The deformation of slow‐moving landslides developed in clays induces endogenous seismicity of mostly low‐magnitude events (ML<1). Long seismic records and complete catalogs are needed to identify the ...type of seismic sources and understand their mechanisms. Manual classification of long records is time‐consuming and may be highly subjective. We propose an automatic classification method based on the computation of 71 seismic attributes and the use of a supervised classifier. No attribute was selected a priori in order to create a generic multi‐class classification method applicable to many landslide contexts. The method can be applied directly on the results of a simple detector. We developed the approach on the seismic network of eight sensors of the Super‐Sauze clay‐rich landslide (South French Alps) for the detection of four types of seismic sources. The automatic algorithm retrieves 93% of sensitivity in comparison to a manually interpreted catalog considered as reference.
Key Points
Landslide seismic sources are automatically classified using the Random Forest supervised classifier with 71 attributes
The sensitivity of the classification is up to 93% in the case of four different classes of seismic events
The large amount of attributes enables the method to be implemented easily and automatically in many contexts
The objective of this work is to present a low-cost methodology to monitor the displacement of continuously active landslides from ground-based optical images analyzed with a normalized image ...correlation technique. The performance of the method is evaluated on a series of images acquired on the Super-Sauze landslide (South French Alps) over the period 2008–2009. The image monitoring system consists of a high resolution optical camera installed on a concrete pillar located on a stable crest in front of the landslide and controlled by a datalogger. The data are processed with a cross-correlation algorithm applied to the full resolution images in the acquisition geometry. Then, the calculated 2D displacement field is orthorectified with a back projection technique using a high resolution DEM interpolated from Airborne Laser Scanning (ALS) data. The heterogeneous displacement field of the landslide is thus characterized in time and space. The performance of the technique is assessed using differential GPS surveys as reference. The sources of error affecting the results are then discussed. The strongest limitations for the application of the technique are related to the meteorological, illumination and ground surface conditions inducing partial or complete loss of coherence among the images. Small movements of the camera and the use of a mono-temporal DEM are the most important factors affecting the accuracy of the ortho-rectification of the displacement field. As the proposed methodology can be routinely and automatically applied, it offers promising perspectives for operational applications like, for instance, in early warning systems.
Recent advances in multi-view photogrammetry have resulted in a new class of algorithms and software tools for more automated surface reconstruction. These new techniques have a great potential to ...provide topographic information for geoscience applications at significantly lower costs than classical topographic and laser scanning surveys. Based on open-source libraries for multi-view stereo-photogrammetry and Structure-from-Motion, this study investigates the accuracy that can be obtained from several processing pipelines for the 3D surface reconstruction of landslides and the detection of changes over time. Two different algorithms for point-cloud comparison are tested and the accuracy of the resulting models is assessed against terrestrial and airborne LiDAR point clouds. Change detection over a period of more than two years allows a detailed assessment of the seasonal dynamics of the landslide; the possibility to estimate sediment volumes and 3D displacement are illustrated for the most active parts of the landslide. Algorithm parameters and the image acquisition protocols are found to have important impacts on the quality of the results and are discussed in detail.
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•Open-source photogrammetry permits reconstructions of natural terrain at accuracies of cm to dm.•Cloud-to-cloud comparison allows us to quantify surface deformation, erosion and accumulation.•Landslide volumes and 3D displacement rates are validated with LiDAR and permanent GPS monitoring.
We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we ...applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.
This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as ...well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.
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Recent advances in image-matching techniques and VHR satellite imaging at submeter resolution theoretically offer the possibility to measure Earth surface displacements with ...decimetric precision. However, this possibility has yet not been explored and requirements of ground control and external topographic datasets are considered as important bottlenecks that hinder a more common application of optical image correlation for displacement measurements. This article describes an approach combining spaceborne stereo-photogrammetry, orthorectification and sub-pixel image correlation to measure the horizontal surface displacement of landslides from Pléiades satellite images. The influence of the number of ground-control points on the accuracy of the image orientation, the extracted surface models and the estimated displacement rates is quantified through comparisons with airborne laser scan and in situ global navigation satellite measurements at permanent stations. The comparison shows a maximum error of 0.13m which is one order of magnitude more accurate than what has been previously reported with spaceborne optical images from other sensors. The obtained results indicate that the approach can be applied without significant loss in accuracy when no ground control points are available. It could, therefore, greatly facilitate displacement measurements for a broad range of applications.
The analysis of landslide hazard requires continuous and high frequency surface displacement monitoring at numerous and geomorphologically relevant locations. Ground-based geodetic methods (GNSS, ...tacheometry) allow very accurate and high frequency temporal observations while remote sensing methods (InSAR, terrestrial and satellite photogrammetry, LIDAR) allow spatially distributed observations at high spatial resolution. A single surface deformation monitoring technique coupling all these capabilities is still missing.
The Geocube system has been designed to partly overcome this pitfall by creating a low-cost, flexible, easy to install and wireless GPS receiver. Dense Geocube monitoring networks can be set easily for operational observations. Furthermore, the monitoring of other landslide properties (micro-seismicity, seismic waves) or triggering factors (meteorology, slope hydrology) is possible with the capacity of integrating additional sensors to the Geocube.
This work presents the Geocube system and the results of a field campaign performed during the summer 2012 at the Super-Sauze landslide, southern French Alps, with a network of wireless low-cost GPS. The objective was to assess the performance of the Geocube system in real field monitoring conditions. Our results document the spatial and temporal evolution of the landslide during a period of 40days. Landslide acceleration periods are detected and correlated to rainfall events.
•The Super-Sauze landslide is monitored by a dense, low-cost and wireless GPS network.•Precise position time series at high temporal frequency are obtained.•The complex spatial pattern of deformation of the landslide is investigated.•A landslide acceleration event is documented.
We apply an image correlation technique to multi-orbit and multi-temporal high-resolution (HR) SAR data. Image correlation technique has the advantage of providing displacement maps in two ...directions; e.g. the Line of Sight direction (LoS) and the Azimuth direction. This information, derived from the two modes of data acquisition (ascending and descending), can be combined routinely to infer the three dimensional surface displacement field at different epochs. In this study, a methodology is developed to characterize the displacement pattern of the large La Valette landslide (South French Alps) using TerraSAR-X images acquired in 2010. The results allow mapping the dynamics of different units of the La Valette landslide at high spatial resolution. The study demonstrates the potential of this new application of High Resolution SAR image correlation technique for landslide ground surface deformation monitoring.
•Image correlation on High Resolution SAR images allows mapping landslide motion.•Application to La Valette (French Alps) site based on TerraSAR-X data is carried out.•Temporal analysis reveals morphological units with different displacement patterns.•Motion pattern identified from SAR data is consistent with local GNSS observations.
Statistical assessment of landslide susceptibility has become a major topic of research in the last decade. Most progress has been accomplished on producing susceptibility maps at meso-scales ...(1:50,000–1:25,000). At 1:10,000 scale, which is the scale of production of most regulatory landslide hazard and risk maps in Europe, few tests on the performance of these methods have been performed. This paper presents a procedure to identify the best variables for landslide susceptibility assessment through a bivariate technique (weights of evidence, WOE) and discusses the best way to minimize conditional independence (CI) between the predictive variables. Indeed, violating CI can severely bias the simulated maps by over- or under-estimating landslide probabilities. The proposed strategy includes four steps: (i) identification of the best response variable (RV) to represent landslide events, (ii) identification of the best combination of predictive variables (PVs) and neo-predictive variables (nPVs) to increase the performance of the statistical model, (iii) evaluation of the performance of the simulations by appropriate tests, and (iv) evaluation of the statistical model by expert judgment. The study site is the north-facing hillslope of the Barcelonnette Basin (France), affected by several types of landslides and characterized by a complex morphology. Results indicate that bivariate methods are powerful to assess landslide susceptibility at 1:10,000 scale. However, the method is limited from a geomorphological viewpoint when RVs and PVs are complex or poorly informative. It is demonstrated that expert knowledge has still to be introduced in statistical models to produce reliable landslide susceptibility maps.
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