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Geosciences
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Landslide Monitoring and Mapping
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Landslide Monitoring and Mapping

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 41776

Special Issue Editors

Dr. Matteo Del Soldato

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Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4 - 50121 Firenze, Italy
Interests: landslide mapping and monitoring; land subsidence; remote sensing data interpretation; geohazard monitoring; EO techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Solari

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Guest Editor
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Division of Geomatics Av. Gauss, 7 E-08860 Castelldefels, Spain
Interests: InSAR; geohazards monitoring; landslides; building monitoring; land subsidence
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Novellino

E-Mail Website
Guest Editor
British Geological Survey, Keyworth, Nottingham NG12 5GG, UK
Interests: natural hazards; landslides; synthetic aperture radar; InSAR; geodesy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landslides are one of the main natural hazards affecting a territory globally. These phenomena have relevant direct and indirect impacts over small and wide areas, causing fatalities and huge socio-economic damages. Population growth and continuous urban expansion often make people move towards areas prone to landsliding. Consequently, the interest in landslides and landslide-prone areas is increasing. Identifying areas that can be affected by damaging events in the near future requires landslide mapping and the investigation of the state of land activity. Several tools and techniques to achieve this goal have been developed. For example, ground instrumentations can be deployed to discover new movements, measure the motion of landslides, and evaluate their temporal evolution. Nowadays, thanks to technological progresses (e.g., cloud computing) and technical advancements (e.g., new processing algorithms), the scientific community can adopt remote sensing approaches for regularly analysing and monitoring land movements in local and national-scale areas, as well as in so far unexplored regions. These applications will also allow developing more correct land use policies and best practices for long-term risk mitigation and reduction. The derived information can be useful to risk management actors to take decisions for civil protection purposes or to more consciously allocate funds.

This Special Issue encourages submissions that include, but are not limited to, analyses of landslides by:

  • using traditional and ground truth approaches
  • using remote sensing techniques
  • combining ground- and satellite-based techniques
  • using innovative computing platforms to manage and process huge volumes of data

Expected applications comprise (among others):

  • mapping of landslides over wide areas
  • monitoring of land phenomena with traditional instruments and methods
  • landslide susceptibility, landslide risk and landslide impact analyses
  • local- and regional-scale applications for landslide post-event rapid mapping
  • interactions between landslides and other hazards (triggering, increased probability, and catalysis/impedance)

Dr. Matteo Del Soldato
Dr. Lorenzo Solari
Dr. Alessandro Novellino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • landslides
  • mapping
  • monitoring
  • ground-based instruments
  • remote sensing

Published Papers (11 papers)

