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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 (31 December 2020) | Viewed by 46662

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Special Issue Editors

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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Prof. Dr. Thomas Oommen

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Department of Geology & Geological Engineering, University of Mississippi, Oxford, MS 38677, 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 approach to the investigation of geological engineering problems has changed dramatically. The advent of new remote sensing sensors and techniques has led to step-change increases in the quality of data available in geosciences and geoengineering. Laser scanning/LiDAR, digital photogrammetry, hyperspectral, and InSAR represent the most used remote sensing techniques in engineering geology and geo-hazard studies. These techniques can be ground-based or, as a result of the high spatial resolution achievable with the newly available sensors, airplanes, drones, and satellite platforms, can be used in the interpretation of geotechnical projects on a large scale.

In this context, this Special Issue invites high-quality and innovative scientific papers that advance the science of remote sensing in geological engineering problems and geo-hazard studies. These will include the analysis and monitoring of landslides and volcanos, the characterization of rock masses and geotechnical sites, ground deformation analyses, and mining applications. Special attention will also be given to the use of GIS 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.

Keywords

Engineering geology
Geological engineering
Remote sensing
Geo-mechanics
Geotechnics
Natural hazards
Photogrammetry and Laser scanning
LiDAR
InSAR
Landslides
Ground deformation and monitoring
Landslide numerical analyses
Mining
Geoinformatics

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Remote Sensing in Engineering Geology (Third Edition) in Remote Sensing (3 articles)

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Research

21 pages, 18492 KiB

Open AccessArticle

A New Method to Predict Gully Head Erosion in the Loess Plateau of China Based on SBAS-InSAR

by Chengcheng Jiang, Wen Fan, Ningyu Yu and Yalin Nan

Remote Sens. 2021, 13(3), 421; https://doi.org/10.3390/rs13030421 - 26 Jan 2021

Cited by 25 | Viewed by 3886

Abstract

Gully head erosion causes serious land degradation in semiarid regions. The existing studies on gully head erosion are mainly based on measuring the gully volume in small-scale catchments, which is a labor-intensive and time-consuming approach. Therefore, it is necessary to explore an accurate method quantitatively over large areas and long periods. The objective of this study was to develop a model to assess gully head erosion in the Loess Plateau of China using a method based on the SBAS-InSAR technique. The gully heads were extracted from the digital elevation model and validated by field investigation and aerial images. The surface deformation was estimated with SBAS-InSAR and 22 descending ALOS PALSAR datasets from 2007 to 2011. A gully head erosion model was developed; this model can incorporate terrain factors and soil types, as well as provides erosion rate predictions consistent with the SBAS-InSAR measurements (R² = 0.889). The results show that gully head erosion significantly depends on the slope angle above the gully head, slope length, topographic wetness index, and catchment area. The relationship between these factors and the gully head erosion rate is a power function, and the average rate of gully head erosion is 7.5 m³/m²/year, indicating the high erosional vulnerability of the area. The accuracy of the model can be further improved by considering other factors, such as the stream power factor, curvature, and slope aspect. This study indicates that the erosion rate of gully heads is almost unaffected by soil type in the research area. An advantage of this model is that the gully head area and surface deformation can be easily extracted and measured from satellite images, which is effective for assessing gully head erosion at a large scale in combination with SBAS-InSAR results and terrain attributes. Full article

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

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25 pages, 20489 KiB

Open AccessArticle

Analysis of the Suitability of High-Resolution DEM Obtained Using ALS and UAS (SfM) for the Identification of Changes and Monitoring the Development of Selected Geohazards in the Alpine Environment—A Case Study in High Tatras, Slovakia

by Ľudovít Kovanič, Peter Blistan, Rudolf Urban, Martin Štroner, Monika Blišťanová, Karol Bartoš and Katarína Pukanská

Remote Sens. 2020, 12(23), 3901; https://doi.org/10.3390/rs12233901 - 28 Nov 2020

