstract Slope movements represent a class of phenomena, which can display a great variety of styles, sizes and displacement rates. Some are extremely rapid, short-lived and highly destructive; others are extremely slow and apparently endless. This paper describes an extremely slow soil slope deformation process that is probably active since thousands of years and will certainly go on for many years to come. The results of in-depth investigations carried out after the 1980 Irpinia-Basilicata earthquake integrated by satellite images covering the time interval 1992–2013 have provided a global and consistent framework that allows to draw a reliable scenario about the effects of the geological processes that are active in the area on slope deformation mechanisms.
  PubDate: 2022-07-01
Quantitative back analysis of in situ tests on guiding flexible barriers
for rockfall protection based on 4D energy dissipation
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  Abstract: Abstract This paper presents a quantitative analysis of system dynamic response, rockfall trajectory control, and kinetic energy evolution of the tests on the guiding flexible protection system (GFPS) based on 4D energy dissipation. The system achieves 4D protection against rockfall hazards on high and steep slopes, with 3D spatial motion and energy evolution control of rockfall trajectories in time history. After clarifying the structural principle of the system, analyzing the kinetic energy characteristics of the rockfall along the hill, and defining the multistage energy-dissipation control principle of the system, an evaluation method for rockfall energy evolution for quantitative back analysis was established for the whole course of system protection. Furthermore, the rockfall’s energy decay characteristics in the guiding section were analyzed. Accordingly, a full-scale model of this system was constructed, and six in situ tests with and without the protection system were conducted. The system model was laid on a slope with a gradient of over 60°, a width of 130 m, and a height of 82 m, where the crushed rock developed. The maximum impacting energy was 2500 kJ. Quantitative back analysis of the experiment was carried out, and the results revealed that, compared with the unprotected tests, after the construction of the protection system, the motion duration of the falling block was extended by over 80%, the impact times increased by approximately five times, the bounce height reduced by roughly 80%, the transverse motion distance reduced by around 50%, and, for blocks that reached the bottom, the residual kinetic energy and the rolling distance away from the slope toe were both decreased by 70%. The combined energy-dissipation ratio was as high as 89%.
  PubDate: 2022-07-01
Recent innovations in the LaRiMiT risk mitigation tool: implementing a
novel methodology for expert scoring and extending the database to include
nature-based solutions
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  Abstract: Abstract This paper presents recent innovations implemented in the LaRiMiT (Landslide Risk Mitigation Toolbox) webtool. These include an innovative methodology for utilising experts’ scoring of landslide risk mitigation measures directly within the decision support tool, and updating of the tool’s database over structural measures to include many nature-based solutions (NBS) for mitigating landslide risk. Landslides are a common and treacherous natural hazard, and due to the effect of climate change on the frequency and intensity of extreme weather events, climate-driven landslides are expected to become more frequent. This negative trend is a driving factor for developing decision-support solutions for risk mitigation, and the LaRiMiT toolbox and the expert scoring methodology presented in this article contribute to meeting these needs. Selecting suitable structural measures is complicated due to factors such as site-specific conditions, local knowledge and resources, socio-economic constraints, and environmental considerations. LaRiMiT uses user-input attributes of site-specific slope movements combined with expert scoring of the suitability of various mitigation solutions to identify and select appropriate mitigation measures from an extensive database of structural solutions. While the LaRiMiT database initially included only conventional (grey) solutions relying on traditional methods, it has been recently expanded to include nature-based solutions (NBS), which are sustainable techniques for managing erosion and mitigating shallow landslides using vegetation and the use of natural materials. NBS and conventional solutions can also be combined to provide hybrid solutions. The proposed methodology for implementing expert scoring is dynamic and iterative, consisting of statistical pooling of experts’ scores collected via online surveys, and consolidating these into an expert scoring utility embedded in the LaRiMiT web portal engine. An initial application of this methodology has been tested by surveying a set of landslides experts, mostly in Europe, and applying it to the structural measures database in the LaRiMiT tool. Although the number of experts in this initial application is limited, dynamic updating of the scoring allows the tool to continuously improve as additional experts contribute.
