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Landslide Studies Integrating Remote Sensing and Geophysical Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 21944

Special Issue Editors


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Guest Editor
Department of Geology, Liege University, Georisk and Environment, Sart Tilman, B-4000 Liege, Belgium
Interests: geohazard assessment; landslide surveys; seismic and seismological investigations; geological and numerical modelling

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Guest Editor
UNOSAT-Division for Satellite Analysis and Applied Research, United Nations Institute for Training and Research (UNITAR),7 bis, avenue de la Paix, CH-1202 Geneva 2, Switzerland
Interests: optical and radar remote sensing; landslide monitoring; natural hazard assessment; susceptibility zonation; geomorphology mapping; climate change; AI4EO
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Guest Editor
Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: landscape evolution; geophysical hazards; archaeology; cultural heritage; remote sensing; earth observation; InSAR; landslides; land subsidence; ground instability
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Guest Editor
Department of Earth Sciences, University of Firenze, 50121 Firenze, Italy
Interests: exploration geophysics; landslides; engineering geology; resilience; natural hazards; remote sensing; seismics; earth sciences
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Guest Editor
Institute of Earth Sciences, Faculté des Géosciences et de l'Environnement, University of Lausanne, 1015 Lausanne, Switzerland
Interests: engineering geology; geohazards; landslides; remote sensing; LiDAR; InSAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landslide investigation and monitoring is increasingly combining inputs from remotely sensed (RS), ground-based, and subsurface data. RS (optical, InSAR, UAV) and geophysical data (electrical, seismic, seismological and electro-magnetic, and 1-/2-/3-D surveys, as well as borehole information) together provide a more comprehensive view of those geohazard phenomena, especially if active mass movements are considered. However, often, these surveys are organised separately, and a full integration of the surface and subsurface information is barely performed. Most data representations lack a model that allows for the joint interpretation of RS and geophysical data, also because of the different scales on which the landslides are generally studied when using these methods, spanning from a regional/wide to local scale. Such models, e.g., based on 3D geomodelling, should also help better cross-validate RS, surface, and subsurface information. In particular, geophysical data interpretation can be affected by high levels of uncertainty—well-integrated and jointly modelled RS, surface, and geophysical data will likely help reduce this uncertainty.

Finally, for large mass movements or a group of investigated massive failures, the surface and subsurface models, even if well-constructed by integrating all processed inputs and outputs, are difficult to analyse and interpret as a whole because of the complexity of information included in the models. Standard visualisation tools (using combined map and section views, as well as animation of models) may not be sufficient to get a deeper insight into the structure and, even more importantly for active landslides, into the dynamics of failure processes. Emerging extended (Virtual) reality (XR) technologies (that have already been used for quite some time in other geoscience fields, mostly those related to exploitation, but less in geohazard research) may help to overcome difficulties of complex model visualisation and interpretation.

Sincerely

Dr. Hans-Balder Havenith
Dr. Romy Schlögel
Dr. Francesca Cigna
Dr. Veronica Pazzi
Dr. Marc-Henri Derron
Guest Editors

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Keywords

  • Joint remote sensing and geophysical surveys
  • Active and old landslides
  • Landslide monitoring
  • 3D data integration models
  • Cross-validation of RS, ground-based, and subsurface information
  • Visualization in XR
  • Virtual subaerial and subsurface field visits

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Published Papers (8 papers)