Cited by 35 | Viewed by 3525

Abstract

The current trend in the use of remote sensing technologies is their use as a tool for monitoring hard-to-reach areas, objects or phenomena in the alpine environment. Remote sensing technology is also effectively used to monitor geohazards and the development of human-made changes in the country. Research presented in this study demonstrates the results for the usability of the publicly available national digital elevation model DEM 5.0 obtained by utilizing the airborne laser scanning (ALS) survey to monitor the development of erosion, morphological changes of talus cones, or the dynamics of movement of rock blocks between stages of measurement in the alpine environment of the High Tatras mountains. The reference methods for this study are the terrestrial laser scanning (TLS) and structure-from-motion (SfM) photogrammetric approach using unmanned aerial systems (UASs). By comparing the created DEMs, the ALS point cloud’s accuracy on mostly rocky areas of different sizes was verified. The results show that the standard deviation of the ALS point cloud ranges from 19 to 46 mm depending on the area’s size and characteristics. The maximum difference ranges from 100 to 741 mm. The value of systematic displacement of data obtained by different technologies ranges from 1 to 29 mm. This research confirms the suitability of the ALS method with its advantages and limits for the detection of movement of rock blocks or change of position of any natural or anthropogenic objects with a size from approximately 1 m². Full article

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

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14 pages, 4847 KiB

Open AccessArticle

Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

by Luigi Guerriero, Diego Di Martire, Domenico Calcaterra and Mirko Francioni

Remote Sens. 2020, 12(21), 3518; https://doi.org/10.3390/rs12213518 - 27 Oct 2020

Cited by 23 | Viewed by 4259

Abstract

An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed providing a specific procedure and related MATLAB™ code to derive displacement field maps using digital image correlation of successive Google Earth images. The suitability of the procedure and the potential of such images are demonstrated here through their application to two relevant case histories, namely the Slumgullion landslide in Colorado and the Miage debris-covered glacier in Italy. Result validation suggests the effectiveness of the proposed procedure in deriving Earth’s surface displacement data from Google Earth images. Full article

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

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17 pages, 2891 KiB

Open AccessArticle

A Novel 2-D Geometry Reconstruction Approach for Space Debris via Interpolation-Free Operation under Low SNR Conditions

by Xi Luo, Lixin Guo, Dong Li, Hongqing Liu and Mengyi Qin

Remote Sens. 2020, 12(12), 2059; https://doi.org/10.3390/rs12122059 - 26 Jun 2020

Viewed by 2149

Abstract

Two unsolved key issues in inverse synthetic aperture radar (ISAR) imaging for non-cooperative rapidly spinning targets including high computational complexity and poor imaging performance in the case of low signal-to-noise ratio (SNR) are addressed in this work. In the strip-map imaging mode of SAR, it is well known that azimuth spatial invariant characteristics exist, and inspired by this, we propose a fast ISAR imaging approach for spinning targets. Our approach involves two steps. First, a precise analytic expression in the range-Doppler (RD) domain is produced using the principle of stationary phase (POSP). Second, a novel interpolation kernel function is designed to remove two-dimensional (2-D) spatial-variant phase errors, and the corresponding fast implementation steps that only require Fourier transform and multiplications are also presented. Finally, a well-focused ISAR image is obtained by compensating the azimuth high-order terms. Compared with current imaging methods, our approach avoids multi-dimensional search and interpolation operations and exploits the 2-D coherent integrated gain; the proposed method is of low computational cost and robustness in the low SNR condition. The effectiveness of the proposed approach is confirmed by numerically simulated experiments. Full article

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

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28 pages, 28517 KiB

Open AccessFeature PaperArticle

The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds

by Paul-Mark DiFrancesco, David Bonneau and D. Jean Hutchinson

Remote Sens. 2020, 12(11), 1885; https://doi.org/10.3390/rs12111885 - 10 Jun 2020

