Article

State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk

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Abstract

A landslide inventory is the most important information source for quantitative zoning of landslide susceptibility, hazard and risk. It should give insight into the location, date, type, size, activity and causal factors of landslides as well as resultant damage. In Europe, many countries have created or are creating national and/or regional landslide databases (LDBs). Yet little is known on their contents, completeness, format, structure, language use and accessibility, and hence on their ability to perform national or transnational landslide zoning. Therefore, this study presents a detailed analysis of existing national LDBs in the EU member states, EU official candidate and potential candidate countries, and EFTA countries, and their possible use for landslide zoning. These national LDBs were compared with a subset of 22 regional databases. Twenty-two out of 37 contacted European countries currently have national LDBs, and six other countries have only regional LDBs. In total, the national LDBs contain 633,696 landslides, of which 485,004 are located in Italy, while Austria, the Czech Republic, France, Norway, Poland, Slovakia, and the UK also have > 10,000 landslides in their LDBs. National LDBs are generally created in the official language of each country and 58% of them contain other natural hazards (e.g. floods and sinkholes). About 68% of the LDBs contain less than 50% of all landslides in each country, but a positive observation is that 60% of the LDBs are updated at least once a year or after a major event. Most landslide locations are collected with traditional methods such as field surveys, aerial photo interpretation and analysis of historical records. Currently, integration of landslide information from different national LDBs is hampered because of differences in language and classification systems for landslide type and activity. Other problems are that currently only half of the national LDBs have a direct link between spatial and alphanumeric information, and that public access is generally restricted or limited. A minimum set of features to be included in national LDBs is suggested, and a flow chart is presented that classifies European countries by ability to perform national-scale landslide susceptibility, hazard and risk analyses.

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... We recommend that landslide forecasters consider the possible (or probable) lack of accurate landslide information when evaluating their forecasts. Event landslide inventory are often incomplete, or are affected by systematic biases e.g., along roads or in populated areas (Van Den Eeckhaut and Hervás, 2012). New advances in detecting event-triggered landslides (Mondini et al., 2019) may improve our ability to validate LEWSs, and to quantify their performances. ...
... Combination of remote sensing and seismic signal processing techniques may be part of the solution of the problem. Automatic, semi-automatic or manual collection of information on landslide occurrence from media sources provides valuable information, but is limited to landslides that cause damage or that occur in built-up areas or along infrastructures (Chleborad et al., 2008;Van Den Eeckhaut and Hervás, 2012;Calvello and Pecoraro, 2018;Pecoraro et al., 2019). Manual collection is labour intensive, and the automatic and semi-automatic methods require independent checking. ...
Article
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The design, implementation, management, and verification of landslide early warning systems (LEWSs) are gaining increasing attention in the literature and among government officials, decision makers, and the public. Based on a critical analysis of nine main assumptions that form the rationale for landslide forecasting and early warning, we examine 26 regional, national, and global LEWSs worldwide from 1977 to August 2019. We find that currently only five nations, 13 regions, and four metropolitan areas benefit from LEWSs, while many areas with numerous fatal landslides, where landslide risk to the population is high, lack LEWSs. Operational LEWSs use information from rain gauge networks, meteorological models, weather radars, and satellite estimates; and most systems use two sources of rainfall information. LEWSs use one or more types of landslide forecast models, including rainfall thresholds, distributed slope stability models, and soil water balance models; and most systems use landslide susceptibility zonations. Most LEWSs have undergone some form of verification, but there is no accepted standard to check the performance and forecasting skills of a LEWS. Based on our review, and our experience in the design, implementation, management, and verification of geographical LEWSs in Italy, we conclude that operational forecast of weather-induced landslides is feasible, and it can help reduce landslide risk. We propose 30 recommendations to further develop and improve geographical LEWSs, and to increase their reliability and credibility. We encourage landslide forecasters and LEWSs managers to propose open standards for geographical LEWSs, and we expect our work to contribute to this endeavour.
... Landslide are one of the most dangerous geomorphic processes (Guzzetti, 2000;Pánek et al., 2008) and cause serious damage to infrastructure and even fatalities each year in many regions of the world (Petley, 2012). Thus, landslides are the most frequently studied process in regions with suitable conditions for their occurrence (Van Den Eeckhaut and Hervás, 2012). The common questions to be answered are associated with landslide activity, spatial distributions, inner structures, predispositions, and triggers. ...
... The Outer Western Carpathians are well known for a dense occurrence of landslides of various types (Van Den Eeckhaut and Hervás, 2012;Pánek et al., 2013Pánek et al., , 2019. Landslides here are frequently activated during extraordinary precipitation events (e.g., 1997(e.g., -Krejčí et al., 2002 and often cause damage to infrastructures (Klimeš et al., 2009). ...
Article
Flow-like landslides are a dangerous landslide type. They often express gradual movement or seeming dormancy, but occasional reactivation can, in extreme cases, result in catastrophic events. To predict their future behaviour, knowledge of past spatio-temporal development and relationships with hydrometeorological triggers is crucial. Moreover, regional data are more robust than case studies. Dendrogeomorphic (tree ring-based) methods are a very precise approach for reconstructing past landslide behaviour. Nevertheless, regional reconstructions are very rare, which is probably due to their time-consuming procedures. This paper presents the results of a regional tree ring-based reconstruction of the spatio-temporal development of flow-like landslides in a selected region in the Outer Western Carpathians. Six selected landslides were studied via analysis of 614 increment cores that came from 307 disturbed trees. The reconstruction provided data for approximately 70 individual landslide reactivation phases that were distributed in 44 event years. Events with regional extension (at least half of the studied landslides were active) were detected in six years (1940, 1941, 1953, 1961, 1985, and 1997). Periods of increased (1950s, 1990s) as well as decreased (1940s, 1970s, 2010s) landslide activity were reconstructed. The use of tree ring data enabled the construction of landslide probability maps. Based on this analysis, all studied landslides exhibit extremely high probabilities of reactivation during a temporal horizon of 100 years, but even over shorter periods (5 and 20 years), their probability of reactivation is very high. Finally, analysis of meteorological triggers suggests the positive effect of precipitation in May (and possibly in September) to activate landslides with regional extent. Extreme short-duration (1-day) precipitation events probably do not play a role in landslide triggering. Moreover, gradual increases in precipitation totals during periods of at least one-half year preceding the event years were detected.
... Lastly, statistical approaches aim at exploiting the "functional" relationships existing between a set of instability factors, and the past and present distribution of landslides obtained typically from a landslide inventory map (Carrara, 1983;Duman et al., 2005;Guzzetti et al., 2012), or a landslide catalogue (Van Den Eeckhaut and Hervás, 2012). The large number of statistically-based approaches proposed in the literature (Reichenbach et al., 2018) almost invariably exploit classification methods, and provide probabilistic estimates suited for quantitative hazard assessments. ...
... Statistical models can be constructed using a variety of thematic and environmental variables obtained from existing maps or by processing remotely sensed images and data, in different landscape and environmental settings, covering a broad range of scales and study-area sizes. The dependent variable is obtained from different types of landslide inventory maps (Guzzetti et al., 2012) or landslide catalogues (Van Den Eeckhaut and Hervás, 2012), and is typically used in a binary structure, expressing the presence or absence of landslides in each mapping unit, where a terrain mapping unit is a regular or irregular geographical subdivision (e.g., a pixel, unique condition, slope or hydrological unit, administrative subdivision (Guzzetti et al., 1999;Guzzetti, 2005;Van Westen et al., 2006)) used to partition a study area. The fitted model is then used to assess the landslide susceptibility for each mapping unit (Guzzetti, 2005). ...
Preprint
Full-text available
Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment. Investigators have for long attempted to estimate landslide hazard to determine where, when, and how destructive landslides are expected to be in an area. This information is useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most attempts at predicting the occurrence of populations of landslides rely on the observation that landslides are the result of multiple interacting, conditioning and triggering factors. Here, we propose a novel Bayesian modelling framework for the prediction of space-time landslide occurrences of the slide type caused by weather triggers. We consider log-Gaussian cox processes, assuming that individual landslides stem from a point process described by an unknown intensity function. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory spanning 1941-2014 is available together with lithological and bedding data. We tested five models of increasing complexity. Our most complex model includes fixed effects and latent spatio-temporal effects, thus largely fulfilling the common definition of landslide hazard in the literature. We quantified the spatio-temporal predictive skill of our model and found that it performed better than simpler alternatives. We then developed a novel classification strategy and prepared an intensity-susceptibility landslide map, providing more information than traditional susceptibility zonations for land planning and management. We expect our novel approach to lead to better projections of future landslides, and to improve our collective understanding of the evolution of landscapes dominated by mass-wasting processes under geophysical and weather triggers.
... According to Hosmer and Lemeshow [49], the aim of an approach based on binary logistic regression (BLR) is to enable the singling out of the best linear relationship between a dichotomous dependent variable (such as 1 or 0 representing the "presence"/"absence" of landslides, respectively) and a set of independent variables representing control geo-environmental factors. In the logistic regression equation, the expected dependent variable f(x) may be expressed as logit(x) = ln(odds) = ln [ π 1−π ] = α + β 1 X 1 + β 2 X 2 + ⋯ + β p X p (2) where logit(x) corresponds to a natural logarithm of odds [π/(1 − π)], expressed as a ratio between the likelihood of the presence of landslides (π) over the likelihood of their absence (1 − π); α is the intercept of the model; and β1, β2 up to βn are the coefficients, which measure the contribution of each independent input variable [50][51][52]. ...
... The quantitative measure of model performance can be tested by computing the area under the curve (AUC) ranging from 0 to 1 [63]. The closer the AUC values are to 1, the higher the predictive performance of the model will be, while the closer the values are to 0.5 (random performance) the higher the inaccuracy of the model will be [50,64,65]. A value equal to 1 denotes a perfect discrimination between positive and negative cases. ...
Article
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Forward logistic regression and conditional analysis have been compared to assess landslide susceptibility across the whole territory of the Sicilian region (about 25,000 km2) using previously existing data and a nested tiered approach. These approaches were aimed at singling out a statistical correlation between the spatial distribution of landslides that have affected the Sicilian region in the past, and a set of controlling factors: outcropping lithology, rainfall, landform classification, soil use, and steepness. The landslide inventory used the proposal of building the models like the official one obtained in the PAI (hydro geologic asset plan) project, amounting to more than 33,000 events. The 11 types featured in PAI were grouped into 4 macro-typologies, depending on the inherent conditions believed to generate various kinds of failures and their kinematic evolution. The study has confirmed that it is possible to carry out a regional landslide susceptibility assessment based solely on existing data (i.e., factor maps and the landslide archive), saving a considerable amount of time and money. For scarp landslides, where the selected factors (steepness, landform classification, and lithology) are more discriminate, models show excellent performance: areas under receiver operating characteristic (ROC) (AUCs) average > 0.9, while hillslope landslide results are highly satisfactory (average AUCs of about 0.8). The stochastic approach makes it possible to classify the Sicilian territory depending on its propensity to landslides in order to identify those municipalities which are most susceptible at this level of study, and are potentially worthy of more specific studies, as required by European-level protocols.
... Lastly, statistical approaches aim at exploiting the "functional" relationships existing between a set of instability factors, and the past and present distribution of landslides obtained typically from a landslide inventory map (Carrara, 1983;Duman et al., 2005;Guzzetti et al., 2012), or a landslide catalogue (Van Den Eeckhaut and Hervás, 2012). The large number of statistically-based approaches proposed in the literature (Reichenbach et al., 2018) almost invariably exploit classification methods, and provide probabilistic estimates suited for quantitative hazard assessments. ...
... Statistical models can be constructed using a variety of thematic and environmental variables obtained from existing maps or by processing remotely sensed images and data, in different landscape and environmental settings, covering a broad range of scales and study-area sizes. The dependent variable is obtained from different types of landslide inventory maps (Guzzetti et al., 2012) or landslide catalogues (Van Den Eeckhaut and Hervás, 2012), and is typically used in a binary structure, expressing the presence or absence of landslides in each mapping unit, where a terrain mapping unit is a regular or irregular geographical subdivision (e.g., a pixel, unique condition, slope or hydrological unit, administrative subdivision (Guzzetti et al., 1999;Guzzetti, 2005;Van Westen et al., 2006)) used to partition a study area. The fitted model is then used to assess the landslide susceptibility for each mapping unit (Guzzetti, 2005). ...
