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Special Issue "Landslide Hazard and Risk Assessment"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (26 April 2019).

Special Issue Editors

Guest Editor
Dr. Filippo Catani

Engineering Geology and Geomorphology Research Group, Department of Earth Sciences, University of Florence, Via La Pira 4, 50121 Firenze, Italy
Website | E-Mail
Interests: basin scale surface processes monitoring and modelling; natural hazards; applications of remote sensing; landslide hazard; scaling processes in geomorphology
Guest Editor
Prof. Ping Lu

College of Surveying and Geo-Informatics, Tongji University, Shanghai, China
E-Mail
Interests: remote sensing, landslide mapping, landslide hazard and risk
Guest Editor
Dr. Federico Raspini

Department of Earth Sciences, University of Florence, Via La Pira, 4 - 50121 Firenze, Italy
E-Mail
Interests: landslide mapping and monitoring; subsidence analysis; remote sensing data interpretation; geohazard monitoring

Special Issue Information

Dear Colleagues,

Landslides are ubiquitous geomorphological phenomena occurring in all geographic regions in response to a wide range of conditions and triggering processes that include rainfall events, earthquakes, and human activities. Landslides can have potentially catastrophic consequences. In several countries landslide mortality can be higher than that of any other natural hazard. In addition to direct losses, landslides also cause significant environmental damage and societal disruption. Assessment of hazard and risk posed by existing or future slope failures is a difficult task that is of both scientific interest and societal relevance but the research community is facing a new challenge in mitigating such risks since the frequency and intensity of landslide disasters is steadily increasing in the last decades, also due to climate change which forces weather extremes.

Therefore, new tools and methods are needed that are able to accurately monitor and map large areas of the globe with suitable time frequency and spatial coverage.

Remote sensing plays a pivotal role in driving the innovation in landslide research, because it can provide data for spatial mapping of large areas, 3D models of the topographic surface, displacement measurements that usefully complement traditional field data, parameters for numerical and empirical models through data assimilation techniques. Specific innovation challenges at the frontier of science are the exploitation of new satellite constellations with specific advantages, such as low-cost, high frequency of acquisition, high spatial or spectral resolution, hyperspectral remote sensing, multi-interferometry, UAV-borne sensors, new ground-based non-strictly SAR radar systems, automated mapping and detection methods, application of artificial intelligence tools to remote sensing data interpretation and analysis.

We would like to invite you to participate in this Special Issue, which will focus primarily on such innovative remote sensing methods to determine landslide hazard and risk, including quantitative evaluation of the associated uncertainties. All landslide types are considered, from fast rockfalls to debris flows, from slow moving slides to very rapid rock avalanches. All climatic and geographical scales are considered, from the local to the global scale, including individual and multiple slope failures.

Submissions are encouraged to cover a broad range of topics on the various applications of remote sensing techniques, which may include, but are not limited to, the following topics: (i) mapping and analysis of landslide conditioning and triggering factors, (ii) supplement of data for landslide hazard models, (iii) landslide motion detection, (iv) remote sensing techniques for the definition of vulnerability and characterisation of elements at risk, (v) remote sensing-based risk scenario modelling.

Prof. Filippo Catani
Prof. Ping Lu
Dr. Federico Raspini
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 papers will be 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. Remote Sensing is an international peer-reviewed open access semimonthly 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

  • Landslide Hazard and Risk
  • Landslide Monitoring
  • Landslide Forecasting
  • SAR interferometry
  • Target and change detection of landslides
  • UAV, airborne and space-borne sensors

Published Papers (13 papers)

