Use of two contemporary remote sensing technologies for mapping the Thracian Cliffs landslide (Northern Bulgarian Black Sea Coast)

In this paper are presented the results from the investigations of the active landslide, located in front of the Thracian Cliffs golf club (Northern Bulgarian Black Sea Coast) for the period 2019–2021. Extensive research by means of in-situ and remote sensing has been carried out on the latest landslide activations. As part of the study, a control GNSS geodynamic network was established. This network was used as benchmark for the results obtained from satellite SAR data processing and UAV surveys targeted at monitoring the modern landslide developments.

Long-Term Monitoring of a Tunnel in a Landslide Prone Area by Brillouin-Based Distributed Optical Fiber Sensors

This paper shows the results of the monitoring of the deformations of a tunnel, carried out using a distributed optical fiber strain sensor based on stimulated Brillouin scattering. The artificial tunnel of the national railway crosses the accumulation zone of an active landslide, the Varco d’Izzo earthflow, in the southern Italian Apennines. Severely damaged by the landslide movements, the tunnel was demolished and rebuilt in 1992 as a reinforced concrete box flanked by two deep sheet pile walls. In order to detect the onset of potentially dangerous strains of the tunnel structure and follow their time trend, the internal deformations of the tunnel are also monitored by a distributed fiber-optic strain sensor since 2016. The results of the monitoring activity show that the deformation profiles are characterized by strain peaks in correspondence of the structural joints. Furthermore, the elongation of the fiber strands crossing the joints is consistent with the data derived by other measurement systems. Experiments revealed an increase in the time rate of the fiber deformation in the first and last part of the monitoring period when the inclinometers of the area also recorded an acceleration in the landslide movements.

Rapid characterisation of the extremely large landslide threatening the Rules Reservoir (Southern Spain)

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of up to 2 cm/yr was obtained by means of Synthetic Aperture Radar Interferometry (InSAR) and Ground Penetrating Radar (GPR) data, during the last 5 and 22 years, respectively. The Structure-from-Motion (SfM) technique provided a higher rate, up to 26 cm/yr during the last 14 years, due to compaction of a slag heap located within the foot of the landslide. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. Differential Global Positioning Systems, inclinometers, ground drilling and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures.

Continuous assessment of landslides by measuring their basal temperature

In this study, we suggest a temperature-based assessment and mitigation approach for deep-seated landslides that allows to forecast the behavior of the slide and assess its stability. The suggested approach is validated through combined field monitoring and experimental testing of the El Forn landslide (Andorra), whose shear band material is Silurian shales. Thermal and rate controlled triaxial tests have shown that this material is thermal- and rate-sensitive, and in combination with the field data, they validate the theoretical assumption that by measuring the basal temperature of an active landslide, we can quantify and reduce the uncertainty of the model’s parameters, and adequately monitor and forecast the response of the selected deep-seated landslide. The data and results of this letter show that the presented model can give threshold values that can be used as an early-warning assessment and mitigation tool.

Identification, Monitoring, and Assessment of an Active Landslide in Tavan-Hauthao, Sapa, Laocai, Vietnam – A Multidisciplinary Approach

The active landslide located in the Tavan-Hauthao, Sapa district, Laocai province, Vietnam was investigated using geophysical methods (2D Electrical Resistivity and Tomography), geotechnical investigations, and a ground survey to assess the geologic condition of the sliding block and surrounding ground. Landslide displacement was measured using 15 surface monitoring points. Numerical modeling was done to assess the behavior of an active landslide. This multi-disciplinary approach helped in interpreting landslide stratigraphy, geotechnical characteristics of the sliding groundmass, depth, and nature of the sliding plane. The surface area of the slide is approximately 1200 m2. Studying this active landslide is important as it affects the road No. 152, which is an important road connecting the Sapa Ancient Rock Field. This study involved surface topographical survey, surface and sub-surface geological, and geotechnical investigations including Standard Penetration Test and Electrical Resistivity Tomography. Geologic and geotechnical data were used to characterize an active landslide block, which is composed of different soil layers underlaid by granitic rock. The surface electrical-resistivity measurements across the Sapa landslide resulted in inverted-resistivity sections with distinct resistivity contrasts that correlated well with the geology and geo-hydrology observed in boreholes.

Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data

The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. In the study area, 83 deformation accumulation areas of potential landslide hazards and 54 deformation accumulation areas of non-potential landslide hazards were identified through spatial overlay analysis with 64 potential active landslides, which have been confirmed by field verification. The Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were trained and tested through attribute selection and parameter optimization. Among the 17 landslide influencing factors, Drainage Density, NDVI, Slope and Weathering Degree play an indispensable role in the machine learning and recognition of landslide hazards in our study area, while other influencing factors play a certain role in different algorithms. A multi-index (Precision, Recall, F1) comparison shows that the SVM (0.867, 0.829, 0.816) has better recognition precision skill for small-scale unbalanced landslide deformation datasets, followed by RF (0.765, 0.756, 0.741), DT (0.755, 0.756, 0.748) and NB (0.659, 0.659, 0.659). Different from the previous study on landslide susceptibility and hazard mapping based on machine learning, this study focuses on how to find out the potential active landslide points more accurately, rather than evaluating the landslide susceptibility of specific areas to tell us which areas are more sensitive to landslides. This study verified the feasibility of early identification of landslide hazards by using different machine learning methods combined with deformation information and multi-source landslide influencing factors rather than by relying on human–computer interaction. This study shows that the efficiency of potential hazard identification can be increased while reducing the subjective bias caused by relying only on human experts.

Active landslides deformations mapping and monitoring in rural areas using satellite radar interferometry

<p>Landslide hazards pose as one of the greatest risks in today’s context of climate change and settlement expansion. The later process occurs both in the urban and rural areas and significantly changes the terrain morphology and contributes as a conditioning factor for the triggering of new landslide events or reactivation of old dormant ones. Usually, the urban areas are of a greater interest to assess the activity of landslides and their associated risks. On the other hand, the remote areas such as the rural settlements are not as much investigated and monitored, mostly because the in-situ investigations requires additional costs for the deployment of various instruments.</p><p>In the last decades, the development of Advanced Differential SAR Interferometry techniques permits to identify and monitor these geomorphological processes from space. They rely on the microwave’s signal properties to quantify with millimeter accuracy possible deformations in time. The advances of satellite’s acquisition capabilities and the increase of computational power allow the mapping of active landslides over wide areas and even detection of failure precursors.</p><p>In our case, we used the DInSAR techniques to identify the active landslides over a large area in the Moldavian Plateau that affects the human settlements. Even though for the urban areas was much easier to detect the landslide induced deformations, in the case of the rural communities this task was much more challenging. We used the COMET-LiCS Sentinel-1 InSAR data (LiCSAR) and the LiCSBAS software for processing the data for the Moldavian Plateau, Northeastern Romania. Based on the results post-processing we classified the landslides activity based on their velocity and we created an active landslide inventory of the area.</p>

Geodetic and UAV Monitoring in the Sustainable Management of Shallow Landslides and Erosion of a Susceptible Urban Environment

Landslides are a worldwide occurring hazard that can produce economic impact and even fatalities. The collection and monitoring of data regarding active landslides are important for predicting future landslides in that region, and is critical to minimize the losses caused. In the expanding metropolitan area of Cluj-Napoca, Romania, drastic changes of land use and increasement of construction zones represent a current evolution issue. The urban sprawl phenomenon imposed the expansion of the city limits and outside the old built-up area, and due to the hilly terrain and geomorphology, natural hazards such as landslides and erosion processes are susceptible to appearance or reactivation. The study incorporates interdisciplinary research composed of evaluation of a landslide susceptible hotspot located in an area of interest to the municipality by means of geodetic and topographic precise measurements, combined with the use of unmanned aerial vehicles (UAV) monitoring of surface movement and GIS spatial analysis. The data obtained in a span of over two years reveal that the investigated slope is subjected to a shallow active landslide of a few centimeters per year, and based on the 64 individual placed landmarks the highest displacement value was 67 mm. Through geomatic tools the exchange rate of the slope surface was evaluated with comprehensive volume calculations, such as displacement, erosion, and accumulation that illustrate a volume of material displaced of 107.2 m3 and the accumulated one of 55.7 m3. The results provide valuable insight into the complex landslide and erosion dynamics that are crucial when predicting future movements and prevention measures.