Article
Full-text available
Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete “point” events in the landscape, we propose a Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide “intensity”, i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide “susceptibility”); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of “unobserved” latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km² area into 889 slope units (SUs). In each SU, we computed the mean and standard deviation of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model. For 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map, we computed the proportion of each thematic class intersecting the given SU. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our “baseline” model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to the predominantly used landslide susceptibility model in the literature. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In particular, our third model (Mod3) also accounts for latent spatial dependencies among neighboring SUs. These are inferred for each of the six time intervals, to explain variations in the landslide intensity and susceptibility not explained by the thematic covariates. By contrast, our fourth model (Mod4) accounts for the latent temporal dependence, separately for each SU, disregarding neighboring influences. Ultimately, our most complex model (Mod5) contextually features all these relations. It contains the information carried by morphometric and thematic covariates, six time-interval-specific regression constants, and it also accounts for the latent temporal effects between consecutive slope instabilities at specific SUs as well as the latent spatial effects between adjacent SUs. We also show that the intensity is strongly related to the aggregated landslide area per SU. Because of this, our most complex model largely fulfills the definition of landslide hazard commonly accepted in the literature, at least for this study area. We quantified the spatial predictive performance of each of the five models using a 10-fold cross-validation procedure, and the temporal predictive performance using a leave-one-out cross-validation procedure. We found that Mod5 performed better than the others. We then used it to test a novel strategy to classify the model results in terms of both landslide intensity and susceptibility, which provides more information than traditional susceptibility zonations for land planning and management—hereafter we use the term “traditional” simply to refer to the majority of modelling procedures in the literature. We discuss the advantages and limitations of the new modelling framework, and its potential application in other areas, making specific and general hazard and geomorphological considerations. We also give a perspective on possible developments in landslide prediction modelling and zoning. We expect our novel approach to the spatio-temporal prediction of landslides to enhance the currently limited ability to evaluate landslide hazard and its temporal and spatial variations. We further expect it to lead to better projections of future landslides, and to improve our collective understanding of the evolution of complex landscapes dominated by mass-wasting processes under multiple geophysical and weather triggers.
... Landslide susceptibility is defined as the relative spatial probability of occurrence for a landslide based on the intrinsic properties of a site (SafeLand, 2011). The concept of susceptibility differs from hazard in that the temporal probability of occurrence, the triggering factors, and the magnitude of the event are not considered in the definition of a susceptibility map (SafeLand, 2011;Van Den Eeckhaut and Hervás, 2012). To produce landslide susceptibility maps, three approaches are usually applied: statistical, physical, and expertbased (SafeLand, 2011). ...
... This does not apply in the landslide domain, where susceptibility and hazard are distinct concepts (e.g. Van Den Eeckhaut and Hervás, 2012). In this study, we implemented the susceptibility feature type. ...
Article
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This study presents a landslide susceptibility map using an artificial intelligence (AI) approach based on standards set by the INSPIRE (Infrastructure for Spatial Information in the European Community) framework. INSPIRE is a European Union spatial data infrastructure (SDI) initiative to standardize spatial data across borders to ensure interoperability for management of cross-border infrastructure and environmental issues. However, despite the theoretical effectiveness of the SDI, few real-world applications make use of INSPIRE standards. In this study, we show how INSPIRE standards enhance the interoperability of geospatial data and enable deeper knowledge development for their interpretation and explainability in AI applications. We designed an ontology of landslides, embedded with INSPIRE vocabularies, and then aligned geology, stream network, and land cover datasets covering the Veneto region of Italy to the standards. INSPIRE was formally extended to include an extensive landslide type code list, a landslide size code list, and the concept of landslide susceptibility to describe map application inputs and outputs. Using the terms in the ontology, we defined conceptual scientific models of areas likely to generate different types of landslides as well as map polygons representing the land surface. Both landslide models and map polygons were encoded as semantic networks and, by qualitative probabilistic comparison between the two, a similarity score was assigned. The score was then used as a proxy for landslide susceptibility and displayed in a web map application. The use of INSPIRE-standardized vocabularies in ontologies that express scientific models promotes the adoption of the standards across the European Union and globally. Further, this application facilitates the explanation of the generated results. We conclude that public and private organizations, within and outside the European Union, can enhance the value of their data by making them INSPIRE-compliant for use in AI applications.
... The areal extents of the study regions used in individual articles in the literature database reveal that only a few present and discuss attempts to do global, synoptic-scale assessments of landslide susceptibility (Nadim et al., 2006;Hong et al., 2007). Very few authors have conducted and discussed landslide susceptibility evaluation at a continental scale (Van Den Eeckhaut and Hervás, 2012;Van Den Eeckhaut et al., 2012a;Günther et al., 2013Günther et al., , 2014Broeckx et al., 2018;Wilde et al., 2018;Depicker et al., 2020). Excluding the continental and the global studies, the study region areas range in size from a few square kilometres to hundreds of thousands of square kilometres, with most of the study areas having an extent of about 100 km 2 (Fig. 2). ...
... The landslide database containing about 150,000 generic landslide locations, was compiled from regional/national geological surveys and/or from the public administrations. No landslide locations were available in the North and Northeast (Iceland, Finland, Estonia, Latvia, Lithuania and Poland) and the Southeast of Europe (Croatia, Bosnia-Herzegovina, Montenegro and Macedonia), even though regional, or national-level inventories do exist in some of these countries (Herrera et al., 2018;Van Den Eeckhaut and Hervás, 2012). ...
Chapter
The variability of landslide phenomena in terms of types, velocity and size, makes it difficult to establish a unique methodology for the definition of landslide susceptibility, with different approaches proposed in the literature for the prediction of landslide occurrence. In addition, the extent of the study area and the characteristics of the available data may influence the selection of the susceptibility models. For these reasons, landslide susceptibility studies described in the literature use different modelling approaches adopting a variety of mapping units and thematic information. In this chapter, we first use a database of 565 articles from a recent systematic review of the literature to illustrate and describe a synthesis of relevant information on landslide susceptibility modelling and terrain zonation. We then present examples of susceptibility zonations prepared at four different scales: (i) continental (Europe), (ii) national (Italy), (iii) sub-national (Umbria Region, Italy), and (iv) catchment scale (Collazzone area, Italy), using different data types and resolutions, different mapping units, and various statistically-based modelling approaches. We use these four examples to provide our reflections on the proprieties of the geo-environmental data, and the main characteristics of the modelling approaches at different scales. We conclude with a few steps that could become a starting point for the discussion and definition of a standard for statistically-based landslide susceptibility methods and zonation.
... Landslide research chiefly relies on digital inventories for a multitude of spatial, temporal, and/or process analyses (Damm et al. 2010;Neuhäuser et al. 2012;Van Den Eeckhaut and Hervás 2012). Generally, landslide inventories are populated with data gathered by two strategies that are applied either alone or in combination (Guzzetti et al. 2012). ...
Article
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Landslide research chiefly relies on digital inventories for a multitude of spatial, temporal, and/or process analyses. In respect thereof, many landslide inventories are populated with information from textual documents (e.g., news articles, technical reports) due to effectiveness. However, information detail can vary greatly in these documents and the question arises whether such textual information is suitable for landslide inventories. The present work proposes to define the usefulness of textual source types as a probability to find landslide information, weighted with adaptable parameter requirements. To illustrate the method with practical results, a German landslide dataset has been examined. It was found that three combined source types (administrative documents, expert opinions, and news articles) give an 89 % chance to detect useful information on three defined parameters (location, date, and process type). In conclusion, the definition of usefulness as a probability makes it an intuitive, quantitative measure that is suitable for a wide range of applicants. Furthermore, a priori knowledge of usefulness allows for focusing on a few source types with the most promising outcome and thus increases the effectiveness of textual data acquisition and digitalisation for landslide inventories.
... Landslide phenomena are widely spread hazards all over the world, they are occurring due to various factors, on various geomorphological conditions and spatial extent, as in different magnitudes (Guzzetti et al., 2006;Van Den Eeckhaut and Hervás, 2012). As a huge geohazard issue, scholars are putting enormous efforts in developing new and reliable methodologies for hazard mapping and mitigation approaches. ...
Article
Full-text available
Landslides are one of the most diffused hazard events in the world, they can occur in different locations under different triggering factors. As such, they are also one of the most studied hazards, while the mechanism of an event is known to the scholars, more difficulties are found in forecasting the location and time of the following event. However, scholars are putting great effort into modelling the phenomena through various tools, as such susceptibility mapping is one of the initial and key steps in the hazard assessment. While effort is put on producing such maps, less is put on the evaluation of those outcomes. The current work aims to analyse the behaviour of two validation metrics – Receiver Operating Characteristics (ROC) and Precision Recall Curve (PRC). The former is widely used in susceptibility modelling, while the latter not so much utilized. However, scholars are highlighting a drawback of the ROC – it is not able to discriminate imbalanced datasets and is providing unreliable outcomes, and as an alternative is proposed the PRC which does not exhibit such flaws. In order to test the performance of both metrics, they were applied to three susceptibility models produced using Statistical Index, Logistic Regression and Random Forest for the area of Val Tartano, Northern Italy. As a result, it was determined that when the metrics are applied to balanced datasets they exhibit similar behaviour; on the contrary when imbalanced classes are introduced PRC is depicting the model performance in a more precise manner.
... The areas exposed to landslide movements pose a serious problem for providing development plans, due to a real risk to the infrastructure and structures [1][2][3][4]. In such areas, the basic tool for assessing the risk of landslides is the depth and surface monitoring of the directly affected area [5][6][7][8][9]. Monitoring should also cover the construction of building and engineering structures exposed to additional interactions from the landslide-affected ground [10][11][12][13]. ...
Article
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The paper presents the procedure and results of monitoring conducted by using a 3D measurement model, taking advantage of integrated surveying technologies developed for a building located within an activated landslide area. Geodynamic interactions within the building have resulted in a spatial deformation condition, leading to significant cracks of structure components and local basement floor upheavals. Conducted site research shows a reactivation of an old landslide form. To provide safe use conditions for the building, it was decided to monitor the structure and the area in its vicinity. Meeting this demand required developing an in-house monitoring system for the landslide form and the very structure. Measurements provided detailed information on the sizes and directions for the displacements of ground surface points and building structure points, as well as the dynamic properties of this phenomenon. Obtained results show the opportunity to use monitoring systems to acquire credible measurements data reflecting the real impact of ground landslide deformations on structures.
... Geohazard database and information system have gained importance in the mitigation by providing data management, data statistics, and public services, etc (Herrera et al., 2018). The rapid developments and innovations of modern information technologies have provided the significant technical support for geohazard database and information system to be more dynamic, elaborated, smart and timeless (Kirshbaum et al., 2009;Van et al., 2012;Pennington et al., 2015). ...
Article
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Geohazards caused significant damages to people and economical properties. The prevention and mitigation of geohazards in China is facing great challenges. To solve the problem, establishing a national geohazard information system is important in accessing the distribution of geohazards, optimizing the efforts of prevention and mitigation, and improving the public awareness and risk reduction capabilities. After two decades’ of development, China national geohazard information system have upgraded from client-server (C/S) to browser-server (BS) architecture, and now to the micro-service based architecture, more than 320 thousands of geohazards which were mainly collected through field surveys and the routine field inspections have been saved in the database; through the information system, data acquisition and integration, classified storage, efficient data processing, real-time statistics and data services have been achieved and standardized by numerous technical specifications. In this paper, the current status of China national geohazard information system, including the development progress, database architecture, system functionality and data applications are introduced, and expected to provide references of establishing an efficient and comprehensive geohazard information system to improve the geohazard risk reduction capabilities for the countries or regions suffering from geohazards.
... Furthermore, according to the assumption that future events are expected to occur under similar conditions as the observed past ones, susceptibility map indicates zones with a potential to experience a particular hazard in the future based solely on the intrinsic local proprieties of a site and expressed in term of relative spatial likelihood. Although these concepts are well-consolidated in the research area related with the risk assessment, especially for landslides [1][2][3][4] the need exist for elaborating susceptibility and risk maps for other natural hazards and to develop new quantitative and robust methods supporting their production. ...