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Research

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26 pages, 55211 KiB  
Article
Assessing the Hazard of Deep-Seated Rock Slope Instability through the Description of Potential Failure Scenarios, Cross-Validated Using Several Remote Sensing and Monitoring Techniques
by Charlotte Wolff, Michel Jaboyedoff, Li Fei, Andrea Pedrazzini, Marc-Henri Derron, Carlo Rivolta and Véronique Merrien-Soukatchoff
Remote Sens. 2023, 15(22), 5396; https://doi.org/10.3390/rs15225396 - 17 Nov 2023
Viewed by 1072
Abstract
Foreseeing the failure of important unstable volumes is a major concern in the Alps, especially due to the presence of people and infrastructures in the valleys. The use of monitoring and remote sensing techniques is aimed at detecting potential instabilities and the combination [...] Read more.
Foreseeing the failure of important unstable volumes is a major concern in the Alps, especially due to the presence of people and infrastructures in the valleys. The use of monitoring and remote sensing techniques is aimed at detecting potential instabilities and the combination of several techniques permits the cross-validation of the detected movements. Supplemented with field mapping and structural analysis, it is possible to define possible scenarios of rupture in terms of volume, mechanisms of failure and susceptibility. A combined observation strategy was applied to the study of major instability located in the Ticinese Alps (Switzerland), Cima del Simano, where the monitoring started in 2006 with the measurement of opened cracks with extensometers. Since 2021, the monitoring has been completed by LiDAR, satellite and GB-InSAR observations and structural analysis. Here, slow but constant movements of about 7 mm/yr were detected along with rockfall activities near the Simano summit. Eight failure scenarios of various sizes ranging from 2.3 × 105 m3 to 51 × 106 m3, various mechanisms (toppling, planar, wedge and circular sliding) and various occurrence probabilities were defined based on the topography and the monitoring results and by applying a Slope Local Base Level (SLBL) algorithm. Weather acquisition campaigns by means of thermologgers were also conducted to suggest possible causes that lead to the observed movements and to suggest the evolution of the instabilities with actual and future climate changes. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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27 pages, 10161 KiB  
Article
Geophysical Surveys for Geotechnical Model Reconstruction and Slope Stability Modelling
by Agnese Innocenti, Ascanio Rosi, Veronica Tofani, Veronica Pazzi, Elisa Gargini, Elena Benedetta Masi, Samuele Segoni, Davide Bertolo, Marco Paganone and Nicola Casagli
Remote Sens. 2023, 15(8), 2159; https://doi.org/10.3390/rs15082159 - 19 Apr 2023
Cited by 4 | Viewed by 1910
Abstract
Performing a reliable stability analysis of a landslide slope requires a good understanding of the internal geometries and an accurate characterisation of the geotechnical parameters of the identified strata. Geotechnical models are commonly based on geomorphological data combined with direct and intrusive geotechnical [...] Read more.
Performing a reliable stability analysis of a landslide slope requires a good understanding of the internal geometries and an accurate characterisation of the geotechnical parameters of the identified strata. Geotechnical models are commonly based on geomorphological data combined with direct and intrusive geotechnical investigations. However, the existence of numerous empirical correlations between seismic parameters (e.g., S-wave velocity) and geotechnical parameters in the literature has made it possible to investigate areas that are difficult to reach with direct instrumentation. These correlations are often overlooked even though they enable a reduction in investigation costs and time. By means of geophysical tests, it is in fact possible to estimate the N-SPT value and derive the friction angle from results obtained from environmental seismic noise measurements. Despite the empirical character and a certain level of uncertainty derived from the estimation of geotechnical parameters, these are particularly useful in the preliminary stages of an emergency, when straight data are not available and on all those soils where other direct in situ tests are not reliable. These correlations were successfully applied to the Theilly landslide (Western Alps, Italy), where the geotechnical model was obtained by integrating the results of a multi-parameter geophysical survey (H/V seismic noise and ground-penetrating radar) with stratigraphic and geomorphological observations, digital terrain model and field survey data. The analysis of the triggering conditions of the landslide was conducted by means of hydrological–geotechnical modelling, evaluating the behaviour of the slope under different rainfall scenarios and considering (or not) the stabilisation interventions present on the slope. The results of the filtration analyses for all events showed a top-down saturation mechanism, which led to the formation of a saturated face with a maximum thickness of 5 m. Stability analyses conducted for the same events showed the development of a shallow landslide in the first few metres of saturated soil. The modelling results are compatible with the actual evolution of the phenomenon and allow us to understand the triggering mechanism, providing models to support future interventions. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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22 pages, 7515 KiB  
Article
New Insights into the Internal Structures and Geotechnical Rock Properties of the Giant San Andrés Landslide, El Hierro Island, Spain
by Jan Klimeš, Yawar Hussain, Anne-Sophie Mreyen, Léna Cauchie, Romy Schlögel, Valentine Piroton, Matěj Petružálek, Jan Blahůt, Miloš René, Stavros Meletlidis and Hans-Balder Havenith
