1,721,088 research outputs found
Risk analysis for the Ancona landslide—I: characterization of landslide kinematics
Full text linkThe Ancona landslide is a complex, deep-seated landslide displaying composite rotational–translational kinematisms and affecting a large urban area in the Ancona municipality on the Adriatic coast of central Italy. The landslide was reactivated with a large and destructive event on 13 December 1982 following a long period of precipitation and has remained active since. This paper focuses on the estimation of the landslide kinematics (more specifically, the horizontal and vertical components of average yearly velocity) for subsequent estimation of risk for a set of 39 buildings as presented in a companion paper. The study relies both on the processing of inclinometer and radar interferometer monitoring data through statistical procedures. Triggering factors are not investigated. Outputs from the two sets of monitoring data are compared quantitatively and qualitatively. The inherent limitations in available data are discussed. The validity of the quantitative results in the context of the risk estimation effort is discussed
Persistent scatterer interferometry (psi) technique for landslide characterization and monitoring
Full text linkThe measurement of landslide superficial displacement often represents the most effective method for defining its behavior, allowing one to observe the relationship with triggering factors and to assess the effectiveness of the mitigation measures. Persistent Scatterer Interferometry (PSI) represents a powerful tool to measure landslide displacement, as it offers a synoptic view that can be repeated at different time intervals and at various scales. In many cases, PSI data are integrated with in situ monitoring instrumentation, since the joint use of satellite and ground-based data facilitates the geological interpretation of a landslide and allows a better understanding of landslide geometry and kinematics. In this work, PSI interferometry and conventional ground-based monitoring techniques have been used to characterize and to monitor the Santo Stefano d'Aveto landslide located in the Northern Apennines, Italy. This landslide can be defined as an earth rotational slide. PSI analysis has contributed to a more in-depth investigation of the phenomenon. In particular, PSI measurements have allowed better redefining of the boundaries of the landslide and the state of activity, while the time series analysis has permitted better understanding of the deformation pattern and its relation with the causes of the landslide itself. The integration of ground-based monitoring data and PSI data have provided sound results for landslide characterization. The punctual information deriving from inclinometers can help in defining the actual location of the sliding surface and the involved volumes, while the measuring of pore water pressure conditions or water table level can suggest a correlation between the deformation patterns and the triggering factors. © 2013 by the authors; licensee MDPI, Basel, Switzerland
TXT-tool 2.039-3.2 Ground-based remote sensing techniques for landslides mapping, monitoring and early warning
No full textThe current availability of advanced remote sensing technologies in the field of landslide analysis allows rapid and easily updatable data acquisitions, improving the traditional capabilities of detection, mapping and monitoring, optimizing field work, and allowing to investigate hazardous and inaccessible areas while granting at the same time the safety of the operators. In the recent years in particular, ground-based remote sensing techniques have undergone a significant increase of usage, thanks to their technological development and quality data improvement, offering advantages with respect to air- or spaceborne remote sensing techniques, in terms of data spatial resolution and accuracy, fast measurement and processing times, and portability and cost-effectiveness of the acquiring instruments. These advantages can be highlighted in the framework of landslide emergency management, when it is often urgently necessary to minimize survey time when operating in dangerous environments and gather all the required information as fast as possible. In this paper, the potential of some ground-based remote sensing techniques and the effectiveness of their synergic use is explored in several case studies, analyzing different slope instability processes at different scales of emergency or post-emergency management. Thanks to them and to the support of existing bibliography, the most common fields of application are suggested for all the considered ground-based sensor technologies and their level of effectiveness is evaluated in relation to the dynamics of landslide types
A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling
No full textClassification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling
Brief communication A prototype forecasting chain for rainfall induced shallow landslides
Full text linkAlthough shallow landslides are a very widespread phenomenon, large area (e.g. thousands of square kilometres) early warning systems are commonly based on statistical rainfall thresholds, while physically based models are more commonly applied to smaller areas. This work provides a contribution towards the filling of this gap: a forecasting chain is designed assembling a numerical weather prediction model, a statistical rainfall downscaling tool and a geotechnical model for the distributed calculation of the factor of safety on a pixel-by-pixel basis. The forecasting chain can be used to forecast the triggering of shallow landslides with a 48 h lead time and was tested on a 3200 km2 wide area. © 2013 Author(s)
Spatial patterns of landslide dimension: A tool for magnitude mapping
Full text linkAbstractThe magnitude of mass movements, which may be expressed by their dimension in terms of area or volume, is an important component of intensity together with velocity. In the case of slow-moving deep-seated landslides, the expected magnitude is the prevalent parameter for defining intensity when assessed as a spatially distributed variable in a given area. In particular, the frequency–volume statistics of past landslides may be used to understand and predict the magnitude of new landslides and reactivations. In this paper we study the spatial properties of volume frequency distributions in the Arno river basin (Central Italy, about 9100km2). The overall landslide inventory taken into account (around 27,500 events) shows a power-law scaling of volumes for values greater than a cutoff value of about 2×104m3. We explore the variability of the power-law exponent in the geographic space by setting up local subsets of the inventory based on neighbourhoods with radii between 5 and 50km. We found that the power-law exponent α varies according to geographic position and that the exponent itself can be treated as a random space variable with autocorrelation properties both at local and regional scale. We use this finding to devise a simple method to map the magnitude frequency distribution in space and to create maps of exceeding probability of landslide volume for risk analysis. We also study the causes of spatial variation of α by analysing the dependence of power-law properties on geological and geomorphological factors, and we find that structural settings and valley density exert a strong influence on mass movement dimensions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry
No full textPreparation of reliable landslide hazard and risk maps is crucial for hazard mitigation and risk management. In recent years, various approaches have been developed for quantitative assessment of landslide hazard and risk. However, possibly due to the lack of new data, very few of these hazard and risk maps were updated after their first generation. In this study, aiming at an ongoing assessment, a novel approach for updating landslide hazard and risk maps based on Persistent Scatterer Interferometry (PSI) is introduced. The study was performed in the Arno River basin (central Italy) where most mass movements are slow-moving landslides which are properly within the detection precision of PSI point targets. In the Arno River basin, the preliminary hazard and risk assessment was performed by Catani et al. (Landslides 2:329-342, 2005) using datasets prior to 2002. In this study, the previous hazard and risk maps were updated using PSI point targets processed from 4 years (2003-2006) of RADARSAT images. Landslide hazard and risk maps for five temporal predictions of 2, 5, 10, 20 and 30 years were updated with the exposure of losses estimated in Euro (€). In particular, the result shows that in 30 years a potential loss of approximate €3.22 billion is expected due to these slow-moving landslides detected by PSI point targets. © 2013 Springer-Verlag Berlin Heidelberg
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