Preprint
Wildfire susceptibility maps display the wildfires occurrence probability, ranked from low to high, under a given environmental context. Current studies in this field often rely on expert knowledge, including or not statistical models allowing to assess the cause-effect correlation. Machine learning (ML) algorithms can perform very well and be more generalizable thanks to their capability of learning from and make predictions on data. Italy is highly affected by wildfires due to the high heterogeneity of the territory and to the predisposing meteorological conditions. The main objective of the present study is to elaborate a wildfire susceptibility map for Liguria region (Italy) by applying Random Forest, an ensemble ML algorithm based on decision trees. Susceptibility was assessed by evaluating the probability for an area to burn in the future considering where wildfires occurred in the past and which are the geo-environmental factors that favor their spread. Different models were compared, including or not the neighboring vegetation and using an increasing number of folds for the spatial-cross validation. Susceptibility maps for the two fire seasons were finally elaborated and validated and results critically discussed highlighting the capacity of the proposed approach to identify the efficiency of fire fighting activities.
... This definition relies on the basic assumption that future events are expected to occur under similar geo-environmental conditions to those observed in past events. Although these concepts are well-consolidated in the research area related with the risk assessment, especially for landslides [4][5][6][7], there is a need for elaborating susceptibility and risk maps for other natural hazards and to develop new quantitative and robust methods supporting their production. ...
Article
Full-text available
Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period (1997-2017) and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.
... Below are definitions for terms used in mass movement zoning and risk management as described by Fell et al. (2008), Hervás and Bobrowsky (2009) and Van Den Eeckhaut and Hervás (2012). ...
Thesis
Mass movement (MM) is a major natural hazard that threatens natural and human environments in Lebanon. It comes under various forms needing a wide range of MM detection, modeling and zoning techniques. MM occurrence is influenced by preconditioning factors and inducing factors such as forest fires. Since the latter has emerged as another hazard destroying over 22% of Lebanon's forests in less than 50 years, this thesis investigates impact of forest fire on MM occurrence under climatic variations and land-use change. Following the standardization of the preconditioning factors in addition to the forest fire burn severity out of the Normalized Burn Ratio (NBR) into layers using geographic information systems (GIS), the Weight factor (Wf) of each was evaluated using the modified InfoVal method. A MM susceptibility map (MMSM) was 5 generated and validated by an independent set of MM. Preceded only by soil type; forest fire burn severity obtained the second-highest Wf. Further investigation into forest fire generating factors was performed. Climatic factors causing prolonged droughts were observed using a meteorological Reconnaissance Drought Index (RDI) and land-use was assessed using Normalized Difference Vegetation index (NDVI) .The associations between NBR and RDI and NBR and NDVI indicate that NBR is a result of the interaction between climate and land-use.
... Using earth observation images (EO images) to detect unknown single landslide events is akin to finding a needle in a haystack. Hence, such surveys tend to focus on mapping landslides within areas that are known to have multiple landslides, due to a specific triggering event [20]. However, the ongoing and rapid developments in the fields of computer vision and cloud computing, as well as in the quality, range, and availability of EO images, mean that continuous, automated landslide monitoring may soon be feasible. ...
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Regional early warning systems for landslides rely on historic data to forecast future events and to verify and improve alarms. However, databases of landslide events are often spatially biased towards roads or other infrastructure, with few reported in remote areas. In this study, we demonstrate how Google Earth Engine can be used to create multi-temporal change detection image composites with freely available Sentinel-1 and -2 satellite images, in order to improve landslide visibility and facilitate landslide detection. First, multispectral Sentinel-2 images were used to map landslides triggered by a summer rainstorm in Jølster (Norway), based on changes in the normalised difference vegetation index (NDVI) between pre- and post-event images. Pre- and post-event multitemporal images were then created by reducing across all available images within one month before and after the landslide events, from which final change detection image composites were produced. We used the mean of backscatter intensity in co- (VV) and cross-polarisations (VH) for Sentinel-1 synthetic aperture radar (SAR) data and maximum NDVI for Sentinel-2. The NDVI-based mapping increased the number of registered events from 14 to 120, while spatial bias was decreased, from 100% of events located within 500 m of a road to 30% close to roads in the new inventory. Of the 120 landslides, 43% were also detectable in the multi-temporal SAR image composite in VV polarisation, while only the east-facing landslides were clearly visible in VH. Noise, from clouds and agriculture in Sentinel-2, and speckle in Sentinel-1, was reduced using the multi-temporal composite approaches, improving landslide visibility without compromising spatial resolution. Our results indicate that manual or automated landslide detection could be significantly improved with multitemporal image composites using freely available earth observation images and Google Earth Engine, with valuable potential for improving spatial bias in landslide inventories. Using the multi-temporal satellite image composites, we observed significant improvements in landslide visibility in Jølster, compared with conventional bi-temporal change detection methods, and applied this for the first time using VV-polarised SAR data. The GEE scripts allow this procedure to be quickly repeated in new areas, which can be helpful for reducing spatial bias in landslide databases.
... Consequently, at regional scale, the realization of landslide inventories and the investigation of their typologies, spatial distribution and time frequency, are useful and valuable information to understanding landscape evolution and assessing landslide susceptibility, hazard and the risks associated with them (Pelletier et al. 1997;Guzzetti et al. 2003Guzzetti et al. , 2009Gull a et al. 2008;Hilker et al. 2009;Damm et al. 2010;Rossi et al. 2010;Neuh€ auser et al. 2012;Van Den Eeckhaut and Herv as 2012;Hurst et al. 2013;Klose et al. 2014;Tonini et al. 2014;Damm and Klose 2015;Sevgen et al. 2019;Kocaman et al. 2020;Karakas et al. 2021). In particular, shallow landslides susceptibility at regional scale represent a key element to manage land use and civil protection planning and for hazard and risk assessment (Gull a et al. 2008;Regmi et al. 2014;Guo et al. 2015;Gholami et al. 2019). ...
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Intense rainfall events often produce a great number of shallow landslides events, which in many cases can hits large areas or an entire regional territory. These slope instabilities cause damage to many roads, buildings, and infrastructures and often human loss. In these conditions, it is useful to refine shallow landslides susceptibility maps at regional scale progressively more reliable and efficacy. To take the highlighted goal it is opportune to promote the use of a circular approach that can considers knowledge (data, methods, models, solutions, etc.) constantly upgraded. To achieve this aims we propose a method that introduces structurally in a possible circular approach (progressive better results with constantly upgraded knowledge) the use of a comprehensive geo-database of shallow landslide events and related implemented through a collection and analysis of numerous sources, including published inventory maps, scientific literature, technical reports and newspapers, integrated by a multi-temporal interpretation of remote sensing images and several field surveys. The method is applied referring to the Calabria region, which is largely affected by this landslide category. The refined geo-database realized includes 22,028 shallow landslides, occurred between 1951 and 2017. The relationship between spatial pattern of the shallow landslides and the analyzed predisposing factors (lithological units, fault density, land use, drainage density, slope gradient, TWI, SPI and LS) showed that the high values of slope gradient, LS factor and drainage density, coupled to low values of TWI, displayed a strong control on the shallow landslide occurrence. The efficacy of the geo-database realization proves their usefulness in order to estimate and validate shallow landslide susceptibility map, which was optimally obtained applied a simple bivariate statistical method. The susceptibility map was classified into five classes and about 26% of the study area falls in high and very high susceptible classes and most of the shallow landslides mapped (76%) occur in the same classes. The AUC value of the prediction rate curve was 0.81, indicating a good prediction capability of the susceptibility map. The interaction between shallow landslide susceptibility map and road network map highlighted that the 20% of the roadways of the region area falls in high and very high susceptible areas, whereas was observed that the high (58.4%) and very high (65.6%) susceptibility classes are mainly distributed within cover materials from weathered crystalline rocks. The results obtained in this study indicate that the proposed method can concur to promote a circular approach and support with efficacy a progressive refinement of regional shallow landslide susceptibility map, from 2008 to now, that may be useful tool for national and/or local authorities to manage land use and civil protection planning, and for hazard and risk assessment from regional to slope scale.
... At global level, 2620 fatal landslides triggered by non-seismic effects were recorded during a 7-year frame period (2004)(2005)(2006)(2007)(2008)(2009)(2010) causing a number of estimated fatalities equal to 32,322 (Petley 2012). In Europe, the national and/or regional landslides databases contain 633,696 landslides in total (at 2012), of which 485,004 are located in Italy (Van Den Eeckhaut and Hervás 2012). Indeed Italy, with almost 8% of the territory affected by landslides, is the most landslide-prone country in Europe (Herrera et al. 2018). ...
Article
The present study addresses the pattern distribution of recent landslides in Italy. The main objective is the detection and mapping of spatio-temporal clusters of landslides that occurred in the period 2010–2017 in the country. To this aim, a subdivision of the national area into 158 warning zones, as identified by the 21 civil protection regional centres to deal with weather-induced hydro-geological hazards, is adopted. Information on landslides comes from FraneItalia, a geo-referenced catalogue developed consulting online news sources. Analyses are performed both at national scale and at a regional scale, focusing on the Campania region. The space–time permutation scan statistics model is applied to detect statistically significant clustering, accounting for the geographical spatial dimension and for the temporal dimension. Two types of analyses are performed: annual, considering each single year; and multi-annual, encompassing the entire 8-year study period. In both cases, spatio-temporal cluster analyses are able to detect areas and frame-periods characterised by relevant and recurrent landslide activity. Finally, the obtained results are compared with a standard landslide density map, highlighting the complementarity of the two approaches.
... Unfortunately, there are still areas for which landslide inventory maps are simply not generated due to financial or reachability issues; they are not available in some hardly accessible areas. The authors of [29] presented a summary of the completeness of landslide inventories for various countries in Europe in 2012, which confirms that there are still areas for which landslide maps have not been generated. For example, landslide inventory in Poland has been generated step by step (commune area after commune area), and the generation of such an inventory sometimes proved to be very time consuming when the geological conditions were complex, resulting in a lack of landslide inventory in such regions. ...
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Featured Application: Landslide Susceptibility Mapping using landslide inventory generated for the surrounding area can be generally carried out. However, the low reliability of such susceptibility map was observed in areas of critical geological structures. Thus, this should be borne in mind, when performing such a modeling in complex geological regions. Abstract: To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions ("Łososina" and "Gródek") separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in "Łososina" and "Gródek" model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for "very low" susceptibility classes and very small values for "very high" susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the "Łososina" model, 90% and 57% of the landslides fell into the "high" and "very high" susceptibility zones in the training and validation areas, respectively. In the "Gródek" model, 86% and 46% of the landslides fell into the "high" and "very high" susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2-3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.
Article
Dendrogeomorphic dating is an effective tool for the assessment of past landslide activity. However, some complex landslide areas include zones of accumulated boulders that can represent geotopes with deteriorated conditions for the stable anchoring of tree roots. Under such conditions, wind gusts can easily cause stem tilting and subsequently be a source of growth disturbances and false landslide events. This study evaluated this possible source of uncertainty in tree rings based on the dating of selected flow-like shaped accumulations of boulders in the Outer Western Carpathians. The data from 94 Norway spruce (Picea abies (L.) Karst.) individuals provided evidence of 154 growth disturbances that were fully comparable with the frequency of active landslides. However, geophysical geoelectrical sounding in combination with field mapping suggests that the studied accumulation is stable. Growth disturbances enabled the reconstruction of nine past events, reaching the minimal threshold of the percentage index. Analysis of triggers detected the occurrence of above-average wind gust speeds in event years compared to other years. Moreover, the dated growth disturbances were not present in the reference trees, probably due to the more stable positions of these trees and thus their better resistance to wind forces. Moreover, the duration of growth disturbances in event years was significantly longer than that of disturbances in other years. The abovementioned evidence suggests that wind gusts are the most effective agents to induce growth disturbances in trees located at boulder accumulations. Subsequently, wind gusts have a strong impact on the resulting chronology of landslide events in the sense of inducing an important proportion of false events. Thus, the findings of this study clearly suggest avoiding boulder accumulations during the dendrogeomorphic dating of past landslide activity.
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Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations.
