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Remote Sensing and Geophysics Methods for Geomorphology Research

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: 31 May 2024 | Viewed by 4007

Special Issue Editors


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Guest Editor
Department of Geo-Informatics, University of Seoul, Seoul 02504, Republic of Korea
Interests: reconstruction and monitoring of topography; InSAR/SAR; detection of geomorphological feature; planetary topography
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Geosciences, University of Aberdeen, King’s College, Aberdeen AB24 3UE, UK
Interests: remote sensing applications in land dynamics; landforms and surface processes on Mars; glacial and periglacial geomorphology; glacial hazards; Mars analogue research; high-resolution terrain modelling and interpretation; UAVs for environmental remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In earlier studies, the principles of geomorphic processes were established through inferences based on spatiotemporally restricted observations of corresponding features. However, at times such limited approaches were insufficient due to vegetation, topsoil, and man-made coverings obscuring the study objects and their slow migrations over time.

Nowadays, in addition to the improved resolution of remote sensing images, science has access to other sophisticated tools and technological approaches with which to observe, track and monitor the dynamics of geogmorphic features. InSAR is a powerful tool for tracking millimeter-scale movements of topographical features such as sliding escarpments, wind-induced degradation, creeping faults, and gradual changes in stream channels. The scientific community is in the process of solving the evolutionary problem of hidden geomorphic features through long-wavelength synthetic aperture radar imaging, the design of super-resolution ranging sensors capable of generating bare-earth terrain images by applying filters, the numerical simulation of the corresponding process and the inter-comparison to target terrains. GPR and high-resolution gravity data now provide information about subterranean layers deep beneath the target terrain. It is also possible to define shallow underwater geomorphology using easily accessible bathymetric data sets and tools. Therefore, the focus of recent research is shifting from securing data sources on concealed terrains and to interpreting, compiling, and fusion of diverse data sets on geomorphic features and their migrations.

This Issue has been organized to provide a platform for presenting advanced techniques and case studies on the observation of the spatiotemporal evolution of geomorphic features. In terms of the dataset methods used, this Issue invites the submission of research onInSAR, SAR backscattering, PolSAR, LIDAR in air/space, gravitational anomalies, and magnetic fields, observing both topographical features and their movement. Target geomorphologies include the fluvial cases, i.e., traces of paleochannels, lacustrine deposits and their changes induced via megaflooding, the avulsion of channels, the aeolian feature such as ripples, dunes and yardangs, glacial and periglacial feature and their changes and the geomorphologies of tectonic and volcanic origin. The spatial domain of this Issue is not only our planet but encompasses extraterrestrial geomorphologies and their changes.

We also invite researchs to institute and implement a novel algorithmic improvement on data sets, new sensor designs to monitor the target morphology, new theoretical and observational findings, and applications of machine learning approaches.

Submissions may cover, but are not limited to, the following themes:

  • SAR/InSAR/PolSAR observations and algorithmic development tracing on morphologic changes
  • Deep subsurface/underwater feature identification involving geomorphic evolutions
  • The techniques to integrate gravitation/magnetic analogies on other remote sensing data over geomorphic features
  • Any case studies to follow the contemporary migrations/evolution of geomorphology
  • The studies of planetary geomorphology using remote sensing data
  • Any studies on algorithmic improvements for observing geomorphic features and their evolutions

Dr. Jungrack Kim
Dr. Anshuman Bhardwaj
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

  • geomorphology
  • geomorphic processes
  • tracing algorithms
  • SAR/InSAR/PolSAR
  • gravitation anomaly
  • machine learning

Published Papers (3 papers)

