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Landslide Monitoring and Mapping II

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

Deadline for manuscript submissions: closed (25 July 2024) | Viewed by 23493

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Dr. Matteo Del Soldato

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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
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Prof. Dr. Roberto Tomás

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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
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Dr. Anna Barra

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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
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Dr. Davide Festa

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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

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Keywords

landslides
mapping
monitoring
ground-based instruments
remote sensing

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Published Papers (12 papers)

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Research

26 pages, 17323 KiB

Open AccessArticle

Linking Inca Terraces with Landslide Occurrence in the Ticsani Valley, Peru

by Gonzalo Ronda, Paul Santi, Isaac E. Pope, Arquímedes L. Vargas Luque and Christ Jesus Barriga Paria

Geosciences 2024, 14(11), 315; https://doi.org/10.3390/geosciences14110315 - 18 Nov 2024

Viewed by 1134

Abstract

Since the times of the Incas, farmers in the remote Andes of Peru have constructed terraces to grow crops in a landscape characterized by steep slopes, semiarid climate, and landslide geohazards. Recent investigations have concluded that terracing and irrigation techniques could enhance landslide risk due to the increase in water percolation and interception of surface flow in unstable slopes, leading to failure. In this study, we generated an inventory of 170 landslides and terraced areas to assess the spatial coherence, causative relations, and geomechanical processes linking landslide presence and Inca terraces in a 250 km² area located in the Ticsani valley, southern Peru. To assess spatial coherence, a tool was developed based on the confusion matrix approach. Performance parameters were quantified for areas close to the main rivers and communities yielding precision and recall values between 64% and 81%. On a larger scale, poor performance was obtained pointing to the existence of additional processes linked to landslide presence. To investigate the role of other natural variables in landslide prediction, a logistic regression analysis was performed. The results showed that terrace presence is a statistically relevant factor that bolsters landslide presence predictions, apart from first-order natural variables like distance to rivers, curvature, and geology. To explore potential geomechanical processes linking terraces and slope failures, FEM numerical modeling was conducted. Results suggested that both decreased permeability and increased surface irrigation, at 70% of the average annual rainfall, are capable of inducing slope failure. Overall, irrigated terraces appear to further promote slope instability due to infiltration of irrigation water in an area characterized by fluvial erosion, high relief, and poor geologic materials, exposing local communities to increased landslide risk. Full article

(This article belongs to the Special Issue Landslide Monitoring and Mapping II)

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15 pages, 2513 KiB

Open AccessArticle

The Evaluation of Rainfall Warning Thresholds for Shallow Slope Stability Based on the Local Safety Factor Theory

by Ya-Sin Yang, Hsin-Fu Yeh, Chien-Chung Ke, Lun-Wei Wei and Nai-Chin Chen

Geosciences 2024, 14(10), 274; https://doi.org/10.3390/geosciences14100274 - 16 Oct 2024

Viewed by 708

Abstract

Rainfall-induced shallow slope instability is a significant global hazard, often triggered by water infiltration that affects soil stability and involves dynamic changes in the hydraulic behavior of unsaturated soils. This study employs a hydro-mechanical coupled analysis model to assess the impact of rainfall on slope stability, focusing on the dynamic hydraulic behavior of unsaturated soils. By simulating the soil water content and slope stability under four different rainfall scenarios based on observational data and historical thresholds, this study reveals that higher rainfall intensity significantly increases the soil water content, leading to reduced slope stability. The results show a strong correlation between the soil water content and slope stability, with a 20 mm/h rainfall intensity threshold emerging as a reliable predictor of potential slope instability. This study contributes to a deeper understanding of slope stability dynamics and emphasizes the importance of proactive risk management in response to changing rainfall patterns while also validating current management practices and providing essential insight for improving early warning systems to effectively mitigate landslide risk. Full article

(This article belongs to the Special Issue Landslide Monitoring and Mapping II)

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17 pages, 4646 KiB

Open AccessArticle

A Unique Conditions Model for Landslide Susceptibility Mapping

by Florimond De Smedt and Prabin Kayastha

Geosciences 2024, 14(8), 197; https://doi.org/10.3390/geosciences14080197 - 24 Jul 2024

Viewed by 834

Abstract

Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic regression. We discuss the assumptions and interpretations of these methods, the relationships between them, and their strengths and weaknesses in case of categorical factors. Of particular interest is the conditional independence of the controlling factors and its effect on model bias. To avoid lack of conditional independence of factors and model bias, we present a unique conditions model that is always unbiased. To illustrate the theoretical developments, a practical application is given using observed landslides and geo-environmental factors from a previous study. The unique conditions model appears superior to the other models. Full article

(This article belongs to the Special Issue Landslide Monitoring and Mapping II)

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31 pages, 23478 KiB

Open AccessArticle

Landslide Susceptibility Assessment by Machine Learning and Frequency Ratio Methods Using XRAIN Radar-Acquired Rainfall Data

by José Maria dos Santos Rodrigues Neto and Netra Prakash Bhandary

Geosciences 2024, 14(6), 171; https://doi.org/10.3390/geosciences14060171 - 18 Jun 2024