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Landslides are important components of global geoheritage, but awareness of their significance and value in such terms seems scanty in the scientific community. Landslides are normally identified among various features of geological and geomorphological interest, and often considered a source of hazard. However, they are seldom identified as geosites and as part of geoheritage. This paper aims at filling these gaps by highlighting the importance of landslides in the global geoheritage. After a short introduction on the values and criteria to define landforms as geosites, based on literature review, we show to what extent landslides have been defined as geomorphosites and as part of geoheritage around the world. We then outline three aspects that should be specifically considered in the identification of landslides as geomorphosites, namely 1) past and present climate changes, 2) anthropic signature, and 3) risk perception. Finally, we describe four cases of spectacular landslides that serve as significant examples worldwide.
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Landslides are natural hazards that cause severe damage and human losses. Japan has succeeded in reducing the number of landslide fatalities and is one of the few countries with long-term databases of landslide fatalities. In this study, we identified the factors that contributed to the decrease in fatalities associated with rainfall-triggered landslides in Japan between 1945 and 2019. We examined trends in landslide fatalities and six factors for Periods I, II, III, IV, and V—each period spans 15 years of the study period—and for Periods I–II, II–III, III–IV, and IV–V. We examined the trends in the number of landslides (NL) and in the ratio between the number of fatalities (NF) and the number of landslides (NF/NL), and considered fatalities as the product of the number of landslides and the probability of fatalities. The number of fatalities decreased continuously between Periods I and IV; the rate of the decrease declined over time. During Period I–II, NF/NL decreased, whereas NL remained unchanged. Decreases in the average number of household members, changes in building structure, and increases in the number of people evacuated may have contributed to the decrease in NF/NL. During Periods II–III and III–IV, NL also decreased. During Period II–III, the area of mature forests increased slowly. During Period III–IV, the implementation of structural measures (i.e., hard measures) was aggressively pursued. The factors that contributed to the decrease in landslide fatalities changed with time, suggesting that measures for reducing landslide fatalities changed according to the degree of maturity of the nation. Furthermore, we identified increases in rainfall and NL in Period V, which might indicate a future increase in landslide fatalities.
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Landslides are one of the most destructive natural hazards in Turkey. In addition to loss of lives, there were many negative impacts of landslides on properties and the environment. To minimize the losses and damages related to landslides, a series of labour-intensive studies starting from landslide inventory to landslide risk mapping is required. Thus, this study aims to assess the landslide risk by a semi-quantitative approach in a landslide-prone area located in the Eastern Mediterranean region of Turkey. This region has been suffering from landslides with its high population and industrial characteristics. A total of 215 deep-seated rotational earth slides were mapped during field studies. Then, landslide-susceptibility mapping was performed by frequency ratio and logistic regression methods. For the hazard stage, the susceptibility map and the triggering indicator maps were used to produce a Landslide Hazard Index (LHI) map. As for the vulnerability analysis, a relative evaluation was performed by considering land use, infrastructure and population density data. All maps were combined at the final stage to produce a Landslide Risk Index (LRI) map of the study area. It was revealed that areal coverages of the produced LRI map were 21.4% very low (VL), 10.8% as low (L), 37.4% as medium (M), 24.8% as high (H) and 5.6% as very high (VH) LRI, respectively. The so-produced LRI map would be beneficial for further and detailed risk analyses to be performed in the future since it highlights the landslide risk hotspots in a regional scale.
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Landslide research relies on landslide inventories for a multitude of spatial, temporal, or process analyses. Generally, it takes high effort to populate a landslide inventory with relevant data. In this context, the present work investigated an effective way to handle vast amounts of automatically acquired digital data for landslide inventories by the use of machine learning algorithms and information filtering. Between July 2017 and February 2019, a keyword alert system provided 4381 documents that were automatically processed to detect landslide events in Germany. Of all those documents, 91% were automatically recognized as irrelevant or duplicates; thereby, the data volume was significantly reduced to contain only actual landslide documents. Moreover, it was shown that inclusion of the document’s images into the automated process chain for information filtering is recommended, since otherwise unobtainable important information was found in them. Compared with manual methods, the automated process chain eliminated personal idiosyncrasies and human error and replaced it with a quantifiable machine error. The applied individual algorithms for natural language processing, information retrieval, and classification have been tried and tested in their respective fields. Furthermore, the proposed method is not restricted to a specific language or region. All languages on which these algorithms are applicable can be used with the proposed method and the training of the process chain can take any geographical restriction into account. Thus, the present work introduced a method with a quantifiable error to automatically classify and filter large amounts of data during automated digital data acquisition for landslide inventories.
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Abstract. This study presents a landslide susceptibility map using an artificial intelligence (AI) approach that is based on standards set by the INSPIRE framework. We show how INSPIRE standards enhance the interoperability of geospatial data, and enable deeper knowledge development for their interpretation and explainability in AI applications. INSPIRE is a European Union Spatial Data Infrastructure (SDI) initiative to standardize spatial data across borders to ensure interoperability for management of cross-border infrastructure and environmental issues. Despite the theoretical effectiveness of the SDI, very few real-world applications make use of INSPIRE standards. We designed an ontology of landslides, embedded with INSPIRE vocabularies and then aligned geology, stream network and land cover data sets covering the Veneto region of Italy to the standards. INSPIRE was formally extended to include an extensive landslide type code list, a landslide size code list and the concept of landslide susceptibility to describe map application inputs and outputs. Using the terms in the ontology, we defined conceptual scientific models of slopes likely to generate landslides as well as map polygons representing real slopes. Both landslide models and map polygons were encoded as semantic networks and, by qualitative probabilistic comparison between the two, a similarity score was assigned. The score was then used as a proxy for landslide susceptibility and displayed in web map application. The use of INSPIRE-standardized vocabularies in ontologies that express scientific models promotes the adoption of the standards across the European Union and beyond. Further, this application facilitates the explainability of the generated results. We conclude that public and private organisations, within and outside the European Union, can enhance the value of their data by bringing them into INSPIRE-compliance for use in AI applications.
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A systematic inventory of landslide events over regional spatial scales and through time is required for investigating changes in landslide frequency along-side changes in landslide triggers. This paper describes the methods used to compile a European-wide landslide inventory and some of the methodological and practical obstacles that inhibit better use and development of such inventories. We argue that these methods can be used more widely to provide a comprehensive picture of landslide populations and to further enrich our understanding of the impact of climate change and other drivers on landslide frequency and magnitude.
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Landslides are a key hazard in high-relief areas around the world and pose a risk to populations and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall-induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that the average upstream angle and compound topographic index are strong predictors of landslide scar location, while the local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modeling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.
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The general assumptions and the most popular methods used to assess landslide hazard and for risk evaluation have not changed significantly in recent decades. Some of these assumptions have conceptual weakness, and the methods have revealed limitations. In this work, I deal with populations of landslides i.e. numerous landslides caused in an area by a single trigger (e.g. a rainstorm, an earthquake, a rapid snowmelt event), or by multiple events in a short or long period. Following an introduction on what we need to predict to assess landslide hazard and risk, I introduce the strategies and the main methods currently used to detect and map landslides, to predict populations of landslides in space and time, and to anticipate the numerosity and size characteristics of the expected landslides. For landslide detection and mapping, I consider traditional methods based on the visual interpretation of aerial photographs, and modern approaches that exploit the visual, semi-automatic or automatic analysis of remotely sensed images. For landslide spatial prediction, I discuss the results of a global review of statistical, classification-based methods for landslide susceptibility assessment. For the temporal prediction, leveraging on a global analysis of geographical landslide forecasting and early warning systems, I discuss short term forecast capabilities and their limitations. Next, I discuss long term landslide projections considering the impact of climate variations on landslide projections. For landslide numerosity and size characteristics, I discuss existing statistics of landslide area and volume obtained from large populations of event-triggered landslides. This is followed by an analysis of the landslide consequences, with emphasis on a spatial-temporal model of societal landslide risk in Italy. I end offering recommendations on what I think we should do to make significant progress in our collective ability to predict the hazard posed by populations of landslides, and to mitigate their risk.
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Landsliding is a major natural hazard; therefore, understanding its activity is an important objective worldwide. For the investigation of the current landslide events, dendrogeomorphic methods are commonly used as they allow quite a precise dating of individual events. Nevertheless, there is still a question of whether dendrogeomorphic methods can successfully work for landslides with low-magnitude movements. To determine their usability and shortcomings, four commonly used dendrogeomorphologic approaches were put to the test: Approach I is based on the detection of abrupt growth suppression; Approach II is focused on the determination of compression wood; and Approaches III and IV that work with eccentric increments. For the detection of landslide events itself, It index thresholds and spatial statistics were used. In total, 329 individuals Picea abies (L.) Karst. Growing on a seemingly dormant landslide in the Outer Western Carpathians were sampled and processed. Overall, obtained landslide chronologies varied considerably although the same trees were used, which allowed pinpointing of the main limitations of each approach. Approach I showed a high sensitivity to water shortages, causing false noise signals. Approach II was not sensitive enough to low-magnitude movements. In contrast, Approaches III and IV recorded many possible landslide events, but most of the events were just noise signals induced by creep movements (and non-geomorphological influences). These conditions made it impossible to filter the real landslide events based on It index thresholds; however, as a substitute, spatial statistics combined with a detailed analysis of real landslide morphology were successfully used. Last but not least, a sensitivity of trees to record possible landslide movements in various stages of their lives was analysed for each approach. Except for Approach IV, all approaches showed high variability in changing sensitivity during a tree's life; thus, during a certain period of the tree's growth, landslide events can hardly be detected. All things considered, findings in our study are crucial for the strategy of the dendrogeomorphic sampling conducted on landslides with low-magnitude movements.
Chapter
One of the remedies for reducing the negative effects of landslide activity is landslide mapping. Landslide detection, carried out by using historical data analysis, stereoscopic photo interpretation and/or field works, is expensive, time-consuming and requires expert knowledge and experience. Automatic approaches for landslide detection can provide benefits such as increased efficiency and reduced costs and time. Many attempts have been made to automate the process of landslide identification but the key information for this process is provided by the high-resolution Digital Elevation Model (DEM) delivered from Airborne Laser Scanning (ALS) data. Having considered this, the objective of this study is to utilise the Object-Oriented Approach (OOA) and DEM for the detection of landslides. In this study, we use the results archived from Pawluszek et al. (ISPRS Int J Geo-Inf 8:321, 2019). The challenges and opportunities of automatic approaches are discussed, based on an investigation conducted in an area heavily affected by landslides. The study area is located close to Rożnów Lake, in Poland and stands out by various land uses. The automatic detection results achieved (OA = 85% and K = 0.6) indicate that there is a huge potential in automatic approaches. However, these approaches face difficulties in landslide detection due to the smoothing of typical landslide features. This situation appears for old landslides and landslides located in areas of active agricultural treatments. Besides the fuzzy delineation of the landslide extent, landslide amalgamation in the OOA results can be observed. Thus, automatic approaches still need to be developed and improved. At the current stage of the development, automatic approaches cannot replace validation based on field reconnaissance but can support an interpreter in their work.
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National and regional historical landslide databases are increasingly viewed as providing empirical evidence for the geomorphic effects of ongoing environmental change and for supporting adaptive territorial planning. In this work, we present the design and current content of the Czech Historical Landslide Database (CHILDA), the first of its kind for the territory of Czechia (the Czech Republic). We outline the CHILDA system, its functionality, and technical solution. The database was established by merging and extending the fragmented regional datasets for highly landslide-prone areas in Czechia. Currently, the database includes 699 records (619 landslides, 75 rockfalls, and 5 other movement types) encompassing the period from the oldest determined records (1132) up to 1989, which represents an important cultural, political, and socioeconomic divide.
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Landslides and related mass movement processes actively pose a threat in the Gerecse Hills (Hungary, Transdanubian Range) by endangering residential and agricultural areas. Several closed or abandoned mining sites and waste deposits are also located in the area. Most of these sites have not been fully remediated, which makes their surroundings dangerous and unsuitable for other use. A multi-hazard map (1: 60,000) was prepared about the geological hazard sources of the Gerecse by collecting and synthetizing data from individual thematic maps and databases. The aim of the map is to provide a comprehensive look at the different but often-interrelated hazard sources of the area. The thematic content of the map consists of three main parts: the result of a statistical landslide susceptibility analysis marking the most landslide-prone slopes, the documented slope failure events, and the areas of hazardous mining sites and their waste deposits.
