Special Issue "Geospatial Approaches to Landslide Mapping and Monitoring"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (29 February 2020).

Special Issue Editors

Dr. Veronica Tofani
Website
Guest Editor
Department of Earth Sciences, Università degli Studi di Firenze, Florence, Italy
Interests: landslide analysis; engineering geology; landslide hazard assessment; geomatics; risk management
Dr. William Frodella
Website
Guest Editor
UNESCO Chair on Prevention and Sustainable Management of Geo-Hydrological Hazards, University of Firenze, Via G. La Pira 4, Italy
Interests: landslide remote sensing; infrared thermography; natural hazards; geomorphological mapping; radar interferometric data interpretation; cultural heritage protection
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Special Issue Information

Dear Colleagues,

Landslides play an important role in the evolution of landscapes and still represent a major threat to urban areas and anthropogenic activities, to infrastructures, cultural and environmental heritage.

In the last decades, advanced remote sensing techniques have undergone a significant increase of usage as effective tools for landslide mapping, inventory and monitoring at various scales (these include but are not limited to Radar inteferometry, Lidar, Digital photogrammetry, Optical and Infrared imaging).

The landslide scientific community, practitioners and end-users, have greatly benefited from both the technological development of these techniques, in terms of spatial resolution, accuracy, fast measurement and processing times, and from the data availability and cost-effectiveness. In this perspective, a key issue is still represented by data management and processing tools which, in turn, can lead to a proper and accurate comprehensive view and interpretation of the slope instability processes, with the final aim of implementing mitigation measures for landslide risk management.

The goal of this Special Issue is to gather high-quality original research articles and reviews on innovative geospatial approaches, synergistic use of remote sensing techniques, and case studies applications for landslide detection, mapping and monitoring, from spaceborne, ground-based to UAV platforms.

Dr. Veronica Tofani
Dr. William Frodella
Guest Editors

Manuscript Submission Information

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Keywords

  • landslide mapping
  • remote sensing
  • geomatics
  • InSAR
  • Lidar
  • digital photogrammetry infrared thermography
  • landslide hazard
  • mitigation measures
  • risk management

Published Papers (14 papers)

