Modern Technologies for Landslide Monitoring and Prediction

In this chapter, the application of close-range photogrammetry for deformation measurements in the field of landslide investigation and monitoring is discussed. Main advantages of this approach are the non-contact operational capability, the large covered area on the slope to analyze, the high degree of automation, the high acquisition rate, the chance to derive information on the whole surface, not limited to a few control points (area-based deformation measurement), and, generally, a lower cost with respect to 3D scanning technology. Applications are organized into two categories: (1) surface-point tracking (SPT) and (2) comparison of surfaces obtained from dense image matching. Different camera configurations and geometric models to transform points from the image space to the object space are also discussed. In the last part of the chapter, a review of the applications reported in the literature and two case studies from the experience of the authors are reported.

Though landslide alert is based on monitoring systems capable of high frequency, highly accurate, continuous-operation photogrammetry has been used since long time to periodically control the evolution of landslides. In this chapter, a fixed terrestrial stereo photogrammetric system is presented. It has been developed to monitor landslides and, in general, changes in digital surface model (DSM) of the scene framed by the cameras. The system is made of two single-lens reflex (SLR) cameras, each contained in a sealed box and controlled by a computer that periodically shoots an image and sends it to a host computer. Once an image pair is received, the DSM of the scene is generated by digital image correlation on the host computer and made available for archiving or analysis. The system has been installed and is being tested on the Mont de la Saxe landslide, where several monitoring systems are active and provide reference data. Instability of the camera attitude has been noticed and corrected with an automated procedure by image resampling. First comparisons with interferometric synthetic aperture radar data show a good agreement of the displacements over time.

Remote-sensing techniques are changing the way of investigating the Earth and its surface processing. Among these, rock fall from vertical cliffs are very frequent and difficult to be investigated because they frequently occur from inaccessible places. At this regard, terrestrial remote-sensing techniques represent a great opportunity for investigating inaccessible cliffs from a remote position. In this paper, a new approach for the investigation of rock cliff and the prioritization of rock fall hazard based on data collected by remote-sensing techniques has been developed and applied to a real coastal cliff located in the southern part of Italy. By the herein presented approach, data derived from a survey performed by the combination of terrestrial laser scanner, ground-based SAR interferometry and infrared thermography are used in order to identify both predisposing factors (mapping of discontinuities) and state of activity indicators of rock instabilities. Hence, a prioritizations map of the cliff in terms of stability interventions is achieved that can be easily used by local authorities in charge of land management.

Terrestrial laser scanning (TLS) has proven to be a very effective technique for landslides monitoring, even if some critical issues exist for providing highly reliable results. This chapter presents the methodology adopted in performing four surveys, carried out over three years on a large slump landslide in order to get effectively comparable data. The first problem concerns the setting up of the reference system, which has been realized by means of global navigation satellite system permanent stations ETRF00 datum. This solution was able to maximize the stability over time even at the expense of a slightly lower precision, which was, however, in the order of 1–2 cm with data recorded during the whole duration of TLS survey. An assessment of geo-referencing accuracy was carried out with respect to the only stable artifact present in the landslide area. This check pointed out that in the central part of the point cloud the repeatability between different surveys was slightly greater than 5 cm. To ensure the quality of the obtained multitemporal digital terrain models (DTM’s) over the entire region of interest, the choice of the interpolation algorithm has been performed and verified with a cross-validation method on the basis of a sample extracted from the data set. To detect the kinematics of the landslide in its several parts, both the DTM’s and profiles have been used, which have proven to be particularly useful for the interpretation of details. After the localization of various landslide bodies (keeping into account slope and aspect maps derived from the DTM), the evaluation of the volumes mobilized over time has been carried out by differencing the DTM’s. This analysis has been separately carried out in the different parts on which the landslide bodies had been subdivided.

This chapter illustrates the concept of passive seismics as a method for monitoring the propagation of cracks within a rock mass as a result of load stress or water freezing in view of the use of this technique for rockfall early warning. The methodology is still far from being a standard and consolidated technique. The research is making progress, but just a few real case studies are documented. They are shortly overviewed in the introduction. Then, an interesting field test where crack propagation was artificially triggered up to full rock detachment, while a small sensor network was active, is discussed to show the existence and the characteristics of precursory signals. It follows the illustration of the microseismic monitoring methodology through the description of the Mt. San Martino (Lecco, Italy) sensor network and the discussion of the preliminary results obtained during the initial months of activity. Apparently, the preliminary results show some correlation with rainfalls, but not with temperature. Microseismic spectra are mainly concentrated in the first 100 Hz. This probably means that the hypocentre distances from the sensors are quite longer than 10 m. Electromagnetic interferences are also observed as mentioned by other authors who have analyzed data sets from other microseismic networks installed in mountain regions. They are automatically discriminated from significant signals by a classification software which works on the time/frequency properties of these events. Hypocenter localization and clustering analysis of the significant events are the planned near-future activities.

Flow-slide disaster is a continuing problem along hillsides in mountainous areas, which always results in numerous casualties and catastrophic destruction of buildings and regional landscapes. Predicting of the propagation stage is of great importance for the disaster mitigation. The smoothed particle hydrodynamics (SPH) method, a mesh-free particle technique, has been widely applied for modelling of flow-slide evolution with some success. The main goal of this chapter was to provide a general view of SPH applications for the analysis of flow-slide disasters including flow-like landslides, landslide-generated waves, and debris flows. The leading features of the proposed SPH models are detailed and the achievements are presented and discussed.

A cost-effective physical model (SLope-Infiltration-distributed Equilibrium—SLIDE) has been developed to identify the spatial and temporal occurrences of rainfall-induced landslides, employing a range of remotely sensed and in situ data. The main feature of SLIDE is that it takes into account of some simplified hypotheses on water infiltration and defines a direct relationship between the factor of safety and the rainfall depth on an infinite slope model. This prototype has been applied to two case studies in Indonesia and Honduras during heavy rainfall events brought by typhoon and hurricane, respectively. Simulation results from SLIDE demonstrated good skills in predicting rainfall-induced shallow landslides by assimilating the most important dynamic triggering factor (i.e., rainfall) quantitatively. The model’s prediction performance also suggested that SLIDE could serve as a potential tool for the future landslide early-warning system. Despite positive model performance, the SLIDE model is limited by several assumptions including using general parameter calibration rather than in situ tests and neglecting geotechnical information and some of the hydrological processes in deep soil layers. Advantages and limitations of this physically based model are also discussed with respect to future applications of landslide assessment and prediction over large scales.

Thousands of landslides are distributed along Yangtze River and its tributaries in the Three Gorges area from Chongqing Municipality in the west to Hubei Province in the east (P.R. China). Since the construction and regular operation of the Three Gorges Dam in the past two decades, many ancient landslides have been reactivated and some new landslides were formed along with the unprecedentedly huge water-level changes. Monitoring landslide activities has then been considered as a high-priority task for geological disaster prevention and management in the reservoir area, while traditional monitoring methods can hardly meet the requirements. In this chapter, we investigated the applications of several methods using Synthetic Aperture Radar (SAR) datasets in landslide monitoring in the Three Gorges area. Multifrequency satellite SAR data sets acquired by ENVISAT/ASAR, ALOS/PALSAR, and TerraSAR-X from different orbits were analyzed to retrieve historic deformations of a few typical landslides. The experimental results suggested that SAR Interferometry (InSAR) methods can be effectively used to monitor slow-moving landslides, while pixel offset tracking method is more suitable for detecting deformation of fast-moving landslides. Furthermore, qualitative correlation analyses indicated that variation of reservoir water level, particularly the rapid water-level decrease due to discharge, should be identified as a key driving factor for landslide deformation in the Three Gorges area.

Landslide detection and monitoring represent a starting point to produce hazard and risk maps useful for proper urban planning and emergency management. In this chapter, different applications of radar interferometric techniques are presented to prove their applicability for detection, mapping and monitoring landslides in different geological settings and with different operative conditions. Data acquisition was carried out through satellite (operating in C-band) and ground-based (operating in Ku-band) sensors. In particular, in this chapter, the application of radar interferometry for landslide detection (Arno River basin), for landslide monitoring (Santo Stefano d’Aveto landslide), for landslide hazard scenario definition (Montebeni landslide) and for landslide emergency management (Santa Trada landslide) are presented and discussed. The different applications highlight the capability and suitability of these techniques to work in different operative settings (i.e.‚ different phenomena and geological framework) and for different aims (survey, early warning and emergency assessment).

Image-to-image co-registration is one of the preprocessing steps needed for the analysis of satellite time series. This chapter presents a new approach where all the available images are simultaneously co-registered, overcoming the limits of traditional techniques. This method was tested on the flood and landslide that occurred in Valtellina (northern Italy) during summer of 1987, resulting in the large rockslide of Val Pola. A data set made up of 13 medium-resolution satellite images collected with Landsat-4 and Landsat-5 Thematic Mapper over a period of 30 years was automatically processed. Results showed that the new approach can provide subpixel accuracy close to manual measurements, which today are considered the most accurate method for image registration. The multi-image co-registration method also demonstrated to be atmospheric resistant and robust against land-cover changes, snow, and cloud cover.