Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 8733

Share This Special Issue

Special Issue Editors

Prof. Dr. Anna Giacomini

E-Mail Website
Guest Editor

Priority Research Centre for Geotechnical Science and Engineering, The University of Newcastle, Callaghan 2308, Australia
Interests: rock mechanics; rockfall analyses; rock mass characterisation; remote sensing for rock mass characterisation; rock slope stability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Riccardo Roncella

E-Mail Website
Guest Editor

Department of Engineering and Architecture, Università degli Studi di Parma, Parco Area delle Scienze, 181/a, 43124 Parma, PR, Italy
Interests: image matching; image orientation; satellite/airborne/UAV photogrammetry; 3D reconstruction; monitoring; laser scanning; vision metrology
Special Issues, Collections and Topics in MDPI journals

Dr. Klaus Thoeni

E-Mail Website
Guest Editor

Priority Research Centre for Geotechnical Science and Engineering, The University of Newcastle, Callaghan 2308, Australia
Interests: numerical modelling; rockfall analysis; proximity remote sensing; slope monitoring and characterisation; machine learning

Special Issue Information

Dear Colleagues,

The advances realized over the last few decades concerning proximity remote sensing techniques and close-range terrestrial digital photogrammetry allow for the fast and direct acquisition of rock mass structural features and surface characteristics for subvertical rock surfaces. Such techniques are particularly effective in monitoring the stability of rock surfaces with limited accessibility or strongly occluded by vegetation, allowing for the collection of large data sets for predictive modelling, hazard assessments and risk analyses. Reliable generations of 3D high-resolution digital surface models (DSM) represent a crucial tool for the accurate mapping of geostructural features, identifying past events, assessing the probability of the occurrence of impending rock slope instabilities and quantifying their evolution.

This Special Issue aims to present novel contributions, case studies and applications of recent advances in remote sensing regarding studying the stability of rock slopes, their hazard and vulnerability assessments and risk management. In particular, the application of advanced machine learning models in rock slope characterisation, hazard and risk modelling are welcome topics. Examples of contributions include:

Rock mass characterisation;
Rock slope stability assessment;
Developments in rock slopes monitoring equipment and fused monitoring sensing;
Application of remote sensing data and numerical modelling for rock slope stability prediction;
Rockfall hazard and risk modelling;
Application of artificial intelligence approaches to analyse large monitoring data sets for rock slope hazard assessment;
Use of remote sensing techniques for the quantification of rockfall events;
The integration of different remote sensing techniques for rock slope stability analyses.

Prof. Dr. Anna Giacomini
Dr. Riccardo Roncella
Dr. Klaus Thoeni
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
photogrammetry
LiDAR
UAV
rock mass characterisation
rock mass stability assessment
rock slope stability
hazard
mapping
rockfall
machine learning

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

13 pages, 8931 KiB

Open AccessArticle

Early Identification of River Blockage Disasters Caused by Debris Flows in the Bailong River Basin, China

by Jianjun Zeng, Yan Zhao, Jiaoyu Zheng, Yongjun Zhang, Pengqing Shi, Yajun Li, Guan Chen, Xingmin Meng and Dongxia Yue

Remote Sens. 2024, 16(7), 1302; https://doi.org/10.3390/rs16071302 - 07 Apr 2024

Viewed by 650

Abstract

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas. Full article

(This article belongs to the Special Issue Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques)

► Show Figures

Figure 1

24 pages, 14488 KiB

Open AccessArticle

A Design Scenario Approach for Choosing Protection Works against Rockfall Phenomena

by Battista Taboni, Gessica Umili and Anna Maria Ferrero

Remote Sens. 2023, 15(18), 4453; https://doi.org/10.3390/rs15184453 - 10 Sep 2023

Viewed by 735

Abstract

Proximity remote sensing techniques, both land- and drone-based, allow for a significant improvement of the quality and quantity of raw data employed in the analysis of rockfall phenomena. In particular, the large amount of data these techniques can provide allows for the use of probabilistic approaches to rock mass characterization, with particular reference to block volume and shape definition. These, in return, are key parameters required for a proper rockfall hazard assessment and the optimization of countermeasures design. This study aims at providing a sort of guide, starting from the data gathering phase to the processing, up to the implementation of the outputs in a probabilistic-based scenario, which is able to associate a probability of not being exceeded with total kinetic energy values. By doing so, we were able to introduce a new approach for the choice of design parameters and the evaluation of the effectiveness of mitigation techniques. For this purpose, a suitable case study located in Varaita Valley (Cuneo, Italy) has been selected. The area has been surveyed, and a model of the slope and a digital model of the rock faces have been defined. The results show that a 6.5 m³ block has a probability of not being exceeded of 75%; subsequent simulations show that the level of kinetic energy involved in such a rockfall is extremely high. Some mitigation techniques are discussed. Full article

(This article belongs to the Special Issue Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques)

► Show Figures

Figure 1

19 pages, 20721 KiB

Open AccessArticle

Slope-Scale Rockfall Susceptibility Modeling as a 3D Computer Vision Problem

by Ioannis Farmakis, D. Jean Hutchinson, Nicholas Vlachopoulos, Matthew Westoby and Michael Lim

Remote Sens. 2023, 15(11), 2712; https://doi.org/10.3390/rs15112712 - 23 May 2023

