Special Issue "Novel Approaches in Landslide Monitoring and Data Analysis"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Jan Blahut
Website
Guest Editor
Institute of Rock Structure and Mechanics, Czech Academy of Sciences, Prague 18209, Czech Republic (IRSM CAS)
Interests: Landslide Monitoring, Landslide Hazard and Risk, Spatial Data Analysis
Prof. Dr. Michel Jaboyedoff
Website
Guest Editor
Institute of Earth Sciences, University of Lausanne, Geopolis 3793, CH-1015 Lausanne, Switzerland
Interests: natural hazards and risks
Special Issues and Collections in MDPI journals
Dr. Benni Thiebes
Website
Guest Editor
German Committee for Disaster Reduction (DKKV), 53113 Bonn, Germany
Interests: Landslide monitoring, landslide early warning systems, disaster risk reduction

Special Issue Information

Dear Colleagues,

It is a great pleasure for me to present this Special Issue of Applied Sciences, “Novel Approaches in Landslide Monitoring and Data Analysis”. In recent years, significant progress has been made in monitoring different types of landslides and analyzing measured data. This progress has expanded the knowledge of landslide processes. It is therefore necessary to summarize, share and disseminate the latest knowledge and expertise.

Our topics of interest include, but are not limited to:

  • Novel instruments for landslide monitoring
  • Advanced data analysis techniques
  • Prediction of landslide behavior from measured data
  • Remote sensing applications in landslide monitoring
  • Combination of several methods and instruments for better understanding of landslide processes
  • Determination of thresholds and alarm states
  • Precision, accuracy and repeatability of measurements
  • Landslide modelling

Dr. Jan Blahut
Prof. Michel Jaboyedoff
Dr. Benni Thiebes
Guest Editors

Manuscript Submission Information

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Keywords

  • landslide instrumentation and monitoring
  • monitoring data analysis
  • remote sensing
  • landslide prediction
  • multi-instrumental data analysis
  • thresholds
  • precision
  • accuracy
  • repeatability

Published Papers (4 papers)

