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

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

The origin of fault scarps & fissures on moorland plateaux & in the vicinity of landslides, in the South Wales Coalfield, UK

Fault scarps and fissures occur on moorland plateaux and in the vicinity of deep-seated landslides in the South Wales Coalfield, UK. These scarps may reach about 4 m in height and 3-4 km in length. The ages of the fault scarps and fissures are difficult to determine. Their relatively fresh and unweathered appearance would seem to suggest they were generated during subsidence as a result of coal mining which has taken place for some 150 years. However, their large magnitude, which make them dramatic features of the landscape, sets them apart from the much lesser features generated during coal mining subsidence in other UK coalfields. Some fault scarps seem to pre-date Ordnance Survey and British Geological Survey maps from the late 1800s-early 1900s. As total extraction (longwall) methods associated with fault reactivation had yet to develop widely at that time it is probable that mining subsidence alone could not have generated such distinct topographic features. The paper reviews the evidence of analogous non-mining fault steps and fissuring, mine abandonment plans and recent fissure treatment works to cast new light on the origin and development of these features. A conceptual model to demonstrate the causative mechanisms and evolution of fissures is also presented. The paper concludes that some fault steps and fissures developed in response to stress relief caused by deglaciation and periglacial activity and have subsequently undergone a later phase of development as a consequence of differential mining subsidence

Semi-automated geomorphological mapping applied to landslide hazard analysis

Computer-assisted three-dimensional (3D) mapping using stereo and multi-image (“softcopy”) photogrammetry is shown to enhance the visual interpretation of geomorphology in steep terrain with the direct benefit of greater locational accuracy than traditional manual mapping. This would benefit multi-parameter correlations between terrain attributes and landslide distribution in both direct and indirect forms of landslide hazard assessment. Case studies involve synthetic models of a landslide, and field studies of a rock slope and steep undeveloped hillsides with both recently formed and partly degraded, old landslide scars. Diagnostic 3D morphology was generated semi-automatically both using a terrain-following cursor under stereo-viewing and from high resolution digital elevation models created using area-based image correlation, further processed with curvature algorithms. Laboratory-based studies quantify limitations of area-based image correlation for measurement of 3D points on planar surfaces with varying camera orientations. The accuracy of point measurement is shown to be non-linear with limiting conditions created by both narrow and wide camera angles and moderate obliquity of the target plane. Analysis of the results with the planar surface highlighted problems with the controlling parameters of the area-based image correlation process when used for generating DEMs from images obtained with a low-cost digital camera. Although the specific cause of the phase-wrapped image artefacts identified was not found, the procedure would form a suitable method for testing image correlation software, as these artefacts may not be obvious in DEMs of non-planar surfaces.Modelling of synthetic landslides shows that Fast Fourier Transforms are an efficient method for removing noise, as produced by errors in measurement of individual DEM points, enabling diagnostic morphological terrain elements to be extracted. Component landforms within landslides are complex entities and conversion of the automatically-defined morphology into geomorphology was only achieved with manual interpretation; however, this interpretation was facilitated by softcopy-driven stereo viewing of the morphological entities across the hillsides.In the final case study of a large landslide within a man-made slope, landslide displacements were measured using a photogrammetric model consisting of 79 images captured with a helicopter-borne, hand-held, small format digital camera. Displacement vectors and a thematic geomorphological map were superimposed over an animated, 3D photo-textured model to aid non-stereo visualisation and communication of results

