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Remote Sensing for Sustainability and Durability of Transportation Infrastructures

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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: 26 May 2024 | Viewed by 6282

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Special Issue Editors

Dr. Wendy Zhou

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Guest Editor

Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO 80401, USA
Interests: remote sensing and GIS applications in geohazard assessment and environmental impact study; SAR and PSInSAR applications in monitoring ground subsidence and landslide deformation rate; underground transportation geotechnics; rock mass characterization; numerical modeling, such as finite element and finite difference methods for the ground thermal regime and stress–strain distribution in the rock or soil mass

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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Dr. Linan Liu

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Guest Editor

School of Engineering and Technology, China University of Geosciences, Beijing 100093, China
Interests: SAR and PSInSAR applications in ground movement monitoring; machine learning applications in civil engineering study; GIS applications in geohazards mitigation and 3D modeling

Special Issue Information

Dear Colleagues,

Transport infrastructures are becoming increasingly susceptible to deterioration due to aging and climate-related hazards. In addition, the increased urbanization, population density, and traffic congestion in urban metropolitan areas worldwide have led to a greater demand for sustainable transportation corridors on both surface and underground. Improving sustainability and durability is more important than ever for tunnels, bridges, highways, roads, railways, airfields, and transit. Geohazards (e.g., landslides, debris flows, and rockfalls) for surface transportation infrastructure are significant concerns in mountainous terrains. Against the background of climate change, geohazard assessment along transportation corridors is more concerning than ever. On the other hand, underground transportation systems offer many positive aspects, such as reducing traffic congestion and travel times, reducing urban sprawl and traffic noise, preserving the landscape and biodiversity, and increasing the resilience of communities from geohazards. However, tunneling-induced ground subsidence is a crucial concern in designing and constructing underground transportation systems.

Many researchers have utilized remote sensing techniques, including optical, microwave, and LiDAR, for geohazards assessments and tunneling-induced ground deformation monitoring over the decades. The retrospective nature of satellite-based remote sensing can provide a time series of ground deformation due to either geohazards or underground excavations. In recent years, intensive research activities include using remote sensing and other techniques, e.g., 3D geological modeling, GIS, and machine learning, which offer a synoptic view and acquire information at different perspectives and time intervals. Assessing geohazards and mapping ground deformations using remote sensing can provide a better understanding of these events’ mechanisms.

This Special Issue aims to publish high-quality articles on all aspects of remote sensing applications for improving the sustainability and durability of surface and underground transportation infrastructures, such as tunnels, bridges, highways, roads, railways, airfields, and transit. RS techniques include, but are not limited to, SAR, InSAR, LiDAR, photogrammetry, and SfM. Sensor platforms include but are not limited to satellite-, airborne-, UAV-, and terrestrial-based sensors.

Topics of interest include:

Characterization of tunneling-induced ground displacements using remote sensing techniques;
Ground displacement pattern recognition though combining remote sensing and machine learning;
Landslides assessments along transportation corridors;
Rockfalls assessment along roadcuts using LiDAR or photogrammetry;
Measurement of ground displacement from optical satellite images, i.e., pixel-tracking or differential DEMs from stereo-optical images;
Bridge monitoring using remote sensing techniques;
InSAR for airfield ground subsidence monitoring;
Integration of remote sensing and geodetic measurements;
Time series ground displacement monitoring, i.e., pre-, during-, and after-events;
Rock mass and fault zone characterization from photogrammetry or point clouds for transportation alignment selection;
Transportation infrastructure monitoring integrating InSAR and GPR.

