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

Journal Browser

Advanced Ground Penetrating Radar Theory and Applications II

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 "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 14644

Share This Special Issue

Special Issue Editors

Dr. Pier Matteo Barone

E-Mail Website
Guest Editor

Archaeology and Classics Program, American University of Rome, Via Pietro Roselli 4, 00153 Rome, Italy
Interests: archaeological methods and science; remote sensing and GIS; landscape archaeology; archaeological prospections; forensic archaeology; forensic geophysics; forensic geoscience; art crime; cultural heritage protection
Special Issues, Collections and Topics in MDPI journals

Dr. Raffaele Persico

E-Mail Website
Guest Editor

Department of Environmental Engineering DIAM, University of Calabria, 87036 Rende, Italy
Interests: ground penetrating radar; electromagnetic wave propagation; magnetic permeability; permittivity; frequency-domain analysis; geophysical techniques; radar imaging; remote sensing by radar; time-domain
Special Issues, Collections and Topics in MDPI journals

Dr. Salvatore Piro

E-Mail Website
Guest Editor

Istituto di Scienze del Patrimonio Culturale, Institute of Heritage Science, Consiglio Nazionale delle Ricerche, National Research Council, C.da S. Loja, 85050 Tito Scalo, PZ, Italy
Interests: applied geophysics; earth sciences; archaeogeophysics; ground penetrating radar; electrical resistivity tomography; gradiometer; integrated geophysical methods; archaeological prospections; landscape geophysics; inverse problems; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After the great success of the first edition of this Special Issue of Remote Sensing, we would like to propose a second one because we are aware about the paramount importance of the advanced Ground-Penetrating Radar (GPR) system providing high-resolution images for inspecting several different media. This 2^nd edition is aimed to collect further most-advanced researches in GPR technology, methodology, and applications. The SI welcomes papers on traditional GPR researches, emerging GPR applications based on (but not exclusively) the use of drones, positioning systems, positioning sensors, 3D and 4D applications, virtual and augmented reality and monitoring. Advanced research and field applications involving other remote sensing methods or geophysical data are also of interest. We invite researchers to contribute original articles presenting the most advanced progresses and interesting case studies regarding the following topics and beyond:

GPR theory
Design, realization, and testing of GPR systems and antennas
GPR data processing and analysis
Modelling and inversion methods for GPR
Applications of GPR in the geosciences
Applications of GPR in forestry, agriculture, and water management
GPR archaeological prospection
New data processing algorithms
Forensic GPR
Combined use of GPR and other remote sensing techniques

Dr. Pier Matteo Barone
Prof. Raffaele Persico
Dr. Salvatore Piro
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

GPR theory
GPR antennas
GPR data processing and analysis
GPR Modelling and inversion
GPR in Geosciences
GPR in Forestry, Agriculture, and Water Management
GPR in Archaeology and Cultural Heritage
Forensic GPR
GPR and Remote Sensing

Published Papers (6 papers)

Download All Papers

Research

Jump to: Other

22 pages, 15413 KiB

Open AccessArticle

A New Approach for Adaptive GPR Diffraction Focusing

by Hamdan Hamdan, Nikos Economou, Antonis Vafidis, Maksim Bano and Jose Ortega-Ramirez

Remote Sens. 2022, 14(11), 2547; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112547 - 26 May 2022

Cited by 2 | Viewed by 1561

Abstract

Several researchers have utilized multipath summation to manage the common problem of scattered energy within GPR sections. Such energy results in degrading the lateral resolution and continuity of reflectors. If detailed velocity models are known, then it is fairly easy to focus the scattered energy by means of conventional migration methods. However, this is rarely the case in GPR sections, as the common-offset antenna array is mostly used, and therefore cannot provide velocity models. This gives an important advantage for the multipath summation method, which has proved to be successful in focusing such diffractions, without the need to build a detailed migration velocity field model. This multipath summation method is based on stacking (summation) of constant velocity migrated sections (weighted or not) over a predefined velocity range. The main drawback of this technique is the high computational cost and the need for user interference to select the appropriate stacking weights. We developed an improved implementation of the weighted multipath summation method that reduces both the computational cost, and the user interference in stacking weights selections. This data adaptive methodology can expedite the migration process, suppress the need for a detailed velocity model, and reduce the user subjectivity. Moreover, a data adaptive spectral scaling scheme was developed. This is applied on the output of the multipath summation process to reduce the expected blurriness in the resulting GPR sections. Full article