Viewed by 1977

Abstract

Rockfall constitutes a major threat to the safety and sustainability of transport corridors bordered by rocky cliffs. This research introduces a new approach to rockfall susceptibility modeling for the identification of potential rockfall source zones. This is achieved by developing a data-driven model to assess the local slope morphological attributes with respect to the rock slope evolution processes. The ability to address “where” a rockfall is more likely to occur via the analysis of historical event inventories with respect to terrain attributes and to define the probability of a given area producing a rockfall is a critical advance toward effective transport corridor management. The availability of high-quality digital volumetric change detection products permits new developments in rockfall assessment and prediction. We explore the potential of simulating the conceptualization of slope-scale rockfall susceptibility modeling using computer power and artificial intelligence (AI). We employ advanced 3D computer vision algorithms for analyzing point clouds to interpret high-resolution digital observations capturing the rock slope evolution via long-term, LiDAR-based 3D differencing. The approach has been developed and tested on data from three rock slopes: two in Canada and one in the UK. The results indicate clear potential for AI advances to develop local susceptibility indicators from local geometry and learning from recent rockfall activity. The resultant models produce slope-wide rockfall susceptibility maps in high resolution, producing up to 75% agreement with validated occurrences. Full article

(This article belongs to the Special Issue Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques)

► Show Figures

Graphical abstract

20 pages, 16481 KiB

Open AccessArticle

3D Discrete Fracture Network Modelling from UAV Imagery Coupled with Tracer Tests to Assess Fracture Conductivity in an Unstable Rock Slope: Implications for Rockfall Phenomena

by Elisa Mammoliti, Alessandro Pepi, Davide Fronzi, Stefano Morelli, Tiziano Volatili, Alberto Tazioli and Mirko Francioni

Remote Sens. 2023, 15(5), 1222; https://doi.org/10.3390/rs15051222 - 22 Feb 2023

Cited by 4 | Viewed by 2182

Abstract

The stability of a rock slope is strongly influenced by the pattern of groundwater flow through the fracture system, which may lead to an increase in the water pressure in partly open joints and the consequent decrease in the rock wall strength. The comprehension of the fracture pattern is a challenging but vital aspect in engineering geology since the fractures’ spatial distribution, connectivity, and aperture guide both the water movement and flow quantity within the rock volume. In the literature, the most accepted methods to hydraulically characterise fractured rocks in situ are the single borehole packer test, the high-resolution flow meters for fractures, and the artificial tracer tests performed in boreholes. However, due to the high cost a borehole requires and the general absence of wells along coastal cliffs, these methods may not be appropriate in rockfall-prone areas. In this study, an unsaturated rocky cliff, strongly affected by rockfalls, was investigated by combining kinematic analysis, Discrete Fracture Network (DFN) modelling, and artificial tracer tests. The DFN model and potential rock block failure mechanisms were derived from high-resolution 3D virtual outcrop models via the Structure from Motion (SfM) photogrammetry technique. An artificial tracer was injected using a double ring infiltrometer atop the recharge zone of the slope to determine the infiltration rate and validate the DFN results. The DFN and tracer test methods are frequently used at different spatial scales and for different disciplines. However, the integration of digital photogrammetry, DFN, and tracer tests may represent a new step in rockfall and landslide studies. This approach made possible the identification of groundwater flow patterns within the fracture system and revealed about a 10-day tracer transit time from the injection area and the monitored slope, with similar conductivity values gathered from both the DFN and tracer test. Planar and wedge failures with volumes ranging from 0.1 and 1 m³ are the most probable failure mechanisms in the areas. The results were consistent with the delay between the intense rainfall and the slope failures previously documented in the study area and with their mechanisms. Full article

(This article belongs to the Special Issue Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques)

► Show Figures

Graphical abstract

19 pages, 22025 KiB

Open AccessArticle

Automatic Extraction of Discontinuity Traces from 3D Rock Mass Point Clouds Considering the Influence of Light Shadows and Color Change

by Jiateng Guo, Zirui Zhang, Yachun Mao, Shanjun Liu, Wancheng Zhu and Tianhong Yang

Remote Sens. 2022, 14(21), 5314; https://doi.org/10.3390/rs14215314 - 24 Oct 2022

Cited by 2 | Viewed by 2008

Abstract

The spatial characteristics of discontinuity traces play an important role in evaluations of the quality of rock masses. Most researchers have extracted discontinuity traces through the gray attributes of two-dimensional (2D) photo images or the geometric attributes of three-dimensional (3D) point clouds, while few researchers have paid attention to other important attributes of the original 3D point clouds, that is, the color attributes. By analyzing the color changes in a 3D point cloud, discontinuity traces in the smooth areas of a rock surface can be extracted, which cannot be obtained from the geometric attributes of the 3D point cloud. At the same time, a necessary filtering step has been designed to identify redundant shadow traces caused by sunlight on the rocks’ surface, and a multiscale spatial local binary pattern (MS-LBP) algorithm was proposed to eliminate the influence of shadows. Next, the geometric attributes of the 3D point cloud were fused to extract the potential discontinuity trace points on the rocks’ surface. For cases in which the potential discontinuity trace points are too scattered, a local line normalization thinning algorithm was proposed to refine the potential discontinuity trace points. Finally, an algorithm for establishing a two-way connection between a local vector buffer algorithm and a connectivity judgment algorithm was used to connect the discontinuity trace points to obtain the discontinuity traces of the rock mass’s surface. In addition, three datasets were used to compare the results extracted by existing methods. The results showed that the proposed method can extract the discontinuity traces of rock masses with higher accuracy, thereby providing data support for evaluations of the quality of rock masses and stability analyses. Full article

(This article belongs to the Special Issue Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques)

► Show Figures

Journal Menu

Journal Browser

Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI