Remote Sensing of the Dead Sea Region
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 1 June 2025 | Viewed by 4807
Special Issue Editors
Interests: radar remote sensing; GIS; geomorphology; tectonics; ground deformations
Interests: hydrogeophysics; remote sensing; photogrammetry; nat. hazards; numerical modelling; soil; subsidence; karst
Special Issues, Collections and Topics in MDPI journals
Interests: InSAR; earth surface deformation; volcanoes; earthquakes; sinkholes and subsidence
Interests: hydrogeology; hydrochemistry; submarine groundwater discharge; thermal remote sensing
Special Issue Information
Dear Colleagues,
In recent decades, remote sensing has become a widely used, cost-effective tool to characterize and monitor geological, hydrological and biological processes on Earth. Established tools and techniques, such as InSAR, high-resolution satellite image analysis, photogrammetry by drones or LiDAR and multispectral analysis, coupled with new technologies, such as machine learning and data science, offer significant opportunities to monitor changes in eco- and geosystems on different spatio-temporal scales. For more than five decades, the Dead Sea region has been subject to very dynamic changes due to the unprecedented regression of the lake and human population pressure. Several processes have attracted particular consideration in research recently: (1) the accelerating appearance of hazardous subsidence, sinkholes and stream channels at the shoreline; (2) subsurface salt karst and submarine groundwater discharge as part of the hydrologic cycle; (3) landslides and erosional processes related to flash floods; and (4) tectonic movements and associated seismic risk along the Dead Sea Rift.
Within this background, this Special Issue aims to provide a concise collection of studies that address knowledge gaps related to the ongoing change in the Dead Sea region, and that provide important information for scientists and stakeholders for sustainable future development of the region. Studies that use one or more of the following classical and novel remote sensing techniques are welcome for submission: multispectral analysis, satellite image analysis, change detection, InSAR, image processing, laser scanning and geometric reconstruction. Furthermore, physical modeling and deep-learning-based analysis of remote sensing data and data fusion techniques are highly encouraged for submission.
Articles and review articles may address, but are not limited, to the following topics:
- Natural and anthropogenic Earth surface changes;
- Geological hazards (subsidence, sinkholes, landslides, soil erosion, piping and active faults);
- Submarine groundwater discharge;
- Vegetation patterns and changes;
- Multispectral analysis of surface waters and floods;
- Monitoring Earth surface processes;
- Land-use analysis;
- Evolution of canyons and stream channels.
Dr. Damien Closson
Dr. Djamil Al-Halbouni
Dr. Gidon Baer
Dr. Christian Siebert
Dr. Jorge Sevil
Guest Editors
Manuscript Submission Information
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Keywords
- dead sea
- earth surface deformation
- submarine groundwater discharge
- multispectral analysis
- InSAR
- photogrammetry
- machine-learning-based remote sensing analysis
- vegetation patterns
- land use
- geomorphology
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Creating benchmark geomorphological mapping data for deep learning: an example from the Dead Sea sinkholes
Author: Schulten
Highlights: - Updated analysis of 500+ new sinkholes and three new uvalas on the eastern shore of the Dead Sea from 2018 to 2022
- Comparing 0.3m GSD vs. 2m GSD increased the mapped sinkhole area by over 100%
- Hausdorff distance showed up to 500 m² differences in annotated sinkhole areas due to human operator variation
Title: Towards Accurate and Innovative Automatic Sinkhole Mapping (AutoSink): A Two-Phase Deep Learning Approach in the Dead Sea Area
Author: AlRabayah
Highlights: Presents a novel U-Net CNN approach for sinkhole detection using high-resolution drone imagery.
Investigates the transferability of the trained model to low-resolution satellite data for sinkhole detection.
Discusses key changes in data preprocessing, highlighting model adaptability for geological studies.