Cited by 69 | Viewed by 10172

Abstract

Rockfall inventories are essential to quantify a rockfall activity and characterize the hazard. Terrestrial laser scanning and advancements in processing algorithms have resulted in three-dimensional (3D) semi-automatic extraction of rockfall events, permitting detailed observations of evolving rock masses. Currently, multiscale model-to-model cloud comparison (M3C2) is the most widely used distance computation method used in the geosciences to evaluate 3D changing features, considering the time-sequential spatial information contained in point clouds. M3C2 operates by computing distances using points that are captured within a projected search cylinder, which is locally oriented. In this work, we evaluated the effect of M3C2 projection diameter on the extraction of 3D rockfalls and the resulting implications on rockfall volume and shape. Six rockfall inventories were developed for a highly active rock slope, each utilizing a different projection diameter which ranged from two to ten times the point spacing. The results indicate that the greatest amount of change is extracted using an M3C2 projection diameter equal to, or slightly larger than, the point spacing, depending on the variation in point spacing. When the M3C2 projection diameter becomes larger than the changing area on the rock slope, the change cannot be identified and extracted. Inventory summaries and illustrations depict the influence of spatial averaging on the semi-automated rockfall extraction, and suggestions are made for selecting the optimal projection diameter. Recommendations are made to improve the methods used to semi-automatically extract rockfall from sequential point clouds. Full article

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

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25 pages, 11311 KiB

Open AccessArticle

UAV Applications for Determination of Land Deformations Caused by Underground Mining

by Paweł Ćwiąkała, Wojciech Gruszczyński, Tomasz Stoch, Edyta Puniach, Dawid Mrocheń, Wojciech Matwij, Karolina Matwij, Michał Nędzka, Paweł Sopata and Artur Wójcik

Remote Sens. 2020, 12(11), 1733; https://doi.org/10.3390/rs12111733 - 28 May 2020

Cited by 53 | Viewed by 5141

Abstract

This article presents a case study that demonstrates the applicability of unmanned aerial vehicle (UAV) photogrammetric data to land surface deformation monitoring in areas affected by underground mining. The results presented include data from two objects located in the Upper Silesian Coal Basin in Poland. The limits of coordinate and displacement accuracy are determined by comparing UAV-derived photogrammetric products to reference data. Vertical displacements are determined based on differences between digital surface models created using UAV imagery from several measurement series. Interpretation problems related to vegetation growth on the terrain surface that significantly affect vertical displacement error are pointed out. Horizontal displacements are determined based on points of observation lines established in the field for monitoring purposes, as well as based on scattered situational details. The use of this type of processing is limited by the need for unambiguous situational details with clear contours. Such details are easy to find in urbanized areas but difficult to find in fields and meadows. In addition, various types of discontinuous deformations are detected and their development over time is presented. The results are compared to forecasted land deformations. As a result of the data processing, it has been estimated that the accuracy of the determination of XY coordinates and the horizontal displacements (RMS) in best case scenario is on the level of 1.5–2 GSD, and about 2–3 GSD for heights and subsidence. Full article

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

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21 pages, 19590 KiB

Open AccessArticle

Ground Deformation of the Chongming East Shoal Reclamation Area in Shanghai Based on SBAS-InSAR and Laboratory Tests

by Qingbo Yu, Qing Wang, Xuexin Yan, Tianliang Yang, Shengyuan Song, Meng Yao, Kai Zhou and Xinlei Huang