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Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, and landslide susceptibility, hazard and risk evaluation. Their quality can depend on the data and the methods with which they were produced. In this work we evaluate the effects of a variable visibility of the territory to map on the spatial distribution of the information collected by four landslide inventories prepared using different approaches in two study areas. The method first classifies the territory in areas with different visibility levels from the paths (roads) used to map landslides, and then estimates the landslide density reported in the inventories into the different visibility classes. Our results show that 1) the density of the information is strongly related to the visibility in inventories obtained through fieldwork, technical reports and/or newspapers, where landslides are under-sampled in low visibility classes; and 2) the inventories obtained by photo-interpretation of images suffer from a marked under representation of small landslides close to roads or infrastructures. We maintain that the proposed procedure can be useful to evaluate the quality of landslide inventories and then properly orient their use.
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Grande parte dos deslizamentos no Brasil são relacionados a eventos pluviométricos extremos, ao tipo e espessura de solo e às características do relevo, e frequentemente causam perdas sociais e danos econômicos. O presente trabalho teve como objetivo analisar a relação entre pontos de ruptura (coroa) e parâmetros morfométricos de deslizamentos, relativos a um evento hidrometeorológico extremo, na bacia hidrográfica do Rio Rolante - RS. A partir de imagens SRTM 30m, foram obtidos dados de declividade, elevação, curvatura horizontal e vertical e aspecto. Com base no mapeamento foram obtidos 143 pontos de ruptura em cicatrizes de deslizamentos. Utilizando agrupamento K-means foram identificados padrões morfométricos relacionados aos pontos de ruptura, e gerados perfis de vertentes. Os resultados mostram 4 tipos de vertentes na bacia do rio Rolante, com ocorrência de processos de deslizamentos. Perfis côncavos e convergentes tiveram menor ocorrência, e perfis convexos e divergentes apresentaram maior ocorrência. Com base nas médias dos principais agrupamentos de clusters, a declividade e as curvaturas tiveram a maior importância em relação à localização das rupturas na vertente, e foram importantes condicionantes de suscetibilidade aos deslizamentos. As médias dos agrupamentos ocorreram acima de 30º de declividade no agrupamento com 4 clusters, que foi o mais representativo.
Article
The main goal of this research is to verify the activity state of landslides provided by an existing landslide inventory map using Persistent Scatterers (PS) Interferometry (PSInSAR). The study was conducted in the Małopolskie municipality, a rural setting with sparse urbanization in the Polish Flysch Carpathians. PSInSAR has been applied using Synthetic Aperture Radar (SAR) data from ALOS PALSAR and Sentinel 1A/B with different acquisition geometries (ascending and descending orbit) to increase PS coverage and mitigate the geometric effects due to layover and shadowing. The Line-Of-Sight PSInSAR measurements were projected to the steepest slope, which allowed to homogenize the results from diverse acquisition modes and to compare the displacement velocities with different slope orientations. Additionally, landslide intensity (motion rate) and expected damage maps were generated and verified during field investigations. A high correlation between PSInSAR results and in-situ damage observations was confirmed. The activity state and landslide-related expected damage maps have been confirmed for 43 out of a total of 50 landslides investigated in the field. The short temporal baseline provided by both Sentinel satellites 1A/B data increase the PS density significantly. The study substantiates the usefulness of SAR based landslide activity monitoring for land use and land development, even in rural areas.
Article
Shallow, rainfall-triggered landslides are an important catchment process that affect the rate and calibre of sediment within river networks and create a significant hazard, particularly when shallow landslides transform into rapidly moving debris flows. Forests and trees modify the magnitude and rate of shallow landsliding and have been used by land managers for centuries to mitigate their effects. We understand that at the tree and slope scale root reinforcement provides a significant role in stabilising slopes, but at the catchment scale root reinforcement models only partially explain where shallow landslides are likely to occur due to the complexity of subsurface material properties and hydrology. The challenge of scaling from slopes to catchments (from 1-D to 2-D) reflects the scale gap between geomorphic process understanding and modelling, and temporal evolution of material properties. Hence, our understanding does not, as yet, provide the necessary tools to allow vegetation to be targeted most effectively for landslide reduction. This paper aims to provide a perspective on the science underpinning the challenges land and catchment managers face in trying to reduce shallow landslide hazard, manage catchment sediment budgets, and develop tools for catchment targeting of vegetation. We use our understanding of rainfall-triggered shallow landslides in New Zealand and how vegetation has been used as a tool to reduce their incidence to demonstrate key points.
Article
The regions of Central and South America most susceptible to the occurrence of landslides will become even more vulnerable in the context of climate change. The Josefina disaster, in 1993, demonstrated both the vulnerability of local infrastructures and communities in the Paute River basin (Ecuador). Since this natural phenomena, several landslide inventories and susceptibility studies were developed, revealing the vulnerability of the Paute River basin to unstable terrain and the need for further studies throughout the basin. Despite this, no studies have been done since then to update the information generated. This paper describes a Mobile Application for Regional Landslide Inventories (MARLI), a simple but efficient open-access platform to report landslide events using the Open Data Kit system. Its design makes reporting fast, simple and cost-effective with an added benefit, and a specialized knowledge is not required for its use. MARLI was tested for the collection of landslides in Cuenca (Ecuador). From the data taken in the field, it was possible to analyze the performance and suitability of collected data and compare the results with regional inventories in the same area. Additionally, these results can be used for the elaboration and update of large-scale inventories or the training of automatic identification systems of landslides and later evaluation of their precision in a small-medium scale. Likewise, this product constitutes a fundamental input for the formulation of mitigation strategies, to formulate the appropriate response and in time, also the elaboration of reconstruction plans before the increase in the occurrence of such phenomena.
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Landslides are a key hazard in high-relief areas around the world and pose a risk to population and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that average upstream angle and compound topographic index are strong predictors of landslide headscarp location, while local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.
Chapter
Earthquake-triggered landslides (ETLs) are among the most significant of all natural hazards. Triggering mechanisms and the continued development of landslides are complex and variable in different environmental settings. Field investigation and/or visual interpretation are two popular ways of landslide mapping. Detailed landslide inventories containing almost all ETLs for a region and the geo-environmental factors provide the basic data to understand the complex factors controlling landslide development. There are more than 86 published inventories for ETLs for individual earthquakes. Significant relationships between the areas affected by ETLs and the maximum distance from the epicenters versus the earthquake magnitudes are evident. Other than the commonly used terrain, geology, and earthquake factors, the characteristics of the underlying stratum, seismogenic fault, and rupture process should consider the occurrence mechanism, spatial distribution, and susceptibility mapping of the regional ETL research. Landsliding activity continues for years to decades, and the erosion and sediment flux change for several decades after a major earthquake.
Article
Inventories of historical landslides play an important role in the assessment of natural hazards. In this study, we used high-resolution satellite imagery from Google Earth to interpret large landslides in Baoji city, Shaanxi Province on the southwestern edge of the Loess Plateau. Then, a comprehensive and detailed map of the landslide distribution in this area was prepared in conjunction with the historical literature, which includes 3440 landslides. On this basis, eight variables, including elevation, slope, aspect, slope position, distance to the fault, land cover, lithology and distance to the stream were selected to examine their influence on the landslides in the study area. Landslide number density (LND) and landslide area percentage (LAP) were used as evaluation indicators to analyze the spatial distribution characteristics of the landslides. The results show that most of the landslides are situated at elevations from 500 to 1400 m. The LND and LAP reach their peaks at slopes of 10–20°. Slopes facing WNW and NW directions, and middle and lower slopes are more prone to sliding with higher LND and LAP. LND and LAP show a decreasing trend as the distance to the fault or stream increases, followed by a slow rise. Landslides occur primarily in the areas covered by crops. Regarding lithology, the regions covered by the Quaternary loess and Cretaceous gravels are the main areas where landslides occur. The results would be helpful for further understanding the developmental characteristics and spatial distribution of landslides on the Loess Plateau, and also provide a support to subsequent landslide susceptibility mapping in this region.
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At variance with conventional landslide susceptibility assessment, non-susceptibility analysis aims at selecting locations in which the likelihood of landslide occurrence is null or negligible. The advantage of this approach is that it does not require estimating different degrees of likelihood outside of the locations of negligible susceptibility. Thus, it entails the use of simplified classification methods. In this work, we tested and validated the existing non-susceptibility model with 18 global and regional landslide datasets, as a prior for the global application. The existing model was applied previously in Italy and the Mediterranean region, and defined by a non-linear relief vs. slope threshold curve, below which landslide susceptibility is negligible. Then, we applied a similar analysis, and proposed a global map, using relief and slope obtained from global elevation data at about 90-m resolution. The global map classifies 82.9% of the landmasses with negligible landslide susceptibility. The non-susceptible areas are broadly consistent with the “very-low” susceptibility class in existing global and continental landslide susceptibility maps and a national non-susceptibility map in the conterminous United States. Quantitative analyses revealed that population and settlements are denser within non-susceptible area than elsewhere, which makes the map of potential interest for non-exposure analysis, land planning and disaster responses at a global scale.
Article
Susceptibility assessments are crucial in identifying hazards in densely populated mountain areas featuring active internal and external dynamic geological processes. A comprehensive analysis of the interactions among ecological, hydrological, and geotechnical factors was performed to develop a quantitative method for assessing regional landslide susceptibility based on slope failure and landslide formation mechanisms. Using slope geomorphic units (SGUs) that can sufficiently describe the comprehensive features of landforms and hazard-forming factors related to landslides, an integrated model was built to analyze potential slip planes and slope stability by considering the different reinforcement effects of roots on soil, the root anchoring capacities of various vegetation types, and the corresponding vegetation weight loads. Using this SGU-based method, the occurrence possibility of regional-scale landslides was determined by analyzing the propagation directions and maximum moving distances of landslide materials in unstable areas. Taking the landslides in the Dadu River Basin, China, as a case study, a landslide susceptibility map was obtained and validated by conducting a field study and remote sensing interpretation of actual landslides; the assessment results were in accordance with the actual disaster situation. The distributions of zones with high or very high susceptibility (i.e., high-altitude valleys with steep slopes, abundant rainfall, and severe soil erosion) are closely correlated with the slope topography and stability. These findings suggest that the proposed assessment methodology can provide scientific support for preventing or mitigating landslide hazards and may serve as pertinent guidance for regional landslide susceptibility assessments in the Dadu River Basin and beyond.
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National and regional historical landslide databases are increasingly viewed as providing empirical evidence for the geomorphic effects of ongoing environmental change and for supporting adaptive territorial planning. In this work, we present the design and current content of the Czech Historical Landslide Database (CHILDA), the first of its kind for the territory of Czechia (the Czech Republic). We outline the CHILDA system, its functionality and technical solution. The database was established by merging and extending the fragmented regional datasets for highly landslide prone areas in Czechia. Currently, the database includes 699 records (619 landslides, 75 rockfalls, and 5 other movement types) encompassing the period from the oldest determined records (1132) up to 1989 which represents an important cultural, political and socioeconomic divide. Along with characterizing the content of the database, we discuss its further developments and applications.
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Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowd-sourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of "landslide events". This source of information allows continuous feedback from real world, and news concerning landslide events can be rapidly collected. In this work, the newspaper articles about landslides in Italy are automatically collected by an existing data mining algorithm, based on a semantic engine. The news has been analysed to assess their distribution over the territory and to verify the possibility of using them for hazard mapping purpose. In 10 years, from 2010 to 2019, the algorithm identified and geolocated 184322 articles referring to 32525 generical events ("news"). At first, the collected data underwent to a manual verification, followed by a classification based on news relevance, localization accuracy and time of publication. Then, these data have been used to identify the areas and the periods most affected by landslide phenomena. The analyses show that almost 42% of Italian municipalities have been affected by landslide. According to the results, the use of data mining is helpful for the creation of landslide databases where the day and the approximative location (municipality) of the possible landslide triggers are known. This database, in turn, can be used for scientific purposes, as the definition of the meteorological condition associated with landslide initiation, the validation of risk maps. It can also be used for a proper land use or risk mitigation planning, since the most landslide-prone municipalities can be defined.
