## 3 Applied Research
It highlights the complementary strengths of satellite-based remote sensing (e.g., SAR and VHR imaging) for large-scale monitoring, ground-based techniques (e.g., ERT and LiDAR) for detailed localized studies, and integrated approaches that combine these tools for a holistic analysis of geohazards. The methods discussed align with established research, such as studies on sinkhole formation, subsidence, and slope instability [2; 12; 26; 27; 34; 38]. including the integration of AI and Machine Learning (ML), which enhance the efficiency and accuracy of hazard detection. Figure 2a-c illustrates the workflow employed to study subsidence and sinkhole evolution [2]. It integrates field and remote sensing data with advanced processing techniques. Field data were collected using time-lapse cameras (TLCs) installed on alluvial fans to monitor floodwater dynamics, hydrometer readings at overpasses to track flood timing and intensity, and drone imagery to capture high-resolution orthophotos and digital elevation models (DEMs). Borehole data provided critical insights into the subsurface salt layers. Figure 2: _Cont._  
Remote sensing methods, including LiDAR and InSAR, complemented field observations. LiDAR data facilitated high-resolution subsidence mapping through annual DSM comparisons, while InSAR provided detailed interferograms to detect surface subsidence patterns and infer subsurface dissolution channels. The integration of these datasets enabled a robust analysis of floodwater recharge and discharge dynamics, sinkhole expansion, and the progressive evolution of the landscape. This comprehensive approach underscores the interplay between subsurface processes and surface geomorphological changes, offering valuable insights into the complex karst dynamics of the region. The Dead Sea presents significant challenges for remote sensing due to its extreme environmental conditions, including high salinity, fluctuating water levels, and active tectonics [9; 19]. Despite these obstacles, advanced remote sensing technologies have  
Figure 2: (**a**) Workflow illustrating the integration of field data, remote sensing data, and data processing to produce actionable insights into subsidence and sinkhole evolution in the Dead Sea region. Key components include data collection from time-lapse cameras, hydrometer readings, drone imagery, and borehole studies, complemented by LiDAR and InSAR analysis. (**b**) Data processing and analysis workflow integrating LiDAR, InSAR, and drone/TLC data. LiDAR facilitated elevation change calculations and sinkhole delineation, while InSAR generated interferograms to trace subsidence lineaments associated with subsurface dissolution channels. Drone and TLC data enabled real-time visualization of flood dynamics and clarified temporal relationships between recharge and discharge events. (**c**) Results derived from the comprehensive workflow showing quantitative measurements such as floodwater flow velocities, sinkhole expansion rates, and floodwater recharge and discharge timings. The integration of field and remote sensing data reveals subsurface dissolution channels and landscape evolution patterns. offered critical insights into the geological, hydrological, and ecological processes [10; 16] (Table 3). Among the most impactful applications is the monitoring of sinkholes and surface deformations, which pose considerable risks to infrastructure, industrial activities, and human safety [21; 22; 56; 26]. Using VHR imagery and InSAR, researchers have achieved precise detection and mapping of sinkholes. InSAR's ability to monitor ground subsidence with millimeter-level precision provides early warning data for sinkhole development (Table 4), while VHR imagery offers detailed spatial information on sinkhole locations, sizes, and growth rates, enabling effective hazard assessment and mitigation [15; 55]. The integration of these techniques has significantly improved the timeliness and accuracy of sinkhole monitoring efforts [11; 14].