  PubDate: 2022-07-01
Enhanced dynamic landslide hazard mapping using MT-InSAR method in the
Three Gorges Reservoir Area
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  Abstract: Abstract Landslide hazard mapping is essential for disaster reduction and mitigation. The hazard map produced by the spatiotemporal probability analysis is usually static with false-negative and false-positive errors due to limited data resolution. Here we propose a new method to obtain dynamic landslide hazard maps over the Wushan section of the Three Gorges Reservoir Area by introducing the ground deformation measured by the spaceborne Copernicus Sentinel-1 synthetic aperture radar (SAR) imagery collected from 9/30/2016 to 9/13/2017. We first determine the spatial probability of landslide occurrence predicted by the support vector machine algorithm. We also conducted the statistical analysis on the temporal probability of landslide occurrence under various rainfall conditions (0, 0–50, 50–100, and > 100 mm for the antecedent 5-day total). We initialize a preliminary landslide hazard map by combining the spatial and temporal landslide probabilities. Meanwhile, the ground deformation velocities during the representative dry and wet seasons can be extracted from multi-temporal interferometric SAR (MT-InSAR). Thereafter, the landslide hazard map can be finalized by an empirical assessment matrix considering both the preliminary landslide hazard map and deformation velocities. Our results demonstrate that false-negative and false-positive errors in the landslide hazard map can be effectively reduced with the assistance of the deformation information. Our proposed method can be used to assess the dynamic landslide hazard at higher accuracy.
  PubDate: 2022-07-01
The role of fluid drag force in the dynamic process of two-phase flow-like
landslides
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  Abstract: Abstract During the landslide mobility process, there is a dynamic interaction between the sliding main body and the different surrounding media. This affects the mobility characteristics of the landslide, particularly the friction force and contact force, which play the significant roles of dissipating and transferring energy in the sliding main body, respectively. In order to analyze the dynamic characteristics of rapid and long-runout landslides, the discrete element method solver of EDEM software and the computational fluid dynamics code of ANSYS-Fluent solver for a two-phase flow-like landslide, combined with flume tests and a field case, were used to research the following points: (1) When the liquid volume fraction is large enough during the mobility process of a two-phase flow-like landslide, the liquid phase provides a certain drag force relative to the solid phase. (2) The drag force was determined from the volume fraction of the liquid, the solid–liquid velocity difference, and the viscosity coefficient of the liquid, which together affect the characteristics of the landslide’s mobility and accumulation. (3) According to the back analysis of the Xianchi Reservoir landslide, fluid drag force plays an important role in the actual landslide mobility process. Therefore, it is necessary to consider the influence of the fluid drag force in hazard zoning and the post-failure mobility process analysis of large-scale flow-like landslides.
  PubDate: 2022-07-01
Two-dimensional deformation monitoring of karst landslides in Zongling,
China, with multi-platform distributed scatterer InSAR technique
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  Abstract: Abstract Zongling landslides are the typical karst landslides located in the southwest of China, which have severely threatened the lives of local people. It is of great significance to monitor the real surface deformation of the unstable slopes for the hazard prevention by using the InSAR technology owing to its high precision and frequent revisit. However, the decorrelation effect and atmospheric delay caused by dense vegetation and alpine terrain result in less effective targets to monitor the full deformation of karst landslides. Besides, the derived deformation along one line-of-sight (LOS) direction can result in the misunderstanding of the real kinematics landslide process. In this study, we combine the multi-temporal distributed scatterer InSAR technology (DS-InSAR) and the multi-platform SAR measurements to derive the multi-dimension deformation time series of Zongling landslides. First, both ALOS/PALSAR-2 datasets and Sentinel-1A images from ascending and descending orbits are employed to acquire the deformation along the LOS direction using the DS-InSAR method, where dense effective targets can be achieved, and the deformation patterns can be revealed. Then, the multi-dimensional small baseline subset (MSBAS) method is used to retrieve the horizontal and vertical deformation of Zongling landslides, which further improve the temporal sampling of the deformation time series. Finally, the comprehensive analyses of driving factors and failure mode are conducted according to the spatial–temporal deformation characteristics, and field investigation. The results indicate that this technical route can not only retrieve the landslide deformation with high spatial–temporal sampling density, but also provide new insight into the driven effect of natural factors and mining activities on karst landslides.
