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Remote Sensing in Engineering Geology - II

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

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 21512

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Special Issue Editors

Dr. Mirko Francioni

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Guest Editor

Department of Pure and Applied Sciences, University of Urbino Carlo Bo, 61029 Urbino, Italy
Interests: engineering geology; remote sensing; natural hazards; landslides; numerical modelling
Special Issues, Collections and Topics in MDPI journals

Dr. Thomas Oommen

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Guest Editor

Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI 49931, USA
Interests: liquefaction susceptibility evaluation at local and regional scales using in-situ measurements and remote sensing observations; estimating liquefaction-induced damage such as lateral spread displacement; active learning to identify data gaps in empirical models; documenting earthquake-induced damages, especially liquefaction, using aerial/satellite images that are sensitive to surficial moisture; transportation geotechnics
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Special Issue Information

Dear Colleagues,

Over the last two decades, the use of remote sensing for the investigation of geological or geotechnical engineering problems has significantly increased. The availability of high spatial and temporal resolution datasets from aerial and satellite platforms, and the use of unmanned aerial vehicles (drones) for data collection has accelerated the adoption of remote sensing in geosciences and geoengineering. The most commonly used remote sensing sensors and techniques include Light Detection and Ranging (LiDAR), Synthetic Aperture Radar (SAR), thermal, hyper-spectral, multi-spectral, and photogrammetry. These remote sensing technologies are being widely used for problems related to ground subsidence, slope monitoring, hydrogeology, site characterization, coastal engineering, erosion, and geo-hazard studies.

In this context, this Special Issue invites high-quality and innovative scientific papers that advance the science of remote sensing in solving problems related to engineering, geology and geoscience. These could include analyzing and monitoring landslides and volcanos, the characterization of rock masses and geotechnical sites, ground deformation analyses, and mining applications. Special consideration will also be given to the use of GIS, big datasets, and artificial intelligence- and machine learning-based methods for remotely sensed data processing and modeling.

Dr. Mirko Francioni
Dr. Thomas Oommen
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.

Published Papers (9 papers)

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Research

36 pages, 392465 KiB

Open AccessArticle

Study of Recent Deformations in the Bogotá Savanna and the City of Bogotá (Colombia) Using Multi-Temporal Satellite Radar Interferometry

by Juan S. Tamayo Duque, Antonio Miguel Ruiz-Armenteros, Guillermo E. Ávila Álvarez, Gustavo Matiz and Joaquim J. Sousa

Remote Sens. 2023, 15(21), 5249; https://doi.org/10.3390/rs15215249 - 05 Nov 2023

Viewed by 1605

Abstract

Bogotá, the largest urban center and capital city of Colombia, is located within the Bogotá savanna, which originated as a lake in the central part of the Colombian Eastern Cordillera. Over time, the lake transformed into a gently undulating plain with horizontally deposited sediments that formed around five million years ago. Over the last few decades, the region has undergone significant population growth and rapid urban development, largely driven by migration from rural areas. This development has substantially impacted the subsidence observed in the city, primarily due to the extraction of groundwater. A previous study by the Servicio Geológico Colombiano (SGC) utilized data from GNSS stations and synthetic aperture radar interferometry (InSAR) with TerraSAR-X SAR between 2011 and 2017 to identify a subsidence pattern in the central region of Bogotá. The purpose of the study was to evaluate the risks and potential disasters associated with the subsidence phenomenon. Our study investigates both the subsidence in Bogotá, previously studied, as well as the rural savanna area, which is currently undergoing significant residential and industrial development. We utilized multi-temporal InSAR (MT-InSAR) techniques with Sentinel-1 SAR images from 2014 to 2021. The analysis results indicate that the outer regions of the city display the most significant subsidence, extending from the center to the north. The subsidence velocities in these areas reach approximately 5–6 cm/year. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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25 pages, 7588 KiB

Open AccessArticle

A Multivariate Time Series Analysis of Ground Deformation Using Persistent Scatterer Interferometry

by Serena Rigamonti, Giuseppe Dattola, Paolo Frattini and Giovanni Battista Crosta

Remote Sens. 2023, 15(12), 3082; https://doi.org/10.3390/rs15123082 - 13 Jun 2023