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Research

Open AccessArticle
Integrated Field Surveying and Land Surface Quantitative Analysis to Assess Landslide Proneness in the Conero Promontory Rocky Coast (Italy)
Appl. Sci. 2020, 10(14), 4793; https://doi.org/10.3390/app10144793 - 13 Jul 2020
Abstract
Rock slopes involved in extensive landslide processes are often characterized by complex morphodynamics acting at different scales of space and time, responsible for different evolutionary scenarios. Mass Rock Creep (MRC) is a critical process for long-term geomorphological evolution of slopes and can likewise [...] Read more.
Rock slopes involved in extensive landslide processes are often characterized by complex morphodynamics acting at different scales of space and time, responsible for different evolutionary scenarios. Mass Rock Creep (MRC) is a critical process for long-term geomorphological evolution of slopes and can likewise characterize actively retreating coastal cliffs where, in addition, landslides of different typologies and size superimpose in space and time to marine processes. The rocky coast at the Conero promontory (central Adriatic Sea, Italy) offers a rare opportunity for better understanding the predisposing role of the morphostructural setting on coastal slope instability on a long-time scale. In fact, the area presents several landslides of different typologies and size and state of activity, together with a wide set of landforms and structural features effective for better comprehending the evolution mechanisms of slope instability processes. Different investigation methods were implemented; in particular, traditional geomorphological and structural field surveys were combined with land surface quantitative analysis based on a Digital Elevation Model (DEM) with ground-resolution of 2 m. The results obtained demonstrate that MRC involves the entire coastal slope, which can be zoned in two distinct sectors as a function of a different morphostructural setting responsible for highly differentiated landslide processes. Therefore, at the long-time scale, two different morphodynamic styles can be depicted along the coastal slopes that correspond to specific evolutionary scenarios. The first scenario is characterized by MRC-driven, time-dependent slope processes involving the entire slope, whereas the second one includes force-driven slope processes acting at smaller space–time scales. The Conero promontory case study highlights that the relationships between slope shape and structural setting of the deforming areas are crucial for reaching critical volumes to induce generalized slope collapse as the final stage of the MRC process. The results from this study stress the importance of understanding the role of morphostructures as predisposing conditions for generalized slope failures along rocky coasts involved in MRC. The findings discussed here suggest the importance of the assessment of the slope instability at the long time scale for a better comprehension of the present-day slope dynamics and its major implications for landslide monitoring strategies and the hazard mitigation strategies. Full article
(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)
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Open AccessArticle
Physical Model Experiments on Water Infiltration and Failure Modes in Multi-Layered Slopes under Heavy Rainfall
Appl. Sci. 2020, 10(10), 3458; https://doi.org/10.3390/app10103458 - 17 May 2020
Abstract
To assess the influence of an intermediate coarse layer on the slope stability during heavy rainfall, knowledge about water movement and how slope failure occurs is important. To clarify the characteristics of water infiltration in a multi-layered slope and assess its influence on [...] Read more.
To assess the influence of an intermediate coarse layer on the slope stability during heavy rainfall, knowledge about water movement and how slope failure occurs is important. To clarify the characteristics of water infiltration in a multi-layered slope and assess its influence on the slope failure modes, eight groups of physical slope models were investigated. It was found that the unsaturated hydraulic conductivity in the coarse layer (5.54 × 10−6 cm/s) was much lower than that of the fine layer (1.08 × 10−4 cm/s), which resulted in the capillary barrier working at a lower water content. Intermediate coarse layers embedded between finer ones may initially confine the infiltration within the overlying finer layers, delaying the infiltration and eventually inducing a lateral flow diversion in the inclined slope. Two different failure modes occurred in the model experiments: surface sliding occurred at the toe in the single-layer slope group and piping occurred at the toe in the multi-layered slope as the rainfall water accumulated, was diverted along the interface, and then broke through in the downslope direction of the intermediate coarse layer. The lateral flow diversion caused by the capillary barrier and the tilt angle may be the major factors influencing the difference of the failure modes. The result also revealed that the coarser layers may have negative effects on the slope stability. Full article
(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)
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Open AccessArticle
Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
Appl. Sci. 2020, 10(7), 2518; https://doi.org/10.3390/app10072518 - 06 Apr 2020
Cited by 1
Abstract
Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal [...] Read more.
Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study. Full article
(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)
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Open AccessArticle
Spatial Proximity-Based Geographically Weighted Regression Model for Landslide Susceptibility Assessment: A Case Study of Qingchuan Area, China
Appl. Sci. 2020, 10(3), 1107; https://doi.org/10.3390/app10031107 - 07 Feb 2020
Cited by 5
Abstract
Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship [...] Read more.
Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship between influencing factors and landslide occurrence within an area, resulting in an inadequate evaluation for the local landslides susceptibility. To address this issue, in this paper we propose a spatial proximity-based geographically weighted regression (S-GWR) model considering spatial non-stationarity of landslide data for assessing the landslide susceptibility. Spatial proximity is the basic input condition for the proposed S-GWR model. The challenge lies in defining the spatial proximity expression that shows the geographical features of landslides and therefore affects the model ability of S-GWR. Our solution chooses the slope unit as spatial adjacency, rather than the grid unit in DTM. The multicollinearity between landslide influencing factors is then eliminated through variance inflation factor (VIF) method and principal component analysis (PCA). The proposed model is subsequently validated by using data in Qingchuan County, southwestern China. Spatial non-stationary is identified for landslide data. A comparison with grid unit and four traditional evaluation models is conducted. Validation results using the area under the ROC (receiver operating characteristic) curve and success rate curve indicate that the spatial proximity-based GWR model with slope unit has the highest predictive accuracy (0.859 and 0.850 respectively). Full article
(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)
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