Remote Sensing of the Dead Sea Region

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

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.

• dead sea
• earth surface deformation
• submarine groundwater discharge
• multispectral analysis
• InSAR
• photogrammetry
• machine-learning-based remote sensing analysis
• vegetation patterns
• land use
• geomorphology

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.

Title: Spatial exponential modeling of temporal NDVI and Climatic Parameters’ relationships for Least-disturbed Plant-Soil-Rock patterns across Desert Fringe gradient
Authors: Maxim Shoshany; Sofia Mozhaeva
Affiliation: Technion - Israel Institute of Technology
Abstract: Estimating the climatic parameters (CPs) of rainfall, potential evapotranspiration, and aridity from normalized difference vegetation index (NDVI) helps better understanding of habitat conditions and vegetation pattern responses to changes in water availability across Mediterranean regions and their dry margins. In this study, we examine relationships between NDVI and CPs for least-disturbed plant-soil-rock patterns along a south to north transect )orthogonal to rainfall isohyets (between the Negev desert and the semi-arid margins of the Judean mountains. Bi-monthly data of these CPs and NDVI from Sentinel images were collected for 38 sites for years representing high, low, and average rainfall. Per-date relationships between each CP on one hand, and the different sites’ NDVI and south-to-north normalized distance (D) on the other hand, were analysed by multiple regression. Per-site correlations were analysed between NDVI and each of the CPs for the different dates. The study is novel in combining the temporal and spatial dimensions of CPs versus NDVI relationships for natural plant-soil-rock patterns, by modelling south-to-north changes in their per-site multi-temporal correlation coefficients. Deviations in the new models’ estimations of bi-monthly winter CPs are below 20 (mm per 2 months) while increasing northwards towards the areas dominated by Maquis. Implementation of these models along sufficiently long time spans may improve our understanding of moderate and high resolution patterns of relationships between natural vegetation and climatic conditions and the formation of thresholds in desert transition zones.