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

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

STOCHASTIC ASSESSMENT OF LANDSLIDE SUSCEPTIBILITY ALONGSIDE “VÍA AL LLANO” HIGHWAY, COLOMBIA

Le frane frequenti lungo la "Via al Llano", una delle più importanti autostrade colombiane, interrompono regolarmente il traffico. Questa rotta cruciale collega Bogotá, la capitale della Colombia, con Villavicencio, la capitale dello stato di Meta, facilitando il trasporto di beni agricoli e industriali e promuovendo lo sviluppo economico regionale attraverso il turismo. La regione circostante la "Via al Llano" è caratterizzata da caratteristiche geologiche come pieghe, faglie, giunti e affioramenti di diverse litologie ed età. Inoltre, pendii ripidi, deforestazione, depositi non consolidati, alte precipitazioni annuali e un paesaggio fortemente sezionato contribuiscono ulteriormente al verificarsi di frane. Pertanto, identificare accuratamente le aree ad alto rischio di frana, in particolare dove la strada interseca, attraverso la modellazione della suscettibilità alle frane, è imperativo.Nonostante studi precedenti, che si basavano prevalentemente sulla modellazione predittiva, risultassero in una correlazione insufficiente con la strada. Pertanto, l'obiettivo di questo studio è migliorare la risoluzione spaziale suddividendo l'area di studio nei cinque comuni attraversati dalla strada: Chipaque, Caqueza, Quetame, Guayabetal e Villavicencio. Per affrontare la complessità dell'area, lo studio ha prima valutato la fattibilità di sviluppare inventari automatici utilizzando dati radiometrici da immagini satellitari ottiche e radar attraverso la piattaforma Google Earth Engine (GEE). In secondo luogo, sono state create mappe pluviometriche dell'area interpolando 15 anni di dati sulle precipitazioni. Inoltre, sono state generate anche mappe geomorfologiche per ciascuno dei cinque comuni, rappresentando un risultato significativo di questa tesi. Queste mappe forniscono informazioni precedentemente non disponibili, essenziali per comprendere i processi naturali regionali e stabilire elementi fondamentali per mappe di rischio e pericolo.Di conseguenza, lo studio ha impiegato la tecnica delle Splines Adattive di Regressione Multivariata (MARS) per modellare la relazione tra le frane e le variabili predittive come altitudine, angolo di pendenza, esposizione, curvatura, litologia, precipitazioni, NDVI. I modelli sono stati rigorosamente calibrati e validati utilizzando dieci campioni di addestramento e dieci campioni di test, valutando le loro prestazioni predittive tramite la curva ROC (AUC). I nostri risultati indicano che le frane sono più probabili intorno ai corsi d'acqua affluenti del Rio Negro, con variabili chiave -Indice di Posizione Topografica (TPI), Indice di Vegetazione Normalizzato (NDVI), elevazione (ELE), precipitazioni (PLV), pendenza (SLO) e litologia (LTL)- che contribuiscono ad accuratezze predittive che vanno dal 74% all'83%.Frequent landslides along the "Via al Llano”, one of the most important Colombian highways, regularly disrupt traffic. This crucial route connects Bogotá, the capital of Colombia, with Villavicencio, the capital of Meta state, facilitating the transportation of agricultural and industrial goods and promoting regional economic development through tourism. The region surrounding the “Via al Llano” is characterized by geological features such as folds, faults, joints, and outcrops of diverse lithologies and ages. Additionally, steep slopes, deforestation, unconsolidated deposits, high annual rainfall, and a highly dissected landscape further contribute to landslides occurrences. Therefore, accurately identifying high-risk landslide areas, particularly where the road intersects, through landslide susceptibility modeling, is imperative.Despite previous studies, which predominantly relied on predictive modeling, resulting in insufficient correlation with the road. Therefore, the aim of this study is to enhance spatial resolution by subdividing the study area into the five municipalities traversed by the road: Chipaque, Caqueza, Quetame, Guayabetal, and Villavicencio. To address the complexity of the area, the study first assessed the feasibility of developing automatic inventories using radiometric data from optical and radar satellite images through the Google Earth Engine (GEE) platform. Secondly, pluviometry maps of the area were created by interpolating 15 years of rainfall data. Additionally, geomorphological maps for each of the five municipalities were also generated, representing a significant outcome of this thesis. These maps provide previously unavailable information, essential for understanding regional natural processes and establishing foundational elements for risk and hazard maps. Consequently, the study employed the Multivariate Adaptive Regression Splines (MARS) technique to model the relationship between landslides and predictor variables such as elevation, slope angle, aspect, curvature, lithology, precipitation, NDVI. The models were rigorously calibrated and validated using ten training and ten test samples, evaluating their predictive performance by the Receiver Operating Curve (AUC). Our findings indicate that landslides are most probable around the tributary streams of the Rio Negro, with key variables -Topographic Position Index (TPI), Normalized Difference Vegetation Index (NDVI), elevation (ELE), precipitation (PLV), slope (SLO), and lithology (LTL)- contributing to predictive accuracies ranging from 74% to 83%