Article
Landslide caused significant damage to people and economical properties. Establishing a comprehensive landslide database is a fundamental task to support landslide mitigation. However, numerous challenges such as non-standardized data collection, inefficient data maintenance, discontinuous data update, and insufficient data service have become the major problems to establish a reliable, dynamic, and comprehensive landslide database. After two decades of development, more than 322,000 landslide events have been collected through professional field surveys, routine field inspections, and public reporting into the China national landslide database. Also, an information system, which was primarily built for data dynamic management, field data acquisition and integration, classified data storage, efficient data processing, real-time statistics, and information services have been achieved. In this paper, the China national landslide database and information system, including the development progress, database architecture, data services, and future developments, are introduced in details. The key considerations and experiences obtained through the development of China national landslide database and information system such as the establishment of massive landslide inventory data acquisition standardization process, comprehensive mechanism of data quality control and dynamic maintenance, services of landslide information system could be helpful for any other countries or regions that also greatly suffering from landslides, and contribute to the improvement of landslide risk reduction in the world.
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Landslides are geomorphological processes that shape the landscapes of all continents, dismantling mountains and contributing sediments to the river networks. Caused by geophysical and meteorological triggers, including intense or prolonged rainfall, seismic shaking, volcanic activity, and rapid snow melting, landslides pose a serious threat to people, property, and the environment in many areas. Given their abundance and relevance, investigators have long experimented with techniques and tools for landslide detection and mapping using primarily aerial and satellite optical imagery interpreted visually, or processed by semi-automatic or automatic procedures or algorithms. Optical (passive) sensors have known limitations due to their inability to capture Earth surface images through the clouds and to work in the absence of daylight. The alternatives are active, “all-weather” and “day-and-night”, microwave radar sensors capable of seeing through the clouds and working in presence and absence of daylight. We review the literature on the use of Synthetic Aperture Radar (SAR) imagery to detect and map landslide failures – i.e., the single most significant movement episodes in the history of a landslide – and of landslide failure events – i.e., populations of landslides in areas ranging from a few to several thousand square kilometres caused by a single trigger. We examine 54 articles published in representative journals presenting 147 case studies in 32 nations, in all continents, except Antarctica. Analysis of the geographical location of 70 study areas shows that SAR imagery was used to detect and map landslides in most morphological, geological, seismic, meteorological, climate, and land cover settings. The time history of the case studies reveals the increasing interest of the investigators in the use of SAR imagery for landslide detection and mapping, with less than one article per year from 1995 to 2011, rising to about 5 articles per year between 2012 and 2020, and an average period of about 4.2 years between the launch of a satellite and the publication of an article using imagery taken by the satellite. To detect and map landslides, investigators use a common framework that exploits the phase and the amplitude of the electromagnetic return signal recorded in the SAR images, to measure terrain surface properties and their changes. To discriminate landslides from the surrounding stable terrain, a classification of the ground properties is executed by expert visual (heuristic) interpretation, or through numerical (statistical) modelling approaches. Despite undisputed progress over the last 26 years, challenges remain to be faced for the effective use of SAR imagery for landslide detection and mapping. In the article, we examine the theoretical, research, and operational frameworks for the exploitation of SAR images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions.
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Comprehensive and sustainable landslide risk management, including the identification of areas susceptible to landslides, requires responsible organisations to collaborate efficiently. Landslide risk management efforts are often made after major triggering events, such as hazard mit-igation after the 2015 Gorkha earthquake in Nepal. There is also a lack of knowledge sharing and collaboration among stakeholders to cope with major disaster events, in addition to a lack of efficiency and continuity. There should be a system to allow for landslide information to be easily updated after an event. For a variety of users of landslide information in Nepal, the availability and extraction of landslide data from a common database are a vital requirement. In this study, we investigate the requirements to propose a concept for a web-based Nepalese landslide information system (NELIS) that provides users with a platform to share information about landslide events to strengthen collaboration. The system will be defined as a web GIS (geographic information system) that supports responsible organisations in addressing and managing different user requirements of people working with landslides, thereby improving the current state of landslide hazard and risk management in Nepal. The overall aim of this study is to propose a conceptual framework and design of NELIS. A system like NELIS could benefit stakeholders involved in data collection and landslide risk management in their efforts to report and provide landslide information. Moreover, such a system would allow for detailed and structured landslide documentation and consequently provide valuable information regarding susceptibility and hazard and risk mapping. For the reporting of landslides directly to the system, a web portal is proposed. Based on field surveys, a literature review and stakeholder interviews , a structure of the landslide database and a conceptual framework for the NELIS platform are proposed.
Article
Rock slope failure (RSF) occurs in different contexts but is typically reported either as (i) single-category inventories or (ii) single-site geotechnical monographs. Few studies have sought to evaluate the spatial incidence of all modes of RSF conjointly, and to infer scenarios of regional landscape evolution from observed patterns of cumulative rock slope overstressing. Here we present the results of a systematic inventory of rock avalanches, rockfalls, rockslides, and gravitational rock slope deformations in the Western Alps (France, Italy, Switzerland) conducted using satellite imagery made available in Google Earth as a detection tool, and aided by preliminary ground-truth checks. The inventory totals 1446 montane RSFs, impacting 9.1% of the study area. Underpinned by GIS tools, the study further examines the spatial distribution of RSF with consideration for (i) predisposing factors (typically: lithology, geological structure), and (ii) preparatory factors (geomorphological process regimes that drives a given slope segment to the point of failure). The latter encompass slower variables (e.g., long-term crustal stress regime, cumulative residence time above equilibrium line altitudes) and faster variables (e.g., short-span glacier-related stresses, permafrost thaw, seismicity). RSF density patterns helped to define seven RSF super-hotspots (large diversity of RSF modes, up to 50% of displaced rock masses / unit area), which define the most intensely overstressed areas of the Western Alps. These super-hotspots occur at sites where highly dynamic, thick, warm-based glaciers above the equilibrium line either intersected (middle Maurienne) or followed the strike of (middle Isère) N–S bands of highly susceptible lithologies and structures during the Quaternary. The widespread incidence of rock slope deformation (cumulative area: 1760 km², i.e. nearly 3 times the total of the other three RSF categories combined) appears further correlated with the low tectonic activity of the orogen and with its areas dominated by an extensional tectonic regime west of the Penninic Frontal Thrust. This contrasts with seismically active orogens, e.g. New Zealand’s Southern Alps, where rock slope deformation is scarce compared to rock avalanches and shallow landslides.
Article
In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of DdLSM. This research begins with an introduction of the theoretical aspects of DdLSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent, inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitative-oriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017–2020. The originality of this research is acknowledged by combining together: (a) a recap of important theoretical aspects of DdLSM; (b) a bibliometric analysis on the topic; (c) a quantitative-oriented review of relevant publications; and (d) a systematic summary of the findings, indicating important aspects and potential developments related to the DdLSM research topic. The results show that DdLSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression or approaches based on frequency ratio are still popular, despite the upcoming trend of applying machine learning algorithms. When analyzing validation techniques, 38% of the publications were not clear about the validation method used. Within the studies that included validation techniques, the AUROC was the most popular validation metric, being used accounting for 44% of the studies. Finally, it can be concluded that the application of new classification techniques is often cited as a main research scope, even though the most relevant innovation could also lie in tackling data-quality issues and research designs adaptations to fit the input data particularities in order to improve prediction quality.
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We have compiled a database of floods and landslides that occurred in Italy between AD 1279 and 2002 and caused deaths, missing persons, injuries, and homelessness. Analysis of the database indicates that more than 50,593 people died, went missing, or were injured in 2580 flood and landslide events. Harmful events were inventoried in 26.3% of the 8103 Italian municipalities. Fatal events were most frequent in the Alpine regions of northern Italy and were caused by both floods and landslides. In southern Italy, landslides were the principal agents of fatalities and were most numerous in the Campania region. Casualties were most frequent in the autumn. Fast-moving landslides, including rock falls, rockslides, rock avalanches, and debris flows, caused the largest number of deaths. In order to assess the overall risk posed by these processes, we merged the historical catalogs and identified 2682 "hydrogeomorphological" events that triggered single or multiple landslides and floods. We estimated individual risk through the calculation of mortality rates for both floods and landslides and compared these rates to the death rates for other natural, medical, and human-induced hazards in Italy. We used the frequency distribution of events with fatalities to ascertain the magnitude and frequency of the societal risks posed by floods and landslides. We quantified these risks in a Bayesian model that describes the probabilities of fatal flood and landslide events in Italy.
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The British Geological Survey (BGS) is the national geological agency for Great Britain. Part of the organisations remit is to provide government and citizens with information on the spatio-temporal occurrence of natural hazards. Since 2000 BGS has developed a series of National Geohazard Assessments, central to which is the new National Landslide Database (NLDB), which contains data on landslides across England, Scotland and Wales. The NLDB began with a series of inherited databases, which between them held around 9000 entries. Even though many of these entries have been removed (through a validation process), the NLDB now holds over 14,000 entries and expands every year. Importantly, BGS is moving towards digital data collection methods which will feed automatically into the National landslide database, providing the most up to date information and images to the users at minimal cost to the tax payer. A project has also been started to map all coastal landslides as well as producing a coastal slope stability assessment using remote sensing. It is hoped this will provide useful information on the nature and extent of coastal landslides and the hazards that these pose to infrastructure. The database is used, alongside other information, to inform a series of National Geohazard Assessments. These GIS based assessments provide information on the susceptibility of the UK landmass to landslide activity. The information is used by government planners, insurance companies and utility operators. The dataset is also made available to citizens through a number of internet based 'resellers'. In 2006/07 1.4 million citizens accessed the information and used the results to support decisions about the purchase or modification to residential or commercial property. 1. Starting Point: The first National Assessment of Landslides Britain, as a whole, does not experience extreme climatic or tectonic events nor have the mountainous regions associated with large scale, destructive landslides. Despite this, landslides are common in Britain and several major landslides have occurred, usually with little warning. Examples of these have caused structural damage (Holbeck Hall Landslide, Scarborough; Lee 1999), interrupting transportation routes (Glen Ogle, Scotland; Winter et al. 2006) and resulting in fatalities (Whitehaven, Cumbria; Jenkins and Hobbs 2007). Prior to a national assessment of landslides being undertaken, the subdued topography and degraded nature of many ancient failures meant that landsliding was not considered to be widespread or problematic in Great Britain. However, costly disruptions to projects in the 1960's such as the Sevenoaks Road By-pass (Skempton and Weeks 1976) and the Waltons Wood motor way embankment (Early and Skempton 1974) by reactivations of previously unknown landslides led to the realisation that research needed to be done to determine the significance and extent of the problem. The first national focussed assessment of landsliding was undertaken for the Government Department of the Environment (DoE) in the mid 1980s. It produced a database of landslides and a review by Jones and Lee (1994). This initial assessment was undertaken as a desk study, collating information from maps, journals, technical reports and books as well as university research and theses. The final number of landslides recorded was 8835, a figure far greater than the initial estimate of 1000 landslides (Jones and Lee 1994). . However, this initial study had several problems similar to those associated with other databases produced through a desk study approach (Jones 1998). These included a bias of information toward areas of concentrated and conspicuous landslide activity, which reflect detailed studies such as those covering the South Wales Coalfield (Conway et al. 1980), Southeast England (Hutchinson 1969), and the Jurassic escarpment (Chandler 1970). Other problems related to gaps in the available data, for instance information on the type and cause of landsliding was very limited, and many database fields remained unpopulated. During the study no distinction was made between small landslides and more extensive areas of landsliding; this lead to a lack of comparability and an overestimation of the overall landslide hazard. This lack of attribution and basic characterization (as opposed to the detailed classification that was adopted) severely limited the analytical potential of the database. A further fundamental flaw of the database (but not of the data itself) was that it used a non-proprietary software system (as might be expected of the time) that quickly became incompatible with newer computer systems. This National Database, which contains information on over 35 landslide attributes, has been incorporated into the British Geological Survey National Landslide Database (NLDB). However, in building a new database, BGS is attempting to deal with a number of the issues encountered by the previous system.