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Open AccessArticle
Measurement of Road Surface Deformation Using Images Captured from UAVs
Remote Sens. 2019, 11(12), 1507; https://doi.org/10.3390/rs11121507
Received: 26 April 2019 / Revised: 30 May 2019 / Accepted: 21 June 2019 / Published: 25 June 2019
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Abstract
This paper presents a methodology for measuring road surface deformation due to terrain instability processes. The methodology is based on ultra-high resolution images acquired from unmanned aerial vehicles (UAVs). Flights are georeferenced by means of Structure from Motion (SfM) techniques. Dense point clouds, [...] Read more.
This paper presents a methodology for measuring road surface deformation due to terrain instability processes. The methodology is based on ultra-high resolution images acquired from unmanned aerial vehicles (UAVs). Flights are georeferenced by means of Structure from Motion (SfM) techniques. Dense point clouds, obtained using the multiple-view stereo (MVS) approach, are used to generate digital surface models (DSM) and high resolution orthophotographs (0.02 m GSD). The methodology has been applied to an unstable area located in La Guardia (Jaen, Southern Spain), where an active landslide was identified. This landslide affected some roads and accesses to a highway at the landslide foot. The detailed road deformation was monitored between 2012 and 2015 by means of eleven UAV flights of ultrahigh resolution covering an area of about 260 m × 90 m. The accuracy of the analysis has been established in 0.02 ± 0.01 m in XY and 0.04 ± 0.02 m in Z. Large deformations in the order of two meters were registered in the total period analyzed that resulted in maximum average rates of 0.62 m/month in the unstable area. Some boundary conditions were considered because of the low required flying height (<50 m above ground level) in order to achieve a suitable image GSD, the fast landslide dynamic, continuous maintenance works on the affected roads and dramatic seasonal vegetation changes throughout the monitoring period. Finally, we have analyzed the relation of displacements to rainfalls in the area, finding a significant correlation between the two variables, as well as two different reactivation episodes. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection
Remote Sens. 2019, 11(7), 760; https://doi.org/10.3390/rs11070760
Received: 12 February 2019 / Revised: 18 March 2019 / Accepted: 26 March 2019 / Published: 29 March 2019
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Abstract
Despite landslides impact the society worldwide every day, landslide information is inhomogeneous and lacking. When landslides occur in remote areas or where the availability of optical images is rare due to cloud persistence, they might remain unknown, or unnoticed for long time, preventing [...] Read more.
Despite landslides impact the society worldwide every day, landslide information is inhomogeneous and lacking. When landslides occur in remote areas or where the availability of optical images is rare due to cloud persistence, they might remain unknown, or unnoticed for long time, preventing studies and hampering civil protection operations. The unprecedented availability of SAR C-band images provided by the Sentinel-1 constellation offers the opportunity to propose new solutions to detect landslides events. In this work, we perform a systematic assessment of Sentinel-1 SAR C-band images acquired before and after known events. We present the results of a pilot study on 32 worldwide cases of rapid landslides entailing different types, sizes, slope expositions, as well as pre-existing land cover, triggering factors and climatic regimes. Results show that in about eighty-four percent of the cases, changes caused by landslides on SAR amplitudes are unambiguous, whereas only in about thirteen percent of the cases there is no evidence. On the other hand, the signal does not allow for a systematic use to produce inventories because only in 8 cases, a delineation of the landslide borders (i.e., mapping) can be manually attempted. In a few cases, cascade multi-hazard (e.g., floods caused by landslides) and evidences of extreme triggering factors (e.g., strong earthquakes or very rapid snow melting) were detected. The method promises to increase the availability of information on landslides at different spatial and temporal scales with benefits for event magnitude assessment during weather-related emergencies, model tuning, and landslide forecast model validation, in particular when accurate mapping is not required. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas
Remote Sens. 2019, 11(6), 647; https://doi.org/10.3390/rs11060647
Received: 26 November 2018 / Revised: 25 February 2019 / Accepted: 1 March 2019 / Published: 16 March 2019
Cited by 2 | PDF Full-text (14343 KB) | HTML Full-text | XML Full-text
Abstract
Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional [...] Read more.
Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives
Remote Sens. 2019, 11(3), 303; https://doi.org/10.3390/rs11030303
Received: 4 January 2019 / Revised: 27 January 2019 / Accepted: 30 January 2019 / Published: 2 February 2019
Cited by 2 | PDF Full-text (20650 KB) | HTML Full-text | XML Full-text
Abstract
Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have [...] Read more.
Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km2 of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m2; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Analysis of Mining Waste Dump Site Stability Based on Multiple Remote Sensing Technologies
Remote Sens. 2018, 10(12), 2025; https://doi.org/10.3390/rs10122025
Received: 10 October 2018 / Revised: 7 December 2018 / Accepted: 10 December 2018 / Published: 13 December 2018
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Abstract
The mining waste of open pit mines is usually piled-up in dump sites, making a man-made hill more than tens of meters high. Because of the loose structure of the dump sites, landslides or debris flow may occur after heavy rainfall, threatening local [...] Read more.
The mining waste of open pit mines is usually piled-up in dump sites, making a man-made hill more than tens of meters high. Because of the loose structure of the dump sites, landslides or debris flow may occur after heavy rainfall, threatening local lives and properties. Therefore, dump stability analysis is crucial for ensuring local safety. In this paper, a collaborative stability analysis based on multiple remote sensing technologies was innovatively conducted at the Xudonggou dump of the Anqian iron mine. A small baseline subset (SBAS) analysis was used to derive the spatial and temporal distributions of displacements in the line-of sight (LOS) over the whole study area. The deformation in LOS is translated to the slope direction based on an assumption that displacements only occur parallel to the slope surface. Infrared Thermography (IRT) technology was used to detect weak aquifer layers located at the toe of possible landslide bodies. Then, numerical simulations based on the limit equilibrium method were conducted to calculate the factor of safety for three profiles located on the dump site. The results, emerging from multiple remote sensing technologies, were very consistent and, eventually, the landslide hazard zone of the Xudonggou dump site was outlined. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery
Remote Sens. 2018, 10(12), 1987; https://doi.org/10.3390/rs10121987
Received: 26 October 2018 / Revised: 26 November 2018 / Accepted: 6 December 2018 / Published: 8 December 2018
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Abstract
Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and [...] Read more.
Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China
Remote Sens. 2018, 10(11), 1756; https://doi.org/10.3390/rs10111756
Received: 7 August 2018 / Revised: 21 October 2018 / Accepted: 2 November 2018 / Published: 7 November 2018
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Abstract
The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, [...] Read more.
The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
The Contribution of Terrestrial Laser Scanning to the Analysis of Cliff Slope Stability in Sugano (Central Italy)
Remote Sens. 2018, 10(9), 1475; https://doi.org/10.3390/rs10091475
Received: 13 August 2018 / Revised: 9 September 2018 / Accepted: 14 September 2018 / Published: 15 September 2018
Cited by 2 | PDF Full-text (6255 KB) | HTML Full-text | XML Full-text
Abstract
In this work, we describe a comprehensive approach aimed at assessing the slope stability conditions of a tuff cliff located below the village of Sugano (Central Italy) starting from remote geomechanical analysis on high-resolution 3D point clouds collected by terrestrial laser scanner (TLS) [...] Read more.
In this work, we describe a comprehensive approach aimed at assessing the slope stability conditions of a tuff cliff located below the village of Sugano (Central Italy) starting from remote geomechanical analysis on high-resolution 3D point clouds collected by terrestrial laser scanner (TLS) surveys. Firstly, the identification of the main joint systems has been made through both manual and automatic analyses on the 3D slope model resulting from the surveys. Afterwards, the identified joint sets were considered to evaluate the slope stability conditions by attributing safety factor (SF) values to the typical rock blocks whose kinematic was proved as compatible with tests for toppling under two independent triggering conditions: hydrostatic water pressure within the joints and seismic action. The results from the remote investigation of the cliff slope provide geometrical information of the blocks more susceptible to instability and pointed out that limit equilibrium condition can be achieved for potential triggering scenarios in the whole outcropping slope. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster–Shafer
Remote Sens. 2018, 10(7), 1029; https://doi.org/10.3390/rs10071029
Received: 28 April 2018 / Revised: 26 June 2018 / Accepted: 26 June 2018 / Published: 29 June 2018
Cited by 5 | PDF Full-text (23575 KB) | HTML Full-text | XML Full-text
Abstract
A detailed and state-of-the-art landslide inventory map including precise landslide location is greatly required for landslide susceptibility, hazard, and risk assessments. Traditional techniques employed for landslide detection in tropical regions include field surveys, synthetic aperture radar techniques, and optical remote sensing. However, these [...] Read more.
A detailed and state-of-the-art landslide inventory map including precise landslide location is greatly required for landslide susceptibility, hazard, and risk assessments. Traditional techniques employed for landslide detection in tropical regions include field surveys, synthetic aperture radar techniques, and optical remote sensing. However, these techniques are time consuming and costly. Furthermore, complications arise for the generation of accurate landslide location maps in these regions due to dense vegetation in tropical forests. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) is typically employed to generate accurate landslide maps. The object-based technique generally consists of many homogeneous pixels grouped together in a meaningful way through image segmentation. In this paper, in order to address the limitations of this approach, the final decision is executed using Dempster–Shafer theory (DST) rule combination based on probabilistic output from object-based support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN) classifiers. Therefore, this research proposes an efficient framework by combining three object-based classifiers using the DST method. Consequently, an existing supervised approach (i.e., fuzzy-based segmentation parameter optimizer) was adopted to optimize multiresolution segmentation parameters such as scale, shape, and compactness. Subsequently, a correlation-based feature selection (CFS) algorithm was employed to select the relevant features. Two study sites were selected to implement the method of landslide detection and evaluation of the proposed method (subset “A” for implementation and subset “B” for the transferrable). The DST method performed well in detecting landslide locations in tropical regions such as Malaysia, with potential applications in other similarly vegetated regions. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides
Remote Sens. 2018, 10(6), 936; https://doi.org/10.3390/rs10060936
Received: 27 April 2018 / Revised: 1 June 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract
This work addresses the suitability of using X-band Synthetic Aperture Radar (SAR) data for operational geotechnical monitoring of site scale slow moving landslides, affecting urban areas and infrastructures. The scale of these studies requires high resolution data. We propose a procedure for the [...] Read more.
This work addresses the suitability of using X-band Synthetic Aperture Radar (SAR) data for operational geotechnical monitoring of site scale slow moving landslides, affecting urban areas and infrastructures. The scale of these studies requires high resolution data. We propose a procedure for the practical use of SAR data in geotechnical landslides campaigns, that includes an appropriate dataset selection taking into account the scenario characteristics, a visibility analysis, and considerations when comparing advanced differential SAR interferometry (A-DInSAR) results with other monitoring techniques. We have determined that Sentinel-2 satellite optical images are suited for performing high resolution land cover classifications, which results in the achievement of qualitative visibility maps. We also concluded that A-DInSAR is a very powerful and versatile tool for detailed scale landslide monitoring, although in combination with other instrumentation techniques. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Quantitative Assessment of Digital Image Correlation Methods to Detect and Monitor Surface Displacements of Large Slope Instabilities
Remote Sens. 2018, 10(6), 865; https://doi.org/10.3390/rs10060865
Received: 1 May 2018 / Revised: 15 May 2018 / Accepted: 30 May 2018 / Published: 1 June 2018
Cited by 3 | PDF Full-text (11448 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We evaluate the capability of three different digital image correlation (DIC) algorithms to measure long-term surface displacement caused by a large slope instability in the Swiss Alps. DIC was applied to high-resolution optical imagery taken by airborne sensors, and the accuracy of the [...] Read more.
We evaluate the capability of three different digital image correlation (DIC) algorithms to measure long-term surface displacement caused by a large slope instability in the Swiss Alps. DIC was applied to high-resolution optical imagery taken by airborne sensors, and the accuracy of the displacements assessed against global navigation satellite system measurements. A dynamic radiometric correction of the input images prior to DIC application was shown to enhance both the correlation success and accuracy. Moreover, a newly developed spatial filter considering the displacement direction and magnitude proved to be an effective tool to enhance DIC performance and accuracy. Our results show that all algorithms are capable of quantifying slope instability displacements, with average errors ranging from 8 to 12% of the observed maximum displacement, depending on the DIC processing parameters, and the pre- and postprocessing of the in- and output. Among the tested approaches, the results based on a fast Fourier transform correlation approach provide a considerably better spatial coverage of the displacement field of the slope instability. The findings of this study are relevant for slope instability detection and monitoring via DIC, especially in the context of an ever-increasing availability of high-resolution air- and spaceborne imagery. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle
Monitoring Surface Deformation over a Failing Rock Slope with the ESA Sentinels: Insights from Moosfluh Instability, Swiss Alps
Remote Sens. 2018, 10(5), 672; https://doi.org/10.3390/rs10050672
Received: 12 March 2018 / Revised: 12 April 2018 / Accepted: 23 April 2018 / Published: 25 April 2018
Cited by 4 | PDF Full-text (7928 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We leverage on optical and radar remote sensing data acquired from the European Space Agency (ESA) Sentinels to monitor the surface deformation evolution on a large and very active instability located in the Swiss Alps, i.e., the Moosfluh rock slope. In the late [...] Read more.
We leverage on optical and radar remote sensing data acquired from the European Space Agency (ESA) Sentinels to monitor the surface deformation evolution on a large and very active instability located in the Swiss Alps, i.e., the Moosfluh rock slope. In the late summer 2016, a sudden acceleration was reported at this location, with surface velocity rates passing from maximum values of 0.2 cm/day to 80 cm/day. A dense pattern of uphill-facing scarps and tension cracks formed within the instability and rock fall activity started to become very pronounced. This evolution of the rock mass may suggest that the most active portion of the slope could fail catastrophically. Here we discuss advantages and limitations of the use of spaceborne methods for hazard analyses and early warning by using the ESA Sentinels, and show that in critical scenarios they are often not sufficient to reliably interpret the evolution of surface deformation. The insights obtained from this case study are relevant for similar scenarios in the Alps and elsewhere. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Other

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Open AccessFeature PaperTechnical Note
Rockfall Simulation Based on UAV Photogrammetry Data Obtained during an Emergency Declaration: Application at a Cultural Heritage Site
Remote Sens. 2018, 10(12), 1923; https://doi.org/10.3390/rs10121923
Received: 15 October 2018 / Revised: 17 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
Cited by 3 | PDF Full-text (10514 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, there was an increasing number of studies focusing on rockfalls due to their impacts on social and sustainable development. This work carries out a three-dimensional (3D) simulation of rockfalls at a cultural heritage site nearby the village of Cortes de [...] Read more.
In recent years, there was an increasing number of studies focusing on rockfalls due to their impacts on social and sustainable development. This work carries out a three-dimensional (3D) simulation of rockfalls at a cultural heritage site nearby the village of Cortes de Pallás (Valencian Community, East Spain). The simulation is based on data collected previously, during an emergency declaration due to the occurrence of a considerable rockfall (7980 m3) on the southern bank of the Cortes de Pallás reservoir, on 6 April 2015. The hydroelectric power plant was damaged, and the main access road to the village of Cortes de Pallás was blocked for eight months. The predominant discontinuities of the rock mass were analyzed by means of the application of structure from motion (SfM) photogrammetry techniques to the set of images taken by remotely piloted aircraft systems (RPAS). The average size of the block was determined as 3.2 m in diameter and 17.6 m3 in volume. Additionally, a digital elevation model (DEM) was generated from an aerial laser scanning (ALS)-derived point cloud using a 1 × 1 grid. These data were implemented in RocPro3D software, obtaining the distances traveled by the blocks detached from different source areas at a cultural heritage site located near the rockfall event, which presents the same geological context. The simulation presented herein shows aggravating circumstances that endanger the cultural heritage area, with higher rockfall hazards than previous official studies (1991) displayed. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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