Remote Sens. 2023, 15(6), 1627; https://doi.org/10.3390/rs15061627 - 17 Mar 2023
Cited by 1 | Viewed by 1909
Abstract
The San Andrés landslide on El Hierro (Canary Islands) represents a rare opportunity to study an incipient volcanic island flank collapse with an extensive onshore part. The presented research improves the knowledge of the internal structure and rock characteristics of a mega-landslide before [...] Read more.
The San Andrés landslide on El Hierro (Canary Islands) represents a rare opportunity to study an incipient volcanic island flank collapse with an extensive onshore part. The presented research improves the knowledge of the internal structure and rock characteristics of a mega-landslide before its complete failure. The investigation combines multiple geophysical measurement techniques (active and passive seismic) and remotely sensed, high spatial resolution surveys (unmanned aerial vehicle) with in situ and laboratory geotechnical descriptions to characterize the rock properties inside and outside the San Andrés landslide. The available geophysical and geological data have been integrated into 3D geomodels to enhance their visual interpretation. The onshore geophysical investigations helped detect the possible San Andrés landslide sliding surfaces at depths between 320 m and 420 m, with a rather planar geometry. They also revealed that rocks inside and outside of the landslide had similar properties, which suggests that the previous fast movements of the landslide did not affect the bulk properties of the displaced rocks as the failure chiefly occurred along the weakened sliding plane. Uniaxial strength tests on basalt rocks further indicate a high variability and spatial heterogeneity of the rock strength properties due to the different types of volcanic rocks and their texture. The new information on the rock properties and structural setting of the San Andrés landslide can now be used to develop realistic geotechnical slope models of the onshore part of the flank collapse that are possibly applicable for slope stability or deformation calculations. It will also help assess related hazards marked by a low occurrence probability and a high impact potential. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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23 pages, 27752 KiB  
Article
Integrated Geophysical Imaging and Remote Sensing for Enhancing Geological Interpretation of Landslides with Uncertainty Estimation—A Case Study from Cisiec, Poland
by Małgorzata Wróbel, Iwona Stan-Kłeczek, Artur Marciniak, Mariusz Majdański, Sebastian Kowalczyk, Adam Nawrot and Justyna Cader
Remote Sens. 2023, 15(1), 238; https://doi.org/10.3390/rs15010238 - 31 Dec 2022
Cited by 4 | Viewed by 2821
Abstract
Landslides, as one of the main problems in mountainous areas, are a challenging issue for modern geophysics. The triggers that cause these phenomena are diverse (including geological, geomorphological, and hydrological conditions, climatic factors, and earthquakes) and can occur in conjunction with each other. [...] Read more.
Landslides, as one of the main problems in mountainous areas, are a challenging issue for modern geophysics. The triggers that cause these phenomena are diverse (including geological, geomorphological, and hydrological conditions, climatic factors, and earthquakes) and can occur in conjunction with each other. Human activity is also relevant, undoubtedly contributing to the intensification of landslide phenomena. One of these is the production of artificial snow on ski slopes. This paper presents a multimethod approach for imaging the landslide structure in Cisiec, in southwestern Poland, where such a situation occurs. In the presented work, the integration of remote sensing with multi-method geophysical imaging was used to visualize landslide zones, and to estimate ground motion. To verify the uncertainty of the obtained data, the combination of electrical resistivity tomography (ERT), multi-channel analysis of surface waves (MASW), and seismic refraction method (SRT) was supported by synthetic modeling. Using geophysical data with accurate GPS-based topography and a terrestrial laser scanning-based digital terrain model (DTM), it was possible to model the spatial variability and surface area of the landslide more precisely, as well as to estimate the velocity field in the nearest surface more accurately. The final result shows displacement up to 1 m on the ground surface visible on the DTM models, while the geophysical methods confirm the change in internal structure. The proposed methodology is fast, cost-effective, and can be used to image the structure of landslides, where the shallowest parts are usually complex and thus difficult to observe seismically. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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19 pages, 23809 KiB  
Article
Multi-Level Data Analyses in the Gajevo Landslide Research, Croatia
by Laszlo Podolszki, Luka Miklin, Ivan Kosović and Vlatko Gulam
Remote Sens. 2023, 15(1), 200; https://doi.org/10.3390/rs15010200 - 30 Dec 2022
Cited by 1 | Viewed by 1663
Abstract
The Gajevo landslide is located in a hilly area of northern Croatia, where numerous landslides endanger and damage houses, roads, water systems, and power lines. Nevertheless, available landslide data are relatively scarce. Therefore, the Gajevo landslide location was chosen for detailed research and [...] Read more.