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We present an updated global earthquake catalogue for stable continental regions (SCRs; i.e. intraplate earthquakes) that is available on the Internet. Our database contains information on location, magnitude, seismic moment and focal mechanisms for over 1300 M (moment magnitude) ≥ 4.5 historic and instrumentally recorded crustal events. Using this updated earthquake database in combination with a recently published global catalogue of rifts, we assess the correlation of intraplate seismicity with ancient rifts on a global scale. Each tectonic event is put into one of five categories based on location: (i) interior rifts/taphrogens, (ii) rifted continental margins, (iii) non-rifted crust, (iv) possible interior rifts and (v) possible rifted margins. We find that approximately 27 per cent of all events are classified as interior rifts (i), 25 per cent are rifted continental margins (ii), 36 per cent are within non-rifted crust (iii) and 12 per cent (iv and v) remain uncertain. Thus, over half (52 per cent) of all events are associated with rifted crust, although within the continental interiors (i.e. away from continental margins), non-rifted crust has experienced more earthquakes than interior rifts. No major change in distribution is found if only large (M≥ 6.0) earthquakes are considered. The largest events (M≥ 7.0) however, have occurred predominantly within rifts (50 per cent) and continental margins (43 per cent). Intraplate seismicity is not distributed evenly. Instead several zones of concentrated seismicity seem to exist. This is especially true for interior rifts/taphrogens, where a total of only 12 regions are responsible for 74 per cent of all events and as much as 98 per cent of all seismic moment released in that category. Of the four rifts/taphrogens that have experienced the largest earthquakes, seismicity within the Kutch rift, India, and the East China rift system, may be controlled by diffuse plate boundary deformation more than by the presence of the ancient rifts themselves. The St. Lawrence depression, Canada, besides being an ancient rift, is also the site of a major collisional suture. Thus only at the Reelfoot rift (New Madrid seismic zone, NMSZ, USA), is the presence of features associated with rifting itself the sole candidate for causing seismicity. Our results suggest that on a global scale, the correlation of seismicity within SCRs and ancient rifts has been overestimated in the past. Because the majority of models used to explain intraplate seismicity have focused on seismicity within rifts, we conclude that a shift in attention more towards non-rifted as well as rifted crust is in order.
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Landslides constitute one of the most important natural hazards in Italy as they are widespread and result in considerable damage and fatalities every year. The Italian Landslide Inventory (IFFI) Project was launched in 1999 with the aim of identifying and mapping landslides over the entire Italian territory. The inventory currently holds over 480,000 landslides and has been available by means of Web services since 2005. The aim of this study is to define quality indices for evaluation of the homogeneity and completeness of the IFFI database. In order to estimate the completeness of the landslide attribute information, a heuristic approach has been used to assign weighting values to significant parameters selected from the landslide data sheet. The completeness and homogeneity of the landslide mapping has been evaluated by means of three different methods: an area-frequency distribution analysis; the proximity of the landslides surveyed to urban areas; variation of the landslide index within the same lithology. The quality indices have allowed identification of areas with a high level of completeness and critical areas in which the data collected have been underestimated or are not very accurate. The quality assessment of collected and stored data is essential in order to use the IFFI database for definition and implementation of landslide susceptibility models and for land use planning and management. KeywordsLandslide-Inventory-Quality index-Natural hazards-GIS-Italy
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A new database on human mortality and morbidity, and civil evacuations arising from volcanic activity is presented. The aim is to quantify the human impacts of volcanic phenomena during the 20th Century. Data include numbers of deaths, injuries, evacuees and people made homeless, and the nature of the associated volcanic phenomena. The database has been compiled from a wide range of sources, and discrepancies between these are indicated where they arise. The quality of the data varies according to the source and the impacts reported. Data for homelessness are particularly poor and effects from ashfall and injuries appear to be under-reported. Of the 491 events included in the database, ∼53% resulted in deaths, although the total death toll of 91,724 is dominated by the disasters at Mt Pelée and Nevado del Ruiz. Pyroclastic density currents account for the largest proportion of deaths, and lahars for the most injuries incurred. The Philippines, Indonesia, and Southeast Asia, as a region, were the worst affected, and middle-income countries experienced greater human impacts than low or high-income countries. Compilation of the database has highlighted a number of problems with the completeness and accuracy of the existing CRED EM-DAT disaster database that includes volcanic events. This database is used by a range of organisations involved with risk management. The new database is intended as a resource for future analysis and will be made available via the Internet. It is hoped that it will be maintained and expanded.
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The quantification of risk has gained importance in many disciplines, including landslide studies. The literature on landslide risk assessment illustrates the developments which have taken place in the last decade and that quantitative risk assessment is feasible for geotechnical engineering on a site investigation scale and the evaluation of linear features (e.g., pipelines, roads). However, the generation of quantitative risk zonation maps for regulatory and development planning by local authorities still seems a step too far, especially at medium scales (1:10,000–1:50,000). This paper reviews the problem of attempting to quantify landslide risk over larger areas, discussing a number of difficulties related to the generation of landslide inventory maps including information on date, type and volume of the landslide, the determination of its spatial and temporal probability, the modelling of runout and the assessment of landslide vulnerability. An overview of recent developments in the different approaches to landslide hazard and risk zonation at medium scales is given. The paper concludes with a number of new advances and challenges for the future, such as the use of very detailed topographic data, the generation of event-based landslide inventory maps, the use of these maps in spatial-temporal probabilistic modelling and the use of land use and climatic change scenarios in deterministic modelling.
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In the initial reconnaissance of a landslide, the activity and the materials displaced in that type of landslide would be described using terms from Table 3-2, the dimensions defined in Table 3-4 would be estimated, and some preliminary hypotheses would be chosen about the causes of the movements. A simple landslide report form is provided in Figure 3-9; its format would allow the creation of simple data bases suited to much of the data-base management software now available for personal computers. The information collected could be compared with summaries of other landslides (WP/WLI 1991) and used to guide additional investigations and mitigative measures. Further investigation would increase the precision of estimates of the dimensions and increase confidence in the descriptions of activity and material and in the hypotheses about the causes of movement. The new information would then be added to the data base to influence the analysis of new landslides. These data bases could form the foundations of expert systems for landslide mitigation.
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We assessed societal landslide and flood risk to the population of Italy. The assessment was conducted at the national (synoptic) and at the regional scales. For the assessment, we used an improved version of the catalogue of historical landslide and flood events that have resulted in loss of life, missing persons, injuries and homelessness in Italy, from 1850 to 2008. This is the recent portion of a larger catalogue spanning the 1941-year period from 68 to 2008. We started by discussing uncertainty and completeness in the historical catalogue, and we performed an analysis of the temporal and geographical pattern of harmful landslide and flood events, in Italy. We found that sites affected by harmful landslides or floods are not distributed evenly in Italy, and we attributed the differences to different physiographical settings. To determine societal risk, we investigated the distribution of the number of landslide and flood casualties (deaths, missing persons, and injured people) in Italy, and in the 20 Italian Regions. Using order statistics, we found that the intensity of a landslide or flood event – measured by the total number of casualties in the event – follows a general negative power law trend. Next, we modelled the empirical distributions of the frequency of landslide and flood events with casualties in Italy and in each Region using a Zipf distribution. We used the scaling exponent s of the probability mass function (PMF) of the intensity of the events, which controls the proportion of small, medium, and large events, to compare societal risk levels in different geographical areas and for different periods. Lastly, to consider the frequency of the events with casualties, we scaled the PMF obtained for the individual Regions to the total number of events in each Region, in the period 1950–2008, and we used the results to rank societal landslide and flood risk in Italy. We found that in the considered period societal landslide risk is largest in Trentino-Alto Adige and Campania, and societal flood risk is highest in Piedmont and Sicily.
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Since 1990, we have maintained a database of historical information on landslides and floods in Italy, known as the National Research Council's AVI (Damaged Urban Areas) archive. The database was originally designed to respond to a request of the Minister of Civil Protection, and was aimed at helping the regional assessment of landslide and flood risk in Italy. The database was compiled in 1991-1992 to cover the period 1917 to 1990, and then updated to cover systematically the period 1917 to 2000, and non-systematically the periods 1900 to 1916 and 2001 to 2002. The database currently contains information on more than 32000 landslide events occurred at more than 21000 sites, and on more than 29000 flood events occurred at more than 14000 sites. Independently from the AVI archive, we have obtained other databases containing information on damage caused by mass movements and inundations, daily discharge measurements and solid-transport measurements at selected gauging stations, bibliographical and reference information on landslides and inundations, and a catalogue of National legislation on hydrological and geological hazards and risk in Italy. The databases are part of an information system known as SICI (an Italian acronym for Sistema Informativo sulle Catastrofi Idrogeologiche, Information System on Hydrological and Geomorphological Catastrophes), which is currently the largest single repository of historical information on landslides and floods in Italy. After an outline of the history and evolution of the AVI Project archive, we present and discuss: (a) the structure of the SICI information system, including the hardware and software solutions adopted to maintain, manage, update, use and disseminate the information stored in the various databases, (b) the type and amount of information stored in each database, including an estimate of their completeness, and (c) examples of recent applications of the information system, including a web-based GIS system to show the location of sites historically affected by landslides and floods, and an estimate of geo-hydrological (i.e. landslide and flood) risk in Italy based on the available historical information.
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Rapid gravitational slope mass movements include all kinds of short term relocation of geological material, snow or ice. Traditionally, information about such events is collected separately in different databases covering selected geographical regions and types of movement. In Norway the terrain is susceptible to all types of rapid gravitational slope mass movements ranging from single rocks hitting roads and houses to large snow avalanches and rock slides where entire mountainsides collapse into fjords creating flood waves and endangering large areas. In addition, quick clay slides occur in desalinated marine sediments in South Eastern and Mid Norway. For the authorities and inhabitants of endangered areas, the type of threat is of minor importance and mitigation measures have to consider several types of rapid mass movements simultaneously. An integrated national database for all types of rapid mass movements built around individual events has been established. Only three data entries are mandatory: time, location and type of movement. The remaining optional parameters enable recording of detailed information about the terrain, materials involved and damages caused. Pictures, movies and other documentation can be uploaded into the database. A web-based graphical user interface has been developed allowing new events to be entered, as well as editing and querying for all events. An integration of the database into a GIS system is currently under development. Datasets from various national sources like the road authorities and the Geological Survey of Norway were imported into the database. Today, the database contains 33 000 rapid mass movement events from the last five hundred years covering the entire country. A first analysis of the data shows that the most frequent type of recorded rapid mass movement is rock slides and snow avalanches followed by debris slides in third place. Most events are recorded in the steep fjord terrain of the Norwegian west coast, but major events are recorded all over the country. Snow avalanches account for most fatalities, while large rock slides causing flood waves and huge quick clay slides are the most damaging individual events in terms of damage to infrastructure and property and for causing multiple fatalities. The quality of the data is strongly influenced by the personal engagement of local observers and varying observation routines. This database is a unique source for statistical analysis including, risk analysis and the relation between rapid mass movements and climate. The database of rapid mass movement events will also facilitate validation of national hazard and risk maps.
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Problems caused by slope movements are the most dangerous geodynamics phenomenon in the Slovak Republic. Primarily they destroy the utility values of the exposed areas, i.e. devastate grassy and forest vegetation, farmland fund and also destroy man-made constructions which are mainly roads, railways, buildings and other important installations constructions. In this paper the slope movement processes in the Slovak Carpathian are described with the relation to geographic and geological subdivision. The regional engineering part and slope movement's parts are quoted from Matula, Pašek (1986) and Nemčok (1982), respectively.
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Within the project "The Temporal occurrence and forecasting of landslides in the European Community" a review of the use of databases and GIS for landslide research has been accomplished. It shows a high potential of these techniques in storing spatial and temporal landslide data (landslide inventories) and in applying different modelling approaches to landslide hazard assessments at various scales.There are three major strategies in European landslide research using GIS and database technologies. At medium and broad scales different combinations of landslide data with factor maps (e.g. slope angle, lithology and geomorphological units) lead to static susceptibility and hazard assessments, which allow probability evaluations for future landslide occurrences. At local scales process models to simulate trajectories of paths for slope processes and deterministic slope stability models are in use. In landslide frequency analysis, temporal database information are correlated with recent and historical triggering factors (e.g. precipitation and precipitation indices) to calculate temporal probabilities for landslide forecasting.However, despite encouraging progress in applying computer technologies in European landslide research, the potential of these tools is still largely untested. Furthermore, it is clear that sophisticated technology cannot replace field work, interdisciplinary research strategies, and critical testing of the reliability of the model results.