Remote Sens. 2020, 12(6), 1016; https://doi.org/10.3390/rs12061016 - 22 Mar 2020

Cited by 31 | Viewed by 4277

Abstract

With the development of the economy, land reclamation, as a result of dredged soil, has become an effective measure to alleviate land scarcity in many coastal cities around the world. Chongming East Shoal (CES), a typical reclamation area in Shanghai that is formed by multi-phase reclamation projects, was selected as the study area. The small baseline subset–interferometry synthetic aperture radar (SBAS-InSAR) method was applied to derive the map of velocity distribution and accumulated deformation with 70 Sentinel-1 synthetic aperture radar (SAR) images collected from 22 March 2015 to 2 December 2019. In addition, 25 undisturbed soil samples, including dredger fill and underlying soil layers, were collected from five boreholes (maximum depth 55 m) through a field investigation. Laboratory tests were then performed on all soil samples in order to facilitate an understanding of geological features, including the measurement of basic physical properties, cation exchange capacity, compressibility, microscale structure, and pores. The present results show that the whole CES was undergoing differential ground deformation, with a velocity ranging from −47.5 to 34.6 mm/y. Fast (−3.4 mm/y) to slow (−0.3 mm/y) mean subsidence velocities were detected in multi-phase reclamation areas from inland areas to the coastline, and were controlled by building load and geological features of soil layers. Urbanization is the main factor that triggers accelerated subsidence and should receive special attention for reclamation areas that have been finished for a long time (over 20 years in this study). The geological features indicated that poor drainage conditions in offshore soil layers resulted in slow subsidence. The field investigation and laboratory test can be powerful explanatory tools to monitor the results from a mechanical perspective. Full article

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

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19 pages, 8444 KiB

Open AccessArticle

Retrospective InSAR Analysis of East London during the Construction of the Lee Tunnel

by Jennifer Scoular, Richard Ghail, Philippa Mason, James Lawrence, Matthew Bellhouse, Rachel Holley and Tom Morgan

Remote Sens. 2020, 12(5), 849; https://doi.org/10.3390/rs12050849 - 6 Mar 2020

Cited by 17 | Viewed by 6812

Abstract

The Lee Tunnel was constructed as the first part of the Thames Tideway Improvement scheme, between 2010 and 2016. With tunnelling for the East section of the main Thames Tideway Tunnel, which joins the Lee Tunnel at Abbey Mills Pumping Station, beginning in early 2020, this paper investigates patterns of deformation in East London during construction of the Lee Tunnel. An unexpected geological feature, later identified as a drift filled hollow, was discovered during tunnelling. This study demonstrates that had eight years of ERS Persistent Scatterer Interferometry (PSI) data been analysed prior to tunnelling, the unusual pattern of displacement may have been recognised and further targeted borehole investigations taken place before the launch of the tunnel boring machine. Results also show how areas of different land use, including cemeteries and historic landfill, exhibit differences in settlement behaviour, compared with surrounding terraced housing. This research highlights the challenges in interpreting PSI results in an urban area with ongoing construction and the value of a long archive of data, which now spans almost three decades in London, that can be used to establish a baseline prior to construction. Full article

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

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15 pages, 4740 KiB

Open AccessArticle

Sliding Time Master Digital Image Correlation Analyses of CubeSat Images for landslide Monitoring: The Rattlesnake Hills Landslide (USA)

by Paolo Mazzanti, Paolo Caporossi and Riccardo Muzi

Remote Sens. 2020, 12(4), 592; https://doi.org/10.3390/rs12040592 - 11 Feb 2020

Cited by 37 | Viewed by 4381

Abstract

Landslide monitoring is a global challenge that can take strong advantage from opportunities offered by Earth Observation (EO). The increasing availability of constellations of small satellites (e.g., CubeSats) is allowing the collection of satellite images at an incredible revisit time (daily) and good spatial resolution. Furthermore, this trend is expected to grow rapidly in the next few years. In order to explore the potential of using a long stack of images for improving the measurement of ground displacement, we developed a new procedure called STMDA (Slide Time Master Digital image correlation Analyses) that we applied to one year long stack of PlanetScope images for back analyzing the displacement pattern of the Rattlesnake Hills landslide occurred between the 2017 and 2018 in the Washington State (USA). Displacement maps and time-series of displacement of different portions of the landslide was derived, measuring velocity up to 0.5 m/week, i.e., very similar to velocities available in literature. Furthermore, STMDA showed also a good potential in denoising the time-series of displacement at the whole scale with respect to the application of standard DIC methods, thus providing displacement precision up to 0.01 pixels. Full article

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

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