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Research

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20 pages, 8365 KiB  
Article
Kinematic and Geometric Characterization of the Vögelsberg Rockslide (Tyrol, Austria) by Means of MT-InSAR Data
by Filippo Vecchiotti, Anna Sara Amabile, Salvatore Clemente, Marc Ostermann, Gianfranco Nicodemo and Dario Peduto
Geosciences 2022, 12(7), 256; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences12070256 - 21 Jun 2022
Cited by 4 | Viewed by 1959
Abstract
This paper focuses on the study of the Vögelsberg landslide located in the municipality of Wattens (Tyrol, Austria), which reactivated in 2016, causing damages to nearby buildings and infrastructures. Since the date of reactivation, a modern monitoring system has been implemented with the [...] Read more.
This paper focuses on the study of the Vögelsberg landslide located in the municipality of Wattens (Tyrol, Austria), which reactivated in 2016, causing damages to nearby buildings and infrastructures. Since the date of reactivation, a modern monitoring system has been implemented with the installation of in-situ geodetic automated tracking total stations (ATTS), an inclinometer and two piezometers. Here, we describe two distinctive methods, the Breaks for Additive Seasonal and Trend (BFAST) and the Vector Inclination Method (VIM) used to characterize the landslide from the kinematic and geometrical point of view. The main input data, used for both methods, derive from processing a stack of several Sentinel-1 differential interferograms with the Multiple Small Baseline Subset (MSBAS) 2D and 3D algorithms. BFAST allowed highlighting the seasonality of the phenomenon from the analysis of the time series as well as the trend and the breakpoints that identify the landslide reactivation phases. These latter were then correlated with the main triggering factors such as rain and snow melting. The application of the VIM through the exploitation of the MSBAS displacement vectors allowed the reconstruction of the depth of the landslide slip surface along both the longitudinal and transversal direction and, in turn, the evaluation of the volumes of material mobilized by the landslide. The results obtained further prove that procedures for the in-depth analysis of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data can contribute to slow-moving landslide characterization, which represents a fundamental step for landslide hazard assessment within quantitative risk analyses. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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17 pages, 8190 KiB  
Article
Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany
by Annika Wohlers and Bodo Damm
Geosciences 2022, 12(4), 170; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences12040170 - 13 Apr 2022
Cited by 2 | Viewed by 1852
Abstract
Mass movements are linked to increasing amounts of damage and disruptions to transportation infrastructures. A valid risk assessment in order to reduce future costs is not always appropriate, as adequate information on landslide data is missing. The presented study estimates the rockfall susceptibility [...] Read more.
Mass movements are linked to increasing amounts of damage and disruptions to transportation infrastructures. A valid risk assessment in order to reduce future costs is not always appropriate, as adequate information on landslide data is missing. The presented study estimates the rockfall susceptibility on a rural road network in the Harz mountains using a bivariate statistical method (information value method). The model is validated using a receiver operating characteristic (ROC) analysis. In addition, the vulnerability of the road network is estimated using vulnerability indicators. The susceptibility model assigns a high or very high susceptibility to 23% of the area in the road network corridor. The relevant road sections are linked to high slope values, NE orientations of road sections, and low-to-moderate vulnerability values. The highest vulnerability values can be found on marginal road sections with high average daily traffic volumes. The combination of the presented methods proposes an easily applicable estimate of vulnerability where conventional methods (i.e., vulnerability curves, matrices) cannot be implemented. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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14 pages, 4339 KiB  
Article
The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis
by Evelina Volpe, Luca Ciabatta, Diana Salciarini, Stefania Camici, Elisabetta Cattoni and Luca Brocca
Geosciences 2021, 11(8), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11080322 - 30 Jul 2021
Cited by 12 | Viewed by 2664
Abstract
The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an [...] Read more.
The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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19 pages, 6880 KiB  
Article
Evaluation of Machine Learning Algorithms for Object-Based Mapping of Landslide Zones Using UAV Data
by Efstratios Karantanellis, Vassilis Marinos, Emmanuel Vassilakis and Daniel Hölbling
Geosciences 2021, 11(8), 305; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11080305 - 22 Jul 2021
Cited by 13 | Viewed by 4221
Abstract
Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aerial platforms such as unmanned aerial vehicles (UAVs). As a result, ultra-high-resolution datasets [...] Read more.
Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aerial platforms such as unmanned aerial vehicles (UAVs). As a result, ultra-high-resolution datasets are available for analysis at low operational costs. In this study, different segmentation and classification approaches were utilized for object-based landslide mapping. An integrated object-based image analysis (OBIA) workflow is presented incorporating orthophotomosaics and digital surface models (DSMs) with expert-based and machine learning (ML) algorithms. For segmentation, trial and error tests and the Estimation of Scale Parameter 2 (ESP 2) tool were implemented for the evaluation of different scale parameters. For classification, machine learning algorithms (K- Nearest Neighbor, Decision Tree, and Random Forest) were assessed with the inclusion of spectral, spatial, and contextual characteristics. For the ML classification of landslide zones, 60% of the reference segments have been used for training and 40% for validation of the models. The quality metrics of Precision, Recall, and F1 were implemented to evaluate the models’ performance under the different segmentation configurations. Results highlight higher performances for landslide mapping when DSM information was integrated. Hence, the configuration of spectral and DSM layers with the RF classifier resulted in the highest classification agreement with an F1 value of 0.85. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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23 pages, 7655 KiB  
Article
Characteristics and Distribution of Landslides in the Populated Hillslopes of Bujumbura, Burundi
by Désiré Kubwimana, Lahsen Ait Brahim, Pascal Nkurunziza, Antoine Dille, Arthur Depicker, Louis Nahimana, Abdellah Abdelouafi and Olivier Dewitte
Geosciences 2021, 11(6), 259; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11060259 - 17 Jun 2021
Cited by 17 | Viewed by 4467
Abstract
Accurate and detailed multitemporal inventories of landslides and their process characterization are crucial for the evaluation of landslide hazards and the implementation of disaster risk reduction strategies in densely-populated mountainous regions. Such investigations are, however, rare in many regions of the tropical African [...] Read more.
Accurate and detailed multitemporal inventories of landslides and their process characterization are crucial for the evaluation of landslide hazards and the implementation of disaster risk reduction strategies in densely-populated mountainous regions. Such investigations are, however, rare in many regions of the tropical African highlands, where landslide research is often in its infancy and not adapted to the local needs. Here, we have produced a comprehensive multitemporal investigation of the landslide processes in the hillslopes of Bujumbura, situated in the landslide-prone East African Rift. We inventoried more than 1200 landslides by combining careful field investigation and visual analysis of satellite images, very-high-resolution topographic data, and historical aerial photographs. More than 20% of the hillslopes of the city are affected by landslides. Recent landslides (post-1950s) are mostly shallow, triggered by rainfall, and located on the steepest slopes. The presence of roads and river quarrying can also control their occurrence. Deep-seated landslides typically concentrate in landscapes that have been rejuvenated through knickpoint retreat. The difference in size distributions between old and recent deep-seated landslides suggests the long-term influence of potentially changing slope-failure drivers. Of the deep-seated landslides, 66% are currently active, those being mostly earthflows connected to the river system. Gully systems causing landslides are commonly associated with the urbanization of the hillslopes. Our results provide a much more accurate record of landslide processes and their impacts in the region than was previously available. These insights will be useful for land management and disaster risk reduction strategies. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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31 pages, 14790 KiB  
Article
Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise
by Pierre Bottelin, Laurent Baillet, Aurore Carrier, Eric Larose, Denis Jongmans, Ombeline Brenguier and Héloïse Cadet
Geosciences 2021, 11(6), 242; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11060242 - 04 Jun 2021
Cited by 5 | Viewed by 2179
Abstract
Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring [...] Read more.
Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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Review