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Research

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Open AccessArticle
Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models
ISPRS Int. J. Geo-Inf. 2020, 9(6), 377; https://doi.org/10.3390/ijgi9060377 - 08 Jun 2020
Cited by 2
Abstract
Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE as a geology and hydrology-related predisposing factor. The Ningdu County [...] Read more.
Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE as a geology and hydrology-related predisposing factor. The Ningdu County of China is taken as a research area. Firstly, 446 landslides are obtained through government disaster survey reports. Secondly, the SE amount in Ningdu County is calculated and nine other conventional predisposing factors are obtained under both 30 m and 60 m grid resolutions to determine the effects of SE on landslide susceptibility prediction. Thirdly, four types of machine-learning predictors with 30 m and 60 m grid resolutions—C5.0 decision tree (C5.0 DT), logistic regression (LR), multilayer perceptron (MLP) and support vector machine (SVM)—are applied to construct the landslide susceptibility prediction models considering the SE factor as SE-C5.0 DT, SE-LR, SE-MLP and SE-SVM models; C5.0 DT, LR, MLP and SVM models with no SE are also used for comparisons. Finally, the area under receiver operating feature curve is used to verify the prediction accuracy of these models, and the relative importance of all the 10 predisposing factors is ranked. The results indicate that: (1) SE factor plays the most important role in landslide susceptibility prediction among all 10 predisposing factors under both 30 m and 60 m resolutions; (2) the SE-based models have more accurate landslide susceptibility prediction than the single models with no SE factor; (3) all the models with 30 m resolutions have higher landslide susceptibility prediction accuracy than those with 60 m resolutions; and (4) the C5.0 DT and SVM models show higher landslide susceptibility prediction performance than the MLP and LR models. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Distribution Pattern of Coseismic Landslides Triggered by the 2017 Jiuzhaigou Ms 7.0 Earthquake of China: Control of Seismic Landslide Susceptibility
ISPRS Int. J. Geo-Inf. 2020, 9(4), 198; https://doi.org/10.3390/ijgi9040198 - 27 Mar 2020
Abstract
On 8 August 2017 an earthquake (MS7.0) occurred within Jiuzhaigou County, Northern Aba Prefecture, Sichuan Province, China, triggering 4834 landslides with an individual area greater than 7.8 m2 over a more than 400 km2 region. Instead of correlating [...] Read more.
On 8 August 2017 an earthquake (MS7.0) occurred within Jiuzhaigou County, Northern Aba Prefecture, Sichuan Province, China, triggering 4834 landslides with an individual area greater than 7.8 m2 over a more than 400 km2 region. Instead of correlating geological and topographic factors with the coseismic landslide distribution pattern, this study has attempted to reveal the control from seismic landslide susceptibility mapping, which relies on the calculation of critical acceleration values using a simplified Newmark block model. We calculated the average critical acceleration for each cell of the gridded study area (1 km×1 km), which represented the seismic landslide susceptibility of the cell. An index of the potential landslide area generation rate was defined, i.e., the possible landsliding area in each grid cell. In combination with PGA (peak ground acceleration) distribution, we calculated such indexes for each cell to predict the possible landslide hazard under seismic ground shaking. Results show that seismic landslide susceptibility plays an important role in determining the coseismic landslide pattern. The places with high seismic landslide susceptibility tends to host many landslides. Additionally, the areas with high potential landslide area generation rates have high real landslide occurrence rates, consistent with dominant small-medium scale landslides by this earthquake. This approach can aid assessment of seismic landslide hazards at a preliminary stage. Additionally, it forms a foundation for further research, such as the rapid evaluation of post-earthquake landslides and identifying highly impacted areas to help decision makers prioritize disaster relief efforts. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Slope Hazard Monitoring Using High-Resolution Satellite Remote Sensing: Lessons Learned from a Case Study
ISPRS Int. J. Geo-Inf. 2020, 9(2), 131; https://doi.org/10.3390/ijgi9020131 - 23 Feb 2020
Abstract
In this study, a highway slope monitoring project for a section of US highway I-77 in Virginia was carried out with the InSAR technique. This paper attempts to provide insights into the complete and practical solution for the monitoring project, including two parts: [...] Read more.
In this study, a highway slope monitoring project for a section of US highway I-77 in Virginia was carried out with the InSAR technique. This paper attempts to provide insights into the complete and practical solution for the monitoring project, including two parts: what to consider for selecting the optimal satellites and configurations for the given area of interest (AoI) and budget; and how to best process the selected data for the monitoring purposes. To answer the first question, the simulated geometric distortion map, cumulative change detection map, intensity map, interferograms and coherence maps from all available historical datasets were generated. The satellite configuration that gives the best coherence and least geometric distortion with the given budget was selected for the monitoring project. For this project, it was the X-band COSMO stripmap with 3 m resolution and eight-days revisit time. To answer the second question, a multi-temporal InSAR (MTInSAR) was applied to retrieve the settlement time series of the slopes along the highway. Several special techniques were applied to increase the level of confidence, i.e., dividing AoI into smaller and independent areas, using a non-linear approach, etc. Finally, fieldwork was carried out for the interpretation and validation of the results. The AoI was overall stable, though some local changes were detected by the SAR signal which were validated by the fieldwork. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Spatial and Temporal Evolution of the Infiltration Characteristics of a Loess Landslide
ISPRS Int. J. Geo-Inf. 2020, 9(1), 26; https://doi.org/10.3390/ijgi9010026 - 02 Jan 2020
Cited by 1
Abstract
Infiltration plays an important role in influencing slope stability. However, the influences of slope failure on infiltration and the evolution of infiltration over time and space remain unclear. We studied and compared the infiltration rates in undisturbed loess and disturbed loess in different [...] Read more.
