## 3 Applied Research
Remote sensing has been pivotal in studying the tectonic dynamics of the Dead Sea Transform (DST) fault system, a major geological feature. InSAR has been widely applied to detect subtle ground movements over time, offering invaluable data for understanding fault slip behavior and assessing seismic hazards [16]. These insights have enhanced the understanding of active faulting and its role in triggering sinkholes and ground subsidence [13; 14]. Remote sensing technologies have also documented water level and shoreline changes as the Dead Sea has experienced significant declines in water levels due to human activities and natural evaporation processes [23; 57]. Multispectral and \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Specific Examples** & **Method/Tool** & **Quantitative Results** \\ \hline Mapping sinkholes on the western shore of the Dead Sea from 2005–2021 using aerial photographs, satellite imagery, and high-resolution UAV photogrammetry (SfM models for 2018, 2021). & Multi-temporal cartographic sinkhole mapping using aerial/satellite images and UAV photogrammetry & 702 new sinkholes identified (2005–2021). An average subsidence rate of 45 cm/year over the total area was calculated. \\ \hline Measuring the 3D morphometry of sinkholes (depth, volume, and surface area). & High-resolution Digital Surface Models (DSM) derived from drone images & Median sinkhole depth: 1.5 m (2021); Maximum depth: 21.2 m; Maximum volume: 228,343 m\({}^{3}\). Total volume of sinkholes in 2021: 329,148 m\({}^{3}\). \\ \hline Identifying clusters of sinkholes and their spatial distribution in the study area. & Kernel density analysis using GIS tools & Density of 191 sinkholes/km\({}^{2}\) for the entire area, rising to 488 sinkholes/km\({}^{2}\) within a smaller, denser zone. \\ \hline Identifying densely packed sinkhole clusters and marking outliers. & DBSCAN clustering algorithm & Sinkholes are highly clustered along a narrow N-S-oriented strip; clusters are enclosed within a 1.13 km\({}^{2}\) area with exceptionally high densities compared to global standards. \\ \hline Assessing the evolution of sinkholes, such as lateral expansion and coalescence. & Morphometric analysis (area, perimeter, circularity ratio) & Median areal growth: 1 m\({}^{2}\)/year for single sinkholes. Areal increase of 5362 m\({}^{2}\)/year (total area). Median deepening rate: 5.9 cm/year; fastest deepening rate: 2 m/year. \\ \hline Assessing the degree of clustering and spatial randomness of sinkholes. & Nearest Neighbor Index (NNI) & Indicates clustering with values close to 0; sinkholes display highly aggregated spatial patterns. \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of the methodologies, specific applications, and quantitative results from [16]. It highlights tools such as UAV photogrammetry, GIS-based analysis, and morphometric assessments, providing insights into sinkhole distribution, dynamics, and hazard implications. hyperspectral sensors, alongside VHR imagery, have enabled precise mapping of shoreline retreats, analyses of water composition, and assessments of the ecological impacts of these changes [26; 54]. Satellite missions like Landsat and Sentinel-2 provide invaluable long-term data, aiding the understanding of these transformations and their implications for regional sustainability [10; 14]. Remote sensing applications extend to environmental and climate studies, including the monitoring of vegetation cover, soil moisture, and land use patterns [58]. By using multispectral sensors and LiDAR, researchers have gathered critical data to address land degradation and guide sustainable resource management [2]. These efforts provide valuable insights into the region's response to climatic variability and inform long-term strategies for managing the Dead Sea basin [13; 14]. The formation of sinkholes presents significant challenges to industrial operations, particularly for the Dead Sea Works (DSW, Israel) and the Arab Potash Company (APC, Jordan).