Cited by 1 | Viewed by 1212

Abstract

Ground deformations in urban areas can be the result of a combination of multiple factors and pose several hazards to infrastructures and human lives. In order to monitor these phenomena, Interferometric Synthetic Aperture Radar (InSAR) techniques are applied. The obtained signals record the overlapping of the phenomena, and their separation is a relevant issue. In this framework, we explored a new multi-method approach based on the combination of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Hierarchal Clustering (HC) on the standardized results to distinguish the main trends and seasonal signals embedded in the time series of ground displacements, to understand spatial-temporal patterns, to correlate ground deformation phenomena with geological and anthropogenic factors, and to recognize the specific footprints of different ground deformation phenomena. This method allows us to classify the ground deformations at the site scale in the metropolitan area of Naples, which is affected by uplift cycles, subsidence, cavity instabilities and sinkholes. At the local scale, the results allow a kinematic classification using the extracted components and considering the effect of the radius of influence generated by each cavity, as it is performed from a theoretical point of view when the draw angle is considered. According to the results, among the classified cavities, 2% were assigned to subsidence and 11% to uplift kinematics, while the remaining were found to be stable. Furthermore, our results show that the centering of the Spatial-PCA (S-PCA) is representative of the region’s main trend, whereas Temporal-PCA (T-PCA) gives information about the displacement rates identified by each component. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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22 pages, 27104 KiB

Open AccessArticle

Multi-Sensor and Multi-Scale Remote Sensing Approach for Assessing Slope Instability along Transportation Corridors Using Satellites and Uncrewed Aircraft Systems

by Marta Zocchi, Anush Kumar Kasaragod, Abby Jenkins, Chris Cook, Richard Dobson, Thomas Oommen, Dana Van Huis, Beau Taylor, Colin Brooks, Roberta Marini, Francesco Troiani and Paolo Mazzanti

Remote Sens. 2023, 15(12), 3016; https://doi.org/10.3390/rs15123016 - 09 Jun 2023

Cited by 1 | Viewed by 2069

Abstract

Rapid slope instabilities (i.e., rockfalls) involving highway networks in mountainous areas pose a threat to facilities, settlements and life, thus representing a challenge for asset management plans. To identify different morphological expressions of degradation processes that lead to rock mass destabilization, we combined satellite and uncrewed aircraft system (UAS)-based products over two study sites along the State Highway 133 sector near Paonia Reservoir, Colorado (USA). Along with a PS-InSAR analysis covering the 2017–2021 interval, a high-resolution dataset composed of optical, thermal and multi-spectral imagery was systematically acquired during two UAS surveys in September 2021 and June 2022. After a pre-processing step including georeferencing and orthorectification, the final products were processed through object-based multispectral classification and change detection analysis for highlighting moisture or lithological variations and for identifying areas more susceptible to deterioration and detachments at the small and micro-scale. The PS-InSAR analysis, on the other hand, provided multi-temporal information at the catchment scale and assisted in understanding the large-scale morpho-evolution of the displacements. This synergic combination offered a multiscale perspective of the superimposed imprints of denudation and mass-wasting processes occurring on the study site, leading to the detection of evidence and/or early precursors of rock collapses, and effectively supporting asset management maintenance practices. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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23 pages, 6059 KiB

Open AccessArticle

Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

by Lukas Fahle, Andrew J. Petruska, Gabriel Walton, Jurgen F. Brune and Elizabeth A. Holley

Remote Sens. 2023, 15(7), 1764; https://doi.org/10.3390/rs15071764 - 25 Mar 2023

Cited by 2 | Viewed by 1730

Abstract

Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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18 pages, 7208 KiB

Open AccessArticle

Stability Analysis of the Volcanic Cave El Mirador (Galápagos Islands, Ecuador) Combining Numerical, Empirical and Remote Techniques

by Guido Rodríguez, Maurizio Mulas, Silvia Loaiza, Michelle Del Pilar Villalta Echeverria, Angel Amable Yanez Vinueza, Erwin Larreta and Luis Jordá Bordehore

