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Remote Sensing
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Remote Sensing for Characterization, Monitoring and Early Warning of Natural...
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Remote Sensing for Characterization, Monitoring and Early Warning of Natural and Engineered Slopes

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 (30 April 2023) | Viewed by 5934

Special Issue Editors

Dr. Tommaso Carlà

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Florence, Via Giorgio La Pira 4, 50121 Florence, Italy
Interests: landslides; slope monitoring; radar interferometry; engineering geology; slope stability; rock and soil mechanics
Dr. Renato Macciotta

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Alberta, 6-207 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB T6G 2H5, Canada
Interests: landslide; rock mechanics; rock mass; monitoring; field survey; remote survey; UAV photogrammetry; rockfall risk assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has become a commonly used tool for assessing and mitigating the risks associated with landslides and underperformance of engineered slopes. During the last few decades, a wide variety of groundbreaking sensors and data processing techniques (e.g., radar interferometry, UAV-based photogrammetry, aerial/terrestrial lidar, etc.) have been introduced to investigate potential and ongoing slope failures of different type and state of activity. This has made it possible to generate very large and detailed data sets, which offer the opportunity to greatly improve the understanding of landslide and slope failure processes, both in space and time. Applications typically focus either on tracking the trends of slope surface displacement or on rigorously mapping morphological/structural features of the soil/rock mass that may give indications about the possible mode and size of instability. In this regard, remote sensing provides a robust framework for data acquisition for topographic and geological characterization; monitoring of changes at the slope surface that provides insight into slope deformation mechanisms and kinematics; and the development of early warning protocols and trigger action and response plans (TARPs). In recent years, practitioners and researchers have been working towards taking advantage of the full potential of remote sensing on these three fronts. While remote sensing for early warning has become a well-established end goal for managing risks associated with natural and engineered slopes, integrating slope characterization and enhanced definition of failure mechanisms and kinematics is proving to support the business case for deploying remote sensing technologies.

This Special Issue is aimed at, but not limited to, research papers illustrating the results of integrated remote sensing campaigns that allowed gaining crucial insights into the dynamics and fundamental characteristics of landslides and potentially unstable slopes in general. Papers describing the experimental use of new sensors and data processing techniques are also greatly welcomed, as well as papers conjugating the results of remote sensing campaigns with numerical modelling of landslide processes. We also encourage papers describing practical applications of novel remote sensing techniques. Emphasis may be put on a wide range of topics and applications, including slope displacement monitoring, acquisition and advanced processing of high resolution point clouds, automatic detection of rock mass discontinuities and related properties, rock mass quality assessment, digital image correlation techniques, quantification of depletion and accumulation rates related to landslide activity, estimation of landslide volume and slip surface depth, retrieval of the runout behavior of past landslides, etc.

Dr. Renato Macciotta
Dr. Tommaso Carlà
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

  • Landslides
  • Slope Monitoring
  • Slope Stability
  • Engineering Geology
  • Rock and Soil Mechanics
  • Radar Interferometry
  • Photogrammetry
  • Point Cloud
  • UAV
  • Risk Analysis

Published Papers (2 papers)

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Research

30 pages, 8495 KiB  
Article
Machine Learning-Based Rockfalls Detection with 3D Point Clouds, Example in the Montserrat Massif (Spain)
by Laura Blanco, David García-Sellés, Marta Guinau, Thanasis Zoumpekas, Anna Puig, Maria Salamó, Oscar Gratacós, Josep Anton Muñoz, Marc Janeras and Oriol Pedraza
Remote Sens. 2022, 14(17), 4306; https://doi.org/10.3390/rs14174306 - 01 Sep 2022
Cited by 6 | Viewed by 2451
Abstract
Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which [...] Read more.
Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models. Full article
(This article belongs to the Special Issue Remote Sensing for Characterization, Monitoring and Early Warning of Natural and Engineered Slopes)
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17 pages, 15131 KiB  
Article
UAV-Based Multitemporal Remote Sensing Surveys of Volcano Unstable Flanks: A Case Study from Stromboli
by Teresa Gracchi, Carlo Tacconi Stefanelli, Guglielmo Rossi, Federico Di Traglia, Teresa Nolesini, Luca Tanteri and Nicola Casagli
Remote Sens. 2022, 14(10), 2489; https://doi.org/10.3390/rs14102489 - 23 May 2022
Cited by 6 | Viewed by 2423
Abstract
UAV-based photogrammetry is becoming increasingly popular even in application fields that, until recently, were deemed unsuitable for this technique. Depending on the characteristics of the investigated scenario, the generation of three-dimensional (3D) topographic models may in fact be affected by significant inaccuracies unless [...] Read more.
UAV-based photogrammetry is becoming increasingly popular even in application fields that, until recently, were deemed unsuitable for this technique. Depending on the characteristics of the investigated scenario, the generation of three-dimensional (3D) topographic models may in fact be affected by significant inaccuracies unless site-specific adaptations are implemented into the data collection and processing routines. In this paper, an ad hoc procedure to exploit high-resolution aerial photogrammetry for the multitemporal analysis of the unstable Sciara del Fuoco (SdF) slope at Stromboli Island (Italy) is presented. Use of the technique is inherently problematic because of the homogeneous aspect of the gray ash slope, which prevents a straightforward identification of match points in continuous frames. Moreover, due to site accessibility restrictions enforced by local authorities after the volcanic paroxysm in July 2019, Ground Control Points (GCPs) cannot be positioned to constrain georeferencing. Therefore, all 3D point clouds were georeferenced using GCPs acquired in a 2019 (pre-paroxysm) survey, together with stable Virtual Ground Control Points (VGCPs) belonging to a LiDAR survey carried out in 2012. Alignment refinement was then performed by means of an iterative algorithm based on the closest points. The procedure succeeded in correctly georeferencing six high-resolution point clouds acquired from April 2017 to July 2021, whose time-focused analysis made it possible to track several geomorphological structures associated with the continued volcanic activity. The procedure can be further extended to smaller-scale analyses such as the estimation of locally eroded/accumulated volumes and pave the way for rapid UAV-based georeferenced surveys in emergency conditions at the SdF. Full article
(This article belongs to the Special Issue Remote Sensing for Characterization, Monitoring and Early Warning of Natural and Engineered Slopes)
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