Stepwise mitigation of the Macesnik landslide, N Slovenia

The paper gives an overview of the history of evolution and mitigation of the Macesnik landslide in N Slovenia. It was triggered in 1989 above the Solcava village, but it enlarged with time. In 2005, the landslide has been threatening a few residential and farm houses, as well as the panoramic road, and it is only 1000 m away from the Savinja River and the village of Solcava. It is 2500 m long and up to more than 100 m wide with an estimated volume in excess of 2 million m(3). Its depth is not constant: on average it is 10 to 15 m deep, but in the area of the toe, which is retained by a rock outcrop, it reaches the depth of 30 m. The unstable mass consists of water-saturated highly-weathered carboniferous formations. The presently active landslide lies within the fossil landslide which is up to 350 m wide and 50 m deep with the total volume estimated at 8 to 10 million m3. Since 2000, the landslide has been investigated by 36 boreholes, and 28 of them were equipped with inclinometer casings, which also serve as piezometers. Surface movements have been monitored geodetically in 20 cross sections. This helped to understand the causes and mechanics of the landslide. Therefore, landslide mitigation works were planned rather to reduce the landslide movement so that the resulting damages could be minimized. The construction of mitigation works was made difficult in the 1990s due to intensive landslide movements that could reach up to 50cm/day with an average of 25 cm/day. Since 2001, surface drainage works in the form of open surface drains have mainly been completed around the circumference of the landslide as the first phase of the mitigation works and they are regularly maintained. As a final mitigation solution, plans have been made to build a combination of subsurface drainage works in the form of deep drains with retaining works in the form of concrete vertical shafts functioning as deep water wells to drain the landslide, and as dowels to stop the landslide movement starting from the slide plane towards its surface. Due to the length of the landslide and its longitudinal geometry it will be divided into several sections, and the mitigation works will be executed consecutively in phases. Such an approach proved effective in the 800 m long uppermost section of the landslide, where 3 parallel deep drain trenches (250 m long, 8 to 12 m deep) were executed in the autumn of 2003. The reduction of the movements in 2004 enabled the construction of two 5 in wide and 22 m deep reinforced concrete shafts, finished in early 2005. In Slovenia, this sort of support construction, known from road construction, was used for the first time for landslide mitigation. The monitoring results show that the landslide displacements have been drastically reduced to less than I cm/day. As a part of the stepwise mitigation of the Macesnik landslide, further reinforced concrete shafts are to be constructed in the middle section of the landslide to support the road crossing the landslide. At the landslide toe, a support construction is planned to prevent further landslide advancement, and its type is still to be defined during the procedure of adopting a detailed plan of national importance for the Macesnik landslide

History and present state of the Slano Blato landslide

The Slano Blato landslide is more than 1290 m long, 60 to 200 m wide and 3 to 11 m deep with a volume of about 700 000 m(3). It is located in the Eocene flysch region of western Slovenia with a limestone overthrust in the direct vicinity, above the landslide. The landslide moves mainly as a viscous earth flow with occurrences of rapid mud flows. In dry periods or in freezing conditions it behaves as a group of several slow to moderate landslides. The landslide follows the course of the Grajscek stream and is presently only 220 m away from Lokavec village. The landslide was first mentioned about 200 years ago. In 1887 it flowed as a liquid flow and reached and destroyed the main road in the valley 2 km away. The Austro-Hungarian monarchy sent one engineer to the site and 17 years later the slide was remediated with a series of torrential check dams. The monarchy prohibited any construction works in the influence, area of the landslide. During the 20th century the region changed from Austrian, Italian, Yugoslav, and finally to Slovenian government in 1991. The relevant Austrian measures and decisions were forgotten during the course of the years, and building permits were issued after the World War II to local people who populated the part of the landslide influence area. Simultaneously, regular maintenance of the excellent past engineering works was neglected. In November 2000 a large landslide of mud and debris was triggered again and it still presents a danger to the relatively new residential houses today. At present, the village is protected against mudflows by a small rockfill dam and by the regulation of the stream bed. In rainy periods removal of mud is necessary to maintain safe conditions for the village. The paper discusses the geological, hydrogeological, hydrological and geotechnical conditions for the occurrence of the Slano Blato landslide. The primary reasons for the Slano blato landslide are the geological and hydrogeological conditions just beneath the overthrust of a Triassic limestone plateau over the Eocene flysch of Vipava valley. The direct reason for triggering the earth flow in 2000 was the intensive precipitation. During the course of years the precipitation threshold for earth flow movements has diminished. The landslide has to be remediated for two main reasons (1) the village below the landslide is endangered, and (2) the landslide is still advancing retrogressively and laterally. The foreseen permanent remediation measures that are currently under construction are briefly presented