Remote Sens. 2023, 15(3), 732; https://doi.org/10.3390/rs15030732 - 27 Jan 2023

Cited by 3 | Viewed by 2974

Abstract

El Mirador de los Túneles is a tube-shaped volcanic cave with a sinuous structure in the Galápagos Islands formed due to cooled near-surface lava flows. Since this natural formation is considered a tourist site, a large number of people frequent it daily; however, its safety conditions have not yet been defined by a comprehensive geotechnical study. In this research, a stability analysis was carried out by combining both empirical methodologies based on geomechanical classifications using Barton’s Q Index and the recently created Cave Geomechanical Index (CGI), and numerical modeling through the finite element method. In addition, three-dimensional modelling was performed using the remote photogrammetric technique Structure from Motion (SfM) to create the numerical calculation sections and dimensions of the different critical parts of the cave. The results of the analysis showed that there is evidence of instability and subsidence along the tunnel. Furthermore, the geotechnical parameters obtained from the different methods complemented each other, resulting in more realistic engineering representation of the subsurface environment. Finally, a graph showing the two empirical methodologies Barton’s Q Index and CGI, with the addition of the Factors of Safety (FoS) obtained from the modeling is presented. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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23 pages, 8134 KiB

Open AccessArticle

Accuracy of Rockfall Volume Reconstruction from Point Cloud Data—Evaluating the Influences of Data Quality and Filtering

by Gabriel Walton and Luke Weidner

Remote Sens. 2023, 15(1), 165; https://doi.org/10.3390/rs15010165 - 28 Dec 2022

Cited by 2 | Viewed by 2014

Abstract

Rockfall processes are now commonly studied through monitoring campaigns using repeat lidar scanning. Accordingly, several recent studies have evaluated how the temporal resolution of data collection and various data-processing decisions can influence the apparent rockfall volumes estimated using typical rockfall database creation workflows. However, there is a lack of studies that consider how data quality and associated data-processing decisions influence rockfall volume estimation. In this work, we perform a series of tests based on an existing reference rockfall database from the Front Range of Colorado, USA, to isolate the influences of data resolution (point spacing), individual point precision, and the filter threshold applied to change results, on the volume estimates obtained for rockfalls. While the effects of individual point precision were found to be limited for typical levels of gaussian noise (standard deviation per coordinate direction ≤ 0.02 m), data resolution and change filter threshold were found to have systematic impacts on volume estimates, with the volume estimates for the smallest rockfalls decreasing substantially with increases in point spacing and change filter threshold. Because these factors disproportionately impact volume estimates for smaller rockfalls, when these factors change, the slope of the apparent power law that describes the relative frequency-volume distribution of rockfalls changes. Evidence is presented that suggests that this phenomenon can explain discrepancies between power law slopes presented in the literature based on studies focused on different scales of rockfall activity. Overall, this study demonstrates the impacts of raw data attributes on rockfall volume estimation and presents an additional effect that tends to bias rockfall frequency–magnitude power law relationships towards underestimation of the relative prevalence of small rockfalls. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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23 pages, 11355 KiB

Open AccessArticle

Modelling the Influence of Geological Structures in Paleo Rock Avalanche Failures Using Field and Remote Sensing Data

by Lingfeng He, Mirko Francioni, John Coggan, Fernando Calamita and Matthew Eyre

Remote Sens. 2022, 14(16), 4090; https://doi.org/10.3390/rs14164090 - 21 Aug 2022

Viewed by 1373

Abstract

This paper focuses on the back analysis of an ancient, catastrophic rock avalanche located in the small city of Lettopalena (Chieti, Italy). The integrated use of various investigation methods was employed for landslide analysis, including the use of traditional manual surveys and remote sensing (RS) mapping for the identification of geological structures. The outputs of the manual and RS surveys were then utilised to numerically model the landslide using a 2D distinct element method. A series of numerical simulations were undertaken to perform a sensitivity analysis to investigate the uncertainty of discontinuity properties on the slope stability analysis and provide further insight into the landslide failure mechanism. Both numerical modelling and field investigations indicate that the landslide was controlled by translational sliding along a folded bedding plane, with toe removal because of river erosion. This generated daylighting of the bedding plane, creating kinematic freedom for the landslide. The formation of lateral and rear release surfaces was influenced by the orientation of the discrete fracture network. Due to the presence of an anticline, the landslide region was constrained in the middle-lower section of the slope, where the higher inclination of the bedding plane was detected. The landslide is characterized by a step-path slip surface at the toe of the slope, which was observed both in the modelling and the field. This paper highlights the combined use of a geological model and numerical modelling to provide an improved understanding of the origin and development of rock avalanches under the influence of river erosion, anticline structures, and related faults and fractures. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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22 pages, 10901 KiB