(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications II)

► Show Figures

Graphical abstract

10 pages, 48659 KiB

Open AccessCommunication

Aventinus Minor Project: Remote Sensing for Archaeological Research in Rome (Italy)

by Elizabeth Wueste, Giulia Facchin and Pier Matteo Barone

Remote Sens. 2022, 14(4), 959; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040959 - 16 Feb 2022

Cited by 2 | Viewed by 2048

Abstract

This paper presents the results of a preliminary survey in a central urban area of Rome, Italy. The results were obtained from both desktop and remote sensing surveys. The Aventinus Minor Project (AMP) is a community archaeological excavation project focusing on an understudied area in Rome with limited modern archaeological excavation: the Aventinus Minor or Little Aventine. The remote sensing (RS) anomalies revealed by the survey illustrate that this area is potentially rich in buried structures potentially correlated with ancient visible remains (i.e., the Servian Walls and Santa Balbina church). The application of RS approaches (such as NDVI, VARI, and GPR) and the creation of a GIS platform lays the foundations for a correct and georeferenced reporting of all collected data, providing a nuanced understanding of the urban archaeology in the dense topography of Rome. Full article

(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications II)

► Show Figures

Figure 1

21 pages, 3252 KiB

Open AccessArticle

Finding Mesolithic Sites: A Multichannel Ground-Penetrating Radar (GPR) Investigation at the Ancient Lake Duvensee

by Erica Corradini, Daniel Groß, Tina Wunderlich, Harald Lübke, Dennis Wilken, Ercan Erkul, Ulrich Schmölcke and Wolfgang Rabbel

Remote Sens. 2022, 14(3), 781; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030781 - 08 Feb 2022

Cited by 5 | Viewed by 2684

Abstract

The shift to the early Holocene in northern Europe is strongly associated with major environmental and climatic changes that influenced hunter-gatherers’ activities and occupation during the Mesolithic period. The ancient lake Duvensee (10,000–6500 cal. BCE) has been studied for almost a century, providing archaeological sites consisting of bark mats and hazelnut-roasting hearths situated on small sand banks deposited by the glacier. No method is yet available to locate these features before excavation. Therefore, a key method for understanding the living conditions of hunter-gatherer groups is to reconstruct the paleoenvironment with a focus on the identification of areas that could possibly host Mesolithic camps and well-preserved archaeological artefacts. We performed a 16-channel MALÅ Imaging Radar Array (MIRA) system survey aimed at understanding the landscape surrounding the find spot Duvensee WP10, located in a hitherto uninvestigated part of the bog. Using an integrated approach of high-resolution ground radar mapping and targeted excavations enabled us to derive a 3D spatio-temporal landscape reconstruction of the investigated sector, including paleo-bathymetry, stratigraphy, and shorelines around the Mesolithic camps. Additionally, we detected previously unknown islands as potential areas for yet unknown dwelling sites. We found that the growth rates of the islands were in the order of approximately 0.3 m²/yr to 0.7 m²/yr between the late Preboreal and the Subboreal stages. The ground-penetrating radar surveying performed excellently in all aspects of near-surface landscape reconstruction as well as in identifying potential dwellings; however, the direct identification of small-scale artefacts, such as fireplaces, was not successful because of their similarity to natural structures. Full article

(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications II)

► Show Figures

Figure 1

19 pages, 9585 KiB

Open AccessArticle

UAV and GPR Data Integration in Glacier Geometry Reconstruction: A Case Study from Irenebreen, Svalbard

by Jānis Karušs, Kristaps Lamsters, Jurijs Ješkins, Ireneusz Sobota and Pēteris Džeriņš