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Research

28 pages, 45922 KiB  
Article
The Dunes of Belvedere–San Marco of Aquileia: Integrating High-Resolution Digital Terrain Models and Multispectral Images with Ground-Penetrating Radar Survey to Map the Largest System of Continental Dunes of Northern Italy
by Federica Vanzani, Alessandro Fontana, Livio Ronchi, Jacopo Boaga, Veronica Chiarini and Irka Hajdas
Remote Sens. 2024, 16(5), 765; https://doi.org/10.3390/rs16050765 - 22 Feb 2024
Viewed by 790
Abstract
The interpretation of high-resolution remote-sensed data (i.e., LiDAR-derived DTMs, aerial photos and satellite images), compared with ground-penetrating radar surveys, historical cartography, geomorphological surveys and stratigraphic data, allowed us to map a large system of dunes near the Grado-Marano Lagoon (NE Italy) and reconstruct [...] Read more.
The interpretation of high-resolution remote-sensed data (i.e., LiDAR-derived DTMs, aerial photos and satellite images), compared with ground-penetrating radar surveys, historical cartography, geomorphological surveys and stratigraphic data, allowed us to map a large system of dunes near the Grado-Marano Lagoon (NE Italy) and reconstruct its evolution. Remote sensing investigations allowed us to recognize, map and interpret the sandy reliefs as a field of continental aeolian landforms extending for over 15 km2 and consisting of parabolic dunes elongated in the WSW direction. Radar soundings, together with the description of stratigraphic sections and cores, documented the internal clinostratification of the dunes, supporting their aeolian origin. Radiocarbon dating documents that the dunes formed 22 ka ago, at the end of the Last Glacial Maximum, and probably evolved until the first part of the Late Glacial, when vegetation was scarce. The landforms were fed by the sands blown from a paleochannel of Isonzo River flowing eastward of the dune’s field and blown by Bora. This is a very strong katabatic wind, still characterizing the area, but that was likely much stronger during last glaciation, when it was probably sustained by a stronger wind pattern in Central Europe. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysics Methods for Geomorphology Research)
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19 pages, 6245 KiB  
Article
Improved Gravity Inversion Method Based on Deep Learning with Physical Constraint and Its Application to the Airborne Gravity Data in East Antarctica
by Guochao Wu, Yue Wei, Siyuan Dong, Tao Zhang, Chunguo Yang, Linjiang Qin and Qingsheng Guan
Remote Sens. 2023, 15(20), 4933; https://doi.org/10.3390/rs15204933 - 12 Oct 2023
Viewed by 1232
Abstract
This paper aims to solve the limitations of traditional gravity physical property inversion methods such as insufficient depth resolution and difficulties in parameter selection, by proposing an improved 3D gravity inversion method based on deep learning. The deep learning network model is established [...] Read more.
This paper aims to solve the limitations of traditional gravity physical property inversion methods such as insufficient depth resolution and difficulties in parameter selection, by proposing an improved 3D gravity inversion method based on deep learning. The deep learning network model is established using the fully convolutional U-net network. To enhance the generalization ability of the sample set, the large-scale training set and test set are generated by the random walk, based on the forward theory. Founded on the traditional loss function’s definition, this paper introduces an improvement incorporating a physical constraint to measure the degree of data fitting between the predicted and the real gravity data. This improvement significantly boosted the accuracy of the deep learning inversion method, as verified through both a single model and an intricate combination model. Finally, we applied this improved inversion method to the gravity data from the Gamburtsev Subglacial Mountains in the interior of East Antarctica, obtaining a comprehensive 3D crustal density structure. The results provide new evidence for the presence of a dense crustal root situated beneath the central Gamburtsev Province near the Gamburtsev Suture. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysics Methods for Geomorphology Research)
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33 pages, 140635 KiB  
Article
Holistic 3D Model of an Urban Area in Norway: An Integration of Geophysical, Geotechnical, Remote Sensing, and Geological Methods
by Ivan Gutierrez, Wiktor Weibull, Lisa Watson, Thomas Meldahl Olsen and Alejandro Escalona
Remote Sens. 2023, 15(11), 2872; https://doi.org/10.3390/rs15112872 - 31 May 2023
Viewed by 1526
Abstract
Understanding the distribution and characterization of natural and non-natural materials on the surface and near-subsurface is important for the development of infrastructure projects. This may be a challenge in highly urbanized areas, where outcrops are scarce, and anthropogenic activities have altered the morphological [...] Read more.
Understanding the distribution and characterization of natural and non-natural materials on the surface and near-subsurface is important for the development of infrastructure projects. This may be a challenge in highly urbanized areas, where outcrops are scarce, and anthropogenic activities have altered the morphological expression of the landscape. This study tests the integration of ground-penetrating radar (GPR), borehole drilling, aerial imagery, geological mapping, and aerial laser scanning as complementary mapping tools for determining the stratigraphy of glacial and post-glacial Quaternary sediments, the depth to the bedrock, and the distribution of anthropogenic material in Mosvatnet, a lake in Stavanger, Norway. The integration proved to be efficient and enabled the generation of a 3D holistic model, which provided a broad understanding of the subsurface geology and the induced anthropogenic changes in the area through time. Bedrock, till, fluvioglacial, and lacustrine geological units were modeled. Accumulations of post-glacial organic matter were mapped, and the distribution of non-natural infill material was determined. The interpreted dataset suggests that the eastern shoreline of Mosvatnet has artificially prograded about one hundred meters westward since the 1930s and the elevation of the corresponding area has increased by about ten meters relative to the lake level. Full article
(This article belongs to the Special Issue Remote Sensing and Geophysics Methods for Geomorphology Research)
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