Search

Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 "Earth observation for seismic hazard assessment and landslide early warning system" are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems. [ABSTRACT FROM AUTHOR]

This paper accompanies the Maps of Geohazard features of the Cilento and the Calabro- Tyrrhenian continental margin in the southern Tyrrhenian Sea (Italy). The main geohazardrelated features were derived from extensive seafloor mapping through the collection of high-resolution multibeam data acquired during several oceanographic cruises. They encompass many fluids seepage features, fault scarps, landslides scars, gullies, channels, and canyons. Hazards related to coastal landslides and shelf-indenting canyons are very high in these sectors (especially in southern Calabria) due to active seismicity coupled with rapid uplift, high sedimentation rates and narrow or totally absent continental shelf, thus promoting a direct connection between steep slopes and coastal areas. In this setting, masswasting features can directly impact coastal or submarine infrastructures or indirectly create local tsunami waves, as observed in historical times. Moreover, this physiographic setting of the margin facilitates the transfer of marine litter toward deep-sea areas. [ABSTRACT FROM AUTHOR]

The MaGIC project (Marine Geohazard along the Italian Coasts) had the aim of mapping the geohazard in the Italian seas and resulted in the production of numerous maps covering parts of the Italian Seas. In this paper, we present the maps: 'The submerged portions of the Aeolian volcanic islands and the north-eastern Sicilian margin', located in the south-eastern Tyrrhenian Sea. Both areas are affected by active geological processes, which represent important geohazards elements. Inthe submarine parts of the Stromboli volcanoremobilization of volcaniclastic deposits occur along the Sciara del Fuoco, where small-scale instabilities may represent a source of geohazard. Hydrothermal activity occurs on Enarete and Enaretino conical seamounts. The north-eastern Sicilian margin has a narrow continental shelf. Numerous canyon heads indent the shelf and, sometimes, reach close to the coast. Canyons have often a retrogradational trend and further eventual landward shift through sliding can iendangeri coastal or offshore infrastructures. Many of the canyons connect with leveed channels with widespread sediment instability. In the Gioia Basin, some of the channels connect to form the Stromboli slope Valley. Volcanic unrest or local and regional earthquakes are proven to have caused submarine landslides and tsunamis. [ABSTRACT FROM AUTHOR]

The assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively. [ABSTRACT FROM AUTHOR]

Traditional landslide susceptibility assessment methods often rely on single models, which can be biased and less accurate. In this article, we introduce a two-tiered strategy to enhance landslide susceptibility predictions. Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. This combination leverages the support vector classifier (SVC) as the key meta-classifier to optimize and refine predictions. Subsequently, we integrate the extreme gradient boosting (XGB), random forest (RF) and gradient boosting decision tree (GBDT) models with SVC to create hybrid approaches. In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. The results demonstrated that the stacking ensemble model outperforms traditional models. The XBGþSVC model achieves the highest AUC value (0.9468), which is followed by the GBDTþSVC (0.9316), RFþSVC (0.9162), XGB (0.9393), GBDT (0.9009), and RF (0.8693). These findings indicate that stacking machine learning approaches hold promise for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

The periodic storage and release of water in reservoirs have the potential to accelerate deformation or trigger ancient landslides. Interferometric synthetic aperture radar (InSAR) technology, renowned for its wide coverage and high precision, has emerged as an effective technique for the identification and monitoring of landslides. This study utilized spaceborne InSAR technology to identify and interpret landslides in the Liujiangxia reservoir area in China, leading to the detection of a total of 20 landslides. Moreover, among these 20 landslides, the landslide with substantial deformation (LJX02) was chosen, and the wavelet quantitative analysis technique was employed to investigate the correlation and time lag between various elevation positions of this landslide, the reservoir water level, and rainfall. The study successfully demonstrated that the deformation of the landslide lagged behind the reservoir water level by 152.10-182.50 days (from low to high elevation) and was delayed by 45.60-68.44 days compared to rainfall (from high to low elevation). [ABSTRACT FROM AUTHOR]

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area's disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation's findings mostly agreed with the survey's actual findings. The study's findings demonstrated that: (1) the FCNN model's AUC value was 0.910, while the I-AHP model's AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened. [ABSTRACT FROM AUTHOR]