  PubDate: 2022-07-01
Failure characteristics and mechanism of a rain-triggered landslide in the
northern longwall of Fushun west open pit, China
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  Abstract: Abstract A rainfall-induced landslide occurred in the north highwall of the Fushun west open pit at 5:00 (UTC + 8) on July 26, 2016, in China. The landslide was about 3.1 × 106 m3 and caused considerable destruction of houses, roads, and railways. Field investigations, laboratory tests, and numerical analyses have been performed to explore the failure characteristics and formation mechanism of the landslide. The landslide was divided into three parts: the crack area, the sliding body area, and the accumulation area. The X-ray diffraction and scanning electron microscope techniques were used to reveal the mineral composition and microstructure of the landslide material. Then, a conceptual model of the landslide mechanism was constructed and the process of the landslide was divided into four stages: unloading and cracking stage; sliding and partial locking stage; shearing out and failure stage; and flowing and accumulating stage. A combined seepage and stability analysis was performed to explore the mechanism of the landslide by numerical simulation. The relationship of volumetric water content and hydraulic conductivity to matric suction was established to describe the hydraulic characteristics of the slope material under infiltration. The results show that the maximum matric suction in the slope decreases continually with the increase of rainfall intensity and duration, and the north highwall will not be stable after 7.2 h for 16 mm/h of rainfall. The original slope was stable and heavy rainfall triggered the landslide, and the obtained critical failure surface matched the field survey closely. The findings improved understanding of the failure mechanism and process of rainfall-induced landslides may be used for evaluating the stability of slopes and early identification for both active and inactive open-pit mines.
  PubDate: 2022-06-24
Correction to: Identifying the role of structural and lithological control
of landslides using TOBIA and Weight of Evidence: case studies from
Romania
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  PubDate: 2022-06-23
Correction to: Landslide detection from bitemporal satellite imagery using
attention‑based deep neural networks
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  PubDate: 2022-06-23
Interrelated impacts of seismic ground motion and topography on coseismic
landslide occurrence using high-resolution displacement SAR data
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  Abstract: Abstract To date, a number of studies have been conducted to examine the relationship between seismic ground motion and coseismic landslides. However, the impacts of seismic ground motion on coseismic landslide occurrence are not fully understood owing to the poor spatial resolution of seismic ground motion data. Recently, seismic observation research has expanded with the use of satellite technology, as crustal deformation can be observed using pairs of SAR (synthetic aperture radar) satellite data. With this technique, obtaining information regarding the ground surface displacement induced by earthquakes is possible at a high spatial resolution, without the need for interpolation or extrapolation. In this study, we focus specifically on the interrelated impacts of seismic ground motion and topography on coseismic landslide occurrence, which has previously been difficult to detect. Using high-resolution ground surface displacement from SAR data, we examine these interrelated impacts in detail and assess coseismic landslide occurrence based on seismic ground motion and topography. Results show that the developed formula accurately reproduces coseismic landslide occurrence and that the impact behaviors of the two factors on landslide occurrence are different. Finally, based on the new formula, we suggest two different trends for the attenuation of seismic ground motion and topography related to coseismic landslide occurrence.
  PubDate: 2022-06-23
Landslide detection from bitemporal satellite imagery using
attention-based deep neural networks
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  Abstract: Abstract Torrential rainfall predisposes hills to catastrophic landslides resulting in serious damage to life and property. Landslide inventory maps are therefore essential for rapid response and developing disaster mitigation strategies. Manual mapping techniques are laborious and time-consuming, and thus not ideal in rapid response situations. Automated landslide mapping from optical satellite imagery using deep neural networks (DNNs) is becoming popular. However, distinguishing landslides from other changed objects in optical imagery using backbone DNNs alone is difficult. Attention modules have been introduced recently into the architecture of DNNs to address this problem by improving the discriminative ability of DNNs and suppressing noisy backgrounds. This study compares two state-of-the-art attention-boosted deep Siamese neural networks in mapping rainfall-induced landslides in the mountainous Himalayan region of Nepal using Planetscope (PS) satellite imagery. Our findings confirm that attention networks improve the performance of DNNs as they can extract more discriminative features. The Siamese Nested U-Net (SNUNet) produced the best and most coherent landslide inventory map among the methods in the test area, achieving an F1-score of 0.73, which is comparable to other similar studies. Our findings demonstrate a prospect for application of the attention-based DNNs in rapid landslide mapping and disaster mitigation not only for rainfall-triggered landslides but also for earthquake-triggered landslides.
  PubDate: 2022-06-17