Viewed by 1994

Abstract

This study is an efficiency comparison between four methods for the production of landslide susceptibility maps (LSMs), which include random forest (RF), artificial neural network (ANN), and logistic regression (LR) as the machine learning (ML) techniques and frequency ratio (FR) as a statistical method. The study area is located in the Southern Hiroshima Prefecture in western Japan, a locality known to suffer from rainfall-induced landslide disasters, the most recent one in July 2018. The landslide conditioning factors (LCFs) considered in this study are lithology, land use, altitude, slope angle, slope aspect, distance to drainage, distance to lineament, soil class, and mean annual precipitation. The rainfall LCF data comprise XRAIN (eXtended RAdar Information Network) radar records, which are novel in the task of LSM production. The accuracy of the produced LSMs was calculated with the area under the receiver operating characteristic curve (AUROC), and an automatic hyperparameter tuning and result comparison system based on AUROC scores was utilized. The calculated AUROC scores of the resulting LSMs were 0.952 for the RF method, 0.9247 for the ANN method, 0.9016 for the LR method, and 0.8424 for the FR. It is also noteworthy that the ML methods are substantially swifter and more practical than the FR method and allow for multiple and automatic experimentations with different hyperparameter settings, providing fine and accurate outcomes with the given data. The results evidence that ML techniques are more efficient when dealing with hazard assessment problems such as the one exemplified in this study. Although the conclusion that the RF method is the most accurate for LSM production as found by other authors in the literature, ML method efficiency may vary depending on the specific study area, and thus the use of an automatic multi-method LSM production system with hyperparameter tuning such as the one utilized in this study is advised. It was also found that XRAIN radar-acquired mean annual precipitation data are effective when used as an LCF in LSM production. Full article

(This article belongs to the Special Issue Landslide Monitoring and Mapping II)

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16 pages, 51049 KiB

Open AccessArticle

UAV, GNSS, and GIS for the Rapid Assessment of Multi-Occurrence Landslides

by Konstantinos G. Nikolakopoulos, Aggeliki Kyriou and Ioannis K. Koukouvelas

Geosciences 2024, 14(6), 160; https://doi.org/10.3390/geosciences14060160 - 9 Jun 2024

Cited by 1 | Viewed by 2241

Abstract

Intense long-duration rainfall or extreme precipitation in a few hours can provoke many simultaneous shallow landslides. In the past, the term multi-occurrence regional landslides (MORLEs) was proposed to describe such phenomena. In the current study, unmanned aerial vehicles in combination with a global navigation satellite system sensor and geographical information systems seem to be the ideal solution for the rapid assessment of many landslides occurring in Aitoloakarnania Prefecture, Western Greece. Fourteen landslides were accurately mapped within a few working days, and precise orthophotos and reports were created and submitted to the local authorities. The analysis of meteorological data proved that there is a peak in precipitation height that triggers the MORLEs in the specific area. Specifically, the value of the daily precipitation was defined at 80 mm. Full article

(This article belongs to the Special Issue Landslide Monitoring and Mapping II)

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18 pages, 7756 KiB

Open AccessArticle

Integrating Dendrogeomorphology into Stress–Strain Numerical Models: An Opportunity to Monitor Slope Dynamic

by Silvia Curioni, Paola Gattinoni and Giovanni Leonelli

Geosciences 2024, 14(5), 129; https://doi.org/10.3390/geosciences14050129 - 9 May 2024

Viewed by 832

Abstract

Monitoring systems are recognized worldwide as fundamental tools for landslide risk management. However, monitoring can be difficult when dealing with large slopes in forested areas. In these situations, dendrogeomorphology can offer a low-cost and low-impact alternative for providing distributed information with an annual temporal resolution. The present study is a first attempt to integrate dendrometric and dendrogeomorphic data into a numerical finite difference model, in order to simulate the stress–strain behavior of the tree-slope system. By using a parametrical approach, the capability of the numerical model to effectively reproduce the tree stem anomalies (i.e., tilting angle, J-shaped feature, and internal stresses causing tree-ring growth anomalies such as eccentric growth and reaction wood) was verified, and the target parameters for the model calibration were identified based on a sensitivity analysis, which emphasized the relevance of the wood deformability; moreover, the interpretation of results allowed to point out different peculiarities (in terms of type of deformation, falling direction, and distribution of internal stresses) for different slope conditions (kinematics and depth of the failure surface) and different zones of the landslide (head scarp, main body, and toe). Afterwards, the modeling approach was applied to the Val Roncaglia landslide (Northen Italy), which involves a complex roto-translational kinematics, characterized by multiple sliding surfaces. The simulated stem anomalies showed good agreement with the ones arising from onsite dendrometric surveys, and they confirmed the conceptual model of the landslide, enabling the planning of further specific investigations. Moreover, the capability of the model in reproducing the tilting angle of trees, if correlated to their eccentricity, could provide a quite long time series (over more than 50–60 years) of the landslide reactivation and allow the use of dendrochronological data for the model calibration, thereby enhancing slope dynamic monitoring and landslide risk management. Full article

(This article belongs to the Special Issue Landslide Monitoring and Mapping II)

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19 pages, 11500 KiB

Open AccessArticle

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 3785

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 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

Open AccessArticle

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 1286

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 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

Open AccessArticle

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 1789

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 2447

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 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

Open AccessEditor’s ChoiceArticle

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 - 3 Dec 2023

Viewed by 3133

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. 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

Open AccessArticle

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

Cited by 1 | Viewed by 1654

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 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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