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15 pages, 18025 KiB  
Review
Landslide Susceptibility Mapping in Brazil: A Review
by Helen Cristina Dias, Daniel Hölbling and Carlos Henrique Grohmann
Geosciences 2021, 11(10), 425; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11100425 - 15 Oct 2021
Cited by 23 | Viewed by 4765
Abstract
Landslide susceptibility studies are a common type of landslide assessment. Landslides are one of the most frequent hazards in Brazil, resulting in significant economic and social losses (e.g., deaths, injuries, and property destruction). This paper presents a literature review of susceptibility mapping studies [...] Read more.
Landslide susceptibility studies are a common type of landslide assessment. Landslides are one of the most frequent hazards in Brazil, resulting in significant economic and social losses (e.g., deaths, injuries, and property destruction). This paper presents a literature review of susceptibility mapping studies in Brazil and analyzes the methods and input data commonly used. The publications used in this analysis were extracted from the Web of Science platform. We considered the following aspects: location of study areas, year and where the study was published, methods, thematic variables, source of the landslide inventory, and validation methods. The susceptibility studies are concentrated in Brazil’s south and southeast region, with the number of publications increasing since 2015. The methods commonly used are slope stability and statistical models. Validation was performed based on receiver operating characteristic (ROC) curves and area under the curve (AUC). Even though landslide inventories constitute the most critical input data for susceptibility mapping, the criteria used for the creation of landslide inventories are not evident in most cases. The included studies apply various validation techniques, but evaluations with potential users and information on the practical applicability of the results are largely missing. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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24 pages, 2290 KiB  
Review
Root Reinforcement in Slope Stability Models: A Review
by Elena Benedetta Masi, Samuele Segoni and Veronica Tofani
Geosciences 2021, 11(5), 212; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11050212 - 13 May 2021
Cited by 63 | Viewed by 8524
Abstract
The influence of vegetation on mechanical and hydrological soil behavior represents a significant factor to be considered in shallow landslides modelling. Among the multiple effects exerted by vegetation, root reinforcement is widely recognized as one of the most relevant for slope stability. Lately, [...] Read more.
The influence of vegetation on mechanical and hydrological soil behavior represents a significant factor to be considered in shallow landslides modelling. Among the multiple effects exerted by vegetation, root reinforcement is widely recognized as one of the most relevant for slope stability. Lately, the literature has been greatly enriched by novel research on this phenomenon. To investigate which aspects have been most treated, which results have been obtained and which aspects require further attention, we reviewed papers published during the period of 2015–2020 dealing with root reinforcement. This paper—after introducing main effects of vegetation on slope stability, recalling studies of reference—provides a synthesis of the main contributions to the subtopics: (i) approaches for estimating root reinforcement distribution at a regional scale; (ii) new slope stability models, including root reinforcement and (iii) the influence of particular plant species, forest management, forest structure, wildfires and soil moisture gradient on root reinforcement. Including root reinforcement in slope stability analysis has resulted a topic receiving growing attention, particularly in Europe; in addition, research interests are also emerging in Asia. Despite recent advances, including root reinforcement into regional models still represents a research challenge, because of its high spatial and temporal variability: only a few applications are reported about areas of hundreds of square kilometers. The most promising and necessary future research directions include the study of soil moisture gradient and wildfire controls on the root strength, as these aspects have not been fully integrated into slope stability modelling. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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Other

