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Geosciences
Special Issues
Landslide Monitoring and Mapping II
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Landslide Monitoring and Mapping II

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 9529

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
Prof. Dr. Roberto Tomás

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Alicante, Campus de San Vicente del Raspeig s/n, 03080 Alicante, Spain
Interests: rock mechanics; landslides; infrastructures; remote sensing; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Barra

E-Mail Website
Guest Editor
Remote Sensing Department, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Avinguda Carl Friedrich Gauss, 7, 08860 Castelldefels, Barcelona, Spain
Interests: DInSAR; PSI; geohazards monitoring; landslide mapping and monitoring; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Festa

E-Mail Website
Guest Editor
Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
Interests: landslide mapping and monitoring

Special Issue Information

Dear Colleagues,

Landslides are one of the main natural hazards affecting territories 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 instrumentation can be deployed to discover new movements, measure the motion of landslides, and evaluate their temporal evolution. At present, thanks to technological progress (e.g., cloud computing) and technical advancements (e.g., new processing algorithms), the scientific community can adopt remote sensing approaches for regularly analyzing and monitoring land movements in local and national-scale areas, as well as in as-yet unexplored regions. These applications will also allow the development of 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
Prof. Dr. Roberto Tomás
Dr. Anna Barra
Dr. Davide Festa
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 (6 papers)