Infiltration plays an important role in influencing slope stability. However, the influences of slope failure on infiltration and the evolution of infiltration over time and space remain unclear. We studied and compared the infiltration rates in undisturbed loess and disturbed loess in different years and at different sites on loess landslide bodies. The results showed that the average initial infiltration rate in a new landslide body (triggered on 11 October 2017) were dramatically higher than those in a previous landslide body (triggered on 17 September 2011) and that the infiltration rates of both landslide types were higher than the rate of undisturbed loess. The initial infiltration rate in the new landslide body sharply decreased over the 4–5 months following the landslide because of the appearance of physical crusts. Our observations indicated that the infiltration rate of the disturbed soil in a landslide evolved over time and that the infiltration rate gradually approached that of undisturbed loess. Furthermore, in the undisturbed loess, both the initial and quasi-steady infiltration rates were slightly higher in the loess than in the paleosol, and in the previous landslide body, the infiltration rate was highest in the upper part, intermediate in the middle part, and lowest in the lower part. This study can help us to better understand the evolution process of infiltration in undisturbed loess, previous landslides, and new landslides. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
An Open-Source Web Platform to Share Multisource, Multisensor Geospatial Data and Measurements of Ground Deformation in Mountain Areas
ISPRS Int. J. Geo-Inf. 2020, 9(1), 4; https://doi.org/10.3390/ijgi9010004 - 18 Dec 2019
Cited by 1
Abstract
Nowadays, the increasing demand to collect, manage and share archives of data supporting geo-hydrological processes investigations requires the development of spatial data infrastructure able to store geospatial data and ground deformation measurements, also considering multisource and heterogeneous data. We exploited the GeoNetwork open-source [...] Read more.
Nowadays, the increasing demand to collect, manage and share archives of data supporting geo-hydrological processes investigations requires the development of spatial data infrastructure able to store geospatial data and ground deformation measurements, also considering multisource and heterogeneous data. We exploited the GeoNetwork open-source software to simultaneously organize in-situ measurements and radar sensor observations, collected in the framework of the HAMMER project study areas, all located in high mountain regions distributed in the Alpines, Apennines, Pyrenees and Andes mountain chains, mainly focusing on active landslides. Taking advantage of this free and internationally recognized platform based on standard protocols, we present a valuable instrument to manage data and metadata, both in-situ surface measurements, typically acquired at local scale for short periods (e.g., during emergency), and satellite observations, usually exploited for regional scale analysis of surface displacement. Using a dedicated web-interface, all the results derived by instrumental acquisitions and by processing of remote sensing images can be queried, analyzed and downloaded from both expert users and stakeholders. This leads to a useful instrument able to share various information within the scientific community, including the opportunity of reprocessing the raw data for other purposes and in other contexts. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software
ISPRS Int. J. Geo-Inf. 2019, 8(12), 551; https://doi.org/10.3390/ijgi8120551 - 02 Dec 2019
Cited by 2
Abstract
With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the [...] Read more.
With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Spatiotemporal Distribution of Nonseismic Landslides during the Last 22 Years in Shaanxi Province, China
ISPRS Int. J. Geo-Inf. 2019, 8(11), 505; https://doi.org/10.3390/ijgi8110505 - 09 Nov 2019
Abstract
The spatiotemporal distribution of landslides provides valuable insight for the understanding of disastrous processes and landslide risk assessment. In this work, we compiled a catalog of landslides from 1996 to 2017 based on existing records, yearbooks, archives, and fieldwork in Shaanxi Province, China. [...] Read more.
The spatiotemporal distribution of landslides provides valuable insight for the understanding of disastrous processes and landslide risk assessment. In this work, we compiled a catalog of landslides from 1996 to 2017 based on existing records, yearbooks, archives, and fieldwork in Shaanxi Province, China. The statistical analyses demonstrated that the cumulative frequency distribution of the annual landslide number was empirically described by a power-law regression. Most landslides occurred from July to October. The relationship between landslide time interval and their cumulative frequency could be fitted using an exponential regression. The cumulative frequency of the landslide number could be approximated using the power-law function. Moreover, many landslides caused fatalities, and the number of fatalities was related to the number of landslides each month. Moreover, the cumulative frequency was significantly correlated with the number of fatalities and exhibited a power-law relationship. Furthermore, obvious differences were observed in the type and density of landslides between the Loess Plateau and the Qinba Mountains. Most landslides were close to stream channels and faults, and were concentrated in cropland at elevations from 600–900 m and on slope gradients from 30–40°. In addition, the landslide frequency increased as the annual rainfall levels increased over a large spatial scale, and the monthly distribution of landslides presented a significant association with the precipitation level. This study provides a powerful method for understanding the spatiotemporal distribution of landslides via a rare landslide catalog, which is important for engineering design and planning and risk management. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds
ISPRS Int. J. Geo-Inf. 2019, 8(10), 460; https://doi.org/10.3390/ijgi8100460 - 16 Oct 2019
Abstract
In this paper, a MATLAB tool for the automatic detection of the best locations to install a wireless sensor network (WSN) is presented. The implemented code works directly on high-resolution 3D point clouds and aims to help in positioning sensors that are part [...] Read more.