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15 pages, 7894 KiB  
Technical Note
Shallow Landslides and Rockfalls Velocity Assessment at Regional Scale: A Methodology Based on a Morphometric Approach
by Antonella Marinelli, Camilla Medici, Ascanio Rosi, Veronica Tofani, Silvia Bianchini and Nicola Casagli
Geosciences 2022, 12(4), 177; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences12040177 - 16 Apr 2022
Cited by 2 | Viewed by 2677
Abstract
Velocity is one of the most important parameters to evaluate the damaging potential of a mass movement, but its assessment, especially for extremely rapid landslides, is a complex task. In the literature, several models to assess mass movement velocity exist, but they usually [...] Read more.
Velocity is one of the most important parameters to evaluate the damaging potential of a mass movement, but its assessment, especially for extremely rapid landslides, is a complex task. In the literature, several models to assess mass movement velocity exist, but they usually require many detailed parameters, and therefore, they are applicable only to a single slope and not usable for regional-scale analyses. This study aims to propose a simple morphometric methodology, based on the spatialisation of the Energy Line method, to determine the velocity of shallow landslides and rockfalls at a regional scale. The proposed method requires a limited amount of input data (landslide perimeters and a digital elevation model), and its application can be carried out using GIS software and a Matlab code. The test area of this work is the Valle d’Aosta Region (Northern Italy), selected due to its peculiar geological and geomorphological setting that makes this region susceptible to the occurrence of both shallow landslides and rockfalls. Since measured velocity values for rockfalls and shallow landslides were not available, the results obtained with the proposed method have been validated through the implementation of a model in the literature, namely the Gravitational Process Path (GPP) model, for some selected landslides. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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16 pages, 9155 KiB  
Technical Note
A New Set of Tools for the Generation of InSAR Visibility Maps over Wide Areas
by Matteo Del Soldato, Lorenzo Solari, Alessandro Novellino, Oriol Monserrat and Federico Raspini
Geosciences 2021, 11(6), 229; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11060229 - 25 May 2021
Cited by 8 | Viewed by 2492
Abstract
Multi-temporal Interferometric Synthetic Aperture Radar (MTInSAR) is a solid and reliable technique used to measure ground motion in many different environments. Today, the scientific community and a wide variety of users and stakeholders consider MTInSAR a precise tool for ground motion-related applications. The [...] Read more.
Multi-temporal Interferometric Synthetic Aperture Radar (MTInSAR) is a solid and reliable technique used to measure ground motion in many different environments. Today, the scientific community and a wide variety of users and stakeholders consider MTInSAR a precise tool for ground motion-related applications. The standard product of a MTInSAR analysis is a deformation map containing a high number of point-like measurement points (MP) which carry information on ground motion. The density of MPs is uneven, and they cannot be extracted continuously at large scale due to geometrical distortions and unfavourable landcover. It is a good practice to assess the feasibility of the interferometric analysis ahead of data processing. This technical note proposes a ready-to-use set of tools aimed at updating existing methods for modelling the effects of local topography and land cover on MTInSAR approaches. The goal of the tools is to provide InSAR experts and non-experts with a fast and automatic way to derive visibility maps, useful for pre-processing screening of a target area, and to forecast the expected density of MP over a specified area. Moreover, the visibility maps are a valid support for users to better understand the available standard and advanced interferometric results. Two workflows are proposed: the first generates the so-called Rindex map (Ri_m) to estimate the influence of topography on MP detection, the second is used to derive a land cover-calibrated Ri_m seen as a probabilistic model for MP detection (MPD_m). The proposed set of tools was applied in the context of the Alpine arc, whose climatic, morphological, and land cover characteristics represent a challenging environment for any interferometric approach. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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15 pages, 7361 KiB  
Technical Note
Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model
by Luca Schilirò, José Cepeda, Graziella Devoli and Luca Piciullo
Geosciences 2021, 11(1), 35; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11010035 - 11 Jan 2021
Cited by 13 | Viewed by 3381
Abstract
In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are [...] Read more.
In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping)
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