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Research

19 pages, 11500 KiB  
Article
Geotechnical and Geophysical Assessment of the 2021 Tamban Chimbo Landslide, Northern Andes of Ecuador
by Isela Salinas, Abelardo Paucar, María Quiñónez-Macías, Francisco Grau, Marysabel Barragán-Taco, Theofilos Toulkeridis and Kervin Chunga
Geosciences 2024, 14(4), 104; https://doi.org/10.3390/geosciences14040104 - 16 Apr 2024
Viewed by 1942
Abstract
The recent landslide at the Tamban site, on 21 December 2021 (23:30 local time), provides relevant information on the trigger mechanisms and their relationship with geological factors. Therefore, the predominant aims of the current study were to identify the lithological units in the [...] Read more.
The recent landslide at the Tamban site, on 21 December 2021 (23:30 local time), provides relevant information on the trigger mechanisms and their relationship with geological factors. Therefore, the predominant aims of the current study were to identify the lithological units in the rocky substrate and subsoil from geophysical surveys, delineating the thickness of the tuff- and lapilli-supported fall layers. Additionally, we evaluated the deformation dynamics from probabilistic and deterministic analysis, where a plane with well-differentiated discontinuities of normal-type geological fault was evidenced. This deformation feature was associated with a planar-type landslide that reached a debris flow up to 330 m distance, with varied thicknesses. Furthermore, we conducted a probabilistic analysis, which started from the characteristics of the post-slide material analyzed through triaxial trials that were conducted to a retro-analysis in order to obtain the parameters of the moment the event occurred. With the base parameters to perform the landslide analysis and determine its safety factors in compliance with current regulations, a reinforced earth configuration was applied using the Maccaferri’s Terramesh method. Hence, it was possible to provide an analysis methodology for further geological scenarios of landslides that occurred in the province of Bolívar, the northern Andes of Ecuador. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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16 pages, 24532 KiB  
Article
Investigation and Monitoring for Ever-Updating Engineering Geological Models: The Example of the Passo della Morte Landslide System
by Angelo Ballaera, Pietro Festi, Lisa Borgatti, Giulia Bossi and Gianluca Marcato
Geosciences 2024, 14(4), 94; https://doi.org/10.3390/geosciences14040094 - 26 Mar 2024
Viewed by 611
Abstract
In mountainous regions, where large valleys are essential corridors for settlements and infrastructures, landslide hazard management is a pressing challenge. Large, slow-moving landslides are sometimes difficult to detect. On the one hand, the identification of geomorphological evidence supported by a detailed analysis of [...] Read more.
In mountainous regions, where large valleys are essential corridors for settlements and infrastructures, landslide hazard management is a pressing challenge. Large, slow-moving landslides are sometimes difficult to detect. On the one hand, the identification of geomorphological evidence supported by a detailed analysis of possible geological predisposing factor is crucial. On the other hand, to confirm the state of activity of the landslide, displacements should also be detected through monitoring. However, monitoring is challenging when large areas and volumes are involved and when cost effectiveness is an issue. This study presents a comprehensive analysis of the Passo della Morte landslide system, located in the Carnian Alps, which has historically posed a significant threat to critical road infrastructures, including a 2200 m long tunnel. The area is exploited as an example of how an iterative 3M approach (Monitoring, Modeling, and Mitigation), can inform and update engineering geological models of unstable slopes by enabling a detailed comprehension of landslide dynamics, facilitating in turn the development of more effective strategies for risk management and mitigation. Through detailed investigation and continuous monitoring over nearly two decades, the engineering geological model has been refined, integrated with new field data, and has progressively improved understanding of slope instability processes. This work underscores the importance of a dynamic and adaptive approach to geological hazard management, providing a valuable framework for similar challenges in other regions. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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16 pages, 3772 KiB  
Article
Effects of Land Cover Changes and Rainfall Variation on the Landslide Size–Frequency Distribution in a Mountainous Region of Western Japan
by Takashi Kimura
Geosciences 2024, 14(3), 59; https://doi.org/10.3390/geosciences14030059 - 23 Feb 2024
Viewed by 999
Abstract
This study investigated the size–frequency distribution of 512 landslides triggered by heavy rain in July 2018 on Omishima Island, western Japan. Since the island has undergone rapid land use and land cover changes in recent decades, this study statistically examined the impact of [...] Read more.
This study investigated the size–frequency distribution of 512 landslides triggered by heavy rain in July 2018 on Omishima Island, western Japan. Since the island has undergone rapid land use and land cover changes in recent decades, this study statistically examined the impact of past land cover changes on the shape of, and local variability in, the size–frequency distribution using the inverse gamma model. The possible influence of rainfall conditions was also examined. The landslides were classified based on the severity of anthropogenic disturbance and rainfall using a 56-year (1962–2018) land cover trajectory map and hourly rainfall distribution data. The results indicated that the land cover change (mainly forest conversion into farmland and its abandonment) affected the size and frequency of landslides that occurred decades after the disturbance. Although all landslide groups had similar small rollovers (location of probability peak; 0.042–0.075 × 10−3 km2), the scaling exponents of the negative power-law decay were lower for landslides in secondary forest and newly developed farmland (ρ = 1.084–1.231) than in old forest and farmland (ρ = 2.504–2.611). This difference is considered significant compared to general exponent values (ρ = 2.30 ± 0.56), suggesting that farmland development after 1962 caused widespread slope instability, leading to an increase in the proportion of large landslides. By contrast, no clear correlations with rainfall intensity were found, primarily due to complex localised variations in rainfall conditions. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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26 pages, 13539 KiB  
Article
Integrating Seismic Methods for Characterizing and Monitoring Landslides: A Case Study of the Heinzenberg Deep-Seated Gravitational Slope Deformation (Switzerland)
by Franziska Glueer, Anne-Sophie Mreyen, Léna Cauchie, Hans-Balder Havenith, Paolo Bergamo, Miroslav Halló and Donat Fäh
Geosciences 2024, 14(2), 28; https://doi.org/10.3390/geosciences14020028 - 24 Jan 2024
Viewed by 1579
Abstract
While geodetic measurements have long been used to assess landslides, seismic methods are increasingly recognized as valuable tools for providing additional insights into subsurface structures and mechanisms. This work aims to characterize the subsurface structures of the deep-seated gravitational slope deformation (DSGSD) at [...] Read more.
While geodetic measurements have long been used to assess landslides, seismic methods are increasingly recognized as valuable tools for providing additional insights into subsurface structures and mechanisms. This work aims to characterize the subsurface structures of the deep-seated gravitational slope deformation (DSGSD) at Heinzenberg through the integration of active and passive seismic measurements. Seismic techniques can hereby deliver additional information on the subsurface structure and mechanisms involved, e.g., the degree of rock mass degradation, the resonant frequencies of the potentially unstable compartments, and the local fracture network orientations that are influenced by wavefield polarization. By employing advanced methods such as H/V analysis, site-to-reference spectral ratios, polarization analysis, surface wave analysis, and the joint multizonal transdimensional Bayesian inversion of velocity structures, we establish a comprehensive baseline model of the landslide at five selected sites. This baseline model shall help identify potential changes after the refilling of Lake Lüsch, which started in 2021. Our results reveal the rupture surface of the DSGSD at various depths ranging from 30 m at the top to over 90 m in the middle of the slope. Additionally, we estimate key parameters including the shear wave velocities of the different rock masses. The 2D geophysical profiles and rock mass properties contribute to the understanding of the subsurface geometry, geomechanical properties, and potential water pathways. This study demonstrates the significance of integrating seismic methods with traditional geodetic measurements and geomorphologic analysis techniques for a comprehensive assessment of landslides, enhancing our ability to monitor and mitigate hazardous events. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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21 pages, 290958 KiB  
Article
Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring
by Doris Hermle, Markus Keuschnig, Michael Krautblatter and Valentin Tertius Bickel
Geosciences 2023, 13(12), 371; https://doi.org/10.3390/geosciences13120371 - 03 Dec 2023
Viewed by 2155
Abstract
Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. [...] Read more.
Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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16 pages, 7895 KiB  
Article
Naïve and Semi-Naïve Bayesian Classification of Landslide Susceptibility Applied to the Kulekhani River Basin in Nepal as a Test Case
by Florimond De Smedt, Prabin Kayastha and Megh Raj Dhital
Geosciences 2023, 13(10), 306; https://doi.org/10.3390/geosciences13100306 - 13 Oct 2023
Viewed by 1238
Abstract
Naïve Bayes classification is widely used for landslide susceptibility analysis, especially in the form of weights-of-evidence. However, when significant conditional dependence is present, the probabilities derived from weights-of-evidence are biased, resulting in an overestimation of landslide susceptibility. As a solution, this study presents [...] Read more.
Naïve Bayes classification is widely used for landslide susceptibility analysis, especially in the form of weights-of-evidence. However, when significant conditional dependence is present, the probabilities derived from weights-of-evidence are biased, resulting in an overestimation of landslide susceptibility. As a solution, this study presents a semi-naïve Bayesian method for landslide susceptibility mapping by combining logistic regression with weights-of-evidence. The utility of the method is tested by application to a case study in the Kulekhani River Basin in Central Nepal. The results show that the naïve Bayes approach with weights-of-evidence overpredicts the posterior probability of landslide occurrence by a factor of about two, while the semi-naïve Bayes approach, which uses logistic regression with weights-of-evidence, is unbiased and has more discriminatory power for landslide susceptibility mapping. In addition, the semi-naïve Bayes approach can statistically distinguish the main factors that promote landslides and allows us to estimate the model uncertainty by calculating the standard error of the predictions. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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