Landslide inventory incompleteness (LII) may significantly affect the model performance in landslide susceptibility mapping (LSM). However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. This hybrid model mitigates the drawbacks of data-heavy statistical approaches and subjective heuristic models by incorporating LII into weight determination. The findings show that the LEO-MAHP model demonstrates superior performance (AUROC = 0.809 and 0.805) over conventional statistical (AUROC = 0.714 and 0.770) and heuristic models (AUROC = 0.738 and 0.741) across different LII levels. We further discuss alternative LII solutions, proposing an updated landslide management strategy that accounts for climate change and human activities. Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM. List of Abbreviations: AHP: Analytic hierarchy process; AUROC: Area under receiver operating characteristic curve; CR: Consistency ratio; CI: Consistency index; CF: Conditional factor; DEM: Digital elevation model; FN: False negative; FP: False positive; FPR: False positive rate; InSAR: Interferometric Synthetic Aperture Radar; IV: Information value; LD: Landslide density; LII: Landslide inventory incompleteness; LS: Landside susceptibility; LSI: Landslide susceptibility index; LSM: Landslide susceptibility mapping; MAHP: Multi-participated analytic hierarchy process; MC: Model construction; MCDA: Multi-criteria decision analysis; MV: Model verification; MPP: Model prediction performance; ORS: Optical remote sensing; P&C: Prevention and control; PGA: peak ground acceleration; ROC: Receiver operating characteristic; RI: Random index; TN: True negative; TP: True positive; TPR: True positive rate. [ABSTRACT FROM AUTHOR]

We present and experimentally demonstrate a seismic ambient noise monitoring optical fibre sensor for early warning detection of sinkholes. The developed optical fibre sensor is designed for warning alert of subsidence and cover collapse sinkholes. The progressive process of sinkhole development causes structural change in the subterranean surface. The impact of this change and its influence on the subsurface acoustic modes was detected in the form of variations in the spectral content of the ambient noise signals monitored in the subsurface. Structural surface integrity was monitored through frequency response as the void increased. Vibrational states relating to unsteady structural conditions were identified. Significant instability events were captured giving timely warnings before collapse. The polarisation based single mode fibre sensor and monitoring method is proposed for implementation in a phase sensitive distributed acoustic sensor setup. Peak frequencies in the micro-seismic noise band of 0.1 Hz to 1.0Hz were observed through cavity development and growth. Extended peak frequency shifts and bandwidth in the band >1Hz were recorded, indicating weakness and imminence of collapse. Early warning detection by the structural field model was achieved prior to the sudden subsurface failure which results in collapse sinkholes. By monitoring variations in the vibrating frequency modes when a subsurface cavity develops within the structure, trigger events and collapse precursor conditions are identified. We have successfully demonstrated an early response warning annunciator by using an algorithm to analyse combinational characteristics of the spectral components of the detected signals. The fibre sensor reduces the risk and socio-economic impact of infrastructural damage due to sudden collapse of sinkholes and has extended potential of monitoring earthquakes and landslides. [ABSTRACT FROM AUTHOR]

The north-eastern border of CA represents the accretionary system developed during its continuous collision with Apulian block. The tardive oblique tectonic component is highlighted by pervasive NW-SE crustal transpressive left-lateral strike slip faults developed from the Upper Miocene to Quaternary tectonic stages. The seismotectonic and landslides Main Map represents the update of the main faults, the style of their arrangement and kinematics, the seismotectonic features and landslide area distribution with risk implications. Seismotectonics allows us to recognise a vertical crustal zonation with the distribution of earthquakes and their focal mechanism solution. The prevalent modality of ruptures depicts a mainly transcurrent and compressive solution in mid-lower crust portions respectively. This data, together with the historical seismicity analysis of the area, lead to consider the NW-SE strike-slip faults improved on the Main Map, as recent and tectonically active. The areal distribution of landslide and susceptibility of the area is due to the interaction between seismotectonic peculiarity, lithological typologies and fault zone architectures. Landslides and flood phenomena of this sector involve many municipalities, major and minor infrastructures and economic and social activities. The landslides and the faults have been identified, mapped and classified, originally at detail scale and, then, represented at 1:50,000 scale in the Main Map, included as supplementary material. The geo-structural and geomorphological data were analysed in a geographic information system allowing data management and implementation. The work presents an updated knowledge framework of risk conditions of the study zone, available to plan and reduce the fundamental elements that determine the landslide and seismic risks in this region. [ABSTRACT FROM AUTHOR]