The Gajevo landslide is located in a hilly area of northern Croatia, where numerous landslides endanger and damage houses, roads, water systems, and power lines. Nevertheless, available landslide data are relatively scarce. Therefore, the Gajevo landslide location was chosen for detailed research and the development of a typical landslide model for this area. During initial research, the geographical and geological settings were reviewed and historical orthophotos were analysed. Due to the complexity and vulnerability of the area, the location required detailed investigations and the integration of multi-level data: remote (based on high-resolution LiDAR data) and field landslide mapping were performed and a map of the landslide area was developed. Precipitation data were reviewed, while shallow boreholes with material sampling and geophysical measurements provided information on material characteristics and 3D (depth) insight. As a result, knowledge was gained about material resistivity and composition along with the depth of sliding surfaces, and an engineering geological map of the Gajevo landslide area with the landslide and directly endangered areas marked was developed to be used by the local community in landslide risk assessment. As it is reasonable to expect that an extreme rainfall event will occur in combination with snowmelt in the coming years, resulting in the reactivation of Gajevo landslide, further research and continuous landslide monitoring are recommended. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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19 pages, 27868 KiB  
Article
A Novel Intelligent Method Based on the Gaussian Heatmap Sampling Technique and Convolutional Neural Network for Landslide Susceptibility Mapping
by Yibing Xiong, Yi Zhou, Futao Wang, Shixin Wang, Zhenqing Wang, Jianwan Ji, Jingming Wang, Weijie Zou, Di You and Gang Qin
Remote Sens. 2022, 14(12), 2866; https://doi.org/10.3390/rs14122866 - 15 Jun 2022
Cited by 11 | Viewed by 2494
Abstract
Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation, land use management, and as a reference for decision-making. Convolutional neural networks (CNNs) in deep learning have been successfully applied to LSM studies and have been shown to improve the accuracy of [...] Read more.
Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation, land use management, and as a reference for decision-making. Convolutional neural networks (CNNs) in deep learning have been successfully applied to LSM studies and have been shown to improve the accuracy of LSM. Although optimizing the quality of negative samples at the input step of a deep learning model can improve the accuracy of the model, the risk of model overfitting may increase. In this study, an LSM method based on the Gaussian heatmap sampling technique and a CNN was developed from the perspective of LSM dataset sampling. A Gaussian heatmap sampling technique was used to enrich the variety of landslide inventory at the input step of the deep learning model to improve the accuracy of the LSM results. This sampling technique involved the construction of a landslide susceptibility Gaussian heatmap neural network model, LSGH-Net, by combining a CNN. A series of optimization strategies such as attention mechanism, dropout, etc., were applied to improve the model structure and training process. The results demonstrated that the proposed approach outperformed the benchmark CNN-based algorithm in terms of metrics (Accuracy = 95.30%, F1 score = 95.13%, and Sensitivity = 91.79%). The Gaussian heatmap sampling technique effectively improved the accuracy of frequency histograms of the landslide susceptibility index, which provided finer-grained mapping details and more reasonable landslide density. By analyzing Gaussian heatmap at different scales, the approach proposed in this paper is an important reference for different regions and other disaster susceptibility studies as well. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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16 pages, 23333 KiB  
Article
Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration
by Krisztina Kelevitz, Alessandro Novellino, Arnaud Watlet, James Boyd, James Whiteley, Jonathan Chambers, Colm Jordan, Tim Wright, Andrew Hooper and Juliet Biggs
Remote Sens. 2022, 14(12), 2836; https://doi.org/10.3390/rs14122836 - 13 Jun 2022
Cited by 5 | Viewed by 2300
Abstract
With the advances of ESA’s Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission, there are freely available remote sensing ground deformation observations all over the globe that allow continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims [...] Read more.
With the advances of ESA’s Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission, there are freely available remote sensing ground deformation observations all over the globe that allow continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims to include these remote sensing data in the effort at forecasting and mitigating hazards across the UK. In this paper, we present a case study of the Hollin Hill landslide in North Yorkshire where a variety of ground-based geophysical measurements are available for comparison with InSAR data. To include Sentinel-1 data in the UK’s Digital Environment, it is important to understand the advantages and limitations of these observations and interpret them appropriately. The Hollin Hill landslide observatory (HHLO) is used by the British Geological Survey to understand landslide processes, and to trial new technologies and methodologies for slope stability characterisation and monitoring. In July 2019, six corner reflectors were installed to improve the coherence of the InSAR measurements. We use Sentinel-1 InSAR data acquired between October 2015 and January 2019 to study the behaviour of this landslide, and find that the line-of-sight component of the down-slope movement is 2.7 mm/year in the descending track, and 7.5–7.7 mm/year in the ascending track. The InSAR measurements also highlight the seasonal behaviour of this landslide. Using InSAR data after the installation of the six corner reflectors, we are able to track the most recent movement on the landslide that occurred in January 2021. This result is in agreement with other ground-based measurements such as tracking of pegs, and soil moisture data derived from electrical resistivity tomography. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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Review

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33 pages, 9288 KiB  
Review
Review on the Geophysical and UAV-Based Methods Applied to Landslides
by Yawar Hussain, Romy Schlögel, Agnese Innocenti, Omar Hamza, Roberto Iannucci, Salvatore Martino and Hans-Balder Havenith
Remote Sens. 2022, 14(18), 4564; https://doi.org/10.3390/rs14184564 - 13 Sep 2022
Cited by 26 | Viewed by 5665
Abstract
Landslides (LS) represent geomorphological processes that can induce changes over time in the physical, hydrogeological, and mechanical properties of the involved materials. For geohazard assessment, the variations of these properties might be detected by a wide range of non-intrusive techniques, which can sometimes [...] Read more.
Landslides (LS) represent geomorphological processes that can induce changes over time in the physical, hydrogeological, and mechanical properties of the involved materials. For geohazard assessment, the variations of these properties might be detected by a wide range of non-intrusive techniques, which can sometimes be confusing due to their significant variation in accuracy, suitability, coverage area, logistics, timescale, cost, and integration potential; this paper reviews common geophysical methods (GM) categorized as Emitted Seismic and Ambient Noise based and proposes an integrated approach between them for improving landslide studies; this level of integration (among themselves) is an important step ahead of integrating geophysical data with remote sensing data. The aforementioned GMs help to construct a framework based on physical properties that may be linked with site characterization (e.g., a landslide and its subsurface channel geometry, recharge pathways, rock fragments, mass flow rate, etc.) and dynamics (e.g., quantification of the rheology, saturation, fracture process, toe erosion, mass flow rate, deformation marks and spatiotemporally dependent geogenic pore-water pressure feedback through a joint analysis of geophysical time series, displacement and hydrometeorological measurements from the ground, air and space). A review of the use of unmanned aerial vehicles (UAV) based photogrammetry for the investigation of landslides was also conducted to highlight the latest advancement and discuss the synergy between UAV and geophysical in four possible broader areas: (i) survey planning, (ii) LS investigation, (iii) LS dynamics and (iv) presentation of results in GIS environment. Additionally, endogenous source mechanisms lead to the appearance of deformation marks on the surface and provide ground for the integrated use of UAV and geophysical monitoring for landslide early warning systems. Further development in this area requires UAVs to adopt more multispectral and other advanced sensors where their data are integrated with the geophysical one as well as the climatic data to enable Artificial Intelligent based prediction of LS. Full article
(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)
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