Dr. Wendy Zhou
Dr. Thomas Oommen
Dr. Linan Liu
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

tunneling-induced ground subsidence
landslide monitoring along transportation corridors
rockfalls along roadcuts
rock mass and fault zone characterization for transportation alignment selection
airfield ground subsidence
bridge monitoring
ground deformation time series
parametric analysis of ground subsidence or deformation

Published Papers (4 papers)

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Research

22 pages, 27029 KiB

Open AccessArticle

Reduction of Subsidence and Large-Scale Rebound in the Beijing Plain after Anthropogenic Water Transfer and Ecological Recharge of Groundwater: Evidence from Long Time-Series Satellites InSAR

by Chaodong Zhou, Qiuhong Tang, Yanhui Zhao, Timothy A. Warner, Hongjiang Liu and John J. Clague

Remote Sens. 2024, 16(9), 1528; https://doi.org/10.3390/rs16091528 - 26 Apr 2024

Viewed by 315

Abstract

Beijing, China’s capital city, has experienced decades of severe land subsidence due to the long-term overexploitation of groundwater. The implementation of the South-to-North Water Diversion Project (SNWDP) and artificial ecological restoration have significantly changed Beijing’s hydro-ecological and geological environment in recent years, leading to a widespread rise in groundwater levels. However, whether the related land subsidence has slowed down or reversed under these measures has not yet been effectively monitored and quantitatively analyzed in terms of time and space. Accordingly, in this study, we proposed using an improved time-series deformation method, which combines persistent scatterers and distributed scatterers, to process Sentinel-1 images from 2015 to 2022 in the Beijing Plain region. We performed a geospatial analysis to gain a better understanding of how the new hydrological conditions changed the pattern of deformation on the Beijing Plain. The results indicated that our combined PS and DS method provided more measurements both in total quantity and spatial density than conventional PSI methods. The land subsidence in the Beijing Plain area has been effectively alleviated from a subsidence region of approximately 1377 km² in 2015 to only approximately 78 km² in 2022. Ecological restoration areas in the northeastern part of the Plain have even rebounded over this period, at a maximum of approximately 40 mm in 2022. The overall pattern of ground deformation (subsidence and uplift) is negatively correlated with changes in the groundwater table (decline and rise). Local deformation is controlled by the thickness of the compressible layer and an active fault. The year 2015, when anthropogenic water transfers were eliminated and ecological measures to recharge groundwater were implemented, was the crucial turning point of the change in the deformation trend in the subsidence history of Beijing. Our findings carry significance, not only for China, but also for other areas where large-scale groundwater extractions are causing severe ground subsidence or rebound. Full article

(This article belongs to the Special Issue Remote Sensing for Sustainability and Durability of Transportation Infrastructures)

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16 pages, 6845 KiB

Open AccessArticle

Land Subsidence Prediction and Analysis along Typical High-Speed Railways in the Beijing–Tianjin–Hebei Plain Area

by Lin Wang, Chaofan Zhou, Huili Gong, Beibei Chen and Xinyue Xu

Remote Sens. 2023, 15(18), 4606; https://doi.org/10.3390/rs15184606 - 19 Sep 2023

Viewed by 920

Abstract

High-speed railways in the Beijing–Tianjin–Hebei (BTH) Plain are gradually becoming more widespread, covering a greater area. The operational safety of high-speed railways is influenced by the continuous development of land subsidence. It is necessary to predict the subsidence along the high-speed railways; thus, this work is of critical importance to the safety of high-speed railway operation. In this study, we processed Sentinel-1A data using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique to acquire the land subsidence in the typical BTH area. Then, we combined the Empirical Mode Decomposition (EMD) and Gradient Boosting Decision Tree (GBDT) methods (EMD-GBDT) to forecast land subsidence along high-speed railways. The results revealed that some parts of the high-speed railways in the BTH plain had passed through or approached the land subsidence area; the maximum cumulative subsidence of the Beijing–Shanghai, Tianjin–Baoding and Shijiazhuang–Jinan high-speed railways reached 326 mm, 384 mm and 350 mm, respectively. The forecasting accuracy for land subsidence along high-speed railways was enhanced by the EMD-GBDT model. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were 0.38 mm to 0.56 mm and 0.23 mm to 0.38 mm, respectively. Full article

(This article belongs to the Special Issue Remote Sensing for Sustainability and Durability of Transportation Infrastructures)