Conference Paper
In the framework of the European Soil Thematic Strategy, and the associated preparation of a directive on the protection and sustainable use of soil, landslides were recognized as a soil threat requiring specific strategies for risk assessment and management. The criteria for harmonized risk area delineation proposed by the Soil Information Working Group (SIWG) of the European Soil Bureau Network (ESBN) adopt a nested geographical approach based on "Tiers" and exploit thematic and environmental data of different type, quality, and resolution using a variety of methodological and technological approaches suitable for the spatial evaluation of any specific soil threat. The main requirement for a continent-wide "Tier 1" assessment for the delineation of areas subject to soil threats in Europe is the availability of relevant input data. At present, such a continent-wide assessment of landslide susceptibility in Europe is feasible only when adopting a qualitative evaluation technique since high-quality, pan-European landslide conditioning-and triggering factor data is available, but a European-wide coverage of landslide locations is missing. "Tier 1" landslide susceptibility evaluations are described to serve for general risk/priority area identification and must at least be able to discriminate areas subjected to more detailed spatial assessments against those where no further action has to be taken. Quantitative evaluations of landslide susceptibility according to a "Tier 2" assessment require the availability of landslide inventory maps and databases. We outline the current advances towards the development of a common methodology for assessing the landslide threat in Europe. We refer to limitations, data needs and future work to be carried out, and present examples of nationwide assessments. 1. Political background The European Union's Thematic Strategy for Soil Protection is a long-term political process that led to the formulation of a draft of a European framework directive devoted to the protection and sustainable use of soil in the European Union (Commission of the European Communities 2006a, 2006b). Within this process, eight individual soil threats that are likely to hamper soil functionalities or lead to soil degradation within the European territory have been identified and are subjected to risk/priority area delineation procedures and the implementation of suitable risk mitigation strategies: erosion, organic matter decline, salinisation, compaction, landslides, contamination, sealing and loss of biodiversity. Landslides are recognized as one of these soil threats. The Soil Information Working Group (SIWG) of the European Soil Bureau Network (ESBN) developed a uniform framework for risk area assessments of the first five soil threats mentioned above in such that hierarchically ordered, nested geographical analysis schemes ("Tiers") are envisaged, leaving the issues of data quality, map resolution and costs open to the individual EU member states (Eckelmann et al. 2006). In this context, European-level continent-wide "Tier 1" risk area delineations for individual soil threats should be conducted with already available data, should render a relatively low spatial resolution (tentatively 1:1 Mill.), and should follow a qualitative zonation approach or a model approach combined with thresholds. "Tier 1" assessments are considered to serve for general risk/priority area identification and should be able to delineate zones where no further measures or spatial analysis have to be taken against those that are subjected to more detailed "Tier 2" assessments. "Tier 2" risk area delineations within areas identified by "Tier 1" should thus render higher spatial resolution, could be conducted by quantitative modelling approaches, and will most likely require data not yet available. For each soil threat, the European Commission is searching for a common methodology for risk area delineations that will enable each member state to conduct the analysis. A set of common criteria for spatial analysis procedures was elaborated by SIWG of ESBN and is already annexed in the current draft of the framework directive. The discussion on a common methodology and on data requirements was more recently put forward by the European landslide experts group hosted by JRC Ispra (Hervás et al. 2007).
Article
Landslides are generally associated with a trigger, such as an earthquake, a rapid snowmelt or a large storm. The landslide event can include a single landslide or many thousands. The frequency–area (or volume) distribution of a landslide event quantifies the number of landslides that occur at different sizes. We examine three well-documented landslide events, from Italy, Guatemala and the USA, each with a different triggering mechanism, and find that the landslide areas for all three are well approximated by the same three-parameter inverse-gamma distribution. For small landslide areas this distribution has an exponential ‘roll-over’ and for medium and large landslide areas decays as a power-law with exponent -2·40. One implication of this landslide distribution is that the mean area of landslides in the distribution is independent of the size of the event. We also introduce a landslide-event magnitude scale mL = log(NLT), with NLT the total number of landslides associated with a trigger. If a landslide-event inventory is incomplete (i.e. smaller landslides are not included), the partial inventory can be compared with our landslide probability distribution, and the corresponding landslide-event magnitude inferred. This technique can be applied to inventories of historical landslides, inferring the total number of landslides that occurred over geologic time, and how many of these have been erased by erosion, vegetation, and human activity. We have also considered three rockfall-dominated inventories, and find that the frequency–size distributions differ substantially from those associated with other landslide types. We suggest that our proposed frequency–size distribution for landslides (excluding rockfalls) will be useful in quantifying the severity of landslide events and the contribution of landslides to erosion. Copyright © 2004 John Wiley & Sons, Ltd.
Article
Despite the availability of studies on the frequency density of landslide areas in mountainous regions, frequency–area distributions of historical landslide inventories in populated hilly regions are absent. This study revealed that the frequency–area distribution derived from a detailed landslide inventory of the Flemish Ardennes (Belgium) is significantly different from distributions usually obtained in mountainous areas where landslides are triggered by large-scale natural causal factors such as rainfall, earthquakes or rapid snowmelt. Instead, the landslide inventory consists of the superposition of two populations, i.e. (i) small (< 1–2 · 10− 2 km²), shallow complex earth slides that are at most 30 yr old, and (ii) large (> 1–2 · 10− 2 km²), deep-seated landslides that are older than 100 yr. Both subpopulations are best represented by a negative power–law relation with exponents of − 0.58 and − 2.31 respectively. This study focused on the negative power–law relation obtained for recent, small landslides, and contributes to the understanding of frequency distributions of landslide areas by presenting a conceptual model explaining this negative power–law relation for small landslides in populated hilly regions. According to the model hilly regions can be relatively stable under the present-day environmental conditions, and landslides are mainly triggered by human activities that have only a local impact on slope stability. Therefore, landslides caused by anthropogenic triggers are limited in size, and the number of landslides decreases with landslide area.
Article
Collapse calderas are one of the most important volcanic structures not only because of their hazard implications, but also because of their high geothermal energy potential and their association with mineral deposits of economic interest. The objective of this work is to describe a new general worldwide Collapse Caldera DataBase (CCDB), in order to provide a useful and accessible tool for studying and understanding caldera collapse processes. The principal aim of the CCDB is to update the current field based knowledge on calderas, merging together the existing databases and complementing them with new examples found in the bibliography, and leaving it open for the incorporation of new data from future studies. This database does not include all the calderas of the world, but it tries to be representative enough to promote further studies and analyses. We have performed a comprehensive compilation of published field studies of collapse calderas including more than 200 references, and their information has been summarized in a database linked to a Geographical Information System (GIS) application. Thus, it is possible to visualize the selected calderas on a world map and to filter them according to different features recorded in the database (e.g. age, structure). The information recorded in the CCDB can be grouped in seven main information classes: caldera features, properties of the caldera-forming deposits, magmatic system, geodynamic setting, pre-caldera volcanism, caldera-forming eruption sequence and post-caldera activity. Additionally, we have added two extra classes. The first records the references consulted for each caldera. The second allows users to introduce comments on the caldera sample such as possible controversies concerning the caldera origin. A further purpose of this work is to construct the CCDB web page. In this web page where registered users can acquire the current database version, as well as to propose corrections or updates and to exchange information with other registered members also involved in the study of caldera collapse processes. Additionally, the CCDB includes a formulary that will facilitate the incorporation of new calderas into the database.
Article
In recent years, growing population and expansion of settlements and life-lines over hazardous areas have largely increased the impact of natural disasters both in industrialized and developing countries. Third world countries have difficulty meeting the high costs of controlling natural hazards through major engineering works and rational land-use planning. Industrialized societies are increasingly reluctant to invest money in structural measures that can reduce natural risks. Hence, the new issue is to implement warning systems and land utilization regulations aimed at minimizing the loss of lives and property without investing in long-term, costly projects of ground stabilization. Government and research institutions worldwide have long attempted to assess landslide hazard and risks and to portray its spatial distribution in maps. Several different methods for assessing landslide hazard were proposed or implemented. The reliability of these maps and the criteria behind these hazard evaluations are ill-formalized or poorly documented. Geomorphological information remains largely descriptive and subjective. It is, hence, somewhat unsuitable to engineers, policy-makers or developers when planning land resources and mitigating the effects of geological hazards. In the Umbria and Marche Regions of Central Italy, attempts at testing the proficiency and limitations of multivariate statistical techniques and of different methodologies for dividing the territory into suitable areas for landslide hazard assessment have been completed, or are in progress, at various scales. These experiments showed that, despite the operational and conceptual limitations, landslide hazard assessment may indeed constitute a suitable, cost-effective aid to land-use planning. Within this framework, engineering geomorphology may play a renewed role in assessing areas at high landslide hazard, and helping mitigate the associated risk.
Article
The IAEG Commission on Landslides and other Mass Movements on Slopes has proposed English and French names for 19 identifiable features of slope movements and for 7 dimensions of those features. The Commission intends to publish this list in other languages and to supplement and revise it from time to time.
Article
The AVI project was commissioned by the Minister of Civil Protection to the National Group for Prevention of Hydrogeologic Hazards to complete an inventory of areas historically affected by landslides and floods in Italy. More than 300 people, divided into 15 research teams and two support groups, worked for one year on the project. Twenty-two journals were systematically searched for the period 1918–1990, 350,000 newspaper issues were screened, and 39,953 articles were collected. About 150 experts on mass movement and floods were interviewed and 1482 published and unpublished technical and scientific reports were reviewed. The results of the AVI project, in spite of the limitations, represent the most comprehensive archiving of mass movement and floods ever prepared in Italy. The type and quality of the information collected and the methodologies and techniques used to make the inventory are discussed. Possible applications and future developments are also presented.
Italy (IFFI), Poland and Slovakia. Again, these characteristics are not always available for all landslides. The LDBs of Andorra and Portugal did not contain any of the characteristics
  • Ldbs Greece
  • Ireland
LDBs of Greece, Ireland, Italy (IFFI), Poland and Slovakia. Again, these characteristics are not always available for all landslides. The LDBs of Andorra and Portugal did not contain any of the characteristics.
Common criteria for risk area identification according to soil threats Mapping the impacts of natural hazards and technological accidents in Europe: an overview of the last decade
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Eckelmann, W., Baritz, R., Bialousz, S., Bielek, P., Carre, F., Houskova, B., Jones, R.J.A., Kibblewhite, M.G., Kozak, J., Le Bas, C., Toth, G., Varallyay, G., Yli Halla, M., Zupan, M., 2006. Common criteria for risk area identification according to soil threats. European Soil Bureau Research Report n. 20, EUR 22185 EN. Office for Official Publication of the European Communities, Luxembourg. EEA, 2010. Mapping the impacts of natural hazards and technological accidents in Europe: an overview of the last decade. European Environmental Agency Technical Report 13. Office for Official Publications of the European Union, Luxembourg.
IAEG Commission on Landslides Landslide Hazard Zonation — A Review of Principles and Practice Volcanic disasters and incidents: a new database
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Varnes, D.J., IAEG Commission on Landslides, 1984. Landslide Hazard Zonation — A Review of Principles and Practice. UNESCO, Paris, France. 63 pp. Witham, C.S., 2005. Volcanic disasters and incidents: a new database. Journal of Volcanology and Geothermal Research 148, 191–233.
Landslide mapping in Austria
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Irish Landslides Working Group
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ICL, 2011. International Consortium on Landslides. www.iclhq.org/Europe.htm. Accessed 25 Jan 2011. M. Van Den Eeckhaut, J. Hervás / Geomorphology 139-140 (2012) 545–558 INSPIRE Thematic Working Group Natural Risk Zones, 2011. D2.8.III.12 Data Specification on Natural Risk Zones — Draft Guidelines, Version 1.9 (29/04/ 2011). INSPIRE Thematic Working Group Natural Risk Zones.
Questionnaire: Risk Mapping — Natural and Technological Risk and Contaminated lands
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Di Mauro, C., Vetere Arellano, A.L., Ranguelov, B., Hervás, J., Peckham, R., Christou, M.D., Duffield, J.S., Wood, M., Nordvik, J.P., Lucia, A.C., 2003. Questionnaire: Risk Mapping — Natural and Technological Risk and Contaminated lands, Special Publication No.I.03.222. JRC, Ispra, Italy.
The Landslide Blog Accessed 15 RAMSOIL, Risk Assessment Methodologies for SOIL Threats
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Petley, D.N., 2011. The Landslide Blog. http://blogs.agu.org/landslideblog. Accessed 15 Oct 2011. RAMSOIL, 2007-2008. RAMSOIL, Risk Assessment Methodologies for SOIL Threats. http://eusoils.jrc.ec.europa.eu/projects/Ramsoil/data.html. Accessed 25 Jan 2011.