Spatial distribution and geometric characteristics of landslides with special reference to geological units in the area of Slavonski Brod, Croatia

A preliminary analysis of landslide spatial distribution and their geometric characteristics is presented for the area of Slavonski Brod, located in the northeastern part of Croatia and belonging to the Pannonian Basin System. A landslide inventory for the study area of 55.1 km2 is accomplished for the first time, based on the visual interpretation of a high resolution LiDAR digital terrain model. In total, 854 landslide polygons are delineated, corresponding to an average density of 15.5 landslides per square kilometre. The average landslide area is 839 m2, and most of the landslides can be classified as small landslides (76 %). The spatial relationship between landslides and geological units is analysed and expressed as a landslide index. The Late Pannonian sands with silts and gravel interlayers and Pliocene clay, sands, gravels, and coal are determined as the units that are most susceptible to landslide processes. The majority of landslides (85 %) are concentrated within these two units, for which a detailed analysis is performed, determining the morphometric parameters (slope and relief) and drainage network. The parameters’ classes that create favourable preconditions to slope instabilities are defined, based on the landslide density within individual classes. Besides, the geometric characteristics of landslides (size and shape) within these two units are compared. The results serve as the basis for further investigations. They help to foresee the area of future landslides through landslide susceptibility maps, and offer a better understanding of the influence of fluvial-denudation and slope processes on recent landscape evolution and form

New application of open source data and Rock Engineering System for debris flow susceptibility analysis

This research describes a quantitative, rapid, and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases. The proposed approach can aid decision makers in land management and territorial planning, by first screening for areas with a higher debris flow susceptibility. Five environmental predisposing factors, namely, bedrock lithology, fracture network, quaternary deposits, slope inclination, and hydrographic network, were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System (RES) methodology. For each parameter, specific indexes were proposed, aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale. The methodology was tested in four basins located in the Upper Susa Valley (NW Italian Alps) where debris flow events are the predominant natural hazard. The proposed matrix can represent a useful standardized tool, universally applicable, since it is independent of type and characteristic of the basin

Historical aerial photographs for landslide assessment: two case histories

This paper demonstrates the value of historical aerial photographs for assessing long-term landslide evolution. The study focussed on two case histories, the Mam Tor and East Pentwyn landslides. In both case histories the variety of data was explored, that could be derived relatively easily using an ordinary PC desktop, commercially available software and commonly available photographic material. The techniques to unlock qualitative and quantitative data captured in the photographic archive were based on the principles of aerial photo-interpretation and photogrammetry. The created products comprised geomorphological maps, automatically derived elevation models (DEMs), displacement vectors and animations. The measured horizontal displacements of the Mam Tor landslide ranged from 0.09-0.74 m/yr between 1953 and 1999, which was verified by independent survey data. Moreover, the observed displacement patterns were consistent with photo-interpreted geomorphological information. The photogrammetric measurements from the East Pentwyn landslide (horizontal displacements up to 6 m/yr between 1971 and 1973) also showed a striking resemblance to independent data. In both case histories, the vertical accuracy was insufficient for detecting significant elevation changes. Nevertheless, DEMs proved to be a powerful tool for visualisation. Overall, the results in this study validated the techniques used and strongly encourage the use of historical photographic material in landslide studies