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18 pages, 6803 KiB

Open AccessArticle

Mapping Tunneling-Induced Uneven Ground Subsidence Using Sentinel-1 SAR Interferometry: A Twin-Tunnel Case Study of Downtown Los Angeles, USA

by Linan Liu, Wendy Zhou and Marte Gutierrez

Remote Sens. 2023, 15(1), 202; https://doi.org/10.3390/rs15010202 - 30 Dec 2022

Cited by 3 | Viewed by 1868

Abstract

Synthetic Aperture Radar (SAR) interferometry is a formidable technique to monitor surface deformation with a millimeter detection resolution. This study applies the Persistent Scatter-Interferometric Synthetic Aperture Radar (PSInSAR^TM) technique to measure ground subsidence related to a twin-tunnel excavation in downtown Los Angeles, USA. The PSInSAR^TM technique is suitable for urban settings because urban areas have strong reflectors. The twin tunnels in downtown Los Angeles were excavated beneath a densely urbanized area with variable overburden depths. In practice, tunneling-induced ground settlement is dominantly vertical. The vertical deformation rate in this study is derived by combining Line of Sight (LOS) deformation velocities obtained from SAR images from both ascending and descending satellite orbits. Local and uneven settlements up to approximately 12 mm/year along the tunnel alignment are observed within the allowable threshold. No severe damages to aboveground structures were reported. Furthermore, ground movements mapped one year before tunnel construction indicate that no concentrated ground settlements pre-existed. A Machine Learning (ML)-based permutation feature importance method is used for a parametric study to identify dominant factors associated with the twin-tunneling induced uneven ground subsidence. Six parameters are selected to conduct the parametric study, including overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, the distance between the two tunnel centerlines, the depth to the tunnel springline, building height, the distance to the tunnel, and groundwater level. Results of the parametric analysis indicate that overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, is the dominant contributing factor, followed by the distance between tunnel centerlines, depth to the tunnel springline, and building height. Two parameters, the distance to the tunnel, and the groundwater level, play lesser essential roles than others. In addition, the geological profile provides comprehension of unevenly distributed ground settlements, which are geologically sensitive and more concentrated in areas with thick artificial fill and alluvium soils, low tunnel depth, and high groundwater levels. Full article

(This article belongs to the Special Issue Remote Sensing for Sustainability and Durability of Transportation Infrastructures)

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25 pages, 10465 KiB

Open AccessArticle

A Railway Lidar Point Cloud Reconstruction Based on Target Detection and Trajectory Filtering

by Hao Liu, Lianbi Yao, Zhengwen Xu, Xianzheng Fan, Xiongfeng Jiao and Panpan Sun

Remote Sens. 2022, 14(19), 4965; https://doi.org/10.3390/rs14194965 - 05 Oct 2022

Cited by 1 | Viewed by 2295

Abstract

The traditional railway survey adopts a manual observation method, such as a total station measuring system. This method has high precision, but the amount of data is small, and the measurement efficiency is low. Manual measurement cannot meet the requirements of dynamic continuous high-precision holographic measurement during railway outages. Mobile laser scanning is a mobile mapping system based mainly on a laser scanner, inertial measurement unit (IMU) and panoramic camera. Mobile laser scanning has the advantages of high efficiency, high precision and automation. However, integrating inertial navigation data and mobile laser scanning data to obtain real 3D information about railways has always been an urgent problem to be solved. Therefore, a point cloud reconstruction method is proposed based on trajectory filtering for a mobile laser scanning system. This paper corrects the odometer data by identifying railway feature points through deep learning and uses Rauch–Tung–Striebel (RTS) filtering to optimize the trajectory results. Combined with the railway experimental track data, the maximum difference in the east and north coordinate direction can be controlled within 7 cm, and the average elevation error is 2.39 cm. This paper applies a multi-sensor integrated mobile detection system to railway detection. It is of great significance to the healthy development of the intelligent railway system. Full article

(This article belongs to the Special Issue Remote Sensing for Sustainability and Durability of Transportation Infrastructures)

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