Remote Sens. 2022, 14(3), 456; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030456 - 19 Jan 2022

Cited by 11 | Viewed by 3336

Abstract

Although measurements of thickness and internal structure of glaciers are substantial for the understanding of their evolution and response to climate change, detailed data about polythermal glaciers, are scarce. Here, we present the first ground-penetrating radar (GPR) measurement data of Irenebreen, and high-resolution DEM and orthomosaic, obtained from unmanned aerial vehicle (UAV) photogrammetry. A combination of GPR and UAV data allowed for the reconstruction of the glacier geometry including thermal structure. We compare different methods of GPR signal propagation speed determination and argue that a common midpoint method (CMP) should be used if possible. Our observations reveal that Irenebreen is a polythermal glacier with a basal temperate ice layer, the volume of which volume reaches only 12% of the total glacier volume. We also observe the intense GPR signal scattering in two small zones in the ablation area and suggest that intense water percolation occurs in these places creating local areas of temperate ice. This finding emphasizes the possible formation of localised temperate ice zones in polythermal glaciers due to the coincidence of several factors. Our study demonstrates that a combination of UAV photogrammetry and GPR can be successfully applied and should be used for the high-resolution reconstruction of 3D geometries of small glaciers. Full article

(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications II)

► Show Figures

Graphical abstract

Other

Jump to: Research

12 pages, 4979 KiB

Open AccessTechnical Note

Particle Swarm Optimization-Based Variational Mode Decomposition for Ground Penetrating Radar Data Denoising

by Sixin Liu, Yuhan Chen, Chaopeng Luo, Hejun Jiang, Hong Li, Hongqing Li and Qi Lu

Remote Sens. 2022, 14(13), 2973; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132973 - 22 Jun 2022

Cited by 8 | Viewed by 1646

Abstract

Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD algorithm determines the central frequency and bandwidth of each Intrinsic Mode Function (IMF) by iteratively searching for the optimal solution of the variational mode and is capable of adaptively and effectively dividing the signal in the frequency domain into the many IMFs. However, the penalty parameter

α

and the number of IMFs K in VMD processing are determined depending on manual experience, which are important parameters affecting the decomposition results. In this paper, we propose a method to automatically search the parameters

α

and K optimally by Particle Swarm Optimization (PSO) algorithm. Then the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are used to judge the best superposition of the IMFs for data reconstruction, and the process is data-driven without human subjective intervention. The proposed method is used to process the field data, and the reconstruction data show that this innovative VMD processing can effectively improve the SNR and highlight the target reflections, even some targets not found in pre-processing are also revealed. Full article

(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications II)

► Show Figures

Figure 1

12 pages, 6245 KiB

Open AccessTechnical Note

Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method

by Shengchao Wang, Liguo Han, Xiangbo Gong, Shaoyue Zhang, Xingguo Huang and Pan Zhang

Remote Sens. 2021, 13(22), 4530; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224530 - 11 Nov 2021

Cited by 2 | Viewed by 1622

Abstract

Crosshole ground-penetrating radar (GPR) is an important tool for a wide range of geoscientific and engineering investigations, and the Markov chain Monte Carlo (MCMC) method is a heuristic global optimization method that can be used to solve the inversion problem. In this paper, we use time-lapse GPR full-waveform data to invert the dielectric permittivity. An inversion based on the MCMC method does not rely on an accurate initial model and can introduce any complex prior information. Time-lapse ground-penetrating radar has great potential to monitor the properties of a subsurface. For the time-lapse inversion, we used the double difference method to invert the time-lapse target area accurately and full-waveform data. We propose a local sampling strategy taking advantage of the a priori information in the Monte Carlo method, which can sample only the target area with a sequential Gibbs sampler. This method reduces the calculation and improves the inversion accuracy of the target area. We have provided inversion results of the synthetic time-lapse waveform data that show that the proposed method significantly improves accuracy in the target area. Full article

(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications II)

► Show Figures