In this paper, a MATLAB tool for the automatic detection of the best locations to install a wireless sensor network (WSN) is presented. The implemented code works directly on high-resolution 3D point clouds and aims to help in positioning sensors that are part of a network requiring inter-visibility, namely, a clear line of sight (LOS). Indeed, with the development of LiDAR and Structure from Motion technologies, there is an opportunity to directly use 3D point cloud data to perform visibility analyses. By doing so, many disadvantages of traditional modelling and analysis methods can be bypassed. The algorithm points out the optimal deployment of devices following mainly two criteria: inter-visibility (using a modified version of the Hidden Point Removal operator) and inter-distance. Furthermore, an option to prioritize significant areas is provided. The proposed method was first validated on an artificial 3D model, and then on a landslide 3D point cloud acquired from terrestrial laser scanning for the real positioning of an ultrawide-band WSN already installed in 2016. The comparison between collected data and data acquired by the WSN installed following traditional patterns has demonstrated its ability for the optimal deployment of a WSN requiring inter-visibility. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling
ISPRS Int. J. Geo-Inf. 2019, 8(9), 397; https://doi.org/10.3390/ijgi8090397 - 05 Sep 2019
Cited by 2
Abstract
This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with [...] Read more.
This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with diverse topographic conditions. The second issue is the mixing problem of landslide inventory that the detection of landslide areas from remotely-sensed data generally includes source and run-out features unless the run-out portion can be removed manually with auxiliary data. To this end, different statistical sampling strategies and the run-out influence on random forests (RF)-based landslide susceptibility modeling are explored for Typhoon Morakot in 2009 in southern Taiwan. To address the construction of models with an extremely high false alarm error or missing error, this study integrated cost-sensitive analysis with RF to adjust the decision boundary to achieve improvements. Experimental results indicate that, compared with a logistic regression model, RF with the hybrid sample strategy generally performs better, achieving over 80% and 0.7 for the overall accuracy and kappa coefficient, respectively, and higher accuracies can be obtained when the run-out is treated as an independent class or combined with a non-landslide class. Cost-sensitive analysis significantly improved the prediction accuracy from 5% to 10%. Therefore, run-out should be separated from the landslide source and labeled as an individual class when preparing a landslide inventory. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment
ISPRS Int. J. Geo-Inf. 2019, 8(8), 332; https://doi.org/10.3390/ijgi8080332 - 27 Jul 2019
Cited by 3
Abstract
Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation [...] Read more.
Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features
ISPRS Int. J. Geo-Inf. 2019, 8(8), 321; https://doi.org/10.3390/ijgi8080321 - 24 Jul 2019
Cited by 5
Abstract
Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in [...] Read more.
Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Monitoring Ground Instabilities Using SAR Satellite Data: A Practical Approach
ISPRS Int. J. Geo-Inf. 2019, 8(7), 307; https://doi.org/10.3390/ijgi8070307 - 17 Jul 2019
Cited by 8
Abstract
Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data [...] Read more.
Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data are delivered, passing from a static view of the territory to a continuous streaming of ground motion measurements from space. Tuscany Region is the first worldwide example of a regional scale monitoring system based on satellite interferometric data. The processing chain here exploited combines a multi-interferometric approach with a time-series data mining algorithm aimed at recognizing benchmarks with significant trend variations. The system is capable of detecting the temporal changes of a wide variety of phenomena such as slow-moving landslides and subsidence, producing a high amount of data to be interpreted in a short time. Bulletins and reports are derived to the hydrogeological risk management actors at regional scale. The final output of the project is a list of potentially hazardous and accelerating phenomena that are verified on site by field campaign by completing a sheet survey in order to qualitatively estimate the risk and to suggest short-term actions to be taken by local entities. Two case studies, one related to landslides and one to subsidence, are proposed to highlight the potential of the monitoring system to early detect anomalous ground changes. Both examples represent a successful implementation of satellite interferometric data as monitoring and risk management tools, raising the awareness of local and regional authorities to geohazards. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Shallow Landslide Susceptibility Mapping in Sochi Ski-Jump Area Using GIS and Numerical Modelling
ISPRS Int. J. Geo-Inf. 2019, 8(3), 148; https://doi.org/10.3390/ijgi8030148 - 19 Mar 2019
Cited by 3
Abstract
The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the [...] Read more.
The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the ski-jump complex area. This study aims to conduct a seismic slope instability map of the area. A slope map was derived from a digital elevation model (DEM) and calculated using ArcGIS. The numerical modelling of slope stability with various slope angles was conducted using Geostudio. The Spencer method was applied to calculate the slope safety factors (Fs). The pseudostatic analysis was used to compute Fs considering seismic effect. A good correlation between Fs and slope angle was found. Combining these data, sets slope instability maps were achieved. Newmark displacement maps were also drawn according to empirical regression equations. The result shows that the static safety factor map corresponds to the existing slope instability locations in a shallow landslide inventory map. The seismic safety factor maps and Newmark displacement maps may be applied to predict potential landslides of the study area in the case of earthquake occurrence. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Review

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Open AccessReview
Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review
ISPRS Int. J. Geo-Inf. 2019, 8(12), 545; https://doi.org/10.3390/ijgi8120545 - 29 Nov 2019
Cited by 6
Abstract
Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are [...] Read more.
Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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