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model

Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations

First insights on the potential of Sentinel-1 for landslides detection

This paper illustrates the potential of Sentinel-1 for landslide detection, Accepted 23 March 2016 mapping and characterization with the aim of updating inventory maps and monitoring landslide activity. The study area is located in Molise, one of the smallest regions of Italy, where landslide processes are frequent. The results achieved by integrating Differential Synthetic Aperture Radar Interferometry (DInSAR) deformation maps and time series, and Geographical Information System (GIS) multilayer analysis (optical, geological, geomorphological, etc.) are shown. The adopted methodology is described followed by an analysis of future perspectives. Sixty-two landslides have been detected, thus allowing the updating of pre-existing landslide inventory maps. The results of our ongoing research show that Sentinel-1 might represent a significant improvement in terms of exploitation of SAR data for landslide mapping and monitoring due to both the shorter revisit time (up to 6 days in the close future) and the wavelength used, which determine an higher coherence compared to other SAR sensors

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

Geophysical-geotechnical sensor networks for landslide monitoring

Landslides are often the result of complex, multi-phase processes where gradual deterioration of shear strength within the sub-surface precedes the appearance of surface features and slope failure. Moisture content increases and the build-up of associated pore water pressures are invariably associated with a loss of strength, and thus are a precursor to failure. Consequently, hydraulic processes typically play a major role in the development of landslides. Geoelectrical techniques, such as resistivity and self-potential are being increasingly applied to study landslide structure and the hydraulics of landslide processes. The great strengths of these techniques are that they provide spatial or volumetric information at the site scale, which, when calibrated with appropriate geotechnical and hydrogeological data, can be used to characterise lithological variability and monitor hydraulic changes in the subsurface. In this study we describe the development of an automated time-lapse electrical resistivity tomography (ALERT) and geotechnical monitoring system on an active inland landslide near Malton, North Yorkshire, UK. The overarching objective of the research is to develop a 4D landslide monitoring system that can characterise the subsurface structure of the landslide, and reveal the hydraulic precursors to movement. The site is a particularly import research facility as it is representative of many lowland UK situations in which weak mudrocks have failed on valley sides. Significant research efforts have already been expended at the site, and a number of baseline data sets have been collected, including ground and airborne LIDAR, geomorphologic and geological maps, and geophysical models. The monitoring network comprises an ALERT monitoring station connected to a 3D monitoring electrode array installed across an area of 5,500 m2, extending from above the back scarp to beyond the toe of the landslide. The ALERT instrument uses wireless telemetry (in this case GPRS) to communicate with an office based server, which runs control software and a database management system. The control software is used to schedule data acquisition, whilst the database management system stores, processes and inverts the remotely streamed ERT data. Once installed and configured, the system operates autonomously without manual intervention. Modifications to the ALERT system at this site have included the addition of environmental and geotechnical sensors to monitor rainfall, ground movement, ground and air temperature, and pore pressure changes within the landslide. The system is housed in a weatherproof enclosure and is powered by batteries charged by a wind turbine & solar panels. 3D ERT images generated from the landslide have been calibrated against resistivity information derived from laboratory testing of borehole core recovered from the landslide. The calibrated images revealed key aspects of the 3D landslide structure, including the lateral extent of slipped material and zones of depletion and accumulation; the surface of separation and the thickness of individual earth flow lobes; and the dipping in situ geological boundary between the bedrock formations. Time-lapse analysis of resistivity signatures has revealed artefacts within the images that are diagnostic of electrode movement. Analytical models have been developed to simulate the observed artefacts, from which predictions of electrode movement have been derived. This information has been used to correct the ERT data sets, and has provided a means of using ERT to monitor landslide movement across the entire ALERT imaging area. Initial assessment of seasonal changes in the resistivity signature has indicated that the system is sensitive to moisture content changes in the body of the landslide, thereby providing a basis for further development of the system with the aim of monitoring hydraulic precursors to failure

Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset