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Remote Sensing for Rock Slope and Rockfall Analysis

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 26340

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Special Issue Editors

Dr. Claudio Vanneschi

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Guest Editor

Centre of GeoTechnologies, University of Siena, Siena, Italy
Interests: remote sensing; engineering geology; photogrammetry; laser scanning; rockfall; landslides; stability analysis; numerical modelling

Dr. Mirko Francioni

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Guest Editor

Department of Engineering and Geology, University G. d'Annunzio of Chieti and Pescara, 66100 Chieti, Italy
Interests: engineering geology; remote sensing; geo-mechanics; natural hazards; landslides; photogrammetry; laser scanning; InSAR; landslide monitoring; landslide numerical analyses
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Dr. Neil Bar

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Guest Editor

Gecko Geotechnics, Cairns, QLD 4870, Australia
Interests: rock engineering; slope stability; empirical methods; natural hazards; surface and underground mining; rockfall management; remote sensing; landslide monitoring

Special Issue Information

Dear Colleagues,

Rock slope instability is a major and widespread phenomenon that can represent significant hazards—especially in areas characterized by high and steep natural or engineered slopes. As a consequence, depending on magnitude, size and velocity, slope failure and rockfall events can cause severe damage, injuries, and casualties. As such, effective mitigation measures are essential to control their effect. Over the last two decades, the approach to rock slope investigation has changed substantially. The application of remote sensing techniques such as LiDAR, radar, and photogrammetry to rock slope analysis have allowed for the rapid and safe acquisition of a huge amount of high-quality information. Such techniques represent valuable tools in rock mechanics, but their use and the management of the generated data is often complex in several contexts.

This Special Issue will present novel contributions including original research, case studies, and new approaches in rock slope and rockfall analysis that take advantage of remote sensing techniques. This can refer, for example, to:

The integration of different remote sensing techniques for rock slope and rockfall analyses;
Rock mass characterization;
Rock slope stability assessment;
Back analysis;
Rockfall intensity, velocity and probability assessment;
Rockfall hazard and risk assessment;
Rockfall mitigation measures design;
Rock slope and rockfall monitoring;
Early warning systems and evacuation planning;
Numerical model calibration.

Dr. Claudio Vanneschi
Dr. Mirko Francioni
Dr. Neil Bar
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. 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 2700 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

remote sensing
rock slope stability
rockfall
runout
hazard assessment
risk assessment
early-warning systems
rockfall mitigation

Related Special Issue

Remote Sensing for Rock Slope and Rockfall Analysis II in Remote Sensing

Published Papers (9 papers)

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23 pages, 12198 KiB

Open AccessArticle

Lidar-Derived Rockfall Inventory—An Analysis of the Geomorphic Evolution of Rock Slopes and Modifying the Rockfall Activity Index (RAI)

by Shane J. Markus, Joseph Wartman, Michael Olsen and Margaret M. Darrow

Remote Sens. 2023, 15(17), 4223; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15174223 - 28 Aug 2023

Viewed by 1001

Abstract

Rockfall presents a significant risk to the safety and economy of communities and infrastructure in mountainous regions. The recently-developed Rockfall Activity Index (RAI) utilizes high-resolution terrestrial lidar-derived digital elevation models (DEMs) of rock slopes to categorize a slope face into seven distinct morphological units, or “RAI classes”. This paper focuses on a comprehensive study conducted at four sites in Alaska, USA, where a robust lidar-based five-year inventory of 4381 rockfall events was analyzed. The primary objective was to investigate variations in failure characteristics, such as cumulative magnitude–frequency distributions, non-cumulative power–law parameters, average annual failure rates, and average failure depths, among the different RAI classes. The findings demonstrate that the seven RAI classes effectively differentiate the rock slope based on unique mass-wasting characteristics. Furthermore, the research explores spatial and temporal variations in these failure characteristics. Based on the study’s outcomes, recommendations are provided for modifying the RAI parameters for each RAI class, specifically the annual failure rate and average failure depth. These modifications aim to enhance the accuracy and effectiveness of rockfall hazard assessments. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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24 pages, 6416 KiB

Open AccessArticle

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

by Martin Štroner, Rudolf Urban and Tomáš Suk

Remote Sens. 2023, 15(13), 3254; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133254 - 24 Jun 2023

Cited by 3 | Viewed by 1119

Abstract

Filtering out vegetation from a point cloud based on color is only rarely used, largely due to the lack of knowledge of the suitability of input information (color, vegetation indices) and the thresholding methods. We have evaluated multiple vegetation indices (ExG, ExR, ExB, ExGr, GRVI, MGRVI, RGBVI, IKAW, VARI, CIVE, GLI, and VEG) and combined them with 10 methods of threshold determination based on training set selection (including machine learning methods) and the renowned Otsu’s method. All these combinations were applied to four clouds representing vegetated rocky terrain, and the results were compared. The ExG and GLI indices were generally the most suitable for this purpose, with the best F-scores of 97.7 and 95.4, respectively, and the best-balanced accuracies for the same combination of the method/vegetation index of 98.9 and 98.3%, respectively. Surprisingly, these best results were achieved using the simplest method of threshold determination, considering only a single class (vegetation) with a normal distribution. This algorithm outperformed all other methods, including those based on a support vector machine and a deep neural network. Thanks to its simplicity and ease of use (only several patches representing vegetation must be manually selected as a training set), this method can be recommended for vegetation removal from rocky and anthropogenic surfaces. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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36 pages, 21824 KiB

Open AccessArticle

Rockfall Magnitude-Frequency Relationship Based on Multi-Source Data from Monitoring and Inventory

by Marc Janeras, Nieves Lantada, M. Amparo Núñez-Andrés, Didier Hantz, Oriol Pedraza, Rocío Cornejo, Marta Guinau, David García-Sellés, Laura Blanco, Josep A. Gili and Joan Palau

Remote Sens. 2023, 15(8), 1981; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15081981 - 09 Apr 2023

Cited by 4 | Viewed by 1701

Abstract

Quantitative hazard analysis of rockfalls is a fundamental tool for sustainable risk management, even more so in places where the preservation of natural heritage and people’s safety must find the right balance. The first step consists in determining the magnitude-frequency relationship, which corresponds to the apparently simple question: how big and how often will a rockfall be detached from anywhere in the cliff? However, there is usually only scarce data on past activity from which to derive a quantitative answer. Methods are proposed to optimize the exploitation of multi-source inventories, introducing sampling extent as a main attribute for the analysis. This work explores the maximum possible synergy between data sources as different as traditional inventories of observed events and current remote sensing techniques. Both information sources may converge, providing complementary results in the magnitude-frequency relationship, taking advantage of each strength that overcomes the correspondent weakness. Results allow characterizing rockfall detachment hazardous conditions and reveal many of the underlying conditioning factors, which are analyzed in this paper. High variability of the hazard over time and space has been found, with strong dependencies on influential external factors. Therefore, it will be necessary to give the appropriate reading to the magnitude-frequency scenarios, depending on the application of risk management tools (e.g., hazard zoning, quantitative risk analysis, or actions that bring us closer to its forecast). In this sense, some criteria and proxies for hazard assessment are proposed in the paper. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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19 pages, 27311 KiB

Open AccessArticle

Digital Rock Mass Analysis for the Evaluation of Rockfall Magnitude at Poorly Accessible Cliffs

by Davide Caliò, Simone Mineo and Giovanna Pappalardo

Remote Sens. 2023, 15(6), 1515; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061515 - 09 Mar 2023

Cited by 7 | Viewed by 2363

Abstract

The analysis of a digital rock cliff model, built by airborne photogrammetric data and infrared thermal images, is herein presented as an alternative tool for rock mass study in restricted and poorly accessible areas. Photogrammetric and infrared thermography techniques were combined for the geostructural and morphological characterization of an unstable cliff located in a nature reserve, where the rock mass extension and the environmental preservation rules required the use of minimally invasive surveying solutions. This methodological approach provided quantitative and qualitative data on both the spatial orientation of discontinuities and the location of major structural features, jutting blocks and past rockfall source areas. The digitally derived spatial data were used to carry out a rock mass kinematic analysis, highlighting the most recurring unstable failure patterns. Thermal images were overlapped to the photogrammetric cliff model to exploit the data combination and to analyze the presence of protruding rock mass volumes to be referred to as potential unstable volumes. Based on this activity, rock volumes were quantified on the digital model and the results were used to provide a zonation map of the potential magnitude of future rockfalls threatening the reserve. Digital data were validated by a field surveying campaign, which returned a satisfactory match, proving the usefulness and suitability of the approach, as well as allowing the quick and reliable rock mass characterization in the frame of practical use and risk management purposes. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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24 pages, 7261 KiB

Open AccessArticle

Hazard Assessment of Rocky Slopes: An Integrated Photogrammetry–GIS Approach Including Fracture Density and Probability of Failure Data

by Claudio Vanneschi, Andrea Rindinella and Riccardo Salvini

Remote Sens. 2022, 14(6), 1438; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061438 - 16 Mar 2022

Cited by 9 | Viewed by 3358

Abstract

Natural rock slopes require accurate engineering–geological characterization to determine their stability conditions. Given that a natural rock mass is often characterized by a non-uniform fracture distribution, the correct, detailed, and accurate characterization of the discontinuity pattern of the rock mass is essential. This is crucial, for example, for identifying the possibility and the probability of kinematic releases. In addition, complete stability analyses of possible rockfall events should be performed and used to create hazard maps capable of identifying the most dangerous parts of a rock mass. This paper shows a working approach that combines traditional geological surveys and remote sensing techniques for engineering–geological investigations in a natural rock slope in Northern Italy. Discontinuities were identified and mapped in a deterministic way by using semi-automatic procedures that were based on detailed 3D Unmanned Aerial Vehicle photogrammetric-based point cloud data and provided georeferenced representations of thousands of fractures. In this way, detailed documentation of the geo-mechanical and geo-structural characteristics of discontinuities were obtained and subsequently used to create fracture density maps. Then, traditional kinematic analyses and probabilistic stability analyses were performed using limit equilibrium methods. The results were then managed in a GIS environment to create a final hazard map that classifies different portions of the rock slope based on three factors: kinematic predisposition to rockfall (planar sliding, wedge sliding, toppling), fracture density, and probability of failure. The integration of the three hazard factors allowed the identification of the most hazardous areas through a deterministic and accurate procedure, with a high level of reliability. The adopted approach can therefore be very useful to determine the areas in which to prioritize remediation measures with the aim of reducing the level of risk. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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31 pages, 5591 KiB

Open AccessArticle

Accuracy Assessment of UAV-Photogrammetric-Derived Products Using PPK and GCPs in Challenging Terrains: In Search of Optimized Rockfall Mapping

by Barbara Žabota and Milan Kobal

Remote Sens. 2021, 13(19), 3812; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193812 - 23 Sep 2021

Cited by 17 | Viewed by 3637

Abstract

Unmanned aerial photogrammetric surveys are increasingly being used for mapping and studying natural hazards, such as rockfalls. Surveys using unmanned aerial vehicles (UAVs) can be performed in remote, hardly accessible, and dangerous areas, while the photogrammetric-derived products, with high spatial and temporal accuracy, can provide us with detailed information about phenomena under consideration. However, as photogrammetry commonly uses indirect georeferencing through bundle block adjustment (BBA) with ground control points (GCPs), data acquisition in the field is not only time-consuming and labor-intensive, but also extremely dangerous. Therefore, the main goal of this study was to investigate how accurate photogrammetric products can be produced by using BBA without GCPs and auxiliary data, namely using the coordinates X₀, Y₀ and Z₀ of the camera perspective centers computed with PPK (Post-Processing Kinematic). To this end, orthomosaics and digital surface models (DSMs) were produced for three rockfall sites by using images acquired with a DJI Phantom 4 RTK and the two different BBA methods mentioned above (hereafter referred to as BBA_traditional and BBA_PPK). The accuracy of the products, in terms of the Root Mean Square Error (RMSE), was computed by using verification points (VPs). The accuracy of both BBA methods was also assessed. To test the differences between the georeferencing methods, two statistical test were used, namely a paired Student’s t-test, and a non-parametric Wilcoxon signed-rank. The results show that the accuracy of the BBA_PPK is inferior to that of BBA_traditional, with the total RMSE values for the three sites being 0.056, 0.066, and 0.305 m, respectively, compared to 0.019, 0.036 and 0.014 m obtained with BBA_traditional. The accuracies of the BBA methods are reflected in the accuracy of the orthomosaics, whose values for the BBA_PPK are 0.039, 0.043 and 0.157 m, respectively, against 0.029, 0.036 and 0.020 m obtained with the BBA_traditional. Concerning the DSM, those produced with the BBA_PPK method present accuracy values of 0.065, 0.072 and 0.261 m, respectively, against 0.038, 0.060 and 0.030 m obtained with the BBA_traditional. Even though that there are statistically significant differences between the georeferencing methods, one can state that the BBA_PPK presents a viable solution to map dangerous and exposed areas, such as rockfall transit and deposit areas, especially for applications at a regional level. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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19 pages, 14310 KiB

Open AccessArticle

A Robust SAR Speckle Tracking Workflow for Measuring and Interpreting the 3D Surface Displacement of Landslides

by Davide Donati, Bernhard Rabus, Jeanine Engelbrecht, Doug Stead, John Clague and Mirko Francioni

Remote Sens. 2021, 13(15), 3048; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13153048 - 03 Aug 2021

Cited by 7 | Viewed by 2744

Abstract

We present a workflow for investigating large, slow-moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post-processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five-year windows, 2010–2015 and 2015–2020. Then, we perform post-processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST-derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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25 pages, 16593 KiB

Open AccessArticle

3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

by Guglielmo Grechi, Matteo Fiorucci, Gian Marco Marmoni and Salvatore Martino

Remote Sens. 2021, 13(5), 957; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050957 - 04 Mar 2021

Cited by 30 | Viewed by 4296

Abstract

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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27 pages, 12143 KiB

Open AccessTechnical Note

Comparison of Remote Sensing Techniques for Geostructural Analysis and Cliff Monitoring in Coastal Areas of High Tourist Attraction: The Case Study of Polignano a Mare (Southern Italy)

by Lidia Loiotine, Gioacchino Francesco Andriani, Michel Jaboyedoff, Mario Parise and Marc-Henri Derron

Remote Sens. 2021, 13(24), 5045; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245045 - 12 Dec 2021

Cited by 11 | Viewed by 3453

Abstract

Rock slope failures in urban areas may represent a serious hazard for human life, as well as private and public property, even on the occasion of sporadic episodes. Prevention and mitigation measures indispensably require a proper rock mass characterization, which is often achieved by means of time-consuming, costly and dangerous field surveys. In the last decades, remote sensing devices such as high-resolution digital cameras, laser scanners and drones have been widely used as supplementary techniques for rock slope analysis and monitoring, especially in poorly accessible areas, or in sites of large extension. Although several methods for rock mass characterization by means of remote sensing techniques have been reported in specific studies, there are very few contributions that focused on comparing the different methods in an attempt to establish their advantages and limitations. With this study, we performed digital photogrammetry, Terrestrial Laser Scanning and Unmanned Aerial Vehicle surveys on a cliff located in a popular tourist attraction site, characterized by complex geological and geomorphological settings, as well as by disturbance elements such as vegetation and human activities. For each point cloud, we applied geostructural analysis by means of semi-automatic methods, and then compared multi-temporal acquisitions for cliff monitoring. By quantitative comparison of the results and validation by means of conventional geostructural field surveys, the pros and cons of each method were outlined in attempt to depict the conditions and goals the different techniques seem to be more suitable for. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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