Open AccessArticle

A Machine Learning Approach to Extract Rock Mass Discontinuity Orientation and Spacing, from Laser Scanner Point Clouds

by Elisa Mammoliti, Francesco Di Stefano, Davide Fronzi, Adriano Mancini, Eva Savina Malinverni and Alberto Tazioli

Remote Sens. 2022, 14(10), 2365; https://doi.org/10.3390/rs14102365 - 13 May 2022

Cited by 10 | Viewed by 2619

Abstract

This study wants to give a contribution to the semi-automatic evaluation of rock mass discontinuities, orientation and spacing, as important parameters used in Engineering. In complex and inaccessible study areas, a traditional geological survey is hard to conduct, therefore, remote sensing techniques have proven to be a very useful tool for discontinuity analysis. However, critical expert judgment is necessary to make reliable analyses. For this reason, the open-source Python tool named DCS (Discontinuities Classification and Spacing) was developed to manage point cloud data. The tool is written in Python and is based on semi-supervised clustering. By this approach the users can: (a) estimate the number of discontinuity sets (here referred to as “clusters”) using the Error Sum of Squares (SSE) method and the K-means algorithm; (b) evaluate step by step the quality of the classification visualizing the stereonet and the scatterplot of dip vs. dip direction from the clustering; (c) supervise the clustering procedure through a manual initialization of centroids; (d) calculate the normal spacing. In contrast to other algorithms available in the literature, the DCS method does not require complex parameters as inputs for the classification and permits the users to supervise the procedure at each step. The DCS approach was tested on the steep coastal cliff of Ancona town (Italy), called the Cardeto–Passetto cliff, which is characterized by a complex fracturing and is largely affected by rockfall phenomena. The results of discontinuity orientation were validated with the field survey and compared with the ones of the FACETS plug-in of CloudCompare. In addition, the algorithm was tested and validated on regular surfaces of an anthropic wall located at the bottom of the cliff. Eventually, a kinematic analysis of rock slope stability was performed, discussing the advantages and limitations of the methods considered and making fundamental considerations on their use. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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16 pages, 11452 KiB

Open AccessArticle

Loess Landslide Detection Using Object Detection Algorithms in Northwest China

by Yuanzhen Ju, Qiang Xu, Shichao Jin, Weile Li, Yanjun Su, Xiujun Dong and Qinghua Guo

Remote Sens. 2022, 14(5), 1182; https://doi.org/10.3390/rs14051182 - 27 Feb 2022

Cited by 33 | Viewed by 4540

Abstract

Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using the image classification method and semantic segmentation method of deep learning. However, there is a lack of research on the automatic recognition of old loess landslides, which are difficult to distinguish from the environment. Therefore, this study uses the object detection method of deep learning to identify old loess landslides with Google Earth images. At first, a database of loess historical landslide samples was established for deep learning based on Google Earth images. A total of 6111 landslides were interpreted in three landslide areas in Gansu Province, China. Second, three object detection algorithms including the one-stage algorithm RetinaNet and YOLO v3 and the two-stage algorithm Mask R-CNN, were chosen for automatic landslide identification. Mask R-CNN achieved the greatest accuracy, with an AP of 18.9% and F1-score of 55.31%. Among the three landslide areas, the order of identification accuracy from high to low was Site 1, Site 2, and Site 3, with the F1-scores of 62.05%, 61.04% and 50.88%, respectively, which were positively related to their recognition difficulty. The research results proved that the object detection method can be employed for the automatic identification of loess landslides based on Google Earth images. Full article

(This article belongs to the Special Issue Remote Sensing in Engineering Geology - II)

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